THE HUMAN FACTORS OF 3D BODY SCANNING (3DBS ...

THE HUMAN FACTORS OF 3D BODY SCANNING (3DBS)

DATA PRESENTATION AND SERVICE INTERACTION

A thesis submitted to The University of Manchester for the degree of

Doctor of Philosophy in the Faculty of Science and Engineering

2020

Monika M. Januszkiewicz

Department of Materials

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List of Contents

List of Tables 8

List of Figures 10

List of Abbreviations 11

Abstract 12

Declaration 13

Copyright Statement 15

Acknowledgements 16

Dedication 17

Publications 18

Quote 19

1. INTRODUCTION 20

Foreword 20

ResearchContext 201.2.1 SolvingFashionIndustryIssueswith3DBodyScanning 20

FundamentalIssuesin3DBodyScanning 221.3.1 TheDesignofVirtualFitInterfacesine-Commerce 241.3.2 TheRelativeAdvantageof3DBodyScanninginFashionRetail 271.3.3 TheCustomerJourneyin3DBodyScanningService 291.3.4 The3DBodyScanningServiceWorkflow 31

ResearchAim 33

ResearchQuestions 33

ResearchObjectives 34

ConceptualFramework 34

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ThesisOutline 38

2. LITERATURE REVIEW 41

Objectives,ScopeandStructure 41

Anthropometry 422.2.1 AnthropometryintheApparelDesignEngineering 432.2.2 PatternDraftingandEngineering 452.2.3 MethodsandToolsforAnthropometricResearch 482.2.4 SummaryofContributiontotheThesis 50

StateoftheArtin3DBodyScanning 512.3.1 DataAcquisitionin3DBodyScanning 512.3.2 3DBodyScanningAccuracy,ReliabilityandPrecision 572.3.3 3DBodyScanningDataStorageandCuration 612.3.4 StandardisationEffortsin3DBodyScanning 672.3.5 SummaryofContributiontotheThesis 71

FashionHumanFactors&UsabilityIssueswith3DBodyScanning 722.4.1 DesignPlatformsforProductDevelopment 732.4.2 TheLostLinkBetweentheDeveloper’sGoalsandSizingPractices 892.4.3 SummaryofContributiontotheThesis 91

ServiceDesignin3DBodyScanningResearch 922.5.1 ServiceDesign-TheoreticalBackground 922.5.2 TheoreticalModelsfor3DBodyScanning 1012.5.3 SummaryofContributiontotheThesis 111

ResearchGapsandOpportunities 1122.6.1 TheExistingSizeandFitOfferings 1132.6.2 StakeholdersPerspectiveon3DBodyScanningDiffusion 1142.6.3 CustomerJourneyin3DBodyScanning 1152.6.4 3DBodyScanningServiceWorkflow 116

3. RESEARCH DESIGN 117

ResearchPhilosophy 118

ApproachtoTheoryDevelopment 120

MethodologicalChoice 122

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3.3.1 QuantitativeResearch 1223.3.2 QualitativeResearch 1233.3.3 MixedMethod 123

ResearchStrategies 126

TechniquesandProcedures 1323.5.1 ResearchQuestion1 1323.5.2 ResearchQuestion2 1353.5.3 ResearchQuestion3 1383.5.4 ResearchQuestion4 140

ResearchEthics 141

4. ONLINE VIRTUAL FIT IS NOT YET FIT FOR PURPOSE: AN ANALYSIS OF FASHION E-

COMMERCE PLATFORMS 147

Introduction 147

TheoreticalBackground 1494.2.1 UserInformationRequirements 1494.2.2 VirtualFitPresentationandAssessment 1504.2.3 SizeandFitPrediction 151

MaterialsandMethods 1514.3.1 SettingandSample 1514.3.2 DataCollection 1534.3.3 DataAnalysis 153

ResultsandAnalysis 1544.4.1 InformationRequirementsforVirtualFitInterfaces 1544.4.2 VirtualFitInterfacesPresentation 1584.4.3 SizeandFitPrediction 160

Discussion 1624.5.1 InformationRequiredfromUsersbyVirtualFitInterfaces 1624.5.2 OutputsPresentedbyVirtualFitInterfaces 1634.5.3 SizeandFitPrediction 165

Conclusions 166

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5. STAKEHOLDERS IN 3D BODY SCANNING: HOW TO CONNECT THE ISOLATED SILOS

OF KNOWLEDGE FOR THE FASHION MASS-CUSTOMISATION 169

Introduction 169

TheoreticalBackground 1725.2.1 DiffusionofInnovation(DOI)theory 172


Results 1785.4.1 RelativeAdvantage 1785.4.2 Compatibility 1805.4.3 Trialability 1815.4.4 Observability 1835.4.5 LowComplexity 184

Discussion 1865.5.1 SynthesisingStakeholdersInterestsandConcerns:RichPictureOne 1865.5.2 AFrameworkforInterdisciplinarityin3DBodyScanning:RichPictureTwo 189

Conclusions 194

6. ‘CUSTOMER JOURNEYS’ IN 3D BODY SCANNING: THE GOOD, THE BAD AND THE

UNEXPECTED 197

Introduction 197

TheoreticalBackground 2016.2.1 CustomerExperiencein3DBodyScanningResearch 2016.2.2 3DBodyScanningInterface 203


ResultsandAnalysis 208

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6.4.1 CustomerExperiencein3DBodyScanning 2086.4.2 InterfaceDevelopment 210

Discussion 2166.5.1 CustomerExperience 2166.5.2 InterfaceDevelopment 218

Conclusions 220

7. HOW CAN FASHION INDUSTRY INTEGRATE 3D BODY SCANNING WORKFLOW: A

CRITICAL REVIEW 222

Introduction 222

TheoreticalBackground 224


ResultandDiscussion 2277.4.1 MeasurementAcquisition 2297.4.2 AvatarProcessing 2327.4.3 DataStorageandCuration 2357.4.4 Applications 240

Conclusions 244

8. DISCUSSION AND CONCLUSION 247

Introduction 247

AttainmentoftheResearchObjectives 2488.2.1 ObjectiveOne 2488.2.2 ObjectiveTwo 2518.2.3 ObjectiveThree 2538.2.4 ObjectiveFour 2558.2.5 ObjectiveFive 2588.2.6 ObjectiveSix 259

TheoreticalContributions 260

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Limitations 265

AgendaforFutureResearch 2668.5.1 ResearchontheVirtualFitInterfaces 2678.5.2 Researchon3DBodyScanningDiffusion 2678.5.3 ResearchonUserExperience 2688.5.4 3DBodyScanningServiceWorkflow 269

SomeFinalReflections 269

9. REFERENCES 271

10. APPENDICES 325

AppendixA:AlvanonBodyFormMeasurements 325

AppendixBVirtualFit:StyleMe-UserJourneyExample 327

AppendixC:DiffusionofInnovationCodingMaterials 33010.3.1 RelativeAdvantageCoding 33010.3.2 CompatibilityCoding 33110.3.3 TrialabilityCoding 33210.3.4 ObservabilityCoding 33210.3.5 LowcomplexityCoding 333

AppendixD:ResearchProjectDescription 334

AppendixE:ParticipantConsentForm 335

AppendixF:ParticipantDemographicQuestionnaire 336

AppendixG:ResearchAdvertisementPoster 337

AppendixH:AnExampleforFocusGroupDiscussionTranscript 338

AppendixI:AnExampleofUserInterviewforInterfaceAppraisal 344

Final Word Count: 84, 981

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LIST OF TABLES Table1OverviewofMeasurementMethodsBasedonBeazley(1996). 49Table2ListofCommercialFirmsfor3DBodyScanners.Source:Author’sOwn. 52Table3MarketOverviewofWhole3DBodyScanners.Source:Author’sOwn. 53Table4RotatoryandHome3DBodyScanningSystems.Source:Author’sOwn. 54Table53DBodyScanningMobileApplications.Source:Author’sOwn. 56Table6SizeSurveysDatabasesBasedonGuptaAndZakaria(2014,2019). 64Table7HumanSolutionISizeDatabase.RetrievedfromHumanSolution(2019). 65Table8ListofStandardsOrganisationsin3DBodyScanning.Source:Author’sOwn. 67Table9KeyISOStandardsfor3DBodyScanningBasedonGill(2015). 68Table10OverviewofCommercialVFI,CADAndCAMInterfaces.Source:Author’sOwn. 73Table11OverviewofCADSoftwarefortheFashionIndustry.Source:Author’sOwn. 83Table12CustomerLiteratureTimelineBasedonRudkowski(2020)Research. 99Table13HumanBehaviourModelsandTheirTheoreticalLocusBasedonVenkatesh(2003). 102Table14Chapter’sMethodsAnalysis.Source:Author’sOwn. 125Table15ServiceDesignThinkingFrameworkBasedonThoringandMüller(2011). 129Table16StakeholdersAnalysis-MethodsOverview.BasedonReedetal.(2009) 137Table17VirtualFitInterfaces.Source:Author’sOwn. 152Table18AnthropometricData.Source:Author’sOwn. 155Table19GarmentData.Source:Author’sOwn. 157Table20VirtualFitInterfacePresentation.Source:Author’sOwn. 158Table21SizeandFitRecommendationModels.Source:Author’sOwn. 160Table22DistributionofSamplebyIndustrySector.Source:Author’sOwn. 175Table23InterviewQuestions.Source:Author’sOwn. 176Table24:PerceivedTrendsinRelativeAdvantageBetweenIndustries.Source:Author’sOwn. 178Table25PerceivedTrendsaboutCompatibilityBetweenIndustries.Source:Author’sOwn. 180Table26PerceivedTrendsinTrialabilityBetweenIndustries.Source:Author’sOwn. 181Table27PerceivedTrendsinObservabilityBetweenIndustries.Source:Author’sOwn. 183Table28PerceivedTrendsinComplexityBetweenIndustries.Source:Author’sOwn. 184Table29DesignBarriersto3DBodyScanningExperience;N=52.Source:Author’sOwn. 208Table30InterfaceBarriersto3DBodyScanningExperience;N=40.Source:Author’sOwn. 210Table31FemaleWeightCategoryDescriptions.Source:Author’sOwn. 213Table32MaleWeightCategoryDescriptions.Source:Author’sOwn. 213Table33AWilcoxon-Mann-WhitneyTest.Source:Author’sOwn. 215Table34Recommendationsfor3DBodyScanningExperience.Source:Author’sOwn. 217Table35ASummaryofRecommendationsforInterfaceDevelopment.Source:Author’sOwn. 219Table36WordsandPhrasesAssociatedwithEachStageoftheModel.Source:Author’sOwn. 227Table37SummaryofContributionsDerivedfromthisThesis.Source:Author’sOwn. 264

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Table38AlvanonAnthropometricDimensions.Source:Author’sOwn. 325Table39FrequencyofRelativeAdvantageThemes.Source:Author’sOwn. 330Table40FrequencyofCompatibilityThemes.Source:Author’sOwn. 331Table41FrequencyofTrialabilityThemes.Source:Author’sOwn. 332Table42FrequencyofObservabilityThemes.Source:Author’sOwn. 332Table43FrequencyofComplexityThemes.Source:Author’sOwn. 333

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LIST OF FIGURES Figure1ServiceDesignFrameworkBasedonPlattnerandWeinberg(2009). 35Figure2LiteratureReviewRoadmap.Source:Author’sOwn 42Figure3ZozoMobileScanInterfaceSource:ZozoSuitUK. 56Figure43dAvatarsDesigns.Source:Belcurves(2018),Style.Me(2018),andMetail(2018). 76Figure5SizeRecommendation.RetrievedfromFitPredictor(2018),andFitAnalytics(2018). 77Figure6BodyShapeClassification.RetrievedfromTrueFit(2018)andFitAnalytics(2018). 78Figure7BodyFFITShapeClassification.RetrievedfromFitsMe(2018). 79Figure8VisualisationMethods.RetrievedfromFitsMe(2018)andVirtusize(2019). 80Figure9PastWardrobePurchases.RetrievedfromTrueFit(2018). 81Figure10DiffusionofInnovationAdoptionCurve.AdoptedFromRogers(2003) 109Figure11ResearchOnionFramework.RetrievedfromSaundersetal.(2015) 117Figure12ResearchOnionInterpretationBasedonSaunders(2015).Source:Author’sOwn. 118Figure13ScreenshotfromNvivoCodebook,BasedonCashetal.(2011)Theme. 131Figure14NvivoExampleofNodeCodingBasedonVirtusize(2019)Interface. 134Figure15EthicsQuestion.Source:(Januszkiewicz,2018). 142Figure16EthicsInformationList.Source:(Januszkiewicz,2018). 143Figure17ConsentFormProcess.Source:(Januszkiewicz,2018). 144Figure18UniversityofManchesterBodyScanningDatabase.RetrievedFromGilletal(2019). 145Figure19RichPictureOne:3DBodyScanningIndustry.Source:Author’sOwn. 189Figure20RichPictureTwo:Stakeholders’ExpertiseinBodyScanning.Source:Author’sOwn. 190Figure21FemaleInterfacewithAOI.Source:Author’sOwn. 207Figure22MaleInterfacewithAOI.Source:Author’sOwn. 207Figure23FemaleInterfaceHeatMap.Source:Author’sOwn. 214Figure24MaleInterfaceHeatMap.Source:Author’sOwn. 214Figure25BoxplotSummary.Source:Author’sOwn. 216Figure263DBodyScanningModelfortheFashionIndustry.Source:Author’sOwn. 228Figure27The3DBodyScanningDataStorageModel.Source:Author’sOwn. 236Figure28Alvanon3DBodyScan(ThreeDifferentAnglesView).Source:Author’sOwn. 326Figure29StyleMeInterface(Steps1-3).Source:(Style.Me,2018). 327Figure30StyleMeInterface(Steps4-6).Source:(Style.Me,2018). 328Figure31StyleMeInterface(Steps7-9).Source:(Style.Me,2018). 329Figure32ResearchProjectDescription.Source:Author’sOwn. 334Figure33StudyThreeParticipantsConsentForm.Source:Author’sOwn. 335Figure34StudyThreeParticipantsDemographicQuestions.Source:Author’sOwn. 336Figure35StudyThreeAdvertisementPoster.Source:Author’sOwn. 337

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LIST OF ABBREVIATIONS 2D – Two-dimension or two-dimensional

3D – Three-dimension or three-dimensional

3DBS – 3D Body Scanning

ADE – Apparel Design Engineering

AOI – Area of Interest

APP – Application

AR – Augmented Reality

B2C – Business to Customer

BRP – Behavioural Reasoning Theory

CAD – Computer-Aided Design

CAM – Computer-Aided Manufacturing

DOI – Diffusion of Innovation

DEXA – Dual-Energy X-Ray Absorptiometry

e-Commerce – Electronic Commerce

HCI – Human-Computer Interaction

HF – Human Factors

ISAA – International Standards for Anthropometric Assessment

ISAK – International Society for the Advancement of Kinanthropometry

ISO – International Organization for Standardisation

IEEE – Institute of Electrical and Electronics Engineers

IT – Information Technology

MIS – Management Information Systems

PLM – Product Lifecycle Management

SD – Service Design

TAM – Technology Acceptance Model

TPB – Theory of Planned Behaviour

TRA – Theory of Reasoned Action

VFI – Virtual Fit Interfaces

VTO – Virtual Try On

UK – United Kingdom

UX – User Experience

UI – User Interface

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ABSTRACT

The anthropometric measures are used extensively in garment development practice.

However, manual techniques reduce the complicated shape of the human body to a set of

limited dimensions that cannot adequately describe the complex three-dimensional (3D)

variations in body shape. 3D Body Scanning has the potential to surpass manual

anthropometric measures by automatically capturing a detailed and accurate human

body dimensions and shape characteristics that can then be visualised in a computer

program in the form of a digital avatar. Nevertheless, much of the unrealised potential

exists for the use of 3D Body Scanners in garment development. Despite research

suggestions that 3D Body Scanning can provide size and fit recommendations that are

more effective and less labour intensive, little evidence in the fashion industry exists to

date to support these claims. The relative appeal of technology is impacted by garment

developer’s inability to describe, interpret, and analyse the data from 3D Body Scanners.

The data from 3D Body Scanners follow the engineering principles that do not connect

well with existing product development practices and standards that could guide the

adoption processes. The past research on the fashion industry adoption has, however,

focused on engineering problems such as calibration, optimisation, and reconstruction

with insufficient focus on customer preferences and garment developers’ requirements.

As a consequence, the fashion industry faces resistance, conflicts and practice

bottlenecks. Therefore, 3D Body Scanning requires collaboration between all

stakeholders to maximise technology useability, engagement, and effectiveness.

This thesis conceptualises opportunities and challenges that garment developers and

fashion customers face when trying to adopt and use 3D Body Scanning. This research

uses stages of the service design thinking framework, and in each iteration step, involves

various empirical methods such as content analysis, interviews, eye tracking, and focus

group workshops. A first study using content analysis evaluates how the existing Virtual

Fit Interfaces (VFI) collect, present, and classify anthropometric information to improve

the size and fit recommendation. The study findings conclude that new garment fit

prediction models, driven by 3D Body Scanning technologies, are needed to satisfy

customer’ relationships with virtual clothing. The second study based on semi-structured

interviews asks 3D Body Scanning stakeholders questions about technology progress

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based on diffusion of innovation theory. The results integrate the stakeholder’s opinions

into two rich pictures frameworks. The first rich picture demonstrates. “wicked”, yet

vital development issues. Wicked problems are mainly characterised by complexity and

related interdependencies, high uncertainty, the divergence of viewpoints and values,

and fluid problem definition. The second rich picture provides a comprehensive guide

for 3D Body Scanning developers on how to align distinct expertise in a way that

reflects stakeholders specific knowledge for service creation, delivery, and retail

outcomes. However, the 3D Body Scanning design must also be in line with the

technology user’ needs and requirements. The third study, therefore, focuses explicitly

on service users’ needs - fashion industry professionals and customers’ - to discuss and

identify problems in service interaction and presentation using focus group workshops

and eye-tracking method. The result provides a list of nine design issues pertaining to

barriers that developers need to address to improve the customers’ journey. Lastly,

building on previous thesis findings, the final study summarises the stages of 3D Body

Scanning workflow and discuss the opportunities and challenges from data acquisition

to the future application of insights in fashion retail. This study emphasises that

technology developers need to adopt broader value frameworks when evaluating 3D

Body Scanning and collaborate with the fashion industry stakeholders.

Keywords: 3D Body Scanning, Fashion Industry, Stakeholders Analysis, User-

Experience, Product Development, Virtual Fit Interface

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DECLARATION

No portion of the work referred to in the thesis has been submitted in support of an

application for another degree or qualification of this or any other university or other

institute of learning.

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COPYRIGHT STATEMENT

i. The author of this thesis (including any appendices and/or schedules to this

thesis) owns certain copyright or related rights in it (the “Copyright”) and s/he

has given The University of Manchester certain rights to use such Copyright,

including for administrative purposes.

ii. Copies of this thesis, either in full or in extracts and whether in hard or

electronic copy, may be made only in accordance with the Copyright, Designs

and Patents Act 1988 (as amended) and regulations issued under it or, where

appropriate, in accordance with licensing agreements which the University has

from time to time. This page must form part of any such copies made.

iii. The ownership of certain Copyright, patents, designs, trademarks and other

intellectual property (the “Intellectual Property”) and any reproductions of

copyright works in the thesis, for example graphs and tables (“Reproductions”),

which may be described in this thesis, may not be owned by the author and may

be owned by third parties. Such Intellectual Property and Reproductions cannot

and must not be made available for use without the prior written permission of

the owner(s) of the relevant Intellectual Property.

iv. Further information on the conditions under which disclosure, publication and

commercialisation of this thesis, the Copyright and any Intellectual Property

and/or Reproductions described in it may take place is available in the

University IP Policy (see

http://documents.manchester.ac.uk/DocuInfo.aspx?DocID=2442 0), in any

relevant Thesis restriction declarations deposited in the University Library, The

University Library’s regulations (see

http://www.library.manchester.ac.uk/about/regulations/) and in The University’s

policy on Presentation of Theses

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ACKNOWLEDGEMENTS

For the past four years, I have had the privilege of being able to peruse knowledge

within an area in which I have become so fond. It has been a most rewarding experience,

both in terms of what I have learnt and whom I have met.

I am incredibly grateful to my supervisors, Dr Steven Hayes and Dr Simeon Gill. Both

were always available for a chat over a cup of coffee and have provided a constant

source of advice, patience, and meticulous criticism, for which I am extremely grateful. I

would also like to thank my co-supervisor, Dr Christopher Parker, for remaining with

the supervisory team and for his enthusiasm and encouragement, which was a constant

source of inspiration for me. I am doubtful whether I would have completed the PhD in

the manner I have without them as guides and mentors.

The ADE research group proved to be a wonderful bunch to work with. Being part of a

dynamic research group that includes talented and friendly individuals from various

backgrounds was a truly rewarding experience. Special thanks to Maryam Ahmed for

being my travel and hiking companion at research conferences in Montréal and Lugano.

Thanks to Domizia Di Maggio and Jonathan Craig for inspiring conversations and

exchange of ideas, criticisms and intellectual stimulation that have helped bring some

clarity to this thesis.

A big thank you to all the stakeholders who participated in this study and contributed

many invaluable insights into the research design and conclusions.

I’m indebted to my family for endless love and support. Jolanta Januszkiewicz, Dawid

Januszkiewicz, and Daniel Marciniak: you taught me how to live in this peculiar world.

Dziękuję!

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IN MEMORY TO MY FATHER WITH LOVE AND ETERNAL APPRECIATION

MARIUSZ JANUSZKIEWICZ (11.01.1965 – 05.11.2020)

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PUBLICATIONS

Januszkiewicz, M., Parker, C. J., Hayes, S. G., & Gill, S (2017) ‘Online Virtual Fit is not

yet Fit for Purpose: An Analysis of Fashion e-Commerce Interfaces’, in Proceedings of

3DBODY.TECH 2017 - 8th International Conference and Exhibition on 3D Body

Scanning and Processing Technologies. Montreal QC, Canada, pp. 210–217. doi:

10.15221/17.210.

Januszkiewicz, M., Parker, C. J., Hayes, S. G., & Gill, S (2019a) ‘3D Body Scanning:

The Next Big Thing in Retail, or Much Ado About Nothing?’, in Textiles and Life 2.

The Textile Institute. Manchester UK.

Januszkiewicz, M., Parker, C. J., Hayes, S. G., & Gill, S (2019b) ‘3D Body Scanning in

the Apparel Industry: Do We Really Know Where We Are Heading?’ 3DBODY.TECH

2017 - 10th International Conference and Exhibition on 3D Body Scanning and

Processing Technologies. Lugano, Switzerland, pp. 204–210. doi: 10.15221/19.204.

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“The most profound technologies are those that disappear. They weave themselves into

the fabric of everyday life until they are indistinguishable from it.”

- Mark Weiser

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CHAPTER 1

INTRODUCTION

Foreword

This chapter introduces a thesis on the subject of ‘The Human Factors of 3D Body

Scanning (3DBS) Data Presentation and Service Interaction.’ The research context in

section 1.2 sets out the key issues and topics relevant to the subject domain that outline

the research context both for the academic researchers and industry stakeholders. The

fundamental issues in section 1.3 justify the research before summarising the aims 1.4,

questions 1.5 and objectives 1.6 of the research. The chapter concludes with a

description of the thesis framework 1.7 and thesis structure 1.8.

Research Context

1.2.1 Solving Fashion Industry Issues with 3D Body Scanning

Fashion, from the outside, maintains an illusion of beauty, creativity, and purpose. The

world made of dreams, imagination, and images; its build upon a corrupt corporate

model that has exploited humans and Earth alike to harvest “beautiful” profits. While

fashion moves faster and faster with more colours, patterns, collections and styles than

ever before; the concept of clothing has not changed much since the invention of

mechanical looms nearly two and half centuries ago (Dana Thomas, 2019).

Clothes are some of the most important inventions in human history, but they are still far

from perfect (Froud et al., 2018). The fashion industry faces many challenges around

sustainability, equity and supply chain management (Gazzola et al., 2020). Garment

development is often central to these issues, with many industry stakeholders unwilling

to let go of outdated views of the discipline and practice habits (Mostafa and Klepper,

2018). However, the fashion industry slow acceptance of the technology innovations

resulted in increased scrutiny from researchers and policymakers (Niinimäki et al.,

2020). Thus, raising a question about the appropriate use of technology in the apparel

supply chain (Bertola and Teunissen, 2018). Nevertheless, technology can open up new

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avenues for innovation and eco-efficiency in fashion (Seymour, 2009). This thesis will

discuss how 3D Body Scanning - as an example of innovative technology - can bridge

the gap between three-dimensional data and product development. The thesis’s central

purpose is to clarify the changes to 3D Body Scanning required when technology is

introduced into retail services. The result will present an expanded view that investigates

the influence of service design in 3D Body Scanning. A service design perspective

acknowledges that technologies are not fixed and immutable (Shaw et al., 2018).

Instead, they are always subject to revision and refinement based on emerging insights

about their usability, effectiveness, and the fashion industry’s evolving needs. From the

supply chain perspective, 3D Body Scanning may have implications for longer garment

use-time (Maldini et al., 2019), reduced carbon footprint with less need for physical

samples (Špelic, 2019) and decrease in sales returns and textile waste (Brownbridge et

al., 2018).

The fashion industry needs a shift from a one-size-fits-all approach to more customer-

centred methods based on 3D Body Scanning to ensure its business model is profitable

and sustainable (Gill, 2015). 3D Body Scanning provides tools and techniques that could

allow the personalised size and fit recommendations (D’Apuzzo, 2007). Personalised

size and fit recommendations are required, especially in e-Commerce, to reduce size-

related returns that cost the UK fashion industry almost £7 billion (Just Style, 2020). In

addition, the inefficiency of the present apparel supply chain forces brands to destroy

$36.8 million worth of unsold merchandise every year (Lieber, 2018). The relatively

fragile fashion supply chain forces product lifecycle management (PLM) directors to re-

think how they design, produce and distribute garments (Russell, 2020a). Thus, the

fashion industry is increasing interest in, and expectations for, the translation of the

personalised, on-demand approaches by using technologies such as 3D Body Scanning,

which could connect with other industry interfaces, such as Virtual Fit Interfaces (VFI),

computer-aided design (CAD) and computer-aided manufacturing (CAM) to create new

product development workflow.

From a theoretical perspective, 3D Body Scanning allows integrating anthropometric

principles and computational approaches with means from garment development and

mathematics (Song and Ashdown, 2015). Researchers such as Gill (2015), Brownbridge

(2018) and Ashdown (2020) envision that 3D Body Scanners could identify subtle yet

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relevant changes in body physique and appearance that could aid customers with more

informed size and fit purchase choices. 3D Body Scanning produces a high-quality

representation of the whole human body surface using non-invasive optical methods

(Heymsfield et al., 2018). The scanned body can be visualised on the computer screen in

a point-cloud form or digital avatar and analysed using CAD software to automatically

detect body landmarks to generate a list of body measurements (Ashdown and Loker,

2010). These measurements, in turn, can be used to create a body form for pattern design

and fabric stitching (Olaru et al., 2012). Moreover, by enabling digital body scan records

of customers; the VFI developers can generate customised avatars for e-Commerce

shopping that could allow for more in-depth analysis of the size and fit criteria in a

customer-centred manner (Silva and Bonetti, 2021). Digital prototyping can direct a

more informed process in manufacturing based on mass-customised or bespoke patterns

generated through CAM software’s, as demonstrated by Harwood et al. (2020).

Accordingly, given technology potential, it would seem appropriate for the fashion

industry to harness 3D Body Scanning to deliver higher customer satisfaction in product

selection, purchase, and retention (Cooklin et al., 2012). However, the fashion industry

has been slow on the uptake of 3D Body Scanning techniques (Vignali et al., 2020). To

date, there remains no broadly adopted guidance or explicit theory on how to implement

3D Body Scanning and its data in the fashion industry (Gill, Hayes and Parker, 2016).

The information from 3D Body Scanning is challenging to convey effectively to meet

the needs of a wide array of users (Peng, Sweeney and Delamore, 2012). Garment

developers struggle to filter through and apply all the information’s available to practice

(Ashdown, Calhoun and Lyman-Clarke, 2009). 3D Body Scanning solutions are not

designed to provide fashion retailers with tools to experiment with pattern drafting

automation, nor are they designed to increase customer understanding of the size and fit

recommendations (Baytar and Ashdown, 2015). The mismatch between what the current

3D Body Scanning can deliver and what it needs to deliver is now of utmost proportion.

Fundamental Issues in 3D Body Scanning

The technique of applying 3D Body Scanning to apparel has been classified as

‘promising’ for over the past 30 years (Jones et al., 1989; Ashdown, 2020). For

technology to be labelled as ‘promising’ for so long is both good and bad news

(Kroemer, 2005). The good news is that the system must really be promising; otherwise,

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it would have been discarded by now. The bad news is that something has held it up in

this merely promising stage. The state-of-the-art in 3D Body Scanning has not advanced

yet sufficiently for the fashion industry to integrate body scan data with established

garment development methods (Yan and Kuzmichev, 2020). There remains what

Churchman (1967, p. 141) calls “wicked problems”.

A wicked problem is a “complex, unpredictable, open-ended, or intractable problem”

(Head and Alford, 2015, p. 712) and involves many stakeholders that often disagree

about the nature of the problem and the desired end-solution (Buchanan, 1992). The

growing disconnect between the hard system approaches (Checkland, 2000) and the

service design research (Prendeville and Bocken, 2017) means the latest developments

in 3D Body Scanning are unsuitable for contexts where the need for innovation is most

significant – the fashion retail industry (Daanen and Ter Haar, 2013). To develop

solutions, stakeholders design their ideas in line with what Kuhn (2012) calls a

‘disciplinary matrix’, that is, the commitment to the theories, values, and methods of

their disciplines. Each disciplinary matrix involves specific ways of problem framing,

investigating, and articulating. Thus, isolated silos of knowledge for 3D Body Scanning

exist within the engineering, technology manufacturing, fashion and computer science

fields (Toti et al., 2019). As a result, while there is increasing appreciation of the

technology potential, there remains limited agreement regarding best practices and

development goals in this nascent field.

This research argues that 3D Body Scanning is a wicked problem because it cannot be

solved but must instead be re-solved as the industry lacks well-defined and stable

problem statements (Bullas et al., 2016). In hard system approaches, tame problems are

ones where it is clear whether a solution has been found, i.e., solving an equation

(Ritchey, 2013). Thus, tame problems require a systematic methodology that can be

analysed and planned for by adopting a rational systems perspective; that is definable,

separable, and solvable (Noordegraaf et al., 2019). In contrast, while progress can be

made with wicked problems, they do not offer a prospect of an overall solution or an

agreed set of metrics (Hillgren, Seravalli and Emilson, 2011). Instead, centuries-old

assumptions about the nature of scientific inquiry and objective knowledge must be

questioned and partially modified (Brown, 2010). Service design tasks are commonly

regarded as wicked or ill-structured characterisations that pervade design research and

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practice (Ritchey, 2013). Therefore, using a service design-led approach is highly

beneficial when tackling complex problems to transform ambiguity into actionable

research statements and solution opportunities (Norman and Verganti, 2014). The

researchers’ fundamental challenge in wicked problems is to focus carefully and

reflexively on the nature of the adoption problems, user experience (UX), and

knowledge of relevant stakeholders (Head, 2019). The limited stakeholders’ interactions,

to date, provided little in-depth, qualitative analysis of the emergence of 3D Body

Scanning as a field understood by reference to the human factors (HF) (BSI British

Standards, 2011), service design (Holmlid and Evenson, 2008) and UX (Stickdorn and

Schneider, 2010). Moreover, past UX studies have focused on the garment tailor

perspective on pattern development practices (Ashdown and Dunne, 2006; Song and

Ashdown, 2012; Dāboliņa et al., 2018; Gill et al., 2018), or collation and analysis of

quantitative data concerning customer demographic or attitude towards certain aspects

of the application (Kim and Forsythe, 2009; Peng, Sweeney and Delamore, 2012; Shin

and Baytar, 2014; Zhang et al., 2019). Quantitative studies have been valuable in

identifying common patterns of technology longevity. However, they have not offered

fine-grained and in-depth accounts of 3D Body Scanning as an academic subfield in all

is disciplinary and complexity. The following sub-sections outline four areas in which existing theory is underexplored

and still in the early stages of research. This thesis argues that the service design

approach can bring a different perspective to inform stakeholder’s decisions before lock-

in is established and further downstream development, which is in interdisciplinary

research - complex and often very costly to replace (Matthews et al., 2019).

1.3.1 The Design of Virtual Fit Interfaces in e-Commerce

The revenue in apparel e-Commerce in 2020 was worth USD 110.6 billion and is

expected to increase to USD 153.6 billion by 2024 (Foysal et al., 2021). In the past

fifteen years, the retail sector has undergone a substantial transformation, such as

integrating novel digital technologies and business models, including platform-based

multi-sided marketplaces, access to information, a global vision, and changes in

computation and mobile shopping (Marceda Bach et al., 2020). Moreover, the COVID-

19 pandemic has highlighted the importance of e-Commerce and has elevated digital as

25

an urgent priority across the fashion supply chain (Russell, 2020a). However, there are

multiple common factors like trust, design quality, atmospherics, and emotions that may

influence customer decision in e-Commerce shopping (Reinartz, Wiegand and

Imschloss, 2019). However, the availability of fit information is often cited as the most

critical element for customers in determining overall satisfaction with garments purchase

(Wang et al., 2021). Through this time, e-Commerce evolved from old-style home

catalogues, in which brands often provided static, vague images alongside incomplete,

and sometimes incorrect size and fit information (Ashdown, 1998). To aid customers in

product selection, most e-Commerce platforms offer product descriptions with high-

quality photos or videos of a model wearing the garment with options to zoom in and

rotate (Lee, 2019), which customer can also share on social media platforms (Blazquez

et al., 2019). Yet troublingly, the online apparel return rate remains substantially high

and exceeds $550 billion worldwide in 2020, that is 75.2% more than four years prior

(Mazareanu, 2018; Sender, 2020). De Leeuw et al. (2016) estimated that around 60% of

the apparel purchased online is returned due to poor fit because e-Commerce websites

do not provide any means of measuring body size and shape. Instead, retail expects

customers to know how to estimate and choose the exact size for a specific garment or

brand while shopping (De Coster et al., 2020). However, most customers do not know

their body measurements when selecting a garment size and can have significant

difficulty estimating their dimensions from model photos (Xia et al., 2018). Thus,

customers often buy a garment in two or more size labels and return the ones that do not

fit, as they do not want to waste their time trying to determine which size would be

correct (Lantz and Hjort, 2013). However, this mindset adds to cost and waste, affecting

overall retail profitability (Choi and Guo, 2018). Therefore, unless the fashion industry

strengthens its digital capabilities in e-Commerce product presentation, retailers will

continue to suffer profit loss (Hernández, Mattila and Berglin, 2019). These research

findings highlight the need to support the established methods with more interactive and

personalised platforms such as Virtual Fit Interfaces, agreeing with Plotkina and Saurel

(2019) and Zhang et al. (2019).

Virtual Fit Interfaces and sizing surveys have been developed to help customers evaluate

garments by delivering information comparable to direct experience with products (Kim

and Forsythe, 2007). The VFI is an application that allows customers to evaluate

garments through physical simulation of clothes on an animated virtual 3D avatar sized

26

to their body measurements. The most effective product presentation is assumed to be

when commercial models’ physical features match customers (Chevalier and Lichtlé,

2012). Thus, VFI focus on amplifying the perceived resemblance between the customer

and the avatar to increase purchase intentions and confidence in apparel fit (Javornik,

2016). VFI aims to provide interactive size and fit prediction methods to guide

customers in product selection, purchase, and retention (Mintel Group Ltd., 2019c).

Although adoption among fashion e-Commerce has been steady, VFI has not displaced

more traditional imaging methods despite often superior and more engaging

performance (Algharabat et al., 2017). One reason for this is that the VFI has mostly

conformed to a ‘do-it-yourself’ attitude, in which customers are required extensive

knowledge to measure their own body thoroughly (Idrees, Vignali and Gill, 2020). Even

avoiding the various usability pitfalls such as the time and stress on the customer (Kim

and Forsythe, 2008), VFI raises questions as to how reliable and accurate the data is for

the requirements of the garment developers (Plotkina and Saurel, 2019). Garment

developers need precise information about the collected data that can be analysed

according to various statistical models (Ashdown, 1998). Data accuracy is a problem

that has wrapped itself around the VFI technology (Kouchi and Mochimaru, 2011). As a

result, VFI data is not being capitalised in a manner, which is of direct observable

benefit to the customer (Gill, 2015). The available VFI systems allow the customer to

evaluate combinations of garments for visual appearance, yet the evaluation of fit or the

assessment of the correct size is still difficult, if not impossible (Magnenat-Thalmann et

al., 2011). In summary, VFI solutions face adoption barriers due to an overcomplicated

process and low user adaption (Gunatilake et al., 2018).

The fashion retailers could capitalise on data from 3D Body Scanners to make the

process easy, relevant, enjoyable (Kim and Forsythe, 2009) and increase accuracy in

product selection (D’Apuzzo, 2007). 3D Body Scanning affords a better understanding

of the customer body, its shape and ratio proportions and its relationship to clothing

(Giri et al., 2017). Integrating 3D Body Scanning within VFI will force garment

developers to re-think the way size and fit analysis is done and provide interactive

communication tools in e-Commerce for customers to engage (Spahiu, Shehi E and

Piperi, 2014). The prevailing practices in product development, which depend on the

craft of the pattern tailor, will thus have to evolve to take full advantage of the

introduction of 3D Body Scanning. As yet, there is no research to articulate the inner

27

workings of emerging VFI that are likely to form the basis of the future of e-Commerce

(Idrees, Vignali and Gill, 2020). The examination of the existing VFI methods and

techniques can lead to more informed and transparent product development practices

that will justify the use of the 3D Body Scanning within VFI (Kim, Kim and Park,

2017). Therefore, theoretical design principles need to be studied and applied

systematically to guide VFI developers with the tools, techniques and insights about

the system development requirements (Durá-Gil et al., 2019).

1.3.2 The Relative Advantage of 3D Body Scanning in Fashion Retail

3D Body Scanning field draws together stakeholders with expertise in fashion,

engineering, manufacturing, computer science, and research. The prevailing diversity in

a number of ‘knowledge silos’ (Finnegan and Willcocks, 2006) and the associated

difficulties of coordinating these disparate knowledge bases have led to problems with

technology deployment in retail (Lewis and Loker, 2014). Garment developers

understand that 3D Body Scanners are good at acquiring data but are not quite ready to

move beyond that stage, as is evident in research by Ashdown and Dunne (2006) and

Harwood (2020). These research papers establish that while fit can be virtually tested,

there is still a lack of approaches that can create mass-customised products from

recorded body scan measurements. The fine-tuning issue can be reflected by ongoing

attempts to diffuse on the ready-to-wear market level: My Virtual Model (1994), Levi

Strauss Original-Spin (1997), Me-ality (2000), Cyberware (2001), QVIT (2009),

Bodi.Me in retail stores: New Look and Selfridges (2012), Artec Shapify Booth in Asda

(2014), Bespokify (2015), or Zozo-Suit Europe (2019). The new supply-chain synergies

and innovation models are therefore needed for the 3D Body Scanning manufacturers to

realise technology contribution to the garment development practice. However, viewed

through the past adoption strategies – 3D Body Scanning has thus far been fragmented,

owing to a lack of relevance to garment developers practice (Song and Ashdown, 2015),

insufficient skills and expertise (Wren et al., 2014) and inadequate UX considerations

(Lewis and Loker, 2017).

The slow adoption of information technology (IT) innovations has lead scholars to seek

to understand, manage and predict diffusion rate (Lyytinen and Damsgaard, 2001).

Many IT studies have documented the uptake of testing, barriers and facilitators to

28

implementation, and the attitudes, preferences, and knowledge of stakeholders (Boudet,

2019). The well-established account to explain and predict rates of IT adoptions is the

diffusion of innovation (DOI) theory (Ryan and Gross, 1943), as propagated by Rogers

(1962; 1995; 2003). DOI theory states that acceptance of an innovation is a function of

the potential adopter’s value and perceptions of the relative advantage, compatibility,

trialability, observability, and low complexity of the innovation. DOI theory seeks to

explain how an innovation spreads through a social system as a group-based

phenomenon (MacVaugh and Schiavone, 2010). Consequently, DOI was chosen as a

theoretical lens for this study because it extends IT theories into concepts, constructs and

issues that specifically relate to technological usage in today’s rapidly changing and

complex world (Chin and Marcolin, 2001). Equally important, the alignment of multiple

stakeholders is required for 3D Body Scanning adoption (Gill, 2015) and the design of

the retail innovation’s acceptable uses (Jin and Shin, 2020). A comparison of various

stakeholder’s views and the role of users’ involvement could provide a more fine-

grained insight into the likely diffusion patterns of 3D Body Scanning. Unless we are

clear on the success measures for various actors involved in adoption (Liedtke et al.,

2015), the value of 3D Body Scanning somewhat becomes limited (Gupta, 2020). Thus,

rather than expending this research effort primarily on yet additional attitude constructs,

dichotomous measures, and personality traits (which ought to be the purview of other

disciplines), diffusion research focuses on the different aspects of people and technology

that shape perceptions and ultimately behaviour towards new customer-facing

innovations (Gaba and Greve, 2019). This approach is the crux of service design – it is

the behind scenes goings-on (processes, governance, organisational structures, change

management or corporate culture) that needs to be designed and crafted in a human-

centred way such that a human-centred front-of-stage experience can be delivered

(Blomkvist and Holmlid, 2011).

3D Body Scanning developers created disconnected solutions buried in systems that do

not automatically integrate well with all commercially available CAD/CAM software’s

and body landmarks because many vendors consider data as proprietary (Peng, Sweeney

and Delamore, 2012). The system fragmentation poses a problem for garment

developers who must decipher how to use 3D Body Scanning in product development

for improved size and fit methods. The data from 3D Body Scanning are complex to

handle, and the research publications that aim to explain how to integrate scan data

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within the practice uses diverse statistical models (Gupta, 2014). To understand the

scientific literature is by itself a significant task for garment developers, and further

developing applications or algorithms based on these works can be yet another milestone

(Gill, 2015). Past research, however, suggested that 3D Body Scanning developers need

a framework that evaluates existing research and expertise gaps linked to tangible UX

outcomes and technical performance (Nash, 2019). This, in turn, requires constructive

analysis of stakeholders’ design practices, perspectives, skills, and knowledge bases that

set the diffusion research agenda for retail adoption and implementation (Reynolds et

al., 2007). Thus, stakeholders in the interdisciplinary research field have to be flexible in

developing products, borrowing the best from each industry expertise, skills and

knowledge, moulding, and shaping it as they go along to excel in 3D Body Scanning

(Steinberg, Horwitz and Zohar, 2015). Therefore, research is needed to generate

consensus between different stakeholders on research questions to increase uptake of

priority setting exercises and design recommendations, as demonstrated by Esmail et al.

(2013). Such a process could enhance the value of research and avoid resource

allocation to efforts that do not address critical priorities.

1.3.3 The Customer Journey in 3D Body Scanning Service

There has also been an exponential growth in the number of publications regarding 3D

Body Scanning applications, yet their primary emphasis is always on the technology

capabilities and the performance itself (Daanen and van de Water, 1998; Daanen and Ter

Haar, 2013). System engineering approaches – as the other hard system approaches –

assumes a relatively well-structured problem situation in which there is virtual

agreement on what constitutes the problem; it remains to organise how to deal with it

(Checkland, 2000). However, this approach ignores the fact that there will be a

multiplicity of views and ontologies, with alternative interpretations fighting it out on

the basis not only of logic but also power, politics and personality (Steinberg, Horwitz

and Zohar, 2015). Therefore, commercial decisions are being made base on technical

capabilities without the ability to realise the process and perception of what fashion

retailers and customers need and want in the garment design process (Robinette et al.,

1997; Kennedy et al., 2020).

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To understand the service experience, it is crucial to walk in the customer’s shoes and

explore a design space, as viewed from the human perspective (Holmlid and Evenson,

2007). Service design applies methods and tools that are fine-tuned and close to

customer practices to inquire and make sense of their experiences and the context they

occur (Wetter-Edman et al., 2014). Numerous methods and tools are available to support

service design providers in getting insights, such as customer journey maps, service

blueprint, mobile ethnography, and desktop walkthrough (Holmlid, 2009). However,

only the customer journey offers quotes and insights from customers about their service

experience to highlight problems, opportunities and values (Rudkowski et al., 2020).

The customer journey reflects the service process that a retailer expects customers to go

through, mapping touchpoints involved in their service (Silva and Bonetti, 2021).

Touchpoints are “any verbal (e.g., advertising) or non-verbal (e.g., product usage)

incident a person perceives or consciously relates to a given firm or brand” (Homburg,

Jozić and Kuehnl, 2017). Likewise, Segelström and Holmlid (2009) found customer

journeys to be a prevalent approach to structure early phase user research in service

design projects. To better understand customers’ journey in 3D Body Scanning, this

study proceeds by classifying technology touchpoints and content features, which in

future research could be used as a foundation for the later stages in service design:

service blueprinting and ethnographic studies.

One consistent finding from 3D Body Scanning literature is that the customer is often

unfamiliar with 3D Body Scanning, and such findings may frustrate technology

stakeholders, who wish for more engaged and informed users (Peng, Sweeney and

Delamore, 2012). The unfamiliarity with 3D Body Scanning resulted in the gap between

technology and the customer that continues to widen, as there is no meaningful depiction

of experience and its diverse representations (Miell, Gill and Vazquez, 2018). The

customer and their personal experiences of size and fit within the apparel systems are

often not considered in the technology elaboration process (Idrees, Vignali and Gill,

2020). One reason for customer hesitancy may also be that the fashion industry

centralised sizing systems, which remove customers from product development practices

(Gill, 2015). Nevertheless, given the management information systems (MIS) field

advances in understanding how customers process information (Saldanha, Mithas and

Krishnan, 2017), it may be the 3D Body Scanning developers who are operating on

outdated assumptions about the human-decision making process (Vehmas et al., 2018).

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Research in MIS aims to understand, describe and explain what customers know and

think about innovation technologies – and equally important, how they have responded

or might respond to their deployment (Nutt, 1986; Baskerville and Myers, 2009).

Understanding different perceptions and responses to 3D Body Scanning could help to

facilitate communication between different stakeholders: retailers, developers,

researchers and customers’ (Lee, Xu and Porterfield, 2020). Thus, this kind of

interdisciplinary interaction can provide critical information for anticipating customers’

reactions, attitudes and inform educational efforts (Henkel, 2006). Boudet (2019)

demonstrated that while stakeholder’s perceptions and responses do not guarantee

acceptance and adoption, its absence is likely to result in technology fail. Inspecting past

research reveals that researchers have employed a wide range of quantitative techniques

to gauge perceptions of and responses to 3D Body Scanning, including surveys,

questionnaires, interviews, and focus groups (Sohn, Lee and Kim, 2020). Quantitative

study methods such as surveys are useful and relatively straightforward for gathering

descriptive information about customer perception in innovation (Freeman, 1984).

However, qualitative studies are also needed to explore why customers perceive

technologies in a certain way, how they came to hold these perceptions and what actions

stakeholders can take to improve technology services (Kawamura, 2020). Therefore,

research into customers journey in 3D Body Scanning, highlighting existing interaction

barriers and solutions, could offer a comprehensive set of design recommendations.

1.3.4 The 3D Body Scanning Service Workflow

3D Body Scanning developments lack common aims and proven methods to translate

theoretical principles into practice workflow (Gill, Hayes and Parker, 2016). To be

applicable in the fashion industry, 3D Body Scanning must prove the feasibility and

enable reliable and reproducible acquisitions of scan data (Parker, Gill and Hayes, 2017;

Tinsley et al., 2020). Nevertheless, 3D Body Scanning processes are based on ideas that

bring them in constant conflict with established product development rules (Lee et al.,

2012). The barrier slowing technology adoption in retail shows a failure of 3D Body

Scanning developers to disseminate technologies that are accessible, affordable and

practical (Gill et al., 2018); too often, impressive technical demonstrations are not

followed by tangible and relevant product development findings (Loker, Ashdown and

Carnrite, 2008; Lewis and Loker, 2017). There are outstanding research questions

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regarding the extent to which, and in what ways, fashion retailers and garment

developers are adapting to new practices with its highly divergent values (Daanen and

Byvoet, 2011; Daanen and Ter Haar, 2013). The problems associated with how the

fashion industry can capitalise on the technology value remain elusive.

3D Body Scanning have long been embedded in “dumb” devices (Reed, Gannon and

Larus, 2012, p. 27) and the significant change is the ability to connect with other retail

product development tools (Ashdown, 2020). 3D Body Scanning has the potential to

offer a platform for building sophisticated services that could make far more than up-to-

date replacements. Moreover, the fashion industry is oriented towards 3D services as a

service model, owing to the automation and democratisation of product customisation

and personalisation processes (Lee, 2021). Therefore, positioning 3D Body Scanning as

a service in research could allow directing more emphasis on infrastructure, networks

and interdisciplinarity required to fully realise technology potential (Silva and Chi,

2020). The objective of service design is to plan and promote the carefully coordinated

action required to execute a high-quality service (Sangiorgi, 2009). Service design is not

about technology per se but about the overall quality of the proposed configurations of

service delivery that might result from the new technology’s comprehensive adoption

(Zomerdijk and Voss, 2010). As Shaw et al. (2018, p. 47) summarised, service design is

about “reinventing the service process to achieve greater (and often different kind of)

impact as opposed to simply improving existing workflow”. The extensive quantity of

information generated from 3D Body Scanning produced research that is dispersed in

many databases and across disciplines (Asbury and Cottle, 2019). This fragmentation

makes it hard to comprehend the interactions and relationships and increase the 3D Body

Scanning field’s underlying complexity (Toti et al., 2019). Thus, research is needed to

integrate data into the interoperable framework (Heymsfield et al., 2018). The integrated

3D Body Scanning framework can be presented to fashion retailers and garment

developers in a user-friendly manner, allowing them to explore, analyse, and navigate

the service requirements with ease (Ashdown, 2013). According to Norman and

Verganti (2014), the concept development and service prototype is a critical stage in

diffusion as a driver of the many decisions made during the design phase of service. In

essence, this study calls for new ‘abstractions’ and a ‘new unit of analysis’ to better

understand innovation – i.e., the ‘service system’ – intended as ‘a configuration of

people, technologies, and other resources that interact with other service systems to

33

create mutual value’ (Sangiorgi and Junginger, 2015, p. 167). Edvardsson and Olsson

(1996) define the service concept as “the detailed description of what is to be done for

the customer (what needs and wishes are to be satisfied) and how this is to be

achieved”. Thus, this is not just about generating evidence of the effectiveness of service

design as an approach to implementation but is about imagining new approaches and

applications that will drive the discipline of 3D Body Scanning forward.

Research Aim

The main aim of this research is to broaden the understanding of the relationship

between Service Design and 3D Body Scanning; this is motivated by the aspiration to

increase the technology adoption and use by customers, retailers, and garment

developers in the fashion industry. Together, this research provides solid foundations to

answer the following research aim:

The research aim:

To situate 3D Body Scanning as a service within the apparel sector in a way that

will be useful in its utility to buy better fitting garments while providing a pleasant

experience to the diverse range of users.

Research Questions

The thesis aims to evaluate the concept of 3D Body Scanning as a service for the fashion

industry. Specifically, it targets the previously presented shortcomings associated with

fragmented research efforts and proposes a service design thinking approach. The following four research questions are investigated in this thesis:

Q1 What are the size and fit tools and techniques that could support the provision

of 3D Body Scanning in the existing fashion services?

Q2 What is the 3D Body Scanning stakeholders’ outlooks for the technology

diffusion within fashion retail? Q3 What does the customer 3D Body Scanning journey look like in retail, and

which factors enable or inhibit the service presentation and interaction?

Q4 What are the critical elements of the 3D Body Scanning service in a fashion

retail scenario?

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Research Objectives

In order to address the research question, a number of research objectives were set:

• To analyse the existing knowledge base on 3D Body Scanning and identify gaps in

connection to the apparel product development and service design (RQ1).

• To evaluate Virtual Fit Interfaces (VFI) potential to connect with 3D Body Scanning

to deliver effective size and fit recommendations (RQ1).

• To apply the diffusion of innovation theory and interview 3D Body Scanning

stakeholders to synthesise different points of view in technology adoption (RQ2).

• To evaluate the existing customer journey in 3D Body Scanning and recommend

design strategies to make the interaction with technology more pleasant (RQ3).

• To analyse the usability of 3D Body Scanning interfaces through eye-tracking

methods to discover barriers in content presentation (RQ3).

• To define a service workflow of 3D Body Scanning that summarise the technology

state-of-the-art and discuss the opportunities and challenges from data acquisition to

the future application of insights in the apparel sector (RQ4).

Conceptual Framework

This research highlights the value of implementing service design through the design

thinking process to situate 3D Body Scanning as a service within the apparel sector.

Service design is a multidisciplinary approach that plays a crucial role in fostering

service innovation (Prestes et al., 2019). The service design is intended to describe the

product development stages that mediate between system characteristics and user

experience (Hillgren, Seravalli and Emilson, 2011). Similarly, design thinking is a

human-centred approach to innovation based on design tools to integrate people’s needs,

potential technologies, and businesses’ requirements (Secomandi and Snelders, 2011).

Specifically, design thinking focuses on solving wicked problems (Rittel and Webber,

1973) and is not limited to classical design problems (such as designing an ergonomic

chair) (Thoring and Müller, 2010). This resonates with the service design approach,

where an assumed solution to a problem is not the driver of development (Pfannstiel and

Rasche, 2019). Thus, the link between service design and design thinking is indubitable

(Clatworthy, 2017). According to Holmlid (2007), service design depends on specialist

competence from the interaction as well as industrial design. In that sense, service

35

design needs analytic processes, depictive representations, experiential aesthetics, and

product deliverables (Blomkvist, Holmlid and Segelström, 2010). Design thinking is

well-suited to meet these needs, as the concepts, skills, and mindset of design thinking

can foster solutions to ill-defined, complex, and unusual problems (McDermott,

Boradkar and Zunjarwad, 2014). The 3D Body Scanning industry is compartmentalised (Toti et al., 2019). However,

service design can help shift the focus of innovation from pure technology to the

contexts of apparel. This research has taken the design approach, rather than

engineering, because before proposing a solution, it is essential to get the problem

perspective right (Dorst, 2011). Jumping directly to solve the problem can lead to a

solution that solves something else and not the original problem at hand (Boradkar,

2010). Thus, adopting the engineering perspective can lead to narrow, discrete, and

highly partial views on 3D Body Scanning, failing to capture the diverse, multiple, and

related technology diffusion methods. In contrast, the research into design increases the

flexibility of knowledge structures because it provides exposure to different domains and

thus loosens up existing linkages, helping to create new ones within and between

domains (Mannucci and Yong, 2018). The first step towards 3D Body Scanning as a

service is a system for visualising the scope of a phenomenon so that services can be

given proper position and weight in the context of any market entity. IDEO created the

proposed ‘Service Design Thinking’ (SDT) framework in Figure 1 at The Hasso Plattner

Institute of Design at Stanford (Plattner and Weinberg, 2009). The iterative process

compromises six stages with principles that relate to the process of business, information

and technology design (Thoring and Müller, 2011).

Figure 1 Service design framework based on Plattner and Weinberg (2009).

36

1. Understand –– The outcome of this stage is to understand the real world, with the

objective of a systematic exploration of the existing research theories and gaps to

ensure the right design challenges are addressed. The SDT framework allows the

researcher to mediate between customer needs and an organisation’s strategic intent.

The focus oscillates between understanding the problematic context and new ideas

for a solution in the process of co-evolution of problem and solution. This stage was

satisfied through a literature review in chapter two, which identify evidence that

supports the proposed thesis structure and determine the extent to which the

proposed structure goes beyond existing research.

2. Observe –– The outcome of this stage is to observe and evaluate the size and fit

applications in the fashion industry. At this stage, the researcher is empathising with

users and can experience the situation first-hand - ideally through observation or

participation. However, it is also important to make an observation in an unbiased

way; thus, the SDT framework suggests the use of personas can increase focus on

the needs of the target users. This was achieved in chapter four through a systematic

content analysis of the existing Virtual Fit Interfaces that have the potential to

connect with 3D Body Scanning to enhance applications usability. Through an

analysis of ten leading VFI’s, the persona of a single female dress form was used to

understand usability issues. The result demonstrates the shortcomings of traditional

sizing metrics and justifies the fashion industry need for the transition toward 3D

Body Scanning methods.

3. Point of View –– The outcome of this stage is to understand different points of view

in the retail diffusion process and what expertise is required to create a 3D Body

Scanning service. This section was addressed in chapter five through semi-structured

interviews with 3D Body Scanning stakeholders (technology manufacturers,

software developers, fashion retailers, and academics) based on diffusion of

innovation theory. The result develops a more comprehensive scope of the problem

by integrating an interdisciplinary perspective in research implementation, showing

–through rich pictures –present and desired collaboration outcomes and reflecting on

directions involved in service co-creation. Furthermore, this research focuses on

studying 3D Body Scanning diffusion in ‘real-time’ and not ex-post, which makes

37

the application of the Rogers (2003) innovation theory most suitable for

investigation.

4. Ideate –– The outcome of this stage is to ideate design guidelines for technology

stakeholders to improve the customer journey in 3D Body Scanning. This is

achieved in chapter six, through the customer evaluation of the 3D Body Scanning

design with the applications of (i) participatory-design technique and (ii) eye-

tracking, respectively. The study contributes to the field by extending developers’

knowledge of design practices and how users factored into the practice; thus, it fills

an essential gap in the field of 3D Body Scanning research. By expanding the

solution space, future research will be able to look beyond the usual methods of

solving problems in order to find more elegant and satisfying design solutions.

5. Prototype –– The outcome of this stage is a set of research directions that propose

five action points that could accelerate the transition towards 3D Body Scanning

services in fashion retail. This is achieved through the creation of the preliminary 3D

Body Scanning workflow model in chapter seven. This chapter aims to develop a

well-defined description of the design intent to make the complex system more

understandable and accessible for all stakeholders through visual abstraction and

research themes integration and elaboration.

6. Test – The outcome of this stage is to redefine research problems and inform

stakeholders about the design requirements and the technology users - how users

think, behave, and feel, and to empathise. Chapter eight summarises thesis findings,

outcomes, and contributions, with implications to the 3D Body Scanning theory and

practice. This 3D Body Scanning interpretation represents a significant step forward

in building a consensus that was previously unattainable due to a lack of

harmonisation, standards, and guidelines. The thesis findings, thus, serve as an open

resource for evaluating interpretations from stakeholders with distinct backgrounds,

development procedures and design objectives into actionable design goals.

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Thesis Outline

The thesis comprises eight chapters, whose contents are summarised below.

Chapter 1: Introduction –– provides an overview of the 3D Body Scanning industry ––

the big picture, research context, challenges, goals, and the comprehensive guide to the

thesis structure. The chapter aims to summarise the need for the investigation and

establish and elaborate on the research focus.

Chapter 2: Literature Review –– explains theory and hypotheses upon which this

research is based and addresses the gaps in existing works, while at the same time

proposes the research design that goes beyond existing theoretical models. The objective

is to create interdisciplinary research based on anthropometry, product development,

human factors, and service design for the 3D Body Scanning deployment in the fashion

industry.

Chapter 3: Research Design –– reviews the research design selected for undertaking

this project. The mixed-methods approach is presented as an option that strategically

combines qualitative and quantitative methods to enable complementary strengths and

reduce weaknesses when carrying out research projects in the ‘real world’. This chapter,

following the research onion framework (Saunders et al., 2015), examines the research

questions and the methods used to tackle them in relation to other design elements.

Chapter 4: “Online Virtual Fit is Not Yet Fit for Purpose: An Analysis of Fashion e-

Commerce Platforms.” To foster e-Commerce growth, VFIs developers must reconsider

how they enact customer body data to offer size and fit recommendations based on a

more appropriate representation of the human body. This study analyses current VFI

approaches to data collection and use, providing insights into how VFIs can use 3D

Body Scanning to facilitate garment development in a more open and informed manner.

The results show a profound disconnect between anthropometric data and garment

development approaches, thus sustaining –– rather than addressing –– existing sizing

concerns. The study contributes to the realisations of more reliable tools and methods,

with further implications for VFI developers, garment developers and 3D Body

Scanning manufacturers on the customer’s self-body classification methods.

39

Chapter 5: “Stakeholders in 3D Body Scanning: How to Connect the Isolated Silos of

Knowledge for the Fashion Mass-Customisation” Fashion retailers and garment

manufacturers do not embrace 3D Body Scanning as the disruptive technology for mass-

customisation it promised to be. To realise mass-customisation, the fashion industry

needs a new supply chain model incorporating 3D Body Scanning. Through 39 in-depth

interviews, this study addresses barriers to the fashion industry adopting 3D Body

Scanning’s social and economic benefits, how to improve channels of communication,

and the problems deploying the technology. This study offers 3D Body Scanning

developers and fashion retails a means to improve collaboration, developing new supply

chain synergies, and deliver on the promise of bespoke garments within mass-

customisation.

