Tilburg University Crossing boundaries Slot, J.H.

Tilburg University

Crossing boundaries

Slot, J.H.

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i

Crossing Boundaries:

Involving External Parties in Innovation

Johanna H. Slot

i

Crossing Boundaries:

Involving External Parties in Innovation

PROEFSCHRIFT

ter verkrijging van de graad van doctor aan Tilburg University

op gezag van rector magnificus, prof. dr. Ph. Eijlander,

in het openbaar te verdedigen

ten overstaan van een door het college voor promoties aangewezen commissie

in de aula van de Universiteit op woensdag 18 december 2013 om 16.15 uur

door

Johanna Hendrika Slot

geboren op 26 augustus 1982 te Brederwiede

ii

Promotor: Prof. dr. Inge Geyskens

Copromotor: Dr. Stefan Wuyts

iii

Committee

Prof. dr. Barbara Deleersnyder, Associate Professor of Marketing, Department of

Marketing, Tilburg School for Economics and Management, Tilburg University, The

Netherlands.

Prof. dr. Inge Geyskens, Professor of Marketing and CentER Fellow, Department of

Marketing, Tilburg School for Economics and Management, Tilburg University, The

Netherlands.

Prof. dr. Katrijn Gielens, Associate Professor of Marketing, Department of Marketing,

Kenan-Flagler Business School, University of North Carolina at Chapel Hill, USA.

Prof. dr. Lisa Scheer, Professor of Marketing, Department of Marketing, College of

Business, University of Missouri, USA.

Prof. dr. Raji Srinivasan, Professor of Marketing, Department of Marketing, McCombs

School of Business, University of Texas at Austin, USA.

Prof. dr. Stefan Wuyts, Associate Professor of Marketing, Department of Marketing,

Tilburg School of Economics and Management, Tilburg University, The Netherlands, and

Associate Professor of Marketing, Department of Marketing, Faculty of Business

Administration, Koç University, Turkey.

i

And ever, as the story drained

The wells of fancy dry,

And faintly strove that weary one

To put the subject by,

"The rest next time -" "It is next time!"

The happy voices cry.

Thus grew the tale of Wonderland:

Thus slowly, one by one,

Its quaint events were hammered out –

And now the tale is done,

And home we steer, a merry crew,

Beneath the setting sun.

Lewis Carroll, Alice’s Adventures in Wonderland,

“All in the Golden Afternoon,” stanza 5 and 6

Acknowledgments

Following a career in business, my first steps in the world of academia made me

feel like Lewis Carroll’s Alice when she started her Adventures in Wonderland. My first

courses in Tilburg, Marnik Dekimpe’s “Marketing Models” and Xavier Martin’s

“Advanced Strategy in Business”, seduced me with a combination of theoretical

challenge, methodological rigor, and managerial relevance. As if research were a cake,

with the words “EAT ME” beautifully printed on it in large letters. After one bite, I

already shrank, and shrank, and shrank some more – and I instantly felt very small,

finding myself in the midst of the great (and sometimes puzzling!) minds of the Tilburg

Marketing faculty. I was attracted to the new challenge that faced me immediately. And

that is how the Adventures of Johanna in Wonderland commenced.

Now I have completed the first leg of my journey in the world of academia. I could

not have accomplished without the support of many. I would like to take this

opportunity to express my thanks.

Acknowledgments

ii

First and foremost, I am greatly indebted to my advisors, Inge Geyskens and Stefan

Wuyts. Inge, meeting you was the very reason for me to come to Tilburg. You were the

one who expressed faith in my research capabilities at a very early stage. You sowed the

seed of my ambition overseas, a career path I had never envisioned myself taking. You

have been a great source of inspiration – your incredible precision, perseverance, and

personal attention have helped me enormously. Stefan, you have taught me to translate

my business experience into research ideas and testable hypotheses. Your everlasting

stream of comments, changes, and critical questions helped me to develop my skills in

developing theory. Even though you are at Koç University most of the time, your

responsiveness to my many, many emails made the distance between Tilburg and

Istanbul disappear. Inge and Stefan, thank you for believing in me (also in moments that

I did not), and for pushing me to higher levels. This dissertation would not be here

without you.

Second, I am equally indebted to Raji Srinivasan. You have helped me a lot in

becoming a better researcher. After taking your online class, you invited me to spend a

semester at McCombs Business School, in Austin, Texas, an amazing opportunity. By

working with me, you taught me lessons I will remember forever. Our research projects

are a true inspiration. On a personal level, you always keep an eye out for me. Thanks

for being my friend. You have changed my life, and I am incredibly grateful for it.

Third, I also would like to thank the members of my doctoral committee: Barbara

Deleersnyder, Katrijn Gielens, Lisa Scheer, and Raji Srinivasan. I feel very privileged to

have such distinguished academics in my committee. I appreciate your comments,

questions, and suggestions; they have certainly improved the essays in this dissertation.

Special thanks go to Barbara Deleersnyder. You have helped me greatly as I developed

my teaching skills. Moreover, you were always available to discuss my career

opportunities, for which I am truly thankful.

In addition, this dissertation would not have been here without the help of the firms

I worked with. Specifically, I would like to thank the many (project) managers and

engineers at the National Aerospace Laboratory. In particular, I would like to thank

Louis Aartman, Marc van Beek, and Rolinde Storm for their help in gathering data,

discussing ideas, and presenting my research. Also, many thanks go “Uncle Harm”, who

Acknowledgments

iii

initiated my collaboration with NLR. Furthermore, I want to thank Maxim Schram and

Eveline van Eekelen of CMNTY, who were so kind to share the data of the Redesignme

community with me. In addition, at ASML, I thank Bert Koek and the many other

managers I met, who sparked my fascination for high-tech industries.

Thanks also to CentER and the Marketing Department of Tilburg for the

coordination of the doctoral program and the financial support that enabled me to go

abroad and to visit conferences. A special word of gratitude goes to the Institute for the

Study of Business Markets. Receiving the Doctoral Support Award facilitated my

research greatly.

My paranimphs have been particularly important to me in the last years. First,

Arjen, my great office mate - I enjoyed discussing papers, issues in retail marketing

(your work), innovation (my work), and our job market adventures. I wish you the best

in Amsterdam. For every one of your many future accomplishments, I will decorate my

office in your name, like in the ‘old days’. Second, Anne, my academic sister – you are

always available for me being next door, both literally in Tilburg and in Leuven. You

have been – and continue to be – an inspiring example to me. Thank you for our many

win-win conversations, for your encouragements, and for your personal advice. Arjen

and Anne, I will never ever forget you. It is an honor to have you by my side.

Being a PhD student at the Tilburg Marketing Department was a great pleasure. Not

in the least, my other fellow PhD students have been very important to me. Millie and

Didi brightened up the day with many ‘hallway’ conversations. Néomie shared my

interest in strategic issues. Jonne sharpened my econometric skills. Mark, Jaione, Femke,

Yufeng, Soulimane, Max, and Kristopher, among other things, thank you for your

passionate participation during our annual Sinterklaas events.

Thanks also to the other members of the Marketing Department, who provided a

very stimulating environment. Bart B., Els, Henk, Marnik, and Rik, thank you for

exposing me to your knowledge, and thank you for the indispensable advice about the

next steps of my academic career. Barbara B., Aurelie, and Anne K., I enjoyed jogging

(and talking, of course) in the Warandebos, I will miss it so much! Anke, Elaine, Ellen,

Marit, and Ernst – thank you for being there. Discussing the things we shared – whether

it is our passion for shoes, clothing, or music, or being from ‘up North’ – was great fun.

Acknowledgments

iv

Robert, I would like to thank you for sharing your view on the world with me. Anick,

Bart S., Carlos, George, Hans, Michel, Natasja, Rutger, and Vincent, your stories livened

up lunch and coffee breaks. Scarlett, Heidi, Nancy, Angelique, and Nienke – you were

always ready to help out when needed. Furthermore, thanks to my friends around the

world. Dear Emine, you cheered up the office during your time in Tilburg and welcomed

me in Istanbul. Dear Leah, you will be my Austin friend forever. Lastly, I would like to

thank my new colleagues at The Smeal College of Business at The Penn State University

for giving me the opportunity to continue my academic career.

Importantly, I have always felt the unconditional support of my loving family and

friends. Pa en ma, voor jullie was het geen verrassing dat ik me zo thuis voelde op de

universiteit. Dank voor jullie onbegrensde liefde en voor jullie hartverwarmende steun

op de moeilijke momenten. Jullie hebben mij altijd gestimuleerd. Albertine, Klaas, en

Hendrieke, ik ben er trots op dat ik jullie ‘grote zus’ mag zijn. Ik kan me geen betere

broer en zussen wensen! Roelant, Linda, Robin, en natuurlijk ook Anne-Marie en Floris,

het is fijn om jullie erbij te hebben. Loek, Afra, Diana, en Luke, dank voor jullie warme

belangstelling (en welkom Fenna! Hoe klein je ook bent, je bent het bewijs dat alles

slechts relatief is). Mijn vrienden van De Keet: hoewel ik weet dat ik soms een rare eend

in de bijt kan zijn, kan ik altijd op jullie rekenen.

Last but not least, my loved one. Lieve Michiel, als jij niet aan mijn zijde had gestaan

in de afgelopen jaren, was me dit niet gelukt. Je kent me door en door, voelt me precies

aan. Je hebt me onvoorwaardelijk gesteund in mijn carrièreswitch aan het begin van dit

traject. Gedurende de afgelopen jaren heb jij ervoor gezorgd dat ik mijn doel voor ogen

hield. En nu gaan we weer verder… Dank je voor je vertrouwen in onze toekomst. Ons

leven gaat verder in Amerika. De volgende stap wordt een hele grote. Maar samen

kunnen we het aan. Ik hou van je, zielsveel, wanneer en waar dan ook.

Johanna

v

Contents

Chapter 1:

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.1 From Closed to Open Innovation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Research Gaps and Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

Chapter 2:

The Role of Supplier and Customer Involvement

in New Product Development: A Meta-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.2 Conceptual Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

2.3 Antecedents of Supplier and Customer Involvement . . . . . . . . . . . . . . . . . . . . . . . 15

2.4 Consequences of Supplier and Customer Involvement . . . . . . . . . . . . . . . . . . . . . 21

2.5 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

2.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

2.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Appendix: List of Studies Included in the Meta-Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 44

Contents

vi

Chapter 3:

The Effect of Customer Participation in Outsourced NPD

on Supplier Task Performance: The Role of Relationship Multiplexity . . . . . . 49

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3.2 Conceptual Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

3.3 Theory and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.4 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

3.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.6 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Appendix: Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78

Chapter 4:

Managing the Crowd:

Prize Structure and Creativity In Online Idea Generation Contests . . . . . . . . . . 81

4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

4.2 Theory and Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84

4.3 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90

4.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

4.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

Appendix: Contest Brief and Submission Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111

Chapter 5:

Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

5.1 Summary and Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113

5.2 Implications for Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

5.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118

5.4 Future Research Directions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120

References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

1

“Begin at the beginning,"

the King said, very gravely,

"and go on till you come to the end: then stop.”

Lewis Carroll, Alice’s Adventures in Wonderland, Chapter 12

Chapter 1

Introduction

To improve the return on investments in innovation, firms increasingly open up

their new product development (NPD) processes by applying open innovation tactics. A

prevalent phenomenon is the involvement of external parties in innovation, which

forms the central topic of this dissertation. Before going over to the empirical core of the

thesis, this chapter will first introduce the topic of open innovation. Next, we describe

the gaps in extant research this thesis aims to address. Finally, we will provide an

outline of the dissertation in which the empirical studies are briefly introduced.

1.1 FROM CLOSED TO OPEN INNOVATION

Innovation is key to business success and a top priority among managers and

academic researchers (Hauser, Tellis, and Griffin 2006). The development of new

products is crucial for firms to create value, generate new customer bases, and develop

new sources of profit. In today’s turbulent business environment, characterized by

globalization, rapid technological developments, and strong competition, successful

NPD efforts are crucial to achieve and maintain competitive advantage. The most

successful firms generate about half of their sales and profits from products developed

in the last five years (Barczak, Griffin, and Kahn 2009). It comes as no surprise that 97%

of the CEOs consider NPD a top priority, as reported in a global survey by

PricewaterhouseCoopers (Percival and Shelton 2013).

Chapter 1 – Introduction

2

Yet, the development of new products is tough, for multiple reasons. First, NPD is a

risky activity. Many NPD initiatives fail before reaching the market. According to a

benchmarking study of 416 firms executed by Barczak, Griffin, and Kahn (2009), seven

ideas are needed for every single NDP project initiated. Unsure technological

developments make that only one out of eleven of the NPD projects started leads to a

market introduction of a new product. Once on the market, only about half of the new

products are successful commercially. Comparing the benchmarking study’s survey

results over time (Page 1993; Griffin 1997, 2002), NPD success rates are remarkably

stable, indicating the difficulty firms experience in improving the return on NPD

investments. Second, NPD is expensive, requiring large investments in the development

of new knowledge, skills, and technologies (Schmidt and Calantone 2002) that have

limited ability to increase short-term cash flows (Srinivasan, Lilien, and Sridhar 2011).

Over the last decades, the costs of technology development have increased dramatically,

a trend that is reported in a wide variety of industries, including computer and

semiconductors, pharmaceuticals, and even consumer products (Chesbrough 2007).

Third, completing the NPD process takes times: the development of new products takes

three years to complete, on average (Barczak, Griffin, and Kahn 2009). Reducing NPD

cycle time such that new products are introduced to the market earlier is imperative for

new product success (Cooper and Kleinschmidt 1994), especially in a business

environment characterized by shrinking product life cycles (Chesbrough 2007).

Traditionally, NPD is organized within the boundaries of the firm. In this model, the

complete development process is internally executed and largely self-contained. All

stages of the NPD process, including the activities for idea generation, marketing and

business evaluation of the product concept, research and development (R&D) of the

technology, product design, and prototype development and testing, are under control

of the firm. For a long time, the internally organized NPD process proved to be an

important source of competitive advantage and a significant barrier to entry for new

entrants (Chesbrough 2003). However, internally organized NPD has its downsides.

NPD organized within the boundaries of the firm can be slow, as all necessary

knowledge, skills, and technologies must be developed internally. In addition, internal

NPD is largely dependent on a firm’s own resources, which limits a firm’s strategic

flexibility.


3

Nowadays, the NPD process has changed. In an attempt to increase the efficiency

and effectiveness of the NPD process, firms increasingly apply open innovation

practices (Chesbrough 2003). Open innovation describes a new paradigm based on the

premise that valuable ideas can come from inside or outside a firm and can go to market

from inside or outside the firm as well (Chesbrough 2003). By crossing firm boundaries,

firms do not need to rely solely on internal research and development as sources for

innovation. Examples include buying externally developed technology on the market or

licensing internally developed technology (Chesbrough 2003; Von Hippel 2005), and

forming joint ventures or alliances for innovation (Rothaermel and Deeds 2004).

This dissertation focuses on the practice of involving external parties in the

formerly internally organized NDP process. The benefits of inviting external parties to

participate in NPD include access to external resources including knowledge and skills

(Fang 2008; Handfield et al. 1999; Jeppesen and Lakhani 2010). Involving external

parties may be the source of new product ideas (Afuah and Tucci 2012; Stump, Athaide,

and Joshi 2002). Depending on their role, external parties could also share materials,

equipment, and machinery, for use in the developing firm’s NPD process.

Gaining access to external party resources may increase the developing firm’s

technological and market knowledge, which may reduce the risk associated with

developing (Walter et al. 2003) and marketing a new product (Chen, Li, and Evans

2012). Furthermore, access to external parties’ resources may reduce the time to

market (Campbell and Cooper 1999), although the additional activities necessary for

managing the external party involvement might also lengthen the NPD process (Bajaj,

Kekre, and Srinivasan 2004). Similarly, external party involvement can affect the cost

efficiency of NPD processes both positively and negatively (Bensaou 1997; Koufteros,

Vonderembse, and Jayaram 2005).

This dissertation focuses on the participation of three types of external parties in

NPD: suppliers, customers, and the “crowd”.1

1 A firm can also involve other types of external parties in its NPD projects, such as research institutes and universities (e.g., Powell, Koput, and Smith-Doerr 1996), consultants (e.g., Knudsen 2007), and competitors (e.g., Luo, Rindfleisch, and Tse 2007).


4

1.2 RESEARCH GAPS AND CONTRIBUTIONS

The overarching objective of this dissertation is to shed light on how to successfully

manage the participation of external parties in NPD. Organized per type of external party

involved, we formulate gaps in extant research that form the basis for the contributions

of this dissertation.

