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
2
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
MaterialsandMethods 1745.3.1 SettingandSample 1745.3.2 DataCollection 1755.3.3 DataAnalysis 177
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
MaterialsandMethods 2046.3.1 SettingandSample 2046.3.2 DataCollection 2046.3.3 DataAnalysis 206
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
MaterialsandMethods 2257.3.1 SettingandSample 2257.3.2 DataCollection 2257.3.3 DataAnalysis 226
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,
23
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
29
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).
30
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).
31
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
32
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?
34
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.
48
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).
2.3.5 Summary of Contribution to the Thesis
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.
2.4.3 Summary of Contribution to the Thesis
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).
2.5.3 Summary of Contribution to the Thesis
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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3.5.2 Research Question 2
“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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3.5.3 Research Question 3
“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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3.5.4 Research Question 4
“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.
4.4.3 Size and Fit Prediction
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.
4.5.3 Size and Fit Prediction
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).
Theoretical Background
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).
Materials and Methods
5.3.1 Setting and Sample
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
5.3.2 Data Collection
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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Theoretical Background
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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Materials and Methods
6.3.1 Setting and Sample
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.
6.3.2 Data Collection
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.
Theoretical Background
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).
Materials and Methods
7.3.1 Setting and Sample
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.
7.3.2 Data Collection
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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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.
267
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.
269
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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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.
THEMES PHASE 1: CHARACTERISTICS IDENTIFIED
PHASE 2: RANKING
PHASE 3: STAKEHOLDERS IDENTIFIED
Scanner Design & location
In Retail
4 Technology Developers
3 Software Developers
1 Fashion Retail
1 Research
Home via Smartphone
4 Technology Developers
8 Software Developers
4 Fashion Retail
4 Research
Technology capabilities
Scan Speed 4 Technology Developers
3 Software Developers
Scan Position 2 Software Developers
2 Research
Reliability
4 Technology Developers
3 Software Developers
3 Research
Amount of Data
4 Technology Developers
3 Software Developers
5 Fashion Retail
2 Research
Design Barriers
Machine Learning 2 Technology Developers
2 Software Developers
Standardisation
6 Technology Developers
4 Software Developers
7 Fashion Retail
4 Research
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10.3.3 Trialability Coding
Table 41 Frequency of trialability themes. Source: author’s own.
THEMES PHASE 1: CHARACTERISTICS IDENTIFIED
PHASE 2: RANKING
PHASE 3: STAKEHOLDERS IDENTIFIED
Usability Sessions
Customer Trials
7 Technology Developers
8 Software Developers
8 Fashion Retail
5 Research
Engagement with Uses in development
1 Technology Developers
Poor Interaction criteria 5 Research
Branding
5 Technology Developers
5 Software Developers
3 Fashion Retail
10.3.4 Observability Coding
Table 42 Frequency of Observability themes. Source: author’s own.
THEMES PHASE 1: CHARACTERISTICS IDENTIFIED
PHASE 2: RANKING
PHASE 3: STAKEHOLDERS IDENTIFIED
Data Presentation
Avatar
3 Technology Developers
2 Software Developers
1 Research
Size Recommendation
3 Technology Developers
4 Software Developers
3 Fashion Retail
Preference Tracking 3 Software Developers
2 Fashion Retail
More Advanced e-Commerce Front Page Communication
1 Technology Developers
1 Software Developers
1 Research
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10.3.5 Low complexity Coding
Table 43 Frequency of Complexity themes. Source: author’s own.
THEMES PHASE 1: CHARACTERISTICS IDENTIFIED
PHASE 2: RANKING
PHASE 3: STAKEHOLDERS IDENTIFIED
Methodology
Practitioner Workflow 5 Fashion Retail
Scaling Resources Up 4 Technology Developers
1 Software Developers
Developing Own Software
6 Technology Developers
8 Software Developers
3 Fashion Retail
1 Research
New Assumptions needed 2 Software Developers
Standards 5 Software Developers
3 Fashion Retail
Metadata
2 Technology Developers
4 Software Developers
2 Fashion Retail
2 Research
Privacy & Security
Data Should Belong to Customer
2 Technology Developers
2 Software Developers
2 Fashion Retail
4 Research
Belong to Fashion Brand 8 Fashion Retail
Belong to Tech Vendor
7 Technology Developers
1 Software Developers
1 Fashion Retail
Data Should be used similarly to credit card (Multi Level Ownership)
1 Technology Developers
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.
335
Appendix E: Participant Consent Form
Figure 33 Study Three Participants Consent Form. Source: author’s own.
336
Appendix F: Participant Demographic Questionnaire
Figure 34 Study Three Participants Demographic Questions. Source: author’s own.
337
Appendix G: Research Advertisement Poster
Figure 35 Study Three Advertisement Poster. Source: author’s own.
338
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.
340
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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