Chapter 6: “Customer Journeys in 3D Body Scanning: The Good, the Bad and the

Unexpected.” The recent customer’s appreciation of customised fashion has highlighted

the need for an industry shift to 3D Body Scanning. The technology is expected to

address customer’s unmet demands in tailor size and fit, enabling the provision of highly

individualised products. However, the deployment of 3D Body Scanning to the retail

remains hindered by the technology design stymied by poor usability and an incomplete

evidence base. For retail adoption, 3D Body Scanning developers need to make the

interaction more effective and less burdensome to customers while providing retailers

with meaningful data. This study set out to establish design recommendations for 3D

Body Scanning using user-centred principles, including a mix of semi-structured

interviews, focus group workshops, and eye-tracking analysis with customers and

fashion retailers. The result provides a list of nine critical design issues that developers

need to address to improve the customer journey.

Chapter 7: “How Can Fashion Industry Integrate 3D Body Scanning Workflow: A

Critical Review.” 3D Body Scanning needs to overcome many challenges to remain fit

for purpose in retail as its methods do not align well with decades-old tailor practices

designed to address the size and fit in apparel. The problems associated with the idea of

how to capitalise on technology value remains elusive. In effect, retailers are faced with

a dilemma that will only become more pronounced as many sophisticated but unproven

solutions continue to proliferate in the market. Technology developers must reassess

how to demonstrate the 3D Body Scanning value to the fashion industry. This study

40

provides an interdisciplinary service workflow framework that helps to articulate shared

concepts and addresses the opportunities and challenges from data acquisition to the

application of insights in retail settings.

Chapter 8: Discussion and Conclusion –– highlights the originality and contribution to

existing theory and practice, connecting the overall findings to the research aims and

objectives and emphasises its originality. The chapter finishes considering the lessons

learnt from the methodology applied in this research, its benefits, challenges, and

limitation.

41

CHAPTER 2

LITERATURE REVIEW

Chapter two presents a critical review of related work to 3D Body Scanning providing

background information concerning underlying concepts relevant for this research

project. This chapter aims to define the research problem and provide a comprehensive

scientific foundation that allows for the identification of knowledge gaps and research

opportunities.

Objectives, Scope and Structure

The literature review is divided into four sections, as shown in Figure 2. To successfully

investigate the research problem in the interdisciplinary field, the researcher needs to

understand and learn from various disciplines’ perspectives. This ideally results in a new

perspective that is more than the sum of its components. The literature review began by

examining the garment developer perspective, focusing on the relationship between

anthropometry and apparel design engineering in section 2.2. This section reviews the

anthropometric product requirements and summarises existing limitations in traditional

manual size and fit approaches. This analysis lays the groundwork on which subsequent

sections are built. Next, section 2.3 evaluates state-of-the-art in 3D Body Scanning from

a technology perspective – its characteristics, capabilities, and concepts. Starting with

general introductions to 3D Body Scanning, the analysis becomes increasingly specific

and eventually pinpointed research problems. The identified issues with 3D Body

Scanning are analysed from a Human Factors perspective and reveal fashion

practitioners’ obstacles when using technology. The most significant obstacles are

related to inappropriate service user understanding discussed in section 2.4 and limited

customer interaction evaluated in section 2.5. Thus, next chapter in section 2.5 examines

theories related to service design that could be of particular interest to the 3D Body

Scanning developments. The chapter culminates in specific research gaps, opportunities

and questions that form the basis for subsequent chapters in section 2.6.

42

Figure 2 Literature review roadmap. Source: author’s own

Anthropometry

Anthropometry is an area of science, which depends upon adherence to the particular

rules of measurement as determined by national and international organisations (Roberts

and Bolton, 1878; Tinsley et al., 2020). The inspiration for the name anthropometry

came from the Greek words ‘anthropo’ meaning human, and ‘metreein’ meaning to

measure (Gupta and Zakaria, 2019). According to Pheasant (1986), anthropometry is an

ancient science, and like many old sciences, has followed a variety of paths. The

diversity of anthropometric paths is both its richness and its bane (Norton and Olds,

1996). The consequence of multiple anthropometric traditions has been the lack of

standardisation in identifying measurement landmarks and measurement techniques

(Bye, Labat and Delong, 2006; Parker, Gill and Hayes, 2017). Nevertheless,

anthropometry becomes a fully-flagged research discipline in 1949 with the ground-

breaking work of Chapanis et al. (1949), who helped to connect data from psychology,

physiology, anthropology, and medicine into the domain of engineering that came to be

known as the field of ergonomics and human factors. As a result, the term

anthropometry refers to scientific knowledge about human physical body characteristics

such as body dimensions, body volumes, masses of body segments, a centre of gravity,

and inertial properties (Heymsfield et al., 2018). Pheasant (1987) expanded the

43

definition further with the phrase ‘applied anthropometrics’ concerning numerical data

of the size, shape and other physical characteristics of human beings with the

applications into design context. Pheasant definition underpins the product development

idea that using anthropometric principles, tools and techniques guided by human factors

approaches can contribute to designing better-fitting apparel products.

Section 2.2 in the literature review aims to define the scope of anthropometric research

in the fashion apparel context. Section 2.2.1 focus on an overview of anthropometry as a

research field in garment development. This section considers the role of anthropometry

in the historical analysis of garment development and suggests how these methodologies

influence the fashion industry practices today. Next, section 2.2.2 discusses pattern

drafting methods, including the grading and fit variables. The anthropometric tools and

protocols are evaluated in section 2.2.3. This section creates a formal structure for the

existing garment development methods and models.

2.2.1 Anthropometry in the Apparel Design Engineering

The measurement of the body is central to the study of apparel product development.

“Anthropometry is one of the oldest methods used to assess the size and shape of the

human body” (Heymsfield et al., 2018, p. 685). Among the anthropometric tools for

clothing construction are callipers, weight balances/scales, tape measures, and calibrated

rulers that have been used for centuries to assess bodily dimensions (Heymsfield and

Stevens, 2017). From the early medieval times, tailoring was recognised as a highly

skilled and strictly hand-made craft in which skill and human resources were the

essential elements of its technology (Antonaglia and Ducros, 2020). The drafting

methods, however, were jealously guarded secrets; consequently, little was published on

pattern drafting prior to the last quarter of the eighteen century (Heisey, Brown and

Johnson, 1988). So, for centuries and well into the middle of the 18th century, the

practices and techniques remained the same (Beazley, 1996).

The human body was measured with a measurement taker – a long and narrow

parchment strip (Aldrich, 2000). Alternatively, tailors used the draping method whereby

fabric was wrapped around the body and thus attained its shape directly (Aldrich, 2007).

The details obtained from either of these methods were then transferred directly to the

44

cloth from which the garment was to be made. Later, patterns were produced on paper

and then laid onto the fabric (Emery, 2014). However, these pattern measurement

methods were inadequate when the development of close-fitting garments of elaborate

design became popularised in Europe (Lemire and Riello, 2008). In the middle of the

18th century, tailors began to search for a more reliable system (Paris, 2010). This search

led to the invention of a standard tape measure –– a measuring instrument that gained

widespread adoption in the 19th century (Kouchi et al., 2012). This invention drew

attention to the comparative relationship that exists between various parts of the body

(Serge, 2007). By realising that there is a relationship between breast measurement and

length to waist, chest width, back and scye – a new approach to garment creation was

introduced based on the application of the geometrical rules and principles to the

anatomical proportions of the human figure (Fortunati, Katz and Riccini, 2003).

The introduction of geometrical rules was a breakthrough into a more sophisticated

tailoring profession that is known today (Ashdown, 2007). By the start of the 20th

century, the increased awareness of fashion clothing led the way to a new practice,

whereby tailors assembled to work under one roof (Riello, 2011). Furthermore, the

behavioural and attitudinal changes in population after World War II had a high bearing

on tailoring craft – a craft which by then begun to disappear (Olds, 2001). The changes

in a freer and more straightforward silhouette made the industry realise that their

manufacturing methods, technology, and techniques with small premises are insufficient

to cope with new market demands. To overcome this difficulty, the workforce and

engineers from other industries were employed to build production lines for producing

simple garments (Yanagisako, 2018). These new techniques were based upon methods

of production engineering that found their knowledge on the war for production line

construction; each garment had to be completed in the shortest possible time, estimated

to the nearest second, with the quality standards maintained. As a result, the tailoring job

was now performed by several machine operations, with no chance for alternations or

reshaping of garments parts subsequent cuts (Olds, 2001).

45

2.2.2 Pattern Drafting and Engineering

Customer satisfaction with fit depends upon the ability to combine the knowledge and

skills of the 19th-century tailor or dressmaker with the strengths of manufacturing and

information technology (Bye, Labat and Delong, 2006). The fundamental element in

apparel design engineering is pattern construction that provides the templates (block) to

cut the fabric (Liu, Zhang and Yuen, 2010). Anthropometric measurement offers the

foundations for two-dimensional pattern construction, usually in the form of size charts

based on a population’s measurements (Davies, 1986). The purpose of developing the

sizing system is to generate an appropriate range of sizes based on actual anthropometric

data for mass-customisation (Aldrich, 2006). Sizing systems and charts have been

developed and improved throughout the years, using more sophisticated methods to

capture peoples dimensions for clothing (Gupta and Zakaria, 2019). Aldrich (2007), in

her research, examined the process of developing a sizing system and concluded that

development encompasses three fundamental stages:

• Anthropometric analysis (fieldwork preparation and anthropometric survey),

• Survey sizing analysis (dependent on chosen method),

• Sizing system development (validation, development, and designation).

Zakaria and Gupta (2014) offer several explanations for current limitations in size and

fit, including lack of anthropometric data to describe the population and the absence of

desirable characteristics of garments for different sizes and shapes. The authors conclude

that by questioning the long-term feasibility of traditional industry practices and a need

for a more robust and standardised anthropometric approach. Gupta and Gangadhar

(2004) presented critical systems for size and fit creation with mathematical formulae,

listed in bullet-points below. The author added a paper reference for each bullet point to

clearly illustrate a detailed empirical example and development focus.

• Mathematical methods such as bivariate classification (Staples and DeLury,

1949); statistical techniques like correlation, coefficients (Mcculloch, Paal and

Ashdown, 1996),

• Multivariate techniques, namely principal component analysis (PCA) (Gupta and

Gangadhar, 2004), and factor analysis (Zheng et al., 2018),

• Programming techniques like linear programming (LP) (Gupta et al., 2006) and

integer programming (Tryfos, 1986),

46

• Data mining techniques such as cluster and decision tree analysis (Hsu and

Wang, 2005); and artificial intelligence techniques including genetic algorithms

(Ding, Hu and Zhang, 2011), neutral network (Tsai and Hsu, 2013), fuzzy logic

(Wang, Chang and Yuen, 2003) and self-organisation method (SOM)

(Doustaneh, Gorji and Varsei, 2010).

The challenge for garment developers is to establish if and how these new approaches

will advance the apparel product development and ultimately translate to useful and

informative applications in a wide range of size and fit. Pattern block used in clothing

design is thought to be the product of diverse body dimensions and fit factors interacting

as a complex system, over multiple levels. Understanding the complex needs of product

developments requires considerable data, which are challenging, costly and time-

consuming to collect through traditional means. However, O’Brien & Shelton (1941),

Kunick (1967), Beazley (1996), Gill and Chadwick (2009), and Song and Ashdown

(Song and Ashdown, 2013) has repeatedly suggested a lack of underpinning theory in

pattern-drafting practice (Gill and McKinney, 2016). The primary tailor practice

challenges are grounded in methods, where the lack of measurement guidance and the

discrepancies of consistency in application between different measurers; suggesting

pattern construction is a highly subjective process and highly dependent upon tailor craft

skills (Gill, 2009). The case exemplified by Gill et al. (2014), as anthropometric

dimensions such as waist circumference and waist-to-hip ratio are frequently used for

pattern making. However, these estimates often vary between tailors and anthropometric

standards (Simmons and Istook, 2003). Much of the inertia in addressing measurement

discrepancy can be attributed to the prevailing and persistent lack of transparency in

forming pattern drafting practices. Ahmed et al. (2019) evaluated ten popular pattern

drafting methods for bodices and trousers and compared them with each other and

against the measurements generated by Size Stream’s 3D Body Scanner. Their research

concluded that sizing methodology needs instructions that provide more detail about

each measurement, equipment used, and clarity of descriptions, aided with a set of

illustrative images. The understanding of variation in methods is essential, but the results

to be comparable must share the same or similar nomenclature.

The process of grading, as described by Gill (2015), is the art of translating a design

concept into proportionally different sizes while retaining the essence of the desired

47

visual appearance of a garment. Sheldon et al. (1940) and Kunick (1967) provided a

comprehensive discussion on the development of the clothing initial sizing systems

methods. However, grading has long remained a neglected area of research in the

fashion industry (Stanley and Baytar, 2016). Thus, practices of contemporary grading

have been called into question by the work of Schofield and LaBat (2005), who implies

the origins of the practices employed precede the collection of anthropometric data

through large-scale surveys. As a result, existing grading methods used are heavily

influenced by proportional theories, determining relationships between measurements

according to systems of scale (Gill, 2009). Nonetheless, Petrova and Ashdown (2012)

asserted that present grading in the fashion industry is not a method of size and scale

adjustment rooted in anthropometric data, but out of practice regimented increments to

match the closest size of the customer before final fitting adjustment. It can be summed

up, however, that the involved drivers of grading are not widely understood by garment

developers, nor included in most fashion curricula or industry training (Gill, 2015).

Addressing grading as an anthropometric derivative requires an understanding of the

pattern rules and the fit criteria (Sohn, Lee and Kim, 2020).

Fit is the relationship between the size and contour of the garment and those of the

human body (Shan, Huang and Qian, 2012). Brown and Rice (2014) defined fit as a

combination of five factors: ease, line, grain, balance, and set. Liechty et al. (2010),

added an additional four criteria: dynamic fit, preference fit, functional fit and look-book

fit. In general, as explained by Ashdown and DeLong (1995), clothing that fits well

provides an adequate amount of wearing ease (e.g., ease to allow for body movement),

and design ease (e.g., ease developed by the designer to create a desired visual effect,

silhouette, and style). Gill (2009) defined functional ease allowances as the minimum

extra amount included in the garment pattern above those of the wearer. Each movement

the human body undertakes causes surface changes of varying levels, and if the garment

restricts this, then it follows that function will be impaired (Rabuffetti et al., 2002).

Thus, the anthropometric dimensions are also influenced by the requirements of

aesthetic, performance and comfort that dictate clothing characteristics (Gupta and

Zakaria, 2014). Ashdown and DeLong (1995) classified personal ease as

a) the threshold of ease variation in apparel perceptible by the wearer,

b) interactions with a variety of fit between different body areas, and

c) the relationship between customer perception and tolerance for misfit.

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The Ashdown and DeLong (1995) research provided a template for full-scale

statistical testing with more replications and for quantifying size increments for ready-

to-wear apparel. To accommodate the best objective and subjective side of fit,

researchers have developed methods for judging the fit based on wearer responses to

the look and feel of the garment and opinions of expert judges to the visual analysis of

garments on the wearer. Following these criteria, the wearer decides how the garment

feels and examines how the garment looks in the mirror. The expert assessment bases

on his/her judgment and visual indication such as seam placement and the location

and orientation of fabric wrinkles. However, both assessments are subjective, and lack

of consistent methods has limited the field (Gill, 2015). Nevertheless, continuing to

neglect fit within the product development stages will have negative consequences for

the fashion industry, including increased customer dissatisfaction rate, increased

return rates, loss in sales, and impeded sustainability goals (Mintel Group Ltd.,

2019b). The biases and gaps in knowledge must be addressed across the supply chain,

involving designers, manufacturers, and garment developers to allow for more

advanced anthropometric methods and tools.

2.2.3 Methods and Tools for Anthropometric Research

This section presents an overview of anthropometry methods and tools, along with a

protocol describing apparel measurement practices. Table 1 shows a range of data

types and categorises that form a basis of data collection practices. The methods for

body measurements diverge, but the resulting data is defined in terms of (a) points, (b)

length (c) surface (d) shapes or (e) volume (Bye, Labat and Delong, 2006). According to Bye, Labat and Delong (2006) measurements can be divided into:

• Linear methods: refers to the distance between two landmarks on the body to

quantify the size of physical dimensions (lengths, widths, or circumferences) such

as hips, thighs, waist, or neck. Landmark identification shows a potential for

human error because of the need for agreement on the landmark locations (and

how to identify it) on the body, including standard definition and

characterisations.

49

• Multiple probe methods: use a combination of linear methods with tools that

evaluate the relationship between and description of body contours. By assessing

and describing various dimensions (linear measurement and contour) of the

human body, academics can study physical parameters that are related to garment

fit and clothing comfort.

• The body form methods: describe the human body by relying on surface and

volume evaluation rather than numerical descriptions. The body form methods

rely less on numeric descriptions and relate information about the surface, shape,

and volume of the human body. Table 1 Overview of measurement methods based on Beazley (1996).

Methods: Point Length Surface Shape Volume Linear Methods • Tape Measure ✔ • Direct Measure ✔ • Proportional Measure ✔ • Anthropometer ✔

• Calipers ✔

Multiple Probe Methods

• Complex Anthropometer ✔ ✔ ✔ • Somatography ✔ ✔ ✔

• Minott Method ✔ ✔ • Planar Method ✔ ✔ ✔ • CIAM ✔ ✔ ✔ • Photography ✔ ✔ ✔

Body Form Method • Draping ✔ ✔ • Casting ✔ ✔ ✔ • Body Scan ✔ ✔ ✔ ✔ ✔

Beazley (1996) research perfectly illustrates how manual techniques are labour-

intensive in interpreting the data, prone to error and highly time-consuming. The

starting point in drafting a pattern should be the unambiguous specification of the

body's form for which the pattern will be drafted. Beazley (1996) formed a

standardised measurement protocol to measure the human body. Each person required

45 minutes and specialised equipment: trolley, harness consisting of two tapes

50

fastened at a right angle at the zero measurements, adjustable elastic tape, landmarker,

chair and the long mirror positioned to the right of the anthropometer. The mirror

enabled the tailor to observe the measuring tape level on the opposite side of the

subject. The recorder was positioned on a chair to the left of the tailor and required a

clipboard with three sheets of A4 paper; each participant took 70 measurements in

millimetres (mm) and noted in blue pen. To reduce the probability of error, tailor

repeated around ten random measures and noted them in red pen, with the acceptable

variation ±5 mm and of ±10 mm on full all-round girth measurements. This case

clearly illustrates that the fashion industry needs an automatic way of collecting

accurate anthropometric measurements to improve data collection protocols. Table 1

demonstrates that 3D Body Scanning is the only known technique that provides all the

available measurements in a fast, reliable, and non-invasive way.

2.2.4 Summary of Contribution to the Thesis

The overall findings from section 2.2 show existing limitations in the methods

traditionally utilised by product developers. The fashion industry shortcomings found

in section 2.2.1 stem from the lack of robust, standardised approaches that allow

researchers to deal subjectivity and bias in practice. The theoretical framework for

modelling the fitting process has yet been developed. This section hoped to

demonstrate that the systematic approach to drafting can improve ready-to-wear

garments. The level of specification necessary for pattern production should be

determined through empirical work, as it will vary for different styles of garments and

areas of the body. For example, areas where the body's curvature changes rapidly and

where the garment fits closely will need to be specified more completely than areas

where the body changes little or the garment does not follow the body closely. Section

2.2.2 initiate that the discrepancy is in many ways due to the lack of technical support

that could take some of the measurement burdens and allow practitioners time for

reflection. However, opportunities to develop innovative strategies using automatic

means in PLM are emerging, as highlighted in section 2.2.30; thus, raising questions

about how 3D Body Scanning methods should be integrated into anthropometric

research. The fashion industry's challenge is to establish if and how 3D Body

Scanning will advance product development and ultimately translate to useful and

informative applications in fashion retail and e-Commerce.

51

State of the Art in 3D Body Scanning

Section 2.3 introduces the state-of-the-art in 3D Body Scanning technologies and

discusses the opportunities and challenges from data acquisition to the application of

insights in fashion retail and customer settings. The conclusion sketches an agenda for

future research based on findings emerging from the analysis in this section.

The last decade of the twentieth century brought about a revolution in anthropometry

with the technology of extracting spatial data out of (stereo-) photographs (Burke and

Hughes-Lawson, 1967). During the mid-nineteen eighties, a request came into

Loughborough University from a textile manufacturer to provide comprehensive

human body shape statistics to facilitate garment manufacturing (Jones, Katherine and

West, 1995). The manufacturing company desired to explore the possibility of

developing a “non-contact machine that is reasonably transportable and sufficiently

speedy in operation to survey a large sample of the British population” (Heymsfield

et al., 2018, p. 680). What emerged in 1987 was the Loughborough Anthropometric

Shadow Scanner “LASS”. The Human Measurement and Growth Research Group at

Loughborough University established the underlying idea of how to measure a body

in terms of radii and angles in conjunction with height (Jones et al., 1989). The first

developed device included a television camera, projector, and a 360° rotating table

upon which the volunteer stood during the evaluation procedure. The slit of light

projected on to the body in a vertical plane passes through the centre of rotation. All

points where the edge of the light falls on the body define the horizontal radii (r) at

points in the vertical plane (h) — the field of “automated anthropometry” was born

(Jones and Rioux, 1997). Since then, there has been a steady growth of the research

papers about 3D Body Scanning that exemplify market potential and diverse

applications available for retail installation and even individual home use.

2.3.1 Data Acquisition in 3D Body Scanning

This section elaborates on the building blocks of the technology to allow for detailed

data capture. 3D Body Scanning aims to create a high-quality representation of the

whole human body surface using non-invasive optical methods (Loker, Ashdown and

Carnrite, 2008). 3D Body Scanning includes laser and structured light systems,

millimetre wave radar, and multi-view camera (Palmer and Alto, 2005; Liechty,

52

Rasband and Pottberg-Steineckert, 2010). 3D Body Scanners rely on visible and

infrared light (IR) and capture information only from the body's surface, thus

requiring customers to wear minimal form-fitting clothing (Gill, Hayes and Parker,

2016).

3D Body Scanners are inexpensive and, compare to medical devices, does not involve

ionising radiation, unlike other whole-body imaging methods such as computed

tomography (CT) and dual-energy X-ray absorptiometry (DEXA) (Pleuss et al.,

2019). Daanen and van de Water (1998) presented the first overview of 3D Body

Scanning technology and demonstrated that early scanner design was bulky, slow,

expensive, and very low in resolution. However, with passing time, Daanen and Ter

Haar (2013) presented a second paper in which scanners rapidly evolved in

technological developments such as megapixel CCD-chips that contributed to higher

resolution and improved accuracy of 3D scan images. Thus, 3D Body Scanners

moved to a more customer-orientated market (Klepser et al., 2020). Therefore, it is

essential to provide the fashion industry with an updated evaluation of commercially

available technology and measuring methods. Table 2 outlines the commercial types

and scanner brands available to diffuse in fashion retail to demonstrate the diversity of

products available in the market. Table 2 List of commercial firms for 3D Body Scanners. Source: author’s own.

Body Scanners Booth Rotary Scanners Mobile Scanners Entertainment Size Stream TC2

3dMD BotSpot Vitronic Telmat Treedys

Styku Fit3D Naked Lab

Zozo Suit Fision Netello Size Stream Shapetrax Body Labs Size Stream Home

Twindom

From a design perspective, 3D Body Scanning often varies in physical characteristics,

including the number and arrangement of cameras and implementation of static or

dynamic (e.g., rotating platform) components. Table 3 and Table 4 presents a very

detailed analysis that compromises data on technical papers by Heymsfield et al.

(2018) and Tinsley et al. (2020), trading website Aniwaa (2019) and product

pamphlets collected from 3D Body Scanning conference (D’Apuzzo, 2019).

53

Table 3 Market overview of whole 3D Body Scanners. Source: author’s own.

Scanner Size Stream

TC2 3dMD BotSpot Vitronic Telmat Treedys

Model SS20 TC2 – 19B

3dMD System

OptaOne+

VITUS Symcad III

Treedys

Model Year 2017 2017 2015 2017 2015 2017 2019

Country US US US Germany Germany France France

Est. 2012 1999 2000 2009 1984 1997 2015

Cost $ 15,000 – 20,000

$ 30, 000 $ 20,000 – 50,000

$150,000 $ 30,000 $15,900 Unknown

Capture Infrared Depth Sensor

Infrared Depth Sensor

Photogra-mmetry

Photogra-mmetry

Optical double triangu-lation

Strctu-red Light

Photogra-mmetry

Accuracy Cylinder test: < +/- 5 mm

Cylinder test: < +/- 1mm 880mm

0.05 RMS mode

0.01 mm RMS mode

Average circum-ference error <1 mm

± 0.15% or 1.5 ‰

0.2 mm

Resolution 600 data points

75 points per cm2

Unknown <16, 348 x 16, 348 pixels.

300 points per cm3

Unknown Nvidia Xavier 20:1

Sensor Heads

20 sensor heads

Turntable and a sensor bar

> 22 modular units/>66 cameras

8 panels /70 photo sensors

8 sensor heads

16 sensor heads

Unknown

Scan Time / Process

<4s / <30s

< 1s / <9s

1.5 ms. / unknown

0.01 s / unknown

14s / unknown

<1.5 s / unknown

1/10s / 3 min

Output Format

OBJ, FBX

WRL, OBJ, STL, PDF,

OBJ, PLY, STL

OBJ, PLY, STL, VRML

OBJ, STL

OBJ, PLY, STL, WRL

Unknown

Dimensions in millimeters

1040 x 1010 x 2015

1870 x 1090 x 2130

Unknown 3140 x 3140 x 2540

1200 x 1200 x 2100

1900 x 1730 x 2100

1900 x 1350 x 2300

54

Table 4 Rotatory and home 3D Body Scanning systems. Source: author’s own.

Scanner Fit 3D Styku Naked Labs Twindom

Model Proscanner V4x S100 Naked Twinstant Mobile

Model Year 2014 2017 2017 2016

Area Fitness & Gym Fitness & Gym Home Fitness Entertainment

Country US US US US

Est. 2012 2015 2015 2016

Cost $ 10,000 – $20,000

$ 10,000 $ 1,395

$ 25,995

Capture Structured Light Infrared Depth Sensor

Infrared light Sensors. Stereovision

Structured Light

Accuracy Unknown +-2.5-5mm 1.5cm large, or 0.5 small circumference

0.05 mm

Resolution The 1,200 images of the body on the rotating plate, stitched to create a 3D avatar

Depth camera resolution 512 x 424. Depth data resolution 2 mm

4 million points of data. Depth map accuracy of a 1/10 of an inch

Max res 0.7 mm – 1300000 points/s

Sensor Heads Turntable and a sensor bar (3 cameras)

Turntable and a sensor bar

Intel ® RealSense depth sensors; scale alignment laser.

89 camera sensors and 25 projectors in 17 scanning poles

Scan Time / Process Time

>40 s/ >4 min

> 40 S/ Unknown

> 15 s/ >3m

>15 s > 10m

Output Format

Cloud, app STL, OBJ Cloud, app OBJ, PLY, STL, WRL

Dimensions in millimeters

1570 x 1980 254 x 254 x 1680 1580 x 370 x 370 2180 x 3050 x 3050

The scan software creates, and measure 3D Body Scanning image and accepts raw

depth frames from the camera(s). From each frame, a list of unconnected (x, y, z)

points in 3D space, called a point cloud, is extracted (Heymsfield et al., 2018).

Multiple point cloud images are captured during the scan and then aligned and

merged to create a single point cloud that combines surface shape information from

many angles around the subject (Heymsfield and Stevens, 2017). The image

processing method is called ‘meshing’ — connecting assembled point clouds and

produces a closed polygonal surface, a 3D mesh (Sobhiyeh et al., 2019). The mesh

function can help identify physiological landmarks on the final image, dictating and

recording various length and circumference measurements to calculate body

composition estimates. The standard open-source such as Blender (Hess, 2007) or the

proprietary software’s such as Autodesk MAYA or ZBrush (Patnode, 2012), can

visualise, clean, and sculpt on the mesh surface to enhance visual effects.

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The two major systems in 3D Body Scanning are structured light and time of flight

(ToF) (Koch and Kaehler, 2009). The structured light scanners employ controlled

visible or IR illumination patterns projected across the imaging field of view. One or

more cameras measure deformations in the light pattern over objects (e.g., a human

body) in the scene (Heymsfield et al., 2018). This deformation information is used to

calculate the per-pixel distance between the camera and the object and thus create a

depth image using geometric triangulation (Remondino, 2003). Light coding is one of

the most common implementations of structured light developed by PrimeSense (Tel

Aviv, Israel) and acquired by Apple Inc. (Cupertino, CA). On the other hand, ToF

systems employ coupled scene illumination (visible light or IR) and image recording

using the charge-coupled device or complementary metal-oxide-semiconductor (CCD

and CMOS) sensors (Taylor, 1998; Woźniak, 2020). However, instead of measuring

pattern deformations, ToF scanners quantify the round trip time (RTT) for reflected

photons to reach the image sensor to calculate depth (Kennedy et al., 2020).

Previously used primarily for architectural and surveying purposes (Puente et al.,

2013), ToF technology has become broadly accessible with the introduction of the

second-generation Microsoft Kinect cameras (Tong et al., 2012).

The hardware in 3D Body Scanning either requires multiple cameras positioned

around the body, e.g. Size Stream and TC2, or a single camera with a mechanism for

rotating either the camera or the scanned subject, e.g. Styku, Shape Labs, Naked Labs

or Fit3D. Systems with multiple cameras are often large and cost more than those

with a single camera that includes a platform that rotates the subject 360° (Bourgeois

et al., 2017). Multi-camera systems with a static configuration (i.e., the sensors and

subject are fixed during a scan) rely on pre-scan calibration for point cloud alignment

(Heymsfield et al., 2018). A calibration object such as a row of spheres or a flat

checkerboard pattern is imaged from all camera angles (Daanen and Ter Haar, 2013).

Typical landmarks are identified and used to calculate the position and orientation of

each camera. This information is applied to orient point clouds from a subject scan in

3D space (Croce et al., 2005).

Alongside the development of large scanning booths, mobile scanning apps are also

developing for engaging with body composition during the clothing size and fit (Xia

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et al., 2018). Models made from mobile phone photographs are proliferating (Li and

Cohen, 2021). Several companies offer mobile apps on a smartphone or tablet that

make photos from which body dimensions can be deducted (Ballester et al., 2018).

Systems to build a 3D model from two-dimensional photographs have been under

development for many years and are now reaching the stage of practical application.

In addition, Xia et al. (2018) claim that new mobile apps solve issues related to

existing systems such as the high cost of 3D whole-body scanning technology, the

measurement extraction dilemma of handheld scanners, and the low-quality results

from manual measuring techniques. The key commercial platforms for 3D mobile

scanning are listed in Table 5, and Figure 3 demonstrates the user interface of Zozo

Suit as an example of a mobile scanning interface. Table 5 3D Body Scanning mobile applications. Source: author’s own.

Scan App ZOZO Fision Netello SizeStream 3DLook.Me Meepl

Measurements Yes Yes Yes Yes Yes Yes 2D Illustration Yes Yes No No No Yes 3D Body No No Yes Yes Yes Yes Country Japan Swiss CA USA USA Swiss Year 2015 2015 2012 2019 2016 2015

Figure 3 Zozo mobile scan interface Source: Zozo Suit UK.

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2.3.2 3D Body Scanning Accuracy, Reliability and Precision

The design and potential retail applicability of 3D Body Scanners differ substantially,

as Table 3 and Table 4 highlights, in which each system has distinct attributes such as

cost, speed, and hardware design. This section aims to critically evaluate the

precision, accuracy, and reliability of 3D Body Scanning based on the past literature

and highlight systems limitations that are mostly correctible when building next-

generation devices.

3D Body Scanning Landmarking Position

3D Body Scanning technologies have been studied to determine product development

effectiveness. Most scholars acknowledged that anthropometric measurements from

3D Body Scanners exhibit higher precision than measurement obtained by fashion

garment developers and trained tailors (Medina-Inojosa et al., 2016; Parker, Gill and

Hayes, 2017). However, some researchers reported slightly higher precision for the

manual measurements when taken by highly trained individuals (Bourgeois et al.,

2017). Gill (2015) highlighted that the disagreement exists because measurements

obtained by 3D Body Scanners are not necessarily equivalent to those obtained by

manual methods. The differences between 3D Body Scanning and manual methods of

measurements are likely, due to landmarking and cut-off point discrepancies

(Thelwell et al., 2020). To seamlessly integrate 3D Body Scanning with garment

development methods, the gap in identifying consistent body landmarks that directly

relate the body to the pattern must be bridged (Bye, Labat and Delong, 2006).

Technical Perspective on Landmarking

Both customer and retail devices across a variety of fields (scanning booths, home

devices, and mobile apps) have become more sophisticated and affordable. However,

comparing performance across different platforms and methods remains a challenge,

and few methods have been validated against well-known standards (Daniell, Olds

and Tomkinson, 2012). Therefore, scanners’ increased availability also results in

greater challenges when optimising the match between the end-application and the

proprietary scanner in use. Gupta and Zakaria (2014) and Simmons and Istook (2003)

defined landmarks as sites on the body that serve as endpoints for measurements.

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Landmarks are located on a bony prominence or other physically definable points on

the human body (detected by feeling the bones beneath the skin) (Serge, 2005). The

major limitation of 3D Body Scanners is that it is not possible to palpate these

protrusions on 3D avatars, making the task of landmarking more challenging.

Simenko and Cuk (2016) suggested the shortcomings can be attributed to insufficient

tailoring of the computer algorithms – the adequate algorithmic methods in 3D Body

Scanning are yet to be developed. The landmarks algorithms need to effectively

encode both the individual geometry and shape variance of real human body shape to

significantly improve the robustness of 3D Body Scanning completion (Allen, Curless

and Popović, 2003). However, landmarks detection is challenging on participants with

high body mass index, where armpits, crotch and bony landmarks may be obscured by

significant soft tissue (Baysal et al., 2016). 3D Body Scanning methods tend to be

sensitive to noise. As 3D Body Scanners fail to remove noise for visualisation

explicitly; thus, rendering fine-grained local structures are impossible to recognise

and analyse (Thelwell et al., 2020). The comprehensive review on landmark detection

algorithms can be found in the work of Alemany et al. (2019), Klepser et al. (2020),

or the review of the industrial standards by McDonald et al. (2018).

Garment Developer Perspective on Landmarking

The traditional methods of measurement often cannot predict difficult to obtain

dimensions, e.g. armhole depth (Fossati, 2013). This limitation can lead to somewhat

significant differences between the person actual body measurements and those

determined by a proportional rule (Gill and McKinney, 2016). According to Gill

(2018), 3D Body Scanning’s application can locate challenging to measure landmarks

with ease. Wren et al. (2014) showed that to improve landmark placement accuracy,

3D Body Scanning design should include refinement of methods for estimating

landmark positions, including exploration of optimal procedures to account for pattern

methods and evaluating other sizing metrics such as body composition, proportions,

shape, ratios and volumes. Gill et al. (2017) discuss the importance of matching

scanner measurements to these of creating garment patterns. This study utilised two

body scanners: Size Stream 14 and TC2, and compared 35 body measurements with

six prominent pattern theories and ISO 8559-1:(2017) and 8559-2:(2017) standards.

Their findings exemplify variations within each measurement and the importance of

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precise landmarking. 3D Body Scanners often do not offer measures defined in the

same way as the measurements required in the drafting methods to create patterns.

Moreover, Parker et al. (2017) measured reliability in Size Stream scanner (Size

Stream, 2017). The protocol included 27 participants, taking 90 body measurements

five consecutive times. This study showed that 3D Body Scanning has suitable

reliability; in which 44 measurements were reliable in 99.73%. A further 22

measurements were reliable in 95.46%, and a final 21 measurements were reliable in

68.26% — failing to meet the required tolerances for garment construction. Similarly,

Kuehnapfel et al. (2016) measured the reliability of laser scanners, using ‘Vitus XXL’

scanner and ‘Anthroscan Basis’ software (version 2.9.9.b, laser class 1) and measured

180 participants. Their research observed that the reliability of 3D Body Scanning

measurements was good with a few exceptions, which need to be considered with

caution –– knee height and the lower edge of the groin. Tsoli, Mahmood, and Black

(2014), however, proved that a range of determinants could influence the performance

of scan reliability methods, from breathing, postural differences to inter-subject

variability in BMI. Kuehnapfel et al. (2016) research further defined 3D Body

Scanning proxies for all standard manual anthropometric measurements and mapped

systematic differences that require further offset corrections.

The Usability & Design Perspective on Landmarking

Xia et al. (2019) compared three measurement methods: a commercial stationary

whole-body scanning system, structure sensor scanning system, and the tape

measurement process. The primary issue found with scanner reliability concerns

participant position and the lack of handlebars to stabilise and guide arm placement,

and the user’s underestimation of the optimal distance. Their research also found that

the sensor scanning system’s structure had the highest validity, and the least amount

of reliability within the three methods. However, it was concluded that the absolute

mean differences between them were still acceptable for garment pattern-making – if

based on the ASTM (2011) standard. Thus, more research is needed on the reliability

of home and mobile scanning devices and applications. For example, how accurately

and reliably can mobile phone applications measure body dimensions when compared

with a very expensive scanning booth? Applications that claim to perform the

functions of established scanning devices should be able to demonstrate equivalence

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according to rigorous standards set for other devices. Similarly, Kennedy et al. (2020)

investigated Naked – 3D Body Scanner’s accuracy compared with DEXA % fat.

Naked 3D Body Scanner is the first optical system for personal use as an extended

home scale with the mirror to use in a home setting. They found home scanners to be

capable of providing customers with accurate and reliable somatic surface

measurements and body composition estimates. Nevertheless, comparing the Naked

Body Scanner’s with the larger 3D optical systems shows that some degree of error

associated with digitally acquired measurements is similar. Thus, home and mobile

scanners can achieve considerably high accuracy but still replicate the same old

assumptions about body measurement techniques (Song et al., 2018).

Medical Field Perspective on Landmarking

The medical field, in contrast, measures 3D Body Scanner reliability by comparing

across established medical standards. Tinsley et al. (2020) compared four systems

FIT3D, Size Stream, Styku, and Naked Labs against two medical standards duplicate

air displacement plethysmography (ADP), and DEXA. Their finding of the test-retest

precision (reproducibility) analysis was that all 3D Body Scanners generally produced

precise circumference and volume estimates. However, all 3D Body Scanning

providers overestimated torso volume and underestimated leg and arm volumes. The

differences in actual circumference values indicate that some variability in the

proprietary specification of estimate location exists between manufacturers and need

to be further examined. The reproducibility and robustness of 3D Body Scanning

capture, processing and data analysis methods must be addressed before the use of

large, longitudinal and multi-modal collaborative studies (Rennesson, 2012).

Summary

The impact that 3D Body Scanning can have on the management and understanding

of product development inner workings and the improved size and fit metrics is set to

be paradigm-changing (Ashdown and Loker, 2010). Consistently pinpointing the

same somatic locations is essential for 3D Body Scanners to accurately predict size

and fit. Therefore, while scanners can independently provide the user with valuable

information, their results cannot be readily compared with those obtained on other

devices nor via tape measure without the institution of universal landmarking

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definitions’ (Kennedy et al., 2020). 3D Body Scanning developers, academics, and

retail stakeholders should collaboratively enable this process of validation to take

place, moving a step closer to personalised fashion.

2.3.3 3D Body Scanning Data Storage and Curation

This section aims to analyse existing 3D Body Scanning databases: goals,

opportunities, and challenges to draw transferable lessons for 3D Body Scanning

developers and fashion retailers. Istook and Hwang (2001) envision 3D Body

Scanning databases as a platform for the fashion industry to engage with real human

bodies in their product development practices. The existing platforms offer the option

to allow or disallow downloads of models, preferably in .stl format or the .fbx and

.obj format used in animation and gaming (Maalin et al., 2019). The objective of the

anthropometric analysis is to profile the demographic data and describe body

dimensions in a way that can distinguish between genders and different age groups for

the selection of crucial dimensions (Gupta, 2014). While there have been many

extensive anthropometric surveys of military populations over the years, surveys of

civilian have been rare (Paquette, 1996). The body dimensions data collected from

military personnel can enhance the general anthropometric knowledge, yet this kind

of data is imprecise for the fashion industry (Keefe, Kuang and Daanen, 2017).

Military personnel have to meet strict conditions and entry and fitness criteria, and

they also tend to be younger than the general population. Through some

anthropometric surveys were conducted at the beginning of the twentieth century

(Gupta and Zakaria, 2014). According to Cooklin (1990), initial size surveys did not

influence the sizing design in any significant way. The problem is even more

cofounded when accounting for the fixed posture used during measurements, often

with arms and legs away from the body (Alemany et al., 2010). The inaccurate

position produces a low level of comparability with the data collected for product

development that uses historic measurement methods (Gill and Parker, 2017). A

parameterisation of scan data can enable posture correction and potentially could

allow correcting the scan posture to one used in historical approaches (Gill, 2015).

However, the workability from transferring historical anthropometric data into digital

files has not yet been investigated.

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The present industry disagreements prevent the comparison of scan data to large pre-

existing datasets from multiple scanning systems. Nevertheless, accessible databases

are core resources for data-rich research, consolidating apparel product development

knowledge and highlighting best practices and challenges (McQuillan, 2020). Sharing

3D Body Scanning data is vital for accelerating the pace of progress in apparel design

engineering research (Sohn, Lee and Kim, 2020) and for fully realising the promises

of fully automated, more sustainable digital fashion (Dāboliņa et al., 2018). However,

collecting cohorts of 3D data at such scales is typically beyond the reach of the

individual company (Henkel, 2006) and thus cannot be achieved without combining

different sources and financial constraints (McDonald et al., 2019). To get to that

future, stakeholders will need to train ‘size and fit’ algorithms with millions of

examples (Acharya et al., 2018). In addition, broad dissemination of 3D Body

Scanning data promotes serendipitous discoveries through secondary analyses, which

are necessary to maximise the use of such data for retail and customers’ (Berg and

Amed, 2020). The available commercial databases are privately own, expensive and

with no open standards to follow (Lee et al., 2015). Further work is needed to create

reliable methods to identify and extract standardised anthropometric measurements,

so databases created from 3D Body Scanning can be added to existing databases

(Heymsfield et al., 2018). At the moment, 3D Body Scanning databases are not only

small but also often lack standards in types of what data is collected and applied.

Sapio et al. (2018) state that the problem is further cofounded as the fashion industry

lack practices and tools for data sharing between product designers and

manufacturers; limiting broader applicability. The following sub-sections will

describe commercially available databases that are available for the fashion industry

to purchase and use in product development data analyses.

Civilian American and European Surface Anthropometry Resource

The survey proposed by a NATO research group was called Civilian American and

European Surface Anthropometry Resource (CAESAR). It was the first international

collaboration between the USA, Netherlands, and Italy to use a 3D Body Scanner as a

data collection tool (Brunsman, Daanen and Robinette, 1997). CAESAR goal was to

scan subjects that are representative of the anthropometric variability of men and

women aged 18 - 60. The WB4 3D Body Scanner from Cyberware used in the

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CAESAR project collected high-resolution data of the surface of the human body

(Robinette and Daanen, 2003). CAESAR North America survey cost $10,000 and

included a population sample of 2,400 male and female subjects, aged 18-65.

CAESAR Europe survey includes 2,000 male and female subjects aged 18-65. Both

surveys were conducted between 1998-2000 and included 1D demographics

measurements in the format of the statistical data in Excel spreadsheets along with a

scanned 3D model.

Size UK

Size UK was a collaboration between the Department of Trade and Industry, leading

by British retailers and academics. Between July 2001 and February 2002, over 1.5

million measurements taken from more than 11,000 people across the UK (Shape

Analysis Ltd, 2002). This survey utilised a TC2 scanner to develop new sizing

information for manufacturers and retailers. The survey aim was to create a

representative sample of the UK population that will allow the re-assessment of the

sizes and body shapes of UK apparel customers and the sizing charts used by fashion

manufacturing. This process included one hundred thirty automatic measurements and

eight manual measurements for each subject (Bye, Labat and Delong, 2006).

The Size UK survey investigated the prospect of bringing forward a new garment

sizing system, which can cater for the improved sizes of the population and describe

the relationship between body shape and garment characteristics more adequately to

achieve a better fit (Bougourd, 2005). The data from Size UK helped to investigate

correlations between body shape/size and marketing/socio-economic information on

the UK population (Bougourd and Treleaven, 2020). According to Treleaven and

Wells (2007), Size UK significant contributions was in developing a protocol to

automate the process of conducting sizing surveys and constructing a national

anthropometric database to be used as a vital national scientific resource. Size UK

survey was a model for Size USA, in which the project goal was to scan 10,000 adults

(ages 18-80) representative of the gender and race diversity in the USA (Bougourd

and Treleaven, 2010). However, the only comparable to Size UK in size and resource

national sizing survey was carried out in 1950 and used manual measuring methods to

measure female subjects. The Size UK report not only compared average sizes in the

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UK but also found how Britons of the 1950s and American counterparts compared

(Shape Analysis Ltd, 2002). The Size UK survey information yielded a 2,400-page

report that included a statistical analysis of data for each measurement (Booradya,

2011). The participating retailers include Arcadia, Bhs, Debenhams, Freemans, GUS,

Marks & Spencer, Grattan, House of Frazer, John Lewis, Littlewoods, Monsoon, N

Brown, Oasis, Redcats, Rohan, Speedo, and Tesco. Data Analysis Team: Richard

Allen, Shape Analysis, Avy Tahan and Bernard Buxton, UCL, Mark Winston

Research, Peter Grant Ross and Jeni Bougourd, LCF, Yannis Duros, Somavision

(Mark Winston Plc; and Shape Analysis Ltd, 2002).

Table 6 summarises the national size surveys created by international organisations,

available to retailers after fee; some of the most significant include: 11,000 scans in

Size UK (2004), 2,400 scans in CAESAR USA & Canadian, 2,000 scans in European

Size CAESAR (2019), and 12,000 scans from Instituto de Biomecánica Size Spain

(Ballester et al., 2015). Table 6 Size surveys databases based on Gupta and Zakaria (2014, 2019).

Database Partners Sample Country Price Year Scanner

Size UK Shape Analysis, Sizemic

11,000 UK Male & Female, 6-90+

United Kingdom

Depends on annual turnover

2004 TC2

Size Germany

Human Solutions, Hohenstein Institute

13.362 Male, Female & Kids 6-90+

Germany 5,000 €

2007- 2008

Human Solutions

Size Italy Human Solutions, Hohenstein Institute.

6,000 Italy Unknown Unknown Human Solutions

Size USA TC2, Retail, Navy

10,000 female & male, 18 - 65+

USA, 12 Locations

Unknown 2004? TC2

Size China Unknown Unknown China Unknown Unknown Cyberware

Size South Korea

Korean Standards Agency

Unknown Korea Unknown Unknown Cyberware

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Alvanon Dress Form Solutions

Alvanon is a central provider of Alva dress form that create mannequins based on

statistically representable data from 3D Body Scanner to represent each target market

size (Ashdown and Vuruskan, 2017). The company can create highly realistic

mannequins based on the extensive Alvanon database that collected data from 30

countries since 2001. The database consists of 6000 scans in standard A and T pose

from an unidentified demographic sample, and the annual subscription costs $3800.

However, their office in Hong Kong is responsible for collecting scan data from

different population surveys and decodes it for interoperability. The digital

mannequins connect well with CAD software’s: CLO 3D, Brozwear and Optitex

(Alvanon Inc., 2019).

Human Solution iSize

Human Solution created a database called iSize that aggregated data from India,

Korea, Germany, and North America size surveys with more than 100,000 scans from

the population aged 6-75 (Human Solution, 2019). iSize database from Human

Solution collects data from six different sizing surveys, as illustrated in Table 7, from

which only two most recent were conducted with 3D Body Scanning. However, the

differences in manual measurement methods and 3D Body Scanning techniques may

have low comparability in landmarking. Table 7 Human Solution iSize database. Retrieved from Human Solution (2019).

Survey Country Year Age Female Male Method

Size Germany

Germany 2007-2008 06 - 66+ 7.210 4.922 3D Scan

CNM 2006 France 2005-2006 05 - 70 6.4486 5.076 3D Scan

Size Korea Korea 2003-2004 18 - 70 3.100 3.100 Manual

HQL Japan 1994 18 - 70 3.000 3.000 Manual

NHANESS III

USA 1990 18 - 70 7.806 6.821 Manual

GB10000-88 China 1985-1988 16-61 male 18-55 female

11.150 11.164 Manual

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Fit 3D - Body Blocks AI database

Body Block AI is a project released by Fit3D 3D Body Scan firm that has launched a

globally available plugin database designed to help brands and retailers recommend

the product size for customers. The company claim to be the world largest, fastest-

growing, and most geographically diverse, approaching 1,000,000 scans by

positioning their scanners around 1500 gym locations since 2012 from 65% female

and 35% male, age 25-85 (Abdulla and Barrie, 2019). However, the measurement

reliability of this sample requires further validation research, as the data collection

protocols for scanning participants in busy public environments such as Gym and

Fitness centres are not yet available.

The University of Manchester

Apparel Design Engineering (ADE) research group from The University of

Manchester created a small open platform database with 100 3D Body Scans from a

White-British and Chinese population, aged 20-30 (Gill, Parker and Hayes, 2017).

The data is collected according to Gill et al. (2016) protocol that creates a uniform and

useful user experience that ensures ethical requirements are met at all stages and

measures are repeatable. 3D Body Scanning experience that can be controlled,

improved, and applied equally to all those who participate in research. Additionally,

collecting data against recognised scan protocols ensures that researchers can collect

full and accurate data about participants, especially details not captured by the scanner

(that can be collected manually by trained personnel in addition to scan data) for

future data analysis and application.

Summary

This section demonstrates that regardless of its intended end-use, all the data collected

using the methods and sensors previously discussed requires appropriate storage,

curation, and processing prior to analysis. The lack of a long-term plan to curate the

data and the absence of consistent landmark positions resulted so far in islands of

information that cannot be aggregated for large scale quantitative analysis. However,

large-scale collection of longitudinal data through 3D Body Scanning databases could

help retailers to explore the impact of body shape and expand it into size, fit and

styling applications.

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2.3.4 Standardisation Efforts in 3D Body Scanning

This section discusses the concept of interoperability and standardisation as a critical

step toward the construction of a product development model for the fashion industry.

Lehne et al. (2019, p. 1) have defined interoperability as “the ability of two or more

systems or components to exchange information and to use the information that has

been exchanged.” Initiatives to standardise or certify 3D Body Scanning applications

as interoperable trace their roots back to the early 2000’ with some organisations

offering paid accreditation to 3D Body Scanning developers to help the fashion

industry separate ‘snake oil’ from legitimate offerings. A summary of existing

representative resources is detailed in Table 8, along with a brief description. Table 8 List of standards organisations in 3D Body Scanning. Source: author’s own.

Framework Launch Membership Focus Reference website IEEE SA P3141

2015 Open to all developers

Interoperability and data standards, expanding into cloud, cyber security.

https://standards.ieee.org/industry-connections/3d/bodyprocessing.html

NATO (RTG) HFM

2017 Military Clothing fit/ logistics http://www.ndc.nato.int

3D.RC 2017 Retail Coalition

Effectiveness, brand criteria, privacy content, operability and advocacy

http://3drc.pi.tv

Web 3D Consortium

1997 Open to all developers

Royalty-free open standards file format and run-time architecture

https://www.web3d.org

The critical barriers incorporating 3D Body Scanning standards are the garment

developers’ lack of access to available scan information. Thus, as a result, the fashion

industry has a knowledge gap and limited expertise to perform robust size and fit

analyses (Choi and Luo, 2019). The fashion industry, however, requires a

comprehensive strategy and fundamental redesign that connects the domains of

fashion, technology, and user experience to improve the value of 3D Body Scanning

for all stakeholders and customers. The following sub-sections outline the ISO

standards and IEEE initiative that aim to standardise the 3D Body Scanning industry;

however, research is still required to comprehend interoperability challenges.

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ISO Standards

The ISO project's goal was that each of the participating 55 country members should

attempt to take account of international standards in their national size surveys (Liu et

al., 2017). The ISO standards in anthropometry intend to standardise the definitions of

landmarks and measurements in 3D Body Scanning, with key standards listed in

Table 9. However, Simmons and Istook (2003) research outlined discrepancies

between ISO standards and 3D Body Scanning definitions. According to Kouchi et al.

(2012), discrepancies exist because no single, agreed-upon set of landmarks and

measurements exists yet for reference. Table 9 Key ISO standards for 3D Body Scanning based on Gill (2015).

Standard number Standard Definition

• (ISO 20685, 2018) Standards for maximum allowable error between extracted and manually measured values

• (ISO 5971:2017, 2017) Size designation of Clothes – Tights • (ISO 8559-1:2017, 2017) Size Designation of Clothes- Part 1: Anthropometric

definitions for body measurement • (ISO 8559-2:2017, 2017) Size Designation of Clothes – Part 2: Primary and

secondary dimensions indicators • (ISO 18825-1:2016, 2016) Clothing-Digital Fittings – Part 1: Vocabulary and

terminology used for the virtual human body. • (ISO 18825-2:2016, 2016) Clothing – Digital Fitting – Part 2: Vocabulary and

terminology used for attributes of the virtual human body • (ISO 18831:2016, 2016) Clothing- digital Fittings-Attributes of Virtual garment.

IEEE Working Group

The growing demands of industry to act have inspired the formation of the IEEE 3D

Body Scanning group (P3141) to bring standardisation across diverse industry

organisations and stakeholders (McDonald et al., 2019). The working group aims to

improve interoperability, which will ease the development and scalability of the 3D

Body Scanning for the industry solutions and applications (Mcdonald, Oviedo and

Ballester, 2017). The IEEE Working Group equates to other standardisation

organisations, including ISO, X3D, AIST, H-Anim, and ASTM. The working group

also closely collaborate with 3D retail coalition groups (3DRC) and the Web3D

consortium. Moreover, the IEEE 3D Body Scanning group is an umbrella of six

working groups focused on quality, metadata, file format, communication/security,

footwear, and fit research:

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• The quality group - intends to provide methods, tools, benchmarks, resources and

testing procedures to define and quantify the quality of 3D avatars.

• The metadata group - intends to define mandatory and optional metadata,

recommend landmark and measurement definitions based on the existing body of

standards, and allow for system-specific metadata (Mcdonald et al., 2018).

• The file format group - intends to narrow the recommended file formats that

support model 3D data, such that all metadata comprised within the same

pertinent file (Fedyukov, 2019).

• The communication and security group - investigates the secure transmission and

storage, as well as the use, protection and privacy of records that contain personal

information as it pertains to 3D Body Scanning.

• The footwear group - aims to address different concerns of footwear and establish

critical definitions for foot measurement and how compression of soft tissue

affects fit in footwear (Mcdonald and Golub, 2018).

• The fit group - investigates elements of fit from a 2D pattern and 3D CAD

perspective in three stages: block 1 (body) based on digital 3D Body Scan, block 2

(wearer) includes minimum wearing ease for translating into style patterns, block

3 (garment) defining digital garment specifications.

3D Body Scanning Recommendation for Standards Creation

The lack of standardisation for data collection and the lack of reliable post-processing

techniques caused that datasets are almost always created anew, limiting the steady

growth of fashion industry databases. To try and conjointly use and analyse datasets

regardless of the source or type of data used can be daunting; thus, expanding

databases and fostering the analysis, use, and exploitation is a herculean task

(Stuckenschmidt et al., 2000). The heterogeneity of 3D Body Scanning devices used

for fashion retail applications poses a unique set of challenges for data storage,

curation, and interpretability. Hence, the identification and standardisation of robust

industry guidelines are of paramount importance. This section shows three major

research gaps in which standards are critical to addressing for technology adoption.

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• 3D Body Scanning developers need to ensure data interoperability.

Section 2.3.3 demonstrated that large parts of today’s anthropometric data are hidden

in isolated corporate silos; however, incompatible systems make data challenging to

exchange, process and interpret results. This interoperability problem means that 3D

Body Scanning databases are characterised by a large number of separate small data

chunks. 3D Body Scanning developers, however, lack efforts and resources to

improve interoperability between systems to facilitate interconnected interfaces.

Nevertheless, Bui et al. (2014) suggested, the 3D Body Scanning industry efforts

should be guided by the big data interoperability principles, as the growth of

accessible data will stimulate novel approaches. The findings in 2.3.2 underline the

critical role of databases as the unifying research progress engines that reflect best

practices and approaches to data generation, organisation and modelling. The gap

between incentives and data generation that can be useful for fashion retailers calls for

urgent action to standardise, structure and assist data collection methods (de Onis et

al., 2004). The connected databases will turn 3D Body Scanning data into meaningful

information and provide a basis for more sustainable practices.

• 3D Body Scanning developers need to create tools for product development.

While the fashion industry has access to a considerable amount of data; firms lack the

right framework to convert them into valuable assets (Scott, Gill and McDonald,

2019). According to Bellemare and Carrier (2017), future developments should

include research on suitable visualisation techniques and data mining protocols, as

well as the investigation of the scalability of these approaches for mass-customisation.

Ning and Dong (2016) suggested creating a virtual collaborative environment for

distributed data mining by making an immersive framework for analysing data and

related patterns possible. Section 2.3 illustrated that a large amount of 3D Body

Scanning data already exists but often cannot be reused by garment developers

because the data lack standardisation and transparent definition that links it back to

pattern drafting practice. 3D Body Scanning developers, thus, need to focus more

resources on developing and combining different data collection methods derived

from various fields such as human factors, ergonomics, marketing and sociology; a

topic further expanded in section 2.4.

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• Retailers must address customers data security and privacy concerns.

While 3D Body Scanners provides garment developers with more benefits than

manual methods (accuracy, speed, time, non-invasiveness, reliability), too much

accumulated data presents a severe challenge regarding keeping all information and

images secure and private (Cai et al., 2008). 3D Body Scanning databases have yet to

become a standard tool in the fashion industry. Thus, the fashion industry must

envision all potential challenges in data curation to form user and data security

guidelines (Gupta, 2014). Treleaven and Wells (2007), however, argue that the

enormous benefits of 3D Body Scanning outweigh any ethical, security and data

protection issues that may arise while using it. In contrast, Cavoukian (2008, 2009),

argues that privacy issues should be considered in the first stages of development of

any information system so that designers can utilise the technology without

compromising privacy. Buxton et al. (2002) recommended industry guidelines that

should base around the idea of having centralised control of user access. This system

database will have a single administrator responsible for providing access to data,

monitoring users’ conduct, and making decisions about barring access to users who

attempt to override their privileges. As it stands now, more research into decision-

makers perspectives and curation algorithms are needed (Paquet, Robinette and

Rioux, 2000), as developers are coming up with systems that are supposed to

ameliorate problems [but] that might end up exacerbating them (Courtland, 2018).


The literature within section 2.3 outlines a practical guide for evaluating technical

capabilities and scanners fit-for-purpose in product development practices. Section

2.3.1 starts with a brief historical description of technology developments and

compares 3D Body Scanners in terms of data acquisition with a focus on sensor

design, cost, and size. Notably, in section 2.3.2, the literature dives deeper and

discusses approaches to accuracy, precision, and reliability research. This section

found that accuracy validation carries widely different meanings for stakeholders. For

instance, the fashion industry may use ‘validation’ based on the repeatability of the

consecutive scans, ISO standards or compare measurements with the existing pattern

theories. However, current body definitions are too unspecific and exclude relevant

body morphology for garment construction (Parker et al., 2021). For software

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developers, validation may be more akin to ‘clinical validation’, referring to the

quality of the sensors and algorithms’ measurements. Additional work is still required

to ensure more robust validation thresholds in the fashion industry, leading to setting

the gold standard, similar to the DEXA scanner (Sobhiyeh et al., 2021). Moving on,

section 2.3.3 provided an overview of the design of 3D Body Scanners databases and

how stakeholders consider specific criteria such as accessibility, usability, safety, and

comfort to make databases more comprehensive for the fashion industry. However,

literature found that 3D Body Scanning developers create highly fragmented datasets,

as they have become progressively narrow in scope, more numerous, and have lost

connection with each other and product development approaches. Thus, in section

2.3.4, the literature emphasised the importance of standards and standardisation

practices. Therefore, to achieve interoperability between systems, stakeholders need

to define shared ontologies that better reflect the user considerations of products and

services in the digital era. Only then will these elegant and powerful techniques fulfil

the promise of becoming rapid, reliable and non-invasive decision-making tools in

product development, enabling mass-customisation and bespoke manufacturing

capabilities. In the 2.4 section, the literature will evaluate how the fashion industry

utilises existing technology and what approaches could allow for teasing out the best

outcomes.