Supplier Involvement

Developments in supply chain management in the 1980s initiated opening up the

innovation process by letting suppliers participate in car manufacturers’ NPD processes

(e.g., Clark 1989; Kotabe, Martin, and Domoto 2003; Takeishi 2001), a practice that was

rapidly adopted in other industries. Supplier involvement ranges from minor

participation to close collaboration with the developing firm. For example, involved

suppliers may provide suggestions during the product’s design phase or share their

technological knowledge with the firm’s engineers during the development phase.

Alternatively, suppliers may co-locate their employees on-site. Suppliers may also take

over design, development, engineering, and testing tasks (Handfield et al. 1999; Wasti

and Liker 1997; Wynstra and Ten Pierick 2000).

The growth in relevant research has paralleled the growth of supplier involvement

in practice. Yet, an integrative review that consolidates prior findings on the role of

supplier involvement in NPD is still lacking. The first contribution of this dissertation is

to take stock of the extant literature on supplier involvement in NPD by analyzing the

antecedents and consequences.

Customer Involvement

Next to suppliers, customers also play a role in the firm’s NPD process (e.g., Fang

2008; Prahalad and Ramaswamy 2004). Customer involvement in NPD focuses on the

buyers and users of the products to be developed. The involvement of customers in NPD

is increasingly used in practice, and can take many forms. For example, customers can

be the source of new product concepts. When selecting lead users, whose current needs

will become general in the future marketplace, customer involvement can contribute to

innovation (Von Hippel 1986). Customers may also participate in testing concepts or

product prototypes. Especially in industrial markets, customers can contribute


5

technological knowledge and skills to the NPD process (Fang, Palmatier, and Evans

2008), whereas in consumer markets, their contribution may come in the form of co-

development (Chan, Yim, and Lam 2010; Franke, Schreier, and Kaiser 2010).

The growing popularity of customer participation is reflected in the academic

literature. Now that a substantial body of research on customer involvement in NPD has

accumulated, an integrative review that consolidates prior findings is opportune. This

dissertation will contribute to the literature by systematically analyzing the antecedents

and the consequences of customer involvement in NPD, in a similar fashion to our

analysis of supplier involvement in NPD. In addition, this dissertation will contribute to

the extant literature by comparing the involvement of suppliers and customers in terms

of their antecedents and consequences.

Although the academic attention for customer involvement in NPD is growing,

extant research on customer involvement focuses on the traditional ‘markets of many.’

In contrast, the role of the involved customer in ‘markets of one,’ in which only one

customer purchases the product developed, is not studied. These interactions are

common in business-to-business markets, where a single customer firm outsources the

development of technology or a product to an external supplier firm. The question rises

whether the outsourcing customer should be involved in the developing firm’s NPD

process. A complicating feature of these industrial relationships is their multiplex

nature: these relationships often include more than one role: the customer may also be

a partner of, a competitor against, or even a supplier to the developing firm (Tuli,

Bharadwaj, and Kohli 2010). We contribute to the literature by empirically investigating

the effects of customer involvement in outsourced NPD in these multiplex relationships.

Crowd Involvement

A more recent application of open innovation is the involvement of the ‘crowd’, an

unidentified group of individuals, external to the firm, in the NPD process. This practice

has been coined as “crowdsourcing” in the business literature (Howe 2006, 2008).

Enabled by recent developments in Web 2.0 technology, firms use crowdsourcing

applications to reach individuals with a wide diversity of knowledge and skills

(Jeppesen and Lakhani 2010). One application of crowd involvement in NPD is the

online idea generation contest, in which individuals external to the firm enter a contest


6

and submit ideas in response to a firm’s open call for ideas. Many firms, including

Heineken, Frito-Lay, Unilever, and Samsung, organize these contests to gather input to

be used in the idea generation phase of the NPD process.

Despite the growing importance of the crowd as an external resource for the NPD

process, motivating the crowd to expend effort in such contests is a key challenge

(McKinsey 2009, p. 53). This dissertation aims to add to the emerging literature on the

involvement of the crowd in NPD by empirically investigating how to motivate the

crowd to contribute to the idea generation phase of the NPD process.

1.3 OUTLINE

This dissertation consists of three essays that focus on the involvement of external

parties in an NPD context. An overview of the chapters’ content, research approach, and

sample is depicted in Table 1.1. Despite their common focus on external party

involvement in NPD, each chapter is self-contained and can be read independently. Each

chapter starts with its own introduction and ends with a discussion of the major

findings. The next sections will outline the three chapters that form the core of this

dissertation.

We start our analysis in Chapter 2 – The Role of Supplier and Customer Involvement

in New Product Development: A Meta-Analysis – by taking stock of the extant empirical

work on supplier and customer involvement in NPD. In the last decades, the

participation of suppliers and customers in a firm’s internal NPD process have become

increasingly important in business. Following practitioners’ interests, researchers in

marketing, strategic management, operations management, and other academic

domains studied the role of supplier and customer involvement in NPD, contributing to

a fragmented literature base on the topic. Using meta-analytic techniques, which are

indispensible for integrating and expanding a field’s knowledge base (Hunter and

Schmidt 1990), we bring sharper focus to these seemingly distinct streams of research.

Specifically, we meta-analyze data harvested from 119 independent samples

reported in 140 empirical studies. The theoretical perspective central to our hypotheses

is the resource-based view (Barney 1991), originally developed in the field of strategic

management, but also applied to explain marketing phenomena (e.g., Day 1994; Hunt

and Morgan 1996; Srivastava, Shervani, and Fahey 1998).


7

We propose and test a framework including both antecedents and consequences of

supplier and customer involvement in NPD. Regarding the antecedents, we focus on (i)

a firm’s resources (in technology and marketing) and (ii) the environmental uncertainty

a firm faces (with respect to technology and the market) as predictors of supplier and

customer involvement in NPD. In terms of the consequences, we focus on (i) product

innovativeness, (ii) speed to market, and (iii) cost performance as outcomes of supplier

and customer involvement. We find that supplier involvement improves speed to

market and cost performance, but lowers product innovativeness. In contrast, customer

involvement improves product innovativeness while lowering speed to market.

TABLE 1.1: Chapter Overview

Chapter 2 Chapter 3 Chapter 4

Research

Question

What are the antecedents and

consequences of supplier and

customer involvement in

NPD?

How does customer

participation in outsourced

NPD affect supplier task

performance for multiplex

relationships?

How does the prize

structure of an online

idea generation

contest affect idea

creativity?

External

Party

Studied

Supplier

Customer

Customer

(in partner, competitor, and

reversed supplier roles)

Crowd

(undefined group of

individuals)

Context NPD projects NPD projects Idea generation

contests

Performance

Measure

Innovativeness

Speed to market

Cost performance

Supplier task performance

(customer and supplier

perspective)

Creativity

Research

Approach Meta-analysis Primary study Primary study

Data

Sources

Empirical work in marketing,

strategic management,

operations management, and

supply chain management

Project administration data

Evaluation reports

Strategic cooperation plans

Procurement records

Surveys

Contest data

Submissions

Panel of judges

Sample 119 samples from 140

studies 140 NPD projects

106 idea generation

contests

Methodology Full information maximum

likelihood

Seemingly unrelated

regression with

randomized intercept

Full information

maximum likelihood


8

Chapter 3 – The Effect of Customer Participation in Outsourced NPD on Supplier Task

Performance: The Role of Relationship Multiplexity – considers the prevalent industrial

context of outsourced NPD. In outsourced NPD, a firm outsources NPD activities to an

external supplier that executes the product development work. Yet, as the outsourcing

customer is highly knowledgeable and may start thinking about technological avenues

to follow in the NPD project, customers increasingly participate in the supplier’s NPD

process. In many outsourced NPD projects, the involvement of the customer in the NPD

project is complicated by the multiplex nature of the relationship between the customer

and supplier: a customer may also be a partner of, competitor against, or even supplier

to its supplier firm (Ross and Robertson 2007; Tuli, Bharadwaj, and Kohli 2010).

Prior research important to this topic can be found in the fields of marketing and

strategic management. Role theory (Biddle 1986; Katz and Kahn 1966), which has its

roots in the sociology literature, forms the theoretical basis of our analysis. Specifically,

we propose how customer participation affects the task performance of the developing

supplier under conditions of (i) customer-as-partner, (ii) customer-as-competitor, and

(iii) customer-as-supplier multiplexity. In this chapter, we consider the effects of

customer involvement at both sides of the dyad by analyzing the effects on the

customer’s perception as well as the supplier’s own perception of supplier task

performance. We test our hypotheses using a proprietary data set on 140 outsourced

NPD projects, composed of multiple sources of archival data, survey data, and key

qualitative insights.

We find that involving a customer that is also one’s partner increases supplier task

performance, but the effect holds only for partnerships that are composed by the parties

themselves and not for partnerships that are engineered by a triggering entity. In

contrast, participation of a customer that also has a supplier role decreases supplier

task performance. Participation of a customer that is also a competitor lowers supplier

task performance in the eyes of the customer, but does not affect the supplier’s self-

reported task performance. Our results provide managerial insight as to when customer

participation helps or hurts.


9

Chapter 4 – Managing the Crowd: Prize Structure and Creativity in Online Idea

Generation Contests – deals with online idea generation contests, a nascent application

of open innovation. Unlike the research contexts central to Chapters 2 and 3, in which

the external party had a supplier or a customer role, online idea generation contests

enable the involvement of external parties without any relationship to the firm. In these

contests, an idea generation challenge is disclosed as an open call to the ‘crowd’: an

undefined group of individuals external to the firm (Howe 2006, 2008). Those that

compete in these crowdsourcing contests do not receive any upfront or guaranteed

payment for their efforts. Instead, they are motivated by the possibility of winning a

prize (Afuah and Tucci 2012). In Chapter 4, we examine the effects of prize structure

characteristics of online idea generation contests on idea creativity, a core element of

innovation strategy (Im and Workman 2004).

Relevant extant work on idea generation contests has been done in both marketing

and strategic management. Our theoretical angle is rooted in psychology. Specifically,

we use arguments from motivation theory (Amabile 1996; Deci and Ryan 1985) to form

our hypotheses. We propose how a contest’s (i) total prize value, (ii) number of prizes,

and (iii) prize spread affect the creativity of the ideas submitted in the contest.

Controlling for the endogeneity of the number of contestants, we test our hypotheses

using a proprietary dataset on 106 online idea generation contests, complemented with

data supplied by expert judges.

We find that total prize value and number of prizes increase idea creativity, while

prize spread decreases idea creativity. Furthermore, the effects of prize structure

characteristics on idea creativity are interdependent. Contest sponsors who are unable

to offer a high total prize value can increase idea creativity by having many prizes of low

value. Contest sponsors should strive to set prizes of equal value as prize spread

decreases idea creativity, especially for contests with few prizes.

Chapter 5 summarizes the main findings and provides general conclusions.

Furthermore, we propose managerial implications of the findings. Finally, we conclude

this chapter with a discussion of the limitations of the studies, and offer potential

avenues for future research.

11

“Consider your verdict,” the King said to the jury.

“Not yet, not yet!” the Rabbit hastily interrupted.

“There's a great deal to come before that!”


Chapter 2

The Role of Supplier and Customer Involvement

in New Product Development:

A Meta-Analysis

2.1 INTRODUCTION

Understanding the drivers of successful new product development (NPD) has been

a long-standing goal of managers and researchers. NPD is associated with high resource

requirements, large investments, long time horizons, and substantial risk (Cooper and

Kleinschmidt 1995). A recent benchmarking study (Barczak, Griffin, and Kahn 2009)

reports that firms require about seven ideas for every single NPD project initiated.

Furthermore, NPD projects take about three years to complete. In addition, firms

commercialize only one product for every eleven NPD projects started, and only about

half of these market introductions are reported to be successful. It is therefore not

surprising that a recent worldwide survey by PricewaterhouseCoopers shows that 97%

of the CEOs consider improving NPD performance a top priority, and a major and

lingering concern (Percival and Shelton 2013).

In an attempt to improve their NPD performance, firms have increasingly opened

up their NPD processes (Chesbrough 2003). A popular practice is the involvement of

parties outside firm boundaries in NPD projects (Wind and Mahajan 1997). Starting in

the 1980s, practitioners and researchers alike have recognized the potential of supplier

Chapter 2 –Supplier and Customer Involvement in New Product Development: A Meta-Analysis

12

involvement as a way to improve NPD performance (Johnsen 2009). In the 1990s, the

role of the customer as an external resource became more important: in an attempt to

find new sources of value, firms let customers participate in innovation processes

(Prahalad and Ramaswamy 2004). Accordingly, academia started to examine customer

involvement in NPD as well. However, despite the accumulating body of research on

NPD, an integrative review that consolidates prior findings on the role of supplier and

customer involvement in NPD is still lacking.

The purpose of this study is to provide an integrative meta-analysis of research on

both supplier and customer involvement in NPD. We limit our analysis to the situation

in which a firm involves suppliers and/or customers in an internal NPD project, and

thus exclude other, more formal forms of collaborative NPD between two firms, such as

joint ventures. Using correlations obtained from 140 studies from a wide range of fields,

including marketing, strategic management, operations management, and supply chain

management, we propose and test a model that encompasses the antecedents and

consequences of both supplier and customer involvement. Specifically, we focus on a

firm’s resources (in technology and marketing) and environmental uncertainty (with

respect to technology and the market) as drivers of its use of supplier and customer

involvement in NPD. We extend prior research (e.g., Barczak, Griffin, and Kahn 2009) by

taking into account the multifaceted nature of NPD success (Griffin and Page 1996) and

by investigating the effects of supplier and customer involvement on three measures of

NPD performance: product innovativeness, speed to market, and cost performance. In

addition, we extend the literature on external party involvement (e.g., Chesbrough 2003)

by explicitly comparing supplier and customer involvement in NPD as two different

strategies to access external resources.

We find that a firm’s internal resources (in technology and marketing) and

environmental uncertainty (with respect to technology and the market) are important

predictors of its use of both supplier and customer involvement in NPD. Further, we find

that supplier and customer involvement affect operational NPD performance differently.

Notably, there are trade-offs between the effects of supplier and customer involvement

on product innovativeness, speed to market, and cost performance, which suggests that

firms should selectively involve external parties in NPD. The results suggest useful

directions for NPD practitioners and scholars.

Chapter 2 – Supplier and Customer Involvement in New Product Development: A Meta-Analysis

13

The rest of this essay is organized as follows. First, we describe the resource-based

view, which is the foundation to our theory. Second, we develop our hypotheses that

explain the effects of a firm’s internal resources and environmental uncertainty on the

use of supplier and customer involvement in NPD. Subsequently, we present our

hypotheses on the consequences of supplier and customer involvement, in terms of

product innovativeness, speed to market, and cost performance. Then, we explain the

data collection procedure and offer the results. We conclude with the study’s

implications for research and practice.

2.2 CONCEPTUAL BACKGROUND

A theoretical perspective that can help to improve our understanding of customer

and supplier involvement in NPD is the resource-based view of the firm (Barney 1991;

Wernerfelt 1984), which has been widely used in other work on NPD (e.g., Kleinschmidt,

De Brentani, and Salomo 2007; Verona 1999). The resource-based view considers the

firm as a unique bundle of tangible and intangible resources. Resources include all of a

firm’s assets, capabilities, organizational processes, information, and knowledge that

enable the firm to deploy strategies that improve its position (Barney 1991).

Importantly, the resource-based view of the firm does not limit itself to a firm’s internal

resources. Crucial resources may also lie outside of the firm (Doz and Hamel 1998).

Specifically, resources that are accessed by means of involving external parties in a

firm’s NPD operations may extend a firm’s resource base (Song et al. 2005). Two

external parties that have received considerable attention of both practitioners and

academics are suppliers and customers (Wind and Mahajan 1997). Using the resource-

based view of the firm, we develop hypotheses on what drives firms to involve suppliers

and/or customers in NPD, and how this affects their performance. We define supplier

involvement in NPD as the extent of supplier participation in the focal firm’s NPD

project (Ragatz, Handfield, and Scannell 1997). Supplier involvement may range from

giving minor design suggestions (e.g., to improve the manufacturability of a component)

to participating in a firm’s NPD project by co-locating supplier employees on-site and

taking over tasks in the design, development, engineering, or testing of particular

components or subsystems (Handfield et al. 1999; Wasti and Liker 1997; Wynstra and

Ten Pierick 2000). During supplier involvement in NPD, the focal firm and the involved

supplier work together. This collaboration allows the firm to use the involved supplier’s


14

knowledge, competences, and capabilities in the NPD project (Ragatz, Handfield, and

Scannell 1997).

Along the same lines, we define customer involvement in NPD as the extent to

which customers participate in the focal firm’s NPD project (Fang 2008). Typically,

customers involved in an NPD project participate by providing information on customer

needs and preferences, by providing feedback on product concepts and prototypes,

and/or by co-developing technology (Fang 2008; Gruner and Homburg 2000). For this

purpose, the focal firm can organize customer visits, customer workshops, and product

tests with customers. During these activities, the interaction between the firm and the

involved customer allows the firm to access external customer knowledge, competences,

and capabilities.