Fashion Human Factors & Usability Issues with 3D Body Scanning

The perspective outlined in section 2.3 gave a unique vantage point to look back on

how decades of research and its inherited product development methods could add to

the technology developments. It demonstrated how the use of 3D Body Scanning

could improve garment developers efficacy, increase body shape inclusivity, and

lower the costs of conducting fit trials (Ashdown, 2020). However, robust empirical

evidence about the value of 3D Body Scanning in their practice is still limited (Sohn,

Lee and Kim, 2020). The question of the method’s effectiveness has been with the 3D

Body Scanning industry for a while (Jones et al., 1989). This section has highlighted

the fundamental challenges in HF and usability practice by the fashion industry in 3D

Body Scanning. The human factors are pragmatic approaches that identify problems

in industrial systems and ergonomics (Stanton et al., 2013). When applied to the field

of human-computer interaction (HCI), human factors research conceptualises

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interaction as a form of ‘man-machine coupling’ (Harrison, Tatar and Sengers, 2007)

with a particular focus to take computer issues beyond the task-related (Overbeeke et

al., 2004). The objective of HF research has been to improve understanding of the

factors that influence the successful development and implementation of computer-

based systems (Venkatesh et al., 2003). As the 3D Body Scanning industry expands

and firms began to array themselves across the investment landscape, it is important

to understand all its applications’ varied facets. The human factors perspective makes

it more possible to ask, well, what is it that the 3D Body Scanning industry is doing?

How does its contribution rate in the problems that there are to solve? This section

reviews 3D Body Scanning platforms and applications with industry examples in

section 2.4.1 highlighting the fashion and technology practitioners’ differences in 3D

Body Scanning usability 2.4.2.

2.4.1 Design Platforms for Product Development

3D Body Scanning can connect with VFI for garment selection in e-Commerce and

provides a foundation for CAD and CAM development for automated pattern

creation. Table 10 exemplifies companies that propose novel solutions to tackle size

and fit issues in apparel. Table 10 Overview of commercial VFI, CAD and CAM interfaces. Source: author’s own.

VFI CAD CAM • My Virtual Model • Belcurves • Fit Analytics • Fit Predictor • Fits Me • Metail • Style Me • Style While • True Fit • Virtual Outfits

• Brozwear (Vstitcher, Lotta, Stylezone)

• CLO 3D • Lectra (Modaris) • Gerber (Accumark), • EFI Optitex • TukaTech (TukaCad)

• Bespokify • Unspun Jeans • Levi Original Spin • Me-ality • Bodi.me • Formcut • Brooks Brothers • Alton Lan • Sur Measur • Nathon Kong tailor truck

Virtual Fit Interfaces

The growth opportunities in apparel e-Commerce are currently limited by the lack of

in-depth information about clothing size and fit (Song, Kim and Ashdown, 2020). The

return rate for apparel is much lower for clothes bought in-store than for those bought

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online (16 per cent versus 25 per cent, respectively) (Berg and Amed, 2020). The

inability to try-on clothing is a major obstacle to online purchases, often called the

suit, fit and match dilemma (Liu et al., 2019). Virtual Fit Interfaces are online tools

developed to help customers with their product selection by providing information

about garment size and fit (Sohn, Lee and Kim, 2020). VFI collect a variety of

information, including customers’ anthropometric data, demographics, style

preference or past purchases, which is then analysed based on 3D Body Scanning

data-sets and retail analytics to recommend the best options from available metrics

(Nam and Kim, 2021). Baytar et al. (2016) argue that retailers adopting VFI

technology can help customers in their decision making and represent the future of e-

Commerce retailing. Similarly, Ashdown et al. (2009) indicated that connecting 3D

Body Scanning with VFI can help retailers provide reliable anthropometric

information and contribute to making e-Commerce an easy and fun experience for the

customer (Sayem, 2019).

The marketing research on VFI focuses on the fit between customers’ physical

features and 3D avatars based on their self–perception. It is suggested that the most

effective product presentation is when commercial models’ physical features match

these of customers (Chevalier and Lichtlé, 2012). Thus, VFI focus on amplifying the

perceived resemblance between the customer and the avatar to increase purchase

intentions and confidence in apparel fit (Javornik, 2016). Therefore, using an avatar

that represents the self (avatar self-congruity) increases the impact of VFI (with more

confidence in apparel fit, greater purchase intentions). Thus, websites should

maximise the perceived resemblance between the customer and the avatar (De Coster

et al., 2020). Furthermore, research on self-representing information processing

suggests that a self-representing effect exists (Symons and Johnson, 1997). However,

Buyukaslan et al. (2019) argue that understanding digital avatars’ impact is critical

because some people tend to have decreased body satisfaction after interacting with

their 3D body image (Grogan et al., 2019). VFI models possess multiple physical

attributes that can influence customer behaviour. Plotkina and Saurel (2019)

examined the VFI avatar look based on attributes, such as ethnicity, body size,

gender, and age. Ethnicity can positively influence perceived hedonic value,

increasing customers’ responses, such as purchase intention (Merle, Senecal and St-

Onge, 2012). Sierra et al. (2009) have argued that customers react more favourably to

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VFI avatars of the same ethnicity. Smeesters et al. (2010) argued, however, that

customers’ reactions to thin or large avatars depend on their own body mass index

(BMI). Their research found that women with low BMIs react positively to thin VFI

avatars, and VFI avatars with larger body sizes can make advertising less effective to

customers with a high BMI. Nevertheless, Berg (2015) found that customers enjoy

viewing highly detailed pictures, including facial and hairstyle details, irrespectively

of gender, age and weight. Lastly, Chevalier and Lichtlé (2012) have shown the

usefulness of a match between the age of the model used in promotion and the target’s

age when the product is aimed at a specific age group. In general, research stresses the

importance of matching suitable VFI avatars with the target customer preferences and

requirements (Brownbridge, Sanderson and Gill, 2016). According to Merle et al.

(2012), when exposed to self-representing or personalised information, customers

process the information differently, which leads to better information recall, a more

favourable attitude, and increased choice confidence. In summary, research suggests

that customers who perceive the VFI as more self-congruent will be more confident in

their product choice (Wallace, Buil and Catalán, 2020; Kaur and Anand, 2021).

The first VFI was called ‘My Virtual Model’ (MVM) and allowed users to create

virtual customer identity based on body self-measurements (Nantel, 2004). MVM

team embarked on a crusade: to convince retailers of their product’s value. The

company was created based on ‘My Virtual Model Dressing Room’, and in 2002 ‘My

virtual in-mail’. According to Nantel (2004) research, the primary customers included

Land’s End, Home Shopping Network, SharminShop, and Levi’s. Their key

competitors included Imaginarix, Click’N’Dress (limited implementation at Spiegel

group), Bodymetrics (B2B), Browzwear (B2B), Compucloz, FitMe (junonia.com),

Toyobo/DressingSim (Benetton Japan). Nevertheless, after 2001-2002 several key

competitors, including Yourfit, LuuLuu, TheRightSize, MySize, and enFashion, have

disappeared from the landscape due to the inability to find customers and the

significant position of MVM. During the same period, MVM absorbed EZsize and

SizeMe. However, due to the low engagement, My Virtual Model interface closed in

2009, and CEO rebranded the interface into a weight management platform. This

suggests that VFI needs a more robust business model and technical improvements

since customers realise that the virtual avatars are not yet a highly accurate

representation of themselves (Brownridge and Twigg, 2014). The existing VFI utilises

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various methods to reflect the customer as a 3D avatar – as shown in Figure 4. For

example, Belcurves avatars are embedded in Mark and Spencer retail and show

limited facial and body features because its target audience is much older and may

have higher concerns with body image (Lee et al., 2012). In contrast, Metail and

Style.me interfaces offered a high level of customisation from pre-defined settings,

including hair colour, length, and style, skin shade, eye-colour, or even lips size.

Despite demands for improving avatar appearance level (Bombari et al., 2015),

realism is still limited in current VFI offerings (Miell, Gill and Vazquez, 2018). The

VFI approaches are further explored in chapter four and provide insights into

improving the design and usability issues.

Figure 4 3D avatars designs. Source: Belcurves (2018), Style.Me (2018), and Metail (2018).

Another important aspect of VFI design is the creation of digital clothes (Porterfield

and Lamar, 2021). Digital clothes can alter the design and skills needed to produce

garments and may result in different industry business models to those used to-date

(Pallant, Sands and Karpen, 2020). In the era of mass-produced clothing, sizing is

used to create ready-to-wear garments to fit a wide range of people in the population

(Petrova and Ashdown, 2012). At the moment, setting the sizing agenda involves

grouping the population into subsets based on common characteristics, which are

often proportionally segmented to enable the delivery of ready to wear clothing. The

retail sizing systems are often unique to the brand and considered mainly by

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researchers as a marketing tool (Staples and DeLury, 1949; Sokolowski, Silbert and

Griffin, 2019). The anthropometric data behind sizing metrics is guarded as

commercially-sensitive, offering few opportunities to fully appreciate how different

retailers’ systems work or how they are developed (Gill, 2015). Nevertheless, the size

labels should provide the customer with clear guidance on the critical body

dimensions that the garment is supposed to fit (Chun‐Yoon and Jasper, 1993).

However, Gill and Brownbridge (2013) focus groups found customers’ can be

resistant to the sizing concept and suggested future research should investigate 3D

Body Scanning as an intervention tool to enable women to have realistic and positive

views of their bodies. Similarly, Oghazi et al. (2018), Yan and Pei (2019), and

Freudenreich and Schaltegger (2020) argues customers are frequently dissatisfied

with the fit of clothing, which leads to a high volume of returns of merchandise and

financial losses for the manufacturers. Despite the research criticism, many Virtual Fit

interfaces, such as Fit Predictor and Fit Analytics in Figure 5, implement size

recommendation as a central element of their marketing tool. However, Loker et al.

(2005) suggested that VFI need to identify design adjustments of acceptable fit within

each size category to address the variety of body shapes.

Figure 5 Size recommendation. Retrieved from Fit Predictor (2018), and Fit Analytics (2018).

To date, sizing and shape remained separate concepts, with few retailers offering

shape categories alongside their sizing charts. However, Gill (2015) demonstrated

examples of retailers beginning to use the shape in the marketing of clothing. The

body shape describes the proportional relationships between dimensions or regions of

the body (Song and Ashdown, 2013). The latest research thrust for size designation is

based on body shape and proportions to establish a body-to-pattern relationship

(McKinney et al., 2017). Shape analysis methods with defined landmark points can be

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used to derive ratios, angles, and their combinations for quantifying body shapes

(Allen, Curless and Popović, 2003). As suggested by Jiang et al. (2019), features

extracted using point cloud outline-based methods requires visualisation tools for

feature interpretation and understanding. Moreover, the shape visualisation tools

could be embedded in 3D Body Scanning software for (i) quickly and anonymously

assessing sizing groups for different target markets and (ii) visualising shape outlines

and body composition to guide new garment development approaches. However,

existing VFI requires customers to classify their body shape using subjective

descriptions, such as those presented in Figure 6, to assist in garment selection.

Figure 6 Body shape classification. Retrieved from True Fit (2018) and Fit Analytics (2018).

The concept of shape in apparel can be traced back to the female figure identification

technique (FFIT) (Devarajan and Istook, 2004), a first body shape classification

system developed in the USA based on data from 3D Body Scanning. The recognised

body shapes are grouped as a triangle, inverted triangle, rectangle, hourglass,

diamond, and rounded shape (Yu and Kim, 2020). As shown in Figure 7, the use of

FFIT was implemented by VFI called fits me. However, Gill (2015) found, the body

shape concept in apparel remains exclusively academic; the fashion industry has yet

to adopt the idea in a product development context. Parker et al. (2021) provided a

detailed description for each FFIT body shape and illustrated how current criteria

omits shoulder measurements, which have little relationship to body shape but is vital

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in garment construction. Therefore, using FFIT with different datasets and definitions

leads to inconsistent conclusions about shape differences (Parker et al., 2021). As

there are no fashion industry initiatives created through the standards and guidelines

for garment developers. (Bougourd and Treleaven, 2020).

Figure 7 Body FFIT shape classification. Retrieved from Fits Me (2018).

3D Body Scanning offers research opportunities that incorporate shape into the

definition of sizing templates to create much more complex body forms and thus

better suit the population’s varied needs (Streuber et al., 2016). However, there are

outstanding questions regarding identifying different body type morphologies

(Thelwell et al., 2020) that could help practitioners recognise the importance of

adapting new measurement charts for different fit requirements (Bougourd, 2007).

The significant research advances include classification of the population by body

shapes in 3D software (Lee and Park, 2017), proportional relationships between key

sizing circumferences (Margerum et al., 2010), considerations of widths and depths

(Li and Chen, 2009), or a combination of crucial circumferences and diameters

(Vuruskan and Bulgun, 2011). Nevertheless, the understanding of body shape is

critical in mass-customised and bespoke manufacturing for a better fit in design (Yan

and Kuzmichev, 2020), especially for customers with disabilities (Park et al., 2019).

The addition of measurements such as pitch, slope length, and front-to-back depth –

may lead to more appropriate body classifications for ergonomists, garment

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construction practitioners, and researchers to work with a higher level of precision

(Parker et al., 2021).

3D product presentation through VFI can enhance information seeking and

entertainment (Algharabat et al., 2017). Meng et al. (2010) suggested that using

interactive VFI technologies enhances customers’ ability to understand sizing

information better because the digital product can facilitate users’ ability to “feel,

touch, and try.” The most popular VFI, however, still produces 2D images, as shown

in Figure 8. In these examples, Fits.me focuses on fit suggestions for three suitable

body areas while Virtusize asks to measure similar style garment and shows how they

match. However, Merle et al. (2012) suggested that software developers should design

VFI with vivid trying-on scenes in order to bridge the gap between the online and

offline shopping experience. Thus, Rennesson (2012) showed how future VFI could

visualise 3D garments on the customer’s avatar, fitting analysis to show the fabric’s

stress map on the body and virtual catwalks to promote products by showing it worn

by the customer’s avatar. Therefore, virtually trying-on apparel can be entertaining,

suggesting the utilitarian and hedonic value of VFI application (Zhang et al., 2019).

Figure 8 Visualisation methods. Retrieved from Fits Me (2018) and Virtusize (2019).

Through VFI, fashion retailers can also capture insights that are more reflective of

customers’ preferences and misfit experiences (Brownbridge et al., 2018), potentially

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improving product delivery (Miell, Gill and Vazquez, 2018). In that manner,

information from VFI can contribute to the creation of big data analytics with

individual customer profiles that harvest data, preferences and turn them into analytics

for advertising (Silva et al., 2019). The most prominent and growing VFI database in

commercial use are from True Fit Genomne™ and Stitch and Fix service (Caro, Kök

and Martínez-de-Albéniz, 2020). Therefore, the fashion industry stakeholders are

increasingly collecting, storing, and analysing substantial granular data and

information (Acharya et al., 2018). Thus, VFI has the potential to support a retailer’s

ability to enhance existing customer relationships in an operative or tactical manner,

such as how to approach customers, communicate with them or influence their

product choices.

Figure 9 Past wardrobe purchases. Retrieved from True Fit (2018).

Computer-Aided Design

Computer-aided design (CAD) tools in product development aim to redefine the

concept of fashion design and pattern creation (Sayem, Kennon and Clarke, 2012).

The existing CAD interfaces allow for the real-time simulation that syncs garment

move, animated avatar, record and plays in a rendered 3D environment (Špelic, 2019).

However, CAD drafting systems currently in use do not adequately model the

physical process of fitting a garment (Jiang et al., 2019). No completely accurate

method exists for drafting an individually fitted pattern (Lagė and Ancutienė, 2019).

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To guide the design of these next-generation CAD interfaces, Sayem et al. (2010)

advocate for creating ubiquitous tools that could shadow garment developer work in

practice. When designing visualisation CAD tools, it is essential to involve expert

users in the requirements gathering process so that the language used represents the

fashion domain and not of the technology developers (Li, Wei and Zhou, 2017). As it

stands now, the garment developers merely navigate in the world designed by the

technologist. However, the garment developer needs to be able to assess the fit of

clothing within 3D environments as well as be able to flatten the 3D surface of a

garment into 2D (Taylor, Unver and Worth, 2003). The garment developer should

have access to a sketch-based interface to keep design workflow familiar with their

practice and design approaches (Loker, Ashdown and Carnrite, 2008). The CAD

interface with an excellent physical approximation of the garment’s behaviour would

make it possible to shorten the design cycle, in which product developer could switch

back and forth between 2D drawing and 3D drape simulation of a virtual toile on a

virtual dress stand transfer 3D scanned measurement to the customised avatar (Mah

and Song, 2010).

CAD technologies have the potential to disrupt traditional approaches in product

development, which might lead to entirely new product styling, design and

verification processes (Goworek, 2010). Proposals for future work should include the

development of a very comprehensive virtual environment for product design that

includes the integration of the design steps into one efficient service workflow.

Detailed documentation of software goals and design intent is thus needed, and this

documentation must be written collaboratively with both 3D Body Scanning

developers and the fashion industry to ensure both its accuracy and relevance to

crucial research problems. In this way, the engineers will be supplied with suggestions

for actual design problems, based on the knowledge delivered by observations of what

designers think and work (Papachristou, Kyratsis and Bilalis, 2019).

The current interfaces and their capabilities are summarised in Table 11. According to

the research by Durá-Gil et al. (2019), the CAD programs allow for:

i. creating and adjusting an avatar (style, pose, size and measurements),

ii. creating 2D patterns (symmetric/ instance design, dart/pleats fold, symbol

annotation, and reference lines),

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iii. grading (add and edit pattern size, create sizing tables),

iv. sewing & tacking on a garment; creating 3D garments (3D lines on patterns,

cutting 3D patterns, flattering, selecting single/ multi meshes,

v. creating fabric kit, arrange patterns and print layouts, and create tech-pack. Table 11 Overview of CAD software for the fashion industry. Source: author’s own.

FEATURE Optitex Lectra Gerber CLO

VIR

TU

AL

BO

DY

Data Generation

Parametric Body ✔ ✔ ✔ ✔ Integration with 3D Scanning

✔ ✔ ✔

OBJ import ✔ ✔ Data Presentation

Posture Adjustment

✔ ✔ ✔ ✔

Simple Walking Movement

✔ ✔ ✔

Full animation ✔

DE

SIG

N Approach 2D to 3D ✔ ✔ ✔ ✔

3D to 2D ✔ ✔

CL

OT

H Drape & Fabric Property ✔ ✔ ✔ ✔

Visual Effects

Pattern and Colour

✔ ✔ ✔ ✔

The non-technical challenge for future CAD represents the increasing product

complexity caused by upcoming technologies. However, how these interface

complexity concerns will play out during the implementation of the CAD in industry,

as standard-practices remains an open question (Gill, 2015). As it currently stands,

few tailors combine expertise in 3D Body Scanners, CAD digital tools and bespoke

tailoring. However, incorporating and implementing garment developers’ knowledge

into CAD software is critical and will lead to more efficient development processes

(Yan and Kuzmichev, 2020). In general, physical simulation research is becoming a

growing field in the games industry to give environments a more natural atmosphere;

but cloth simulation is only emerging research discipline (De Silva, Rupasinghe and

Apeagyei, 2019). In the past, owing to high processing in the draping cost, designers

choose tight-fitting or stretchy clothes for their virtual models that can paint on the

skin of character by using texture maps. Often clothes are modelled directly on a

character, and when animated, they move with the skeleton structure of the character

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but do not show any drape behaviour (Dāboliņa et al., 2018). In the graphic

simulation, the main cloth measurements taken into account are sheer, stretch and

bending, while most nonlinearities and hysteresis effects – are ignored (Miguel et al.,

2012).

Mathematical models to describe cloth are complex systems of a partial differential

equation that derive from mechanical laws and material behaviour. There are two

approaches to cloth modelling on computer system Finite Elements Methods (FEM)

and Mass Spring Particle Systems (MSPS) (Chen, Magnenat Thalmann and Allen,

2012). While FEM can be very accurate in describing the drape behaviour of fabric

properties, they are very slow. MSPS are also capable of modelling cloth drape

behaviour to a reasonable degree of accuracy, but they offer possibilities for a trade-

off between realism and speed. The cloth is a complex mechanical system made of

fibres. The mechanical behaviour of cloth is inheritably dependent on the material and

made structure of the fibres. Common fibre materials are wool, cotton and synthetics,

and they differ in thickness and internal fibre or yarn structure. The fabric structure is

inherited to the process of manufacturing and can be tight or loose (Feng et al., 2019).

Woven, knitted and non-woven are the three main unique fibre structure types.

Woven structures are made of orthogonally aligned, and interlaced threat patterns

created by the device called a loom, where warp and weft directions are distinguished.

On a loom, the warp threads are spaced equally and held under tension, and the weft

threads or fillings interlaced into the warp threats. Warp and weft directions usually

exhibit different elastic properties and are therefore responsible for the antistrophic

behaviour of woven cloth. According to Sinha (2020), knitted fabrics are hand-made

or by knitting machines, and they consist of interlaced curls in successive rows. They

are loose and elastic and used for woollens and underwear. Non-structured fabrics are

fabrics with no specific pattern structure.

CAD research follows one of the two approaches to visualise garments models in 3D.

The first, called the 2D-to-3D approach, allows positioning 2D clothing patterns to be

virtual human and automatically fitting the drape simulation (Meng, Mok and Jin,

2012). Magnenat-Thalmann and Volino (2005) provided a framework for an

interactive design environment to edit patterns in 2D and immediately visualised the

garment draping results in 3D. Meng et al. (2010) used physical-based real-time

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simulation to visualise design effects by virtually sewing up intricate garment patterns

on human models. In contrast, the 3D-to-2D approach represents a procedure in which

garments are first modelled as 3D objects and then flattened into 2D patterns (Huang

et al., 2012). The 3D-to-2D approach provides a way for designers to create garments

in a virtual 3D environment; nevertheless, the resulted garments are usually of simple

style, for example, with a single layer. These solutions might be suitable for 3D

characters but not for real garment development and production (Huang et al., 2012).

For example, Decaudin et al. (2006) modelled 3D virtual garments as ruled surfaces

that can be flattened to give 2D patterns. Lu and Wang (2008) proposed a 3D garment

design system involving customer’s participation for mass personalisation, and they

employed style surface and curves to represent garments. In addition, each new style

is a distinct 3D object; the design process of these 3D garments is very different from

the traditional practice in the textile industry, where complicated styles can be easily

created by manipulating some template garments, called basic blocks (Harwood, Gill

and Gill, 2020). On the other hand, all the 3D-to-2D applications involve a process

called surface flattening, in which a 3D virtual garment is flattened to give 2D

garment patterns. So far, research focused on areas such as assessment of fit within

the 3D environment (Ashdown and O’Connell, 2006), 3D Body Scanning for avatar

and catwalk animation (Kartsounis et al., 2003; Brownridge and Twigg, 2014) and the

creation of tools to support virtual fit from 2D images (Chuang, Chen and Liu, 2018).

However, research agrees that a new way of modelling and flattening 3D garments in

CAD should be developed for clothing production and application (Kang, Oh and

Kim, 2021). Nevertheless, 3D Body Scanning is an important technology for apparel

CAD because the digitalised human body can be readily obtained, from which 3D

garment can be created to evaluate the size and fit features. The near future may see

further CAD applications where the whole process of scanning, designing and

manufacturing customised products for the human body becomes tightly integrated,

intuitive and automated.

Computer-Aided Manufacturing

Computer-aided manufacturing (CAM) is a means of automating the manufacturing

process through the use of software and computer-controlled machinery (Rizkiah,

Widiaty and Mulyanti, 2020). Research on computer-aided manufacturing (CAM)

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systems is essential for scalable fashion manufacturing processes to reduce production

lead-time, as well as improve process throughput (Ashdown and Dunne, 2006). CAM

for a fully scalable production has yet to be achieved, and many challenges remain for

mass-customised manufacturing (Tseng and Jiao, 2001). The accuracy of body chart

data, reliability of body measurements, and methods to quantify fit preferences are

critical elements in a system designed to automate the pattern drafting process (Gill et

al., 2018). However, anatomical knowledge does not appear to be prevalent in

clothing measurement literature, except for Watkins (1995). To provide sufficient

insight into landmarks, garment developers requires consultation of research for both

ergonomic (Pheasant, 1986; Cameron, 2002), anatomy (Basmajian, 1983), and

clothing measurement (Sheldon, Stevens and Tucker, 1940; Kemsley, 1957; Bolton et

al., 1975; Beazley, 1996). The connected analysis of these three disciplines allows

recognition of critical features and considerations of a measurements survey, which

need to be recorded and presented informatively (Gill and Chadwick, 2009). This, in

turn, can pave the way for the automated creation of clothing patterns (Sayem,

Kennon and Clarke, 2010). 3D Body Scanning can provide reliable and valid data

about the body that can better inform the drafting of garment pattern blocks (Slopers)

(Gill, 2018). One example of such developments is Scan To Pattern JBlock Software

(Harwood, Gill and Gill, 2020). JBlock software introduces a new method of

automatically drafting patterns from measurements extracted from scan data.

JBlockCreator may be coupled to 3D Body Scanners, CAD software or plotters to

form a connected pipeline for the mass-customised or bespoke apparel manufacturing.

Scan data can transform and allow discussion on how the manual method is accessible

and understandable by current practitioners. Moreover, Harwood et al. (2020)

research summarise critical points for further discussion:

i. Placement of the crease line on trousers

ii. Location of the apex of the bust

iii. The shape of the trouser crotch curve

iv. When should the bodice have a bust dart into the armhole?

The first brand that connected 3D Body Scanning with CAM in retail was Levi

Strauss & Co., which in 1998 has made a name for itself in technology circles as they

were the first large apparel company to try to provide a model of mass-customisation

through jeans offerings. Through the program called Original Spin’, Levi offered

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customers choices in style, fabric, finish, colour, and inseam length (Itzkowitz, 2015).

To measure customers and provide customised patterns, Levi used Intellifit 3D Body

Scanning in the San Francisco flagship store. The introduction of mass-customised

jeans created a boom for personalised products (Anderson, 2006) in the fashion

industry (Tseng and Jiao, 2001). Following Levi’s initial success, Lands’ End

launched its first products in 2001, and JC Penney followed suit in 2005 (Moon and

Lee, 2014). Unfortunately, none of these businesses survived the test of time and

made 3D Body Scanning their integrated part of the company. For Levi Strauss & Co.

primary reason for the program closure was the problematic business situation,

including cost-cutting efforts, in which the US factory focused on producing the

customised jeans was closed (Piller, 2004). The company failed to build into a

personalised relationship with its customers — as, during all its existence, re-orders or

customer feedback were never easily possible. In customer knowledge management

literature, the personalisation concept is highly emphasised (Kang and Johnson, 2015;

Parker and Wang, 2016). JCPenney and QVC eventually suspended all custom

clothing operations, and Lands’ End decided to stop producing custom pants for men

and women with no background information about the reason why (Song and

Ashdown, 2012). Twenty-three years later, a new brand called Unspun began a

collaboration with retail giant H&M to pilot a project called Body Scan Jeans in

Sweeden. Customers can book an in-store contactless scan and then select from two

jeans styles and further personalise it by selecting fabric, waist and hem options, and

various trims (Wright, 2019a). The final version of Genesis jeans by Unspun include a

QR code powered by the Internet of Things (IoT) Eon platform that makes it possible

to track the origins and record the life of the garment; ensuring the product and its

materials can be identified and authenticated for resale and recycling from one life

cycle to the next (Wright, 2021).

Unique Design solutions opened the first 3D Body Scanning booths called Me-ality

— a size and style recommendation service in King of Prussia Mall (Huffington post,

2013). The process began with 3D Body Scanning, where the user entered a circular

booth, fully dressed, and waits as the scanner takes about 200,000 measurements in

360 degrees using low-power radio waves (Accardo and Chaudhry, 2014). The

scanner has not displayed the user’s 3D avatar, but instead, the body measurements

numbers were translated into personalised shopping guides for customer body size

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and shape. A kiosk next to the scanner advised on a list of brands from the mall-based

on the style and size the user could be interested in trying. ‘My Best Fit’s’ database

included about 50 retailers, including Old Navy, Eddie Bauer, and Talbots (Romeo,

Stannard and Bourgeois, 2017). The company planned to open in the USA additional

300 speciality kiosks between 2013-2015 and create a sizeable information-rich

database for marketers keen to observe customer trends, with tie across several other

industries such as health, fitness, insurance, ergonomics and gaming (Huffington post,

2013). In 2015 according to Dawes (2015), the company raised funding of $15

million, and the government officially valued the pipeline at 250 million worth.

However, the kiosks vanished with no information about what happened to them and

no information as to what happened to the raised public funding (Bousquet, 2014).

3D Body Scanning was also tried across different markets and different demographic

groups for female customers. The company called Bodi.me collaborated with two

leading retailers: New Look and Selfridges Co to find well-fitting jeans for a very

different price point. New Look is the UK’s largest high-street jeans retailer, and

Selfridges & Co is s a chain of high-end department stores. However, the scanning

procedure and technology presentation was identical for both stores. In the process,

customers entered the 3D Body Scanning booth in their underwear, and seven seconds

later, Bodi.me, body-shape analytics, used their proprietary algorithms to find

garments that best suit the customer’s unique shape and size. The Bodi.me team then

email the customer their ‘unique body map’ for future reference – based on the theory,

as more stores implement the technology, the details could help with accurate fitting.

However, bodi.me concept has not stood the test of time and closed its retail operations.

The service focused too exclusively on creating efficient transformation and sales

processes while ignoring crucial questions about how firms should decide what to offer

and to which customers. Thereof, retailers making a strong assumption may often be

challenging to meet, i.e., that the manufacturer can properly predefine a solution space

from which customers can be served (Baden-Fuller and Haefliger, 2013).

The most prosperous 3D Body Scanning business models come from the menswear

tailoring services such as shirts and suits. A high-quality suit is hard to find in the

ready-to-wear category because it is defined, among other things, by the fact that it

fits perfectly. Many made to measure retailers adopted scanners to enhance tailor

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practice with an automated machine and keep customer information is in an organised

manner. The most prominent companies include Formcut (UK), Brooks Brothers

(USA), Alton Lane (USA), Sur Measur (CA), Nathon Kong tailor truck (CA).

Moreover, these stores offer customisation services in terms of both material and

design and choose the type of lapels, vents, pockets, and linings to complement the

customer experience. Moreover, Piller (2004) research showed that a manufacturer’s

ability to leverage the known elements of a long-tail business model (i.e., practical

customer toolkits) is crucial for business success. It is contingent on retailer efforts to

understand customer demand characteristics better to incorporate this knowledge into

the manufacturer’s core functions. Should the customer orientation be too low, the

manufacturer may struggle to align the range of flexibility embedded in its products

and processes with the target customers’ personal needs.

2.4.2 The Lost Link Between the Developer’s Goals and Sizing Practices

3D Body Scanning has the potential to push size and fit methods forward, but so far

achieved very little to no success in apparel (Huang et al., 2012; Huang, Liang and

Wang, 2018). Istok and Hwang (2001) early on have proposed that the 3D Body

Scanning industry’s promise has not been realised, in part, because the enterprise is

modelled on those of the high-tech information technology (IT) industries, that may

not be suitable for the fashion industry methods. Loker et al. (2008) uncovered a

substantial discrepancy between how garment developers understand and engage in

product development practices and technical research based on modelling in

algorithms. The discrepancy between approaches may be one reason why the ‘size

and fit’ literature often has an ethereal flavour, seeming to be both mathematically

correct and yet irrelevant to garment developers who must make things work with real

clothes, human bodies and pattern techniques (Scott, Gill and McDonald, 2019).

There is still a need for more work on ‘the raison d’être’ (the reason for existence)

(Galle, 2011) of 3D Body Scanning in the fashion industry, to provide practically

applicable solutions, as well as a contribution to knowledge that can be applied to

solve real-world problems. What is missing from this curricular usability approach in

the literature is that there is little or no direct emphasis on developing practical skills

for working in 3D Body Scanning (Wang and Ha-Brookshire, 2018). Gill (2015) also

suggested that the challenges inherent in trying to use 3D Body Scanning are that

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garment developers require a novel business architecture that is better suited to

connect these divergent stakeholders activities. For example, the fashion industry’s

involvement in developing 3D Body Scanning would require an extension of

designers’ activities to areas previously covered solely by the engineering domains

(Gu and Liu, 2019).

Despite recent technical advances in 3D Body Scanning, a central component of

successful innovation remains mostly unaddressed in academic literature: the intimate

connection between 3D Body Scanning tools and the changes they necessitate to the

actual delivery of retail services (Loy and Canning, 2016). Today, product

development typically focuses more on generalised customer adoption. In essence, the

costs and bulk volume requirements of modern fashion consumption make it very

difficult to treat each product as customisable and instead focus on the broadest

applicability across the target market (Kang and Kim, 2012). In contrast, 3D Body

Scanning has shown the potential to enhance bespoke retail services’ through better

customer experience, improved size and fit outcomes and reduced manufacturing

costs, but their actual impact remains variable and limited in scope (Gustafsson,

Jonsson and Holmström, 2019). Fashion retail acceptance is still growing, and

dominant service models have yet to emerge (Silva et al., 2019). This is partly due to

the disconnect between the traditional fashion industry approaches based on

incremental changes and the flexible way in which 3D Body Scanning solutions are

developed. In meccas of digital innovation like Silicon Valley, there is often a desire

to move more rapidly with less tolerance for regulations (Bindahman, Zakaria and

Zakaria, 2012). However, allowing unregulated claims has the potential to backfire by

further detracting from the credibility of this emerging sector, such as Virtual Fit

Interfaces – the VFI usability theme is further explored in chapter four.

The ability to undertake the interdisciplinary type of research is complicated; as it

requires collaboration between academics, garment developers, engineers and

computer scientists who often have different approaches and drivers to their

engagement with technology (Seyed and Tang, 2019). The fashion industry, therefore,

needs practical tools to identify underlying principles in product development

approaches. The fashion industry barriers are rooted in the lack of usable data to

investigate theoretical models, and they often struggle with limited access to

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commercially sensitive data (Gill and McKinney, 2016). Unfortunately, the topic is

not yet covered appropriately at a practical level and in academic publications that

should demonstrate the benefits and shortcomings of the theories (Scott, Gill and

McDonald, 2019). In addition to being attentive to the licences of their project

dependencies, Black (1990) was among the first to suggest that the garment

developers should document the full creative process from design to pattern drafting

and draping, a case shown by Harwood et al. (2020) for trousers pattern and Jiang et

al. (2019) for traditional Chinese dress.


This section illustrates the proof of the ever-increasing interest in 3D Body Scanning

and the growing number of commercial solutions that appeared in the past ten years.

Section 2.4 revealed that 3D Body Scanning could be used to build strong, reciprocal

business-to-customer relationships with apparel product development or

manufacturing departments. Thus, this section provided theoretical insights into the

fundamental problems that garment developers face using 3D Body Scanning. In

doing so, this section illustrates a dichotomy between technology producers and

fashion practitioner’s ontologies, methods and practices. A challenge for those who

work in the fashion field is to establish if and how 3D Body Scanning will advance

product development and ultimately translate to useful and informative applications in

a wide range of strategies. In section 2.4.1, this research evaluated existing industry

tools and technologies that may support 3D Body Scanning adoption. Specifically,

section 2.4.1.1 discusses VFI’s and section 2.4.1.2 CAD programs from three

perspectives: the interface design, the technology barriers, and the software

integration level. Section 2.4.1.3 discusses challenges in CAM and the potential of

scan-to-pattern theory and illustrates existing market approaches. As such, the study

sheds light on past attempts to diffuse technology in retail, as it is important to draw

on lessons from past technology developments. These solutions grow to accommodate

a wide range of methods, such as virtual fit, retail try-on, scanning booths, mobile

applications, and innovative retail concepts. These products are finding an eager

market and opening up new research opportunities. The above examples demonstrate

many instances in which the expectations from the customers and the actual features

provided by the system are not well correlated. As highlighted in section 2.4.2,

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fashion retailers and technology developers’ discrepancies need to be further

evaluated and resolved. In the next section, the literature will discuss 3D Body

Scanning as a service concept to get an initial understanding of how service design

practice is performed in regard to involving stakeholders and get a sense of which

areas there were which might warrant further studies.

Service Design in 3D Body Scanning Research

This section maps the relationships between technologies, meaning, and innovation –

and illustrates the movement of trends within the industry. Section 2.5.1 introduces

the existing theories and models of service design that will provide a foundation for

further research and analysis. Specifically, it discusses the service design theories

such as user experience, stakeholder analysis, customer experience and customer

journey to understand the full range of challenges in building interactions, i.e., how

customers respond to the information, interface design, and interactive functions.

Section 2.5.2 contextualises existing MIS theories on the research related to 3D Body

Scanning service presentation and customer interaction. The overview of MIS

literature provides an opportunity to pause and reflect upon both what has been

accomplished by past work and what needs to be accomplished in the future.

However, most of the research approaches employed in the empirical articles included

in the review can be characterised as traditional approaches reflecting a positivist

orientation. Complementing the dominant positivist MIS research paradigm in 3D

Body Scanning with alternative perspectives and philosophical bases is important to

develop the field further. The last section 2.5.3, therefore, argues that the qualitative

service design perspective can open new avenues for gaining insights about 3D Body

Scanning integration with fashion retail practice.

2.5.1 Service Design - Theoretical Background

Service Design is a design practice and theory of designing desirable, usable and

useful service interactions on the fringe of an organisation (Junginger and Sangiorgi,

2009). Service Design is increasingly considered as ‘approach’ or ‘thinking’ that can

be transferred to a wide variety of methods for service innovation (Yu and Sangiorgi,

2014). Therefore, service design has been defined as planning and organising people,

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communication, infrastructure, and material components to improve service quality

and the interaction between stakeholders and users (Andreassen et al., 2016). The

core of service design, termed design thinking, represents a solution or experience-

focused means of interpretation that puts the customer first and the organisation

second (Gabrysiak, Giese and Seibel, 2011). The service design relies on methods to

incorporate elements of interaction between stakeholders and customers, duration of

the interaction, and the sociotechnical context where value takes place (Steen,

Manschot and de Koning, 2011). The outcome of a service design process is in itself a

process where value is co-created between users and service organisations (Patrício,

Gustafsson and Fisk, 2018). Therefore, the creative transition from understanding the

user experience to devising service solutions is crucial in service design (Teixeira et

al., 2012). An interdisciplinary approach is a strong anchor for service design projects

since it connects experts, users and stakeholders to orchestrate solutions that do not

yet exist (Ostrom et al., 2010). Edvardsson & Olsson (1996) imply that services

cannot be created, but the prerequisites for service concepts can be researched and

designed. Service concepts can be translated into service specifications, and building

on the specifications service delivery system is configured (Goldstein et al., 2002). A

service delivery system involves how service concepts are realised (Roth and Menor,

2003). Thus, aligning the service concept with service delivery system design is vital

for achieving successful service performances (Ponsignon, Smart and Maull, 2011).

The service design process will allow stakeholders to contribute to the development of

the service concept and focus on creating service forms, outcomes and experiences

(Clatworthy, Van Oorshot and Lindquister, 2014).

A good starting point for discussion about the fundamental issues for designing the

technology service is the Ehn (1988) book Work-Oriented Design of Computer

Artefacts. His contribution to the field presents three main points that would

foreshadow significant developments and lasting approaches in the service design

field. Firstly, Ehn (1988) – among others, Dreyfus (1992) and Winograd and Flores

(1987) or Richardson (2019) – pointed towards the shortcomings of Cartesian dualism

in computer science. Ehn stressed the need for an alternative philosophy of

technology based on existential phenomenology. Secondly, Ehn (1988) proposed

trans-disciplinarily as a necessary step to take in humanising the design of computer

and argued for the expansion of the existing methodological foundations to include

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the rich traditions found in the humanities and arts. Thirdly, Ehn (1988) emphasised a

need for practical knowledge, which he described as the fundamental component of

the design, in addition to theoretical knowledge – the hallmark of Cartesian dualism.

Björgvinsson et al. (2010) actualised these positions by proposing and dissecting an

approach where user co-design participation in the innovation process is vital:

building on the skills of both users and stakeholders to develop technologies that

promote digital democratisation. According to Ehn (2014), there is a genuine call for

innovation through service design methods. Research that demands extensive

collaboration across professional and institutional boundaries is not only multi-sited

but also multicultural (Von Hippel, 2001). Various institutional cultures – with

distinct values and norms – need to learn how they differ and how they can

collaborate. Distributed innovation, as Ehn (2014, p. 230) call it, therefore demands

that differences be acknowledged and that people with conflicting views and practices

understand and settle on what to agree upon and deliver service blueprint for

sustainable business model (Cheung, Prendeville and Kuzmina, 2019). This means

resolving conflicts on a paradigmatic level – for example, what is considered new and

what the focus should be in the trajectory of the diffusion of innovation (Steen, 2013).

Based on the literature review, this research found that the people involved in

technology design struggle to articulate precisely and realistically which specific

benefits they aim to achieve for their users (Li and Cohen, 2021). The main challenge

is how 3D Body Scanning firms can harvest users’ innovations into useable and

pleasant retail services (von Hippel, 2005). In the existing design scenario, technology

developers have become the main stakeholders deciding what it means that a system

is well designed and have at the same time monopolised user involvement (Daanen

and Ter Haar, 2013). The design decisions have tended to be based on task-oriented

analysis (Daanen and Byvoet, 2011). However, this task-oriented focus has been

criticised for creating an insufficient understanding of users’ perspectives and actual

usage contexts (Mironcika et al., 2020). As a consequence, cooperative nature has

been reduced and institutionalised under a logic of technology development.

Therefore, it is quite hard for researchers and developers to benefit from each other’s

work (Toti et al., 2019). The mismatch between the firms goals resulted in 3D Body

Scanning producing fewer benefits than stakeholders could have realised (Gill, 2015).

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Motivated by this situation, this research proposes to frame 3D Body Scanning as a

service design problem to emphasise the generation and combining of knowledge by

bringing different perspectives necessary for developing a shared understanding of

what should be done and how to cooperate (Kimbell, 2011). Therefore, service design

is critical in transforming 3D Body Scanning because it allows stakeholders to

communicate and cooperate across disciplines and organisations (Sanders and

Stappers, 2008). This research plan to identify relevant stakeholders and users, and

their specific goals and the possible benefits through service design methods.

However, as section 2.4 in the literature imply, fashion retailers were often excluded

from user evaluations and co-design activities (Papachristou, 2015). Nevertheless,

Parker and Heapy (2006) advocated for organising cooperation between retail

professionals, who deliver the service, and the customers, who experience the service

because both their perspectives are needed for implementation. That means that the

experiences and needs of retail managers, garment developers and customers are all

considered as users throughout the service design process. This contrasts with the

existing 3D Body scanning literature, as retailers were not viewed as technology

users. Service design, however, shows that by setting up user involvement in

particular ways, where, e.g., users and frontline retail personnel are provided with

generative tools and techniques, they can produce innovative services and more in-

depth research about diffusion barriers (Holmlid, 2009).

Stakeholders Analysis

In service design theory, services are often networked in many ways and are dynamic;

that is, the user and the service provider both influence the service experience that is

co-created and experienced in the interaction (Thomas et al., 2009). Stakeholder

analysis is an approach or set of tools for generating knowledge about actors –

individuals and organisations – to understand their behaviour, intentions,

interrelations and interests; and assess the influence and resources they bring to bear

on decision-making or implementation processes (Varvasovszky and Brugha, 2000).

Only by understanding who has a stake in an initiative and through understanding the

nature of their claims and inter-relationships with each other can the appropriate

stakeholders be effectively involved in decision-making (Freeman, 1984). Mossberg

(2008) suggested the whole system in service design works as a staged performance:

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the stage with trained actors, the user experience, the performance, and the backstage

that offer invisible support. This metaphor emphasises the requirement of well-trained

retail staff and the need for various stakeholders to develop services (Qi, Shangxue

and Weiping, 2015). Analysing stakeholders power and interest can assure the

number of participants who respectively launch contribution and demand in service

(Idoughi, Seffah and Kolski, 2012).

Some preliminary attempts to fill this gap of understanding stakeholders’ relationships

in 3D Body Scanning can be found in recent research in varied disciplines: medical

(Haleem and Javaid, 2019), ergonomic (Hernandez-Sandoval et al., 2020),

engineering (Hu, Kong and Lv, 2021) or even archaeology (Eve, 2018) and focusing

on the practices that can be used to manage stakeholders and users involvement in

service development processes (Toti et al., 2019). However, this topic’s available

knowledge for the fashion industry remains siloed and scattered (Gill, 2015). The

creation of integrative methods, tools, and languages that unify stakeholders different

perspectives is crucial for developing the service design field (Patrício et al., 2011).

This research argues that a real challenge throughout any service design project is

aligning stakeholders’ business objectives with the underlying operational

arrangements when moving from ideation to commercialisation. Involving retail

stakeholders in study design and development can make research findings more

relevant to the garment development decisions these professionals face and thus more

useful and likely to be taken up in practice – especially for research intended to

address pattern drafting theories disparities in a real-world setting (Lewis and Loker,

2017). Thus, acknowledging retail users along with technology, manufacturing and

engineering stakeholders may offer a more dynamic view of the involvement and

alignment of different stakeholders and their knowledge, leveraging the opportunities

offered by 3D Body Scanning. This alignment requires an in-depth understanding of

stakeholders and their multiple, mutual relationships, which can only be achieved by

breaking open the stakeholder interactions (Solaimani, Guldemond and Bouwman,

2013). This research zooms in on stakeholder interactions, which means that the

analysis is separated from the traditional, strategic definition, and the focus shifts

towards a framework that is developed and applied in the area of service design

(Blomkvist and Holmlid, 2011).

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User Experience

Human-computer interaction is only one of many names used for the design of

interfaces of technology over time. The name of the mainstream lines of inquiry has

changed with the trends over time and has been known as usability, interaction design

and user experience design, among others (Segelström, 2011). User experience is

holistic, encompassing every contact with an organisation, so all service encounters

need to be seamlessly orchestrated (Andreassen et al., 2016). User experience is about

technology that fulfils more than just instrumental needs in a way that acknowledges

its use as subjective, situated, complex and dynamic encounters (Löwgren, 2002).

Thus, the user-centred design suggests a concern for people in their roles as users.

According to Hassenzahl and Tractinsky (2006) – user experience is a consequence of

a user’s internal state (predispositions, expectations, needs, motivation, mood), the

characteristics of the designed system (complexity, purpose, usability, functionality)

and the context (or the environment) within which the interaction occurs (the

meaningfulness of the activity and voluntariness of use). Therefore, research into user

experience creates innumerable research opportunities to better understand, define,

operationalise and translate 3D Body Scanning into service quality (Silva and Bonetti,

2021). Diverse benefits are associated with user experience research: from improving

idea generation and service development to improving decision-making and

cooperation, creativity and satisfaction (Steen, Manschot and de Koning, 2011).

Realising the importance of user experience in service design, the research addresses

the issues beyond pragmatic functionality and usability with hedonic user motivations

such as stimulation, identification and self-expression (Vaananen-Vainio-Mattila and

Wäljas, 2009). The outcome of these suggests the accomplishment of user needs

contribute to motivation and to use of systems, products and services again

(Ahsanullah et al., 2015).

Customer Experience

The customer experience concept dates back to 1909, when the first Selfridges

department store opened on Oxford Street in London. The store welcomed customers

with fresh flowers, natural light and attractive product displays that encouraged

shoppers to browse and enjoy the experience, a concept that was unheard of at the

time (Rudkowski et al., 2020). For the past one hundred years, retailers have invested

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in customer experience to competitively position themselves and realise key business

objectives such as market share and sales revenue (Ameen et al., 2021). Services are

becoming more commoditised as leading-edge companies compete on customer

experience (Pine and Gilmore, 1998). Laming and Mason (2014, p. 15) defined

customer experience as “the physical and emotional experiences occurring through

the interactions with the product and/or service offering of a brand from the point of

direct, conscious contact, through the total journey to the post-consumption stage”.

The design thinking methods suggests that successful design requires the “exchange

between people who experience products, interfaces, systems and spaces and people

who design for experiencing” (Sanders and Dandavate, 1999, p. 3). Hence, customers

are central to the service design process (Goldstein et al., 2002). Moreover, research

suggests that capturing customers’ unique knowledge about the usage and latent needs

is key to innovation and service success (Mahr, Lievens and Blazevic, 2014). Yet,

research has also argued that customers cannot contribute or even hamper the

innovation process because they lack the imagination of new products that do not yet

exist (Magnusson, 2009). However, the customer will always have an experience:

good, bad or indifferent, as service always comes with an experience (Carbone and

Haeckel, 1994). As a result, it is agreed that generally, customers’ have a more

accurate and more detailed model of their needs, while manufacturers have a better

model of the solution approach in which they specialise (von Hippel, 2005).

The past fifty years of research has contributed to a holistic understanding of

customer experience as a decision-making process or journey. Table 12 summarises

the customer experience literature timeline based on the Rudkowski (2020) research.

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Table 12 Customer literature timeline based on Rudkowski (2020) research.

Timeline Theme Key Authors

1960 Consumer Buying Behaviour Models (Howard and Sheth, 1969) 1970 Consumer Satisfaction and Loyalty (Buskirk and Rothe, 1970) 1980 Service Quality (Berry, Parasuraman and

Zeithaml, 1988) 1990 Relationship Marketing (Berry, 1995; Sheth and

Parvatiyar, 1995) 2000 Customer Relationship Management (Berry, Carbone and Haeckel,

2002; Payne and Frow, 2004; Rust, Lemon and Zeithaml, 2004)

2010 Customer Engagement (Brodie et al., 2011, 2013; Kumar and Pansari, 2016)

2020 Experimental Marketing (Rudkowski et al., 2020; Ameen et al., 2021)

A key characteristic of experience-centric services is that they are designed to engage

customers and enable them to connect with the service in a personal, memorable way

(Zomerdijk and Voss, 2010). When customers’ experience a service, it always results

in evoking emotions – powerful, subjective feelings and physiological states

(Johnston and Kong, 2011). The main ones being happiness, surprise, love, fear,

anger, shame and sadness, and those feelings may range from, for example,

discomfort to depression or warm to intimate or at ease to ecstatic (Grace and O’Cass,

2004). The benefits the customer gets from using the service includes how they

perceive if they have profited or gained from the service provided and their

experience of it, i.e., how well their requirements and needs have been met (Johnston

and Kong, 2011). Another outcome of the service from a customer’s perspective

would be their conscious or unconscious appraisal of the service provided (Johnston

and Jones, 2004). These judgments, good, bad or indifferent, will result in the

intention to purchase and recommend a product or the intention to complain

(Stickdorn and Zehrer, 2009). Thus, all service encounters provide an opportunity for

emotional engagement despite how mundane the service might be (Johnston and

Kong, 2011).

Customer Journey

The importance of perceived service quality (Edvardsson and Olsson, 1996;

Edvardsson, 2005) and customer experience (Walter, Edvardsson and Öström, 2010)

are widely acknowledged concepts within service management and marketing

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literature. However, customers’ encounters with service providers in retail often

represent fragmented and frustrating experiences (Halvorsrud, Kvale and Følstad,

2016). More recently, the customer journey approach has emerged, whereby the

process of service delivery is mapped from the perspective of the customer to help

stakeholders understand service complexity (Halvorsrud, Kvale and Følstad, 2016).

Parker and Heapy (2006) pamphlet ‘The Journey to the Interface’ has been

instrumental in sparking interest in customer journeys within the field of service

design (Holmlid, 2009). A typical customer journey focuses on the customer and

actions and experiences from the beginning to the end of the service process

(Heuchert, 2019). Research about customer journey is often qualitatively rich and

have a strong value in communicating and sharing insights (Holmlid and Björndal,

2016). Combining customer journey within service design approach provides a more

in-depth level of detail in methodologies while still allowing for a more empathetic

approach to the problem space (Tueanrat, Papagiannidis and Alamanos, 2021).

Customer journey highlights that customers evaluate the service holistically (i.e.,

through a series of experiences they derive from different service touchpoints), which

indicates that the provider should not treat each touchpoint independently from the

rest (Rudkowski et al., 2020). The customer journey touchpoints are the building

blocks of services (Stickdorn and Schneider, 2010). Moreover, Halvorsrud et al.

(2016, p. 846) described touchpoints as “moments of contact between the customer

and the organisation”. Therefore, designing customer experience-centric services

involve managing a chain of service touchpoints surrounding service delivery

(Halvorsrud, Kvale and Følstad, 2016). This way, service design looks at the

experience by focusing on the entire customer journey, including the experiences

before and after the service encounters (Ho, Lee and Sung, 2013). This is an

important and distinguishing characteristic of the design object of service design

(Holmlid, 2007).

Retailers are positioned to design and deliver engaging customer experiences

(Ailawadi and Keller, 2004). Retailers’ recent strategic emphasis on customer-centric

experiential strategies lend relevance and importance to customer journey mapping as

a research topic (Rudkowski et al., 2020). Therefore, retailers need insight into the

dynamic, subjective experiences of individual touchpoints and how the overall

experience is shaped to alleviate customer dissatisfaction (Meyer and Schwager,

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2007). Yet, retailers often focus on the customer journey as one single encounter

within a customer journey with the lack of emphasis on the pre-purchase and post-

purchase touchpoints (Lowe, Maggioni and Sands, 2018). This research proposes that

3D Body Scanning in retail is a complex customer journey process, where the

customer travels through pre-scan (pre-purchase), scan (purchase) and post-scan

(post-purchase) stages and interacts with multiple touchpoints throughout the retail

journey (Lee et al., 2012). With a lack of awareness about the end-to-end service

delivery process, fashion retailers cannot excel in building customer interactions but

still provide a less-than-satisfactory overall experience (Lee et al., 2012; Peng,

Sweeney and Delamore, 2012). Therefore, understanding service delivery from the

customer’s perspective is an important topic for stakeholders that seek to improve

their services (Lemon and Verhoef, 2016); especially given the current low adoption

rates of 3D Body Scanning within retail services (Russell, 2020b). With these developments in service design research and its prominent concepts in

mind, the next section describes the 3D Body Scanning approaches for usability and

experience in MIS context.

2.5.2 Theoretical Models for 3D Body Scanning

While this research aims to draw upon a service design approach to uncover new

ideas or dimensions of 3D Body Scanning acceptance, the previously explored MIS

concepts should also be acknowledged. While there is almost certainly an overlap

between service design and MIS theories, it may also illuminate important distinctions

among these central concepts, strengthen the theoretical potency of these concepts,

and create a tighter nomological network for diffusion theory. Researchers studying

customer perceptions of and responses to fashion technology innovations draw on a

wide array of theoretical frameworks and models. MIS studies on user involvement

generally operationalise "successful implementation" through the dependent variable

system quality or system acceptance (Ives and Olson, 1984). Table 13 summarises

these behavioural models and their theoretical locus. The theories reviewed are: the

theory of reasoned action by Fishbein and Ajzen (1975), the theory of planned

behaviour by Ajzen (2011), technology acceptance model TAM1 by Davis (1989) and

TAM 2 Davis and Venkatesh (2000), and diffusion of innovation by Rogers (2003).

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Table 13 Human behaviour models and their theoretical locus based on Venkatesh (2003).

Theory Core Definition

Theory of Reasoned Action (TRA) (Fishbein and Ajzen, 1975)

Drawn from Social Psychology TRA is one of the most fundamental and influential human behaviour theories to predict a wide range of behaviours (Sheppard, Hartwick and Warshaw, 1988). The theory is based on four premises for attitude toward behaviour: (a) The degree to which the measure of intention and the behavioural criterion correspond concerning their level of specificity. (b) The stability of intentions between the time of measurement and performance of the behaviour. (c) The degree to which carrying out the intention is under the individual’s volitional control.

Theory of Planned Behaviour (TPB) (Ajzen, 2011)

TPB extended TRA by adding the construct of perceived behavioural control. In TPB, perceived behavioural control theorised to be an additional determinant of intention and behaviour. This model postulates that one’s intention to act is the result of a rational decision-making process that considers attitudes towards the behaviour, perceived social pressure to perform the behaviour, and evaluating one’s capability to perform. TRA and TPB’s combination created a new MIS framework called Behavioral Reasoning Theory (BRT) (Westaby, 2005).

Technology Acceptance Model (TAM1): (Davis, 1989); (TAM2): (Venkatesh and Davis, 2000)

TAM aims to predict information technology acceptance and usage (Venkatesh et al., 2003). Unlike TRA, TAM excludes the attitude construct in order to explain intention better parsimoniously. TAM2 extended TAM by including the subjective norm as a predictor of intention (Venkatesh and Davis, 2000). TAM 1 and 2 theories base on three basic constructs: (a) Perceived usefulness, the degree to which a person believes that using a particular system would enhance their job performance. (b) Perceived ease of use, the degree to which a person believes that using a particular system would be free of effort. (c) Subjective norms adopted from TRA/TPB - only included in TAM2.

Diffusion of Innovation Theory (DOI): (Rogers, 2003)

DOI explains how an innovation spreads through a social system and how information flows through the media and interpersonal channels. DOI based on five constructs: (a) Relative advantage, the degree to which an innovation is perceived as being better than its precursor. (b) Compatibility, the degree to which an innovation is perceived as being consistent with the existing customer values and needs. (c) Trialability, the degree to which an innovation may be experimented with on a limited basis. (d) Observability, the degree to which the results of an innovation are visible to others. (e) Complexity, the degree to which an innovation is perceived as challenging to understand and use.

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Behavioural Reasoning Theory

Behavioural Reasoning Theory (BRT) is a combination of Theory Of Reasoned

Action and Planned Behaviour that was proposed by Westaby (2005), to test the

relative influence of adoption and resistance factors in a single connected framework.

Westaby (2005) theorised BRT aims under two broad dimensions – reasons for and

reasons against performing a behaviour. According to Sebald and Jacob (2018), the

reasons for and reasons against has been conceptualised in research as to subsume

pro/com, benefit/cost, and facilitator/constraint. Following this research framework,

Istook (2008) suggested that 3D Body Scanning need to have more than one channel

of presence and that e-Commerce apps for mobile use may not be sufficient to

mitigate customers’ confidence to adopt and use technology. Gupta and Arora (2017)

investigated determinants and barriers of fashion m-Commerce tools adoption based

on a survey with 638 respondents. They found that customers adopt mobile shopping

because of (a) convenience, (b) variety of choice, and (c) price. Although research

in 3D Body Scanning suggests that the values of hedonic and utilitarian goods are

similarly represented (Zhang, Cao and Wang, 2017), it remains mostly unknown,

however, how these values are mapped – through different channels – during

customer purchase decisions and judgments. Past research suggested that people rely

more on hedonic factors when adopting new technology (Xue, Parker and Hart, 2020)

and that this is amplified by trait-reward seeking (Parker and Wang, 2016). To better

understand customer value, based on the BRT framework, this section clarifies how

3D Body Scanning affects hedonic and utilitarian values.

The utilitarian strategies emphasise rationality, utility, are task-related and aim to

minimise the cognitive load. Sachdeva and Goel (2015) defined utilitarian experience

as offering attributes such as convenience, value, customer service, and

confidentiality by retailers to enhance the customers’ positive attitude. Loker et al.

(2004) found participants were willing to pay for the scan up to $15 and were keen to

travel 30 minutes to scanning location, spent another 30 minutes on the scan process.

In addition, Lee et al. (2002) found that customers were comfortable to wear

underwear or street clothes, and needed strong assurance about their privacy. Loker et

al. (2004) found that some participants were even comfortable wearing a spandex

bodysuit in focus groups. Moreover, Daanen and Byvoet et al. (2011) evaluated

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methods in VFI that can help customers choose a product that fit well based on

customers self-reported metrics such as age, height, weight, bra size perception of

arm’s length, and size of hip and waist. The results suggested that self-reported

measurements are suitable for attaining suitably sized garments. However, for

achieving a satisfactory level, the garments still required an individual fitting session.

In a similar study, Ashdown and DeLong (1995) explored customer preferences for fit,

although limited to a few selected dimensions for lower body garments. The result

found that participant can be quite sensitive in their ability to perceive variations in

the fit of their pants, e.g. waist +0.5 cm. Song and Ashdown (2015), further

investigated the accuracy of virtual fit interface for garment fit evaluation by having

customers compare the fit between virtual and real trousers. The result indicates that

the display of the 3D virtual model in a slightly unbalanced posture did not look as

precise in the virtual environment, despite being correct in the fit session. The above

studies collectively add to the garment developer’s knowledge of how to specify fit by

identifying customers’ perceptions thresholds, which are necessary prerequisite to

quantify size increments and increase customer choice in e-Commerce.

The hedonic experience has been defined as the customer’s experience of creativity,

playfulness and exploration when interacting with a VFI interface (Holbrook and

Hirschman, 1982; Lee, Kim and Fiore, 2010; Kashfi, Feldt and Nilsson, 2019). Song

and Ashdown (2013) measured customer perception of fit in digital and real

environments. They found that customer often does not base their decision on

objective fit criteria but subjective values and trends. To enhance hedonic value,

retailers become interested in constructing VFI tools based on augmented reality (AR)

concept (Javornik, 2016; Beck and Crié, 2018) to achieve multiple benefits (Rese et

al., 2017). AR superimposes computer-generated virtual objects on real environments

and allows real-time interactions to enrich a user’s experience of reality (Poushneh,

2018). AR-based VFI tools have been developed and studied primarily in the context

of jewellery, glasses, and make-up. Nevertheless, AR research must enlarge the

investigated contexts of use and settings (Hilken et al., 2017). AR-based VFI tools

represent expensive investments for apparel e-retailers, but they are not yet well

established. The question remains, whether they are more efficient than traditional

pictures of human models wearing garments (Shin and Baytar, 2014). The question of

the effectiveness of AR as compared to conventional ways of product presentation is

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crucial for online retailers, and it has to be investigated thoroughly in order to bring

strong evidence (Bonetti, Warnaby and Quinn, 2018).

The utilitarian and hedonic strategies in the BRT framework are also discussed in the

context of body image and 3D avatar design. 3D Body Scanning has changed the way

that people can view and interact with their bodies and clothes (Ridgway, 2018).

Loker et al. (2004), pointed to a fundamental question: would we feel differently

about acquiring and wearing a dress if we were involved with design in more

technologically interactive ways through 3D Body Scanning, video game avatars, or

product configurators? Kim and Sundar (2012) asserted that utilising realistic avatars

in virtual environments will allow customers to see themselves from a third-person

perspective to enhance shopping decision. However, in Blum et al. (2007) research,

one of the raised concerns of avatars and their appearance was that they were too

much attention-grabbing rather than the presented products itself. They found

participants often make remarks about specific characteristics of their avatar, and how

they could improve its appearance, rather than concentrating on the product itself.

Furthermore, Loker et al. (2004), ascertained a perceived lack of control by customers

to the limited demand for product design involving customers and lack of availability

of product configurators online and in in-store kiosks. Their research also suggested

that fashion corporations should help customers learn how to use the product

configurators and test their designs with limited time and cost risks to make

applications more playful and engaging.

Customer’s reactions to 3D Body Scanning are complex due to the psychological and

emotive implications of visualising the body in 3D. Schilder (1935) introduced and

defined the ‘body image’ term as the means of one’s perceptions toward his or

herself. Grogan et al. (2013) found that scan images can be informative in providing a

realistic and objective view of their bodies, but the data is often de-contextualised

from the retail experience. For example, Grogan et al. (2019) found customers’

positive responses about how their bodies appeared on the scans. In contrast, Kim and

Sundar (2012), found that the majority of people in the population were unhappy

about their physical appearance irrespective of age, gender, race, and social class. In

general, researchers have found that feelings about the body are correlated with

feelings about self (Mendelson and White, 1982; Lee et al., 2012; Grogan et al., 2016;

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Beck and Crié, 2018). Nevertheless, more research is needed on the communication

of result and customers understanding of 3D Body Scanning data. Cash (2012)

asserted that the pressure on the good look had resulted in the loss of the wholeness of

the body as people began to see it as a group of separate components, each to be

tweaked, sculptured and forced into shape; further ignoring the mental health

consequences. Consequently, the fashion industry confronts customers repeatedly

with several toxic messages: that if one only tries hard enough, the perfect body can

be obtained, and that the body can be shaped and modelled at will (Sentilles and

Callahan, 2012). The result suggested that customers from very young ages enter into

an unnecessary, damaging, lifelong conflict with their bodies. Nevertheless, 3D

avatars have the potential to counterattack the frequent criticism of the Internet

regarding its ‘virtuality’ and support the humanisation of the Internet (Mull et al.,

2015). The features of avatar-based on 3D Body Scanning can be used to express the

personality of its user. This trait may be useful in different scenarios where distinctive

personalities, unique appearances, and even individualised behavioural patterns are

beneficial, for example, for social interaction in the digital environment (Nantel,

2004; Baytar and Ashdown, 2015). Therefore, to increase technology acceptance,

developers need to find a new way, in which customers’ avatars should not distract

the customer from the product on sale, and not become an end in itself. Instead,

customers should use VFI as a tool to support the actual shopping-related tasks. In

addition, Gill et al. (2018) implied that retailers mostly see 3D Body Scanning as a

device for aggregating anthropometric data rather than a tool helping customers with

styling suggestions appropriate to their body shape composition.

Together, the TRA and TPB frameworks integrated into BRT research provides

theoretical insights into how garment developers and customers understand the use of

3D Body Scanning based on utilitarian and hedonic values. 3D Body Scanning

developers and software stakeholders can use the BRT framework to examine further

how to build social networks and communication channels around 3D Body Scanning.

However, while BRT theory has yielded information about the customer decision-

making process, classical conditioning principles do not account for customers’

spontaneous behaviour (Alahmad, 2020). BRT theory was not chosen for this research

because it views users as passive learners; this highly contrasts with service design

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theories, which view users as active agents and technology co-creators who can

control and determine technology development (Bossen et al., 2014).

Technology Acceptance Model

The fundamental aspect of designing a 3D Body Scanning is the interest customers

will have in being scanned, and in allowing retailers and manufacturers to use the

scan data (Gill, 2015). Lewis and Loker (2017) emphasise, for 3D Body Scanning to

become a commercially viable endeavour, research needs to investigate general

customer comfort with and willingness to participate in repeat scanning sessions. This

also raises an essential question about how far customers should be expected to

change their actions and behaviour to utilise a new technology that represents an

emerging practice.

Lee et al. (2006) applied TAM to investigate Virtual Fit Interfaces and found that all

three aspects of TAM, ‘perceived usefulness’, ‘perceived ease of use’ and ‘perceived

enjoyment’, significantly enhanced customer attitude and behavioural intention

towards an online retailer. Hedonic shopping orientation had a significant impact on

one aspect of perceived enjoyment, and utilitarian shopping orientation had a

significant effect on perceived usefulness and perceived ease of use. In addition, Kim

and Forsythe (2008) found that the effect of perceived ease of use differs between

technology, as she measured impact on 2D photos, the 3D rotating image on customer

adoption attitude. This study emphasised that technology anxiety had a significant

negative influence on the use of virtual fit interfaces, and innovativeness had a

significant favourable influence on the use of 3D rotation views. However, neither

technology anxiety nor innovativeness had a substantial impact on the use of 2D

views. Also, Kim and Forsythe (2008) found that customers with a high level of

technology anxiety were not as likely to use virtual fit. In contrast, innovative

customers are likely to try the 3D rotation view regardless of their attitude toward

using such technologies. The result stresses that Virtual Fit Interfaces play an active

hedonic role, increasing the entertainment value of the online shopping process in

customers who are considered early adopters. Consequently, 2D views (larger view

and alternate views) showed a robust utilitarian role. The 3D rotation view served

both functional and hedonic purposes. Pookulangara et al. (2018) presented the

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extended TAM Model. This study collected a sample of 577 responses and using

factor analysis and found that the critical values for the customer were: usefulness,

convenience, ease of use, and confidence. This finding was further challenged by

Plotkina and Saurel (2019), with a smaller sample of 48 University students and found

similar values for customer acceptance: enjoinment, convenience, ease of use and

usefulness. The findings together highlight that that ease of use, convenience and

usefulness are the most critical factors towards increased technology acceptance. This

narrow view, however, often focus on outcomes and ignores the important underlying

cognitive and motivational characteristics of individual affected by the change

(Beaudry and Pinsonneault, 2005).

In sum, TAM can be construed as simply the relationships between antecedent factors

such as perceived usefulness and ease of use that aim to predict that particular type of

intention connected to the amount of IT usage (Davis, 1989). This research did not

choose TAM theory to investigate 3D Body Scanning acceptance because it expands

the view of IT acceptance as occurring during the initial adoption stage in which goals

such as training, optimisation and adoption of IT become the central thrust (Schwarz

and Chin, 2007). Instead, following service design guidelines (Shaw et al., 2018), this

research postulates that acceptance involves holistic conjunction of a user’s

behavioural interaction with the IT over time and resistance/acceptance that develops

within a specific social, environmental or organisational setting. This way, TAM

contrasts with DOI theory, which acknowledges that acceptance is multidimensional

(Rogers, 2003), including specific dimensions salient in certain temporal usage

contexts-and not necessarily the same as an attitude or intention to use an IT

extensively (Chin and Marcolin, 2001).

Diffusion of Innovation Model

The diffusion of innovation research suggests that knowledge is not a panacea for

improving the size and fit selection (Gill, 2015). Public perceptions and responses are

not formed in a vacuum — the views of others matter (Rogers, 2002). The media,

elites, peers and trusted messengers (for example, government, industry, academics,

or social media) shape customer perception about the adoption or rejection of a

technology (Lyu, Hahn and Sadachar, 2018). However, the role of ‘social

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representations’ — or socially constructed summary views about 3D Body Scanning

is missing in public perception, tangled in the highly inaccessible technical language.

Instead, innovation studies increasingly show that preconceived attitudes often

determine how new information is processed (Boudet, 2019). By approaching the

transition toward 3D Body Scanning in retail and e-Commerce, scholars emphasise

the need to move beyond aggregating individual opinions about technology (personal

acceptance) to consideration of social acceptance (Loker, Ashdown and Carnrite,

2008). They also emphasise the importance of incorporating the views of multiple

stakeholders and how these groups interact in facilitating or impeding change in

customer behaviour. The diffusion theory provides the guiding question of a

deliberation process is ‘what should users do collectively’ (von Hippel, 2005, p. 74).

This reflection can allow 3D Body Scanning stakeholders to understand what are the

elements most central to each organisation and improve knowledge about the contexts

in which the industry operate. Thus, research is needed on industry and stakeholders

perceived barriers to adapt 3D Body Scanning innovation (Gupta, 2020).

Adopter categories are the classifications of the members of a social system on the

basis of innovativeness. In this case, adoption refers to continuous use. Therefore,

non-adoption or incomplete adoption is not part of DOI theory (Masuda et al., 2018).

The rate of adoption is measured by the relative length of time required for a certain

percentage of the members of a group to adopt an innovation. Rogers (2003) referred

to Innovators, Early Adopters, Early Majority, Late Majority and Laggards as

innovation categories, as shown in Figure 10. The gentle bell-shaped curve represents

the groups of customers adopting new technology, and the S-curve represents the

market share that reaches 100% following complete adoption. This is the point of

market saturation (MacVaugh and Schiavone, 2010).

Figure 10 Diffusion of Innovation adoption curve. Adopted from Rogers (2003)

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The socio-demographic factors, such as gender, age, ethnicity, income and education,

have been tested to determine customer characteristics in terms of how they shape

attitude towards 3D Body Scanning. In 3D Body Scanning research, early adopters

were described as mostly female (Ridgway, 2018), Caucasian (Peng, Sweeney and

Delamore, 2012), with family income lower than $40,00 (Loker, Cowie, Ashdown

and Lewis, 2004). Higher levels of income and education are often associated with the

early adoption of technology trends in retail and purchasing power of high-cost

fashion (Cantista and Sadaba, 2019). However, Loker et al. (2004) research proved

otherwise – 3D Body Scanning deployment factors such as high income and

education are not consistently linked. Their study was designed around household

income and marital status and found that single women or women with household

incomes below $40,000 were significantly more comfortable with the scanning

process than married women and women with household incomes over $100,000.

Their research highlighted the importance of these two variables in defining a target

market for 3D Body Scanning. Lee et al. (2012) suggested to identify other

characteristics of customers who would be most interested in using 3D Body Scan

data, e.g. what level of interest will have in severe fit problems, large-sized or older

women. Research is still needed on how customers will drive the adoption of 3D

Body Scanning data to increase their involvement in product development and quality

of fit in the retail.

The future research also needs to discuss in what form should 3D Body Scanning be

presented in retail – as a service by an independent firm, in every primary retail

location, in speciality firms and kiosks in the gym, or as a research tool to improve

clothing size offerings and fit exclusively in an academic setting. Retail floor space is

expensive; instead of dedicating space solely for scan use, changing rooms could be

used to house scanners (Delamore and Sweeney, 2010). Lewis and Loker (Lewis and

Loker, 2017) interviewed fashion retail employees attitudes towards technology

applied in the retail store and found a large segment of employees to be often

ambivalent about less well-known technologies such as 3D Body Scanning and

product configurators. The findings highlight the potential in 3D Body Scanning

applications to build a relationship and improve communication through fun,

engagement and interactions. However, retail employees often lack clear instructions

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on how to do so. Moreover, SME retailers often struggled to see a clear return on

investment and saw 3D Body Scanners as an obstacle that can make the experience

stressful and take too much of the trading space (Xia et al., 2018).


This section develops a set of propositions for designing service experience in 3D

Body Scanning, drawing on relevant service design literature. Starting with section

2.5.1, it summarises the relevance of service design theories in 3D Body Scanning

research. This section summarises critical concepts in service design such as:

stakeholder analysis, user experience, customer experience, and customer journeys.

Understanding customer responses and their adoption drivers are important, as

customer support can influence new technology deployment. However, researchers

studying customer responses to 3D Body Scanning draw on a wide array of

theoretical frameworks, leading some scholars to lament the fragmented nature of

insights in the field. Even more troubling, many articles do not explicitly draw on a

theory base, remaining mostly descriptive, making advancements in the field difficult.

Therefore, section 2.5.2 provides a theoretical typology, in which five prominent

models are identified, with different implications for research priorities and actions to

address adoption and diffusion. While the presented theories are distinct, there is

considerable overlap and interaction between them. This theoretical typology enables

an improved understanding of perspectives, and so has the potential to facilitate

discussion of the options available to provide a more nuanced perspective on

diffusion. Nevertheless, research is needed to create a framework for assessing

interactions, suggesting that interactions should be designed in terms of customer

capabilities and needs to create adoption hotspots. Adopting 3D Body Scanning,

however, requires involvement for all stakeholders and new enterprise models – thus,

diffusion of innovation framework was chosen. The literature review found that the

main reason for slow adoption is that the requirements for the users are almost totally

incompatible with the current state-of-the-art.

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Research Gaps and Opportunities

This section consolidates the presented analysis of the literature and deduces specific

research focus areas that address the formulated research questions. 3D Body

Scanning can be seen as a paradigmatic shift in fashion toward mass-customisation

and more bespoke offerings. The influencing factor for research direction was the

current lack of knowledge on service design in the field of 3D Body Scanning. The

literature review has provided an overview of 3D Body Scanning that can be used as a

basis for reflecting on technology capabilities and the present state-of-the-art.

Together, the selected articles offer contributions to the progress of 3D Body

Scanning through the examination of the opportunities and challenges, from data

acquisition to future applications of insights in retail and e-Commerce. Remarkably

little is known about how different stakeholders perceive and evaluate 3D Body

Scanning diffusion, and how their expertise influences the collective service outcome.

So far, the process has been muddied by different framesets (engineering, computer

science, fashion, manufacturing, and marketing) within which challenges and options

are discussed, with varying interpretations of diffusion needs and their implications to

service development. The literature review demonstrates that fashion practitioners do

not know how to integrate 3D Body Scanning in garment development and does not

appear to have a strong sense of practical need for technology. However, there has

been a long-held desire among garment developers for a tool that offer visualising

body and necessary dimensions for the garment design (Gill, 2015). What has

emerged is a ‘wicked’ problem (Buchanan, 1992) – in which stakeholders are unable

to agree on the technology development agenda and the most desirable solutions to be

applied in retail. The effectiveness of 3D Body Scanning in retail is thus never just

examining the technology itself. But examining how well the technology enlists retail

providers, product developers, manufacturers, and customers to see and understand

the value in a new way of doing things. This section suggests that shifting from a

perspective focused on “engineering” toward one focused on “service design” will

help stakeholders’ in more nuanced and productive conversation about the role of 3D

Body Scanning in the fashion industry. In the following, the identified research

opportunities are further specified, resulting in overarching research questions.

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2.6.1 The Existing Size and Fit Offerings

Virtual Fit Interfaces has the potential to enable the customer to engage with size and

fit in e-Commerce offerings. However, the VFI heterogeneity, usability, and data

quality issues present a substantial barrier to adoption. Customer attitudes towards

VFI will continue to evolve, and one agent that will drive this change is the 3D Body

Scanning (Choi and Nam, 2009). It is, therefore, important to objectively assess VFI

metrics to understand how 3D Body Scanning could facilitate better garment

selection, purchase, and retention in e-Commerce (Gill, 2015). Virtual Fit Interfaces

have the potential to leverage 3D Body Scanning as a data-driven platform,

integrating research from imaging, artificial intelligence, and machine learning

(Vaccaro et al., 2018). The prevailing assumption in past VFI research is that the

customer understands what constitutes the correct size and fit and can adequately

measure his/her body (Daanen and Byvoet, 2011). However, a precise definition of

customer learning curves is often missing, and it is still unclear what information’s are

needed from customers to provide garment in the right size and comfortable fit

(Ahmed et al., 2019). Song and Ashdown (2020) observed the significant differences

in the actual values of anthropometric variables and variation in the customer

assessment – making 3D Body Scanning a superior reference method. In this

perspective, the research is needed to critically appraise VFI offerings to define the

principles on which the next generation of e-commerce tools for customer use should

be based. So far, there has been only a limited attempt to conduct an assessment of

digital tools, and the present techniques solely focus on fashion practitioner expertise

and fit appraisal (Ashdown and DeLong, 1995). Thus, previous research offers little

insight or guidance as to where developers should focus on future innovations. For

VFI to have a more significant impact, the validity of anthropometric estimates for

product development and data visualisation techniques must be established. To that

end, chapter 4 review the existing Virtual Fit Interfaces and gaps, highlight the

evolving approaches, and detail 3D Body Scanning framework that addresses the

current limitations in the marketplace with a path toward implementation. The following research question can be formulated:

RQ1: What are the size and fit tools and techniques that could support the

provision of 3D Body Scanning in the existing fashion services?

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2.6.2 Stakeholders Perspective on 3D Body Scanning Diffusion

The evidence on the impact of 3D Body Scanning in the fashion industry is still

inconclusive, and little is known of the service relative advantage. Incorporating a

perspective focused on how the technology value proposition and how distinct

stakeholders understand it can help to set informed diffusion goals (Rotolo, Hicks and

Martin, 2015). However, the evidence gathering and experimentation is further

complicated by the fact that 3D Body Scanning is developed and applied largely by

and within companies (Ashdown, 2020). A growing service design literature

emphasises the need for an open-ended approach characterised by experimentation

(Costa et al., 2018; Zheng et al., 2018); this body of literature also recognises that

traditional, top-down “command and control” management approach is insufficient

for solving wicked design problems (Henkel, 2006). 3D Body Scanning is highly

complex and presenting an issue that span enables interdisciplinary collaboration –

bringing together experts from technology, software design, fashion, research, and

manufacturing domains. To spark innovation in the field, however, stakeholders need

to experiment, and such a process involves evidence gathering from multiple domains

and interdisciplinary knowledge generation. The literature found a lack of consensus

on the meaning of ‘value’ and priorities in design, and retail deployment. The

technology manufacturers and developers’ value are not always aligned with fashion

retails’ view, which raises the importance of stakeholder’s interaction service to co-

produce an understanding of what matters in service delivery. The gaps regarding

stakeholder’s expertise must be addressed if existing developments are to be better

informed by sound scientific evidence. DOI theory by Rogers (2003) represents a

facilitated framework for bridging disciplines and sectors, which emphasise active

discussion and exchange of arguments to explore how different skills address industry

challenges and realising the relative advantage of 3D Body Scanning in fashion retail.

To that end, chapter 5 will be investigated how 3D Body Scanning stakeholders

perceive service diffusion and its relative advantage. In so doing, this chapter provides

an analysis of comparative foresight that can be read and applied to address decision

making based on stakeholder’s expertise for future developments. The following research question can be formulated:

RQ2: What is the 3D Body Scanning stakeholders’ outlooks for the

technology diffusion within fashion retail?

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2.6.3 Customer Journey in 3D Body Scanning

In the more than 30 years since the first 3D Body Scanning technologies were put into

use in retail little has changed regarding the fundamental design (Jones et al., 1989;

Daanen and Ter Haar, 2013). Although stakeholders have begun to find stopgap

solutions to retail – they remain ill-prepared to handle the needs of users (Peng,

Sweeney and Delamore, 2012). The research by Ashdown (2020) highlighted issues

with existing designs that should be improved, including façade presentation, data

information, usability, and interaction. To get an understanding of the service value, it

is crucial to “walk in the customer’s shoes” (Holmlid and Evenson, 2008, p. 343).

According to Lemon and Vehoef (2016), customer journey exercise allow to

understand and experience the service in a way a customer would. The research into

user experience can also highlight areas in which existing processes can be improved

(Kashfi, Feldt and Nilsson, 2019). Gill (2015) and Gupta (2020) found strong

evidence that 3D Body Scanning customers’ and users are struggling to apply the

knowledge learned into product development and garment selection. Therefore, more

research is needed on connecting user experience with actionable outcomes and

exploring how the user interprets data from 3D Body Scanner. Future research should

involve motivating design goals by what best supports the needs of the users, rather

than by what is technologically feasible (Schweitzer, Gassmann and Rau, 2014). The

customer journey as explained by Lemon and Vehoef (2016), is an exercise that

provides an immediate view on the service gaps, for instance, identify opportunities

for cutting useless time, improving customers’ experience, or provide critical

information that would increase the service efficiency. The work is needed to

formalise subjective aspects of usability, such as utility and user delight and

satisfaction. Usability viewed in this way, along with product development relevance,

creates the opportunity to impact customer engagement. To this end, chapter 6 will

outline an approach that is guided by end-user requirements and demonstrate

comprehensive customer journey, based on Size Stream 3D Body Scanner. In

addition, this study used focus groups to encourage group interaction. This enriched

the generation of information and supported a co-productive approach as advised in

service design literature (Stickdorn and Schneider, 2010). Focus groups provide a

mechanism to uncover and appreciate users’ opinions, understanding and thoughts

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around topics of interest by co-constructing meaning in action, in which users learn to

challenge each other, thus developing their ideas through repeated interactions. The following research question can be formulated:

RQ3: What does the customer 3D Body Scanning journey look like in retail,

and which factors enable or inhibit the service presentation and interaction?

2.6.4 3D Body Scanning Service Workflow

The service workflow framework represents a process analysis typology that aims to

codify knowledge, skills and particular events happening in service to generate

support for its reproducibility (Caro, Kök and Martínez-de-Albéniz, 2020). Building a

service design model can be viewed as orchestrating an integrated series of clues that

collectively meet or exceed customers’ needs and expectations. The research

directions are needed because the plethora of research disciplines keeps 3D Body

Scanning information invisible due to the low interoperability between systems, thus

impacting setting up the retail diffusion agenda. The industry model is meant to foster

interdisciplinary collaborations and knowledge transfer because they are more

accessible and concise, compared with the large volumes of technical literature, and

because they emphasise critical questions and approaches to addressing them. As a

referee, the model is not supposed to be exhaustive overviews of the literature but

rather a heuristic evaluation of the status of research and forward-looking discussion.

In doing so, chapter 8 provides stakeholders with an overview of the field and the

potential of research directions. This study introduces the 3D Body Scanning model

and evaluates the service workflow components from data acquisition, processing,

storage, and curation, to potential applications. In practice, the analysis will aim to

support and connect the previous research that may influence the deployment of 3D

Body Scanning in retail. The following research question can be formulated:

RQ4: What are the critical elements of the 3D Body Scanning service in a

fashion retail scenario?

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CHAPTER 3

RESEARCH DESIGN

Research design evaluates the relationship between theory and practices based on

Saunders et al. (2015) ‘Research Onion’ framework to set the orientation of this PhD

thesis. The research design considers factors beyond the data collection and analysis

and gives thought to what Saunders et al. (2015) called the theory of research. First,

an overview of the philosophy and approaches to theory development are discussed

with the methodological choice outline. Second, specific research strategies are

proposed as a means for fulfilling the thesis aims. Finally, the methods used in the

individual empirical studies are evaluated in terms of their suitability for extracting

information from the selected research methods. However, the detailed specifics of

each study are expounded in the methods sections of each chapter.

This thesis research design interpretation – from Saunders et al. (2015) research onion

framework – as shown in Figure 12, and these themes are further elaborated and

discussed in the following sub-sections.

Figure 11 Research Onion framework. Retrieved from Saunders et al. (2015)

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Figure 12 Research onion interpretation based on Saunders (2015). Source: author’s own.

Research Philosophy

A research philosophy implies the strategies and techniques of the research. Tennant

(2015) depicts philosophy as a conceptual discipline with introspective and reflective

methods in five areas: metaphysics, epistemology, ethics, logic and aesthetics. From a

historical point of view, there are two classical – positivism and interpretivism, and

two recent – critical realism and pragmatism, research philosophies (Melnikovas,

2018). The dichotomy between positivism and interpretivism is a matter of constant

critics base on the distinction between natural and social sciences (Fischer, 2001).

Positivism reflects the philosophical stance of a natural scientist. Ontology is based on

objectivist assumptions that entities are observed, atomistic events, existing external

to social actors. Therefore, only observation and empirical data may be referred to as

“credible” (Gale, 1984). Conversely, interpretivism reflects subjectivist ontological

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assumptions that entities are constituted of discourse. The existing constructed reality

is researched through social constructions as consciousness or language. Ontology is

based on subjective assumptions that are constructed and continually evolving. Thus

knowledge and facts are relative and subjective (Russell, 1996). Subsequently, critical

realist perceives the sensations and images of real entities, not the real entities

themselves. Ontology is based on the knowledge that is obtained by discovering

generative mechanisms (Wynn and Williams, 2012). Finally, pragmatism is based on

the assumption that the research dealing with complex phenomena should utilise the

strengths of both philosophies (Sieber, 1973). Pragmatism encourages a reflexive

account of how theoretical thought and practical activity are understood (Wardhani,

1910). Ontology is based on integrating purpose and function into understanding

knowledge and inquiry by grounding intellectual activity in experience (Russill,

2016).

This research has chosen pragmatism as a theoretical lens. In research, pragmatism is

a holistic endeavour that requires prolonged engagement, persistent observation and

data triangulation (Aliseda, 2006). A key theme in pragmatism is its focus on people’s

practices and experiences rather than on abstract theories (Steen, 2013). In essence,

this thesis is in line with pragmatism because it focuses on stakeholders’ and users’

concrete practices, their experiences, and the role of shared knowledge; with the aim

to promote cooperation to improve users garment development and buying behaviour.

Pragmatism stresses the primacy of situated practice and the existential condition of

being placed in a world of emerging and unfolding phenomena, a “world brimming

with indeterminacy, pregnant with possibilities.” (Shalin, 1986, p. 10). The world of

pragmatism is emergent, in the making, through the ongoing interactions between

stakeholders, users and surrounding environments (Dalsgaard, 2014). This aesthetic

resonates with the service design as a situated and systemic activity to get an

understanding of the challenge and in the ongoing design process in which various

components of the situation “talk back” to the designer (Schön, 1983). As such,

pragmatism and service design coincide on a fundamental level – pragmatism is very

responsive to designer thinking in that it offers articulations and insights regarding the

notions of situation, emergence, and interaction (Dalsgaard, 2014). For a study with

the concept of service design at its focus, it was considered proper to select strategies

that not only permitted the observation of participants interaction in flight but that

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also (at the appropriate time) facilitated a collaborative approach to data collection

and analysis. The dynamism of the industrial context and the accessibility of the

research sample also affected which methods were employed at different times.

Hence, planning and flexibility were essential to provide a methodology rigid enough

to mitigate the risk of scope creep detracting from the core aim of the study. Yet, fluid

enough to allow the researcher to adapt to research opportunities as and when they

arose to make the best use of the data available (Rheinhardt et al., 2018). As such,

pragmatic researchers have the opportunity to combine the macro and micro levels of

a research issue (Onwuegbuzie and Leech, 2005). In what follows, this chapter

evaluates the connections between Saunders et al. (2015) research onion and the

philosophy of pragmatism.

Approach to Theory Development

The approach layer of the Saunders et al. (2015) onion discusses the deductive,

inductive and abductive methods to the theory development. Deductive reasoning

reaches back to ancient philosophy at which Plato denied the validity of inductive

sense-making from experience, and asserted that only logical deduction is a valid

method for developing theory, i.e., the hypothetical-deductive method (Baskerville

and Pries-Heje, 2010). Deductive reasoning involves deducing a conclusion from a

general premise, i.e., a known theory, to a specific instance, i.e. an observation (Lee,

Pries-Heje and Baskerville, 2011). The deduction is always certain: if the premises are

true, a logically deduced conclusion is necessarily true (Howe, 1988). The deductive

process appears very linear – one step follows the other in a very logical sequence

(Weick, 1989). Aristotle introduced inductive reasoning as a valid method for

generating knowledge, proceeding from particulars to generals (Shadish, 1995). In

inductive reasoning, a sample of entities from a population is observed, and

observations are generalised to all entities of the population. Bacon (1627), later

conceptualised Aristotle inductive reasoning by arguing that a theory can be

inductively developed through discovering the essential nature of observations –

concluding specific instances. Peirce (1905b) introduced the third approach to theory

development: abductive reasoning. This approach explored the new kind of inference

as reasoning a posteriori that replaces the three classical terms, major premise, minor

premise, and conclusion, by terms: rule, case, and the result (Johnson and

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Onwuegbuzie, 2004). The abductive approach purpose is not to derive a hypothesis

from the existing body of knowledge and test it in a closed system (deductive); nor

does it intend to infer a conclusion from an observation in an open system (inductive)

(Strauss and Corbin, 1994) Abductive reasoning involves drawing a possible

precondition from a specific consequence. The abductive approach is useful for

research in service design because it enables the search for a ‘satisficing’ solution for

a given problem (Lee, Pries-Heje and Baskerville, 2011). Martin (2009) argues that

design thinking relies on abductive reasoning in which sense-making of observation

occurs through drawing inference to the best explanation.

This research has chosen the abductive approach that is grounded in service-design

thinking to solve wicked design problems (Lee, Pries-Heje and Baskerville, 2011).

The abductive approach allows the researcher to integrate the fashion industry

practices with hard-system engineering and computer science methods. Poincaré

(1905, p. 157) reasons that “science is built up of facts, as a house is built of stones;

but an accumulation of facts is no more a science than a heap of stones is a house.”

The present methodological and hard-system approaches will only help ensure solid

stones, yet they do not help build the house – the big industry picture. The literature

review found that a large part of the diffusion problem is the lack of a cumulative

theoretical service model. The abductive reasoning provides a scaffolding of

integrative and cumulative theory that motivates using diverse empirical approaches

and, often, provides a natural means to integrate findings from across the disciplines

(Muthukrishna and Henrich, 2019). An essential part of design theorising involves

abductive reasoning because there is a purpose aimed at guiding learning and

problem-solving. These topics – practices, experiences, and knowledge, and

communication, cooperation, and change – are intimately intertwined through the

abductive concept of inquiry (Steen, 2013). Thus, contributing to the model

development through abductive reasoning will allow 3D Body Scanning stakeholders

to make sense of three decades of now-distrusted data, and move toward a more

collaborative understanding of the service design methods.

There are, however, several drawbacks to the abductive approach, which were

carefully considered. The first is the lack of control of the variables; therefore, it is

difficult to seek answers to specific questions or set of hypotheses, as in an

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experimental approach (Campbell and Stanley, 2015). A second drawback is the

inability to manipulate variables; therefore, it is not feasible to specify the exact

conditions of the investigation. One of the important criticisms of the non-

experimental approach is of validity. The understanding and the interpretation of the

phenomenon is subject to the investigators’ bias and explanation (Fernández,

Lehmann and Underwood, 2002). Although these drawbacks were important

considerations, the first two were not considered to affect the research because the

research aims did not require control and manipulation of variables. However, the

third issue of validity was relevant to the research. Therefore, the validity of the field

study findings was carefully considered when developing the methodology and

techniques for the investigation. The concept of validity in research is further

discussed in each of the studies in chapters four-seven.

Methodological Choice

For a long time, the advocates of both the qualitative and quantitative methods have

been engaged in a debate about research philosophy, the objectives, scope, and nature

of the inquiry, with a result of the significant divide between quantitative and

qualitative methodologies (Wagner, Glaser and Strauss, 1968).

3.3.1 Quantitative Research

Quantitative purists articulate assumptions consistent with positivist philosophy

(Wolgemuth et al., 2018). This approach believes that social observations should be

treated as entities in much the same way that physical scientists treat physical

phenomena. The observer is separate from the things that are subject to observation

(Gould and Lewis, 1985). Quantitative purists maintain that social science inquiry

should be objective. That is, time and context-free generalisations are desirable and

possible, and real causes of social scientific outcomes can be determined reliably and

validly (Nagel, 1986). In quantitative methods, researchers should eliminate their

biases, remain emotionally detached and uninvolved with the objects of study, and

test or empirically justify their stated hypotheses. Sieber (1973) called the quantitative

researchers “the virtues of hard, generalisable survey data.” Positivist research

follows a pattern of writing in rhetorical neutrality, characterised by impersonal

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passive voice and technical terminology, in which establishing and describing social

laws is the central focus (Tashakkori and Teddlie, 1998).

3.3.2 Qualitative Research

Qualitative purists articulate assumptions consistent with constructivists and

interpretivists that reject the positivist doctrine. Qualitative researchers argue for the

superiority of constructivism, idealism, relativism, humanism, hermeneutics, and,

sometimes, postmodernism (Lincoln and Guba, 2009). Qualitative purists contend that

multiple-constructed realities abound, that time and context-free generalisations are

neither desirable nor possible. The research is value-bound, that it is impossible to

differentiate causes and effects fully. The logic flows from specific to general (e.g.,

explanations are generated inductively from the data), and that knower and known

cannot be separated because the subjective knower is the only source of reality (Guba,

1990). Respectively, this doctrine is characterised by a dislike of a detached and

passive style of writing, instead of applying detailed, vibrant, and empathic

description, written directly and somewhat informally, professing the superiority of

low, full observational data (Myers, 2000).

3.3.3 Mixed Method

Both qualitative and quantitative sets of purists view their paradigms as the ideal for

research, and, implicitly if not explicitly, they advocate the incompatibility thesis,

which posits research associated methods should not be mixed (Howe, 1988). The

distinctions exist between quantitative and qualitative researchers concerning:

ontology, epistemology, axiology, rhetoric, logic, generalisations and causal linkages

(Onwuegbuzie and Leech, 2005). However, despite inherited distinctions, the

dominant school of thought have evolved from the quantitative–qualitative paradigm

wars: pragmatism (Rossman and Wilson, 1985). The philosophy of pragmatism

considers that no single viewpoint can ever give the entire picture, but is multiple

(Peirce, 1905a). Pragmatism aims to clarify ideas, by giving meanings to objects

according to those habits of action involved; thus it puts forward an epistemic aim

with an experimental solution, precisely providing an “inseparable connection

between rational cognition and rational purpose” (Peirce, 1905b).

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This research has chosen mix-method research as a methodological choice. 3D Body

Scanning research has become polarised: with the majority taking quantitative hard-

engineering approaches (Checkland, 2000) to study phenomena as factual,

generalisable and causative; while research into understanding human factors and

design issues is often lacking. Ranger et al. (2019) explain the drive for 3D Body

Scanning research to take the positivist approach –– to be fundable and generalisable

– has meant quantitative methods have dominated past research. Developers focused

on what, rather than why 3D Body Scanning should be applied in retail. Tseëlon

(2001) criticises approaches with positivist roots in fashion research for not being able

to capture social aspects that influence choice holistically. 3D Body Scanning

research lacks critical reflection and fails to provide a deep and nuanced

understanding of fashion practice. The positivist approaches distance the findings

from their social context (Scaturro, 2008). As explained by Smith (2020),

anthropometric research, seen only through the positivist lens, not only failed to create

a sustained interest in comprehensive anthropometric examinations but in the past led

researchers such as Galton to promote the eugenics movement.

On the other hand, within qualitative research in 3D Body Scanning, there is a

tendency to focus on applications – pushing theories to become too useful –– too

quickly (Ashdown and Dunne, 2006). Nevertheless, the value of qualitative

approaches stems not from their statistical generalisability but instead from drawing

inferences from data to create theoretical underpinning. Interestingly, while there has

been some research in the qualitative domain, results have been mixed due to

conflicting priorities between customers and garment developers. Textile researchers

in the qualitative study provide pre-defined frameworks based on own practice

experience, thus hinder individual users from elaborating on their needs fully. This

rationalism has hindered developers understanding of UX. Highlighting the

qualitative data on human factors is thus flawed with industry-standard bias. The

fashion industry often privileges specific categories, depending on methodological

and theoretical choices. Two types of studies have dominated the field. The first

aimed at explaining what kinds of individuals do (or do not) adopt technology and

seek to explain what factors facilitate (or impede) adoption. Second, focus strictly on

methods and how garment developers perceive the useful benefits for users. Place and

process-based elements have been incorporated into these models but are not yet part

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of the ‘standard’ model for these types of studies. To more closely match the reality of

customer perceptions and response requires examining how technology, people, place

and process interact.

This research takes a pragmatic approach, to avoid reductionist bias, allowing the

depth and nuances of resilience to be fully understood before moving towards the

model for its application. A pragmatic perspective is needed to cope with “wicked

problems”— problems that cannot be clearly defined using “facts” at the start of a

project and that cannot be solved by selecting a “best” solution (Steen, 2013). This

research uses a mix-methods approach as the route to address the research questions,

as shown in Table 14 – gathering insights into the capabilities, experiences, desires

and needs of stakeholders and customers to build industry 3D Body Scanning model.

Greene et al. (1989) further identified that this approach allows for:

• Data triangulation - looking for convergence and corroboration of results from

the different methods;

• Complementarity - looking for elaboration, enhancement, and clarification of

the results from one method with the results from the other;

• Development - looking for the use of results from one method to help develop

or inform the other method;

• Initiation - looking for the discovery of paradox and contradictions that help to

adjust the research questions; and

• Expansion - looking for extending the breadth and range of inquiry by using

different methods for different inquiry components. Table 14 Chapter’s methods analysis. Source: author’s own.

Studies Methods Applied Participants Methodological Choice Chapter 4 Content Analysis 10 Virtual Fit Interfaces QUAL Chapter 5 Interviews 39 industry stakeholders QUAL Chapter 6 Focus Groups and

Interviews 52 individuals (six groups with 39 participants and 13 individual interviews)

QUAL

Eye-tracking experiment followed by individual interviews

40 individual experiments

QUANT/ QUAL

Chapter 7 Heuristic Service Typology

Heuristics analysis of the research findings

QUAL

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Research Strategies

Peeling away – the philosophical and choice layers – leads to the next layer of the

onion: research strategies. 3D Body Scanning research tends to focus on surveys and

experiments, see Loker et al. (2004), Domina et al. (2008), Lee et al. (2012), and

Waltemate et al. (2018). Surveys are a relatively straightforward method for gathering

generalisable information (Straub, 1989). User can provide additional information for

developers by showing whether specific phrases, for example, ‘custom clothing’

rather than ‘body scanning’, garner greater support (Lee and Chow, 2020). However,

the standardised question wording and response choices capture customer attitudes at

a single point in time and rely on customer self-reports. According to Boudet (2019),

experimental methods are less suited to explore why people perceive technologies in a

certain way and what actions they have taken as a result. For these questions,

researchers conduct qualitative approaches with interviews, focus groups and

participant observation.

The qualitative approaches are often embedded in thematic analysis, grounded theory,

ethnography, archival research and narrative inquiry. However, while the data

collection stage may involve some similarities, in the analysis stage, the differences

are more pronounced (Baskerville and Myers, 2009). Archival research may involve

tracing back methods of practice, i.e., demonstrating how pattern drafting techniques

evolved; see Kunick (1984) or Ahmed et al. (2019). Yet, in innovation studies, this

approach means the limit to studying the discourse life cycle (the research and

practitioner literature), not the diffusion life cycle (the actual use of techniques)

(Hislop et al., 1997). In narrative research, the focus is on the stories told from the

individual and arranges the important themes in those lived experiences, see (Selin,

2008). Similarly, in ethnography, the focus is on setting the individuals’ stories within

the context of the culture-sharing group, see Woodward (2007). However, these

methods do not fit well with research-oriented toward change in information systems

and innovation studies (Baskerville et al., 2019). Moreover, this research focuses on

studying 3D Body Scanning in ‘real-time’ and not ex-post. The action research was

not chosen because it operates over a longitudinal time framework of several iterative

cycles, focusing on the refinement of existing processes or products and not

necessarily on new product development (De Villiers, 2005). Lastly, grounded theory

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was not chosen because the basic principle behind the grounded theory is that the

hypotheses and theories emerge bottom-up from the data rather than top-down from

existing theory (Mahoney, 2007). However, this research rests on the premises and

principles borrowed from other theories and paradigms such as service design

(Holmlid, 2004), wicked problems (Buchanan, 1992) or diffusion of innovation

(Rogers, 2003) and connect them with 3D Body Scanning and service design

research.

This research has chosen thematic analysis (Holton, 1988) integrated within the

service design thinking framework (Thoring and Müller, 2011) to build a holistic

picture of 3D Body Scanning service. Thematic analysis is the process of articulating

basic conceptual categories prior to the process of formulating hypotheses or

generalised relations among the themes (Holton, 1996). ‘Theme’ is the main product

of data analysis that yields practical results in the field of study (Vaismoradi et al.,

2016). A structural thematic analysis mode serves to investigate “the interrelations of

various themes” (Merton, 1975, p. 188). Therefore, thematic analysis is descriptive,

not prescriptive (Mantere and Ketokivi, 2013). The various perspectives in thematic

analysis appear supplementary rather than antithetical, with each chapter having its

own problematics, its own set of basic questions and derivative puzzles (Merton,

1975). Material collected through qualitative thematic methods are unstructured,

unwieldy and often consists of verbatim transcriptions of interviews or discussions,

field notes or other written documents (Ritchie and Spencer, 1994). Moreover, the

internal content of the material is usually in detailed and micro-form (e.g., accounts of

experiences, descriptions of interchanges, observations of interactions, etc.) (Bulmer,

1984). Themes are essentially a sense-making tool, a form of capturing the underlying

phenomenon one seeks to understand (Dorst, 2011). The thematic analysis provides a

flexible and useful research tool through its theoretical freedom, providing a rich and

detailed yet complex account (Braun and Clarke, 2006). Braun and Clarke (2019)

argue that themes are creative and interpretive stories about the data, produced at the

intersection of the researcher’s theoretical assumptions, analytic resources and skill,

and the data themselves. All of this has implications for the methods of analysis that

are developed. Therefore, thematic analysis has to provide some coherence and

structure to this cumbersome data set while retaining a hold of the original accounts

and observations from which it is derived (Braun and Clarke, 2020). This is where the

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way design practice deals with themes and frames in the context of open, abductive

reasoning (Dorst, 2011).

To strike a balance between demarcating thematic analysis clearly (explaining what it

is and how to do it) and ensuring flexibility concerning how it is used (Jodi, 1994),

this research applied a service design thinking framework to make (epistemological

and other) assumptions explicit (Steen, 2013). This way, thematic analysis can reveal

broad trends that may facilitate communication between stakeholders and customers.

The ‘Service Design Thinking’ framework has been applied when dealing with open,

complex problem situations. The service design thinking process consists of six steps

(as described in Figure 1), which are visually connected by curved lines to indicate

that these steps can and should be performed in iterative loops, if it appears necessary

to go back to a previous step. ‘Framework’ is an analytical tool or process which

involves a number of distinct though highly interconnected stages (Graebner, Martin

and Roundy, 2012). Although the process is presented as following a particular order

– indeed, some stages do logically precede others – there is no implication that

‘Framework’ is a purely mechanical process, a fool-proof recipe with a guaranteed

outcome (Ritchie and Spencer, 1994). For this research purpose - ‘framework’ has

been adopted from service design thinking as articulated by Holmlid (2004), Muller

and Thoring (2011) to help these aims and outputs to be achieved. This approach’s

strength is that it is possible to reconsider and rework ideas precisely by following a

well-defined procedure because the analytical process has been documented and is

therefore accessible (Gale et al., 2013). In order to illustrate the method and to reflect

the context and diversity of its applications in service design, Table 15 summarises

additional descriptions of the input and output of each step, as well as a definition of

the aspired goal of each step, including the methods of how to achieve this goal.

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Table 15 Service Design Thinking Framework based on Thoring and Müller (2011).

Process Goal How-to Input Output Understand • Collect

existing information become an expert.

• Structure all insights.

• Secondary desk research.

• Grouping of similar insights, finding titles for each group.

Research journals and academic publications.

Chapter Two: Literature review.

Observe • Review existing VFI applications.

Evaluate VFI design in terms of: • information

collected from customers,

• data presented to the customer,

• grouping data into a coherent framework.

• Problem definition,

• design challenge,

• content analysis and usability evaluation.

Chapter Four: Online Virtual Fit is not Yet fit for purpose.

Point of View

• Gather insights about stakeholder’s diffusion agenda

• Micro theory about required expertise.

Diffusion of Innovation theory categories: • relative

advantage, • compatibility • trialability • observability • complexity.

• Identify industry stakeholders and how they factor in the diffusion research.

• Insight about expertise required for 3D Body Scanning.

Chapter Five: Stakeholders in 3D Body Scanning: How to Connect the Isolated Silos of Knowledge for the Fashion Mass-Customisation.

Ideate • Micro theory about users’ needs.

Thematic analysis - based on customer journey framework

Insights about user’s needs (interview transcripts, audio recordings, notes) and service touchpoints.

Chapter Six: Customer Journey in 3D Body Scanning: The Good, the Bad and the Unexpected.

Prototype Generate ideas for possible solutions to the defined problem or needs

• Secondary desk research.

• Heuristic evaluation

Self-explanatory representation of the service concept.

Chapter Seven: How Can Fashion Industry Integrate 3D Body Scanning Workflow: A Critical Review.

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In thematic analysis, the tasks of defining, categorising, theorising, explaining,

exploring and mapping are fundamental to the researcher role (Strong and Volkoff,

2010). When conducting data analysis, the researcher becomes the instrument for

analysis, making judgments about coding, theming, decontextualising, and

recontextualising the data (Nowell et al., 2017). The themes are creative and

interpretive stories about the data produced at the intersection of the researcher

theoretical assumptions, their analytic resources and skill, and the data themselves

(Braun and Clarke, 2019). The qualitative analyses require interpretation, but the

researcher minimised personal interpretation bias by (a) frequent supervisory

discussions, (b) continuous reflection and (c) constant comparison; three steps

recommended by Stenbacka (2001). According to Braun and Clarke (2020) most

recent articulation, thematic analysis is: 1) data familiarisation and writing

familiarisation notes; 2) systematic data coding; 3) generating initial themes from

coded and collated data; 4) developing and reviewing themes; 5) refining, defining

and naming themes; and 6) writing the report.

Reliability is the extent to which studies can be replicated, using the same methods

and getting the same results. To understand reliability, it is necessary to clarify what

can be reliable in a qualitative study. Eason (1991, p. 84) notes that “reliability is a

characteristic of data”, and Sax (1980, p. 261) adds that “it is accurate to talk about

the reliability of measurements (data, scores and observations)”. Moreover, Goetz

and LeCompte (1984) observe that while no study can ever be replicated exactly

because human behaviour is not static, reliability directly affects the degree to which

study results are credible to others. Thus, generalizability asks how accurately

observed scores permit us to generalise about a person’s behaviour in a defined

universe of situations (Twining et al., 2017). To reliably and systematically analyse

qualitative data, the researcher used NVivo (QSR, 2019) codebook within each study

to develop themes subsequently. Codebook’ captures a cluster of methods that

broadly sit within a qualitative paradigm (albeit with some pragmatic compromises).

Having imported data into NVivo 12, the researcher began creating nodes based on

each theoretical study framework (Hutchison, Johnston and Breckon, 2010). NVivo

allowed the researcher to organise data in a hierarchical tree nodes structure, moving

from a general category at the top – the parent node – to more specific categories or

child nodes. A feature of thematic analysis is that the creation of nodes (themes)

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becomes less frequent with analysis progress because data (references) begin to fit

free nodes that already exist - thus increase research generalizability (Bringer,

Johnston and Brackenridge, 2006). For each study, subthemes were formed based on

pre-existing coding frameworks, often represented as child nodes in NVivo; as shown

in Figure 13. The screen shoot from study three, in which users evaluated Areas of

Interest (AOI) based on the body classification framework developed by Cash (2014).

Moreover, cases in NVivo are nodes with attributes that represent participant type,

age, occupation or institutes, sites or other entities involved in research. To analyse

data, the option ‘matrix coding query’ was utilised to probe data, find patterns and

evaluate concepts (Van et al., 2019). This allowed viewing the number of cases and

references coded at free nodes by the selected attributes and comparing how they

were further coded to parent and child nodes (Deterding and Waters, 2018).

Figure 13 Screenshot from NVivo codebook, based on Cash et al. (2011) theme.

The coding is also often labour-intensive: “Typically, verbal data tend to be

voluminous: 1hr of recording may take up to 10 hr to transcribe, which can result in

15 to 50 pages of text (to code)” (Chi, 1997, p. 283). This study’s data collection

consisted of 83 recordings that were transcribed and coded in their entirety. However,

to minimise the risk of bias, the author discussed themes creation and growth with the

supervisory team each month to evaluate coding protocols and compare opinions

(Richards, 1999). The purpose of following the NVivo procedure was not to create a

model; instead, it was used to visualise the themes and creatively think about how the

parts fit together (Feng and Behar-Horenstein, 2019). Each analysis’s details are

discussed more in-depth in section 3.5 and expounded in each study.

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Techniques and Procedures

This section describes the techniques and procedures chosen for tackling the research

questions, focusing on the human factor’s perspective for 3D Body Scanning service

interaction and data presentation. This thesis includes data collected from content

analysis, stakeholders, users’ interviews and focus groups, and heuristic evaluation.

Although each study was conducted independently, the common methodological

features will be discussed in this chapter, the specifics of each study are detailed in the

relevant subsequent chapters.

3.5.1 Research Question 1

“What are the size and fit tools and techniques that could support the

provision of 3D Body Scanning in the existing fashion services?” This research question covers the first two stages of the service design framework:

understand and observe. This question was tackled first by undertaking a literature

review in chapter two and through content analysis in chapter four. The first objective

was to analyse the existing knowledge base on 3D Body Scanning and identify gaps

in connection to the apparel product development and service design. The literature

review provided direction for the empirical work, looking at the principles, methods

and practices of 3D Body Scanning and the possibilities of integrating them in service

design for fashion retail use. The literature implicitly recognises that the current

technical focus in 3D Body Scanning developments is problematic and that for

technology to be adopted, it needs to align more with fashion industry practice. The

technological focus created the tendency in most companies and developers to get

stuck in specialised boxes, where they are required to focus on a narrow part of the

picture (Ashdown, 2020). However, the service design through a pragmatic lens

focuses on the “empirical method” of moving back and forth between practices

(primary experiences) and reflections (secondary experiences) to develop practical

knowledge – knowledge that is based on practices, and that is practically applicable

(Dewey, 2005). Thus, the proposed service design approach argues that the

knowledge should explore alternative futures, promote communication and

cooperation, and organise positive change.

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Chapter four comprises a usability evaluation of the existing size and fit applications

to corroborate (triangulate) and clarify literature findings with real-world examples.

To answer the first research question, chapter four evaluated Virtual Fit Interfaces

(VFI) and their potential to connect with 3D Body Scanning to deliver effective size

and fit recommendations in e-Commerce. Chapter four, “Online Virtual Fit is Not Yet

Fit for Purpose: An Analysis of Fashion e-Commerce Platforms”, offered a

‘snapshot’ of the context in which the size and fit services are operating, but it did not

yet provide the users’ or stakeholders perspective on the service. Instead, this study

focused on VFI usability, adopting measurements from Alvanon (2019) body form to

create a virtual persona of the user. This study defined persona as a descriptive model

of the user, encompassing information such as user body measurements, goals and

needs (Idoughi, Seffah and Kolski, 2012). The Alvanon body forms are accurately

shaped like the human body because they were created from current customers’ body

scan data in various demographics (Baytar and Forstenhausler, 2020). Unlike typical

mannequins, they are designed with legs instead of in a skirt form and can be used to

create patterns for anything from swimwear to outerwear (Ashdown and Vuruskan,

2017). Moreover, this study focused only on female customers’ because the VFI

applications for male were not yet readily available online at the time of this study

(Sohn, Lee and Kim, 2020). Nevertheless, according to Mintel (2019b), women under

the age of 35 claims that viewing items on a 3D model would help them shop online,

with females favouring retailers that provide such technology.

This study undertook a qualitative content analysis of VFIs; a technique used to make

systematic, replicable, and valid evaluations by interpreting and coding textual

material (Krippendorff, 1980). Once the study captured each platform’s interface

screenshots, the researcher performed thematic content analysis using NVivo (QSR,

2019) qualitative analysis software. This study was considered to be a valuable source

of information on the representation of users’ requirements in existing applications

and provided an opportunity to obtain information on a design from a retrospective

point of view. For these reasons, a study based on content analysis was considered

worthwhile. Thematic content analysis is based on coding, an essential process in

usability studies (Følstad, Law and Hornbæk, 2010). Chapter four coding process

comprises three steps: (a) open coding, (b) axial coding, and (c) selective coding, as

discussed by Braun and Clarke (2006). Open coding is described as the process of (a)

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breaking down, (b) examining, (c) comparing, (d) conceptualising and (e)

categorising by which concepts and their proprieties and dimensions are identified

from data that are transcribed by the researchers. In open coding, themes are

compared with others in terms of similarities and differences in order to offer them,

when similar, the same name (Hutchison and Mitchell, 2011). The next step, axial

coding, is the process of relating categories to subcategories for continuous

comparison of the similarities and differences between such theme concepts. Lastly,

selective coding is the process of integrating and refining the final theory. In this

study, main open coding categories were grouped as information collected from the

user, information presented to the user and the VFI classification framework. The

example of coding is demonstrated in Figure 14, and the example of complete VFI

interface coding is further shown in Appendix B and Mendeley database

(Januszkiewicz et al., 2019).

Figure 14 NVivo example of node coding based on Virtusize (2019) interface.

This study introduces some of the key themes shaping 3D Body Scanning use in the

fashion context. The purpose of content analysis is to build up a conceptual system

and categories to describe how the industry uses size and fit prediction platforms. The

following study focuses on what 3D Body Scanning integration with existing size and

fit practices might mean for the fashion retail, engineering, and technology industries.

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“What is the 3D Body Scanning stakeholders’ outlooks for the technology

diffusion within fashion retail?” This research question covers the third stage of the service design framework: point of

view. The question was undertaken in chapter five, “Stakeholders in 3D Body

Scanning: How to Connect the Isolated Silos of Knowledge for the Fashion Mass-

Customisation.” This study assesses the adoption goals and development

requirements through the lens of the diffusion of innovation theory (Rogers, 2003) via

semi-structured interviews with 3D Body Scanning stakeholders. Diffusion of

innovation theory can link existing ‘knowledge to need’ (Biagini et al., 2014, p. 282)

as demonstrated in the literature, section 2.5.2.3 and can be defined as the movement

of knowledge, skills or technical expertise from one organisational setting to another.

DOI theory provides a framework for analysing which types of expertise are being

transferred for adaptation and where these transfers fit into the innovation process.

The endpoint of this study develops a more comprehensive and nuanced

understanding of the problem and possible ways to address it through the integration

of the stakeholder’s knowledge into an evidence-informed strategy for the fashion

industry. To realise the technology relative advantage, this study constructed a rich

picture to illustrate the relationship between stakeholder groups to corroborate and

triangulate the diffusion dialogue (Avison, Golder and Shah, 1992).

To elicit data from stakeholders’ various methods and approaches have been

developed, as shown in Table 16. This research has chosen semi-structured

interviews. Interview research aims to understand the ‘universal essence’ (Creswell,

2012) or a phenomenon of human experience – in this case, the experience of 3D

Body Scanning stakeholders and their role in the process of technology diffusion in

retail. Data is collected from a heterogeneous group, all of whom have experience of

this phenomenon and actively construct its meaning (Norman and Verganti, 2014). In

contrast to survey research, qualitative methods allow investigators to probe into

participants’ answers; offer reassurance and encouragement; gauge participants’

understanding of new concepts; see logic or contradiction in their ideas; and ascertain

a level of certainty or enthusiasm in participants’ responses with cues such as tone of

voice, speed, or body language (Hakoköngäs, 2020). However, due to the high level

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of industry competitiveness, interviews were more suitable than focus groups. The

research also targeted individuals with long tenure and representing enterprises that

systematically innovate. Thus, the snowball sampling was rejected because

interviewers explicitly asked for those who were responsible for innovation, i.e.,

business owners, R&D managers or staff managing new business development

activities. To check for non-response bias, respondents and non-respondents were

compared across industries and size classes (van de Vrande et al., 2009).

Nevertheless, one criticism of the interview technique is that data is consciously

constructed by the participants rather than being a reflection of objective truth.

Participants’ responses may be influenced by the design of the study, or the

interviewer’s characteristics, opinions, or reactions (Bryman and Bell, 2015).

Mitigating this potential for bias requires the researcher to establish a rapport with

interviewees and be able to adapt the schedule to follow interesting leads (Mann,

2016).

Interviews can take a structured or unstructured format. Structured interviews use

closed questions and gather standardised answers, which can be quantified or

generalised; however, they can be restrictive, as they cannot investigate issues in-

depth, and are not suited to complex issues (Aberbach and Rockman, 2002). A semi-

structured interview format provides the best designs – limiting the questions to a

loose schedule to achieve a rich (dense and meaningful), saturated dataset (Darnall

and Jolley, 2004). Semi-structured interviews were used as it allows a balance

between gathering rich and focused data and keeping the feel open and

conversational; this builds rapport between the participant and investigator,

encouraging them to share stories and examples from their own experience (Rabionet,

2014). Thus, mitigating the potential for bias in research. The philosophy was based

on the view that all information is perceived as a potential contribution to the area due

to the lack of knowledge in the field. For these reasons, the researcher decided to base

the investigation on diverse stakeholder groups (Bunn, Savage and Holloway, 2002).

Additionally, the difference in knowledge and expertise context from the distinctive

stakeholders has offered an opportunity to look at the existing design and technology

deployment issues in contrasting contexts.

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Table 16 Stakeholders Analysis - Methods Overview. Based on Reed et al. (2009)

Method Description Resources Strengths Weaknesses Focus Groups

A small group of stakeholders, brainstorm their interests, influence and other attributes, and categorise them.

High-quality facilitation: room hire; food and drink; facilitation materials, e.g., flip-chart paper and post-its.

Rapid and adaptable; useful for generating data on complex issues that require discussion to develop understanding.

Less structured than some alternatives, so require effective facilitation for good results

Semi Structured

Interviews with a cross-section of stakeholders

Interview time; transport between interviews; voice recorder

Useful for in-depth insights into stakeholder relationships and to triangulate data collected in focus groups.

Time-consuming and hence costly; difficult to reach consensus over stakeholder categories.

Snowball Sampling

Individuals from initial stakeholder categories are interviewed, identifying new categories and contacts.

As above: successive respondents in each stakeholder category are identified during interviews.

Easy to secure interviews without data protection issues; fewer interviews declined.

The sample may be biased by the social networks of the first individual in the snow-ball sample.

Interest Influence matrices

Stakeholders are placed on a matrix according to their relative interest and influence.

Can be done within focus group setting or individually by stakeholder during interviews or by researcher.

Possible to priorities stakeholders for inclusion; makes power dynamics explicit.

The prioritisation may marginalise certain groups; assumes stakeholder categories based on interest–influence

Q Methodology

Stakeholders sort statements are drawn from a concourse according to how much they agree with them, analysis allows social discourses to be identified.

Materials for statement sorting; interview time; transport between interviews.

Different social discourses surrounding an issue can be identified and categorised according to their ‘fit’ within these discourses.

It does not identify all possible discourses, only the ones exhibited by the interviewed stakeholders.

Social Network Analysis

Used to identify the network of stakeholders and measuring relational ties through questionnaires.

Training in the approach.

Gain insight into the boundary of stakeholder network; the structure of the network; influential and peripheral stakeholders.

Time-consuming; questionnaire is a bit tedious for respondents; need specialist in the method.

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“What does the customer 3D Body Scanning journey look like in retail, and

which factors enable or inhibit the service presentation and interaction?”

This research question covers the fourth stage of service design framework: ideate.

The question was embarked in chapter six “Customer Journey in 3D Body Scanning:

The Good, the Bad and the Unexpected.” In this study, the researcher used a wide

variety of design thinking methods, including focus groups, interviews and eye-

tracking to understand the in-depth customer experience in their service journey.

The literature review highlight that 3D Body Scanning developers has de-emphasised

the importance of the design process and maintained the role of a retailer as a mere

recipient of the information (Silva and Bonetti, 2021). In result, the engineering teams

were responsible for addressing the customer requirements. Nevertheless, the

diffusion studies continue to be sited using more traditional, less collaborative

methods. Thus, the past research lacks the objectivity in data collection that is

desirable, particularly in terms of presenting evidence to stakeholders to promote

service improvement (Gupta, 2020). The mix-method approach in this study ensured

the scope of the questions would cover a sufficient breadth of human and technical

factors at all levels of the system, in accordance with innovation and design theories

(Baskerville et al., 2019). The qualitative phase was evaluated through focus groups

and semi-structured interviews, and the quantitative phase was tackled through eye-

tracking method.