The goals of our meta-analysis are to examine the antecedents and consequences of

supplier and customer involvement in NPD. The conceptual framework guiding our

analysis is depicted in Figure 2.1.

FIGURE 2.1: Antecedents and Consequences

of Supplier and Customer Involvement in NPD

First, we argue that the extent to which a firm involves suppliers and customers to

access external resources depends on its internal resources and the external

Supplier involvement

Customer involvement

NPD performance

• Innovativeness• Speed to market

• Cost performance

Firm resources

• Technology• Marketing

Uncertainty

• Technology• Market

Product importance

Firm size

= hypothesized relationship= control


15

circumstances the firm faces in the form of environmental uncertainty (cf. DeSarbo et al.

2005). We distinguish between a firm’s technological and marketing resources, and

between technological and market uncertainty. Second, we argue how supplier and

customer involvement in NPD contribute to NPD performance, in terms of product

innovativeness, speed to market, and cost performance.

2.3 ANTECEDENTS OF SUPPLIER AND CUSTOMER INVOLVEMENT

We start our theory development by examining the antecedents of supplier and

customer involvement in NPD. We distinguish between internal resources and

environmental uncertainty.

Effects of Firm Resources on Supplier and Customer Involvement in NPD

Firm resources refer to all assets, capabilities, organizational processes, information,

knowledge, etc. controlled by the firm that enable the firm to formulate and implement

strategies to improve its competitive position (Barney 1991). Extant research finds that

firm resources play an important role in the NPD process. For example, Cooper (1979)

shows that technical proficiency and marketing knowledge are key to the NPD process.

Moenaert and Souder (1990) study R&D and marketing personnel as important actors

in the NPD process. Similarly, Calantone and Di Benedetto (1988), Song and Parry

(1997), and Song and Montoya-Weiss (2001) describe how technical and marketing

resources are key inputs to the innovation process. Thus, both technological resources

as well as marketing resources have been recognized as playing a role in the

development of new products.

A firm’s technological resources relevant to NPD are the inputs to the technological

development of a new product (Calantone and Di Benedetto 1988), including its

machinery and installations, R&D personnel, engineering expertise, design capabilities,

production skills, patents, the stock of previously accumulated technological knowledge,

etc. (Song and Montoya-Weiss 2001). A firm’s marketing resources relevant to NPD

include market information as well as the skills to screen, use, and disseminate the

resulting marketing knowledge in the firm (Day 1994; Hunt and Morgan 1995).

Specifically, knowledge about customer needs and preferences, channel partners, and

competitors comprise important resources during the NPD process (Day 1994; Song

and Montoya-Weiss 2001). In addition, marketing resources include the firm’s test


16

marketing and product launching capabilities (Cooper 1979). We expect that both types

of firm resources – technology and marketing – affect the extent to which firms involve

suppliers and customers in the NPD process.

Technological resources and supplier involvement. We argue that a firm’s internal

resources are a necessary condition for firms to deploy the resources sourced from

external parties. As a firm has accumulated more domain-specific prior knowledge and

skills, it is better able to recognize, value, and employ externally sourced knowledge

(Cohen and Levinthal 1990).

Firms with more internally developed technological resources have a higher

absorptive capacity to process supplier knowledge in the NPD project (Cassiman and

Veugelers 2006), which may lead to higher levels of supplier involvement in NPD for

four reasons. First, a firm with more internally developed technological resources is

more likely to understand its current technological knowledge deficiencies, which

increases the involvement of suppliers as a way to use the externally sourced

knowledge to fill its internal knowledge gaps. Second, a firm with more internally

developed technological resources is better able to assimilate supplier know-how and

subsequently employ it in its own NPD project (Cohen and Levinthal 1990), which may

also increase the extent of supplier involvement in NPD. Third, a firm with high levels of

internal technological resources is more proficient to jointly develop new knowledge

with the involved supplier. Fourth, a firm with a strong R&D track record can better

judge and select fruitful technologies among those offered among the supplier base

(Narasimhan, Rajiv, and Dutta 2006), making supplier involvement more attractive.

Turning to the supplier’s perspective, LaBahn and Krapfel (2000) note that a

supplier is more willing to participate in a firm’s NPD project when the latter has a

stronger internal technological resource base, because this also offers the supplier more

learning opportunities. In sum, we expect that firms with stronger internal

technological resources will more intensely involve suppliers in their NPD projects. We

hypothesize:

H1SI: A firm’s technological resources increase supplier involvement in NPD

Technological resources and customer involvement. We expect that a firm’s

internally developed technological resource base also affects the involvement of


17

customers in NPD projects. Prior research has suggested that new technology-intensive

products are best developed through extensive involvement of customers in the NPD

project (Neale and Corkindale 1998), because customer involvement leads to better

insight into market opportunities for these products. We argue that a stronger

technological resource base improves a firm’s ability to evaluate, select, and translate

these market opportunities into new products. First, a firm that has accumulated more

technological knowledge and skills can better evaluate the technical feasibility and

realism of customer input (cf. Kim and Wilemon 2002) and can therefore select the

most promising customer ideas (Huston and Sakkab 2006). Second, strong

technological knowledge and high levels of design and engineering skills are necessary

to effectively translate unmet customer needs and preferences into new products

(Narasimhan, Rajiv, and Dutta 2006). Thus, customer involvement is likely to be a more

attractive strategy for firms with a stronger technological resource base.

Turning to the perspective of the customer, one key motivation for customers to be

involved in the firm’s NPD activities is to increase the fit between the product and their

requirements (Fang, Palmatier, and Evans 2008). However, involvement in an NPD

project also requires time and effort from the customer’s behalf (Brockhoff 2003) for

which the firm possibly competes with other manufacturers seeking the customer’s

participation. The translation of customer input into usable products requires

technological resources. Therefore, the likelihood that a customer is willing to

participate in a firm’s NPD project is higher when the latter has more technological

resources, because it has a higher ability to turn the customer input into a product that

suits the customer’s needs. In sum, we expect that a firm with more internal

technological resources will more intensely involve customers in its NPD efforts.

Reflecting our thinking, we hypothesize:

H1CI: A firm’s technological resources increase customer involvement in NPD

Marketing resources and customer involvement. We argue that a firm with strong

internally developed marketing resources is more likely to involve customers in NPD,

for three reasons. First, a firm with more marketing resources has a stronger external

orientation (Day 1994; Vorhies and Morgan 2005), which fuels the development of

strong channel bonding capabilities. The resulting improved customer relationships


18

offer easier access to customer input (Chen, Li, and Evans 2012), which makes it more

likely to involve customers in NPD. Second, a firm with more marketing resources is

more likely to understand its current market knowledge deficiencies (Atuahene-Gima

2005), which may increase the use of customer involvement as a way to fill its

knowledge gaps. Third, a firm with more marketing resources is better able to translate

information about customer needs and preferences into promising market

introductions. Specifically, a firm’s market sensing abilities and its ability to formulate

product concepts based on customer needs and preferences render customer

involvement in NPD a more attractive strategy.

Turning to the perspective of the customer, a firm’s marketing resources improve

its channel bonding capabilities, which strengthen the relationship between the

customer and the firm. It is expected that the resulting improved customer relationships

will make customers more willing to share their input with the firm (Ritter and Walker

2003). In sum, a firm with more marketing resources is more likely to intensely engage

its customers in NPD. We hypothesize:

H2CI: A firm’s marketing resources increase customer involvement in NPD

Marketing resources and supplier involvement. We expect that a firm’s marketing

resources affect supplier involvement in NPD as well, for three reasons. First, a firm that

has superior access to marketing assets and that has stronger marketing capabilities is

more externally oriented (Day 1994). As a result, the firm will be more motivated to

source external knowledge from suppliers. Second, a firm with an external orientation

has stronger market-sensing abilities (Day 1994), which will aid in selecting the

technological assets that are most promising from a market point of view from those

available in the supplier resource base. This will make supplier involvement in NPD a

more attractive strategy for firms with a strong marketing resource base.

Turning to the perspective of the supplier, a firm with higher levels of marketing

resources typically has stronger channel bonding capabilities with its supply chain

partners as a result of its external orientation (Day 1994; Vorhies and Morgan 2005).

Close, collaborative relationships between the firm and its suppliers will make it easier

to realize supplier involvement in NPD as close suppliers will be more willing to invest

time and effort in the firm’s NPD project (Joshi and Stump 1999; Walter 2003). In sum,


19

we expect that a firm with stronger internally developed marketing resources will

demonstrate higher levels of supplier involvement in NPD. Accordingly, we hypothesize:

H2SI: A firm’s marketing resources increase supplier involvement in NPD

Effects of Environmental Uncertainty on Supplier and Customer Involvement in NPD

The strategic choices of a firm are not only influenced by its resources but also by

its environment (Porter 1980). We distinguish between two types of environmental

uncertainty: technological uncertainty and market uncertainty. We define technological

uncertainty as the extent to which technology in an industry is in a state of flux

(Jaworski and Kohli 1993). Under high levels of technological uncertainty, firms

struggle to understand new and incompletely specified processes or products

(Burkhardt and Brass 1990; Rindfleisch and Heide 1997). We define market uncertainty

as the speed and the unpredictability with which customer needs and preferences

change (De Luca and Atuahene-Gima 2007; Rindfleisch and Heide 1997).

Technological uncertainty and supplier involvement. We expect technological

uncertainty to increase a firm’s need to involve suppliers in NPD projects. Under

conditions of technological uncertainty, a firm needs to remain flexible in terms of

technological resources (John, Weiss, and Dutta 1999). By involving suppliers in NPD, a

firm can broaden its technological options and assure itself of the resource flexibility

that is necessary to face technological uncertainty. Furthermore, accessing externally

developed technological resources reduces the focal firm’s technology development risk

substantially (Bidault, Depres, and Butler 1998), which also increases the attractiveness

of supplier involvement to the focal firm. We hypothesize:

H3SI: Technological uncertainty increases supplier involvement in NPD

Technological uncertainty and customer involvement. Technological uncertainty is

also likely to increase the need to involve customers in the NPD process. Under

conditions of rapid technological change, a firm may be unsure which of the multiple

possible technological trajectories to select for further development. Technological

trajectories differ in terms of the customer benefits offered (Anderson and Tushman

1990). In these circumstances, a firm needs market resources for making informed

choices about the customer needs that can be solved with the various technological


20

avenues available. Interacting with customers results in increased first-hand market

insight, which provides direction in such changing product markets (Narver and Slater

1990). Customer involvement under conditions of technological uncertainty may be

beneficial to the involved customer as well, as being involved may contribute to

choosing the technology that offers the most customer benefits. Thus, in technological

uncertain environments, customer involvement helps a firm to align technological

choices with customer needs (Von Hippel 1986). Accordingly, we hypothesize:

H3CI: Technological uncertainty increases customer involvement in NPD

Market uncertainty and customer involvement. We expect that a high level of market

uncertainty increases customer involvement in NPD. Changing customer needs and

preferences increase a firm’s need for market resources. Customer involvement can

fulfill the firm’s need for additional market resources by providing first-hand market

insights about customer needs and preferences (Fang 2008). External resources from

customers can reduce the market uncertainty a firm faces in several ways. For example,

customers can offer feedback about product concepts that fit their needs best. Involved

customers can also assure their needs are met by assisting in designing a product, which

is expected to increase customers’ willingness to be involved in the focal firm’s NPD

project. Further along in the NPD process, customers may play a role in field testing,

which contributes to a better fit between the newly developed product and a customer’s

usage situation. In sum, under conditions of market uncertainty, involving customers in

NPD allows the firm to respond to new demand curves (Slater and Narver 1995). We

hypothesize:

H4CI: Market uncertainty increases customer involvement in NPD

Market uncertainty and supplier involvement. Under conditions of market

uncertainty, a firm has greater needs for technological resources, because a firm that

faces rapidly changing customer needs and preferences requires strategic response

flexibility (Grewal and Tansuhaj 2001). New-to-the-firm technologies may be required

to meet changing customer needs. This may spur supplier involvement in NPD, as it

offers a firm access to a variety of technological resources which increases the firm’s

flexibility to respond to new demand curves (cf. Slater and Narver 1995). Thus, we


21

expect that market uncertainty increases the likelihood to involve suppliers in the NPD

project:

H4SI: Market uncertainty increases supplier involvement in NPD

2.4 CONSEQUENCES OF SUPPLIER AND CUSTOMER INVOLVEMENT

We continue our theory development by examining the consequences of supplier

and customer involvement in NPD. Prior research has reported inconsistent results as

to the effects of supplier and customer involvement on NPD performance. For example,

Potter and Lawson (2013) report positive effects of supplier involvement on NPD

performance, whereas Millson and Wilemon (2002) show negative effects of supplier

involvement. As to customer involvement, Brettel and Cleven (2011) find that it

improves the performance of NPD projects, but Knudsen (2007) reports a negative

influence of customer involvement. These inconsistencies in prior research may be

attributable to the multifaceted nature of NPD performance (Griffin and Page 1996).

Recognizing that no single measure is able to completely gauge the performance of an

NPD project (Griffin and Page 1996), we set out to examine the effects of supplier and

customer involvement on three interrelated, yet distinct measures of NPD performance:

product innovativeness, speed to market, and cost performance.

A product’s level of innovativeness is defined as its newness in terms of technology

and market (Kleinschmidt and Cooper 1991). A product is high on technological

newness when its development requires the use of new technology, engineering, design,

and production processes (Kleinschmidt and Cooper 1991). A product is high on market

newness when it serves new customers, fills new customer needs, and faces new

competitors on the market (Kleinschmidt and Cooper 1991). Innovative products can

disproportionally contribute to firm profitability (Wind and Mahajan 1997), and are

crucial to maintain a competitive advantage (Abernathy and Clark 1985).

However, developing innovative products comes with challenges. Developing

radically innovative products takes longer than developing incrementally innovative

products (Griffin 1997): trade-offs may have to be made between product

innovativeness on the one hand and speed to market on the other hand (e.g., Fang 2008;

Swink, Talluri, and Pandejpong 2006). We define speed-to-market as the time it takes

from concept screening and evaluation, technical development and testing, up to the


22

production startup, excluding market introduction (Cooper and Kleinschmidt 1986).

Speed-to-market is also referred to as time to market (Tatikonda and Montoya-Weiss

2001), project completion time (Terwiesch and Loch 1999), product development time

(Lilien and Yoon 1989), and lead time (Ulrich et al. 1993).

In turn, developing products with a high speed to market comes – literally – at a

cost. Increasing the speed of development lowers a project’s cost performance (Bayus

1997; Langerak, Rijsdijk, and Dittrich 2009). Thus, we also study cost performance. We

define cost performance as the firm’s ability to keep developing costs within budget

(Kessler 2000). Increasingly intense competition forces firms to improve the efficiency

of their product development activities (Rothwell 1994), which underlies the

importance of paying attention to cost performance in NPD.

We now discuss the effects of supplier and customer involvement in NPD on each of

these performance measures: product innovativeness, speed to market, and cost

performance.

Effects of Supplier and Customer Involvement in NPD on Product Innovativeness

Supplier involvement and product innovativeness. We argue that supplier

involvement in NPD increases product innovativeness. First, involving suppliers in NPD

increases the developing firm’s resource base, including knowledge and skills, which is

crucial for innovation (Håkansson & Eriksson 1993; Ragatz, Handfield, and Petersen

2002; Takeishi 2002; Wynstra, Van Weele, and Weggeman 2001). As the focal firm and

the involved supplier share a common goal (Un, Cuervo-Cazurra, and Asakawa 2010),

relevant explicit and tacit information is shared openly (Brown and Eisenhardt 1995;

Ragatz, Handfield, and Petersen 2002). Open information sharing may increase

innovation spillovers from the supplier to the developing firm (Inkpen 1996) and may

enhance knowledge creation between the two parties (Inkpen 1996), thereby

increasing a firm’s ability to develop radically innovative products by employing new

technologies in the NPD project (Koufteros, Cheng, and Lai 2007). Second, involving

suppliers may contribute to the selection of promising nascent technologies, because of

the suppliers’ scout function in the innovation process. Suppliers may have more

technological insights and experience in particular areas, such that they can share

reliable, meaningful, and relevant knowledge about emerging technologies with the


23

focal firm (Walter et al. 2003). Third, suppliers can also be a source of market insight

(Song and Thieme 2009). A supplier may have invested in marketing activities directed

at its supply chain and may have developed insights about customer demands and

unfulfilled market opportunities that can be relevant and valuable to the developing

firm. In turn, the focal firm can use these external marketing resources to increase its

product innovativeness. We offer the following hypothesis:

H5SI: Supplier involvement in NPD increases product innovativeness

Customer involvement and product innovativeness. We expect that intensively

involving customers in the NPD process also contributes to product innovativeness.