The focus groups and semi-structured interviews were used to explore customer

interaction and their attitudes towards technology usability and processes. Using

participatory design methods in which user act as co-creator (Stickdorn and

Schneider, 2010), this study goal was to gain an in-depth understanding of customer

journey in 3D Body Scanning service. Each question focused on a different facet of

soft systems methodology (SSM) – design, interaction, and application. In total, 52

participants completed this scenario. The second part of the study involved the eye-

tracking technique to probe into interface developments and user’s perception of the

human body composition. Main outcome measures were customers’ gaze, dwell time

(total amount of time a participant fixates certain areas of interest). Eye-tracking

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glasses can enhance interface developers understanding of visual perceptions of

participants’ gaze and perspective during evaluation. In total, 40 participants

completed the scenario. Each participant assigned to the task wore Tobii Pro 2.0 eye-

tracking glasses (Tobii Pro AB, 2019). The recording produced a first-person view

video with an overlying pupil fixation showing where the participant was looking in

real-time. The data were then uploaded to a secured research computer and analysed

with the Tobii Pro Glasses Analyzer software (Tobii Pro AB, 2018). The Wilcoxon-

Mann-Whitney (WMW) was used to assess significant differences in the overall

proportion of time spent on the interface Areas of Interests (AOIs).

Eye-tracking systems require calibration, a method of algorithmically associating the

eye’s physical position with the point in space that the participant is looking at (gaze)

(Sharafi et al., 2020). Therefore, the researcher had to ensure the ambient light levels

were not too high for the raw eye picture to become washed out (Zhu, Fujimura and

Ji, 2002). If the lighting is too high, the software will have trouble tracking the pupil

and corneal reflection. Eye-tracking also can only be done at people with good vision

- when vision is normal and healthy, the eyes automatically move (track) smoothly,

accurately, and quickly (Singh and Singh, 2012). Typically, glasses and contact lenses

may cause reflections that can either obscure the corneal reflection or be regarded by

the eye tracker as being actual corneal reflections (Jongerius et al., 2021). The corneal

reflection can also be obscured if there are small dark round clumps of mascara on the

eyelashes – the eye tracker can be tricked into thinking that the mascara is the pupil.

Therefore, this study excluded people with poor vision or strong makeup from the

sample. Moreover, in some instances data quality was impacted by racial bias, as

researcher found that accuracy and precision for Asian participants was worse than

that for African and Caucasian participants. This finding corresponds with Blignaut

and Wium (2014), who found that the narrow eyes of Asian participants or eyes with

a narrow cleft cause the eye-tracker to lose the glint, and therefore, trackability is

worse for Asians than for the other two ethnicities (Caucasian, black). Therefore,

researcher had to take cognizance of the small vertical tolerance and take care to

position participants such that their eyes are on the same level as the vertical centre of

the eye-tracker. The specifics of eye tracker are further discussed in study three

materials and methods, section 6.3.

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“What are the critical elements of the 3D Body Scanning service in a fashion

retail scenario?”

This research question covers the fifth stage of the service design framework:

prototype. The question was undertaken in chapter seven: “How Can Fashion

Industry Integrate 3D Body Scanning Workflow: A Critical Review.” Recent advances

in 3D Body Scanning allow for truly ubiquitous and unobtrusive anthropometric

measurement for product development. However, challenges to realise the benefits of

technology for customers, retailers, and researchers remain. A consensus is lacking

about strategies on how 3D Body Scanning should diffuse in the fashion retail

context. This research offers a topology by which the fashion industry can identify the

benefits of new technologies and encourage the changes to practice routines necessary

to realise 3D Body Scanning service. Similarly, the service design approach takes

developers out of their comfort zones toward enacting more comprehensive service

innovations that requires creativity and collaboration with different disciplines. There

are outstanding questions regarding performance evaluation and data storage,

curation, processing, integration, and interpretation that need a multidisciplinary

perspective. Therefore, this study proposes a service framework of 3D Body Scanning

that summarises the technology state-of-the-art and discusses the opportunities and

challenges from data acquisition to the future application of insights in fashion and

customer settings.

Most of the heuristics are design guidelines and refer to the system design with

common factors, such as consistency, appropriate visual presentation, task analysis,

user experience, guidance and support (Norman and Draper, 1986). A heuristic is

defined as “a context-dependent directive, based on intuition, tacit knowledge, or

experiential understanding, which provides design process direction to increase the

chance of reaching a satisfactory but not necessarily optimal solution”(Fu, Yang and

Wood, 2016, p. 4). Several concepts that figure in heuristic explanation, namely,

agent, action, intention, purpose, desires, and offered ideas for the service

investigation (Norman, 2005). In particular, the focus on ‘purpose’ provided clues that

perhaps the research should look at the existing gaps in the design process to

understand the factors underlying the adoption issues. However, the heuristic

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evaluation does not require users, who can be hard to get, and can be used initially to

rinse out a number of usability problems (Hvannberg, Law and Lárusdóttir, 2007).

This study is created to codify and formalise design knowledge so that innovative

practices may be communicated and used to solve design problems, especially the

pinnacle, wicked problems that face the world and cross-cutting interdisciplinary

markets.

The first step in heuristic evaluation is to decide which set of heuristic principles to

evaluate (Khajouei, Hajesmaeel Gohari and Mirzaee, 2018). This research followed a

set of principles for the 3D Body Scanning workflow as outlined by Daanen van de

Water (1998), Daanen and Ter Haar (2013), and Heymsfield et al. (2018). Other

researchers can reuse the framework for comparing evaluation methods described in

this study because of its thorough structure. From the practical point of view, a

topology for capturing and recording usability problems in 3D Body Scanning

evaluation is recommended (Vuruskan, Seider and Detering-Koll, 2011). However,

3D Body Scanning heuristic development has yet to be compiled into a

comprehensive list. In this method, the researcher independently examines the 3D

Body Scanning service design against a set of workflow principles. Thus, the previous

studies act as a guide that helps to mitigate personal bias and increase result

reliability. The experience in empirical studies provides a good background for

recognising and conceptualising usability problems.

Research Ethics

Research Ethics Committees was responsible for assessing ethics application to

ensure that adequate consideration has been given to the ethical aspects of a research

project. The PhD’s ethical approval was granted through the Ethics Committee of the

University of Manchester. The research is stored according to the Data Management

Planning Tool. A research data management plan (DMP) describes the arrangements

that will be put into place to manage research data throughout the project life cycle

(Whyte, 2016). The ethic application form explains what will happen with data during

the research project and after it has been completed. The reference for this project

data management is RDMP8037. The codes of ethical conduct are enunciated as sets

of principles aimed at safeguarding or assuring participants’ rights by providing

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adequate information to make informed decisions about their initial and continuing

participation in the research (Shilton, 2018). The form, as shown in Figure 15,

detailed what type of participation this research undertook. The participants were

recruited through the Eventbrite website, social media posts related to events in

Manchester and posters advertised in Sackville Street Building at Manchester

University and The Northern Quarter. This way researcher could target a broad range

of participants and remain representative of the population, as shown in section 6.3.1.

Figure 15 Ethics question. Source: (Januszkiewicz, 2018).

Managing the ethical process represented an opportunity to prepare the researcher to

think about best research practice- and not only in terms of ethics - in a very in-depth

and constructive way. For example, due to the inherent fluidity and responsiveness

required when working with human participants – especially using focus groups and

interviewing approaches – the researcher cannot always say with any certainty how

aspects of the research will develop. Therefore, justifying the creditability of this

research methodology was a critical step towards convincing the committee that the

project was 'doable' and therefore ethical in terms of being a worthwhile thing to ask

people to take part in. The defined list of information for the research committee is

presented in Figure 16. Consequently, the application and, in particular, the research

proposal was extremely thorough, but of course, it was also very lengthy and took a

significant amount of time to prepare - in this research case, about 4-5 months.

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Figure 16 Ethics information list. Source: (Januszkiewicz, 2018).

The most effective way to address the informed consent issue is by using an

information sheet, which was provided to all those invited to participate. More

specifically, the researcher was worried about the scanning phase as participants had

to undress and stand in an uncomfortable ‘A position’ in the scanning booth (Gill,

Hayes and Parker, 2016) and then discuss and reflect on their experience with a

broader audience, potentially invoking a feeling of being ‘spied’. However, this was

mitigated as a researcher could participate in ADE monthly scan sessions and observe

and learn how other, more experienced researchers facilitated the scanning process:

the presentation skills, wording choice, body language etc. Moreover, the researcher

could investigate scan ethic application from ADE research group Project Ref 14111,

which outlined how to plan a research project in the 3D Body Scanning field.

Explanation of the 3D Body Scanning process and details of consent based on

Gill et al. (2019) ethical guideline:

• Each participant had the opportunity to have the scanning process explained to

them and be referred to the step-by-step procedure detailing the process from

start to finish.

• The step-by-step guide and a copy of the consent form were provided for the

participant to read prior to scanning. The information used clear, jargon-free

language so that participants could understand what they are being asked to do

(Lloyd and Hopkins, 2015). The consent form was retained, though a blank

copy is available on the website for scrutiny by participants.

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• A general body scanning information sheet, and a sheet related to specific scan

projects, was provided to each participant before scanning. The general sheet

indicated the type of underwear that should be worn during the scan process

and the types of scan outputs that will be provided. Therefore, before starting

the session, participants were presented with an information sheet to fully

understand the research and their participation rights, as shown in Figure 17.

• An encrypted and password protected database available on a single PC was

used to record participant contextual data separate from any scanned image, as

shown in Figure 18. This is backed up to encrypted and password-protected

hard disks that are kept separate at secured locations.

• The database can only be accessed by members of the ADE team with a

password and store personal details of each participant, including name; age;

ethnicity; gender; email; postcode and the manual measurements not recorded

by the scanner, as well as the name of the main scanning personnel responsible

for collecting the body scan data.

Figure 17 Consent form process. Source: (Januszkiewicz, 2018).

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Figure 18 University of Manchester body scanning database. Retrieved from Gill et al (2019).

The database code consists of a four-digit unique identifier, a code for gender (M or

F), a letter code for ethnicity and two digits for age. Having this data in the code helps

with managing the scans and later matching of scan with participant data during

analysis. The following details are recorded in the database; those marked with * are

merged with the measurement data during later analysis:

• Name: ensures each participant is an individual and allows researchers to code

for repeat body scans of the same participant as well as manage the data.

Names are only recorded in a database and on the printed copy of the consent

form and are not exported with other details for use in the analysis of the scan

data.

• Date of scan*: helps with mapping details of the participant to captured scan.

• Postcode: the first three digits are recorded to enable the population data to be

grouped into demographic regions in future.

• Email Address: this allows to contact participants who have agreed to take part

in future studies.

• Participants are asked to tick a box signifying their agreement to be contacted

about future scanning projects.

• Age*: this data allows to group scans into demographics.

• Gender*: allows for the classification of scans into gender groups.

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• Ethnicity*: using the major NHS ethnicity groupings participants’ scans can

be grouped by ethnicities. There are a number of studies showing that some

ethnic groups often have distinct morphological characteristics.

• UK Shoe Size: the scanner often gets insufficient data from the feet; having a

record of this will help ensure that data can be used in much more application,

and errors to data collection of the feet can be moderated suitably.

• Occupation: this allows for scans of students and non-students to be identified

and ensures researchers can clearly indicate ratio of participants between those

who are/were students and those who are not.

• Bra size worn during scan: scans are captured of participants in their

underwear; it is well documented that the bra will modify/shape the breast;

having this data opens up later possibilities of using the data collaboratively

with other research groups, like the breast health research centre.

• Manual measurements*: these are all measurements not handled by the

scanner that has an application in clothing development.

To summarise, the ethic application process represented an opportunity for the

researcher to prepare a thesis plan and think about best study practice in a very in-

depth and constructive way. By obtaining ethical approval, a researcher demonstrated

that this thesis has adhered to the accepted ethical standards of a genuine research

study which increased recruitment potential.

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CHAPTER 4

ONLINE VIRTUAL FIT IS NOT YET FIT FOR PURPOSE:

AN ANALYSIS OF FASHION E-COMMERCE

PLATFORMS

Introduction

Virtual Fit Interface (VFI) developers attempt to reduce the cost of garment returns in

Electronic Commerce (e-Commerce) by providing users with size and fit

recommendations. Reducing costs is essential as e-Commerce generates 24.3%

(Amed and Berg, 2017) of the fashion industry’s market worth, an estimated $1.4

trillion annually (Lissaman, 2020). Moreover, the COVID-19 pandemic elevates e-

Commerce as a critical channel for profitability (Craven et al., 2020). However, the

VFI has yet to offer fast and precise methods to collect anthropometric measures from

users to determine the correct size and fit (Sohn, Lee and Kim, 2020). The results

provided by VFI are not based on reliable data provided by garment developers

(Hernández, Mattila and Berglin, 2019). The VFI approaches instead focus on user

self-reported questions to solicit body dimensions and shape characteristics, which do

not fully account for the garment developers’ needs and are, therefore, not fit for

purpose (Zulkifli, Kim and Takatera, 2020).

Correct size and fit are the garment’s relation to the wearer’s body, as intended by the

designer, presenting the garment’s style through contour and drape. The designer

achieves intended contour and drape through translating anthropometric

measurements, categorised to pre-specified size groups, to pattern blocks that, when

stitched together, curve to the body (Harwood, Gill and Gill, 2020). While VFI

developers believe users can measure their body’s information well enough to identify

the right landmark palpation, self-reported metrics are found unreliable by garment

developers (Song and Ashdown, 2013). The manual anthropometric approaches are

relatively simplistic and use a limited number of measurements, which are prone to

human error and do not fully describe the complex three-dimensional variations in

body shape (Thelwell et al., 2020). Therefore, despite the first VFI entering the

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market in 2000 (Nantel, 2004), the anthropometric data required by VFIs to produce

correct size and fit recommendations and how users understand those parameters

remains unclear (Zulkifli, Kim and Takatera, 2020).

The point cloud data from the 3D Body Scanning contains all of the differential

geometric properties of the body’s shape surface, providing a higher degree of

precision and complexity than manual methods (Kuehnapfel et al., 2016). Previous

studies have investigated the use of 3D Body Scanning’s integration with VFIs, based

on technical abilities and reviewed the approaches to sensors capabilities (Sapio,

Marrella and Catarci, 2018), human modelling and cloth simulation (Lin and Wang,

2014; Liu et al., 2017), or AI developments (Giri et al., 2017). These studies

demonstrate that 3D Body Scanning can connect with VFI on a technical level but

overlook the suitability requirements for users (Petrova and Ashdown, 2008; Baytar

and Ashdown, 2015; Feng and Xie, 2019). Furthermore, multiple sizes and fit

prediction methods are extant within the literature (Meng, Mok and Jin, 2010; Beck

and Crié, 2018). The assessment of which waist measurement a 3D Body Scanning

should take is one example of this discord, a decision that will transform the user’s

predicted size if not specified (Gill et al., 2014; Gill and Parker, 2017).

Assuming 3D Body Scanning can reliably predict the size and fit, there is a lack of

agreement on the information users require to make size and fit judgments (Gill,

2015). Information presented to users through VFIs has a history of being abstract (De

Coster et al., 2020), compared with the real experience of trying a garment on; driven

by self-perception misconceptions (Ketron and Williams, 2018). Nevertheless, there is

a considerable mismatch between users’ perceived body measurements and their body

image (Linkenauger et al., 2017). The past studies also have observed that idealised

avatars are preferred to avatars that are more truthful (Linkenauger et al., 2017).

Therefore, it is important to review the presentation methods and find the elements

that contribute to the dissatisfaction with online purchases and associated return rates

that substantially limit e-Commerce profitability. Until research bridges the gap of

human shape, size, and measurement, VFI cannot progress beyond a playful

marketing tool. It is, therefore, necessary to define the magnitude of space and

acknowledge the shortcomings of current applications for VFI to be successful.

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The lack of evidence for the effectiveness of Virtual Fit Interfaces and the tendency of

publicly available apps to be developed without theory or empirical evidence

highlights the need for the rigorous evaluation of new VFI’s. The study aims to

analyse existing Virtual Fit Interfaces to understand how 3D Body Scanning can help

e-Commerce retail deliver well-fitting garments. Thus, the research objective is to

investigate the existing criteria for how VFI collect information, available

presentation features, and alignment of the app and its features with existing

frameworks, as highlighted by Gill (2015, p. 13). In doing so, this study describes the

challenges for engineering, computer vision, and fashion industry research that must

be addressed to realise promised benefits of 3D Body Scanning in e-Commerce.

In pursuing this aim, this study addresses the following three objectives:

1. This study evaluated the information required from users by VFIs to assess

how 3D Body Scanning can complement the existing anthropometric

techniques.

2. This study evaluated the VFIs presentation features to understand the degree

to which the anthropometric data from body scan can benefit users in size and

fit evaluations.

3. This study examined how VFIs establish the size and fit recommendations to

understand how VFI developers can standardise the size assessment against

recognised criteria.

Theoretical Background

4.2.1 User Information Requirements

Technology advancements – such as VFIs – aim to help users choose the correct size

of garments while shopping online by providing more detailed fit information. In

addition, VFI may provide fashion practitioners with interactive tools and analytics to

help them reconsider their approach to garment prototyping, sizing, and fitting

(Kamali and Loker, 2006). However, VFIs still lack anthropometric standards of what

information to include when defining and collecting measurements (Bye, Labat and

Delong, 2006). The absence of standards translates into a lack of comparable datasets,

most pronounced in garment developers’ transferability methods and practices

(Ahmed et al., 2019). Thus, for more robust VFI predictions, it is vital for size and fit

prediction to consider how practitioners obtain body measurements (Beazley, 1996)

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and how they place those measurements for pattern creation, i.e., in garment block

development (Yan and Kuzmichev, 2020). The adoption of 3D Body Scanning within

VFI can help fashion e-Commerce capture detailed and accurate external dimensions

and shape the human body’s characteristics to progress beyond promotional

marketing tools (Thelwell et al., 2020). However, Gill et al. (2018) pointed out that

while it is evident that product development methods can be problematic in terms of

generality, the approaches must be grounded in the fashion practices to stimulate

adoption. Understanding fashion pattern drafting and fitting parameters will help VFI

developers classify the body and also describe how users perceive themselves relative

to these classifications (Ridgway, 2018). Such harmonisation may lead garment

developers to formally adopting new sizing and fit approaches (Finsterwalder, 2018).

However, further work is needed to create reliable methods to identify and extract

standardised anthropometric measurements (Heymsfield et al., 2018).

4.2.2 Virtual Fit Presentation and Assessment

For retailers to embrace VFIs in e-Commerce, VFIs must communicate to users the

size and fit with minimal error and reflect the real-world garment fitting experience

(Xue, Parker and Hart, 2019). VFIs can offer a variety of ways of engaging with users

during online shopping to enhance the virtual product experience (de Bellis and

Venkataramani, 2020). However, despite advances in user experience and user

interface design, size and fit’s nature remains problematic (Gill and Brownbridge,

2013). The key issues are a lack of explicit garment fit assessment guides or theory to

explain fit assessment and achievement (Song and Ashdown, 2013). Research

suggests that using an avatar that represents the self-increases the impact of virtual

try-on (e.g., more confidence in apparel fit, greater purchase intentions), and that e-

Commerce provider should focus on maximising the perceived resemblance between

the user and the model (Pan and Steed, 2017). Nevertheless, different visual

characteristics may have different effects on user behaviour based on a complex

interplay of self-image, age, personal characteristics and garment aesthetics (Lee et al.,

2012). Ashdown and DeLong (1995) researched how viewers can perceive fit with 3D

virtual technology, recommending the provision of scan slices or measurements from

clothed - and minimally clothed - participant at garment fit locations. However, the

major limitation of VFIs is no underlying Computer-Aided Design (CAD) pattern

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data used in the constructions of the 3D garments and fit evaluation or ‘correct’ size

assessment (Gill, 2015). CAD environment could help garment developers understand

the influence of different and often complex factors, such as mathematics, anatomy,

physics, and mechanics. Thus, provide a reliable and valid method to form the

theoretical foundation for new product development approaches (Nikolova et al.,

2021).

4.2.3 Size and Fit Prediction

Gill (2015), classified VFI developments into three categories: ‘size recommendation,’

‘fit recommendation,’ and ‘fit visualisation.’ Gill positions these interface-based user

data collection characteristics, and how the interface communicates the result back to

the user. The existing discrepancy in product development practice, however, has led

to limited planning and prediction of size and fit outcomes, and it has also made it

difficult to correlate the identified anthropometric data with functional outcomes. By

having a set of frameworks, developers can position users with a clear context of

product requirements (Hwangbo, Kim and Cha, 2018). In order to optimise user

experience, the design must meet basic user needs like functionality (most

fundamental), reliability, and usability before promoting enjoyment (most advanced)

(Plante et al., 2018). Miell et al. (2018) research, discusses how companies adopt

proposed interface design, without applying (additional) methods for interpreting and

contextualising these visualisations into the user experience principles (Kashfi, Feldt

and Nilsson, 2019). Consideration of the context of product requirements is necessary

to achieve a complete picture of the effects of size and fit tools and decide on a

suitable framework on which to base the research (Reid et al., 2020). The multi-

disciplinary approaches of technology development criteria and garment fit

assessment need to be considered in order to fully understand the area of digital fit

and sizing in research.

Materials and Methods

4.3.1 Setting and Sample

This study discovered leading e-Commerce VFIs through purposive sampling

(Kassarjian, 1977), examining academic journals in Fashion Marketing, and

Management as well as anthropometry and Interactive Design literature. The search

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regarded papers published over the period from 2017 to 2019 for the keywords

‘Virtual Fit’, ‘3D Body Scanning’ and ‘Virtual Try-on,’ from the Web of Science.

The result consisted together of 52 records including, author, title, abstract, keywords

and all the references. This study followed the protocol of sciento-metrics analysis set

by Toti (2019) for digital anthropometry and used Citespace software written in Java

(Chen, 2006) to generate interactive visualisations of nodes and links (i.e. networks)

in research. The leading industry databases were also searched for the same keywords

in WGSN, BOF-Business of Fashion, Just Style, and Drapers. The database draws

from and integrates several data sets, thereby providing information about the

innovation and market activities of companies and institutions involved in VFI

development in recent years. The search strategy was designed to capture the current

state-of-the-art in VFIs website. To be eligible for inclusion, the VFI websites had to

be:

1. Embedded within a live fashion e-Commerce website

2. Available via non-subscription platforms

3. Offered in an English language interface

4. Situated within the High Street and Luxury Market segments At the time of research, ten fashion retailers that met the criteria above use Virtual Fit

on their websites. All ten retailers were, therefore, selected for this study as presented

in Table 17. The resultant sample size, while limited, exceeds the minimum

requirements of content analysis research, six or higher (Braun and Clarke, 2019). Table 17 Virtual Fit Interfaces. Source: author’s own.

Virtual Fit Technology Embedded in Retailer

1. Fit Analytics Tommy Hilfiger (Tommy Hilfiger, 2018) 2. Fit Predictor Boden (Boden, 2017) 3. Fits Me Henri Lloyd (Henri Lloyd, 2018) 4. Metail House of Holland (House of Holland, 2018) 5. True Fit Phase Eight (Phase Eight, 2018) 6. Virtusize Filippa K (Filippa K, 2018) 7. Belcurves Mark and Spencer (Belcurves, 2018) 8. Style-While Seezona (Seezona, 2018) 9. Virtual Outfits Yoox (Virtual Outfits, 2018) 10. Style.ME Style.ME (Style.Me, 2018)

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4.3.2 Data Collection

This study collected 79 screenshots exploring the potential user journeys through the

different VFIs, nine journey steps per platform on average. Each step of the journey

involved inputting persona body dimensions and other demographic information on

the platform to receive size and fit recommendation. For this analysis, the researcher

focused on the purchase of the simple cut black dress to utilise both upper and lower

body measurements (Kuzmichev, 2020). To ensure reliability, the researcher used the

standardised measurements based on Alvanon™ body form1 - used extensively in

product testing and development in the fashion industry research (Ashdown and

Vuruskan, 2017). To make sure the body form’s measurements are correct, the

researcher scanned the body form using a size stream 3D Body Scanner (Size Stream,

2017), a technology shown to be reliable for anthropometric research (Parker, Gill and

Hayes, 2017).

4.3.3 Data Analysis

This study undertook a qualitative content analysis of VFIs, a technique used to make

systematic, replicable, and valid evaluations by interpreting and coding textual

material (Krippendorff, 1980) using NVivo software (QSR, 2019). This technique

focused on the objective, systematic, and quantitative description of the visual content

(Weber, 1990), allowing common themes among the images to be identified for

coherence, relevance, and clarity. The data analysis was concerned with

the content information displayed by each VFI, and the ‘word counts’ suggested the

frequency of repeating words during the virtual user journey for a qualitative

comparison of themes. The grouped themes demonstrate underpinning patterns that

manifest across the body of responses. Thematic analysis was based on the principles

of Braun and Clarke (2006), which can be summarised as

a. data familiarisation,

b. codebook development with initial coding,

c. identifying themes in data,

d. reviewing themes,

1 Dress Form size code UMR-WMSK12H-1504, dress form code AVF58535

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e. defining and naming themes, and

f. producing the result. To ensure validity, the lead researcher analysed the screenshots, with the research

team performing second coding. Any discrepancies between the research team were

discussed and solved in consensus. Based on the thematic analysis, a structure of main

and subthemes was created. Comparative coding within the same coding tree is taken

forward into this study’s results.

Results and Analysis

4.4.1 Information Requirements for Virtual Fit Interfaces

This study evaluated the information required from users by VFIs to assess the

potential of 3D Body Scanning to connect with Virtual Fit Interfaces. Through this,

this study discovered two information categories:

• Anthropometric Data – Describing the user’s body measurements VFIs ask

for garment size/fit calculation.

• Garment Data - Describing the user’s past garment purchase, fit preferences,

and style preferences; to offer garment size/fit recommendations.

Anthropometric Data Requirements

Table 18 presents the information required from users by VFIs to assess how 3D

Body Scanning can complement the existing anthropometric techniques. This study

found that despite all VFIs having similar fit and style objectives, there was no strong

agreement on data collection methods, producing a total of 15 pieces of various

anthropometric measures.

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Table 18 Anthropometric data. Source: author’s own.

Bel

curv

es

Fit A

naly

tics

Fit P

redi

ctor

Fits

Me

Met

ail

Styl

e. M

E

Styl

e -W

hile

True

Fit

Virt

ual O

utfit

s

Virt

usiz

e

Age 0 1 0 1 0 0 0 3 0 0

Arm Length 1 0 0 0 0 0 1 0 0 0

Belly Shape 0 1 0 0 0 0 0 1 0 0

Body Shape 0 0 0 1 0 1 0 0 2 0

Bra size 0 2 0 0 1 1 0 3 0 0

Bust size 1 0 0 0 0 0 1 0 1 0

Calves 0 0 0 0 0 0 0 1 0 0

Foot Arch 0 0 0 0 0 0 0 1 0 0

Height 1 1 0 1 1 1 0 3 1 0

Hip Shape 1 1 0 0 1 1 1 2 1 0

Inside Leg 0 0 0 0 0 0 1 2 0 0

Shoulders 0 0 0 0 0 0 1 0 0 0

Torso 0 0 0 0 0 0 0 2 0 0

Waist 1 0 0 0 1 1 1 0 1 0 Weight 0 1 0 1 1 1 0 3 0 0

Table 18 shows that VFI’s categorise individuals based on a group of body

dimensions. To recommend garments, some interfaces utilised as little as four (Fits

Me) or five (Belcurves, Virtual Outfits, and Style.Me) body measurements with the

highest number of ten (True Fit). The most prevalent anthropometric data utilised by

the eight VFIs were height and hip shape (n=8), along with the waist measurement

and weight (n=5). However, this study found a high level of inconsistency as each

platform referred to a different placement definition on a body. Even within the same

measurement - e.g., hip (n=8) - three formats varied in definition: the numeric value

presented in inches or centimetres (Belcurves, Metail, Virtual Outfits and Style.Me),

visual descriptive image (Fit Analytics, True Fit), or in XS, S, M, L, or XL metrics

(StyleWhile). Therefore, the critical question remains how designers should create

VFIs and how users should use VFIs to achieve reliable performance, despite

theoretical imperfections. Moreover, none of the interfaces provided information on

the direct relationship between each measurement to the patterns used in product

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development. These measurements should, therefore, be clearly defined, showing how

the VFI places the collected data in a product development context.

The measurement problem was further confounded when users were asked by VFI

about their body shape; for example, describing their belly, hip, or shoulders. VFI

developers estimated that body shape measures could complement existing

anthropometric techniques in the prediction of the correct size and fit. In these

instances, VFI platforms assumed the users could classify their own body against

loose criteria, such as narrow shoulders and curvy belly. Nevertheless, VFIs

constructed their predictions around broadly defined body shapes such as the Inverted

Triangle, Hourglass, or Triangle. Although, it is currently unknown what all of the

shape features captured by VFI represents in terms of product development methods.

As no universal descriptors for body shape and proportion exist; thus, subjective data

has the potential to be inaccurate. Grounding VFI predictions on potentially

inaccurate body shape classifications means that fit and style recommendations can

have an added erroneous influence.

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Garment Data Requirements

Table 19 presents the range of garment data required of the users for each VFI, along

with the frequency of occurrence. VFIs collect the user’s personal information - past

purchases and current fit preference - to predict the size and fit, providing VFIs with

contextualised, and meaningful, data on users’ shopping habits, brand inclination, and

size preferences. Table 19 Garment data. Source: author’s own.

Bel

curv

es

Fit A

naly

tics

Fit P

redi

ctor

Fits

Me

Met

ail

Styl

e M

E

Styl

e -W

hile

True

Fit

Virt

ual O

utfit

s

Virt

usiz

e

Purchase History

0 0 2 0 0 0 0 16 0 12

Preferred Fit

0 1 0 0 1 0 0 2 0 0

Preferred Style

2 0 0 0 0 0 0 4 0 0

Unlike the anthropometric data requirements, spanning 17 measurements, nine VFIs

required only three interfaces included users garment preferences, with the tenth

ignoring garment data. The garment preference data was described based on purchase

history, preferred fit and preferred style. • Purchase History – Users create an account based on their email address, with

VFIs expecting the user to answer questions on their earlier shopping purchases.

The user’s answers inform retailers about which brands and sizes variations they

prefer. Retailers can then customise product recommendations within marketing

communications.

• Preferred Fit – User’s perception of a good fit, ranging from the desire for a

garment to conform to the body (i.e., give comfort), to perfect conformation to the

body (i.e., give maximum form appearance).

• Preferred Style – Style ease and the amount of fullness added to garments to

create a desired visual effect. Style ease results in an added volume of fabric to

body areas, depending upon personal preference and current fashion in clothing

categories.

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Considering the use of garment fit data, there was no strong preference in VFI’s use

of one method over another: Past Purchase (n=3); Preferred Fit (n=3); Preferred Style

(n=2). Nevertheless, only True Fit uses multiple methods to classify Virtual Fit, with

high dependency upon Purchase History.

4.4.2 Virtual Fit Interfaces Presentation

Table 20 presents the outputs as presented by VFIs to understand the degree to which

the anthropometric data from body scan can benefit users in size and fit evaluations.

The key finding here is a lack of visual methods and tools to consistently

communicate the size and fit criteria between all nine of the VFIs. Table 20 Virtual Fit Interface presentation. Source: author’s own.

Bel

curv

es

Fit A

naly

tics

Fit P

redi

ctor

Fits

Me

Met

ail

Styl

e M

E

Styl

e -W

hile

True

Fit

Virt

ual O

utfit

s

Virt

usiz

e

360° view 4 0 0 0 0 2 0 0 0 0

Avatar 3 0 0 0 2 6 2 0 2 0

Branding 1 1 3 0 0 1 0 20 0 1

Buy Button 5 1 0 0 2 0 1 1 1 0

Heat Map 2 0 0 0 0 1 0 0 0 0

Personalisation 0 0 0 0 2 2 0 0 3 0

Size Information 3 6 1 3 2 1 0 1 1 3 Social Media 3 0 0 0 0 1 0 0 1 0 Style Library 2 0 0 0 0 1 1 1 1 0

Virtual Catwalk 0 0 0 0 0 0 0 0 2 0

Size prediction was the most common variable across eight VFIs with different sizing

metrics, reflecting manufacturing size practices’ inconsistency. Stylewhile is the only

platform omitting size or fit information in a numeric form. Instead, Stylewhile

focuses on garment style visualisation on a personalised virtual model. A Virtual Fit

Interface can give feedback either through a near photo-realistic visualisation of the

attired garment or through fitting visualisations such as fabric stress and strain colour

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mapping. Nevertheless, most of the VFI (n=5) attempts to mimic the in-store fitting

experience online by creating a virtual avatar. Users’ can visualise how an item of

clothing would look on their body by inputting necessary body measurements and

dress the avatar, allowing them to judge the fit before buying. The virtual avatar

allows users’ different customisation options to increase the level of realism and

engagement. Metail, designed it with considerable realism to imitate relevant aspects

of the user’s appearance. StyleWhile and Style.ME apply a similar approach, allowing

users to select a stranger’s photograph with similar facial characteristic, eye colour,

and hairstyle to their own before entering size information to display an avatar

wearing their chosen garment. In contrast, Belcurves’ rejects lifelike avatars, aiming

to mimic only the user’s body shape, with the skin tone remaining Caucasian and

facial features remaining neutral. VFIs consider interactive avatar presentation

features, enabling better presentation of product information; through rotating,

zooming in and out, or changing the avatar body pose. Users can quickly get fit

feedback in the avatar, displaying their body from any angle and distance (as applied

in Belcurves) or from six different angles (as applied in Metail and Style.Me). VFIs

offer few accessories to accompany avatars, limiting their User representation

effectiveness. Animated avatar movements were possible (by Virtual Outfits) that

came in the virtual catwalk, in contrast to the motionless picture. Animation can allow

the user to understand better how the garment reacts to the wearer’s movements.

However, there is still a lack of technological developments that can make the

avatar’s movement and posture look natural.

Standardisation, to enable the exchange of garment descriptions between VFIs, is a

significant barrier to overcome as these innovative tools need suitable guidance on

how to interpret VFIs with more consistent methods to present closeness of fit.

Nevertheless, Virtusize allows users’ to either pick an item they already own from a

retailer’s online store for comparison or enter the measurements of a piece of clothing

from personal wardrobe to see an overlay of the products to help the user understand

if the item will fit their body type. However, no consistency exists, visual methods

need users to develop the skills of how to align meaningful feedback, since size and

fit are often comparative experiences. Heat maps - as applied by Belcurves - show

differences between a garment and a person. Heat maps use colour coding - with

darker red showing higher tension and paler green showing looser fit - to

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communicate varying levels of fit. These indicators have, however, no context in

terms of suitable ease allowances to judge against user fit preferences. Applying 3D

Body Scanning means users can view a virtual representation of themselves. Thus,

software or CAD tools automatically extract size and landmark information from the

user’s 3D body shape data.


Table 21 presents how VFIs establish the size and fit recommendations to understand

how VFI developers can standardise the size assessment against recognised criteria.

The key finding here is a lack of VFI that combines all three criteria. Table 21 Size and Fit Recommendation Models. Source: author’s own.

Bel

curv

es

Fit A

naly

tics

Fit P

redi

ctor

Fits

Me

Met

ail

Styl

e M

E

Styl

e-W

hile

True

Fit

Virt

ual O

utfit

s

Virt

usiz

e

Fit Recommendation

0 1 0 1 0 0 0 1 0 0

Size Recommendation

1 1 1 1 1 1 0 1 0 1

Fit Visualization 1 0 0 0 1 1 1 0 1 0

Table 21 shows the size recommendation is the most common method, applied by

seven of the ten interfaces. In contrast, half of the VFIs (n=5) applied a Fit

Visualization, usually presented in the form of an avatar and digital outfits. The least

applied method was Fit Recommendation (n=3), which was due to the complexity of

technology and the need for an explicit and more holistic method of describing fit for

users to understand recommendations with ease. Interestingly, the analysis also

presented that many VFIs (n=4) combine two methods, although none of the

platforms tried to combine all three methods, which leaves an unexplored market.

Size Recommendation

Size recommendation was the most popular variable applied by (n=8) of VFIs. The

VFIs provide users with labels and charts for garment sizing. These labels charts are

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universal metrics to classify the body within size brackets based on manufacturers

specification sheets. This method provides the user with a definition of their size,

differing between retailers and even a given retailer’s departments. In this approach,

VFIs describe garment sizes through a variety of names, and - within each sizing

system, a retailer adopts – a variety of body types and shapes. The sizing systems are

reviewed according to how they defined figure types, how they described garment

sizes, dimensions the sizing system uses, and groupings of garment type. VFIs use

existing sizing charts to map predictions. Still, this method prohibits the multitude of

potential body shapes and how pattern drafting methods can reflect these realities.

Fits,me’s approach to size matching exemplifies the use of sizing charts to map

prediction, where the systems search for a ‘body-double’ by finding users with similar

body measurements and classifying them into ‘fit profiles.’ Fits.me measures how

many body-doubles returned the purchased garment, implying that a low return rate

within a cluster means perfect size. The variation in body size and type within each

segmented target market makes it challenging to achieve compatible sizing systems

and consistent labelling. VFI learning and referencing the specific sizing relating to fit

are, therefore, inaccurate because research developed them based on false definitions.

Fit Visualisation

Fit visualisation was the second most popular variable, applied by (n=5) of the VFIs.

The realistic visualisation of a garment is the most intuitive way to give feedback

about its fit. In this approach, the user selects a garment of a predetermined size

before overlaying an image (2D or 3D) of the selected garment on a virtual avatar

representing the subject wearer. This method provides the user with the ability to

compare and contrast selections of garments through visual means. A fully developed

fit visualisation could illustrate the stresses and strains the material is exposed to

when the user wears the garment. However, due to technological limitations, the VFI

image presentation does not consider the nature and reaction of the fabric chosen with

the user’s individual body type and shape. Belcurves and Style.ME are the only

interfaces that focus on fit feedback through heat maps to show the areas where the

garment fits; from very tight to very loose.

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Consequently, this method of viewing garments in a virtual environment does not

offer an accurate, ‘true fit’ representation of how the selected garment would look on

the actual user. Instead, such prior systems represent the clothing as virtually ‘worn’

by a generic human form through presenting a garment’s photograph on a fashion

model’s body, rarely representing the user’s body size, shape, or other features.

Alternatively, VFIs present a photograph of the garment in isolation without the

necessary anthropometric context. Such platforms do not help users determine how a

particular garment will look when worn. The users’ resultant confusion is especially

true when the user’s proportions do not match a fashion model, nor of the 3D virtual

‘mannequin’ chosen by the clothing retailer with their chosen VFI. Such situations are

almost ubiquitous.

Fit Recommendation

The least applied method of prediction is a fit recommendation, with the application

of only (n=3) of VFIs. The fit recommendation is measured by asking the user fit-

related questions (varied across platforms), including classifications for style

preference, earlier garment purchase, and body satisfaction. Some applications

(Fits.me, True Fit) aim to combine user body data with their wardrobe data, as they

created an extensive database of garment related information, including textiles and

cloth composition: fabric, stretch, cut, and ease. These descriptions begin to consider

the complexity of personal preferences and fashion influence, including art, style,

craft, and fashion. The developer must consider such influences, along with the

technician’s specialised knowledge, to link these devious factors together. However,

to be accurate, retailers need to access the manufacturer’s garment data to implement

this approach wholeheartedly. Moreover, there are no interfaces to engage the user in

the process, as fit recommendation platforms only use text or illustration-based

descriptions. The interfaces assume text or illustration-based descriptions provide

enough detail for users to accurately assess suitability before purchase.

Discussion

4.5.1 Information Required from Users by Virtual Fit Interfaces

Anthropometric data required from users in section 4.4.1 demonstrated that existing

VFIs methods have a limited connection to measurements that garment developer’s

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need (Ahmed et al., 2019). The measurement’s limited connection created no

consistent relationship between VFI’s garment size predictions and pattern drafting

and grading methods. The existing methods in VFI aim to match well with the

difference between the estimated body dimensions and the results from the sizing

table (Daanen and Byvoet, 2011). These efforts, however, were reported to be not

enough to facilitate the process of apparel fit in mass-customisation (Sohn, Lee and

Kim, 2020). The inconsistencies in VFI methods confirmed that VFI developers

understand clothing’s fit variables by different degrees. Loker et al. (2004) research

supports these findings and shows that identically measurements are, in practice,

never identically defined. This discrepancy is further exacerbated by VFI platforms

requiring the user to specify their body shape based on many assumptions about

proportional norms, which may not reflect individual variation within a population

(Gill, 2015). However, the past research highlighted that users have limited

knowledge of their own body shape’s classification (Yu and Kim, 2020). However,

Parker et al. (2021) demonstrated that FFIT classification limit how body shape

inform pattern drafting because it excludes measurements in the critical upper-body

control zone/ the shoulders’ anchor area. Moreover, given the full range of body

proportions, shapes, and postures, measurement interactions may cause misfits that

single linear measurement comparisons cannot identify (Thelwell et al., 2020).

Therefore, the use of 3D Body Scanning’s allows body landmarks information’s

automatic extraction from shape features. The point cloud data from the 3D Body

Scanner contain all the differential geometric properties of the real body’s surface.

The use of 3D Body Scanning thus allows for theories to be investigated based on the

increased type and volume of measurements available. Measures acquired by these

devices can be used to describe, interpret, and analyse the human body dimensions for

applications in apparel sizing. Though it is currently unknown what all the shape

features from 3D Body Scanner represent in terms of garment development practice,

it further illustrates the wealth of information regarding shape and size distribution

which cannot be captured by linear measurements used in current practice.

4.5.2 Outputs Presented by Virtual Fit Interfaces

Section 4.4.2 evaluates the visual output users receive after sharing their

anthropometric and garment purchase information. The study aimed to systematically

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identify basic visual features that influence the VFI developments. However, the

findings indicate that VFI still lacks universal methods of communication as different

platforms utilised different, often contrasting techniques for size and fit presentations.

This study found that half of the VFI utilised avatar as a presentation medium, but

only three VFI used avatar in a form that the allowed user to customise facial features.

The non-realistic generic avatars represent a faster and more economical approach

that requires less computational power to produce. The detailed facial features, on the

other hand, requires a lot of computational efforts (Hauswiesner, Straka and Reitmayr,

2013). Nonetheless, De Coster et al. (2020) proved that generic avatars do not lead to

a more accurate body size estimation and that realistic avatars can lead to higher

subjective measures of positive social interaction. However, it was also noted that the

quality of the face was less important than its mere presence. Other studies have

indicated that while the appearance of an avatar is significant (e.g. people spend much

time customising avatars) (Plotkina and Saurel, 2019), the effects of avatar realism is

highly related to users’ personalities (Idrees, Vignali and Gill, 2020), body size (St-

Onge et al., 2017), and emotions (LaBat and DeLong, 1990). Therefore, this study

recommends that VFI researchers and developers should investigate through 3D Body

Scanning how different formats of avatar production affect users’ reactions to viewing

their own avatar.

Similar attention should be upheld when looking at the findings of clothing

visualisation methods. Since size and fit are often comparative experiences, VFIs

must deliver meaningful comparisons, as e-Commerce users are motivated by

efficient fashion idea fulfilment (Parker and Wenyu, 2019). The development of

clothing visualisation methods requires new approaches that recognise fit in terms of

body and garment relationships and interactions. Miell et al. (2018), emphasises the

need for the synthesis of technical garment interfaces, user fit, and sizing preferences

into a single understandable interface. Nonetheless, this study found that half of VFIs

offer 3D avatars of the user based on a limited series of measurements or other critical

criteria, including bra size and age. However, adjusted avatars will not offer the level

of detail of a scanned avatar (Istook, 2008; Xia et al., 2019). Instead of VFIs

modelling their avatars on body approximations, 3D Body Scanning can display the

user’s actual body from any angle and distance, besides visualising the worn

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garment’s stress and strains values through colour representations. This inability of

users to determine own body measurements has been observed repeatedly; for

example, Ashdown and DeLong (1995) proved, both perception and acceptance of fit

variations can vary in different areas of the body. 3D Body Scanning allows for more

linearity between the individual, the product development process, and the final

garment and fit assessment.


Section 4.4.3 applied the conceptual model suggested by Gill (2015) to current VFIs.

A comparison between systems for Virtual Fit analysis also demonstrated that

practitioners understand clothing’s fit variables based on different criteria. Kim and

Forsythe (2008) argue VFI may help to position user in a more centric manner and

enhance the user perception of size and fit in an online environment. This study

emphasises the need for research to develop accurate, up-to-date size specifications,

as current practices fragment the population by dividing standard bodies into

numerous disparaged categories. Brownbridge et al. (2018), however, emphasises that

few users understand the dynamics of sizing and fit responsible for creating the

modern apparel sizing systems. Thus, the lack of sizing standards - combined with

unreliable labelling - causes apparel fit problems. Apparel fit problems, in turn, causes

an excessive rate of apparel returns, lost sales, brand dissatisfaction, intense users

frustration, and may lead to the rejection of future use of VFI.

Shin and Baytar (2014), highlighted that most of the VFIs do not consider the

idiosyncratic nature of fit and complexity of personal preferences, which may relate to

body shape, body satisfaction, and current fashion trends. Ashdown and O’Connell

(2006) found that the user’s subjective measure did not correspond with experienced

professionals’ garment fit evaluation because the manual measurement process often

compresses the subject’s body. The amount of tension applied depends on the

subjective assessment of the measurer, and therefore may be difficult to standardise.

As a result, some of the most utilised data in recommending ‘correct’ garment fit

embodies limitations in users’ skills to judge and assess fit dimensions appropriately.

Song et al. (2020) found that users’ do not acknowledge fit criteria such as the

verticality of the side seam, hem ease, ease at neckline, bust point position, or

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grainline. The garment developers fit assessment skills were consistently more

detailed and focused and thus can provide a model for training novice designers.

Nevertheless, the skills deficiencies lead to users repeatedly purchasing ‘incorrect’

sizes rather than achieving ‘perfect’ fit, as VFIs promise. 3D Body Scanning,

therefore, has an opportunity to enhance fit recommendations due to the reliable non-

ambiguity of the technology; if the research can quantify style and fit (Scott, Gill and

McDonald, 2019). Lastly, VFIs do not offer a ‘true form fit’ experience for the user or

a means for them to customise their digital profile. Neither do any of the ten VFI

platforms provide a method of viewing a virtual representation of a selected garment

on a specific user. The user is, therefore, incapable of accounting for realistic garment

material deformation that would occur as the garment is worn (Sohn, Lee and Kim,

2020). Moreover, none of the methods presented above allows the user to create the

specifications for a custom-tailored garment (Harwood, Gill and Gill, 2020). VFIs,

instead, focus on a selection of existing garment sizes - from a category - for viewing

on a standard, non-user specific avatar. Without using the user’s actual body, the VFI

visualisation may unrealistically represent the fit experience.

Conclusions

This study explores how VFI developers can improve VFI state-of-the-art as a

garment size and fit prediction tool and help e-Commerce users in their clothing

evaluation. By examining the existing VFI approaches, this study aimed to advance

the understanding of current problems and possible solutions related to erroneous fit

and body size identification judgments. The study results demonstrated that the

existing VFI approaches are insufficient to address size and fit concerns, and to

reduce the e-Commerce apparel return rate. The VFI integration with 3D Body

Scanning can improve the existing size and fit methods in e-Commerce.

This study offers the following guidance for VFI developers in improving technology

relevance and precision in garment selection, purchase, and retention in e-Commerce

websites. The data required from users by VFIs are currently based on a plethora of

abstract metrics that are not grounded in garment development theories or ISO

standards. VFI does not offer information relating to garment development

approaches or how users classify their bodies or how they experience a garment’s size

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and fit. Hence, 3D Body Scanning integration with VFI can help developers provide

robust and transparent evaluation metrics with evidence that garment developers can

easily interpret. This, however, is fundamental for paving the way for technology

acceptance in e-Commerce. Moreover, technology platform integration can support

the creation of a standardised approach to data type and collection processes (to

ensure consistency), with information about the associated data required to support

later analysis and application. This research shows that VFIs does not replicate the

user’s shopping experience beyond existing fit practices - which are usually

subjective – and evolve towards virtual methods. Lastly, to develop new methods of

appreciating garment fit, representative of the user’s garment try-on experience, new

fit prediction models are needed. 3D Body Scanning can offer resources for these

models by visualising and quantifying fit, helping practitioners engage with the users

for whom they are making clothing.

The in-depth analyses of the existing VFIs made it possible to summarise the research

challenges that should be taken on by VFI developers, researchers and fashion

retailers. This analysis refines 3D Body Scanning relevance by highlighting strategic

research directions that can simultaneously tackle multiple interconnected knowledge

gaps and support the VFI developer’s agenda. VFI developers should include 3D

Body Scanning in their platforms, identifying anthropometric measurements from

scans and size and fit protocol to increases the reliability of their size and fit

predictions. As such, the user’s ability to correctly assess a garment’s size and fit shall

be increased, along with their website experience. Garment developers must develop

new methods for creating patterns from anthropometric analysis as current VFIs do

not associate with body shape and proportions, enabling automated and precise

systems of mass-customisation and virtual tailoring. Fashion retailers can, in turn,

offer better garment suggestions due to better information about the garments they are

selling. 3D Body Scanning manufacturers must collaborate with VFI developers and

pattern cutters to enable their technology to influence the design of better fitting

garments with more explicit resources for user size and fit selection. 3D Body

Scanning can, thus, enable higher e-Commerce profitability through reduced costs,

lower return rates, and more efficient supply-chain management. The findings

summarised in this study make substantial contributions to the VFI state-of-the-art.

The results also help to broaden fashion retailers and garment developers

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understanding of the application fields of the 3D Body Scanning on VFI, as well as

assist further studies intended to meet the up-to-date research challenges presented.

As only a few English language VFIs exist at the time of the study, future research

has tantalising possibilities to appraise these study insights as the second generation of

VFIs become available. Such appraisal shall contextualise future innovations and

direct VFI’s development within the service industry. Future research should focus on

3D Body Scanning from a service perspective for designers to produce VFIs for

fashion users. To design such services, research must investigate a user perspective

and expert thoughts on barriers to and incentives for 3D Body Scanning deployment.

Moreover, the sampling method was purposive; accordingly, data were collected from

a limited sample of interfaces. Therefore, the findings cannot be generalised to other

size and fit mobile apps that may not have similar cultural and technical

characteristics.

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CHAPTER 5

STAKEHOLDERS IN 3D BODY SCANNING: HOW TO

CONNECT THE ISOLATED SILOS OF KNOWLEDGE

FOR THE FASHION MASS-CUSTOMISATION

Introduction

The recent proliferation of 3D Body Scanning devices, and apps, has resulted in

fashion retailers exploring new size and fit approaches to deliver mass-customised

apparel (Ashdown, 2020). In 2020, 72% of UK customers found problems buying

well-fitting clothing, and 43% required a free return option to purchase online (Mintel

Group Ltd., 2020). Nevertheless, the free return costs the fashion industry £9bn a year

(Just Style, 2020) COVID-19 pandemic’s continuing influence forces retail to use

more data-driven approaches (Craven et al., 2020). Retailers could use 3D Body

Scanning’s data to characterises customers according to body size and shape, to

higher precision and complexity than manual methods (Thelwell et al., 2020).

However, fashion retailers struggle to connect all the size and shape features captured

by 3D Body Scanners with garment development practice (Gill, 2015).

3D Body Scanning developers attempt to support fashion retail through sophisticated

devices, principles, and frameworks that describe, interpret and analyse the human

body (Gupta, 2020). Nevertheless, most retailers misunderstand these technologies

(Lewis and Loker, 2014) because developers’ ignores established pattern drafting

principles and rarely integrate with the fashion industry’s pre-existing knowledge

(Harwood, Gill and Gill, 2020). Instead, developers focus on overcoming engineering

problems such as ‘parameterisation’, ‘optimisation’ and ‘calibration’ without

tackling the practical aspects of applying the scan-data in pattern blocks (McKinney et

al., 2017). Nevertheless, the fashion industry knowledge – found on decades-old

tailoring practices – bears little relationship to 3D Body Scanning’s methods;

hampering the technology’s translation into usable-product outcomes (Park, Kim and

Sohn, 2011). 3D Body Scanning’s manufacturers, academics, and software developers

are, therefore, creating an advanced measuring tool that the fashion retail cannot use.

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3D Body Scanning promises to push mass-customisation - and size and fit

improvements - in garment development since the LASS scanner’s invention (Jones et

al., 1989). The mass-customisation promise has not, however, come to fruition. One

reason for this challenge is that relationships between stakeholders - and the processes

they influence - are often evaluated without a robust conceptual framework that

connects underlying technology principles (Pantano and Gandini, 2018). Gill et al.

(2016) highlight that stakeholders’ processes are inextricably linked and require

simultaneous attention. An integrated framework of stakeholders’ perspectives is thus

a requirement to fully understand and improve the capacity to harness technology

relative advantage (Arnold and Narang Luthra, 2000). Moreover, enabling

compatibility between approaches can enhance technology effectiveness in delivering

customer-driven outcomes (Childs et al., 2020). Research, however, documents little

about the prevalence of customer trials and how retailers use the available information

from 3D Body Scanning to answer product development questions (Gill, 2015). The

documented customer trial sessions focused on testing customer acceptability of

clothing fit variations after the experience of being scanned. However, the

development of more general guidelines to specify fit parameters for all garments is

yet to be formed (Ashdown and DeLong, 1995). As a result, the existing innovation

efforts are misaligned with pattern-drafting systems (Reid et al., 2020). TC2’s

scanners demonstrate this by pre-programming the ‘wrong’ waist location in their 3D

Body Scanners from the waist location used in pattern construction (Gill et al., 2014).

To incorporate 3D Body Scanning within retail, stakeholders must connect their

methods to ensure scan-data integrity. 3D Body Scanners may, otherwise, produce

conflicting results, undermining retail acceptance and customer applications (Peng,

Sweeney and Delamore, 2012).

3D Body Scanning’s low observable impact and high complexity push retailers to

focus more on adopting size and fit tools such as Virtual Fit Interfaces (VFI) (Plotkina

and Saurel, 2019). VFI demand from customers the ability to match one’s body image

with visual bodily cues or information to build a ‘virtual twin’ in e-Commerce

(Januszkiewicz et al., 2017). However, research indicates that basing VFI on the

customer’s self-perception is inaccurate (De Coster et al., 2020) because people often

have a distorted perception of self (Linkenauger et al., 2017). These strategies also

result in the loss of geometric detail (Heymsfield et al., 2018). Nevertheless, 3D

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avatars can help customers make a correct purchase decision and increase positive

responses to retailers offerings (Shim and Lee, 2011). Research must, therefore,

investigate more sophisticated methods that capture the human body’s complex

curvatures (Thelwell et al., 2020) and improve the current understanding of

anthropometry in garment development. 3D Body Scanning is the most promising

technology.

This study aims to understand how 3D Body Scanning manufacturers, academics, and

software developers can increase technology usage within fashion retail to benefit

from mass-customisation. To address this aim, this study undertook the following

research objectives:

1. This study evaluated economic and social benefits to understand 3D Body

Scanning’s relative advantage over traditional anthropometric methods.

2. This study assessed design principles to understand 3D Body Scanning’s

compatibility with fashion retail and manufacturing systems.

3. This study examined the usability sessions to understand how customers can

trial 3D Body Scanning before implementing it in their systems.

4. This study examined communication channels to understand the degree to

which stakeholders can observe 3D Body Scanning’s benefits.

5. This study evaluated deployment problems to understand 3D Body Scanning’s

complexity in incorporating the technology within fashion retail and

manufacturing systems. This study conceptualises technology usage as tied to consequent individual and

performance outcomes (Chin and Marcolin, 2001). This study examined usage

through 30 semi-structured interviews with 39 stakeholders from the 3D Body

Scanning industry and the diffusion of innovation (DOI) framework, as articulated by

Rogers (2003). To date, IT research has introduced the general themes and theories,

including Davis (1989), Azjen and Fishbein’s (1975), Moore and Benbasat’s (1991)

and Cooper & Zmud’s (1990). Although many of these IT frameworks differ in their

theoretical structures, constructs, and relationships posited, they all address the use of

technology. However, only DOI incorporates the effects of institutions, knowledge

barriers, and social bandwagons, which push beyond simple dichotomous measures

such as use or not use or amount of usage (e.g., frequency or time spent) (Prescott,

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1995). Thus, DOI extends past IT frameworks into concepts, constructs, and issues

that specifically relate to technological usage in a rapidly changing and complex

world (Rogers, 2002).


5.2.1 Diffusion of Innovation (DOI) theory

DOI theory has been applied across fields to analyse and explain the process by which

a new technology spreads through a population (Rogers, 2003). DOI theory argues

that new technologies are more readily adopted when they: 1) are perceived to have

advantages over the status quo (relative advantage), 2) are compatible with existing

values (compatibility), 3) can be tried out without fully committing to them

(trialability), 4) the customer can observe that others have successfully adopted them

(observability), and 5) be simple to acquire or use (low complexity) (Masuda et al.,

2018).

Relative Advantage

Relative advantage is the degree to which the customer perceives innovation as better

than the idea it supersedes (Rogers, 2003). Relative advantage is often expressed in

economic profitability or in convening social prestige. The innovation’s nature

determines adoption’s rate and pattern (Linton, 2018). 3D Body Scanning operates in

the traditional closed model, forcing stakeholders to develop, build, market, distribute,

and support technology independently. This closed model expects firms to be strongly

self-reliant, encouraging internal research and development (van de Vrande et al.,

2009). While this insula model is historically beneficial, the innovation landscape has

changed (Masucci, Brusoni and Cennamo, 2020). Dispersed knowledge makes

internal innovation too expensive. Firms must combine external and internal ideas,

and paths, to the market to achieve a relative advantage (Padma and Wagenseil,

2018).

Compatibility

Compatibility is the degree to which the customer perceives innovation as consistent

with their existing values, experiences, and needs. Innovation can be compatible or

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incompatible (i) with sociocultural beliefs, (ii) with previously introduced ideas, or

(iii) with customer needs for the innovation (Rogers, 2003). Moreover, Tornatzky and

Klein (1982) argue that there are two types of compatibility: cognitive compatibility,

referring to what people feel or think about innovation, and operational compatibility,

relating to compatibility with people’s activities (Sina and Wu, 2019). Hence,

identifying appropriate design interventions that can change values to be more

compatible with customers’ needs can positively impact the diffusion curve (Curtin,

2019).

Trialability

Trialability is the degree to which the customer may experience an innovation; on a

limited basis (Rogers, 2003). Trialability is expressed by the new ideas that the

customer can try before purchase, giving an innovation meaning. Innovation is

defined as the commercialisation of an enabling technology based on customer

insights to address unmet needs and industry awareness to identify the respective

enabling technology. Von Hippel (2005) was among the first to regard customers as

active designers that may develop their innovations; perspective technology

developers can imitate. Therefore, to increase diffusion speed, firms must ensure the

emerging design is relevant to customer’s preferences, needs, and market trends (Sick

et al., 2019).

Observability

Observability is the degree to which the customer can see an innovation’s effect

(Rogers, 2003). Observability is expressed in how easily the customer observes an

innovation in a way they may communicate to others (Gilliam and Rockwell, 2018).

Stakeholders must become intelligent customers of what is around externally, be

active in the field, up to date, current with whom is who, which ideas will diffuse

successfully, and which ones will be temporarily popular (Von Hippel, 2001). Firms

must, therefore, frame messages in a way that convenience customer to make the time

investment and engage in something new to increase diffusion speed (Wu, Kim and

Koo, 2015).

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Low Complexity

Complexity is the degree to which an innovation is perceived as relatively difficult to

understand and use (Rogers, 2003). Any new idea may exist on the simplicity-

complexity continuum (Rogers, 2002). Defining 3D Body Scanning’s role will,

however, help retailers to understand better detailed technical information that may be

difficult for customers to comprehend (Otieno, Harrow and Lea‐Greenwood, 2005).

Defining and sharing processes is, therefore, essential to increase the diffusion speed

(Perdana, Ciptono and Setiawan, 2019). Future requirements must have a well-

articulated plan and process for informing stakeholders about how their input and

knowledge will be used (de Jong et al., 2015).



Interviews with stakeholders have been proposed as a suitable strategy to tackle

wicked problems because, in a multidisciplinary field, stakeholders have different

views on the problem and insights to contribute to its solution (van Woezik et al.,

2016). This study recruited stakeholders through purposive sampling to analyse 3D

Body Scanning’s trends, motives, and management challenges. Purposive sampling

involves the identification and selection of individuals that are proficient and well-

informed with a phenomenon of interest and have the ability to communicate

experiences and opinions in an articulate, expressive, and reflective manner (Etikan,

2016). The researcher searched the Internet for evidence of activity in 3D Body

Scanning through LinkedIn Groups. Estimation under purposive sampling was proven

reliable for qualitative research (Guarte and Barrios, 2006) and allowed targeting

specific individuals (Tongco, 2007). The researcher then sampled company

employees responsible for innovation, including business owners, managers, and

research and development (R&D) managers and executives. First, the researcher

asked respondents if they were involved in the research, dissemination, or

implementation of technology in the apparel sector. Second, to reliably identify

trends, respondents had to work in a job related to 3D Body Scanning for at least five

years. The long-term tenure ensured the respondent could adequately judge if and

how innovation processes develop.

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This study sampled 39 respondents from 30 organisations, with professions

comprising: Founder/Co-founder (n=16, 42%), Executive Level (n=11, 28%),

Developer/Engineer (n=7, 15%), and Research (n=5, 10%), all involved in digital

innovation for body-worn products. According to Boddy (2016, p. 429), a qualitative

sample size of 30 in-depth interviews is considered significant and achieves data

saturation in qualitative research. The saturation was also determined at 30 interviews

because the researcher could not find additional themes and found a high topic

repeatability level (Robinson, 2014).

Table 22 shows the respondent distribution across size class and industry. The sample

was stratified across two size classes (10-99 employees and 100-499 employees), as

van de Vrande et al. (2009) use. The sample size also helps to achieve a representative

number for each industry sector, being of at least three key actors (Pelz, 1983;

Nowicki, Koehler and Charles, 2020). The diversity of expertise generates more

innovative ideas and thus reduces bias towards homogeneous views. The study

preserved the respondent’s anonymity by removing identifiable data and keeping

commercially sensitive material confidential. All procedures were approved by The

University of Manchester Research Ethics Committee, reference number 2018-1811-

5145. Table 22 Distribution of sample by industry sector. Source: author’s own.

Industry Sector Sample Size Domain Sample Size Fashion N = 13 Omni-channel N = 10 e-Commerce N = 1 Apparel PLM N = 2 Software N = 13 CAD systems N = 7 Virtual Fit N = 6 Hardware N = 8 Body Scanners N = 6 Foot Scanners N = 2 Research N = 5 Academic research N = 2 Industry research N = 3 TOTAL 39


This study conducted 30 interviews with 39 individuals – one interview per

organisation – lasting between 55 and 85 minutes to gain sufficient depth (Parker,

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2021). This study undertook interviews face-to-face (n=9) and online (n=21) through

the respondent’s firm’s internal communication software, e.g., Skype. The researcher

organised interviews in a semi-structured format, with open-ended questions, which

Table 23 defines. The semi-structured format allows the research team to learn about

the stakeholders’ primary areas of interest and permit them to articulate their

responses (Reed et al., 2009). Following the provision of participant information - and

an opportunity to ask questions - the researcher asked the stakeholders to provide

verbal consent for participation.

Existing research is yet to establish questions for 3D Body Scanning practitioners.

This study, therefore, followed Wilson’s (2014) guidelines for designing semi-

structured interviews. This study recorded the respondent’s answers via a Dictaphone

voice recorder before the lead author transcribed the interviews verbatim. Table 23 Interview questions. Source: author’s own.

Attribute Participant Question Information to Collect Opening Statement

Can you introduce yourself and the firm you work for, and what is your role in the organisation?

The general firm overview, management responsibilities, firm size & market size

Relative Advantage

With regard to your firm experience, could you outline what the success stories are? And what do you feel are the areas that still need improvement?

Firm perspectives and goals, financial advantages and planned R&D investments, technology portfolios

Compatibility In your words, what are the existing problems we should be trying to solve with the 3D Body Scanning industry?

Skills and knowledge lag, task division and discords, mapping end-to-end processes.

Trialability What, in your opinion, is the best way to communicate the size and fit for the customer in e-Commerce? How, in your opinion, should the 3D Body Scanning be integrated with the retail in-store experience?

Usability and ease of use, skills development, technology framing, design trends & directions.

Observability Why do you think 3D Body Scanning so far has very limited observability or even (in some cases) failed to diffuse?

Peers influence, External influence, Opinion leadership: peer, expert

Low Complexity

Have you perceived any barriers & complexities to implement innovation, and if so, could you describe them?

Standardisation methodology, internal & external support

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5.3.3 Data Analysis

The thematic analysis followed Braun and Clarke’s (2006) six stages to employ a

replicable and transparent methodology. The six stages involved are

a. Data familiarisation,

b. codebook development with initial coding,

c. identifying themes in data,

d. reviewing themes,

e. defining and naming themes, and

f. producing the result. The lead researcher conducted coding using NVivo 12 (QSR, 2019), qualitative data

analysis software. For consistency and reliability, the lead researcher first read all

open-ended answers and identified preliminary categories. Next, codes about similar

themes were grouped to generate categories based on the conceptual framework:

diffusion of innovation (Rogers, 2003). Finally, the researcher compared all

classifications with the rest of the supervisory research team, with different opinions

discussed and resolved. For ease of presentation, the thematic analysis’s key themes

have been averaged based on the frequency of occurrence; following Tuckett’s (2005)

data analysis guide. The complete coding tree is provided in Appendix C.

The last stage of the analysis was to apply the rich picture framework to explore

topics’ interconnections and structure the discussion around expertise and knowledge

silos (Dearden and Wright, 1997). This study chose the rich picture because it is

designed to support the collaborative development of research agendas through

interviews between relatively small groups of participants, and it has been used

extensively within the diffusion of innovation studies (Hansen and Kautz, 2004;

Sinfield, Sheth and Kotian, 2020). The rich picture in

Figure 19 identifies stakeholders, their interest, interrelationships, and conflicts

(Avison, Golder and Shah, 1992). Figure 20 also presents a rich picture to compose

the stakeholder requirements in development and reasoning about design processes

(Checkland, 2000); as used in previous research (Parker, May and Mitchell, 2010).

Comparing the work-context rich picture with the desirable design-context rich

picture provides a way of checking whether appropriate stakeholders represent each

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stage of the design process to help move industry from silos to systems (Monk and

Howard, 1998).

Results

5.4.1 Relative Advantage

This study evaluated economic and social benefits to understand 3D Body Scanning’s

relative advantage over traditional anthropometric methods. The economic benefits

were expressed in stakeholder’s growth strategies and social benefits in gaining

leadership position. Table 24 compares the frequency of different industry sectors

towards relative advantage. Table 24: Perceived trends in relative advantage between industries. Source: author’s own.

Relative Advantage:

Fashion (n=13)

Software (n=13)

Hardware (n=8)

Research (n=5)

Growth Strategy 0.85 0.77 2.38 0.40 Leadership 0.46 0.69 0.25 0.20

Growth

In the hardware sector, technology exploitation defines growth; buying or using

intellectual property, such as (n=6, 75%) patent or (n=2, 25%) trademark to engage in

outward I.P. licensing. Stakeholder 15, industry researcher, confirms: “Companies do

not collaborate because manufacturers particularly try to sell you the software, the

widget, or scanner.” This result shows closed silos in facilitating the knowledge from

scan manufacturers into the appropriate retail dissemination methods.

Software stakeholders, however, favoured growth on technology exploration;

venturing activities, such as (n=5, 42%) R&D outsourcing, (n=2, 18%) external

networking, and (n=3, 25%) participation with retailers to better understand customer

behaviour. For example, “My platform is meant to integrate with other CAD systems.

I have not reinvented the wheel.” stakeholder 10, software, founder. This result

indicates that software developers are more likely to engage in open innovation.

However, due to systems disparity, the digital tools developers built were typically

disconnected from the enterprise I.T. infrastructure; therefore, not designed to

integrate into the existing garment development workflows.

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Contractual agreements - or mutually beneficial non-contractual alliances - created

these means of collaboration. Outsourced firms were, interestingly, often local. Their

reliance on local partnerships made communication more accessible - speeding up the

commercialisation process. While all the developments came from technology firms –

hardware and dedicated software firms – technological growth was only favoured by

(n=5, 38%) fashion stakeholders. Fashion stakeholders are less time-pressure and

have lower emotional involvement for growth. For example, “Fit is wonderfully

complex, and I do not think that just 3D scan will solve the whole

problem” stakeholder 7, apparel, developer. The notion that fashion stakeholders play

a minor role in 3D Body Scanning development is thus supported.

Leadership

The leadership role is prominent in (n=3, 38%) apparel and (n=5, 24%) software and

within firms that must abide by a formal hierarchical ranking structure. These

stakeholder groups perceive establishing a leadership position as essential to exploit

3D Body Scanning’s economic benefits and influence standards creation. All the

stakeholders from fashion groups exemplify this view; a coalition to build a set of

standards was underpowered to steer a clear vision. The coalition, therefore, relies on

their instincts. For example, stakeholder 18 from the apparel group noted, “We tend to

be at the forefront of the ladder (…). We see standards developments, but at the same

time, we cannot wait for standards to form. We kind of push ahead and hope we make

good decisions that are aligning with whatever standards get created.” This attitude

means that leadership motives do not necessarily dictate innovation objectives.

However, (n=7, 20%) of stakeholders spoke against the leadership role and

unnecessary hype. This group argued that industry overuses the ‘3D’ prefix, and

grand promises that the technology cannot - yet - deliver dominates 3D Body

Scanning language. For example, “We have seen much overpromising in the market,

and many people overpromised that they have solved the problem, but does little” -

stakeholder 1, scan developer and founder. This finding implies that firms have

different objectives and do not engage in open innovation as potential partners cannot

meet the expectations or deliver the required quality of a product or a service.

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5.4.2 Compatibility

This study evaluated design principles to understand 3D Body Scanning’s

compatibility with fashion retail and manufacturing systems. Table 25 compares the

frequency of different industry sectors towards compatibility. Table 25 Perceived trends about compatibility between industries. Source: author’s own.

Compatibility:

Fashion (n=13)

Software (n=13)

Hardware (n=8)

Research (n=5)

Design 2.00 1.54 2.25 0.80

Design

3D Body Scanning’s design is heterogeneous, partly described by the firm’s insula

characteristics. Exploring technology heterogeneity, this study found the location,

device size, and capabilities are the key differences related to 3D Body Scanner’s

deployment. For example, “I was in Australia, and there was a 3D scanning sitting in

the mall, and nobody went there. I was very curious, but even knowing this

technology, I hesitated because I could not feel comfortable inside closed-door and

undress in the shopping mall.” stakeholder 24, software, executive position. This

quote highlights that the 3D Body Scanning design must be considered jointly with

privacy concerns, staff presentation skills and scan location.

Stakeholders listed a lack of standardisation (n=21, 53%) or slow developments in

machine learning algorithms (n=4, 10%) as the significant design barriers.

Stakeholders also listed technical factors such as (n=4, 10%) position, (n=7, 18%)

speed, (n=10, 26%) reliability, and (n=14, 36%) the amount of available data as key

3D Body Scanning capabilities. In terms of location and size, (n=9, 23%) prefer

scanning booths to be located in retail while (n=20, 51%) prefer solutions with

scanning from home, e.g., via smartphone apps. This finding suggests a strong focus

on technology efficiency, with skills that still replicate the same old product

development practices. For example: “If you put the scanner in the middle of the

store, this is not a comfortable or normal shopping experience. Technology needs to

compliment on what retailers are comfortable with” stakeholder 16, apparel, founder.

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Overall, this study found many barriers to compatibility in design are related to

corporate organisation and culture. The interviews found that for (n=7, 90%) of

hardware, the primary reason for this direction is to remain competitive with the

current retail model, which prioritises multiple sizes buys and free returns. Moreover,

(n=5, 63%) of the hardware and (n=11, 85%) of software group members agreed that

‘generic’ requirements from fashion clients limit their growth potential. This finding

suggests that stakeholders may not be aware of potential barriers because they cannot

yet compare them with best practices and successful outcomes.

5.4.3 Trialability

This study examined trialability from usability sessions to understand the degree to

which stakeholders can trial 3D Body Scanning before implementing it in their

systems. Table 26 compares the frequency of different industry sectors towards

trialability. Table 26 Perceived trends in trialability between industries. Source: author’s own.

Trialability

Fashion (n=13)

Software (n=13)

Hardware (n=8)

Research (n=5)

Usability 0.69 1.38 0.88 1.80

Usability Sessions

Customer trials of 3D Body Scanning are - often - run at corporation Head Quarters

and other internal firm settings. A low level of openness characterised these sessions

in methods, samples, and recruitment practices. While most stakeholders (n=28, 72%)

try to involve their customers in market research, they limit their involvement in

monitoring their interaction with the scanner or general product modifications. Only

(n=1, 5%) of R&D participation included full customer involvement. Hence, (n=5,

100%) the research group’s stakeholders agreed, trialability sessions have poor

integration with social interactions, and those technology providers isolated firms

from broader user communities. For example, “This sector is going the same way as

the tech companies, i.e., Dell computer powered by Intel with NVidia graphics cards.

For shoes, it could be Stella McCartney shoes, scanned by Size Stream, fitted by Else-

Corp and available on Rodeo Drive with Amazon, Sarenza, or Zappos service

provider to purchase” - stakeholder 13, scan developer and co-founder. This quote

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demonstrates that organisational cross-disciplinary collaboration is essential for

digital innovation; thus, siloed expertise and processes slow capacity for diffusion.

According to (n=13, 33%) of the stakeholders, customers accept innovation if well-

known apparel brands support market research and usability trials. Interviews show

that fashion brand engagement creates higher process optimism, and customers

believe their participation will solve their sizing issues. Our findings imply that

customers will spend more time - and money - if fashion brands in open-manner

support and articulate the scan purpose. For example: “It was shocking for me that

people were coming and willing up because their immediate impression was, you guys

are going to make this work, and we [are]§ going to be able to shop better with you”

stakeholder 31, apparel, executive position. Nevertheless, scan manufacturers

continue to be cited using more traditional, less collaborative methods, despite the

diversity of expertise required in the process. Yet, none of the stakeholders mentioned

a list of skills needed to present and interpret technology in the retail setting. The lack

of a skills list reveals that space-competitiveness may increase the ability to create

high-end technology design but reduce the ability to exploit this design with maximal

efficiency.

Access to 3D Body Scanning remains limited and geographically sparse because of

high technology cost and skills requirements - dictating long-distance travel and

limiting market penetration. However, a larger technological distance between

partners might lead to adverse adoption due to information asymmetries. For

example: “If I want to do anything around body scanning, I have to send my

customers to London to get a scan and get the set of right information’s. The

measurements are different from every manufacture” stakeholder 11, software,

founder.

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5.4.4 Observability

This study examined channels of communication to understand the degree to which

stakeholders can observe 3D Body Scanning’s benefits over traditional anthropometry

methods. Table 27 compares the frequency of different industry sectors towards

observability. Table 27 Perceived trends in observability between industries. Source: author’s own.

Observability

Fashion (n=13)

Software (n=13)

Hardware (n=8)

Research (n=5)

Channels 1.46 1.38 3.13 1.40

Channels of Communication

The proliferated 3D Body Scanning features set different channels of communication

that often reflected the stakeholders’ taste. This study found a significant

disagreement in data presentation. Based on our sample data, avatar creation is chosen

by (n=6, 15%) of stakeholders. In contrast, others prefer methods, including (n=10,

26%) size recommendation, or (n=5, 12%) preference tracking. For example, “We

started working with body scans as a source of anthropometric information, and we

realised that apart from measurements that we are extracting; the 3D object that we

get from the scanner was useless.” - stakeholders 14, industry researcher. This study

result shows that different knowledge bases and the lack of prior cooperation to

overcome information asymmetries may impact anthropometric measurements’

accuracy.

The primary reason for rejecting avatar is related to technological limitations, making

avatar look cartoonish or fake, the lack of research on a psychological dimension, and

customers being distracted from purchase activities. For example, “I essentially said

unless it is real; unless it looks like you; unless you can see the waves in your hair

and wrinkles in your face and tone of the skin; unless all of that is there – it can give

this super weird real authentic feeling as we are looking at our self. Because if it is

kind of like you, but is not you, it is distrustful” - stakeholder 19, scan developer and

founder. However, research stakeholders (n=2, 40%) suggested that idealistic images

may overwhelm customers. Customers are, therefore, likely to disengage from

activities that portray them in an unflattering light. For example, “The Mark and

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Spencer’s women advert, where they did a survey and decided that the average

women were now a size 16, so they had the naked size 16 women shouting: I am

normal. The idea was to try to give a more realistic view to body scanning, but it

backfired because the public did not respond to this advert well” - stakeholder 5,

industry researcher. Moreover, customers - especially older or with fit issues - may

not want to hear objective comments. For example, the outfit is designed for young

body shapes, as (n=5, 13%) of stakeholders observe. Nevertheless, different customer

markets facilitate access to different types of technologies, and it is important to

disentangle them to estimate their individual impact. Scan manufacturers, therefore,

need to think about the type of customer they are targeting and craft technology in a

more user-centred way.

5.4.5 Low Complexity

This study evaluated deployment problems to understand 3D Body Scanning’s

complexity in incorporating the technology within fashion retail and manufacturing

systems. Table 28 compares the frequency of different industry sectors complexity. Table 28 Perceived trends in complexity between industries. Source: author’s own.

Complexity:

Fashion (n=13)

Software (n=13)

Hardware (n=8)

Research (n=5)

Methodology 1.00 0.92 1.88 0.60 Privacy 1.31 0.31 1.38 1.20

Methodology

The methodology relating to space (or platform) and methods should help the fashion

group with the digital transition. The fashion stakeholders (n=5, 39%) complained

that developers create platforms to digitise and optimise engineering workflows and,

therefore, neglect the design as a creative and artistic medium. Developers (n=5,

24%), however, said ‘scaling up’, with existing knowledge, could help achieve a

significant reduction in garment returns. The idea of scaling-up triggered a trend in

creating new software applications, as (n=18, 46%) of all stakeholders confirmed to

develop - or are developing - personalised size and fit tools, protocols, and

methodologies. For example, “There is a gap between these two [technology &

retail], that is why we are developing our own stuff, and at the moment we got

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programmer in house, and so we try to get better and better” stakeholder 22, apparel,

founder. However, the results on proliferation in research views leave an important

gap. First, each information system is a custom design with few consistent patterns to

follow and even less experience from previous efforts. Second, it may confuse

customers of why they need distinct concepts for the same purpose.

Regarding methods, none of the apparel stakeholders could justify what body

dimensions designers require from the scanner or how it can influence current gaps in

pattern drafting. Software (n=5, 36%) and apparel (n=3, 24%) stakeholders confirmed

that while designing their systems and following ISO guidelines, they had problems

translating standards into something tangible for product development. 3D Body

Scanning databases might often need reanalysis because fashion brands work with

different measures or after acquiring additional datasets. Thus, this study found

significant problems with data reusability; new resource instances cannot easily be

linked to existing ones. The complexity in methodology becomes even more co-

founded when software includes metadata. Nevertheless, only (n=10, 26%)

stakeholders acknowledged metadata in their software. The major components of the

metadata listed were style, fit preferences, and format file specificity. For

example, “Even if you have a perfect measurement of the human body, there is an

artistic dimension. The intended fit between these two things makes fashion and style

so wonderfully interesting. It is somehow reflective to look back into the fashion and

say, OK, I going to respond to this trend, and this is fascinating because if you try to

build a machine learning model on top of that, there is this incredibly human

element” - stakeholder 7, apparel and developer. This implies that providing retail

with access to 3D Body Scanning as such is not sufficient; firms need to further

develop connecting portfolios that are more oriented towards services and end-user

outcomes.

Privacy

The topic of privacy involved scan storage and data access. While (n=10, 28%)

agreed that the scan should belong to the customer, (n=9, 23%) implied it should

belong to technology vendors, who could act as a middleman between apparel and

customer. Similarly, (n=8, 62%) of apparel stakeholders claimed the data should stay

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within their firms for further use. However, researchers (n=3, 60%) place privacy

education as a critical topic and believed that ownership should be multi-pronged with

strong privacy protections and firms’ accountability. This way, customers could

decide who can interact with their artefacts. Fashion retailers (n=3, 23%) similarly

suggested that customers would place similar meaning in digital data as in credit

cards, stipulating multi-level ownership. For example, “If we look at the medical

records as a guide, then the original scans would be the property of the scanning

company. The customer would be allowed to inspect, review, and receive a copy of

their scans. This will probably be a third party and not the brands themselves”

stakeholder 9, scan developer, co-founder. This study found that the existing measures

for data protection rely on consent and de-identification of scan rather than on

assuring its appropriate use. The significant limitations for progress were lack of

government actions and lack of policies to form and govern 3D data use. The privacy

dilemma of each group is interrelated and mutually reinforcing, with each justifying

their lack of priority for privacy based on the others not prioritising it. As suggested

by (n=5, 63%) of hardware stakeholders, the lack of government accountability,

disempowerment and lousy media coverage resulted in customer’s distrust. Progress

is, however, limited as governments have little urgency to act.

Discussion

5.5.1 Synthesising Stakeholders Interests and Concerns: Rich Picture One

3D Body Scanning has complex, diverse and coupled multisectoral dynamics that

extend beyond the scope of a single discipline. The rich picture in Figure 19

represents the stakeholders’ point of view, following their professions, which

considers accommodating the firm’s interest in technology diffusion. It represents

different knowledges, epistemic practices, and divergent values, which required

bridging and connecting practices. Figure 19 present knowledge disparity in the 3D

Body Scanning industry through relationship clusters, flow of information (arrows),

tensions (crossed swords), and concerns (thought bubbles), as Monk and Howard

(1998) describe. The rich picture shows that stakeholders have their specific

conventions and design technology that brings together only a limited set of skills,

and socialising into different traditions appears challenging. Barriers include

challenges with discipline-specific terminology; challenges in coalescing around a

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common research question, goal, or motivation; and difficulty pursuing a shared

vision with disagreements in device size and access point, user posture, attire and

landmarking locations. Thus, the innovation becomes limited by prevailing practices,

in which it is not easy to create, replicate, share, and expand work. To that end, the

assumption that retailers adopt new technology only to maximise their existing utility

dominates the 3D Body Scanning industry, agreeing with Kim and Forsythe (2007).

Several empirical studies show the heterogeneous, partly insufficient, application of

3D Body Scanning in practice (Jiang et al., 2019; Harwood, Gill and Gill, 2020;

Kuzmichev, 2020).

The customer’s rights to control their private data and the needs of researchers and

stakeholders to access data create significant tension. Scan data can rightly be seen as

containing uniquely personal, powerful and sensitive information about individuals

(Sekhavat, 2017). Thus, by regulating the creation, maintenance and disclosure of

information, stakeholders can reduce privacy risks and reassure those who might not

otherwise participate in 3D Body Scanning. However, existing laws, as stakeholders

elaborated, have significant gaps and inconsistencies. The chief ethical issues raised

by stakeholders are: (a) informed consent for manufacturers to store the data in the

database and the ability to share it with collaborating retailers. (b) informed consent

for inclusion of personal information in the database, such as ethnicity, gender, age, or

styling preferences. (c) whether participants can effectively exercise the right to

withdraw from the research. The research community also suggested that a step in

protecting customers is to ensure there are data-protection staff with training in

fashion design and ethics on the front lines of every database. Keeling et al. (2013)

indicated that technological databases in retail raise new questions concerning the

nature of relationships between retailers and customers, in which retailers must

demonstrate high levels of transparency, accountability and expectations of

reciprocity. Specific databases can be so detailed in their requirements for data

curation that they scare away retail users, who do not always have the resources and

capacity to learn the relevant skills (Peirson-Smith and Peirson-Smith, 2020).

Moreover, it could be easy for retailers to get lost in a large range of metadata,

ontologies and thesauruses, especially as each type of data option is tailored to

different pattern-drafting purposes (Wang and Ha-Brookshire, 2018). Therefore, a

new role of the well-trained fashion digital curators is needed to facilitate precise

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curation of digital databases that need to be reliably established and documented

according to the scope and purpose. Stakeholders believed that privacy protections

would prove as necessary for the future of 3D Body Scanning and retail applications

as they will be for the future of e-commerce. Therefore, the sooner that reasonable 3D

Body Scanning privacy protections are in place, the better it will be for all

stakeholders.

Another dichotomy that was made visible through the rich picture is the absence of

‘knowledge brokers’ that act as a necessary conduit between the interdisciplinary

spheres (Duncan, Robson-Williams and Edwards, 2020). The results indicates that

fashion stakeholders have a central role in exchanging product information to increase

diffusion speed and maintain customer trust. However, scan manufacturers have no

intent to integrate beyond confined methodological transactions. Through interviews,

it becomes clear that the fashion industry sees a relative advantage of 3D Body

Scanning in faster and more accurate body measurements. Yet, the compatibility

difficulties inherent in measurement definition have, thus far, prevented its usefulness.

Manufacturers provide scanners with a set of anthropometric data that is inconsistent

with pattern-drafting methods (Gill et al., 2017; Ahmed et al., 2019). For example,

the fashion industry, measurements are correct when they align with ISO standards or

pattern theories (Parker, Gill and Hayes, 2017). However, for scan manufacturers,

validation is more akin to clinical standards referring to the quality of sensors and

algorithm (Tinsley et al., 2020). The findings draw attention to fundamental tensions

that should be considered in future research, notably stakeholders’ assumptions about

measurement reliability and validity in product development.

In recognising how intermeshed data from scan and pattern drafting methods are; this

study found a development gap in the lack of industry-standard software that could

help retailers exploit information from digital 3D avatars. The software could improve

guidance on using different garment development measures and help identify

assumptions, inherent rules, and empirical relationships between measurements and

resulting pattern shapes (Harwood, Gill and Gill, 2020). As Gill (2015) suggested,

many of these relationships have little or no underlying theory, and whilst addressed

in practice, have no foundations to support automation. The present lack of

intermediary that could translate retail requirements into engineering specifications

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highlights the need for academics to act as knowledge brokers and retain control of

the information channels. The need for a transparent, coordinated process became

apparent as gaps in implementation make it difficult for retailers to adopt and use 3D

Body Scanning. This study results correspond with Ashdown (2020), who argues that

3D Body Scanning’s fundamental problem is commercialisation instead of the

invention. Failure to acknowledge the complexities of 3D Body Scanning, the diverse

expertise it requires, and the context within which it is co-produced will make

technology diffusion more difficult.

Figure 19 Rich picture one: 3D Body Scanning industry. Source: author’s own.

5.5.2 A Framework for Interdisciplinarity in 3D Body Scanning: Rich Picture

Two

In light of the information presented in the previous section, a framework that would

capture tacit knowledge is needed to link dispersed knowledge silos. The proposed

rich picture framework in Figure 20 grouped stakeholders by their discipline as a

central element to the diffusion process (Lohman, 2020). The pyramid shape

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encourages stakeholders to start at the bottom and work their way up. If the service is

unable to provide the data governance standards that an individual seeks, for example,

the evaluation need not continue up the rest of the pyramid. To develop the

framework, each of the diffusion question was discussed in the context of discipline-

specific perspectives and expertise (Hristov and Reynolds, 2015). The interviews

enabled the researcher to ‘inhabit’ each group’s values and discuss where each

domain’s expertise and knowledge is to be drawn (Le Dantec, Poole and Wyche,

2009). Therefore, rich picture 2 intend to help develop a richer set of definitions,

metrics and methodologies to understand 3D Body Scanning practices and design

appropriate service representations (Kent, 2007). In the aggregate, these components

aim to provide a structure to tease out what sectors are responsible for prioritising

explicit research areas. To deliver this, a rich picture framework comprises:

• the top-down architecture, starting at the bottom with the most critical questions,

• the group of stakeholders responsible for answering these questions,

• the desired outcomes that garment developers can validate.

Figure 20 Rich picture two: stakeholders’ expertise in Body Scanning. Source: author’s own.

Research Stakeholders

The pyramid’s first level represents the academics with common themes that arose

from interviews about data libraries, governance (Cavoukian, 2008), standards, and

education efforts (Scott, Gill and McDonald, 2019). This study found that the absence

of guidance from academia caused the feedback loop to stagnate, with a lack of

principles that frame the pursuit of robust, workable outcomes. It is, therefore,

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currently much more manageable for academics to ‘reinvent the wheel’ by duplicating

efforts than to build on past findings. As a result, industry stakeholders perceived

collaborations with academics as unappealing, with projects terminated suddenly,

capriciously, or because they no longer valued developing supply-chain synergies.

This study recommends that academics create an open reference platform to support

the industry in becoming more skilled and responsive to challenges and change. This

study found that manufacturers and developers needed background information in

translating disciplinary standards when designing systems and would benefit from

relevant, up-to-date guidelines in one location. The guideline should establish scan

landmarks positions, data collection standards and encompass burgeoning variations

in nomenclature. The findings highlight that building a platform, therefore, also

involves building a coalition of stakeholders in which researchers provide an

authoritative voice in integration, ensuring that expertise is appropriately valued.

Technology Stakeholders

The pyramid’s second level represents scan manufacturers as key technology

stakeholders. This study found, through interviews, that this group influence product

design, file format, and metadata. However, when building scanners, manufacturers

struggled to elicit from the fashion industry how much data to record, store, query,

classify, and use. Scan manufacturers, therefore, require much closer collaboration

with the fashion industry. At the moment, manufacturers review design activities

against other technology firms’ actions, overexploiting their unique skills. Hence, 3D

Body Scanning providers began to look alike (Daanen and Ter Haar, 2013). However,

despite similarities in outer design, the past literature found a lack of agreements in

landmarking between instruments in all the body components measured (Kistorp and

Svendsen, 1997; Thelwell et al., 2020). The fact that the results vary with

manufacturer scanner, scan and calibration procedure means that individual results on

the two or more systems cannot be directly compared. Discrepancies in the approach

may, besides the calibration, cause different accuracy errors and leave large databases

under-exploited, a case exemplified by the CAESAR survey (Robinette and Daanen,

2003). The manufacturers of the 3D Body Scanners should consider standardising and

intercalibrate their instruments, following guidelines set in open academic reference

platforms. This way, scan manufacturers could focus more resources on building

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‘scan-to-pattern theories’ (Gill et al., 2018, p. 236) that would, in manual drafts, be

corrected by the experience of the garment developers and direct experimental

evidence.

Manufacturing Stakeholders

The pyramid’s third level depicts garment manufacturing: mill owners and garment

developers with interview themes relating to automation and customisation. This

study found that mill owners perceive 3D Body Scanning in conflict with their

production workflow because fast-fashion value large orders, short lead-time and low

cost (Camargo, Pereira and Scarpin, 2020). Yet, the marketing literature emphasises

the high demand for personalised products (Miell, Gill and Vazquez, 2018; Xue,

Parker and Hart, 2020); thus, providing a rationale for technology adoption. However,

this study found low modularity between scanners and existing software incentivised

garment developers to develop siloed, in-house systems. This finding agrees with Yan

and Kuzmichev (2020), who show isolated scan applications’ when designing a

bespoke shirt. The workflow starts with body morphology created by the VITUS

Smart XXL (Vitronic) and Anthroscan (Human Solution), measured by Rhinoceros

(Rhino) with the bone rigging in Mixamo (Adobe). 2D pattern blocks are sketched in

ET CAD (BUYI), analysed in SPSS (IBM) and executed in CLO 3D virtual try-on.

Nevertheless, to fully automate processes with 3D Body Scanning, the various CAD

applications have to be re-compiled into the plug-in apps. This way, the integrated

interface could bridge the gap between design and manufacture, providing real-time

feedback about fit, ease or landmarks between three-dimensional design and two-

dimensional pattern layout.

Software Stakeholders

The pyramid fourth level represents software developers who, as interviews

highlighted, are most interested in VFI applications with data analytics about

customer demographic and style/trend desirability. In particular, as this study found,

much attention has been devoted to creating 3D avatars from customer descriptions.

However, customers cannot identify avatars that best represent their own body (De

Coster et al., 2020), and lack of objectivity in self-representation makes datasets

highly biased (Plotkina and Saurel, 2019). Thus, connecting 3D Body Scanning with

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VFI for fast and reliable data acquisition could help software developers re-focus

resources on building interactions between virtual shopping environments and 3D

avatars to improve VFI commercial feasibility. This can make VFI more informative

and playful, with interfaces defined and annotated with terms grounded in fashion

ontologies and actionable insight. As it stands now, the output from 3D Body

Scanners yield hundreds of measurements that emphasise highly inaccessible medical

jargon and out of context numerical data that customers’ often do not understand.

Thus, software stakeholders need to work with garment developers and academics to

explicitly define how each measurement is grounded, defined, and influence sizing/ fit

and styling decisions with UX design principles about design interface – to

personalise VFI by turning on/off some measurements. The large user base could

support personalised sales and marketing that could be sold to advertising companies

and translated to VR or AR for an immersive shopping experience (Xue, Parker and

Hart, 2020).

Fashion Retail Stakeholders

The pyramid’s fifth level considers fashion retailers who, to embrace 3D Body

Scanning, must assess the location, skills, and process at adoption’s inception. This

study results identify fashion stakeholder’s leadership as being essential. The fashion

industry must engage with the customer to successfully implement any new retail

technology. Fashion retailers must anticipate early on how to break into customer

attention and ensure the investment is lucrative (Lewis and Loker, 2017). Customer

awareness of 3D Body Scanning should not be assumed. The retail inability to watch

customers in their activities slows down diffusion as they seldom refer to customers’

requirements. Our results confirm that fashion endorsement and strong branding

provide a basis for customer trust (Frasquet, Mollá Descals and Ruiz-Molina, 2017).

Fashion retailers, therefore, require strong collaboration with developers. This paper,

however, further show that many stakeholders rely on algorithms for garment

development, despite algorithms propagate the developer’s ignoring customer

activities. Therefore, retailers need to develop functional interactions and design

agenda to ensure it is consistent with the way people consider their shopping activities

(Edvardsson et al., 2012).

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Customers

Customers represent the pyramid’s top-level, yet a customer experience is still unclear

because of the disparity in 3D Body Scanning offerings. Customers cannot, therefore,

learn to interact with 3D Body Scanning systems. When customers see the phrase ‘3D

Body Scanning’, we found, from fashion retailers, that they are unsure how to interact

with it in their shopping. The customer, consequently, ignores 3D Body Scanning.

This reluctance for retail diffusion might also reflect general uncertainty on how

customers currently view 3D Body Scanning (Bonetti, Warnaby and Quinn, 2018).

The possibility that customers may reject services contributes to 3D Body Scanning’s

commercialisation lagging behind its technical development. Guidelines that aim to

introduce 3D Body Scanning design features will need to determine and define the

user experience and interaction criteria. What is also clear from the interviews about

the user trials is that it is important to form a more explicit understanding of what is

required from customer-user research.

Conclusions

This study set out to offer guidance to retailers, scan manufacturers, software

developers, and academics to increase 3D Body Scanning’s diffusion in retail.

Today’s 3D Body Scanning industry has been trained primarily in scientific

backgrounds (software, engineering, or mathematics) that prepared them well for

many technical topics but left them lacking sufficient knowledge of pattern drafting

methods. The result is that stakeholders sometimes ignore elementary design

principles routinely applied by garment developers. The lack of interoperability

among the tools built by this community, to some extent, reflects the heterogeneity of

the backgrounds in the community itself. This study shows that reversing this

tendency will call for an unusual mix of skills in which stakeholders must collaborate

to borrow – or build off – each other’s expertise. In particular, the process of drawing

a rich picture was hoped to serve as a constructive tool in interprofessional and

interdisciplinary dialogue. The rich picture analysis supports the understanding of

what 3D Body Scanning stakeholders could capture when building a 3D Body

Scanning service, the diversity of views on what needs to be governed, and what

options exist for interventions to attain such objectives.

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3D Body Scanning’s relative advantage can be increased if manufacturers,

developers, and researchers include pattern-drafting principles throughout all aspects

of a 3D Body Scanner’s development. To increase compatibility, fashion retail

stakeholders should consider a new set of design principles that would allow breaking

with continued over-reliance on traditional fast-fashion approaches. In particular, this

study found that the incorporation of machine-learning techniques within 3D Body

Scanning could provide the digital-hub that focuses on mass-customisation. In this

digital hub, customers can augment specific capabilities with actionable steps, such as

style-assistant, based on available body shape and ratio data from the scanner.

Customers interacting with such customisable parameters related to their body-

measurements offers a level of observable advantage to 3D Body Scanning that has

yet to be seen in a fashion retail environment. However, to achieve this goal, the

fashion retail trial sessions require further evaluation of the reliability and accuracy of

such metrics. The existing evidence, as this study found, is lacking on how 3D Body

Scanning information can be used to change existing garment manufacturing practices

and improve fashion retail’s size and fit metrics based on substantial longitudinal

data. The fashion industry must, consequently, establish the appropriate standards and

regulations that 3D Body Scanning must meet to be relevant in retail practice and

decrease the level of perceived complexity.

This research has implications for 3D Body Scanning stakeholders to identify areas

where gaps in the development exist. This study discussion allows developers to

move towards producing evidence that fashion retailers can easily apply to product

development practices. This study urges manufacturers, developers, and researchers to

capitalise on the fashion practitioner’s metrics and further contribute to their

validation and acceptance.

• Manufacturers should focus on designing scanners with relevant

product development landmarks, metrics, and metadata. They should also help

to illuminate the expected time, cost, and degrees of complexity that may be

expected to complete the assessment.

• Software developers should create a variety of tools to translate their

data into actions for customer engagement efficiently. In particular, the ability

for the customers to personalise their experience by turning on/off some

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measurements and to customise avatar based on their external features may

add more reality and credibility in design.

• Academic researchers should focus on the critical evaluation of robust,

fit-for-purpose analysis frameworks that could improve retail understanding

and expectations on technology utility, desired outcomes, and limitations.

This study methodology is limited to describe this moment in time, as stakeholders’

interconnections will become more complex over time, increasing the complexity of

competing interests and data flow. Stakeholders with strong views may, hence, be

more likely to participate in interviews. This limitation of qualitative research presents

an opportunity for future research: quantitatively surveying stakeholders across

computer science, engineering, fashion, manufacturing, and academia. This study

sample is also broad, drawing from global stakeholders: USA (n=14, 47%), Europe

(n=10, 33%), Canada (n=4, 13%), Asia (n=1, 3%), and Middle East (n=1, 3%). Future

research could sample from a broader range of geographical regions. Drawing from a

broader range of geographic regions will illuminate where technology diffusion is at

the highest point. Future research could also focus on the customer perspective of the

service and information provision. Focusing on service and information provision

may illuminate early adopters of the system and opinions about propositions put

forward in this study.

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CHAPTER 6

‘CUSTOMER JOURNEYS’ IN 3D BODY SCANNING: THE

GOOD, THE BAD AND THE UNEXPECTED

Introduction

3D Body Scanning is critical for the fashion industry’s profound transformation

towards more customer-centred size and fit methods (Gill, 2015). Anthropometric

data is needed to describe the human body’s diversity and shape in product

development (Gupta, 2020). The adoption of 3D Body Scanning by fashion retail can

bring many advantages, including AI-based 3D avatars to better understand and cater

to customer taste and fit preferences (Silva and Bonetti, 2021). However, the 3D Body

Scanning services struggle to develop, despite growing support from researchers and

garment developers who see it as a promising medium for more customised retail

offerings (Harwood, Gill and Gill, 2020). The ability to produce results depends

critically on the level of customer sustained engagement as 3D Body Scanning

developers need customer-driven data to evidence technology effectiveness (Peng,

Sweeney and Delamore, 2012). However, developers tend to be uninformed about the

issues related to the interface between customer experience and technology design

(Kaur and Anand, 2021). As a result, the fundamental design of 3D Body Scanners

has not changed significantly for many years despite growing evidence that it should

(Mironcika et al., 2020). The existing research suggests that due to poor design and

interaction, customers have low confidence in these methods’ reliability and little

incentive to engage in 3D Body Scanning (Ashdown, 2020; Silva and Bonetti, 2021).

The commercial efforts in 3D Body Scanning are deeply ingrained in the hi-tech

culture that employs engineering principles to improve the design outcomes, i.e.,

calibration techniques or increasing scanner speed (Daanen and Ter Haar, 2013).

Subsequently, 3D Body Scanners are smaller, more mobile, and faster, often imitating

familiar home objects such as scale or mirror (Grazioso, Selvaggio and Di Gironimo,

2018). However, 3D Body Scanning developers rarely consider technology design

and usability as areas worth exploring with customers. The usability problems are

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usually treated as technical issues that will work themselves out with further

efficiency gains and widespread adoption (Kennedy et al., 2020). However, this

approach leads to interaction problems because customers had to adapt to a new way

of performing familiar tasks (Stern, 2018). The developer’s focus has been mainly on

technology as a solution on its own, though technologies by themselves are rarely

transformative (Weber and Rohracher, 2012). Although advances in technical areas

are necessary, the developers’ lack of familiarity with the non-technical aspects of the

problem limited the ability to deploy technology in retail (Lewis and Loker, 2017).

The literature on customers in 3D Body Scanning is scarce and has been mainly

studied through fashion practitioners’ lens. For example, scholars such as Lee et al.

(2012) and Song and Ashdown (2015) focused on garment tailoring problems, and in

their studies, researchers provided participants with specific usability scenarios to

measure technology uptake. These studies collectively found that customer values a

service that is easy to use, offers suggestions about garment fit and style, and allows

final adjustments. Moreover, the usability outlook focused on developing verbal

protocols for interaction techniques to identify system design problems (Gill, Hayes

and Parker, 2016). However, these studies rarely allowed the participants to try and

experience the service beyond brief verbal descriptions. Therefore, there is little

information about customer experience in 3D Body Scanning. Vecchi et al. (2015)

point out, it is unclear which 3D Body Scanning elements are the most compelling for

customers and which are the main barriers to interaction. Thus, while past studies

addressed some of customer experiences’ elements, there is no representation of a

more holistic service view beyond dichotomous measures (Teixeira et al., 2012). The

service design approach can create a rich and varied understanding of technology

purpose to guide stakeholders in the design and development when investing in 3D

Body Scanning solutions.

The design challenges are further exacerbated as customers are expected to

understand and interpret the 3D avatars. Silva and Bonetti (2021, p. 4) defined 3D

avatars as “digital character representing an online user and that the user can

interact with and talk to via text and voice chat functions”. Research on customer

experience with 3D avatars has started to emerge but mainly in engineering and

computer science contexts (Golyanik et al., 2017). This research stream focuses

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mainly o increasing the resemblance between 3D avatars and human customers (Saint

et al., 2019) to increase self-congruity (De Coster et al., 2020) and telepresence (Lee,

Xu and Porterfield, 2020). However, the customers’ skills and fashion context are

barely considered when providing customers with scan output (Sohn, Lee and Kim,

2020). Therefore, a clearer distinction needs to be made between the characteristics of

expert users (fashion retailers) and lay users (customers). Fashion retailers will be

familiar with analysing scan datasets, manipulating them in CAD packages, and

understanding the technical terminology (Yan and Kuzmichev, 2020). Moreover, the

scan results are reported using a variety of templates containing different amounts,

levels, and layouts of information (Januszkiewicz et al., 2017). The inconsistency in

communication may prove difficult for customers to contextualise 3D Body Scanning

data into existing approaches of size and fit provision. The past literature on

consumers’ interaction was oriented on surveys and interviews and measured

participants satisfaction when interacting with customised 3D avatars. However, the

research found that female participants reported feeling threatened and vulnerable

when seeing their bodies on-screen and focused on perceived flaws and related self-

defeating interpretations with negative emotions (e.g., anxiety, depression, shame)

that further increase selective attention (Grogan, et al., 2019). Similarly, male

participants found areas of the body to critique, including new concerns resulting

directly from seeing the scanned image (Brownbridge et al., 2018). It is thus unclear

to retailers whether 3D Body Scanning may be beneficial or harmful to retail and its

increased relevance in virtual fit applications (Grogan, 2017). If any of the service

delivery steps associations are negative, the customer is more likely to resist the

innovation (Zarazua de Rubens, Noel and Sovacool, 2018). Therefore, to increase

adoption – the 3D avatar from 3D Body Scanning must be considered within the

overall customer experience and journey.

Eye-tracking provides an opportunity to explore direct point-of-attention; and expand

upon prior findings as little is known about participants viewing patterns and the

overall literacy when viewing the data for clothing purchase (De Coster et al., 2020).

The past approaches shed light on how sensitive the scan content may be to

participants but also have led to the emergence of biases (Chen and Jackson, 2005).

Thus using alternative methods, such as eye-tracking, may help to overcome such

difficulties and gradually bring a fresh research perspective (Miell, Gill and Vazquez,

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2018). The use of eye-tracking can examine customers’ point of visual attention when

engaging with a body scan stimulus. This indicates that viewing patterns are

associated with attention, and the visual cognitive systems direct the gaze toward the

important and informative body part (Hotta et al., 2019). Thus, the eye-tracking

approach may provide initial suggestions on the visual arrangements on how to

effectively communicate 3D Body Scanning data (Bera, Soffer and Parsons, 2019).

The scan interface needs to be able to deliver the results and their implications clearly

and unambiguously to customers who have no training in anthropometry or pattern

drafting methods.

Fashion retailers are uniquely positioned to design and deliver engaging customer

experiences. However, retailers need insight into the dynamic, subjective experiences

of individual touchpoints and how the overall experience is shaped to alleviate

customer dissatisfaction. Therefore, this study examines the customer experience in

3D Body Scanning through the customer journey. This proposition leads to the

following questions:

(1) This study evaluated the design of the 3D Body Scanning to understand the

necessary technology changes to fit more with retail experience.

(2) This study examined the visual elements of 3D Body Scanning to understand

the customer’s level of understanding and interface readability.

(3) This study tested the female and male total fixation level in the 3D Body

Scanning interface to understand the difference in gender viewing pattern.

This study addresses the research objectives by analysing 52 participants opinions

based on mixed-method research that utilised participatory design techniques such as

focus group workshops and semi-structured interviews, and eye-tracking methods.

The study findings reveal a compelling list of nine non-technical barriers that need to

be addressed if 3D Body Scanning is to be deployed the fashion retail.

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6.2.1 Customer Experience in 3D Body Scanning Research

The customer experience represents a complex relationship between a person and a

technology (Edman, 2011). A good customer experience enables the customer to learn

how an application work, operate it efficiently, and ‘enjoy’ its use (Bolton et al.,

2018). Therefore, understanding the service delivery process from a customer’s

perspective is key to the successful design and management of services (Halvorsrud,

Kvale and Følstad, 2016). The customer experience is a measure of the user’s

willingness to utilise a service and their emotional connection with it (Hwang, Kim

and Lee, 2021). It is commonly agreed that service experience is to be understood on

the level of the individual customer (Bitner, Ostrom and Morgan, 2008). Therefore,

research efforts have been made to assess the socio-demographic factors, such as

gender, ethnicity, income, and education, to elicit the early-adopters profile. First,

early adopters were often described as female (Song and Ashdown, 2013), between

18-35 years old (Reid et al., 2020) and Caucasian (Ridgway, 2018). The level of time

and monetary investment in the body scan was also examined. Interestingly, early

adopters were connoted with low spending power and family income lower than

$40,000 (Peng, Sweeney and Delamore, 2012). In addition, Loker et al. (2004) found

that customers were willing to travel 30 minutes to scan location and spent another 30

minutes on the process. Paradkar et al. (2015) found that they were keen to pay $15

for the scan. Moreover, Loker et al. (2004) found that female students were

comfortable wearing underwear or even a spandex bodysuit. Nevertheless, as

Almousa (2019) pointed some customers may not feel comfortable scanning wearing

underwear in a public space because of cultural differences. These findings suggest

that customers may have different preferences, and 3D Body Scanning must be able to

accommodate for flexibility in design (Rajan et al., 2005). However, research so far

only touched upon some of the design issues through surveys and questionnaires,

which focused too narrowly on developers engaged in technical tasks (Thackara,

2001). In contrast, design research offers a wide range of concepts and methods, such

as methods commonly used in the fields of empathy design and participatory design.

These approaches could help generate insights into the customer’s lifeworld (Visser et

al., 2005; Kouprie and Visser, 2009), or tap customers’ collective creativity for

innovation purposes.

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The strong literature focus on task-related efficiency and effectiveness in research

arises from the implicit notion that the 3D Body Scanner is only a tool when applied

at retail. Yet, service providers interact with their customers through a multitude of

offline and online channels and touchpoints. (Rudkowski et al., 2020). However, in

the context of 3D Body scanning, fashion retailers often focus on individual

touchpoints (vs journeys) when assessing their customers’ experiences. This silo

approach diverts attention from the bigger issue: the customer’s end-to-end

experience or journey (Grewal and Roggeveen, 2020). The process of customer

journey shows that consumers go through the pre-purchase, purchase and post-

purchase stages and across a multitude of online and offline touchpoints (Lemon and

Verhoef, 2016). The touchpoint, as defined by Halvorsrud (2016, p. 846), must meet

the following criteria: it must be visible to the customer, that is, if the customer does

not encounter it in any way, it is not a touchpoint; it must be a discrete event that can

be appointed in time, and it must involve communication or interaction between the

customer and a service provider. Moreover, Hassenzahl et al. (2000) described two

quality dimensions in touchpoints, hedonic quality and ergonomic quality. Ergonomic

quality is defined as task-related quality dimensions such as usability. In comparison,

hedonic quality is described as originality, innovativeness, and beauty. Nevertheless,

Hassenzahl et al. (2006) described customer experience as a border concept than

usability (which only focuses on pragmatic quality aspects). The customer experience

includes pragmatic aspects, hedonic aspects, and user emotions resulting from the

interaction between the customer and product (Hassan and Galal-Edeen, 2017). Hence

usability is a part of customer experience, which affects the overall customer journey

(Halvorsrud, Kvale and Følstad, 2016). The mechanical clues are the sensory

presentation of the service: the sights, smells, sounds, tastes, and textures of the

service experience. However, some additional clues emerge from the behaviour and

appearance of service providers and become the “how” of the service: the provider’s

dress, voice, choice of words (Sandström et al., 2008). Hence, the service experience

is anything the customer perceives by its presence or absence. The service experience

consists of functional clues that indicate whether the different parts of the service are

working as they are supposed to.

The potential service scenarios were developed by Loker et al. (2004) and Lee et al.

(2012) and described potential applications based on the usability of 3D body

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Scanning: virtual try on, size prediction, custom fit, personal shopper, codesign,

pattern development, and research. In these studies, the moderator presented

PowerPoint descriptions of possible usages of 3D Body Scan data with a full verbal

explanation of each scenario. However, the customer experience was limited to well-

crafted stories and visual cues, which exposed planned journeys touchpoints but

provided no details about the actual customer journey and potential service

inconsistencies. This highlighted an essential gap in the research of customer

experience in 3D Body Scanning that was considered worthy of further investigation.

It was also hypothesised that design principles developed from an understanding of

customers’ requirements offer developers a rational basis for the design and reference

points in the process (Nielsen and Molich, 1990; Norman and Verganti, 2014).

6.2.2 3D Body Scanning Interface

The 3D Body Scanning interface provides an abundance of perceptual information

with multiple measurements specifying body dimensions. Consequently, it seems

reasonable for fashion retailers to assume that customers have an accurate perception

of the body (Ketron and Williams, 2018). Many VFI scenarios present customers’

with scenarios that demand the ability to match one’s own body image with visual

bodily information in order to make decisions on body and apparel fit (De Coster et

al., 2020). In contrast to this intuition, Linkenauger (2017) has shown substantial,

striking distortions in visual perceptions of their own body. Grogan et al. (2016)

found that scan images are informative, but customer reactions may be complicated

due to the psychological implications. Loker et al. (2004) found customers’ positive

responses about how their bodies appeared on the scans. In contrast, Kim and Sundar

(2012), found that the majority of people in population were unhappy about their

physical appearance irrespective of age, gender, race, and social class. It has generally

been shown that feelings about the body are correlated with feelings about self. More

research is, however, needed on the customers understanding of 3D Body Scanning

data. To produce significant results, developers have not only to play to the strength

of technology but also apply the relevant design principles to increase engagement.

Increasing technologies efficiency and reducing their cost is not, therefore, enough.

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This study recruited participants using the stratified sampling method (Lee et al.,

2012; Grogan et al., 2016; Beck and Crié, 2018), through posters in The University of

Manchester campus, Manchester’s Northern Quarter district, and via social media:

Twitter, LinkedIn, and Reddit. In total, the 52 participants were persons who had a

high interested in technology and who represented different demographics and apparel

needs.

1. Participants represented: 75% female (n-39) and 25% male (n-13), between age

18-25 (n-28), 26–35 (n-11), 36-50 (n-10), 51+ (n-3), years old.

2. The majority of the sample was University students (n-35); other professions

consisted of apparel designers (n-7), technology developers (n-6), academic

lecturers (n-2), and academic administrators (n-2).

3. The sample background was 64% Caucasian (British n-25, Cypriot n-2, Greek n-

1, Russian n-1, Italian n-3, German n-1); 30% Asian (Chinese n-11, Chinese-

British n-1, Taiwanese n-3, or Indian n-1); 4% Middle East (Saudi Arabia n-1,

Egyptian-Belgian n-1); 1% African (Nigerian n-1).

The sample size satisfies a criterion for external validity because the average causal

relationships observed in our sample is likely to be similar to any other random

sample drawn from the UK target population (Straub, 1989). The diversity of the

sample allowed to elicit distinct opinions for an inclusive 3D Body Scanning (Ning

and Dong, 2016). The experimental procedures described in this study were carried

out following the University of Manchester research ethics committee, ref

RDMP8037. Informed consent was obtained from all participants; a copy of consent

form and demographics information’s are attached in appendix D, E, and F

respectively.


These results of the study are based on two design activities:

• Phase one - focus group workshops or semi-structured interviews, and

• Phase two - eye-tracking interface assessment.

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Sessions were undertaken between September 2018 – January 2019 at University of

Manchester academic setting and began with each participant being scanned by Size

Stream SS14 scanner (Rovelli, 2018).

In phase one, 52 participants were randomly allocated to mix groups and after their

scan, invited to discuss their experience in focus groups (n- 39) that varied in length

between 70 minutes to 85 minutes; participants who could not fit schedule were

interviewed on an individual basis (n=13) that were approx. 30 minutes long. The

participants were asked to reflect on 3D Body Scanning experience using post-it-note

in order to count the number of design solutions and ideas evoked by each identified

problem, and the arguments given in favour or against each design solution. The data

from audio files of each focus group were transcribed verbatim. The notes and quotes

recorded were coded and evaluated on the basis of frequency analysis.

In phase two, this study further selected 40 participants (female n=29, male n=11)

suitable for the eye-tracking experiment, following the Tobii Pro Glasses guidelines

(Tobii Pro AB, 2019). The participant selected had no dimorphic body disorder or an

eating disorder, also due to the technical limitations; participants with heavy eye

makeup or wearing glasses were excluded. The experiment utilised two separate

pieces of equipment: Toshiba laptop with Size Stream interface (V5.2.8), and portable

eye tracker: Tobi Pro Glasses 2 (head unit and recording unit) (Tobii Pro AB, 2018).

Portable eye trackers Tobi Pro Glasses 2 was utilised as it has the advantage of

affording a more extensive breadth of head movement that tracks both eyes allowing

more considerable latitude for movement (Olsen, 2012). The eye movements were

recorded with the sampling rate 50 Hz (scene camera, video resolution: 1920 ×1080 at

25 fps, sensors: gyroscope and accelerometer 3.3); calibration was accepted if less

than 1° of visual angle error (both on the x-axis and y-axis) was achieved. This setting

means that if the gaze samples were 100%, this would mean that 50 samples per

second were identified and able to be stored as gaze points accurate to 5mm

(Duchowski, 2007).

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6.3.3 Data Analysis

Qualitative Data

Due to the exploratory nature of this study, a rich, qualitative descriptive analysis was

undertaken to make a systematic, replicable and valid evaluation of transcribed

recordings and ‘open coded’ themes based on Robson and McCartan (2016) criteria.

The lead researchers conducted the transcription coding process using NVivo 12

(QSR, 2019) qualitative data analysis software. For consistency and reliability, the

lead researcher first read all open-ended answers and identified preliminary

categories. Next, codes pertaining to similar themes were grouped to generate sets of

categories that related to each of the research questions. Finally, the lead researcher

compared all classifications with the rest of the research team, with different opinions

discussed and resolved. The answers recorded were coded and evaluated on the basis

of frequency analysis (Hutchison, Johnston and Breckon, 2010) in section 6.4.1.

Statistics

During the experiment, all participants were seated in front of stimulus material that

displayed a standardised female or male 3D avatar along with other interface

elements. Using a standardised model helped generate cumulative output and added

more objectivity in people’s reflections, avoiding body distress or personal biases.

Time spent on each AOI was analysed as a proportion of total fixation duration (TFD)

and the completion time of the scenario varied between 4-6 minutes. The total fixation

was chosen with a Tobii I-VT filter to reduce noise while still preserving the features

of the sampled data needed for classification (Olsen, 2012).

This study determined eight main AOIs, as demonstrated in Figure 21 for female

participants and Figure 22 for male participants. The human body AOI were based on

Cash (2014) scale. Holmqvist et al. (2011) define AOI as a region in the stimulus that

the researcher is interested in gathering data. The eight identified AOI’s are face

(facial features, shape) and hair (texture) (Rectangle 1), upper torso (chest or breasts,

shoulders, arms) (polygon), mid-torso (waist, stomach) (Rectangle 2), lower body

(buttocks, hips, thighs, legs) (rectangle 3), list of measurements (Rectangle 4), menu

panel 1 (Rectangle 5), menu panel 2 (Rectangle 6), menu panel 3 (Rectangle 7), Size

Stream Logo (Rectangle 8). Once drawn, each fixation point was manually mapped

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from the glasses recording per frame onto the relevant snapshot (Duchowski, 2007).

The aggregated data was plotted through heat maps to visualise regions of interest.

Figure 21 Female interface with AOI. Source: author’s own.

Figure 22 Male interface with AOI. Source: author’s own.

A Wilcoxon-Mann-Whitney (WMW) was conducted to examine whether men and

women differed at the fixation level in their viewing pattern during interface

evaluation. The WMW test is an alternative to t-test when the underlying distribution

of the outcome variable is not normally distributed. Non-parametric tests are most

useful for small size studies (Fagerland, 2012). The data from descriptive statistics

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was visualised through boxplots. All statistical analyses were performed with SPSS

25.0 (IBM Corp, 2019). The level of significance was set at P < 0.05 (two-tailed),

𝑒𝑓𝑓𝑒𝑐𝑡𝑆𝑖𝑧𝑒𝑓 = 0.25; 𝑃𝑜𝑤𝑒𝑟(1 − 𝛽𝑝𝑟𝑜𝑏𝑒𝑟𝑟𝑜𝑟) − 0.8, 𝛼 = 0.05, 𝐺𝑟𝑜𝑢𝑝𝑠 =

2,𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑠 = 8, 𝐶𝑜𝑟𝑟𝑒𝑙𝑎𝑡𝑖𝑜𝑛𝐴𝑚𝑜𝑛𝑔𝑠𝑡𝑀𝑒𝑎𝑠𝑢𝑟𝑒𝑠 = 0.5.

Results and Analysis

6.4.1 Customer Experience in 3D Body Scanning

Table 29 presents the most common barriers found by the customers in 3D Body

Scanning design. The findings demonstrate that customers experienced a discrepancy

between the technology external image and system capabilities. Table 29 Design barriers to 3D Body Scanning experience; n=52. Source: author’s own.

Design Barriers Freq. Example • Name N=19 Participant 08, “I think the name body scan in itself – that

what attracted us, like body scan, whoa!” • Layout N=13 Participant 23, “Yes, it would better, if it were more going for

a fancy bra fitting, with elements like a nice trolley for your jewellery; sort of more for customers.”

• Camera N=21 Participant 22, “A lot of people would be worried about cameras, if they are x-rays or normal, can they take a picture, and/or what they are for?”

• Interaction N=20 Participant 09, “It would be better if this machine could create more interaction for me; it will decrease my feeling of being very nervous, not knowing what to do.”

• Purpose N=20 Participant 23, “I think it lacks rationale for why you would do that, why would you go in there.”

Technology Name

The primary factor that attracted (36%, n=19) participants to attend scanning, before

having any direct contact with technology, was the name ‘3D Body Scanner’. The

name evoked an intense technical curiosity and was interpreted with the flavour of

futurism and high technical design expectations.

Layout

The scanning booth was divided into two connected spaces: the changing and the

scanning space. The changing space was for (17%, n=9) of participants

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uncomfortable, as having to undress in a small room and was considered an

annoyance. The suggested by (10%, n=5) action for improvements included adding

shelves and clothing racks; this could help to ‘add texture and depth into space and

mimic the retail try-on experience.’ Moreover, participants (12%, n=6) worried that

gaping curtains would make them feel exposed. The curtain facade evoked feelings of

(6%, n=3) a photo-booth or even (6%, n=3) a clinical trial. Participants recommended

using the doors instead that could additionally help to subdue the external noise.