Customers have been recognized as a source for many innovative ideas (Feng et al. 2012;

Von Hippel 1986) of a broad nature (Un, Cuervo-Cazurra, and Asawaka 2010) that

stimulate product innovativeness. Exposure to customer needs is critical for developing

superior products (Clark and Fujimoto 1991). A firm that closely interacts with

customers during NPD builds an in-depth understanding of customer interests as well

as their expressed and unexpressed needs (Griffin and Hauser 1993; Ramani and Kumar

2008; Vargo and Lusch 2004). This way, a firm can identify market trends early on,

which allows it to act on unexplored market opportunities (Chen, Li, and Evans 2012),

increasing product innovativeness. Next to being a source of improved market insight,

customers can also offer technological knowledge. Customers with needs that precede

the majority of the market are likely to try developing a solution themselves and to

build early technological insights (Von Hippel 1986), which can help increase the

innovativeness of the product developed. However, a strong customer orientation has

also been associated with lower product novelty (Im and Workman 2004). Customers

may define their needs in terms of existing products (Bonner and Walker 2004), such

that customer involvement may lead to more application-oriented developments

associated with lower investments and less risk (Knudsen 2007), which restricts the

exploration of very innovative alternatives (Danneels 2003). Acknowledging this risk of

customer involvement in NPD, prior research has shown that involved customers

should be carefully managed, and customer input needs to be thoroughly processed

before being put to use in the NPD project (Sethi 2000). Under these conditions,

customer input is well-channeled and improves, rather than deteriorates, product


24

innovativeness. Hence, overall, customer involvement in NPD has the potential to

increase product innovativeness. We hypothesize:

H5CI: Customer involvement in NPD increases product innovativeness

Effects of Supplier and Customer Involvement in NPD on Speed to Market

Supplier involvement and speed to market. Prior research has suggested three ways

in which supplier involvement may help reduce the speed to market of a newly

developed product. First, by involving suppliers, additional personnel join the focal

firm’s project team and may take over non-core tasks, such as the development of

particular components or the execution of testing activities. This may reduce the

workload of the focal firm (Clark and Fujimoto 1991), and allows it to specialize into

tasks that require the firm’s key competencies and skills (Eisenhardt and Tabrizi 1995).

As a result, the critical path of development projects may be shortened (Brown and

Eisenhardt 1995; Ragatz, Handfield, and Petersen 2002), improving speed to market.

Second, by involving suppliers more intensively in the NPD process, the focal firm is

more likely to identify potential technical problems in the product specification, product

design, or production early on (Knudsen 2007; Zirger and Hartley 1994). This

eliminates rework, thereby speeding up the NPD process (Ragatz, Handfield, and

Petersen 2002). Third, involving suppliers increases the number of technological

perspectives held among the NPD team members (Eisenhardt and Tabrizi 1995), which

reduces the time needed to solve technical problems, in case any occur. Fourth, closely

involving suppliers in an NPD project contributes to a better coordination of

communication and information exchange between the parties, which reduces delays

and helps to achieve time goals (Ragatz, Handfield, and Petersen 2002). Therefore, we

hypothesize:

H6SI: Supplier involvement in NPD increases speed to market

Customer involvement and speed to market. In contrast to involved suppliers, who

typically take over tasks from the firm’s NPD team, involved customers do not take over

tasks, but rather join the firm’s NPD team to provide feedback and co-development

assistance to the firm’s engineers (Fang 2008). Listening to involved customers and

translating customer input into usable knowledge of customer needs and preferences

requires substantial time commitment on behalf of the developing firm (Bajaj, Kekre,


25

and Srinivasan 2004), as customer input requires processing before it can be used

(Sethi 2000). Thus, instead of speeding up the NPD process, involving customers adds

extra tasks that the focal firm must execute, thereby increasing development time and

reducing speed to market. The reduced speed to market as a result of involving

customers in the NPD project can be partially compensated for. A firm that involves

customers in NPD is likely to identify product benefit misspecifications and product

design flaws in an earlier stage, which could save rework in later stages of the NPD

process (Campbell and Cooper 1999; Koufteros, Vonderembse, and Jayaram 2005).

However, such time benefits can only be attained when customers become involved in

various stages of the NPD process. Specifically, customer involvement is valuable during

the preliminary market and technical analysis, because the insight gained can reduce

costs and problems in the more costly and risky stages of (post-) development

(Campbell and Cooper 1999). In addition, customer involvement is critical during the

testing and evaluation stages of the project, because it provides pre-commercialization

feedback (Campbell and Cooper 1999). However, the more NPD stages in which

customers are involved, the more customer-related project tasks are added to the focal

firm’s task list in a project. Thus, net, we expect that customer involvement in NPD

lengthens rather than shortens the NPD project. Reflecting our thinking, we hypothesize:

H6CI: Customer involvement in NPD decreases speed to market

Effects of Supplier and Customer Involvement in NPD on Cost Performance

Supplier involvement and cost performance. We expect that supplier involvement in

NPD improves an NPD project’s cost performance, for the following reasons. First,

specialized supplier personnel can take over tasks otherwise executed by the focal firm.

As both the supplier and the focal firm specialize into tasks based on their core skills

and competencies, the NDP process is likely more efficient (Eisenhardt and Tabrizi

1995), reducing the cost of development. Furthermore, supplier involvement may spur

joint investments in new technologies while sharing the development cost (Dröge,

Jayaram, and Vickery 2000; Hartley et al. 1997; Langerak and Hultink 2008). In addition,

intensive collaboration leads to an improved relationship, which facilitates coordination

and communication, which, in turn, lowers the cost of problem solving (Cannon and

Homburg 2001; Scannell, Vickery, and Dröge 2000). Finally, involving suppliers in the


26

NPD process allows firms to eliminate design and production problems early on in the

NPD process (Knudsen 2007; Zirger and Hartley 1994), which reduces costly rework

(Ragatz, Handfield, and Petersen 2002). We expect:

H7SI: Supplier involvement in NPD increases cost performance

Customer involvement and cost performance. Also customer involvement is likely to

affect the cost performance of NPD. On the one hand, involved customers provide

resources to the NPD process that the company may not have access to (Feng 2012;

Gruner and Homburg 2000), saving the focal firm the costs of acquiring these resources

on the market. In addition, interacting with customers during the NPD project can

improve a firm’s understanding of customer needs (Brown and Eisenhardt 1995), which

helps it to prevent mistakes in the design phase of the project. As a result, costly

changes to the product design later in the project can be avoided (Campbell and Cooper

1999; Koufteros, Vonderembse, and Jayaram 2005). On the other hand, the coordination

of involving customers in the NPD process is associated with additional costs (Bajaj,

Kekre, and Srinivasan 2004; Bensaou 1997). For example, additional customer visits,

workshops and product tests with customers, and development team meetings aimed at

gathering customer knowledge must be organized. In addition, processing the newly

gained customer information and translating it into usable resources for the NPD

project takes substantial resources from the focal firm (Sethi 2000), which ultimately

lowers the project’s cost performance. We expect that these additional costs for

coordination and processing, which can be substantial as they affect each project task in

which customers participate, outweigh the potential cost savings achieved. We

hypothesize:

H7CI: Customer involvement in NPD decreases cost performance

2.5 METHOD

Literature Search

The role of supplier and/or customer involvement in NPD have been studied in

multiple fields, including marketing, strategy, operations management, and supply chain

management. We therefore employed a wide search to retrieve the relevant

publications for inclusion in the meta-analysis, covering the period 1990-2013. We used


27

the following search strategies. First, via a computerized search, we retrieved studies on

either supplier or customer involvement in the ABI Inform Global and ECONLIT

databases using the keywords “supplier involvement,” “supplier participation,”

“supplier collaboration,” “supplier integration,” “customer involvement,” “customer

participation,” “customer collaboration,” “customer integration,” “customer co-creation,”

“customer co-development,” and “customer coproduction.”

Second, we executed an issue-by-issue search for the following journals: Academy of

Management Journal, Administrative Science Quarterly, International Journal of Research

in Marketing, Industrial Marketing Management, Journal of the Academy of Marketing

Science, Journal of Management, Journal of Marketing, Journal of Marketing Research,

Journal of Product Innovation Management, Management Science, Marketing Science,

Organization Science, and Strategic Management Journal. Third, we examined the SSRN

network for unpublished studies and work in progress to address the “file-drawer”

problem (Rosenthal 1991). Fourth, we examined the reference sections of eight reviews

on NPD (e.g., Calantone, Harmancioglu, and Dröge 2010; Cankurtaran, Langerak, and

Griffin 2013; Henard and Szymanski 2001; Evanschitzky et al. 2012). Finally, we

examined the reference sections of all studies identified in the previous four steps to

retrieve any study that might have been overlooked in the process.

After identifying studies for potential inclusion in the dataset, we evaluated the

appropriateness of each study. We excluded studies on collaborative NPD in joint

ventures and alliances (e.g., Rothaermel and Deeds 2004). We excluded studies that

reported on supplier and/or customer involvement in domains other than NPD, such as

manufacturing (e.g., Jayaram, Xub, and Nicolae 2011). Furthermore, we excluded studies

on suppliers (e.g., Carson 2008) or customers (e.g., Franke, Schreier, and Kaiser 2010)

fully taking over the NPD process. In addition, we excluded studies for which we could

not identify which type of external party (supplier or customer) was involved (e.g.,

Kessler, Bierly, and Gopalakrishnan 2000; McNally, Akdeniz, and Calantone 2011).

Further, we excluded studies reporting on the willingness or intention to (be) involve(d)

in NPD rather than actual involvement in NPD (e.g., Porter and Donthu 2008). Lastly, we

excluded studies that failed to report a Pearson correlation coefficient between

supplier/customer involvement in NPD and one of the other constructs in our model,


28

and that also lacked sufficient statistical information to allow the computation of a

correlation coefficient using the formulas in Hunter and Schmidt (1990, p. 272).

A total of 140 studies were eligible for inclusion in the meta-analysis (see the

Appendix for an overview). Three of these studies were unpublished at the time of data

collection. Some studies merely reanalyzed previously reported data (e.g., De Toni and

Nassimbeni 1999, 2000), or reported subsamples of data that were expanded in later

studies (e.g., Liker, Kamath, and Wasti 1998; Wasti and Liker 1997, 1999). Other studies

examined data from more than one sample (e.g., Lin et al. 2005). In all, we obtained 119

independent samples, reported in 140 studies. Supplier (customer) involvement in NPD

was studied in 91 (54) independent samples.

Data Collection Procedure

Correlations between variables of interest were recorded. In case correlations were

not reported, we converted phi, standardized beta, and univariate F to correlation

coefficients using the formulas provided by Hunter and Schmidt (1990, p. 272) and

Peterson and Brown (2005, p. 179).

All harvested correlations were categorized on the basis of operationalizations of

the construct. In a number of cases, multiple variables within one sample referred to the

same underlying construct. To correct for the interdependence of the recorded

correlation coefficients, these correlations were combined into a composite correlation

following the formulas of Hunter and Schmidt (1990, pp. 435-348).

Testing our hypotheses requires that we collect the correlations between every pair

of constructs in our model, rather than only the correlations between supplier

(customer) involvement and their hypothesized antecedents and consequences (cf.

Geyskens, Steenkamp, and Kumar 1999). As a result, we also extracted correlations for

every pair of constructs in our model (e.g., marketing resources and technology

resources) from the same set of primary studies.

Table 2.1 presents an overview of the constructs in the model, including definitions

and representative measures.


29

TABLE 2.1: Construct Definitions and Representative Measures

Construct Definition Representative measures

Product

innovativeness

Degree to which the developed product is

innovative (is new or technologically

groundbreaking, and/or fulfills new customer

needs)

Kouferos, Cheng, and Lai

(2007); Lau, Tang, and

Yam (2012)

Speed to

market

Degree to which the developed product arrives

on the market timely, measured by e.g. time to

market and development speed

Feng et al. (2012); Wynstra

et al. (2012)

Cost

performance

Degree to which the developed product's cost

targets are met, measured by e.g. percentage

over budget

Primo and Amundson

(2002); Ragatz, Handfield,

and Petersen (2002)

Supplier

involvement

The extent to which suppliers work together

with the focal firm in the NPD project, measured

by e.g. intensity of supplier collaboration,

supplier integration, and supplier participation

Sánchez and Pérez

(2003a,b); Sherman,

Souder, and Jenssen

(2000)

Customer

involvement

The extent to which customers work together

with the focal firm in the NPD project, measured

by e.g. intensity of customer collaboration,

customer participation, and customer co-

development

Athaide and Zhang (2011);

Mishra and Shah (2009)

Technological

resources

Technological assets, knowledge, capabilities,

and organizational processes of the focal firm,

measured by e.g. R&D investments, patents, and

technological proficiency

Cousins et al. (2011); Un,

Cuervo-Cazurra, and

Asakawa (2010)

Marketing

resources

Marketing assets, knowledge, capabilities, and

organizational processes of the focal firm,

measured by e.g. level of market forecasting

ability, market knowledge and market response

capability

Chen, Li, and Evans (2012);

Souder, Sherman, and

Davies-Cooper (1998)

Technology

uncertainty

The rate of change in the technological

environment

Jean, Sinkovics, and

Hiebaum (2013); Petersen,

Handfield, and Ragatz

(2003)

Market

uncertainty

The rate of change with respect to the

composition of customers, and their needs,

preferences, and demand; and nature of

competitive actions

Chen, Li, and Evans (2012);

Lau, Tang, and Yam (2012)

Product

importance

Strategic importance of the product(s)

developed, e.g. project criticality

Fang (2008); Gulati and

Sytch (2007)

Firm size Scale and scope of organizational operations, e.g.

number of employees and plant size

Atuahene-Gima (2003);

Dabhilkar et al. (2011)


30

After extracting the correlations, we corrected them for measurement error in both

measures as well as for dichotomization of truly continuous variables, following

Geyskens et al. (2009). We subsequently transformed the corrected correlations into

Fisher’s z-coefficients (Lipsey and Wilson 2001). Next, we pooled the correlation

coefficients for each pair of constructs in our model, by averaging and weighting the

individual corrected study effects by an estimate of the inverse of their variance (Lipsey

and Wilson 2001) to give greater weight to more precise estimates. Finally, we

reconverted the pooled z-transformed study effects back to correlation coefficients

(Hedges and Olkin 1985).

Estimation

We estimate our model on the pooled meta-analytic correlation matrix and use the

harmonic mean of the sample sizes of each entry in the meta-analytic correlation matrix

(N = 1,513) as the sample size for our analysis. Our hypotheses require that we

simultaneously test the impact of internal resources and environmental uncertainty on

the involvement of suppliers and customers in NPD, as well as the effects of supplier

and customer involvement on NPD performance. Testing these equations independently

would result in biased estimates due to the endogeneity of the decision to involve

suppliers and customers in NPD in the performance equation (cf. Hamilton and

Nickerson 2003). We therefore estimate the following system of equations using full

information maximum likelihood:

(1) SI = β1 * RESTECH + β2 * RESMKT + β3 * UNCTECH+ β4 * UNCMKT + β5 * FSIZE

+ β6 * PRODIMPO + ε1

(2) CI = γ1 * RESTECH + γ2 * RESMKT + γ3 * UNCTECH+ γ4 * UNCMKT + γ5 * FSIZE

+ γ6 * PRODIMPO + ε2

(3) PERF_INNO = δ1 * SI + δ2 * CI + δ3 * RESTECH + δ4 * RESMKT + δ5 * UNCTECH

+ δ6 * UNCMKT + δ7 * FSIZE + ε3

(4) PERF_SPEED = ζ1 * SI + ζ2 * CI + ζ3 * RESTECH + ζ4 * RESMKT+ ζ5 * UNCTECH

+ ζ6 * UNCMKT + ζ7 * FSIZE + ε4

(5) PERF_COST = η1 * SI + η2 * CI + η3 * RESTECH + η4 * RESMKT+ η5 * UNCTECH

+ η6 * UNCMKT + η7 * FSIZE + ε5


31

where SI (CI) = supplier (customer) involvement in NPD, RESTECH (RESMKT) =

technological (marketing) resources of the focal firm, UNCTECH (UNCMKT) =

technological (market) uncertainty, FSIZE = firm size, PRODIMPO = strategic importance

of the product developed, PERF_INNO = product innovativeness, PERF_SPEED = speed to

market, and PERF_COST = cost performance.

We control for firm size (e.g., Fang 2008; Lau, Tang, and Yam 2010) in the

involvement and the performance equations. Larger firms are expected to be more

likely to involve customers and suppliers in their NPD as they have more financial

resources to allocate to strategic collaborations (Koufteros, Cheng, and Lai 2007). In

addition, larger firms have more personnel that they may dedicate to innovation, and

are thus more likely to develop more innovative products. Also, larger firms are more

likely to have NPD procedures in place which help speed up the development process

and contribute to keeping costs down (cf. Hitt, Hoskisson, and Kim 1997).

Furthermore, we control for the influence of product importance (e.g., Athaide,

Stump, and Joshi 2003) in the involvement equations, since firms may be more prone to

allocate external resources to strategically important development projects.

Finally, in the performance equations, we also control for potential direct effects of

the firm’s technological and marketing resources and technological and market

uncertainty (cf. Gatignon and Xuereb 1997; Kessler 2000; Tomlinson 2010).

Because the decisions to involve suppliers and customers in NPD may be related in

ways other than the model accounts for, we allow the residuals of equations (1) and (2)

to be correlated. Similarly, because the different performance indicators may have

common antecedents other than those specified, we allow the residuals of equations (3),

(4), and (5) to be correlated (see Franke and Park 2006 for a similar practice).

2.6 RESULTS

Table 2.2 reports the meta-analytic correlations for the focal relationships in our

model. We report the following summary statistics for each bivariate relationship of

interest to our study: the number of samples reporting on the bivariate relationships k,

the total sample size N, the average corrected correlation (��), the corresponding

standard error (SE), and the 95% confidence interval around ��.


32

TABLE 2.2: Meta-Analytic Bivariate Effects for the Hypothesized Relationshipsa

95%

Confidence

Interval Predictor k N ��b SE

Predictors of supplier involvement

R&D resources 12 4450 .15 * .02 .11 .19

Marketing resources 2 142 .21

.11 -.01 .43

Technological uncertainty 13 1736 .46 * .03 .40 .52

Market uncertainty 5 733 .09

.05 .00 .18

Predictors of customer involvement

Technological resources 11 4091 .20 * .02 .15 .24

Marketing resources 5 609 .31 * .05 .21 .41

Technological uncertainty 11 1524 .15 * .03 .08 .21

Market uncertainty 8 1050 .07 .04 .00 .15

Predictors of product innovativeness

Supplier involvement 21 6467 .21 * .02 .18 .25

Customer involvement 23 6605 .25 * .02 .22 .29

Predictors of speed to market



Predictors of cost performance



* p < .01 a k = number of samples; N = total sample size; �� = estimate of corrected population correlation; SE = estimated standard error of ��. b The corrected mean correlation coefficients (ρ) are sample-size weighted, measurement error- and dichotomization-corrected estimates of the population correlation coefficients.

Table 2.3 shows the full meta-analytic correlation matrix that we used to estimate

Equations (1) – (5). Each cell in this matrix represents a meta-analysis of several

samples. With the exception of the “marketing resources – supplier involvement”

relationship (k = 2, N = 142), all focal relationships included data from at least 5 samples

(�� = 12.2, �� = 2,480).2 Note that no individual sample contained all correlations of

interest. Thus, the total number of samples analyzed is much larger than the number of

samples contributing to any individual meta-analytic correlation.

2 Some relationships in our meta-analytic correlation matrix were based on rather small numbers of samples and sample sizes. The magnitudes of those relationships should be interpreted with caution. However, we note that the problem of few samples is less likely to seriously affect the estimates of average correlations than the variation of the correlations harvested (Hunter & Schmidt 1990).


33

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34

T

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ce

Pre

dic

tors

β

t

β

t

β

t

β

t

β

t

Tec

hn

olo

gica

l res

ou

rces

.0

4

1.9

1 *

* .0

7

2.5

3 *

**

.07

2

.70

†††

.3

1

12

.71

†††

.4

3

18

.44

†††

Mar

ket

ing

reso

urc

es

.23

1

0.0

0 *

**

.29

1

1.2

5 *

**

.41

1

6.1

3 †

††

-.0

4

-1.5

8

-.3

1

-12

.46

†††

Tec

hn

olo

gy u

nce

rtai

nty

.5

1

22

.24

***

.1

5

5.6

0 *

**

.17

5

.92

†††

-.

18

-6

.31

†††

-.

15

-5

.18

†††

Mar

ket

un

cert

ain

ty

-.1

3

-5.6

6 †

††

.03

1

.00

.0

6

2.5

5 †

† .1

6

6.3

6 †

††

.24

9

.96

†††

Fir

m s

ize

.23

1

1.2

5 †

††

.15

6

.57

†††

.2

3

10

.07

†††

-.

05

-2

.20

††

.13

5

.86

†††

Pro

du

ct im

po

rtan

ce

.18

8

.73

†††

.1

2

5.0

4 †

††

Sup

pli

er in

volv

emen

t

-.

07

-2

.27

††

.46

1

4.8

5 *

**

.26

8

.97

***

Cu

sto

mer

invo

lvem

ent

.08

3

.09

***

-.

08

-2

.87

***

.0

2

.93

***

p <

.01

, **

p <

.05

, * p

< .1

0 (

on

e-si

ded

); †

†† p

< .0

1, †

† p

< .0

5, †

p <

.10

(tw

o-s

ided

); h

arm

on

ic m

ean

= 1

51

3; χ

2 (

4)

= 2

54

.2 (

p <

.01

);

GF

I =

.97

; CF

I =

.94

; NF

I =

.94

; RM

SR =

.04

.


35

Table 2.4 presents the results. The overall fit statistics of the model are satisfactory:

χ2 = 254.2, d.f. = 4, p < .01; goodness of fit index [GFI] = .97; comparative fit index [CFI]

= .94; normed fit index [NFI] = .94; and root mean square residual [RMSR] = .04.

Hypothesis Testing

Turning to the antecedents of supplier involvement first, we find that internal

technological resources (β = .04, p < .05) and internal marketing resources (β = .23, p

< .01) increase supplier involvement in NPD, confirming hypotheses H1SI and H2SI.

Furthermore, we find that technological uncertainty (β = .51, p < .01) increases supplier

involvement in NPD, confirming H3SI. However, counter to H4SI, market uncertainty (β = -

.13, p < .01) reduces supplier involvement in NPD.

Customer involvement in NPD is positively affected by the focal firm’s internal

technological resources (β = .07, p < .01), its internal marketing resources (β = .29, p

< .01), and the technological uncertainty in its environment (β = .15, p < .01), confirming

hypotheses H1CI, H2CI, and H3CI. We do not find support for the positive effect of market

uncertainty on customer involvement in NPD (H4CI: β = .03, p = .16).

We continue with the consequences side of the model. Interestingly, we find that

supplier involvement in NPD has a negative effect on product innovativeness (β = -.07, p

< .05), counter to what we hypothesized in H5SI. We do find evidence of the hypothesized

positive effect of customer involvement on product innovativeness (β = .08, p < .01),

confirming H5CI. With respect to speed to market, we find support for the hypothesized

positive effect of supplier involvement (H6SI: β = .46, p < .01) and customer involvement

(H6CI: β = -.08, p < .01) in NPD. Lastly, the positive effect of supplier involvement on an

NPD project’s cost performance (β = .26, p < .01) confirms H7SI. In contrast, we do not

find a significant effect of customer involvement on cost performance (H7CI: β = .02, p

= .18).

Robustness Checks

The individual correlations may be affected by idiosyncratic study characteristics.

We explored this issue in depth for the correlations involving our two focal constructs

using the Parametric Adjustability approach (Farley, Lehmann, and Sawyer 1995).

Specifically, we regressed the individual study correlations involving supplier (91


36

correlations) and customer (80 correlations) involvement on a set of (i) sample, (ii)

study scope, and (iii) publication characteristics. With respect to the sample

characteristics, we examined the effects of type of distribution channel (industrial

versus consumer), industry composition of the sample (single industry versus a mix of

industries), product type (goods versus services), and level of technology (samples with

exclusively high-technology products versus otherwise, following the industry

classification developed by Eurostat of the European Commission). In addition, we

tested for the effect of location of the sample firms (developed versus developing

countries) for a subset of 48 studies on supplier involvement (73 correlations) and a

subset of 32 studies on customer involvement (57 correlations) that disclose country

information. Furthermore, we examined the effects of study scope characteristics. We

tested for the effect of involvement scope (involvement examined for the whole NPD

project versus a part of the NPD project), and external party scope (focus on supplier or

customer involvement versus focus on supplier and customer involvement). Lastly, we

examined the effects of publication characteristics: publication year, outlet (top tier

versus other), and academic field (marketing versus strategy versus operations and

supply chain versus innovation and technology). We controlled for the constructs

related to customer and supplier involvement using dummy variables. Specifically, we

regress the variables discussed above on the Fisher’s z-coefficients by estimating the

following equations:

(6) �� = � + ∑ �� + ∑ ��

�� + ��

(7) �� = � + ∑ �� + ∑ ��

�� + ��

where �� and �� are the z-transformed correlations between supplier (customer)

involvement and their antecedent or consequence construct i in study j; � and � are

intercepts, � , �� , �, and �� are the parameters to be estimated, �� are dummy variable

matrices that specify which antecedent or consequence construct is related to supplier

(customer) involvement, �� are the variable matrices of the moderators discussed

above, and �� and ��are the residual terms. For the models specifying correlations with

supplier involvement, none of the study characteristics were significant at the .05 level.

For the models specifying correlations with customer involvement, only one of the

study characteristics was significant at the .05 level. Specifically, the correlations


37

between customer involvement and its antecedents and consequences are weaker for

samples based on goods than those for services (b = -.26, p = .04).

2.7 DISCUSSION

Our study provides insight into the antecedents and consequences of supplier and

customer involvement in NPD. Analyzing data obtained from 119 independent samples

reported in 140 studies, we show how a firm’s internal technological and marketing

resources influence the involvement of suppliers and customers in the firm’s internal

NPD projects. In addition, we show how supplier and customer involvement improve or

deteriorate three dimensions of NPD performance.

Implications for Theory

In terms of antecedents, we find that firms with stronger technological and

marketing resource bases demonstrate higher levels of supplier and customer

involvement in NPD. Rather than using externally obtained resources as substitutes for

lacking internal resources, our results suggest that high levels of firms’ internal

resources increase their access to external resources. Marketing resources have

stronger effects on the involvement of suppliers (β2 = .23) and customers (γ2 = .29) in

NPD than technological resources do (β1 = .04 and γ1 = .07, respectively). This suggests

that internal marketing assets and skills increase the value of externally obtained

resources to a larger extent than internal technological assets and skills do.

Further, we find that technological uncertainty stimulates firms to involve suppliers

and, to a lesser extent, customers. This suggests that firms try to deal with a

technologically turbulent environment by increasing their technological options (by

involving suppliers) and by obtaining technological directions from the market (by

involving customers). Contrary to our expectations, firms reduce their use of supplier

involvement under conditions of market uncertainty. A possible explanation is that

uncertain customer needs and preferences increase a firm’s difficulty of articulating the

necessary external technological assistance, which may hamper the use of supplier

involvement in NPD (Salvador and Villena 2013). An alternative explanation is that

firms develop a stronger technology orientation as a result of supplier involvement in

NPD. A strong technology orientation pre-empts a fast market response to changing

customer needs (Gatignon and Xuereb 1997), which makes supplier involvement a less


38

attractive strategy. From the perspective of the supplier, one could argue that increased

market uncertainty in an industry might reduce the supplier’s willingness to become

closely involved in the NPD project of one particular firm, because participation in one

firm’s NPD would strengthen the relationship with that specific firm and reduce the

supplier’s future strategic flexibility.

While controlling for the direct effects of firm resources and environmental

uncertainty, we find that both supplier and customer involvement affect NPD

performance. Importantly, the effects of supplier involvement on NPD performance

differ from the effects of customer involvement on NPD performance. Thus, the type of

external party involved matters in NPD. Future research should therefore always take

into account the nature of the external party when investigating the benefits of

externally sourced resources in NPD contexts. Failing to differentiate between suppliers

and customers (as in, e.g., Faems, Van Looy, and Debackere 2005; Jung and Wang 2006;

Kessler, Bierly, and Gopalakrishnan 2000; McNally, Akdeniz, and Calantone 2011)

potentially leads to misleading results.

Furthermore, we find that the involvement of suppliers and customers in NPD has

different effects on different dimensions of NPD performance. Specifically, supplier

involvement positively affects speed to market and cost performance, but negatively

affects product innovativeness. The latter finding is somewhat unexpected: although

prior research has repeatedly argued that supplier involvement should lead to

increased product innovativeness because of the sourcing of new technological

knowledge, the results of our meta-analysis suggest that involved suppliers take over

work, thereby saving money and increasing speed to market, but do not enrich a project

technologically with innovative inputs. Future research may further examine in which

conditions involved suppliers could improve the innovativeness of new products.

With regard to customer involvement, the results suggest a trade-off between

higher product innovativeness and lower speed to market. To reflect these trade-offs,

researchers examining NPD success are advised to investigate multiple dimensions of

performance in combination (e.g., Atuahene-Gima 2003; Fang 2008), instead of focusing

on one aspect of NPD performance only (e.g., Allocca and Kessler 2006; Feng et al. 2012).

Not only does the use of multiple performance measures reflect the multifaceted nature


39

of NPD success (Griffin and Page 1996), it increases the measurement precision of data

collection efforts (Silk and Kalwani 1982). By structuring the assessment task into

multiple dimensions, the complexity of key informants’ evaluation task is reduced,

decreasing measurement error (Silk and Kalwani 1982).

Finally, the positive effect of customer involvement on product innovativeness

deserves to be emphasized. The benefits of involving customers in innovation have not

always been evident. Many captains of industry have questioned the use of listening to

customers during the development of new products. To quote Steve Jobs, the late co-

founder and former CEO of Apple Inc., “A lot of times, people don’t know what they want

until you show it to them.” He was not the first to ignore customer inputs. Henry Ford

said: “If I’d listened to customers, I’d have given them a faster horse.” These beliefs have

been reflected in extant research on strategic orientations, which finds that being

customer oriented negatively affects the innovativeness of new products (e.g., Gatignon

and Xuereb 1997; Im and Workman 2004). Our meta-analysis shows that involving

customers in NPD increases the innovativeness of newly developed products. This

finding shows that letting customers participate in the NPD project is of a different

nature than merely listening to customers or having a customer orientation, and that

customers can contribute to the development of innovative products.

Managerial Implications

This study offers three key takeaways for managers:

Takeaway 1: Plan supplier and customer involvement differently, depending on

objectives. In case a project development team strives for innovative outputs, let

customers rather than suppliers participate. For products that require to be introduced

on the market fast, involve suppliers and refrain from the participation of customers. In

case product development is under cost pressure, consider involving suppliers and let

them take over development tasks.

Takeaway 2: Take into account the trade-offs associated with involving suppliers and

customers in NPD projects. Involving suppliers contributes to fast market introductions

and efficient development processes but lowers the innovativeness of the product

developed. Involving customers contributes to increasing product innovativeness but

slows down the speed to market.


40

Takeaway 3: The internal resource base and external environment influence the

attractiveness of involving suppliers and customers. The stronger the internal

technological and marketing resource bases of a firm are, the more that firm implements

supplier and customer involvement in NPD projects. Furthermore, a firm’s environment

influences the involvement of suppliers and customers in NPD. Specifically, uncertain

technological environments contribute to the involvement of suppliers and customers,

whereas uncertain market environments lower the involvement of suppliers in an

internal NPD project.

Limitations and Future Research Suggestions

Our study has limitations, a number of which offer opportunities for future

research. First, our meta-analysis is restricted to the constructs measured in extant

studies. While it is also interesting to investigate the effects of a firm’s strategic

orientation on supplier and customer involvement, this is not possible in the current

study due to the lack of primary research. Second, the constructs of technological and

marketing resources have been defined rather broadly, which is common in meta-

analysis. Future empirical research could improve our understanding of the impact of

firm resources on the involvement of suppliers and customers by taking a more fine-

grained perspective by distinguishing between different types of technological and

marketing resources.

Meta-analysis takes stock of the knowledge available about a topic. The ultimate

goal of meta-analysis is to give directions for future research. The current meta-analysis,

like many of the collected empirical studies, took the point of view of the focal firm: the

antecedents as well as the consequences of supplier and customer involvement in NPD

were studied from the perspective of the developing firm. Future research could extend

our understanding of supplier and customer involvement by taking the perspective of

the involved party: What are the characteristics of suppliers and customers that make

them fit for involvement in an NPD project? For example, does a supplier’s customer

orientation improve its value as an external resource in an NPD project, or is a

technology orientation more beneficial? In addition, characteristics of the relationship

between the focal firm and the involved party, such as the length of the relationship,

trust, and dependence, could enrich the current state of knowledge.


41

The current meta-analysis shows that supplier/customer involvement in NPD

has different effects on product innovativeness, speed to market, and cost performance.

Future studies on supplier and customer involvement could extend these insights by

focusing on other outcomes, such as knowledge transfer to the focal firm, firm image,

relational consequences, and market performance of the new product.