Cameras

Participants (38%, n=20) reiterated that the room covered in cameras made them feel

uncomfortable and uneasy. Some participants (15%, n=8) also mentioned feeling

‘vulnerable’ while standing still in underwear and in an unnatural “A” pose pulling

the handles up. This evoked concerns about data leakage; mostly because participants

(19%, n=10) mentioned a lack of understanding on what information scan collect and

in what format the data will be displayed for future use. Therefore, (35%, n=18)

participants mentioned that to allow companies to use personal data they would need

extra reassurance about how the data is collected, stored and used, and how they can

access this data and control how other firms access their personal information.

Interaction

During the scan process, (38%, n=20) of participants reflected that the high feeling of

anxiety and nervousness led to unexpected actions, for example, (30%, n=16) pulled

support handles upwardly resulting in a bizarre scan or (10%, n=5) stopped breathing

to ensure correct posture and position. The inability to determine the outcome of their

position quickly made participants feel (15%, n=8) confused, (10%, n=5) frustrated,

(10%, n=5) embarrassed, or even (5%, n=3) stupid. As a help, guide participants were

keen to download additional mobile apps (13%, n=7) or receive visual and vocal aid

with instructions, e.g., on-screen with voice chat-box or Augmented Reality (AR)

(12%, n=6), along with the fact that highly trained assistants are needed to support the

user throughout the process – before and after the experience.

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Purpose

Participants (17%, n=9) identified that they had confused the anthropometric body

scanner purpose with the hospitals’ X-ray scanners such as DEXA. In this matter,

participants were worried about the health consequences, as they believed continuous

use of body scanning might be harmful, in a way that exposes persons to radiation.

Participants (15%, n=8) proposed the use of an app that will connect with digital

mirrors as a complementary tool near or inside scanning booths to virtually try-on

garments on the digital avatar could reflect more retail setting. Participants (30%,

n=16) proposed sharing an avatar with a store employee or an e-Commerce website to

receive curated style recommendation. In addition, participants (27%, n=14) were

hoping 3D Body Scanning would cut all the unnecessary hassle of trying garments on,

as some (20%, n=10) expected that 3D Body Scanner would help them decide on the

right style and ‘true’ size.

6.4.2 Interface Development

Table 30 presents the most common design issues found by the customers when using

the interface. The key findings demonstrate that the link between body dimensions

and garment fit is unclear, and customers lack the skills to interpret data to receive a

personalised shopping experience. Table 30 Interface barriers to 3D Body Scanning experience; n=40. Source: author’s own.

Interface Barriers Freq. Example • Amount of

Information N=33 Participant 11 “At the moment, it is too much information for

me; it would be helpful if you could see key measurements.”

• Page Layout N=21 Participant 31 “Maybe the colour could contrast between body and where the measurement is being taken.”

• Actionable Next Step

N=18 Participant 6 “Like a summary of something, like this are full measurements but how these will affect how do you shop?”

• Avatar Design N=38 Participant 21 “Yes, it looks a bit, cartoony, something out of Kung-Fu movie for some reason.”

Amount of Information

In terms of information provision, participants (33%, n=13) felt overwhelmed or

exhausted by the amount of data displayed in both the personal paper printed output

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and the Size Stream interface. Participants (26%, n=10) identified that they lacked the

skills and knowledge to interpret their body measurements as reported. The

information presented made participants feel alienated, as participants felt the

information was intended only for professional garment makers. The suggested

changes reflected a desire for lay terms. The notable struggles were related to (31%,

n=12) vocabulary, and (20%, n=8) acronyms used as well as (65%, n=20) finding

connections between measurements listed and landmarks on the body. In general,

participants preferred the scanning data to have more contexts and less jargon as it

was felt to be personal and less ‘brusque’.

Page Layout

The layout themes related to the overall structure as participants (44%, n=17)

suggested that the measurement list could be more organised, as they struggled in

selecting the measurement, and proposed a select-de-select button on the interface.

Participants proposed that the output should have delineating sections to avoid dense

blocks of small text and have clear section headings to convey information.

Participant (10%, n=4) appreciated the use of colour to differentiate sections of the

document and suggested this option could be used to link landmarks with body

measurements better. Lastly, participants were expecting a much glossier look from

the technology that is linked with the fashion industry, as participant 23 summarised

“more like a digital magazine or a fancy brochure”.

Actionable Next Step

Participants wanted more information and detail on the topics that were already

present in the report. Some participants suggested that to see the outcome as

meaningful; they would need (27%, n=11) relevant background information about

each measurement, or (30%, n=12) recommendations from their peer reference group.

Participants also wanted more information about the fashion side itself and

recommendation for size, fit and style. As participants (37%, n=15) suggested

providing links to further information could help guide customers towards trustworthy

online information; customers are otherwise likely to search for online guidance

themselves, which as participants reflected can lead to misleading information.

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Avatar Design

Most participants (55%, n=22) preferred to display the 3D avatar on a computer

screen, or mobile device. Moreover, participants (30%, n=12) agreed on benefits from

the digital interactive design. The avatar on computer scream allowed rotating and

zooming avatar and seeing the body from multiple angles and perspectives.

Participants called it more playful and enjoyable. However, (46%, n=18) suggested

the avatar should be customisable, ‘not plain and purple’ so the skin colour looked

more realistic mimicking the properties and texture of human skin. At the same time,

others (10%, n=4) disagreed and suggested the abstract colour adds more objectivity

to the image and helps to detach from it. The adjectives used to describe avatar were:

shiny plastic (13%, n=5), cartoony (5%, n=2), sludge (5%, n=2), CGI (3%, n=1),

flattered (3%, n=1), clay (3%, n=1), or mire (3%, n=1).

Surprisingly, we found that participants were unable to distinguish between one’s own

and someone else’s avatar; leading (36%, n=10) female and (27%, n=3) male to

believe they saw their 3D Body Scan on the computer screen. The female participants

found similarity in detail for the hairstyle (40%, n=12), legs length (30%, n=9), or

waist with a straight-line silhouette (30%, n=9). The male avatar participants found

similarity in the (18%, n=2) height, (18%, n=2) posture or age variable (9%, n=1). In

both cases, participants very much agreed on avatar height, for female adjective used

were short (25%, n=7) or average height (14%, n=4), while for (73%, n=8) male the

avatar appeared tall - at least 180 cm. Body distortions were, however, most apparent

when describing avatar weight. Female participants elicited dissimilar shapes, with

distinction divided between four categories: average (32%, n=9), sporty (29%, n=8),

fat (25%, n=7), and skinny (11%, n=3). Table 31 illustrates the adjectives and

frequency of words used to describe avatar weight.

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Table 31 Female weight category descriptions. Source: author’s own.

Body Descriptions Adjectives Used Number (N) Average Average & ordinary N= 9 Sporty Toned body N=2

Well-build N=2 Sporty calves & muscle tights N=4

Fat Overweight N=3 Fat -belly N=2 Stocky N=1 Wide N=1

Skinny Small N=1 Slim N=2

Similarly, male participants could not agree in the weight category, as participants

elicited two quite distinct categories: (55%, n=6) fit & sporty and (45%, n=5) fat.

Table 32 illustrates the adjectives and frequency of words used to describe avatar

weight. Table 32 Male weight category descriptions. Source: author’s own.

Body Descriptions Adjectives Used Number (N) Sporty Large N=2

Strong N=4 Muscular N=6

Fat Big Belly N= 1 Chubby N=3 Heavy N=1

The attention also focused on avatar legs, with disagreeing judgments. Female

participants (32%, n=9) described them as: ‘short and stubby’ (21%, n=6), ‘long with

sporty calves’ (18%, n=5), or ‘strong and masculine’ (14%, n=4). Moreover, when

describing legs, female (11%, n=3) referred to body ratios and described the avatar as

having a long torso and short legs. Male avatar described legs as ‘nice’ (10%, n=1),

‘strong’ (10%, n=1), and highlighted ‘well-defined tights’ (19%, n=2). Nevertheless,

there were disagreements in terms of the leg-torso relationship. Participants (19%,

n=2) saw legs as proportional, while others (27%, n=3) saw legs as shorter with broad

shoulders and long torso. The female participants described body posture as lean

(10%, n=3) or striking (14%, n=4) and silhouette as straight (25%, n=7), narrow

(n=14%, n=4), or flat (7%, n=2). In addition, female participants (14%, n=4)

described the waist as not distinct or pronounced, and only one person noticed the

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curve on the back spine while rotating the avatar. Male participant (10%, n=1)

summed that the avatar represented ‘typical alpha male with big forearms and broad

chest’ other (18%, n=2) criticised model muscle definition as exaggerated.

Statistics

The gaze data was visualised as heat maps in Figure 23 and Figure 24. The critical

finding unfolds that majority of data were clustered around the avatar AOIs and

directed towards the head and shoulders. The study findings also found a visual

similarity between female and male viewing pattern.

Figure 23 Female interface heat map. Source: author’s own.

Figure 24 Male interface heat map. Source: author’s own.

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A Wilcoxon-Mann-Whitney was performed to investigate gender differences in total

fixation level when viewing the 3D Body Scanning interface. Three dependent

variables were used based on selected AOIs. The independent variable was gender.

Table 33 demonstrates that this study found statistically no significant difference

between males and females on the combined dependent variables. Thus, the study

concludes that there is no significant difference between female and male in the

fixation pattern when viewing different elements of AOIs. This means that there is no

significant difference between genders when inspecting 3D Body Scanning interface. Table 33 A Wilcoxon-Mann-Whitney test. Source: author’s own.

Mann-Whitney U Wilcoxon W Z Asymp. Sig. (2 tailed) Polygon 105.000 171.000 -1.651 .099 Rectangle 1 158.500 593.500 -.030 .976 Rectangle 2 147.000 582.000 -.379 .705 Rectangle 3 159.000 594.000 -.015 .988 Rectangle 4 137.000 572.000 -.682 .496 Rectangle 5 141.000 207.000 -.560 .575 Rectangle 6 127.000 193.000 -.987 .323 Rectangle 7 144.000 210.000 -.470 .639 Rectangle 8 145.500 211.500 -.424 .671 a. Grouping Variable: Gender

The boxplots show representative data from descriptive statistics. The data show no

substantial difference, and the mean was reliably distributed. Whiskers represent the

minimum to the maximum data range, and the median is signified by the central

horizontal line. The upper and lower limits in the box plot outline the first and third

quartiles.

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Figure 25 Boxplot summary. Source: author’s own.

Discussion

6.5.1 Customer Experience

In the more than thirty years since the first 3D Body Scanning was put into use in

retail, little has changed regarding the fundamental design agenda of these scan

technologies (Daanen and Ter Haar, 2013). The device size shrunk, and computing

power increased, but the interaction protocols remained the same (Gill, Hayes and

Parker, 2016). The increasing evidence of a discrepancy between the intended

technology use and the use by customers made the design of 3D Body Scanning an

important area for investigation. This study establishes that the discrepancies could be

attributed to an inadequate representation of the user’s requirements in the design of

3D Body Scanning. This requires shifting from siloed to cross-functional approaches

and changing from a touchpoint to a journey orientation. Table 34 identified design

and usability issues and proposed five critical issues and guidelines on how to

improve technology design and interaction effectiveness.

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Table 34 Recommendations for 3D Body Scanning Experience. Source: author’s own.

Recommendation Detail

• Employ simple space design considerations and sections.

Good design can enhance trust, ease of comprehension, and lead to a reduction in stress.

• Make the right atmosphere Avoid embellishments, use neutral lights, and add doors, and shelves and hangers to put clothing.

• Explain the process through retails salesperson in plain language

Use well trained retail salesperson to walk the customer through the process. Avoid any technical terms and words that can be interpreted differently by people with different backgrounds or expectations.

• Explain the process through user-friendly apps and chat-box

Use the app to explain data use and storage, and retail policy. Use the app as a medium for additional references to brand design and practice. Make a chat-box option available to walk the customer through the process during the interaction.

• The appropriate use of graphics is helpful

Work with the UX team to create posters and presentations around store and scanning space. Graphics can help people understand numbers and put the process in context.

A significant component of the research findings concerns the design context in which

this study found that developers often work with inadequate information on user

requirements. This study findings indicate that relying on this engineering angle may

have led to accounts of framing that are insufficient in discussing how to design 3D

Body Scanning. This study suggests that technology developers need to understand

the relevant design space and find an appropriate solution within that space (Baek and

Lee, 2012). Harvey et al. (2014) noted that the perception of space and design

elements in 3D Body Scanning complicates the process of linking scanning with

customer try-on habits. Indeed, this study found that the scanning process is not fit-

for-purpose for the intended context of use in which participant’s needs had not been

adequately met. Most participants reported that they needed to repeat the scan

procedure to accommodate the poor system instructions. In many cases, this involved

more time and effort for the users.

The design atmosphere themes appear to be intertwined with the theme of privacy and

retail responsibility. This study found that the lack of clear design guidelines leads to

growing customer concerns about data privacy. This finding is in line with the

research of Aloysius (2018) and Urbinati (2019) that emphasis customers’ privacy

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concerns about the amount of their personal information that is collected negatively

impact the adoption rate. Because references to privacy outnumber those related to

beneficence, it appears that issues of the standards regarding security, retail integrity,

and content are necessary to be communicated more clearly through design. This

study, however, found that developers lack a mechanism for understanding users’

requirements concerning system solutions. These study findings conclude that

developers lack design references and strategies that are appropriate for customer-

related tasks in a retail shopping environment.

6.5.2 Interface Development

This study found that the interface design has a significant influence on the way

customers approach 3D Body Scanning, and as a consequence, on the overall process.

Nevertheless, customers struggled to find a useful reference to retail shopping

practices or pattern evidence-based theories. The interface design relates to the

information on users’ requirements in terms of the form in which scan information is

presented to customers. Table 35 identified interface issues and proposed strategies to

improve technology effectiveness. These study guidelines propose to use appropriate

language that non-specialists can understand and apply for their specific buying

purpose (Gill and Brownbridge, 2013). This is important not only because it is

aesthetically enjoyable but perhaps because it helps users understand the content of

the document. What emerges - as being particularly crucial in the information

customer require - is clear details on the functionality and connection to the retail

experience. Information in the existing form, however, leaves the functions unclear.

As a result, it becomes difficult for customers to work out how to change or make

improvements in their garment selection and purchase routine.

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Table 35 A summary of recommendations for interface development. Source: author’s own.

Recommendation Detail • Make report easier for

non-specialists to understand

Use layman terms, plain language, avoid jargon as most reports are incomprehensible even to a retail person. Make the result as unambiguous as possible.

• Consider the structure and appearance of the interface

The structure of the interface affects understanding and ease of reading.

• Make the main result prominent

The result of the scan should stand out and be easily found within the interface. Do not dilute the main message.

• Keep technical details separate

Put fine-grained technical details into a separate section. Avoid dense blocks of text and lengthy reports.

• Provide ‘action to be taken’ section

Include a section of recommendations and concrete next steps for product engagement.

• Provide a ‘what result means’ section

Explain what the implications of the result are (brand size guidance, style info).

• Use colours to make things easy to read

The use of colours can help with understanding, linking, and appearance of the document.

• Present results in neutral terms

Do not use positive or negative message framing or colour result. Aim instead for a statement of fact.

The study findings showed a strong relationship between the form in which

information was provided to customers and how they were able to apply the

information to their shopping scenarios. Additionally, considerations of target users’

preferences and data privacy input are minimal. The isolation of the user interface

from the overall fashion retail purpose created a design context that centres on

technical orientation, in which ‘providing more information is always better’. Nevertheless, narrowing user interface information can significantly improve design

context that lies in the careful mapping between users’ requirements and the system

design, and also in the validation process that develops (Blum et al., 2007). Moreover,

the current interface lacks educational information, which is a crucial component of

innovation, to foster skills necessary to transition into 3D Body Scanning. The

development of the design process appears to depend upon the resolution of issues

related to readability, which require closer collaboration between fashion and

technology developers, as demonstrated in the previous chapter.

In terms of avatar design, most individuals do not seem to notice drastic differences

between their own and other morphologies. Indeed, researchers have agreed that the

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ability to perceive conspecifics as being “like me” is at the core not only of social

development but also our sense of self (Plotkina and Saurel, 2019). This paper

confirms findings by Linkenauger (2017) by showing substantial distortions across

the whole body and measuring its significance with eye-tracking data. Thus, this study

explicitly shows that some of the interface adoption barriers are in the eye of the

beholder. Additionally, this paper found that participants, despite their gender,

exhibited a more definite filial preference toward the configuration of features

associated with the face despite its low quality (Meltzoff, 2007). This is in line with

the findings of Hotta et al. (2019), who stated that humans, when viewed the pictures,

tend to gaze more on the face region.

Conclusions

This study set out to discover the customer experience in 3D Body Scanning based on

Size Stream scanner and interface in a controlled academic setting. The study findings

revealed that developers’ weak relationship with the customers, a lack of evidence, a

lousy reputation in retail integrity, and the participants’ limited perception of 3D

Body Scanning role in retail compelled the technology use. The analysis has

highlighted nine design characteristics, which can contribute to developers

understanding of the representations of users. The concepts introduced in this study

provide a starting point for shifting thinking away from traditional engineering

methods and moving toward a more comprehensive service design approach.

This research has highlighted issues with existing designs that could (and should) be

improved, including acoustical properties, control of lighting, shielding and layout

design. This study put forth a set of design guidelines necessary for improved

customer involvement. This study results reveal that the critical design barriers are

related to the technology image and interior, ambiguity in interaction, and privacy.

The results found that the scanner name was the most desirable thing, as participants

attached a high-technological imagination and curiosity. However, the study also

found that the present experience was associated with the clinical procedure and

interior look. What emerges from study findings is a discrepancy between the design

intentions underlying the customer expectations, and the actual representation of the

design. The study also found that the scanning interface does not fit with the purpose

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of shopping – customers exclusively focus on the avatar face and shoulders features

and struggles to link data with shopping outcomes. Moreover, the measurement list

was often too complicated for customers to understand. The study recommended that

retailers strip out all the unnecessary jargon and provide additional references in the

form of an app to help customers interpret and better contextualise scan data in e-

Commerce.

There is still a need for more work on the raison d’être of in 3D Body Scanning to

provide practically applicable solutions, as well as a contribution to knowledge that

can be applied to solve retail diffusion problems. The presented guidelines provide an

avenue for technological developers to better understand and become more responsive

to the users when designing their services. The technology design should become part

of the stakeholders’ developments agenda that take part in actualising and putting into

motion theories, models, and the very propositions of user experience. Thus, the

developers should ensure that research findings are used and integrated into practice,

to produce benefits for customers and retailers.

As of today, this study is the most comprehensive depiction of customers’ barriers

regarding the use of 3D Body Scanning. These findings are strengthened by the use of

robust methods for both the quantitative and qualitative part. It is, however, worth

considering that results are not static – they change over time.; thus, revising these

findings may prove useful as both technology and events evolve, and the expectations

increase. Successful design is a dynamic process that needs to be adjusted and

redefined systematically to meet changing retail demands. There was generally broad

consensus within each group as to what was important to them, and the

recommendations can be implemented without compromising the information.

Nevertheless, there was a bias toward female participants in the study sample.

Although males did take part in all research stages, any future work implementing

these recommendations would benefit from an equal balance in sample.

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CHAPTER 7

HOW CAN FASHION INDUSTRY INTEGRATE 3D BODY

SCANNING WORKFLOW: A CRITICAL REVIEW

Introduction

The preceding three decades have seen rapid advances in methods designed to

describe and quantify the human body through 3D Body Scanning (Gupta, 2020). The

term 3D Body Scanning accommodates a wide range of processes, including scanning

booths, mobile apps, and body metrics tools, i.e. Virtual Fit Interfaces (Ashdown,

2020). 3D Body Scanning allows personalised data to be collected quickly, at high

volume, for low cost and in real-time using sensors and apps (Heymsfield et al.,

2018). The ability to gather digital anthropometric data cheaply across retail target

markets means that: (i) more styles for diverse body shapes can be pursued; (ii) sizing

and fit metrics can be statistically validated; and (iii) new brand relationships can

emerge from the data that would never have been contemplated before (Ashdown and

DeLong, 1995; Song and Ashdown, 2010; Gill, 2015).

Through technology lens, 3D Body Scanning state-of-the-art appears capable of

solving most of the fashion size and fit issues. Despite technological advances, much

of the 3D Body Scanning workflow still requires time-consuming, manual input by a

diverse team of garment developers, retailers, and manufacturers. 3D Body Scanning

poses challenges for the fashion industry that need to adopt a new set of expertise,

which means that the retailers capacity to perform and analyse data has not expanded

as widely as the technical sophistication of scanning devices itself (Daanen and Ter

Haar, 2013). As a result, the retail adaption is not keeping pace with technological

innovations; thus, the ‘adoption deficit’ is getting wider (Lewis and Loker, 2017).

Therefore, the number of steps necessary for service provision for fashion practice

must be characterised and mitigated before data can be applied and utilised in apparel

product development.

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The growing number of case studies and theoretical work has produced an extensive

collection of development issues based on various research framesets – engineering

(Khalili and Zeraatkar, 2017), computer science (Streuber et al., 2016), manufacturing

(Apeagyei and Otieno, 2007), marketing (Pookulangara, Parr and Kinley, 2018), and

fashion (Petrova and Ashdown, 2012). Taken together, these framesets subsume a

wide variety of specific barriers that are highly context and industry-specific, with

little grounds yet for generalisation. In result, although there are many people

interested in technology, there is little agreement on the most effective way of

creating it. The technology developers have strong assumptions about the value of

their offering, but that value is not interpreted the same way by retailers,

manufacturers, and researchers. 3D Body Scanning is faced with a dichotomy of what

technology developer’s claim is possible versus what is realisable and appropriate

from a fashion perspective. A mismatch between the developer’s frameworks and the

practical realities in which garment developers operate translates into the poorly

informed 3D Body Scanning design. This problem is most pronounced in technology

interfaces with complex, practice-focused and sometimes arcane drafting methods

(Gill et al., 2018).

Although progress towards 3D Body Scanning not yet achieved its expectation and

potential; there is increasing recognition of its value (Xia et al., 2019). Several fashion

brands begin to partner directly with 3D Body Scanning developers to access

innovation without internal commitments. Most of these collaborations are

informational, but companies are becoming aware of the benefits that can come from

more engaged offerings (Wright, 2019b). For example, 3dMD partnered with Under

Armour to create custom clothes for athletes (Sokolowski, Silbert and Griffin, 2019),

Size Stream with Alton Lane for tailor-made suits (Gill, 2015). TC2 with Nike

(Buckner, Ashdown and Lyman-Clarke, 2007), who also bought FeetID for products

improvements (Butler-Young, 2018), and Amazon purchased Body Labs AI to create

the Amazon Halo watch (Abdulla, 2020). Smaller brands have leapt into the breach as

well. There are already close to 40 apps for iOS or Android about 3D Body Scanning

(Cherdo, 2020). So far, however, most of the fashion brands have approached

technology as an afterthought or, at best, as a supporting customer-engagement versus

a long-term opportunity (Ashdown, 2020). As the knowledge gaps present a

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significant barrier to progress that unless resolved, may continue to limit the use of

3D Body Scanning in fashion retail applications.

This study aims to examine the complete process from data acquisition to end-user

applications in 3D Body Scanning workflow and to identify and explain the reasons

why fashion industry adaptation does not yet take place. This critical review takes

stock of existing knowledge and outlines the contours of research to identify and

explain barriers with directions on how they might be addressed without being tied to

one particular normative view. The proposed service workflow does not introduce

new concepts into the field but instead integrates the existing concepts in a way that

can align the 3D Body Scanning developer’s priorities with fashion practice. Given

the widening adaptation deficit, this research is not only beneficial from a theoretical

perspective but also highly relevant in practice.


The model typologies are aimed towards the future in the sense that they can show

that something is possible to build (Alexiou and Zamenopoulos, 2008) or reveal what

is otherwise unknown or inaccessible without an external representation (Buur and

Larsen, 2010). This design technique acknowledges the involvement of all

stakeholders in the development process (Lauff et al., 2020) and ensures that concepts

are shareable and open to communication (DiSalvo et al., 2011). This study took

inspiration from a series of recent theoretical model typologies exercises in service

innovation studies, as suggested by Gudowsky and Rosa (2019). In the context of

emerging technoscientific innovations, there is increasingly an acknowledgement of

the need for the development of interdisciplinary models to unpack the heterogeneity

of stakeholders and the contexts for their actions (Stilgoe, Owen and Macnaghten,

2013).

3D Body Scanning process flow has been demonstrated by Daanen and Ter Haar

(2013), ranging from (i) data acquisition, (ii) data processing, (iii) data curation and

storage, and finally, (iv) applications. The model was then applied and reinforced by

Heymsfield et al. (2018) into the clinical and health context. This conceptual

framework provides a solid foundation for this study and ensures that the full ranges

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of issues are appropriately addressed. In that sense, the 3D Body Scanning framework

can offer a potential solution to interoperability by providing a common platform for

defining characteristics needed for service deployment in fashion retail as well as

insights into how these issues diverge and separate in different contexts, i.e., clinical,

health, and fitness. The scope of 3D Body Scanning continues to grow, and the

diversity of applications continues to multiply; thus, the connected service framework

is needed to connect distinct industries and stakeholders. This framework sets an

agenda for service that resist framing research only as orientated towards (and

constrained by) technology concerns (Lauff, Kotys-Schwartz and Rentschler, 2018).

Too often, the fashion stakeholders are brought in only at the end of diffusion

procedures, to inform implementation or communication plans, rather than to help

determine development directions (Gill, 2015). However, this insular approach has

been challenged by innovation studies, which promotes intensely collaborative

approaches to interdisciplinary research (Fisher and Schuurbiers, 2013).



This study applies a heuristic evaluation that involves having the author to examine

the service and judge its compliance with recognised usability principles – the

heuristics (Nielsen and Molich, 1990). The heuristic evaluation aims at explaining

each observed usability problem concerning established principles that identify parts

of the design that may induce errors in the use or in other ways to hinder the effect the

design is meant to achieve (Norman, 2005). The study proposes a 3D Body Scanning

workflow framework that links the technology components and development barriers

that must be overcome into appropriate research agenda for the fashion industry

adoption.


The empirical results are based on previous research findings (chapters 4-6) and

include 30 interviews with 39 key stakeholders, carried out in October 2017 – May

2018, and focus groups and interviews with 52 technology users: fashion customers

and fashion retailers between September 2018 - February 2019. This technique

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allowed the study to limit bias by capturing the range of almost 100 stakeholders

involved in the diffusion issues but with different views and expertise. This research

process involved sustained engagement with core stakeholders and users (early

adopters), generating broader impact through dialogue, and documenting the process

to co-produce new insights.

7.3.3 Data Analysis

Preliminary categories were constructed based on Daanen and Ter Haar (2013) to

demonstrate the 3D Body Scanning process flow and map inputs, analyses, and

outputs. The preliminary categories were devised and then refined based on (i) the

authors experience in 3D Body Scanning research; (ii) analysis of research presenting

the structure of a range in 3D Body Scanning; and (iii) input and critique from the

research team to test whether interpretation resonated with their own experiences of

the technology.

This study applied the heuristic evaluation that is based on model typologies for

research analysis and presentation. Model typologies are well-established analytical

tools (Collier, LaPorte and Seawright, 2012). They are used to form and refine

concepts, draw out underlying dimensions, and create classification categories for

classification and measurement (Boyd et al., 2017). Based on rigorous qualitative

work, model typologies have conceptual power to provide new insight into underlying

dimensions of concepts (McKnight and Chervany, 2001). There is, of course, a certain

amount of subjectivity involved in this analysis, and a different research group might

have developed a different typology of perspectives (Henry et al., 2020). The analysis

of words and their associated meaning provides a basic template for this study

typology. To identify the words and phrases in Table 36, focused on stakeholders and

users’ interviews, which resonated most strongly with each perspective, and then

revisited the transcripts and codes for these interviews to identify words, which were

used frequently or emphasised. The volume and frequency of the words may also

reflect which stages were more important to stakeholders and which were often

omitted in discussion.

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Table 36 Words and phrases associated with each stage of the model. Source: author’s own.

Perspective Key Words Data Acquisition Measurement, dimension, landmark, definition, anatomical meaning,

accuracy, consistency, reliability, statistically valid, directory, reference, vocabulary, product development, protocol, engagement, comparison, methodology, documentation, automatic extraction, target market, presentation, fit variables, ease, line, balance, grain, set, subjective, objective, customer perception.

Data Processing Point Cloud, data heterogeneity, clean, smooth, noise, holes, process, skills, software, scan position, demographic, posture, dress code, avatar, bones, joints, vertices, mesh, format, texture, Alvanon, customisation, facial features, hair, look, applications, engagement.

Data Storage Data storage, search, manage, process, privacy, flexibility, access, visualise, exchange, privacy, security, speed, curating tools.

Data Applications

CAD, CAM, data modelling, mobile applications, Virtual Fit, mobile apps, validation, interface design, digital mirrors, VR, AR, MR.

Result and Discussion

This section outlines the structure and content of the 3D Body Scanning model,

presented in Figure 26. The model covers the path from data acquisition to when

insights are used for retail through customer-facing technologies. The model begins

with the acquisition of measurement-related data using a variety of sensors, following

the landmark definitions and fit parameters. Once data has been appropriately

acquired, the processing step takes place whereby data is transformed and integrated

based on the end-model. For example, data may undergo different transformations

from a generic point cloud to avatar customisation. The avatar customisation option

indicates that there are no one-size-fits-all solutions, probably reflecting the multiple

and context-specific orientations of 3D Body Scanning. This processed data is then

curated, a step that comprises storing, cleaning, and filtering. The resulting model can

be deployed for customer applications, which can consist of Virtual Fit Interfaces,

CAD design or for the scan to pattern drafting CAM applications.

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Figure 26 3D Body Scanning model for the fashion industry. Source: author’s own.

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7.4.1 Measurement Acquisition

The first variable in the framework typology relates to data acquisition. 3D Body

Scanners aim to create a high-quality representation of the human body surface using

non-invasive optical methods to obtain anthropometric dimensions. Gill (2015) has

listed critical measurements from 3D Body Scanners as circumferences, widths, linear

dimensions and volumes. The model demonstrates that each of the measurement

dimensions requires (i) measurement landmarking, and (ii) fit indication.

Measurement Landmarking

The landmark is referred as body marking the location of anatomical guideposts

(Pleuss et al., 2019), rooted in manual techniques using palpable or visual aspects of

the body surface (Gill and Chadwick, 2009). The use of landmarks is motivated by a

desire to enable acquired data to have the assignment of anatomical meaning to the

scanned mesh surface. However, the key challenges remain as the successful

applications of landmarks will depend on a measurement name and definition to

ensure accuracy, consistency, and reliability (Serge, 2007). The landmark location

should be based on validated standards to ensure the data algin with the garment

developer’s needs (Kincade, Regan and Gibson, 2007). Manual identification of

landmarks is time-consuming, requires anthropometric training, is subject to intra-

and inter-observer variability (Javaid et al., 2020), and not suitable for the analysis of

large data sets (Gill, 2018). Thus, the use of 3D Body Scanning in landmark

identification provide reproducible results with high precision (Kennedy et al., 2020).

However, the measurement reproducibility is still relatively low (Parker, Gill and

Hayes, 2017). Developers practices lack the strict validation of 3D Body Scanning,

which that undergone systematic reliability assessment paired with an industry-

standard (Kouchi and Mochimaru, 2011).

The reliability issues usually arise from a lack of technology protocols to ensure

consistency in the process of measurements acquisition (Simmons and Istook, 2003).

As a first step, technology developers should aim to create a landmarking directory

that allows tailoring the available techniques to particular retail needs and

applications. The directory interface would enable representing better the numerous

anatomical variations found in the human body. Technology developers need to work

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interactively with fashion and research teams to define landmarking locations and to

allow an acceptable level of reproducibility by individuals with different expertise

(fashion designers, apparel manufacturers, engineers, and computer scientists). The

landmarking definitions should permit better data exchange and comparison of

results. The landmarks must also reflect the fashion industry needs that exemplify the

applications of related methods in product development (Harwood, Gill and Gill,

2020).

Pargas et al. (1998) have shown that the measurement data should be accompanied by

metadata such as landmarking documentation and data sets from multiple sources to

identify similarities and differences between scanners and landmarking

methodologies. The inaccuracy in landmarking can lead to discrepancies in the

interpretation of the data, whatever the quality of the hardware used in the

measurements (Serge, 2007). In essence, the landmarking directory is necessary to

facilitate the identification of measures for product development. The landmarking

directory will provide garment developers with automatic measurements of any

number of parts in body dimensions. The precise landmarking will also allow writing

internal macros, i.e., short programs, to take any of the measurements automatically.

The program would enable the fashion industry to automate the process of analysis by

defining, for example, how to recognise and measure a person’s chest. The scanning

software may then be instructed to take chest measurements of any number of images

without human intervention. For instance, macros can take linear surface

measurements (follows the contour of the body); straight-line measurements (provide

Euclidean distances between points); and circumference measurements (trace around

the convex hull of the points). Besides, as Lee and Park (2017) suggested the

landmarking, tools may provide segmentation functions, to select and isolate specific

segments of the body, with precision for analysis taken at any angle, for example,

detailed views of shoulder slopes and back postures.

While landmarking directory and macros are valuable for fashion retail, as well as

research, very little is known about how to create interface design to generate a highly

valid standardised dataset to train, validate and compare automated measurements.

The fashion industry needs an empirically tested template that displays 3D Body

Scanning data in a user-friendly manner (Nicod, Llosa and Bowen, 2020). However,

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there is no research on the assessment techniques that suggest apparent gaps in

stakeholders’ knowledge in terms of user needs, language and graphical design (Lee

et al., 2010). The development of scalable and economically viable interfaces that

deliver on the promise of personalised fashion must be co-designed with the fashion

industry and target market customers. The interface design represents the focal point

at which technology and fashion meet and enable decision-making ultimately to lead

to the creation of bespoke offerings.

Fit Parameters

The collection of measurements for product development requires not only precise

landmarking but also the satisfaction of complex fit parameters to ensure the finished

product function correctly on the body (Ashdown and DeLong, 1995). The link

between anthropometric data and products requirements can be demonstrated through

fit attributes – (i) ease, (ii) line, (iii) balance, (iv) grain, and (v) set (Erwin, Kinchen

and Peters, 1979).

3D Body Scanning presents opportunities to incorporate fit parameters into pattern

drafting to refine the existing fit sessions (Ashdown and O’Connell, 2006). However,

the key technical limitations remain. First, most of the fit data parameters within these

systems have not been collected using accurate and well-validated procedures

(Ballester et al., 2015). The fit parameters necessary for accommodating grading

practices are frequently not available from one unique source (Ashdown and Loker,

2010). Often, garment developers obtain definitions for fit parameters from various

resources (their in-house fit sessions, the tailor personal experience, past literature,

i.e., public, or commercial data repositories) without extensive documentation. It is

then up to the software developers to find a way to combine the different pieces of the

puzzle into one coherent fit system (Ballester et al., 2015). In practice, data

combination from various invalidated sources requires much more manual tuning and

data editing to achieve ‘acceptable’ results (Hamad, Thomassey and Bruniaux, 2014).

Thus, the collection of fit places great importance on the expertise and subjectivity of

the tailors involved in the stages of product development (Niwa et al., 1998).

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Fit parameters are paramount for successful application and analysis in product

development and require appropriate referencing in order to be realised. Fit

parameters (definitions, standards and assumptions) should be integrated within

pattern libraries and repositories in which CAD software could read and reason over

(Gill, 2009). The universal fit parameters could provide reliable, high-quality data as a

basis for the grading formulation and implementation of fit strategies to satisfy the

requirements of different end-users and apparel styles. To this end, fashion

practitioners should provide software developers with the grading assumptions and

theories that underlie most pattern development practices. Relevant information for

different sizes should be then estimated from quantifiable information available in fit

parameters (Scott and Sayem, 2018). This transparency in the process can reveal

inferences hidden in unstated assumptions, identify potential contradictions or

undesired fit implications and potentially generate explanations for quantifying ease

requirements (Gill and Chadwick, 2009).

The optimal communication and consensus between the various stakeholders – and

the customer are crucial. Thus, garment developers need to understand what

constitutes a good fit from the customer’s point of view (Ashdown and DeLong,

1995). The design software requires the agility to accommodate customer style

preferences (Huang, Liang and Wang, 2018) and novel channels and methods of

communication (Lee and Chow, 2020). Nevertheless, as Loker et al. (2004) argue, the

customers may not recognise what constitutes a good fit, even when wearing a

perfectly fitting garment. Therefore, it is essential to observe and analyse customer

behaviour when they shop online. To this end, as suggested by He et al. (2010), AI

tools within size and fit applications such as VFI can be linked with customer’s

preferences to identify patterns and create management sales reports. A further area of

application for 3D Body Scanning is the evaluation of preferences, including the link

of customer behaviour, trends interactions, and evolution of individual style

preferences based on body shape (Pookulangara, Parr and Kinley, 2018).

7.4.2 Avatar Processing

3D Body Scanning data must be processed before it can be used for storage and

applications (Romeo, Stannard and Bourgeois, 2017). The second variable in the

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framework demonstrates that the processing stage is composed of (i) point cloud, and

(ii) avatar topology. The processing stage is essential to ensure the generated

geometry from the obtained 3D scan is validated against actual body measurements to

ensure accuracy and reliability of avatar dimensions (Hale, Linley and Kalaskar,

2020). Even the small distinction between customer actual body and avatar can cause

dissatisfaction with size and fit that could lead to garments returns (Petrova and

Ashdown, 2012). As discussed in the previous section, there is a growing trend

towards the integration of data from various 3D Body Scanning technologies. As

such, there is a multitude of unstructured multi-modal data with substantial noise

(Cheng et al., 2018). For example, different devices may be equipped with unique

sensors, including quality, size, and position that result in different noise topology.

Point Cloud

The point cloud is a pre-stage of avatar creation, and it determines the validity of

collected measurements. The software utilised to create, and measure 3D Body Scan

creates raw depth frames from the camera(s) sensor(s). From each frame, a list of

independent (x, y, z) points in 3D space, called a point cloud, is extracted (Heymsfield

et al., 2018). Multiple point cloud images are captured simultaneously during the

scan; aligned and merged to generate a single point cloud that combines surface and

shape information from many different angles (Saint et al., 2018). The captured data

then need to be cleaned and filtered; removing artefacts before any feature extraction

or modelling can take place (Yu et al., 2012).

Starting with the clean image is essential to the success of automatic measurement

extraction, as extraneous points can give false measurement points (Li and Li, 2009).

Data collected from different body scanners may have varying noise topology, and its

reconstructed geometry often has holes or gaps in occluded or hard-to-view regions

such as the top of the head, under the arms, or between the legs in the groin area

(Golyanik et al., 2017). The ‘unwanted points’ are the result of various problems,

primarily due to the poor lighting or sensors positioning (Puente et al., 2013).

Performing point cloud repair is thus sometimes necessary, especially when the

missing regions are large (Morlock et al., 2016). These data gaps may impact the

accuracy of anthropometric measurements, a case demonstrated by Sobhiyeh et al.

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(2019). The image files generated by 3D Body Scanners frequently include

extraneous points outside the body. At present, the cleaning point cloud requires skills

in 3D modelling using open source (i.e., Blender) or commercial software (i.e.,

Autodesk Maya or ZBrush); or even mathematical modelling. There are a plethora of

methods available, and algorithms show promising improvements for the processing

accuracy, as researched by Rashidi and Brilakis (2016), Heymsfield (2018), and

Tinsley et al. (2020). However, several outstanding questions remain, regarding

handling the heterogeneity of the data and variability in the demographics, scan

position and dress code; and research needs to investigate these topics more

comprehensively.

Avatar Topology

A 3D mesh consists of finite points with each element defined with specific physical

properties. In the topological form, points from the point cloud (organisation, flow

and structure of vertices) are connected to form a visualisation of the body (Allen,

Curless and Popović, 2003). 3D modelled object with a specific volume and

dimension can be ‘meshed’ with a finite number of elements that helps in solving for

accuracy in pinpointing regions of strain at a higher resolution. 3D Body Scanning

software should be able to organise vertices in the 3D model in a way that it is

efficient, clean and well-detailed to create a 3D model that can then be saved in

various formats including OBJ or FBX (Fedyukov, 2019). The 3D model may include

bones, joints, textures, vertex weights, and mesh (Cordier and Magnenat-Thalmann,

2005). The model represents a complete object with various elements like 3D mesh,

texture, and animation. The early work on this has been completed by Magnenat-

Thalmann (2005), continuing with new methods of extracting and predicting body

dimensions from photographs and descriptions (Streuber et al., 2016).

The 3D avatar can impact virtual garment fit evaluations (Buckner, Ashdown and

Lyman-Clarke, 2007), and modelling (Olaru et al., 2012), mass-customisation

(Harvey et al., 2014), virtual try-on (Beck and Crié, 2018), and VR shopping

simulations (Novotny, Gudmundsson and Harris, 2020). However, little has been

written about how fashion designers are reacting to or using 3D avatars in product

development. The level of details and customer engagement with customised 3D

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avatar need to be further investigated (Januszkiewicz et al., 2017). While there is a

substantial body of research on customers reactions to 3D Body Scanning image

(Grogan et al., 2020), little is known on how user 3D Body Scanning interface drive

the use of 3D Body Scanning in practice. Thus, fashion designers who increase the

self-awareness without clear information on body relation to fashion design and use of

realistic avatar models may be at risk for increased body concerns (Linkenauger et al.,

2017). In addition, there are many different methods of avatar presentation, from

generic Alvanon body forms and cartoonish looking, to highly personalised avatars in

which customers can choose from hair colour, size of lips and skin shade. Thus,

customers should be able to decide on the level of details as well as pose presentation,

garment design and animation. Loker et al. (2004) implied that customers’ would feel

differently about acquiring and wearing clothes if they were involved with it in more

technologically interactive ways through 3D Body Scans. However, how the use of

3D Body Scanning will influence the actual purchases has yet to be tested. These are

the questions researchers need to study as interactivity helps us become involved with

product development in new ways (Merle, Senecal and St-Onge, 2012).

7.4.3 Data Storage and Curation

The kernel of the 3D Body Scanning is based on an extensive and detailed database

entirely collected from validated and objective data acquisition procedures.

Regardless of its intended end-use, all the data collected using the methods previously

discussed requires appropriate storage, curation and processing (Hirst, White and

Smith, 2018) prior to analysis. The databases provide integrated platforms to organise,

search, manage, process, analyse, visualise and exchange data across different devices

and sensors, alongside privacy, security and access concerns. Since the CAESAR

survey in the year 1999 (Robinette and Daanen, 2003), more than 20 large-scale

surveys have been conducted by researchers across the world using 3D Body

Scanning technologies of a different kind (Ballester et al., 2018). However, all the

collected data is incompatible, and lack the universal resources enabling to conjointly

use and analyse datasets, making data obsolete in the fashion practice.

The third variable in the model highlights that cloud-computing integration with 3D

Body Scanning can help databases gain traction and develop suitable guidelines for

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practice. In that sense, as demonstrated in Figure 27, the database is based on two-

layered architectures composed of (i) a fog-computing layer and (ii) a cloud layer for

data management. Multi-tier computing could be used to develop user-centric

approaches in which different services are autonomously customised according to

specific applications and user preference.

Fog Computing Layer

3D Body Scanning cloud computing alone cannot support such ubiquitous

deployments and applications because of connectivity shortcomings (Yang, 2019).

The multi-tier computing resources are required to ensure timely data processing, as

depicted in Figure 27. Fog (or mini-cloud) computing entails data analysis on edge

devices (Chen and Ran, 2019), which enables real-time data that performs processing

locally (Alrawais et al., 2017). Fog layer also invokes the idea of ‘remote access’, in

which a fashion industry can work with scan data using a standard WI-FI Internet

connection. This kind of technology is referred to as a ‘client-server architecture’

where the main server application holds and manages the data and delivers a view of

the analytic output into a retail client edge device or as a web browser.

Figure 27 The 3D Body Scanning data storage model. Source: author’s own.

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The illustration in Figure 27 provides an overview of the process starting with the

device layer (which includes fast, real-time processing and data visualisation,

embedded systems, gateways, and microdata storage), followed by the fog layer

(which includes local networks, virtualisation, and data analysis) and finally cloud

layer (which consists of data centres, storage, and processing). The fog computing

layer emphasises the 3D Body Scanning developer’s dual role as both data depositors

and beneficiaries of the integrated data frameworks. According to Yu et al. (2010),

multi-layer practices also underlines the critical role of databases as unifying engines

of research progress that reflect best practices and approaches to data generation,

organisation and modelling. The fog-computing layer also enables the interoperability

of heterogeneous sources of the data that abstracts incoming data formats and

protocols from the specific retail or brand. The specific brand gateways can facilitate

the sharing of information and the driving of components that meet the required needs

of the database. In this way, databases can consolidate data from different 3D Body

Scanners to enable their curation and harmonisation in which curated data are

organised based on specific and detailed features such as age, gender, ethnic

background, or other metadata details – accessible for retail reuse.

Until the turn of the century, Microsoft Excel databases or even personal notebooks

were standard practice for the fashion industry in storing data about brand specific

size and fit guidelines. The vast increase in apparel volume and the complexity of 3D

Body Scanning data has rapidly made a paper notebook an unsuitable solution for

keeping track of information. To this end, the fashion industry needs to invest in data

curators job roles to administer and interpret the large volume of scanning data that is

central to developments. The fashion curators are needed to facilitate precise curation

of digital databases that need to be reliably established and documented according to

the scope and purpose. Data curators will be responsible for locating all pertinent

characterisation of data and metadata within the database, interpret data outliners or

assays scan positions, ensure the study’s integrity and, finally, enter all additional

relevant to retail demographic pieces of information into the database, including legal

consents requirements.

However, gaining anthropometric insight into product development from such a large

quantity of data is an impossible expectation to place upon a practice-based industry

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that focuses on experimentation rather than scientific schemes (Gill, 2015). In order to

solve the future problem of the exploding numbers of results, databases will need to

be changed from being fixed records of body scans in digital folders to be the retail

size and fit aids (i.e., predictive and analytical tools). In that sense, the data curator

should be assisted by an electronic curation tool with smart features, such as drop-

down menus and controlled value relationships that enable the intelligent analysis of

trends in data (Lord et al., 2014). This can allow for data models to be further

explored by designers and researchers to identify or design new target markets.

However, broader adoption across a variety of providers – and the transparency and

portability of the models generated will also be vital. Thus, decision-making support

will need to be auditable to avoid technology homogeneity, racial bias and other

potential pitfalls (Banakou, Hanumanthu and Slater, 2016). Additionally, to support

deep curation efforts, developers should build plug-in applications that enable fashion

users to develop new functionalities within the database, to support novel analysis and

ensure the brand practices and brand IP are protected.

Cloud Storage Layer

Cloud computing architectures include servers, networking, software, databases and

data analysis over the internet, which enable economies of scale (Schadt et al., 2010).

Cloud computing is often considered the centralised paradigm, while the fog

computing layer previously described would be a decentralised paradigm (Eisele et

al., 2017). 3D Body Scanning allowed retailers to better tailor products, messages,

and service to customers’ needs (Beck and Crié, 2018). However, retail technologies

are often seen as too invasive of customers’ privacy and lead to resistance that

undermines their benefits. The challenges of 3D Body Scanning deployment in retail

are the lack of regulations, incentives, and systems to manage retail ownership and

responsibilities for data. Successful applications should hide unnecessary technical

details from the users, minimise the computational overhead and enable legitimate

research. However, 3D Body Scanning developers have yet to achieve this aim fully.

Nevertheless, the ability to continuously generate and use customer information is

essential for the fashion industry to create superior value, and therefore customers

must have a chance for the follow-up elaboration of the quality of garment fit.

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A key consideration for the 3D Body Scanning database is obtaining an appropriate

form of consent from users and knowing what that consent means for how data can be

released, accessed, and reused. 3D Body Scanning data integrity is paramount for

successful application and analysis in the fashion industry and requires suitable data

storage to be realised. Thus, a trusted third party should act as a gateway to monitor

how databases accept requests from retailers, instructs the researchers to carry out

computation on the encrypted data, and decrypts and disseminate the results back to

the retailers. One of the most pressing challenges is that the 3D Body Scanning

industry does not have a global data governance framework that permits stakeholders

to adequately take advantage of the rapid growth in 3D Body Scans. Therefore,

stakeholders need to find an adequate balance that ensures data security and the

ability to protect the privacy of the 3D Body Scan (Koops et al., 2017). These

concerns, however, may be mitigated through the active involvement that empowers

users to decide what data they want to send to the server and what to the retailers.

Informed consent is the underlying backbone in research participation, ensuring

ethical principles in line and protects the individual’s freedom of choice and respects

the individual’s autonomy, thus safeguards the research integrity (Kouchi et al.,

2012).

Concerns around breaking security and protection laws prevent complete and open

data-sharing agreements – blocking a path to the validation of models needed for the

next generation of research from being achieved, and also throws a wrench into the

retail application. The data-sharing practices become bogged down by nebulousness

surrounding a lack of customers trust in retail actions. Ribaric et al. (2016) suggest

that data de-identification (that is, the removal of any personal identifiers) is a

potential route to resolve data sharing and privacy demands. The feasibility of this

approach for 3D Body Scanning is yet to be investigated. In that sense, the fashion

curator who originally collected the data replaces directly identifiable information

with a code that typically retains a key to the data subject’s identity (Gill, Hayes and

Parker, 2016), which permits resort to primary data in the event of any data integrity

questions. In addition, in multiple user modes, customers can make their API requests,

rather than have the administrator make all requests on their behalf, thereby retaining

accountability which in turn leads to more positive attitudes (Feng and Xie, 2019).

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7.4.4 Applications

The fourth variable in the model shows that there is a wide and growing range of

commercial, data-driven applications for 3D Body Scanning. These applications serve

a critical role by allowing new approaches to be rapidly and flexibly tested and

potentially complement product development workflows, enabling fashion

practitioners to manage their size and fit selection better in ready to wear garments.

The model in Fig 26 demonstrates that present 3D Body Scanning applications can be

divided into three key applications: (i) Virtual Fit Interfaces, (ii) Computer-Aided

Design (CAD), and (iii) Computer-Aided Manufacturing (CAM).

Virtual Fit Interfaces

Virtual Fit Interfaces are the tools that aim to provide opportunities for the customer

to engage in size selection in e-Commerce to try-on clothes on the digital avatar

(Hernández, Mattila and Berglin, 2019). VFI’s have gained traction in recent years

and have the potential to enhance customer product interaction in e-Commerce

(Plotkina and Saurel, 2019) and increasing responsiveness to individual customer

feedback (Weathers, Sharma and Wood, 2007). Retail interfaces can harness customer

fit experience alongside descriptive criteria of the body that could help generate the

banks of data needed to navigate and create suitable garment fit more dynamically.

Many scholars have described the marketing benefit (Merle, Senecal and St-Onge,

2012; Shin and Baytar, 2014), but few studies are focused on current commercial

VFIs offerings and the techniques implemented. Chapter four provides an in-depth

overview of this area of research as well as the strengths and limitations of the

ongoing VFI developer’s efforts.

At present, customer-oriented VFI technologies lack validation, and their underlying

models frequently change, resulting in usability issues. Technical validation defines

the precision and accuracy with which the customer body can be measured, whereas

fashion validation establishes the association between the body measurements and the

underlying product development process. The validity issues must be overcome if 3D

Body Scanning potential is to be realised in VFI. A convincing demonstration of a

significant improvement in VFI is still required to justify the added complexity of

using 3D Body Scanning in e-Commerce. Most VFI’s have little or no information

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regarding their reliability and validity, the testing they underwent or how the data is

acquired and processed. The customer data collected from different devices vary

greatly, from self-questionnaire, through mobile apps to body scans booths. For

example, some VFI based their conclusion on customer self-measure and data-

interpretation methods that are then duplicated in big data models (Pookulangara, Parr

and Kinley, 2018). Therefore, retail validation can be restricted, prone to bias and

generate a different range of interactions, ultimately resulting in misleading

conclusions. To create maximum benefit to the end-user or other stakeholders (e.g.,

fashion retail, researchers, and manufacturers) – VFI needs an effective standardised

system with algorithmic transparency and interoperable components that allow for

high integrity measurement systems. This will enable fashion practitioners to verify

and validate 3D Body Scanning in e-Commerce.

Converting the data acquired into relevant VFI avatar information involves

establishing image-interpretation criteria needed to deliver size and fit information

adequately. The avatar visualisation is only meaningful if the resulting data make

sense to the end-user. Thus, the size and fit data visualisation should be tailored to

end-users and their specific needs. Chapter six found that interpreting 3D Body Scan

data could be challenging for most of the customers. Research by Grogan et al. (2017,

2019, 2020) establishes that 3D Body Scanning could be linked with reduced body

satisfaction. The scan data ambiguousness coupled with fashion industry standards

may be the reason why so many customers feel negative after the scan viewing.

However, if 3D avatars can potentially increase body satisfaction that could be used to

persuade fashion marketers to use more realistic and representative models for

marketing purposes rather than unrealistic ideals.

Computer-Aided Design

The recent advances in CAD design and simulation provide novel capabilities that

involve a combination of an extensive range of techniques, involving mechanical

simulation, collision detection, and user interface techniques. In the quest for

breakthroughs, CAD developers are often confronted with the challenge of processing

digital data of increasing complexity and richness, which demands an informatics

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infrastructure with tools to collect, store, manipulate, analyse, interpret and visualise

vast amounts of data.

As technology progresses, the modern garment developer must become increasingly

familiar with computational techniques and software tools. In a sense, the CAD

designer of the future must be a concept artist, pattern maker and 3D illustrator

combined. The CAD designer is required to undergo a long process, as described by

Yan and Kuzmichev (2020), with many process steps that utilise multiple software’s.

In their method, researchers went from Vitus 3D Body Scanner to the compatible scan

software Anthroscan to generate a mesh model. Then, researchers uploaded mesh

form to the Adobe Mixamo to rig the scan, and in ET CAD to sketch in 3D the pattern

blocks for the shirt. Finally, the scan was ready to be uploaded in CLO to sew and

visualise fit. The new project workflow is needed to reduce the practitioner need to

transfer data between multiple software packages tediously. Workflow systems can

enable a field to transition from requiring that experts manually string together many

single-purpose software tools, to enabling non-experts fashion practitioner to create a

seamless workflow using a single integrated tool. In thinking about the steps of using

such image-analysis workflows sustainably, the critical issues that must be addressed

by developers are CAD connectivity, reproducibility, and the ability to share files and

workflows. The latter are open frameworks that allow the smooth integration of

various other libraries and tools, including specific tech-packs in such a way that

designers can easily swap one tool for another one. Once available for industrial use,

this technology will directly support styling and shaping work because of the

possibility to integrate intuitive-oriented artwork.

Computer-Aided Manufacturing

To describe the CAM process, Gill et al. (2018) have coined the term ‘scan to pattern

relationship’. Ahmed et al. (2019) provided an in-depth review of some of the pattern

drafting approaches in product development, offering a qualitative analysis of

dimensions and requirements. Pattern automation can facilitate sustainability by

allowing modelling techniques to evolve over time. However, its potential remains

relatively untapped in the 3D Body Scanning industry (Harwood, Gill and Gill, 2020).

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‘Scan to Pattern’ is an example of software that interfaces between customer

anthropometric data and fit indications (Gill et al., 2018). Often this software exists at

the nexus of hard engineering approaches and fashion tailors’ specifications, further

adding to the confusion of what is needed, and by whom, because historically, the

first were exempt from fashion curricula. Baytar and Ashdown (2015) research

illustrated that garment developer trained in traditional 2D methods are not able to

integrate vast amounts of 3D data into the pattern drafting, and that only a few people

have had the experience of using and operating a 3D Body Scanner. This model

delved deeper into the problem and showed that the problem exists because (i)

developers use different landmarking locations and definitions for pattern

construction; (ii) fashion retailers’ reference to different pattern making approaches

without fully understanding its underlying assumptions. (iii) fit variables are often not

taken into account, but instead, the generic grading models are utilised.

For 3D Body Scanning to continue into the more mainstream retail environment, it

needs to have credibility, particularly with garment developers. 3D Body Scanning

developers must focus on the credibility of the evidence for pattern automation

validity and scalability in product development. Gill et al. (2018) found that digital

automation of the drafting process has the potential to cut time frames, costs and

complexity significantly. In JBlockCreator software, Harwood et al. (2020)

demonstrated that the software is capable of reading measurements taken manually or

recorded using 3D Body Scanning software. Garment developers may, therefore,

integrate this tool as part of a fully automated scan-to-pattern process. However, more

research is needed on retail integration of the software into product development, and

on presenting its successful case studies. McKinney et al. (2017) acknowledged that

the conceptual knowledge must be reinforced with pragmatic experience. Allowing

designers to have a sketch-based interface to accommodate the requirements and

validate design option through the analysis of virtual garment prototypes should

reduce the number and role of physical prototypes in fit sessions (Fontana, Rizzi and

Cugini, 2005).

Gill (2015) implied, such procedures into software instructions, may help fashion

practitioner uncover inherent rules and empirical relationships between measurements

and resulting pattern shapes to support full pattern block automation; a case

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exemplified by Harwood et al. (2020) and McKinney et al. (2017) for trousers pattern

development or by Jiang (2019) for traditional Chinese dress. The conformity of data

into the basic patterns (methodology of their drafting) integrated into automatic

systems is not yet clear (2019). The automatic use of data from 3D measurement

acquisition in 2D design systems is not provided (Dāboliņa et al., 2018). Ahmed et al.

(2019) exemplified inconsistency in measurements captured in pattern drafting and

3D Body Scanning approaches. Furthermore, Harwood (2020) have identified

difficulties in placing bodice block drafts in the armhole curves, particularly when 3D

Body Scanners often extract measurements, which differ from those expected in

manual methods. Nevertheless, the developments of approaches to automating the

product development will expand the ability to study scan to pattern relationships; by

allowing fashion stakeholders to test underlying assumptions. Furthermore, the body

scan software must offer a clean user interface for tailoring professionals while also

providing a robust, extensible class framework for developers to build on existing

capabilities and grow the library of implemented drafting techniques. Studies

examining pattern-automation interfaces under conditions resembling real-world

practice can improve the understanding of scan design and what knowledge and skills

users should have to be most effective.

Conclusions

The impact that 3D Body Scanning has on the future of fashion retailing and product

development is undeniable. However, challenges remain to the realisation of the

benefits of this service for customers, research, and the fashion industry. This section

elaborates on these research themes further based on the 3D Body Scanning workflow

framework and makes methodological suggestions where specific interventions can

aid the greater harnessing of the benefits of technology.

This study found that the barriers to data acquisition are the lack of landmark

definitions, standards, and guidelines for fit parameters. The creation of user-friendly

landmark directories can allow writing macros and automatically references best-fit

solutions. These guidelines could, in turn, aid fashion practitioners to investigate and

develop their own procedure for product development. The study found that the avatar

processing stage is limited by the scan inability to create a 3D model that can then be

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saved in various formats to suit customers’ requirements. The solution suggested was

for fashion practitioners to test and provide more empirical evidence for different

outputs and target markets. The data storage and curation are limited by cloud

architecture and slow ubiquitous processing procedures. To avoid further pitfalls, 3D

Body Scanning developers should create multi-tier (fog) computing, in which the

fashion industry creates a new role of fashion curators to facilitate precise curation of

digital databases and provide customers with clear privacy instructions. By addressing

these three major barriers, body scanning applications such as VFI, CAD and CAM

could be further tested and validated in fit sessions and the manufacturing process.

This study offers guidance for retail providers, fashion managers, researchers,

software, and technology developers in understanding the implications of 3D Body

Scanning tools for the innovation in services throughout the stages of product

development. The workflow framework enables stakeholders to anticipate

systematically how 3D Body Scanning may arise in future retail services and suggests

consideration for how they may adopt and develop their use of the technology.

Applying the framework to research on 3D Body Scanning service innovation helps to

standardise contractual references, enhance research insight, speed up traceability of

findings, and ultimately should lead to more positive outcomes. This study also serves

as a basis for interdisciplinary discussions that can lead to a definition of fashion retail

needs and long-term goals for technology adoption. It also focuses on finding a

consensus on how to answer these needs by improving state-of-the-art technology.

Distinct stakeholders identified in the discussion can draw upon the framework to

standardise vocabulary usage in their interest domains of fashion, technology, and

user experience. By documenting the systematic processes for translating the exercise

guidelines to stakeholders and the development of supporting resources, this study

serves as a template for similar technology innovation processes in other settings.

A future framework would ideally expand this to incorporate stakeholder’s

perspectives on the framework viability based on human-centred design workshops.

The model does not map all possible aspects of 3D Body Scanning research, and thus

further refinement with experts from each domain are needed to sharpen the debate.

This study does, however, draw clearly who these stakeholders are and suggest

considerations for synthesis. The introduction of a service topology is recognised as a

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valuable step in causal inference within a quantitative study. The explanatory

adaptation research needs to consider the dynamics of barriers to avoid an

inappropriate static picture. The content and processes of 3D Body Scanning services

will likely change as new technologies are developed, and the service topology

presented in this chapter will provide a foundation from which to evolve to adopt

these innovations accordingly.