In addition, and as is commonplace in meta-analytical work, the model estimated

main effects of supplier and customer involvement on the three dependent variables. It

is conceivable that involving both suppliers and customers simultaneously leads to

additional benefits/downsides not accounted for by this additive model. For example,

market insights offered by involved suppliers could be verified by participating

customers. Similarly, the technological feasibility of market opportunities emerging as a

result of involving customers can be directly explored using resources of involved

suppliers. Unfortunately, none of the 31 samples included in the data set measuring

both supplier and customer involvement has been analyzed with an interaction term

between the two involvement variables, such that the data offered by these studies does

not allow the further analysis of interaction effects of supplier involvement * customer

involvement on the three dependent variables. Given that involving external parties is

an increasingly used business practice, future research should examine the interaction

effects of involving multiple types of external parties.

On a similar note, it is interesting to test curvilinear effects of a firm’s resources

on its use of supplier/customer involvement. Possibly, the positive effects of internal

firm resources on the use of supplier/customer involvement become weaker when

firms have very strong resource positions. In such cases, a firm might be able but not

need to involve suppliers/customers in its NPD process. A comparable issue pertains to

possible curvilinear effects of supplier and customer involvement on performance. Very

high levels of supplier and customer involvements could cause firms to focus too hard

on the currently available technologies of suppliers and currently immediate needs of

customers, which could blind firms to future challenges and opportunities, lowering

product innovativeness. Unfortunately, the data offered by extant research does not

allow an empirical test of quadratic effects of supplier and customer involvement

(Williams and Livingstone 1994). Future research on supplier and customer

involvement could extend the state of the literature by studying non-linear patterns.


42

A related issue is the effect of combining high technology and marketing resource

levels with the development of strong supplier and customer relationships, which

follows from our theory. Combining both high levels of internal resources and external

relationships may negatively affect a firm’s profitability. Unfortunately, we lack the

necessary data for an empirical test. Future research could extend our understanding of

the combination of involving supplier and customer involvement with resource

positions.

In the current study, we find that involving suppliers has different effects on NPD

performance than involving customers. This finding has a number of implications for

further research. First, future research on external party participation in NPD should

distinguish between the various parties involved. In several cases, authors of prior

studies could contribute to the literature simply by reanalyzing existing data sets on

external party involvement and presenting their findings per type of party. In addition,

future research could extend its theoretical lens by examining the involvement of other

types of external parties, such as competitors, research institutes and universities, and

consultants.

Second, these findings have implications for research on product development

for business markets. In these markets, the relationship between two parties often

includes more than one role, and thus is ‘compound’ (Ross and Robertson 2007) or

‘multiplex’ (Tuli, Bharadwaj, and Kohli 2010): a customer may also be a partner of, a

competitor against, or even a supplier to the same firm. Given our finding that the

effects of involving a customer differ from the effects of involving a supplier, what are

the effects of involving a customer that also is a supplier to the firm? In a similar vein,

should firms involve customers that are also their partners, or their competitors? We

will take up this issue in Chapter 3 of this dissertation.

Third, the finding that the role of the party involved is important for managing

NPD is interesting in the light of current developments in open innovation. Increasingly,

NPD projects are opened up to the “crowd”: an undefined group of individuals external

to the firm that generate ideas (Howe 2006, 2008; Poetz and Schreier 2012). In these

crowdsourcing settings, the relationship of the involved party with the focal firm

disappears: especially external parties without a relationship with the focal firm are


43

targeted to contribute to the NPD project. Thus, as a result of crowdsourcing, a new type

of external party emerges. An emerging issue for future research is, then: what

influences the effectiveness of crowdsourcing, or, formulated differently, how to

manage the crowd? This question is central to Chapter 4 of this dissertation.


44

APPENDIX: List of Studies Included in the Meta-Analysis

Reference1 Journal2

Sample

size

Industries

in sample3

Level of

technology 3,4

Data on

supplier

involvement

Data on

customer

involvement

Allocca & Kessler 2006 C&IM 158 mix 2-4 x

Al-Zu'bi & Tsinopoulos 2012 JPIM 313 n.s. n.s. x x

Atuahene-Gima 2003 AMJ 104 mix 4

x

Bidault, Depres, & Butler 1998 IJTM 24 mix 3 x

Bonner 2010 IMM 134 mix 1-4

x

Brettel & Cleven 2011 C&IM 254 mix 3-4 x x

Callahan & Lasry 2004 R&DM 55 single 4

x

Callahan & Moretton 2001 IJPM 44 single 4 x

Carbonell, Rodríguez-Escudero, & Pujari 2009

JPIM 102 mix 1-4

x

Carr et al. 2008 IJO&PM 166 n.s. n.s. x

Chen & Paulraj 2004 JOM 138 mix 3-4 x

Chen, Li, & Evans 2012 IMM 159 single 4

x

Chien & Chen 2010 SIJ 125 single 2 x x

Corsten & Felde 2005 IJPD&LM 135 single 3 x

Dabhilkar et al. 2009 JP&SM 136 mix 3-4 x

Danese & Filippini 2010 IJO&PM 186 mix 3-4 x

Das, Narasimhan, & Talluri 2006 JOM 322 mix 3-4 x

Dowlatshahi & Contreras 1999 IJPR 171 mix 3-4 x

Dröge, Jayaram, & Vickery 2000; Scannell, Vickery, & Dröge 2000; Vickery et al. 2003; Dröge, Jayaram, & Vickery 2004; Jayaram, Vickery, & Dröge 2008

JPIM; JBL; JOM; JOM; JOM

57 single 3 x

Dvir et al. 2003 IJM&DM 117 mix 3-4

x

Eisenhardt & Tabrizi 1995 ASQ 72 single 4 x

Ellis, Henke, & Kull 2012 IMM 233 single 3 x

Fang 2008 JM 134 mix 3-4

x

Fang, Palmatier, & Evans 2008 JAMS 188 mix 3-4

x

Feng et al. 2012 IMM 176 mix 1-4

x

Feng, Sun, & Zhang 2010 IMM 139 mix 2-4 x x

Fillippini, Salmaso, & Tessarolo 2004

JPIM 85 mix 3 x x

Flynn, Schroeder, & Sakakibara 1994

JOM 42 mix 3-4

x

Gales & Mansour-Cole 1995 JE&TM 44 single 3

x

González & Palacios 2002 IMM 54 mix 3-4

x

González-Benito, Da Rocha, & Queiruga 2010

IJPE 96 mix 3 x

Griffith, Harmancioglu, & Dröge 2009

JWB 200 mix 1-4 x

Gruber et al. 2010 SMJ 130 mix 3-4

x

Gulati & Sytch 2007 ASQ 151 single 3 x

Handfield et al. 2009 IJO&PM 151 mix 3-4 x


45

APPENDIX: List of Studies Included in the Meta-Analysis – continued

Reference1 Journal2

Sample

size

Industries

in sample3

Level of

technology 3,4

Data on

supplier

involvement

Data on

customer

involvement

Hartley, Zirger, & Kamath 1997; Hartley et al. 1997*

JOM; ITEM

79 mix 3-4 x

Hoegl & Wagner 2005 JMAN 28 mix 4 x

Hong & Hartley 2011 JSCM 313 mix 3-4 x

Inemek & Matthyssens 2013 IMM 189 mix 1-4 x

Ittner & Larcker 1997a; Ittner & Larcker 1997b; Ittner & Larcker 1995*

JMR; MANSCI;

JAR;

184; 249; 184

single 3-4 x x

Jayaram 2008 IJPR 338 mix 3-4 x

Jean, Sinkovics, & Hiebaum 2013 JPIM 170 single 3 x

Joshi & Stump 1999 JAMS 184 mix 3-4 x

Knudsen 2007 JPIM 210+207 n.s. n.s. x x

Koufteros, Cheng, & Lai 2007 JOM 157 mix 2-4 x

Koufteros, Rawski, & Rupak 2010 DS 191 single 3 x x

Koufteros, Vonderembse, & Jayaram 2005

DS 244 mix 3-4 x x

Laamanen 2005 RP 85 single 4

x

Lai et al. 2011 TA&SM 126 n.s. n.s. x x

Lakshman & Parente 2008 JMS 136 single 3 x

Langerak & Hultink 2005 ITEM 233 mix 3-4 x x

Langerak & Hultink 2008 JE&TM 93+122 mix 3-4 x

Langerak, Rijsdijk, & Dittrich 2009

ML 129 single 2

x

Lau, Tang, & Yam 2010; Lau, Yam, & Tang 2010; Lau, Yam, & Tang 2007*

JPIM; JPIM;

IM&DS 251 mix 2-4 x x

Lawson et al. 2009; Cousins et al. 2011

JPIM; JPIM

111 mix 2-4 x

Li et al. 2007 IJPE 142 single 4 x

Liker, Kamath, & Wasti 1998 IJQS 365 single 3 x

Lin & Germain 2004 EMJ 191 mix 1-3

x

Lin & Huang 2013 JB&IM 179 single 4

x

Lin et al. 2005 IJPE 103+109 mix 3 x

Maylor 2001; Maylor 1997*

OME; IJO&PM

46 n.s. n.s. x x

Millson & Wilemon 2002; Millson & Wilemon 2006

IMM; TECH

118; 58

mix 3-4 x x

Mishra & Shah 2009 JOM 189 mix 3-4 x x Naor et al. 2008; Kristal, Huang, & Schroeder 2010; Luo, Mallick, & Schroeder 2010; Peng et al. 2013

DS; IJO&PM;

EJIM; JSCM

189; 167; 189; 155

mix 3-4 x x

Nassimbeni 1996; De Toni & Nassimbeni 1999* De Toni & Nassimbeni 2000

IJPE; IJPR; OME

50 mix 3-4 x

Naveh 2005; Naveh 2007

IJPR; JOM

62 single 4 x


46


Reference1 Journal2

Sample

size

Industries

in sample3

Level of

technology 3,4

Data on

supplier

involvement

Data on

customer

involvement

Nieto & Santamaría 2007 TECH 1300 mix 1-4 x x

Nijssen et al. 2012 JPIM 136 mix 3-4

x

O'Cass & Ngo 2012; Ngo & O'Cass 2013

IMM; JBR

155 mix 1-3

x

Parker, Zsidisin, & Ragatz 2008 JSCM 116 mix 3-4 x

Perols, Zimmerman, & Kortmann 2013

JOM 116 mix 3-4 x

Petersen, Handfield, & Ragatz 2003

JPIM 88 mix 1-4 x

Phan, Abdallah, & Matsui 2011 IJPE 27 mix 3-4

x

Potter & Lawson 2013 JPIM 119 mix 3-4 x

Primo & Amundson 2002 JOM 38 single 4 x

Pujari 2006 TECH 66 mix 1-4 x

Pujari, Wright, & Peattie 2003 JBR 151 n.s. n.s. x

Quesada, Syamil, & Doll 2006 JSCM 406 single 3 x

Ragatz, Handfield, & Petersen 2002

JBR 83 n.s. n.s. x

Ramaswami, Srivastava, & Bhargava 2009

JAMS 88 mix n.s. x x

Ritter & Walter 2003 IJTM 233 mix 3-4

x

Salvador & Villena 2013 JSCM 165 mix 3-4 x

Sánchez & Pérez 2003a; Sánchez & Pérez 2003b

TECH; JPIM

60; 63

single 3 x

Sethi 2000 JM 141 mix 1-3

x

Sherman, Souder, & Jenssen 2000 JPIM 65 mix 4 x x

Sing & Power 2009 SCM 418 n.s. n.s. x

Sobrero & Roberts 2001; Sobrero & Roberts 2002*

MANSCI; RP

50 single 3 x

Song & Di Benedetto 2008 JOM 173 mix 3-4 x

Song & Thieme 2009 JPIM 205+110 mix 3-4 x

Souder, Sherman, & Davies-Cooper 1998

JPIM 101 mix 4

x

Spina, Verganti, & Zotteri 2002 IJO&PM 67 mix 3-4 x

Stock & Zacharias 2013 JPIM 180 mix 3-4

x

Stuart 1993 IJPMM 240 n.s. n.s. x

Stump, Athaide, & Joshi 2002; Athaide, Stump, & Joshi 2003; Athaide & Zhang 2011

JPIM; JMT&P;

JPIM 296 mix 4

x

Swink 1999 JOM 91 mix 3-4 x

Swink, Narasimhan, & Wang 2007; Narasimhan, Swink, & Viswanathan 2010*

JOM; DS

224 mix 2-4 x x

Takeishi 2001; Takeishi 2002*

SMJ; OS

45 single 3 x

Tan & Tracey 2007 JSCM 175 n.s. n.s. x

Tavani et al. 2013 IJPR 161 mix 1-4 x


47


Reference1 Journal2

Sample

size

Industries

in sample3

Level of

technology 3,4

Data on

supplier

involvement

Data on

customer

involvement

Tomlinson 2010 RP 436 mix 1-4 x x

Tracey & Tan 2001; Tracey 2004

SCM; JSCM

180 n.s. n.s. x

Tsai 2009 RP 753 mix 1-4 x x

Un, Cuerzo-Cazurra, & Asawaka 2010

JPIM 781 n.s. n.s. x x

Vonderembse & Tracey 1999 JSCM 268 n.s. n.s. x

Wagner 2012 JSCM 67 mix 3-4 x

Walker 1994 OS 95 mix 3 x

Walter 2003 JBR 247 mix n.s. x

Wasti & Liker 1997 JPIM 122 single 3 x

Wasti & Liker 1999 TEM 174 single 3 x

Wu & Ragatz 2010 IJISM 124 mix 3-4 x

Wynstra et al. 2012 JPIM 185 mix 3-4 x

Wynstra, Von Corswant, & Wetzels 2010

JPIM 161 single 4 x

Zhang, Henke, & Griffith 2009 JOM 2012 single 3 x

Zirger & Hartley 1996 ITEM 44 mix 4 x

1 Studies with a (partially) overlapping sample are grouped together

2 AMJ = Acad of Man't J; ASQ = Adm Sci Quarterly; C&IM = Creativity & Innovation Man't; DS = Decision Sciences; EJIM = Eur J of Innovation Man't; EMJ = Eur Man't J; ITEM = IEEE Transactions on Eng Man't; IM&DS = Ind Man't & Data Systems; IMM = Ind Marketing Man't; IJISM = Int J of Integrated Supply Man't; IJM&DM = Int J of Man't & Decision Making; IJO&PM = Int J of Operations & Prod Man't; IJPD&LM = Int J of Physical Distr & Logistics Man't; IJPE = Int J of Prod Econ; IJPR = Int J of Prod Res; IJPM = Int J of Project Man't; IJPMM = Int J of Purch & Materials Man't; IJQS = Int J of Quality Sci; IJTM = Int J of Tech Man't; JAR = J of Accounting Res; JB&IM = J of Bus & Ind Marketing; JBL = J of Bus Logistics; JBR = J of Bus Res; JE&TM = J of Eng & Tech Man't; JMAN = J of Man't; JMS = J of Man't Studies; JM = J of Marketing; JMR = J of Marketing Res; JMT&P = J of Marketing Theory & Practice; JOM = J of Operations Man't; JPIM = J of Product Innovation Man't; JP&SM = J of Purch & Supply Man't; JSCM = J of Supply Chain Man't; JAMS = J of the Acad of Marketing Sci; JWB = J of World Bus; MANSCI = Man't Sci; ML = Marketing Letters; OME = Omega; OS = Org Sci; R&DM = R&D Man't; RP = Res Policy; SIJ = Service Industries J; SMJ = Strat Man't J; SCM = Supply Chain Man't; TA&SM = Tech Analysis & Strat Man't; TECH = Technovation 3 n.s. = not specified 4 1 = low technology; 2 = medium-low technology; 3 = medium-high technology; 4 = high technology; following the classification of Eurostat (European Commission) * These studies did not add new information over and above the other studies reporting on the same sample and are therefore excluded from the meta-analysis

49

“But it’s no use now,” thought poor Alice,

“to pretend to be two people!

Why, there’s hardly enough of me left to make one respectable person!”


Chapter 3

The Effect of Customer Participation

in Outsourced NPD on Supplier Task Performance:

The Role of Relationship Multiplexity3

3.1 INTRODUCTION

In a wide variety of business markets, such as aerospace, automotive, chemicals,

ICT, and software, firms increasingly outsource new product development (NPD)

activities to external suppliers (Engardio and Einhorn 2005). Business customers are

typically highly knowledgeable (Appleyard 2002) and may start thinking of technology

solutions themselves beyond merely expressing their technology needs to the supplier

(Urban and von Hippel 1988). As a consequence, they regularly participate in the

supplier’s NPD process. For example, Airbus has outsourced the development of engines

for its new A350-1000 model to Rolls-Royce, but is a close participant in Rolls-Royce’s

fundamental research program to develop new materials for the engine. Similarly, Volvo

has outsourced the development of its electric motor to Siemens, but instead of being a

passive buyer, it closely participates in the electric motor’s inverter design.

3 We sincerely thank the executives and the project managers who participated in the interviews and the survey for sharing their time and insights. Furthermore, we gratefully acknowledge the financial support that was received from the Institute for the Study of Business Markets at the Pennsylvania State University.

Chapter 3 – Customer Participation in Outsourced NPD: The Role of Relationship Multiplexity

50

The growing popularity of customer participation is reflected in the academic

literature, which falls into two research streams. The first stream focuses on co-

production by consumers and has largely reported positive effects of customer

participation, e.g. on customer value (Chan, Yim, and Lam 2010) and customer

satisfaction with the firm (Franke, Schreier, and Kaiser 2010; Troye and Supphellen

2012). The second stream, which focuses on industrial customers’ participation in

suppliers’ NPD processes, has established that customer participation is beneficial on

average (Fang, Palmatier, and Evans 2008) but does not always lead to desirable

outcomes (Fang 2008). As both research streams focus on ‘markets of many,’ they are

silent on a key complicating feature of customer participation in ‘markets of one.’ In

markets of one, where a single customer outsources an NPD project to an external

supplier, the relationship between customer and supplier often includes more than one

role, and thus is ‘compound’ (Ross and Robertson 2007) or ‘multiplex’ (Tuli, Bharadwaj,

and Kohli 2010): a customer firm may also be a partner of, a competitor against, or even

a supplier to its supplier firm. Roundtable discussions with executives from three high-

tech firms underscore that firms struggle with the question whether to involve

customers in the NPD process when these customers also play other roles. The

marketing director of a large contract R&D firm conveyed that sentiment as follows:

“I am not sure whether we should allow customer X to participate in the NPD

process, as we also compete against each other. […] Should we let customer Y

participate in the development process, as they are also our partner in a

consortium? I don’t know. […] I believe the other roles played by our customers

in relation to our firm may matter and should be taken into account when we

decide on customer participation.”

The main goal of this essay is to investigate how customer participation in

outsourced NPD affects supplier task performance for multiplex relationships. The

study makes the following contributions. First, we address recent calls for examining

customer integration in the innovation process (ISBM Research Priorities 2012; MSI

Research Priorities 2010-2012). The general hypothesis that emerges from the extant

literature is that customer participation in NPD is beneficial. However, previous

empirical research has not been unequivocally supportive of the positive effects of

customer participation (e.g., Stump, Athaide, and Joshi 2002). Following up on Fang,

Palmatier, and Evans’ (2008, p. 334) call that “future research should also investigate


51

the conditions when customer participation negatively affects performance,” we build a

contingency framework and systematically analyze when customer participation helps

or hurts.

Second, extant research on customer participation analyzes a single tie between the

customer and the supplier (e.g., Fang 2008; Noordhoff et al. 2011), whereas we

incorporate the multiplex nature of customer-supplier relationships. Specifically, we

theorize and test how relationship multiplexity moderates the effect of customer

participation on supplier task performance, a key outcome in outsourced NPD projects

(cf. Carson 2007). Research on role theory forms the theoretical basis for our

examination. Following Ross and Robertson (2007), we distinguish between three types

of multiplexity. First, we consider customer-as-partner multiplexity, the situation where

a customer that participates in the supplier’s NPD process simultaneously shares

partner ties with the supplier. Second, we study customer-as-competitor multiplexity,

which occurs when the participating customer also competes with the supplier. Third,

we examine customer-as-supplier multiplexity, which happens when the participating

customer shares role-reversal ties with the supplier such that the customer and the

supplier also face each other in reversed roles. We extend the Ross and Robertson

(2007) framework by distinguishing between two sub-types of partnerships, viz. self-

initiated partnerships and engineered partnerships (Doz, Olk, and Ring 2000; Koza and

Lewin 1999). Although both are in use in technology development and innovation,

extant marketing research has not distinguished between them. By examining the

interplay between customer participation and three types of multiplex relationships, we

also address recent calls for more research on relationship multiplexity (e.g., Lilien et al.

2010; Rindfleisch et al. 2010; Ross and Robertson 2007; Tuli, Bharadwaj, and Kohli

2010).

Finally, we test our hypotheses regarding the effects of customer participation on

supplier task performance at both sides of the customer-supplier dyad. First, we

examine the customer’s perception of supplier task performance, as a function of its

participation in the NPD project and the multiplexity of its relationship with the

supplier. Next, we compare the results for the customer with the results for the

supplier’s self-reported task performance. By incorporating both the evaluations of the

customer and the supplier in the analysis, we do not presume consensus about supplier


52

task performance across the dyad (see Rokkan, Heide, and Wathne 2003 for a similar

practice). Any differential effects of customer participation on the customer’s versus the

supplier’s perception of supplier task performance may have important implications for

how to manage customer participation in NPD. For example, even if customer

participation does not affect the supplier’s self-reported task performance, the supplier

may still want to involve the customer if doing so improves the customer’s perception of

supplier task performance, as this may create opportunities for future business.

To test our hypotheses, we assembled a unique, proprietary data set on 140

outsourced NPD projects. Our data set combines multiple sources of archival data,

including project administration records, project evaluation reports, strategic

cooperation plans, and procurement records. We supplement these data with survey

data gathered specifically for this study, and key insights taken from three roundtable

discussions with senior executives and eighteen in-depth interviews with project

managers.

The remainder of this essay is organized as follows. First, we advance our

conceptual framework and we present hypotheses. Next, we describe the empirical

context and the data collection procedures, followed by the method and results. The

final section discusses implications for researchers and managers, and provides

suggestions for further research.

3.2 CONCEPTUAL BACKGROUND

Following Fang (2008, p. 91), we define customer participation as “the extent to

which the customer is involved in the manufacturer’s NPD process.” The specific context

of this study is an outsourcing customer’s involvement in the executing supplier’s NPD

process. Customer participation in outsourced NPD projects varies in terms of the

activities performed and can range from fundamental research over product design to

prototype testing. Customer participation also varies in terms of the intensity with

which those activities are performed by the customer (Appleyard 2002; Fang 2008).

Sometimes, customers provide mere assistance in tasks mainly executed by the supplier;

in other situations, customers play a more central role in the NPD process (Fang,

Palmatier, and Evans 2008).


53

Although the potential benefits of customer participation have been well

articulated in the business press, customer participation may not always be beneficial.

Fang (2008) has taken a first step to follow up on his and his colleagues’ call for

research on the conditions when customer participation negatively affects performance

(Fang, Palmatier, and Evans 2008, p. 334) by studying, among others, the moderating

role of downstream network connectivity. A key difference in our approach is that, in

contrast to Fang who studies ‘markets of many,’ we study ‘markets of one.’ Such a

context, where the participating customer constitutes the ‘market,’ requires a shift in

focus from downstream connectivity (the relationship between the customer and its

distributors) to upstream connectivity (the relationship between the customer and the

supplier). In outsourced NPD, that relationship often extends beyond the tie in which

the customer participates in the supplier’s NPD process, as the customer and the

supplier may also share other ties with each other in which both parties play different

roles. We argue that the effect of customer participation on supplier task performance is

contingent on the types of ties constituting the multiplex relationship between the

supplier and the customer.

We use role theory to argue how relationship multiplexity moderates the effect of

customer participation in outsourced NPD on supplier task performance. Role theory is

the study of characteristic behavioral patterns (roles) of actors within contexts (Biddle

1986; Katz and Kahn 1966). The roles that are played are evoked by the situation in

which actors find themselves. Based on the role of an actor, specific behavioral

expectations are formed (Biddle 1986) – sometimes also referred to as rules

(Montgomery 1998) or norms (Heide and John 1992) – that serve as explanations for

behavior. In the context of our study, the behavioral expectations associated with the

role of a participating customer in outsourced NPD relate to the sharing of various

resources with the developing supplier, including intangible resources such as

information, expertise, and human capital, as well as tangible resources such as

materials and equipment (Campbell and Cooper 1999; Fang 2008).

A largely unaddressed issue in role theory pertains to the specific way in which the

roles played in different ties may affect one another. To address this issue, we rely on a

branch of role theory that has recently received attention in the marketing literature,

viz. functional role theory (Heide and Wathne 2006). In functional role theory, the


54

‘identity’ of an actor is based on his position within a social system, i.e. the function of

the actor (Biddle 1986). In our context, the use of terms such as ‘customer,’ ‘partner,’

‘competitor,’ and ‘supplier’ are applications of functional role theory (cf. Heide and

Wathne 2006). Building on functional role theory, and in line with Ross and Robertson

(2007), we distinguish between three types of relationship multiplexity: customer-as-

partner multiplexity (when the participating customer also shares partner ties with the

supplier), customer-as-competitor multiplexity (when the participating customer also

shares a competitor tie with the supplier), and customer-as-supplier multiplexity (when

the participating customer also shares role-reversal ties with the supplier).

3.3 THEORY AND HYPOTHESES

Examining customer participation from a relationship multiplexity perspective

moves our research into uncharted territory. To ground our approach in managerial

practice, a qualitative pre-phase preceded the development of the hypotheses.

Grounded Theory Development

To get more insight into the effects of customer participation in multiplex

relationships, we held three roundtable discussions with senior executives in general

management, marketing, and R&D, and we conducted eighteen in-depth interviews with

project managers who led NPD projects for their respective firms. To capture a broad

set of perspectives, we selected interviewees from three firms in different industries

(aerospace, IT system development, and photolithography), with annual revenues

ranging from $85 million to $8.5 billion. On average, the roundtables lasted ninety

minutes and the in-depth interviews lasted fifty minutes.

The roundtable discussions underscored that customer participation in outsourced

NPD is a pervasive phenomenon and that customers are increasingly pushing suppliers

for increased participation in their outsourced NPD projects. From these roundtables, it

further became clear that the concerns the executives expressed centered on whether

or not they should let customers participate if these customers also fulfilled other roles.

Several interviewees indicated that they were inclined to let partners participate.

When asked why, they indicated “because they are frequently at our premises anyway.”

That said, they added that they were unsure whether this was the right strategy.


55

Opinions diverged with regard to the consequences of participation by a competing

customer. Some interviewees argued that “it seems wise to keep competitors at a

distance” and “we tend not to involve competing customers in their outsourced NPD

projects – why enlighten them more than is absolutely necessary?” This belief stands in

direct contrast with other interviewees noting just the opposite: “we don’t mind

involving competitors, as they will get to learn the technologies and knowledge we

develop sooner or later anyway. However, competitors need to bring something to the

table as well – it’s not a one-way street.” With regard to participation by customers that

also play a supplier role, one interviewee explained that “it signals our intent to

strengthen and deepen our relationship with this important stakeholder,” while also

pointing to a potential problem: “A bad experience with the customer in its supplier role

may negatively affect the way we deal with it in its participating-customer role […], a

situation we should try to avoid because termination of the customer relationship

would imply a lose-lose situation.”

In sum, the roundtable discussions and in-depth interviews in this qualitative pre-

phase underscore that (1) relationship multiplexity is a pervasive phenomenon in the

context of customer participation in outsourced NPD, and (2) managers are either

unsure or do not converge on whether or not they should let customers participate if

these customers also fulfill other roles.

Hypotheses Development

Next, we build hypotheses on how customer participation affects supplier task

performance. Specifically, we draw on role theory to hypothesize how the nature of the

multiplex relationship (customer-as-partner, customer-as-competitor, customer-as-

supplier) moderates the impact of customer participation in outsourced NPD on

supplier task performance.

Customer participation. The potential benefits of customer participation in the

supplier’s NPD process are considerable. The role of a participating customer is to share

resources with the developing supplier. An important resource to be shared by the

participating customer is information (Fang 2008). Information does not only help the

supplier gain a better understanding of the customer’s problems and needs at the outset

of the NPD process, but may also help to better understand how customer needs evolve


56

as the customer is confronted with new ideas, concepts, and prototypes across the NPD

stages (Fang, Palmatier, and Evans 2008; Stump, Athaide, and Joshi 2002). This may

enable the supplier to develop a better solution (Bonner and Walker 2004). Besides

information sharing, customer participation may also involve the sharing of other

resources, such as human capital (experienced personnel), materials, equipment, and

machinery (Campbell and Cooper 1999). These resources may help the supplier to

better perform its product development task, by opening up alternative development

paths that otherwise might not have been accessible to the supplier.

In sum, customer participation creates a platform for sharing information and other

resources during the NPD process, which may provide the supplier with better

customer insight and improved development paths, thereby contributing to supplier

task performance. In line with the generally positive tone of the academic and business

literature on customer participation, we hypothesize:

H1: Customer participation in outsourced NPD positively affects supplier task

performance

While customer participation potentially increases supplier task performance

through the sharing of resources, this potential may not be realized. Specifically, we

examine how the nature of the multiplex relationship may facilitate or impede resource

sharing and thereby moderate the effect of customer participation on supplier task

performance.

Customer-as-partner multiplexity. Customer-as-partner multiplexity refers to the

situation where a customer that participates in the supplier’s NPD process

simultaneously shares (one or more) partner ties with the supplier. A partner tie is a

horizontal collaborative arrangement among two or more organizations, intended to

jointly acquire and utilize information and resources to develop new technology

(Rindfleisch and Moorman 2001). In partnerships, firms work together as equals on the

basis of the complementarity of their knowledge and resources, in the pursuit of

mutually beneficial outcomes (Jap 1999).

Partners tend to develop norms that specify how information is shared (Dyer and

Singh 1998; Gundlach, Ravi, and Mentzer 1995). These norms prescribe behaviors that

facilitate communication. The improved communication between partners stimulates


57

the emergence of heuristics for processing knowledge (Hansen 1999). Especially when

the knowledge to be shared is complex in nature or tied to technologies, such heuristics

facilitate knowledge transfer (Reagans and McEvily 2003). Therefore, the supplier is

likely to perform its NPD task more effectively for a participating customer when both

parties are also partners.

This may, however, not hold for all partnerships. Based on their formation process,

we distinguish between two types of partnerships, namely self-initiated partnerships

and engineered partnerships (Doz, Olk, and Ring 2000; Koza and Lewin 1999), both of

which are prominent in technology development and innovation (Millson, Raj, and

Wilemon 1996; Wallsten 2000). In self-initiated partnerships, the partners themselves

(the supplier or the customer) initiate the partnership and decide on the composition of

the partnership. In this type of partnership, all partners know each other’s identity in

advance and deliberately agree to jointly develop new technology. For example, the

collaboration between Nike and Philips Electronics to develop audio sports products,

and USCAR – the collaboration between Ford, Chrysler, and GM for automotive research

– are self-initiated partnerships. Self-initiated partnerships typically emerge in response

to environmental changes, when managers of different organizations recognize they

have similar interests (Doz, Olk, and Ring 2000). As such, self-initiated partnerships

tend to have precisely formulated objectives (Koza and Lewin 1999). Therefore,

partners in self-initiated partnerships are expected to easily coordinate and achieve

consensus over the domain of collaboration (Doz, Olk, and Ring 2000).

Conversely, engineered partnerships have been orchestrated by an overarching

initiator, or triggering entity (Doz, Olk, and Ring 2000), which often is a governmental

or other non-profit organization that wants to leverage innovation (Link and Scott

2010). These top-down induced innovation projects are announced publicly, and

organizations are invited to join. Partnering organizations are teamed up without the

partners’ full control of and consent to the match-making process. As a result, the

partners have to cooperate, regardless of their desire to do so. Sematech, an R&D

partnership between 14 U.S.-based semiconductor manufacturers that have been

brought together by the U.S. Department of Defense, is an example of an engineered

partnership (Browning, Beyer, and Shetler 1995). Other examples are the R&D

consortia which develop new technologies under the European Framework


58

Programmes supported by the European Commission. Engineered partnerships are

formed in response to environmental changes that the managers of the different firms

either interpret differently or overlook or ignore (Doz, Olk, and Ring 2000). In that case,

a triggering entity is required to make firms coalesce into a partnership.

Since the firms’ different interpretations of their environment complicate

communication (Doz, Olk, and Ring 2000), heuristics related to information sharing are

less likely to emerge in engineered partner ties than in self-initiated partner ties. We

therefore expect that the synergistic effects between the participating-customer role

and the partner role are stronger for self-initiated partnerships than for engineered

partnerships. We hypothesize:

H2: (a) Self-initiated partner ties and (b) engineered partner ties between a

supplier and a customer strengthen the effect of customer participation in

outsourced NPD on supplier task performance

H3: The moderating effect on the relationship between customer participation in

outsourced NPD and supplier task performance is stronger for self-initiated

partner ties than for engineered partner ties

Customer-as-competitor multiplexity. Customer-as-competitor multiplexity occurs

when the participating customer also competes with the supplier. Thus, the customer

holds two relationship roles, a participating-customer role and a competitor role.