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CHAPTER 8

DISCUSSION AND CONCLUSION

Introduction

This chapter concludes with the outcomes and achievements of the research, the

contribution to knowledge, and the scope for further investigations.

The rationale for this study stemmed from the fact that 3D Body Scanning has shown

great potential to enhance the customer experience in size and fit (Miell, Gill and

Vazquez, 2018) and improve garment development design outcomes (Harwood, Gill

and Gill, 2020). However, actual technology impact remains variable (Ashdown,

2020). The adoption so far has been haphazard, owing to the inadequate consideration

about the suitability of current approaches in the realisation of 3D Body Scanning

(Gill, 2015). The literature found that the fashion industry does not have a good

concept of how to use the actual 3D data, limiting its broader applicability and usage

(Jiang et al., 2019). Consequently, developers strive to help make processes in retail

more efficient, though they rarely integrate readily with existing product development

knowledge.

With this in mind, this thesis designed four studies (chapter 4-7) based on a service

design research framework (Stickdorn and Schneider, 2010). The research question

has been answered through: a) conducting analysis of existing Virtual Fit Interfaces to

understand how the use of 3D Body Scanning can potentially help e-Commerce retail

deliver well-fitting garments; b) identifying critical diffusion challenges that might

impair the technology value and proposing solutions through stakeholders

interdisciplinary expertise in the diffusion process; c) offering a new perspective on

customer research in 3D Body Scanning with design and interaction guidelines that

could allow capturing value from customer experience better; and d) identifying four

critical workflow design elements that are necessary for the 3D Body Scanning

service. Taken together, this thesis answers the original research question and

contributes to the fields of 3D Body Scanning.

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Attainment of the Research Objectives

This research contributes to the evaluation and design of 3D Body Scanning, situating

technology as an inextricable part of product development in the fashion industry. The

following sub-sections describe how the objectives were addressed through the

research activities — providing the main findings, outcomes, and conclusions.

8.2.1 Objective One

“To analyse the existing knowledge base on 3D Body Scanning and identify

gaps in connection to the apparel product development and service design.” To achieve objective one, a literature review was undertaken on the domain of 3D

Body Scanning pertaining to apparel product development, innovation, and service

design. The past literature suggested that 3D Body Scanning is transforming dormant

and static areas of the fashion industry with novel applications and research

opportunities. The technology turned anthropometry from old tools such as callipers,

weight balances/scales, tape measures, and calibrated rulers to the modern ‘digital

age’. To illustrate this claim – the literature investigates 3D Body Scanning methods

and tools to enhance product development methods and provides an overview of

operational details, early validation studies, standardisation incentives and

applications. The findings from the literature provided direction for this research.

According to service design principles, understanding technology developments

demands to pay attention to what is happening within firms, industries, and markets as

well as what particular individuals are doing (Maglio and Spohrer, 2008). The ability

to undertake service design research is complicated, as it requires collaboration

between academics, industry professionals and customers – bringing their respective

expertise to the debate (Costa et al., 2018). Specifically, service design literature

contends that there is a need for academics to attend to sources of data that document

the activities of a wide variety of organisational stakeholders, and it observed that this

contrasts with the traditional fashion approaches (Hollebeek and Andreassen, 2018).

Past research on 3D Body Scanning was driven mainly by technology fluctuations,

without a design agenda or analysis of user needs. For example, Pargas et al. (1997)

and Streuber et al. (2016) have proposed a series of methods for evaluating technical

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advances. However, the lack of publications that have clarified how user requirements

influenced technical breakthroughs or justify how retailers interpret scan data for

improvements in garment development practice (Liu, Chow and Zhao, 2020).

Following service design recommendations, the literature evaluates past examples of

diffusion attempts as a critical and under-exploited resource.

The first two steps of the framework –understand and observe – explores technology

capabilities and uses. Virtual Fit Interfaces represent ‘new, trendy and hot’

applications (Feng and Xie, 2019) with the potential to connect with 3D Body

Scanning to facilitate higher product engagement in e-Commerce (Zhang et al., 2019).

However, Beck and Crié (2018) found that customers often struggle with the

interpretation of VFI results, and it is unclear to what extent VFI successfully

influence purchase intention and garment retention (Grogan et al., 2019). Moreover,

some researchers, such as Yang and Xiong (2019) argue against VFI because it allows

customers to virtually try-on an item before purchase and realises it actual look,

potentially diminishing the impulse to purchase stimulated by appealing promotional

photos. The ongoing VFI debate prompted this research to explore commercial

offerings through thematic content analysis (Robson and McCartan, 2016). To analyse

interfaces, chapter four extends Gill (2015) framework that classifies VFI based on

how they enact and present data into size recommendation, fit recommendation, and

fit visualisation, see section 8.2.2. This study found that the fit visualisation method

has been underexplored, partly because flattening an avatar into a polygon shape to

explore body-to-pattern is not easily relatable compared to established apparel

manufacturing try on practices. This research result fits into a broader discussion

along with Gill and Parker (2017) and Scott et al. (2019), who postulates the need for

new theory formulation surrounding the heuristic aspects of garment fit.

In the third step of the framework – point of view – research focuses on stakeholder’s

perspective on technology diffusion. The robust evidence of what is valued in 3D

Body Scanning design, development and deployment are sparse. The literature offers

narrow opinions from specific groups. For example, garment developers are interested

in the validity of 3D scans as a tool for visual analysis (Song and Ashdown, 2010),

while computer scientist (Kim et al., 2019) and engineers (Klepser et al., 2020) are

looking for ways to improve it. However, none of these studies focuses on the holistic

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picture and expertise required to improve the outcome. More rigorous research for the

development of the industry requirement is needed to determine the effectiveness of

3D Body Scanning to facilitate moving into retail practice. A recent study by

Papachristou et al. (2019) reflects on the attributes that service developers and fashion

retailers consider when adopting new digital tools for innovation in retail. Their

findings identify that stakeholders assign value to the final look of the prototype and

how elegantly it fits within the entire product development cycle and sales

applications; thus, ensuring their competitiveness in the market. This study finding is

also in line with Bonetti et al. (2018), who states that fashion leaders do not see

customer-facing technologies as part of their strategic planning agenda. This study

builds on Papachristou et al. (2019) findings by constructing semi-structured

interviews with stakeholders to understand the value in 3D Body Scanning based on

DOI theory (Rogers, 2003). The result in chapter five indicates that 3D Body

Scanning is based on ideas that bring them in constant conflict with established rules,

thus, making the garment developers resistant to change, see section 8.2.3.

The fourth step of the framework – ideate – focuses on customer needs and design

requirements. Peng et al. (2012) asserted that stakeholders adoption goals should be

evaluated based on customer experience at each stage of the user journey. This

statement provided a foundation for chapter six that unfolds the service journey from

the customer’s point of view. Furthermore, Peng et al. (2012) state that “no matter

how accurate the measurement a 3D Body Scanner can achieve, users will not adopt

it unless it offers them a service, content or features that provide them with something

meaningful.” Chapter six aims to show that the link between service delivery and the

actionable outcome is broken, as customers lack the skills to connect scan data with

purchase intention, see section 8.2.4. The findings also position 3D Body Scanning in

the broader discussion by Linkenauger (2017), suggesting that full-body

representation is distorted and influenced by personal perceptions of other people’s

bodies, especially if the other person body is similar to own body. Thus, chapter six

evidenced that the visual representation of customer without other metrics could not

provide adequate information about garment size and fit.

The last two steps of the framework – prototype and test – provides a formal analysis

of the service. The model typology is a strategic approach to help organisations chart

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technology issues relevant to their future service success. A group of researchers has

attempted to utilise technology assessment in the 3D Body Scanning sector. For

example, Trieb et al. (2013), in the Eurofit project, explores the impact of 3D Body

Scanning so far had on fashion consumption. Harwood et al. (2020) developed an

assessment tool for automatic pattern construction, and Ashdown et al. (1995) used a

multi-criteria model for evaluating fit variables. Ahmed et al. (2019), Gill et al.

(2018), and Parker et al. (2017) also conducted a pattern drafting assessment for the

landmarking in 3D Body Scanning. So, technology assessment has not been a foreign

concept to the 3D Body Scanning industry. However, the industry still lacks a

pragmatic guide of putting stakeholders’ expertise and user requirements into practice

to craft a connected service in retail. Daim and Oliver (2008) state that the holistic

model needs appropriate assessment, including identification of market needs (section

8.2.1 and 8.2.2), capabilities (section 8.2.2 and 8.2.3) and actors analysis (section

8.2.3 and 8.2.4) for picking the right objectives to fill the gaps on the model. Chapter

seven provide research directions for stakeholders in 3D Body Scanning and discuss

the opportunities and challenges from data acquisition to the future application of

insights in customer settings (section 8.2.6).

8.2.2 Objective Two

“To evaluate Virtual Fit Interfaces (VFI) potential to connect with 3D Body

Scanning to deliver effective size and fit recommendations.” To achieve objective two, chapter four investigated Virtual Fit Interfaces through

content analysis of ten available platforms in fashion e-Commerce. The number of

companies offering personalised size and fit prediction services in e-Commerce is

increasing (Idrees, Vignali and Gill, 2020). Still, little was known on the relative

effectiveness of different types of VFI for product selection and purchase intention.

The question of the efficacy of 3D Body Scanning compared to traditional ways of e-

Commerce product presentation is crucial for fashion retailers, and it has to be

investigated thoroughly to bring strong evidence for mainstream adoption (Reid et al.,

2020). VFI offers the promise of a significantly reduced return rate, higher product

conversion, enhanced engagement with virtual product development and realistic

representation of self in the web (Mintel Group Ltd., 2019a; Plotkina and Saurel,

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2019). To date, existing research by Buyukasla et al. (2019) and Miell et al. (2018)

has proposed a series of frameworks for evaluating VFI interactivity, vividness, and

customer uptake. However, no publications known to the author have provided a

systematic review that refers to a clear theoretical basis for product development. This

study addressed this gap by critically examining current VFI in e-Commerce to

understand the underlying concepts and possible pitfalls of this approach entirely.

This study investigated a crucial topic in apparel e-Commerce: how VFI collects

information and the communication strategies, including the extent to which avatar

exhibits visuals corresponding to customers’ appearance (Lee and Xu, 2020). A

theoretical framework based on the Gill (2015) research was adopted to explain the

impact of the application and the mediating role of determining fit, from

classifications of the body to direct engagement with the tools of fit visualisation. Gill

(2015) suggested that while many of these methods may appear quite abstract

compared with the traditional size and fit approaches, they provide additional ways of

positioning the customer more intimately within the process of selecting garments

online. However, the problem of adoption could refer to the inconsistencies in

approaches and methodologies (Hsieh et al., 2019). Chapter five found that current

VFI produce non-compatible size and fit recommendations based on a multitude of

anthropometric and self-reported body measurements, with little to no consensus on

which are used within the systems. This lack of agreement leads to existing ‘bad

habits’ of customers to buy incorrect fitting garments is sustained rather than

addressed through VFIs. In contrast, 3D Body Scanners can provide precise, reliable

and relevant anthropometric data (circumferences, areas, and volumes) and body

shapes estimates (Parker et al., 2021).

Chapter four proposes how 3D Body Scanning could help to lower the demands

placed on customers by distilling and prioritising the critical information through 3D

Body Scanning while simultaneously attending to the usability elements. This study

concludes that allowing unregulated claims has the potential to backfire by further

detracting from the credibility of this emerging sector. Thus, reporting standards can

be an essential component in making data reusable by ensuring that VFI creates

suitable target market information for retailers to respond to (Yang and Xiong, 2019).

The results exemplified and confirmed that VFI misrepresents customers’ actual size

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and shape, partly because its product-based approach focuses by default on outdated

retail practices. This study provides evidence that the fashion industry needs to move

toward 3D Body Scanning to allow for ubiquitous data collection, CAD data in

pattern drafting and in the constructions of the 3D garments for new fit evaluation

methods. Gray (1998) and Gill (2015) argues that CAD allows for adding geometric

principles of pattern construction, especially the nature of a pattern as a 2D system of

connected Cartesian points. Thus, the system based on CAD has a better intuitional

and cognitive visual effect for customers using VFI technology (Jiang et al., 2019). In

the absence of standards, it is recommended that fashion professionals need to

communicate better with developers to create meaningful content. This kind of effort

requires unbiased interaction to ensure stakeholders can reference good practice at all

levels. To move from conflicting perspectives to strategic action – it is important to

develop a process for reconciling differences among stakeholder groups. Clarifying

these details early in the process can help reduce misaligned expectations and aid

implementation efforts. The findings from chapter four – the VFI framework is

intended to be a foundation on which future studies can refine or expand selected

definitions or stimulate a broader view on size and fit research; see section 8.5.1.

8.2.3 Objective Three

“To apply the diffusion of innovation theory and interview 3D Body Scanning

stakeholders to synthesise different points of view in technology adoption.” To achieve objective three, chapter five investigated diffusion strategies in 3D Body

Scanning. This was done through 39 semi-structured interviews with industry

stakeholders. The use of 3D Body Scanning in retail service is gathering pace, as is

evidenced through these interviews and broader literature in marketing. As it does so,

stakeholders must understand the complexities and opportunities that arise from early

implementation instances to allow appropriate strategies to formulate. Before 3D

Body Scanning goes mainstream, this moment in the time provided a unique

opportunity to ensure that technology is designed and delivered in line with what

retailers and customers value. Nevertheless, 3D Body Scanning stakeholders come

from distinct industries with different knowledge, practices, vocabularies, and

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ontological perspectives for service innovation that often lack a credible and

coordinated strategy for diffusion.

Chapter five improves the communication between distinct stakeholders to drive

innovation by the collective’s output, vision, and understanding of service design. The

study found that stakeholder’s narratives are archetypal for the industry sector and

highlight the work progress in 3D Body Scanning. In this perspective, the research

puts the development puzzle together by suggesting the design guidelines based on

diverse expertise within the service, thus exploring the bigger picture. The diffusion

problems of implementing 3D Body Scanning bring to light the sector-specific

challenges and gaps in knowledge. The interviews found that each piece of expertise

is understood as a strategic narrative that begins with assumptions and tends to lead in

a specific research direction. However, the sector-specific challenges have the root

cause in growing system complexity (Liu, Chow and Zhao, 2020). This study found

that most of the interviewed 3D Body Scanning firms are trying to build a service

pipeline independently to profit from their incremental IP. Additionally, this study

found that knowledge gaps are based on the power structure, power dynamics,

industry culture, and lack of skills that pose potential adoption barriers – hindering the

use of 3D Body Scanning.

This chapter outcome provides a visual tool – rich picture (Monk and Howard, 1998),

forming a basis for further research into stakeholders’ knowledge gaps that may serve

as foundations for educational efforts to build a coherent industry narrative. The rich

picture foundation starts with the evaluation of standards, governance, and libraries to

ensure interoperability and technology feasibility. The rich picture then moves up to a

metadata and file format. The problem becomes even more relevant with the

increasing number of unstructured data in non-relational databases, which

manufacturers have a problem understanding (Robinette and Daanen, 2003; Bougourd

and Treleaven, 2010). Providing standards in terms of data structure should help

manufacturers to work more efficiently. More generally, if 3D Body Scanning data

are structured according to international standards, data sets are much easier to

analyse, and efforts needed for data cleaning and pre-processing are reduced (Stoter et

al., 2020). This, in turn, can speed up the research process and provides new avenues

for pattern automation, as defined in the third level of the rich picture. 3D Body

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Scanning data need to be extended from merely being ‘body data records’ in digital

static folders to being analytics in product development aids. This allows for the

customisation of support without the need to reinvent what is provided, thus ensuring

maximum added value across contexts (Zhou, Park and Koltun, 2018). In the fourth

level of the rich picture, this study discusses the industry need for common workflows

that enable a seamless provision of 3D Body Scanning. As chapter five found, the

data from 3D Body Scanning do not integrate well with many different analytical

tools into usable information for retail application. Thus, overcoming organisational

challenges would lead to a significant increase in the evidence base of developments

that can successfully drive innovation in fashion retail. This study finding offers

tentative lessons for other companies that can be drawn from the experience of the

stakeholders and future research directions; see 8.5.2.

8.2.4 Objective Four

“To evaluate the existing customer journey in 3D Body Scanning and

recommend design strategies to make the interaction with technology more

pleasant.” To achieve objective four, the first part of chapter six investigated user experience and

data presentation in 3D Body Scanning. The final product represents a customer

journey that illustrates the service touchpoints, barriers in interaction and suggests

design guidelines. This study followed design thinking principles with 52 participants,

who engaged with a set of mix-method activities: focus groups, interviews, co-design,

eye-tracking, and interface analysis. Combinations of research approaches produced

valuable insights that can fuel technology design and inform the best ways in which

these technologies and services can be marketed and packaged. To this end, this study

reports a full description of the development and usability testing process of 3D Body

Scanning experience based on Size Stream (2017) body scanner and interface. Such

an organic approach maximises the research process within the real world and often

chaotic circumstances.

The objective of the research was to understand the user’s perception of the

technology. Stakeholders must recognise that to serve the users: customer and

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retailers better, a deeper understanding of the user-defined service journey is required

and prepared to coordinate their activities with complementary providers. Study six

provides new insights into the design challenges and an important contribution to the

existing 3D Body Scanning literature because no similar study is currently available

addressing touchpoints in the customer journey. The usability testing with users as a

research practice has traditionally been a lab-based activity, and as such, it relied

heavily upon controlled experimental methods and quantitative means of analysis. It

has often combined questionnaire administration to evaluate how garment developers

and researchers’ vision of the future service setups align with customers preferences

and uptake, where manipulation of certain interface elements provides an independent

variable (Lewis and Loker, 2014). However, in these papers, one can see that the

qualitative voice of the consumer-user only functions as an initial and relatively minor

influence within the processes of conversion (Peng, Sweeney and Delamore, 2012).

These experiments informed initial recommendations made to the technology

manufacturers regarding interactions on the original design. Yet, as study five found,

the emphasis of the deployment concentrates on the conversion of design parameters

to engineering parameters with no subsequent link to manufacturing and production

parameters. The lack of being able to reference new kinds of characteristics,

attributes, features, and functionalists to existing products may, thus, provide useless

data (Latour, 1989). Dumas (2007) and Norman (2005) reiterate this when they claim

that the specific instances that researchers see in a usability test are more often

symptoms of broader and deeper problems with both the product and the process.

These included issues regarding customers thoughts and feelings about the 3D Body

Scanning experience, what they imagined the scanners to be, how this compared with

what they were apprehending, its design and content, and the practice of viewing.

This droves a need to approach study six with a more open-ended approach, which

would understand the service usability in context with an individual’s perceptions and

understandings. If one begins a deployment with badly articulated or sampled user

needs and requirements, then it will lead to a poor product. 3D Body Scanning

offerings constrained customer experience by the incoherent vision of the service and

the usability paradox – in which customers must go through the process that often

feels uncomfortable to receive a garment that may not fit quite well (Peng, Sweeney

and Delamore, 2012; Almousa, 2019). This study found that customers struggled to

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find answers to what benefits 3D Body Scanning could bring to their shopping

experience. The study highlighted that 3D Body Scanning design lacks an ongoing in-

depth analysis that thoughtfully considers customer involvement in the process. The

research findings indicate a lost connection between the retail environment and 3D

Body Scanning design. This study found that the strong focus on efficiency gains

reduced design to almost clinical experience in which users were unsure even how to

breathe or potential radiation impact. Results indicate that participants emphasised the

importance of the design of the scanning booth as the most significant adoption factor.

This study found that according to participants, the excellent design reflects and

communicate safety and privacy well. Participants paid particular attention to the

layout and overall layout structure – the interior decoration, whether the signs and

marks are clear, and the shelves and hangers are available. Customers also considered

intangible factors and image of service, that is, music, noise, room temperature, and

attitude of service personnel.

Service design means that researchers should understand the needs of system users

and design technologies specifically to meet those needs before the design phase

(Norman and Verganti, 2014). In contrast with technology-centred design, this kind of

research represents a better approach to improving services (Krippendorff, 2004).

This is because users are not forced to conform to technologies or adapt to a new way

of performing familiar tasks, leading to system problems when users resist these

changes. This study found that the 3D Body Scanning problems concern data quality

as scanners design affected participants posture and influenced breathing problems.

Yet, in the current outlook, experience issues are typically treated as technical

problems that will work themselves out with further technology efficiency

improvement and widespread adoption (Daanen and Ter Haar, 2013). The service

design provides a framework for considering the design of services in a more holistic

manner (Holmlid and Evenson, 2008). There is no published research on the

systematic development of a 3D Body Scanning targeting early adopters to the best of

the author knowledge. This is the first study to bring together customers and retail

providers to co-produce what is of value when focusing on service in 3D Body

Scanning. Data gathered from this study can now be used to develop tools to quantify

technology value, for example, using Kano questionnaires or concept mapping based

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on the study’s customer journey framework, to further build upon these findings; see

section 8.5.3

8.2.5 Objective Five

“To analyse the usability of 3D Body Scanning interfaces through eye-

tracking methods to discover barriers in content presentation.” To achieve objective five, the second part of chapter six evaluated the 3D Body

Scanning interface to understand how different factors influence the level of

attraction, impinging principally on the customers’ unconscious mind. Tacit

behaviours are of relevance and of interest to marketers, and chapter six drew

attention to the potentials that interactive 3D software have in tracking user actual (as

opposed to realised and perceived) usage patterns. At the same time, the quantitative

analysis of participants’ viewing patterns is based on aggregated user viewing

behaviours (Wedel and Pieters, 2006). This part of the study found that participants

use the face as a special visual stimulus and exhibited stronger filial preference

toward the configuration of features associated with the head, neck, and chest. Yet,

the facial features in 3D Body Scanning are underdeveloped due to privacy issues

(Cai et al., 2008). Thus, this study found strong incongruency, in which customers

were either unable to identify or wish not to view avatars that best represent their own

body. Future research must answer if 3D avatars are essential to an effective e-

Commerce experience that satisfy both customers and retailers, or if potentially 3D

Body Scanning could contribute to general dissatisfaction with e-Commerce.

The eye-tracking facilitates an understanding of the subject matter under

consideration because of how they show the interrelation of the underlying factors

(Duchowski, 2007). An eye-tracking study on the deployment of visual attention

among 3D Body Scanning users found substantial distortions in the customer’s

perceptions of the relative proportions of their bodies. These results fit into research

findings by Linkenauger et al. (2017), showing that, contrary to our intuitions, our

perceptions of our body are quite distorted. This study findings agree with these

research statements and show that participants look at the informative head and

shoulders regions disproportionately longer than other features or parts of the body in

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the picture. However, the head region is not as detailed due to the existing privacy

issues (Cai et al., 2008). In that sense, this chapter revealed that most participants do

not seem to notice drastic differences between their own and other’s morphologies.

The study demonstrated that customer subjective judgements are inaccurate for e-

Commerce product selection and purchase because customers’ lack sufficient

understanding of how to connect body data with shopping outcomes. Nevertheless, as

chapter four demonstrated, the industry existing approaches in VFI strongly base their

results on customers’ self-perception. However, this study results indicate that the

perception of our body is largely inaccurate. Quantitative eye-tracking assessments of

the 3D Body Scanning interface have helped to isolate and understand better the

various factors contributing to customers’ confusion and proposes design

improvements to increase interface readability.

8.2.6 Objective Six

“To define a service framework of 3D Body Scanning that summarise the

technology state-of-the-art and discuss the opportunities and challenges from

data acquisition to the future application of insights in the apparel sector.” Progress in the debate about 3D Body Scanning requires a new visual tool to allow

comparison and in-depth analysis of interconnected pathways that make up the body

scanning service. 3D Body Scanning is still in a state of flux (Ashdown, 2020). While

plausible trends are beginning to emerge, they still need to be cultivated and

coordinated (Gupta, 2020). The number and scope of 3D Body Scanning technologies

continue to grow, and the diversity of size and fit solutions and commercial

applications continues to multiply. The fashion retailing structure is evolving in

response to changing patterns both in demand for retail services and how they are

provided (Berg and Amed, 2020). The debate over how to manage 3D Body Scanning

research has generated a considerable amount of literature on technical capabilities

and progress. However, past research guidelines have shed little light on the role of

3D Body Scanning in retail. Chapter seven shows a model typology that has the

potential to advance and connect a wide range of otherwise disparate knowledge

processes. It presents the development and application of a visual diagram that

advances the discussion of how to create a 3D Body Scanning service. The visual

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mapping tool allows for a systematic and standardised depiction of (i) the pathways in

the 3D Body Scanning service, (ii) the stakeholders involved, and (iii) the applications

created. This chapter creates a heuristic evaluation for the constructive path forward

through engagement and the involvement of all the key stakeholders and users.

Chapter seven proposes that 3D Body Scanning functions at four levels: measurement

acquisition, avatar creation, data storage, and applications. If implemented effectively,

the model can be used as an evolving and malleable means of linking definitions with

action and communicating with the retail and customers, which can generate support

for future design concepts and faster diffusion, see section 8.5.4. This function

explored the characteristics of 3D Body Scanning elements that can be followed from

different angles for technical, fashion, and usability discussions. In this manner, the

3D Body Scanning service framework may have valuable applications as:

• A precise and transparent guideline to capture customers’ requirements;

• An indicator to exhibit the weights of customer requirements and design tasks;

• A visualisation tool to display the relationship between the garment developers

design tasks and customer requirements; and

• An immediate aid for collaborating develops an account of what service

entails and what role each stakeholder plays within the 3D Body Scanning.

Theoretical Contributions

The research situates 3D Body Scanning as a service for fashion retail by considering

theoretical knowledge from the service design thinking that leads to solving a real-

world problem. The research also contributes towards the practical application of the

service design theory by providing a comprehensive overview of the action, effects,

and benefits of service design in retail through visual frameworks and

recommendations for the technology implementation process. The theoretical

contribution can be of interest to the retail innovation management disciplines who

wish to understand the extensive effects of service design in fashion technology. The

retail management theories address the importance of using design as a strategic tool

(Holmlid and Evenson, 2008), but there is still little empirical research about how

different areas of design – service design and design thinking - apply to the 3D Body

Scanning context (Silva and Bonetti, 2021). Fragmented theories of design and

innovation add to the confusion about the specific benefits of the service design

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approach in 3D Body Scanning research (Vink et al., 2019). Therefore, this research

adds knowledge to the theory to comprehend the complexity of expanding design in

the 3D Body Scanning through following the stages of service design thinking

framework (Thoring and Müller, 2010). The innovative apparel retail companies and

design supporting organisations can systematically use these research findings to

understand design innovation in their practice to increase innovativeness,

competitiveness, and sustain growth.

The influence of design on innovation is well documented in service design research

(Vink et al., 2019), where innovation is often regarded as a direct outcome of the

design process (Bjögvinsson, Ehn and Hillgren, 2012). Yet, in the 3D Body Scanning

industry, the innovation was often limited to technical discipline-based design (i.e.,

computer science and engineering) (Toti et al., 2019) with no evaluation of overall

service quality (Shostack, 1982). The industry often put heavy reliance upon

pronounced claims of improved or superior technical performance and/or

specifications (Daanen and Ter Haar, 2013). Thus, the stakeholders’ desire to exploit

the 3D Body Scanning outstripped a field ability to impose a common standard for

description and methods documentation (McDonald et al., 2019). 3D Body Scanning

is, therefore, predominantly interpreted as a tool for optimising the existing size and

fit methods (Scott, Gill and McDonald, 2019). In result, the 3D Body Scanning, and

in particular the content aspects of the system, never matured or was never developed

to the extent necessary to create a platform for its users. Instead, it remained an

artefact, transparent not in use but through lack of use, usage, and usefulness. Lack of

content options led to lack of participation, and therefore the technology can be

considered only evocatively - capable of providing a base by which participants were

able to comment, and project upon 'if it did work' or did fulfil all it was built up to

provide (Loker, Ashdown and Carnrite, 2008). This research demonstrated that unless

efforts are redoubled to connect the fragmented, inconsistent practices and ambiguous

assumptions, the field could see an era of a black box in which outside retailers cannot

work out the technology value (Lewis and Loker, 2014). The implementation gap is

driven by stakeholders’ lack of understanding of how garment development practices

are rooted in tailoring craft, the science of pattern drafting, and mathematical

principles involved in the fitting process. This research has shown that 3D Body

Scanning is an interdisciplinary field with a need for skills from engineering,

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computer vision and science, mathematics as well as apparel product development,

retail management, and user experience. In addition to this, this research offers

several major points of departure from current industry thinking.

The contextual review established that no other academic study into 3D Body

Scanning design practice has engaged with a range of diverse critical experts and

users in the way that this research has. A range of design approaches has been applied

to improve 3D Body Scanning, such as stakeholders’ interviews, customer journeys

and eye-tracking. In this respect, this study presents an original contribution to

knowledge. Service design is a stakeholder-centred discipline, which means that

various stakeholders are involved in the process of designing new services

(Blomkvist, Holmlid and Segelström, 2010). Developing a solid theoretical and

empirical grounding for how stakeholders think about 3D Body Scanning provides

insights into the roots of the normative debate (Rose, Flak and Sæbø, 2018). Service

design researchers focus on understanding people and their needs and thorough

analysis is needed to formulate insights (Segelström, 2011). These insights are

visualised in frameworks to provide easily accessible depictions of service systems,

skill, and practice, which distinguishes service design from other service management

fields (Segelström, 2009). However, it is argued by Blomkvist et al (2010) the service

design researchers do what they think is the best thing in the current situation, and

worry less about adhering to specific methods and tools. Thus, chapter five

interviewed stakeholders based of the Roger’s theory (2003), and following the

diffusion attributes to draw of a clearer picture about who is implicated in technology

adoption. Following the interviews, the discussion part initial suggestions about

stakeholder’s participation in the various stages of innovation decision were made.

Finally, the identification of stakeholders made in this chapter helped at the expansion

of the descriptive part of the theoretical framework proposed in Figure 20 – adding

two groups of stakeholders not previously mentioned in literature namely diffusion

intermediaries and facilitators.

The thesis illustrates the relationship between these elements and how stakeholder’s

and user’s perspective must be jointly considered to effectively design 3D Body

Scanning in a service delivery system. The service interface is the site where not only

do all the functional aspects of the service’s purpose must converge in relevant,

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purposeful, and useful ways but must also interface with the user in a representational

and meaningful way. Therefore, evaluating customer experience in service is often a

task of not only engineering but also of aesthetics, and of social learning, cognitive

sensitivity and understanding (Teixeira et al., 2012). To account for this, customer

journeys were created by following and documenting actual customers in the service

setting. The customer journeys showed the various channels through which users

would access the service and how the users would move between different

touchpoints at different stages of the service. Therefore, this research strongly

suggests that process characteristics are contingent on the degree of customisation of

the service concept and the type and variability of customer inputs.

The research outcome provides a comprehensive overview of the effects of different

areas of service design in innovation through the accumulation of theories, and design

innovation. With that, it is time to summarise the in a series of contributions to the

field of 3D Body Scanning. Table 37 re-iterates the main findings and arguments

made in a condensed form with references to each chapter they pertain to. This

research represents an important step towards the identification of the design

characteristics of service delivery processes in an information-intensive service

delivery system.

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Table 37 Summary of contributions derived from this thesis. Source: author’s own.

Contribution Statement Chapter Service-centric view of the 3D Body Scanning. 1-8 Consolidation of existing research into an integrated model that shows how 3D Body Scanning uses of the anthropometric measurements in fashion product development.

2

The current role of 3D Body Scanning in fashion, the reasons for technological change, transformational barriers and the stakeholders involved.

2

Up to date investigation of existing Virtual Fit interfaces adopted by fashion e-Commerce, with a strong focus on usability and communication strategies.

4

The research shows that VFI produces considerable variations in the reliability and validity of collected data that is open to personal bias and are, therefore, ineffective in addressing size and fit issues in e-Commerce.

4

3D Body Scanning can increase the effectiveness of VFI by improving product interactivity, vividness, and size and fit literacy.

4

Stakeholders perform different roles, have different viewpoints, and use a different vocabulary at various stages in the development. This leads to a perception gap, a difference between the logical understanding of different stakeholders when talking about different parts of the same whole.

5

Lack of consensus on goals, lack of multidisciplinary communications, and lack of empirical efforts and resource for sharing practices are the most significant barriers in the diffusion of 3D Body Scanning.

5

The rich picture provides a framework for the systematic development and evaluation of the stakeholder’s expertise in 3D Body Scanning service.

5

Aesthetics and interior design, information capacity, and personalisation options are the key design dimensions that need to be considered when creating 3D Body Scanning service in retail.

6

Information overload and unclear or lack of instructions are the key inhibitors for increasing customer adoption in 3D Body Scanning services.

6

The research shows that when using 3D Body Scanning interface customers do not perceive morphology of their own body as it is, but instead, customers see it as being systematically dimorphic.

6

The customer journeys’ framework proposes together twelve guideline steps for improving 3D Body Scanning that all the stakeholders can collectively use to inform guide and structure their trans-disciplinary efforts.

6

The research provides guidance for creating a holistic framework based on 3D Body Scanning workflow, taking into account measurements, avatar, database, and applications for setting up retail service.

7

The research articulates each model variable based on technical, fashion, and usability perspective to add to the holistic perspective.

7

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Limitations

This research has limitations due to the limited scope that a PhD study can cover. One

limitation is the generalisability of the findings over time. The concept of analytic

generalisation seems appropriate here, i.e., to develop theoretical propositions but not

to enumerate frequencies (Sørensen, Mattsson and Sundbo, 2010). In this way,

research can provide knowledge that does have a general interest because other

stakeholders can learn from research and because the findings provide indications of

how specific actions will influence certain diffusion outcomes even though guidelines

cannot be applied blindly but must be adapted to specific firms’ particular context.

In the Virtual Fit Interface analysis in Chapter 4, mobile body scanning applications

are left out for clarification and simplification. This is because of how difficult it is to

translate them into a single usability table and because this research wished to take

into account already implemented size and fit services in e-Commerce. However, the

mobile apps are still in the early experimental stage and not yet fully available on the

market. However, the author has attempted to cover all the currently existing Virtual

Fit Interfaces from different fashion firms. The ten Virtual Fit Interfaces are still at an

early stage of commercialisation and may change quickly in response to accumulated

knowledge, limiting their usefulness in future research. Future research, therefore,

should follow the development of the Virtual Fit industry and investigate emerging

B2C business models and trends in e-Commerce over time.

The outcome of chapter five is limited to the number of interviewees: 39. The future

research could benefit from a larger number of participants, although the sample size

was sufficient for a qualitative study. Nonetheless, it is important to note, for

example, that in chapter five, a larger number of stakeholders from a more diverse

range of institutions, i.e., manufacturing and production, would have strengthened the

results. However, the innovative, experimental character of this exploratory study

must also be acknowledged. Its objectives were to highlight the importance of

accounting for stakeholders’ knowledge, perceptions and expertise and outline the

benefits of diffusion of innovation theory for retail innovation management.

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In chapter six research, the author noted that some people are more able to articulate

their activities than others or are perhaps more vocal and opinionated regarding what

3D Body Scanning means to them. Some people seem naturally more introspective

and/or articulate regarding their thoughts, beliefs, and opinions on things. This, too,

can shape and direct responses. This places emphasis on awareness, tools, methods,

and procedures that can balance feedback from all types of individuals to not

predicate the design of systems towards those more vocal or able to articulate and

express their beliefs and experiences. In addition, the participants recruited for chapter

six were cognitively competent and physically able. When users are from a population

that comprises a broader range of nationalities and capabilities, such as age, mobility

and expertise, the effects of customer’s experience may be less than homogeneous.

Equally, issues regarding computer literacy were not acknowledged. Although

multiple sample participant acquisition streams were utilised, including posters at the

University of Manchester and Norther Quarter street, the remaining stream relied

upon web-related media and therefore required internet access and a degree of

computer literacy. This may have caused bias or introduced an extraneous variable

within the participant recruitment process that may have affected individual

performance. Thus, this research focused more attention on early adopters, and early

majority and more research are still needed to understand the laggard’s perspective.

Agenda for Future Research

The results of this research project are constrained by a number of limitations that can

serve as starting points for future work. The four case studies have provided valuable

insights into this under-theorised concept of 3D Body Scanning as a service. The

purpose of this section is to briefly outline a series of topics on the theoretical and

applied aspects that can build on thesis findings. The evaluation of the 3D Body

Scanning service in the retail industry is difficult due to challenges such as ethical

approval and resources. Thus, full validation of the model is beyond the timeline of a

PhD study but could be potential work for post-doctoral research. Below, this section

outlines a research agenda is proposed to guide future research.

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8.5.1 Research on the Virtual Fit Interfaces

This research has proposed the use of the 3D Body Scanning data on Virtual Fit

Interfaces to move away from methods based on the customer’s subjective self-

judgements in product selection and purchase. Designing a product in accordance

with ergonomic principles can increase profits, reduce garment returns and need for

training and after-sales support. This study demonstrated that existing methods for

predicting the usability of VFIs have become so entrenched that their validity is

simply assumed and seldom tested. This point was further echoed and supported in

chapter six, in which, with the use of eye-tracking device, research found that

customers struggle to differentiate between the morphology of their body and other

customers. The next step is to test how 3D Body Scanning data can be ‘plugged’ into

VFI to demonstrate the feasibility of the technology and investment efforts. Future

research must answer cost-benefit questions, that is, does it cost more to use a method

than is gained from the result? If 3D Body Scanning is really to improve the design of

VFI, their methods must be shown to be useful, usable and easy to integrate. A range

of activities, including specification of user requirements, the development of VFI

design guidelines, the evaluation of VFI prototypes, and analysis of user trials in VFI

environments, must be tested to diffuse in e-Commerce fully.

8.5.2 Research on 3D Body Scanning Diffusion

Expertise in research integration and implementation is an essential but often

overlooked component of tackling innovation in research diffusion problems. The

intense focus on the engineering side of the problem explains stagnation in the

feedback loop, in which stakeholders are miring the system in its own inertia. More

broadly, an example of system stagnation was illustrated in chapter four, in which

VFI can be seen as small ‘tribes’ around a particular approach. Instead, study five

argues that the good balance in expertise must be well-articulated, accessible and

useable. This study lays the foundation for defining the expertise needed in 3D Body

Scanning. It assesses where stakeholder’s actions align and where they conflict, based

on diffusion of innovation attributes. The outcome of this study created a rich picture

that illustrates a pyramid of expertise, bringing together different ways in which

stakeholder’s integration and implementation is conceived and put into practice. In the

opening up discussion on expertise, it is worth in future research to probe deeper in

268

characterising necessary skills for 3D Body Scanning. In doing so, researchers can

create stakeholders’ workshops to capture, assess and transmit required skills and

methods in 3D Body Scanning. This assessment should allow stakeholders for a better

understanding of how their views interact in the overall development agenda and if

guidelines could be drawn up for their relative contributions to the complete

evaluation of the quality of services.

8.5.3 Research on User Experience

This study wanted to gain more in-depth insight into the design of the 3D Body

Scanning that can address the various needs of the service users. This study was

conducted in academic settings, examining psychosocial, ergonomic, and usability

issues in the customer service journey. Given that the fundamental design of 3D Body

Scanning has not changed significantly for many years despite evidence that it should,

this study proposed design recommendations that represent a valuable guide for

enhancing customer experience and outcomes. The holistic approach used in this

research has opened several research opportunities related to the design and use of 3D

Body Scanning. Given the breadth of design considerations that could be addressed,

3D Body Scanning developers must be careful to not focus too narrowly on a small

set of design goals. The risk in satisfying the chosen purposes may lead to

exacerbating other existing problems or introducing new ones. One way to handle

conflicting design goals in future research is to analyse 3D Body Scanners role in

different retail settings: luxury, contemporary, and fast fashion, for various

demographic target markets and consider different heuristics evaluations and see its

potential impact on customers and overall service quality. In addition, the eye-

tracking findings also draw attention to the way in which 3D avatar image or virtual

self is socially and semiotically constructed by different companies and fashion trends

or by certain individuals. Further, it is a route by which the way in which such

‘institutionalised fashion images may constrict, impact, and otherwise affect research

processes in 3D Body Scanning, and indeed strategy regarding product development

and design. Thus, the socio-cultural impact of 3D body images should be further

examined.

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8.5.4 3D Body Scanning Service Workflow

A consensus definition of 3D Body Scanning service has considerable scope to

improve customer experience in garment selection, purchase, and retention, and this

study model provides a clear roadmap on how such a definition may be developed.

Knowledge of system uncertainties enables stakeholders to make informed

judgements about the appropriateness of diverse pathways in service. The logical next

step is to apply and disseminate the model more broadly than it already has been.

Future research can be focused to determine how each stakeholder interest is

accommodated in the model. This will also allow validating the research with industry

experts, expanding definitions, and adding any missing links. Further work could also

consider how this may be mapped in such a way as to constitute guidelines of ‘best

practice’ for designers and those who are stakeholders in the design and

implementations of technology and marketing trials. The finished product may create

a network roadmap of all variables and provide the much-needed foundation for

‘lingua franca’ that can help stakeholder’s reference and see new research despite

their limitations in the knowledge domain. In addition, the Wizard of Oz approach to

prototyping method could be used to help researchers avoid getting locked into a

specific design or working under an incorrect set of assumptions about customers

preferences. Wizard of Oz allows research to explore and evaluate designs before

investing the considerable development time needed to build a complete prototype.

3D Body Scanning model can complement other upcoming retail technologies such as

VR/ AR and AI analytics facilitating fashion industry inter-connectives, which is

becoming critical in this age of increasing globalisation of digital networks.

Some Final Reflections

Through this thesis, I have hoped to illustrate that the human factor dimension is

necessary for developing a 3D Body Scanning service capable of inquiry,

commentary, debate, and provocation in social, scientific, and disciplinary matters of

concern. I have hoped to articulate that the service design field warrants analysis to

develop and make a valuable contribution to successful technology diffusion. The

thesis premise is that to develop the practice, more people from outside service design

need to engage and challenge it. The field would have been much more interesting,

engaging, and useful if stakeholders from outside the hard-system practice

270

contributed. It would have been useful if those who know and advocate the practice

were challenged to defend it in order to advance it. This research provided a

theoretical apparatus by which to engage with the potential stakeholders and users in

the discourse on critical design practice by offering a gate of entry into its discourse.

Developing the terminology and laying down precise definition’s fixes specific points

around which the practice can be discussed and enhance the meanings of shared

concepts through value co-creation. The call made here is to challenge and critique

the critical design agenda and to question how technology might adapt to remain

meaningful. Such engagement will add value to 3D Body Scanning practice and, by

extension, add value to the theoretical foundation of the technology design and

human-computer interaction disciplines.

Hunt (1994) and Henkel (2006) argued that although we know a lot about how

companies compete in the market place, we know little about how they collaborate. In

this research, I have scratched only the surface of what is perceived to be an area of

outstanding importance for the further development of digital size and fit solutions in

the future. The stakeholders and participants involved offered a rich environment for

exploring the multiple dimensions in the process of design of a 3D Body Scanning

service. In this thesis, I strongly argue that technologies are successful - when they

fulfil fashion designer-producer and customer expectations. To date, the thesis

findings have been disseminated and used at a series of conferences providing

grounds for discussion with developers and users in sessions at the 3D Body Scanning

conference in Montreal (Januszkiewicz et al., 2017) and Lugano (Januszkiewicz et

al., 2019b), and introduced at the Textile institute conference on Textile and Life in

Manchester (Januszkiewicz et al., 2019a). The research efforts have been well

received, the feedback has been positive, and it has provoked discussion at these

events. There is now a scope to formalise the feedback to disseminate further and test

the model quantitatively. Moreover, it will be interesting to see how different types of

design scholars and the 3D Body Scanning stakeholders can potentially benefit from

the use of the 3D Body Scanning framework.

271

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APPENDICES

Appendix A: Alvanon Body Form Measurements

The Alvanon measurements collected followed the Beazley (1996) measurement

protocol to ensure the data was collected in a valid and reliable manner. Table 38 Alvanon anthropometric dimensions. Source: author’s own.

Dress Form Size Code UMR-WMSK12H-1504 Dress Form Code AVF58535 Code Body Measurement 12

1 Height 168.5 2 Nape Level to Ground 146.2 3 Depth of Scye to Ground 126 4 Side Waist to Ground (direct) 106 6 Inside Leg from Ground" 77.1

~6a~ Ankle Height 7.3 8 Scye (Armhole) Width 11.6

~9~ Right Shoulder Drop 4.9 11 Nape to arms scye level 20.7 12 Nape to back Waist Level 41.6 13 Waist Girth 71.8 14 Hip Girth 97.7 15 Centre Back Waist to Hip* 21 16 Upper Hip Girth 88.8 23 Nape to End of Shoulder 19.3

~25~ End of shoulders point to Elbow Prominence 36.2 ~26~ End of Shoulder to waist 58.2

28 Head Girth 54.1 32 Neck Base Circumference 38.8 33 Mid Neck Circumference 34 34 Cross Back Shoulders 38.6 35 Across Back (midway nape and scye level) 34.2 38 Across Front Shoulders 37.6 39 Across Front (midway CF neck to bust level) 33 40 Right Shoulder Length 12.9 42 Back Scye Level to waist 21.5 45 Side Waist to Knee* 60.7 46 Side waist to Ankle* 100.3 48 Chest Girth 86.2 50 Bust Girth 89.8

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51 Naper to Bust Level 34.9 52 Under Bust Girth (normal) 76.2 55 Width of Bust Prominence 19.2 57 Armhole Girth 42.2 58 Upper Arm Girth 28.3 59 Elbow Girth ^ 27.5 60 Wrist Girth 15.2

~60a~ Min-Max Hand Girth (calculated) 21.2 62 Croth Length 69.5 63 Thigh Girth 56.9

~63a~ Knee Girth 35.4 64 Calf Girth 35.4 65 Ankle Girth 23.4 66 Instep- Heel Girth (calculated) 29

Figure 28 Alvanon 3D Body Scan (three different angles view). Source: author’s own.

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Appendix B Virtual Fit: Style Me - User Journey Example

Figure 29 Style Me Interface (steps 1-3). Source: (Style.Me, 2018).

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329


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Appendix C: Diffusion of Innovation Coding Materials

Table 39 – 43 are showing the flow of the stakeholders’ interviews from each

innovation attribute. The characteristics identified as important in the stakeholder’s

interview are grouped by category and ranking (frequency of occurrence).

10.3.1 Relative Advantage Coding

Table 39 Frequency of relative advantage themes. Source: author’s own.

THEMES PHASE 1: CHARACTERISTICS IDENTIFIED

PHASE 2: RANKING

PHASE 3: STAKEHOLDERS IDENTIFIED

Technology Exploitation

Patent 6

Technology developers Trademark 2

Technology Exploration

External Networking 2

Software developers R&D Outsourcing 5

Retail Participation 3

Fashion Perspective

Interest in Technological Growth 5 Fashion Retail

Leadership Role

Positive 3 Fashion Retail

5

Software Developers

Negative

1

3 Technology Developers

3 Research

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10.3.2 Compatibility Coding

Table 40 Frequency of compatibility themes. Source: author’s own.


PHASE 2: RANKING


Scanner Design & location

In Retail


3 Software Developers

1 Fashion Retail

1 Research

Home via Smartphone



4 Fashion Retail

4 Research

Technology capabilities

Scan Speed 4 Technology Developers


Scan Position 2 Software Developers

2 Research

Reliability



3 Research

Amount of Data



5 Fashion Retail

2 Research

Design Barriers

Machine Learning 2 Technology Developers


Standardisation



7 Fashion Retail

4 Research

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10.3.3 Trialability Coding

Table 41 Frequency of trialability themes. Source: author’s own.


PHASE 2: RANKING


Usability Sessions

Customer Trials



8 Fashion Retail

5 Research

Engagement with Uses in development


Poor Interaction criteria 5 Research

Branding



3 Fashion Retail

10.3.4 Observability Coding

Table 42 Frequency of Observability themes. Source: author’s own.


PHASE 2: RANKING


Data Presentation

Avatar



1 Research

Size Recommendation



3 Fashion Retail

Preference Tracking 3 Software Developers

2 Fashion Retail

More Advanced e-Commerce Front Page Communication



1 Research

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10.3.5 Low complexity Coding

Table 43 Frequency of Complexity themes. Source: author’s own.


PHASE 2: RANKING


Methodology

Practitioner Workflow 5 Fashion Retail

Scaling Resources Up 4 Technology Developers


Developing Own Software



3 Fashion Retail

1 Research

New Assumptions needed 2 Software Developers

Standards 5 Software Developers

3 Fashion Retail

Metadata



2 Fashion Retail

2 Research

Privacy & Security

Data Should Belong to Customer



2 Fashion Retail

4 Research

Belong to Fashion Brand 8 Fashion Retail

Belong to Tech Vendor



1 Fashion Retail

Data Should be used similarly to credit card (Multi Level Ownership)


3 Fashion Retail

3 Research

Government Accountability 5 Technology Developers

1 Fashion Retail

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Appendix D: Research Project Description

Figure 32 Research Project Description. Source: author’s own.

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Appendix E: Participant Consent Form

Figure 33 Study Three Participants Consent Form. Source: author’s own.

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Appendix F: Participant Demographic Questionnaire

Figure 34 Study Three Participants Demographic Questions. Source: author’s own.

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Appendix G: Research Advertisement Poster

Figure 35 Study Three Advertisement Poster. Source: author’s own.

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Appendix H: An Example for Focus Group Discussion Transcript

Group Discussion 6

Participants: 21 - 25

Task outline

On this session, we will try to design a 3D body scanning service together. Thinking

about your experience in 3D Body Scanning and your potential ideas for the future

service, could you write down all your thoughts on post-it notes. Write positive

suggestions on the red cards and negative on the red cards. I will give you 5 minutes

to think and begin brainstorming. Next, we will start our discussion, and we will try to

build on each other ideas.

M: Who's ready to start?

P21: I felt like it was quite an uncomfortable position.

P22: Yes, I've got that as well

P21: and I didn't like to be standing on this position, and posture as well.

P22: Yes, it wasn't very pleasant to standing up properly.

P23: I have written this as a need for posture guidance, yes.

P22: I felt like I need to do this more than once to understand how to properly

position myself.

P25: Yes, I put this as well. I went large, (laugh), I got it on a big post-it note.

P21: I think it is also the fact what you expect, when you see like a picture of

somebody, you see it with all the clothes on. I used to see the upright image of myself,

but when you see a scanned image, dots simulating yourself, and you see yourself in

an odd position. I don't look like that, but maybe that is how you usually look. So it

may not be comfortable, and even make me feel wrong about myself.

P23: Because you usually don't stand with your hand in a position like this. Also, the

thing is because you lift it, yes, I lifted it at first try.

P21: I quite imagine like a would, look different in a different position and like it also

depends on what you would use this for.

P22: With regards to position, it is also a bit the machine is in control. It is quite

intimidating, like depending on the machine.

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P23: Yes, like I could not relax. I wonder if this gives you different data when you are

holding and tensing, does tense gives you different measurement?

M: The reason we use the handles is to make sure the posture is standardised in some

way so that we can capture and analyse the larger quantity of data.

P23: oh yes, so it is for comparison, I suppose.

P22: Yes, it might be.

P24: I get that, but what I mean is not a fact, that if everybody uses that, but more the

tension effects it creates.

M: Shall we move to the next point?

P25: I put it was quick, music works, yes you know, there was explicit instruction

overall.

P23: The instruction was clear, but I think it was the whole experience, actually I

wrote it was a teamwork project, calm and professional, you know, just the fact that

someone is there makes you more confident.

P21: I thought the voice doesn't walk me through much, right?

P23: Yes, I knew there was a voice, but only reacted when it said your measurements

are done now, I guess it would be helpful if it meant we are starting the measurement

now, get the position to breathe.

P21: Yes, I have like I wonder if I just breathe in just tortured myself on this, thinking

I will end with a weird shape or something because I wasn't very like normal?

P23: Oh, you hold yourself, really?

(Group laugh)

P21: You know I was trying to get some straight shoulder position -ish

P22: Be more like a photo-booth, you go in there and tells you exactly like, and they

are having like 3,2,1 and scan!

P21: Yes, it would be useful to have a count or something, like that, rather than just

you know quickly done, it is great but also surprising. I felt not ready.

P24: I don't remember anyone saying to me, don't move and so I have to keep still,

and I just stayed still just in case, it was weird.

P25: Also, with wearing hair buns, it would be nice to have more instruction on that,

know it before the scan.

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P25: I just put the changing clothes felt a bit lie a doctor surgery (laugh), think about

that, thinking about that

P23: Yes, it would be more like you feel like you are going for a fancy bra fitting

there, which like elements like you have a nice trey for your jewellery

P23: Yes, this kind of experience, sort of more for customers

M: What would be your reaction if you have seen 3D Body Scanning in store?

P22: I think it needs to have an explanation or be goal-oriented?

P23: Why would you do that (3D body scanning in-store)

P22: And also, for a machine, like I guess a lot of people would be worried about

cameras like is it x-rays or camera base, can they make a picture, what is this camera

for, like all these questions need to be communicated somehow before the process.

P24: And also mention what happens with your data, with who will be shared with,

you know I work in it in Manchester University, so I know all the rules, what is going

to happen to your data. But if you like walking to the Top Shop, I would be quite

happy to be scanned if I knew what would happen with my data and who would

manage and share my data with.

P23: And you have to think about what is for, because if you are going to get this

print out if going to have such a negative impact that you may not want to go

shopping. You got to think about that because if you look at yourself, you know you

look on printout versus, flattering mirror and soft lighting it is a different experience.

P21: I think that would depend on what is the item you are shopping for because I

would not look at that when I go shopping in store. But if I was buying a wedding

dress and I would have that and show me styles specific to what would suit my body

or if I am trying to buy online maybe or something like that. Something that will show

you how to look at your best and in being truthful in that then perhaps I wouldn't mind

looking at that. I mean they have a software, running system where you could like

running on a treadmill so you can figure out what is going to be the best shoot of you

and doing something seemingly, and good and it feels like yes right. Unless you

conceive them in so obvious marketing

P23: And I think it lacks rationale for why you would do that, why would you go in

there?

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M: Have you ever had the opportunity to try this tech in-store?

P21,22,23,24,25: No

M: Do you think this data; somehow can help you with your shopping?

P23: I think it needs to be simplified because it is really not clear how this data relates

to the parts of the body

21,22,24,25: Yes

23: You know, its need to be presented better, like a better visual layout.

25: Like a summary of something, like these are full measurements, but only keys

measurements will affect how do you shop online?

P24: Yes, the thing is there is never the measurements in clothes presented if you get

a length, you are lucky, it is you know a complete bollocks because you know your

top part will be 10, your bottom part will be 12, and so it is great. But manufacturers

don't know the situation when you are shopping online, and I can see why, because

the clothing comes from the manufacture of 10 different people and so for them to put

down the measurements for every single item is a huge amount of time and effort to

figure it out, so I am not sure it is worth it.

P25: there might be a correlation between size specs?

P24: If they base this on real women that would be nice

P23: What it could be your data could be assumed, and your style like this is size 12

or 14 maybe with capturing your body measurement

P24: there is somebody who does that, they put your measurement, and they say that

somebody with the same measurements bought a size 10 or 12 or 8, that has helped

me in the past, so I think there is something already available in the market.

P21: I wouldn't like looking what the norm for your weight and being compared, yes,

I mean my waist-hip length is very unusual for my body. I just like going around

everybody else and being like you know actually I longer than you but kind of thing.

And that is with studying fashion and being interested in these things.

M: Ok, let's move on, any more points, here:

P23: I though the general environment was like if you are in here, you can hear what

is going in this room so maybe you want to make it more private, add more ambience.

I think my point is that when you in there, in your underwear, you sort of realising

how someone else can hear everything, and it makes you more nervous.

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P25: But I don't mind it that much because it is at the University, well if it were

somewhere else then yes, I would be slightly concerned.

M: What about if we as mentioned before making it more like a changing room, in

the changing room we use to hear other people being around and we are also in the

underwear, right?

P22: I suppose yes (laugh)

P24: As long as they are not walking near the door.

P21: Yes, I think what makes the difference is curtain versus the door, kind of thing.

Like if you have a door, you feel kind of closed and in control. Whereas when you

with curtain it is more vulnerable like you are at the mercy of other people.

P24: And it is quite neat at the changing rooms so yes, making it look more like

changing room.

P21: I was going to say that maybe add some clear visual output like I can see myself

in it, how I wanted, but it does seem identifiable and unusual.

P23: Sometimes you don't want the reality, this is especially true for people who

shop, I don't know.

P24: That was one thing it is a really detailed printout with lots of things and

measurements, but one measurement I need is not on there, that's the measurement for

me when I am knitting. This output is going to be great for me when I will neat the

jumper and all because I got exact measurements to compare, but that one

measurement would be good, really handy.

P25: I get that because it would be difficult to get a measurement for underarm.

M: Next point, do we have anything for positive green space?

P23: I think it is an interesting process that is that thing, body positive.

(Group Silence)

M: So, if you could re-design the page, how would you attempt to do so?

P23: I think I would simplify, I would have two levels of it, depending for what users

are going to be if it's for the retail environment you may have the core measurements

and some other that somehow correlates to them in some way to shop for this, so there

is a connection between them if you are to use it in a retail environment.

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P22: I think just having a better explanation, just having that one thing to compare

against whether it is UK standard or some other data.

P21: I think also have the information few columns like left side and right side ones,

so it’s easier to have more links and maybe have graphics the other way up because it

sort of feel they cut half a bit, they are too far on the page, and I probably have like a

small avatar of yourself with almost the body cut on four chunks and maybe have the

measurements highlighted in rows because if you look at it and you have lines then

that could be a new box and a new box here with information relating to that section,

M: What version of yourself would you prefer to see, realistic or more anonymous, or

maybe not at all just a list of measurements?

P24: realistic

P22: Yes, or the one I've got

P25: Giving people a choice would be good, if somebody is self-conscious especially,

or if somebody is into sports maybe more muscle definitions or something like that,

P22: Yes

P21: it would be interesting to see the body without the lines, because they sort of

distorts the image and how you look, like and maybe the dots are denser for a

particular reason, to have them it is interesting to see them without lines.

P25: That is an example of another body scan, and you can see how all the muscles

definitions, it is from size scream, it would be nice to have an example to show how

could this look like if somebody wants it this way or not

P22: That is how I imagine the scan output would be like.

P25: I think the size stream it wasn't as blurred as this one, maybe automatically

tweak it a little bit so we can see it more polished with muscle definition

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Appendix I: An Example of User Interview for Interface Appraisal

Size Stream Interface interview 34ET Participant 34 Female, 26 Me: Can you tell me more about the image how does appear to you? P34: Do you guys develop all of the systems yourselves? It doesn’t seem tall to me. I don’t know it look fit in some ways because the shape is not too pronounced. I cannot see muscles, so it is not well gym/ trained person, but still, it is not an overweight person. It seems like the legs are shorter, they look pretty short. I don’t know if the grey bit between legs the final part of the triangle is, she got a very long central body and quite short legs. And I don’t know the arms look normal, shoulders parallel. Me: Can you see a relationship between the body and the measurements? P34: I know the names, some of them, maybe if I look at the shoulder width horizontally, yes. If I look at bust circle yes, I can see this one. The chin height yes, collar yes, and some others, but others not really, and is very detailed, and there are measurements like I don’t even know. There is, what I am thinking about the yellow circles and red dots on the body, do they relate to measurements, right? Can I click on them? I think they are clear the lines, but I am trying to link them together, the red dots and measurements, they are indicated here, ‘si’, ‘This is a chin height, but it is not showing it, so wait... No, it does not work well. You know what I am realising, the measurements do not follow any order or direction like go from a-z or here to here, but instead, they are kind of confused. I think it would be easier to keep them in some order. Well, I am looking at these three dots here. I cannot find them, because we got abdomen here that is the central part, then we got the knees and the ankles, and we have the arm and neck. It goes back to the posture, and the calves and the chest, so it is difficult for me to find eventually what I want to find because it does not follow an order. But if it goes from ankles to shoulder, I knew that was the end, but hereafter the elbow we got the hip the knee, the neck the armhole, the shoulder the side neck, the knee again. I looking for this the three dots here, I looking for I don’t know head, I cannot find it, so uh, what is the inseam? Maybe if we knew the exact names of measurements, the alphabetical order would work, but not for me when I have to guess the names incorrectly. Me: Do you see any similarities between your body and the example presented? P34: I think it is different, in some ways, it is pretty, yeah it is different in some ways, but as I would say, for example, this part is very pronounced, but here is not. Also, I know I don’t have a lot of breasts but here is not visible, and here it seems like I got the almost flat stomach, the chin, it looks pretty smooth, but here it is more

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pronounced, and also my hair bun. On the screen, it seems more realistic, here is not well defined and also the hair is different. It is the same person. Me: Lastly, would you prefer the output to be more or less realistic? P34: Well, I think it is better on screen than on paper. I like it this way, not cartoonish. I want an avatar to look more realistic and not cartoonish, it gives me more sense of authenticity, but if you provide me with cartoon version, I don’t think it is very credible, so yeah.

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