Competitors have similar knowledge bases (Lane and Lubatkin 1998), which facilitates

knowledge absorption (Cohen and Levinthal 1990) when the competing customer

shares information with the supplier while participating in the NPD project. Thus, the

supplier is better able to apply the newly gained knowledge, thereby increasing supplier

task performance.

However, the potential benefits of customer-as-competitor multiplexity may be

overshadowed by role conflict. In its participating-customer role, the customer is

expected to share information and other resources with the supplier with the purpose

of joint value creation (Fang, Palmatier, and Evans 2008). The role of competitors, on

the other hand, is to focus on value division and appropriation (Jap 1999). Hence, on the

basis of its competitor role, the participating customer may worry that the supplier will

opportunistically appropriate its knowledge (cf. Hamel, Doz, and Prahalad 1989; Lado,


59

Boyd, and Hanlon 1997). Although sharing information always carries the risk of

knowledge leakage and opportunistic exploitation, this risk amplifies when the

collaborating parties have conflicting objectives (Mohr and Sengupta 2002). Thus, the

behavioral expectations (sharing knowledge) associated with the participating-

customer role may conflict with the behavioral expectations (protecting knowledge)

associated with the competitor role. A likely consequence of such role conflict is that

interactions between the participating customer and the supplier are hampered by

discussions about ownership and the customer’s attempts to hedge against knowledge

leakage. The supplier may be similarly concerned about knowledge appropriation by

the participating customer, and also hedge against knowledge leakage. As more

resources are dedicated to cope with appropriation concerns and reconcile role conflict,

the supplier can devote fewer resources to performing job responsibilities (Nygaard

and Dahlstrom 2002), thereby decreasing supplier task performance.

In sum, although competitor multiplexity facilitates knowledge absorption, this

potential benefit may not manifest itself because of motivational concerns.

H4: A competitor tie between a supplier and a customer weakens the effect of

customer participation in outsourced NPD on supplier task performance

Customer-as-supplier multiplexity. Customer-as-supplier multiplexity refers to the

situation where the participating customer shares (one or more) role-reversal ties with

the supplier. Role-reversal ties arise when the customer and the supplier also face each

other in reversed roles. Customer A and its supplier B share a role-reversal tie if A also

acts as a supplier to B. In business markets, role-reversal ties often occur when firms

operate in multiple market segments. For example, Nokia is known worldwide as a

device manufacturer, but it is also a leading provider of network equipment (Basole

2009). In case of role-reversal ties, Nokia may encounter the same firm in two roles: in a

supplier role for its device production and, contemporaneously, in a customer role for

its network equipment.

In contrast to customer-as-partner multiplexity – which creates role synergy – and

customer-as-competitor multiplexity – which creates role conflict – we argue that the

presence of role-reversal ties leads to role ambiguity. Role ambiguity refers to the lack

of clarity regarding role expectations and the consequences of one’s role performance


60

(Biddle 1986; Netemeyer, Johnston, and Burton 1990). The popular notion that “the

customer is king” illustrates the prototypical nature of customer-supplier ties in which

the customer poses demands that the selected supplier is expected to adhere to

(Cannon and Perreault 1999). In case of role-reversal ties, however, the customer may

be unsure how strictly it can impose its demands. Can the customer still be exigent or

should the customer be more compliant, given the presence of a role-reversal tie? It is

not only unclear how the customer is expected to behave when simultaneously acting as

a supplier in another project; also the consequences of its behavior are unclear. The

supplier – who is equally uncertain about how to behave in its supplier role when

simultaneously acting as a customer in the role-reversal tie – may or may not adhere to

the requirements of the exigent customer and may or may not expect reciprocation in

the role-reversal tie. In sum, role-reversal ties cause ambiguity for the customer and the

supplier by blurring behavioral expectations and creating uncertainty regarding the

consequences of both parties’ behaviors. The consequences of role ambiguity are well

documented. Prior research has shown repeatedly that role ambiguity leads to lower

(job) performance (Bagozzi 1980; Singh 1993). For example, Singh (1993) shows that

sales agents’ performance wanes in the presence of customer-based role ambiguity.

Role ambiguity is expected to affect supplier task performance especially when

both parties cooperate, such as in customer participation. From a cognitive perspective,

as customer and supplier role expectations become more ambiguous, they must

dedicate more cognitive resources to clarify role expectations (cf. Jackson and Schuler

1985). As a result, meetings between the customer and the supplier risk being

dominated by efforts to sort out role obligations, which may detract from the project at

hand (Nygaard and Dahlstrom 2002, p. 70), and result in behaviors that are more likely

to be inefficient, misdirected, or insufficient (Jackson and Schuler 1985; Van Sell, Brief,

and Schuler 1981). From a motivational perspective, role ambiguity has been shown to

reduce firms’ ‘effort-to-performance’ expectancy (Jackson and Schuler 1985). When

uncertainty regarding role expectations reduces the customer’s confidence in the

efficacy of its participation efforts, the customer may become discouraged to fully

deploy its innovative potential to the project (Solomon et al. 1985). Consequently, the

effect of customer participation on supplier task performance should decrease. We

hypothesize:


61

H5: Role-reversal ties between a supplier and a customer weaken the effect of

customer participation in outsourced NPD on supplier task performance

3.4 METHOD

Empirical Context

We obtained access to a unique sample of NPD projects carried out by a single

contract R&D organization (the supplier) for a variety of customers. The contract R&D

organization is a globally operating organization that is active in various high-

technology markets that are associated with the aerospace industry, including

aerospace craft, materials, onboard equipment, and avionics software. The firm is

sufficiently large to ensure an adequate sample size, and its customers and the NPD

projects it performs for its customers are diverse enough to ensure variation in our key

constructs. Gathering data within a single (supplier) firm allows us to capture rich and

detailed data on individual projects that otherwise would be difficult to obtain. In

addition, it enables us to control for industry effects as explanations for supplier task

performance (cf. Mayer and Nickerson 2005) and thus “provide(s) for greater

comparability of key dependent and independent variables” across projects (Chandy

2003, p. 353).

The aerospace industry is ideal to test our hypotheses, for two reasons. First, the

development of components, products, and technologies used in aerospace craft are

often outsourced to specialized suppliers. For example, in developing the Boeing 787

Dreamliner, as much as 70% of the research and manufacturing of the plane was

outsourced (Kotha and Nolan 2005). Second, the industry is characterized by many

different types of ties between organizations, leading to multiplex relationships. Partner

ties are prominent: numerous new products are developed in self-initiated partnerships

or in engineered partnerships. Many firms in this industry are active in multiple market

domains, which increases the prevalence of customer-as-competitor multiplexity and

customer-as-supplier multiplexity.

When a customer outsources an NPD project to the focal contract R&D organization

(the supplier), this supplier appoints a project manager, who writes a ‘project plan.’ The

project plan is a formal document used to manage project execution. It defines the

objectives of the project, documents each activity included in the project, estimates the


62

time and resources required to complete each activity, and specifies the parties that will

be involved in each activity. During the project, the project manager provides regular

project-status updates in a ‘project status report.’ Together, the project plan and the

project status report include whether, when, and how customers participate in NPD.

Immediately following project completion, both the supplier’s project manager and the

customer’s representative formally appraise the project by, independently, filling out

project evaluation reports.

Sample

We obtained permission from the board of the contract R&D organization to study a

sample of 150 NPD projects, executed between 2003 and 2009, representative for all

three operational divisions of the firm and all seventeen underlying departments. The

board facilitated our research by providing access to a wealth of internal documentation.

In addition, we were granted access to several managerial levels ranging from the

project managers to the corporate office. We ensured that the collected sample was

representative for the firm, by comparing the 150 sampled projects with the firm’s

entire portfolio of 311 NPD projects spanning the period 2003-2009. Along dimensions

we could measure (viz., project budget and project duration), we found no significant

differences between the sampled projects and the non-sampled projects.

Data Sources

We combined data from a wide variety of sources. First, the organization’s quality

assurance officers supplied us with project evaluation reports for our sample of 150 NPD

projects. These project evaluation reports are filled out by the customer and by the

supplier’s project manager independently upon completion of the project, and are filed

by the organization’s quality assurance officers. Second, the contract R&D organization

granted us access to their project administration records, for all projects executed

between 2000 and 2009. These records contain key descriptors of the projects,

including project duration (start date, end date), project budget, and identification of

the customer. Third, we reviewed and coded the organization’s strategic cooperation

plans for the years 2000-2009 (2500+ pages in total). These plans describe all

partnerships – self-initiated and engineered – the firm engaged in. Fourth, we obtained

access to the organization’s procurement records, which contain all its purchase orders


63

between 2000 and 2009 and thus provide insight into the organization’s role-reversal

ties. Fifth, the board facilitated a focus group session with five senior managers to identify

all competitors of the contract R&D organization. Sixth, we obtained permission to

administer a brief (one-page) survey to the project managers, in which they were asked

to report on customer participation by referring to their own (confidential) project

plans and project status reports. The project managers were sent a package containing a

letter from the executive management endorsing the study and the survey instrument.

Overall, 140 surveys were returned.4

Measurement

Supplier task performance. Supplier task performance (STP) measures are taken

from the project evaluation reports that are filed by the company’s quality assurance

officers. STP as perceived by the customer (STP_CUS) is measured through two five-

point items that capture the customer’s evaluation of (1) the supplier’s performance

with respect to the end result in general, and (2) the developed product. The supplier’s

self-reported STP (STP_SUP) is measured through one five-point item that captures the

supplier’s evaluation of project execution. Although we would have preferred identical,

multi-item scales for the supplier and the customer to measure STP, the advantage of

the available scales may compensate for this limitation: by using data that were

collected by the firm immediately following project closure, the STP measures do not

suffer from the memory-bias problems that are prone to affect survey-based research.5

Customer participation. The extent of customer participation (PARTIC) in the

supplier’s NPD process is documented in the project managers’ project plans and

project status reports. Since we did not have permission to consult these (confidential)

plans and reports, we measured customer participation using a survey among the

project managers. However, we specifically instructed the project managers to consult

their project plans and status reports to retrieve the necessary details. Following Fang,

4 For ten projects, the survey was not returned. We compared these ten projects with the 140 remaining projects in terms of project budget, project duration, relationship multiplexity, and supplier task performance as perceived by the supplier and by the customer. We did not find statistical differences. 5 Nevertheless, we tested the robustness of our findings for an alternative measure of the supplier’s self-reported STP. Specifically, in the survey that we administered to the project managers, we included the same two items that were used to measure the customer’s perception of STP (STP_CUS) to measure the supplier’s self-reported STP. Results remained substantively the same. See the Robustness Checks section for more details.


64

Palmatier, and Evans (2008), our measure of customer participation captures both

breadth (number of activities in the supplier’s NPD process in which the customer

participates) and depth (how deeply the customer participates in each of these activities)

of customer participation. Based on interviews with five project managers, we identified

twelve activities that can be part of an NPD project executed by the contract R&D

organization. For each of the twelve activities, we asked whether the activity was

included in the sample project and, if so, whether the customer participated in that

activity (0 = did not participate, 1 = participated). If the customer participated, we

subsequently asked about the customer’s depth of participation using a seven-point

Likert scale, ranging from ‘very superficially involved’ to ‘very deeply involved.’ We

measure customer participation as the average of customer participation breadth (the

ratio of the number of activities in which the customer participated to the number of

activities present in the project) and customer participation depth (the average depth

across the activities the customer participated in), after standardization.

Relationship multiplexity. Self-initiated partner ties (SELFPART) and engineered

partner ties (ENGPART) are measured as the number of self-initiated and engineered

technology development projects in which the contract R&D organization and the

customer collaborate during the focal project period. Competitor tie (COMPET) is

operationalized as a dummy variable that equals one if the customer is also a

competitor to the contract R&D organization, and is zero otherwise. Role-reversal ties

(REVERSE) are measured as the number of projects in which the customer serves as a

supplier to the contract R&D organization during the focal project period. We log-

transform SELFPART, ENGPART, and REVERSE to correct for non-normality.

Control variables. In addition to the focal theoretical variables, several control

variables are also included as main effects. At the project level, we account for the

project’s size (PR_SIZE), measured by the project budget. To normalize the project

budget, a log-transformation was used. We further include project innovativeness

(INNOV) since a radically innovative project may result in more excitement (Schmidt

and Calantone 1998) and thus more positive task performance evaluations than a less

innovative project. In a similar vein, we control for time pressure (TIME_PRESS), since

task performance evaluations may be affected negatively in projects where time

pressure is intense (Payne, Bettman, and Luce 1996). Project innovativeness (INNOV)


65

and time pressure (TIME_PRESS) are both operationalized using seven-point Likert

scales included in the survey among project managers.

At the firm level, we control for the firm size of the customer (CUS_SIZE), measured

as the number of employees (log-transformed). In addition, customers may take more

credit for the outcome if they have already made many investments ex ante, i.e. prior to

outsourcing the NPD project, in the technology to be developed or if they have been

involved intensively in developing the project plan. We therefore control for ex-ante

customer investments in the technology to be developed (INV) and customer

involvement in project-plan development (PLAN). Both constructs are measured using

seven-point Likert scales included in the project-manager survey.

Finally, we take into account the number of concurrent projects the supplier

executes for the customer. The development of technology takes long (Rosenberg 1990),

which increases the occurrence of concurrent outsourced NPD projects for the same

customer. Executing multiple projects simultaneously generates cooperativeness

between the supplier and the customer (cf. Heide and John 1990), which may increase

the effectiveness of customer participation. Therefore, we control for the main effect of

the number of concurrent projects executed for the customer as well as the interaction

effect between the number of concurrent projects and customer participation. We

measure concurrent projects (CONCURR) as the number of other projects (besides the

focal project) the supplier executes for the customer during the focal project period

(log-transformed). The Appendix provides an overview of the measures used in our

study, and their data sources.

Estimation

The customer and the supplier provided independent evaluations of STP at the

completion of the outsourced NPD project. Since we do not presume consensus about

STP across the dyad (cf. Rokkan, Heide, and Wathne 2003), we estimate two equations

with, respectively, the customer’s perception of STP and the supplier’s self-reported STP

in project i executed for customer j as the dependent variables. The first of these

equations captures the perspective of the customer and is specified in the following

fashion:


66

(1) STP_CUSij = β0j + β1 * SELFPARTij + β2 * ENGPARTij + β3* COMPETij

+ β4 * REVERSEij + β5 * CONCURRij + PARTICij [β6 + β7 * SELFPARTij

+ β8 * ENGPARTij + β9 * COMPETij + β10 * REVERSEij + β11 * CONCURRij]

+ β12 * PR_SIZEij + β13 * INNOVij + β14 * TIME_PRESSij + β15 * CUS_SIZEj

+ β16 * INVij + β17 * PLANij + εij

The second equation, which we estimate using STP_SUPij as the dependent variable,

captures the perspective of the supplier and is specified in a parallel fashion:

(2) STP_SUPij = γ0j + γ1 * SELFPARTij + γ2 * ENGPARTij + γ3* COMPETij

+ γ4 * REVERSEij + γ5 * CONCURRij + PARTICij [γ6 + γ7 * SELFPARTij

+ γ8 * ENGPARTij + γ9 * COMPETij + γ10 * REVERSEij + γ11 * CONCURRij]

+ γ12 * PR_SIZEij + γ13 * INNOVij + γ14 * TIME_PRESSij + γ15 * CUS_SIZEj

+ γ16 * INVij + γ17 * PLANij + μij

We use mean-centering prior to forming the interactions to ease interpretation.

Estimation of our model is complicated by three factors. First, customer

participation may be strategically chosen to optimize STP. This may result in samples in

which only maximizing choices are included. To examine whether such endogeneity is

present, we conduct a Durbin-Wu-Hausman test, which augments the two STP

equations with the first-stage structural residual of a model estimating customer

participation. In this model, we include all independent variables and control variables

from our main model, supplemented with additional exclusion instruments for

identification purposes (Wooldridge 2002). We use three exclusion instruments, viz. the

project’s duration (in number of activities included in the project), the customer’s level

of technological expertise (two items on a seven-point scale),6 and a dummy variable

that is 1 if the customer is a government agency and 0 otherwise.7 In the augmented

regression equations, the parameter estimates for the residuals do not reveal any

violation of the assumed exogeneity of the customer-participation variable (p > .10).

Consequently, endogeneity is found not to be an issue.

Second, our sample contains more than one project for twenty-one customers. The

inclusion of multiple projects for the same customer in the sample is representative for

6 Customer technological expertise is measured through two seven-point items that were included in the survey among project managers. Cronbach’s alpha was .90. 7 Instrumental variables must satisfy two requirements: they must be correlated with the potential endogenous variable (i.e., customer participation), and they must be orthogonal to the error. The F-statistic suggested by Staiger and Stock (1997) confirms that the first condition is satisfied (F = 14.0, p

< .01), while the J-statistic of Hansen (1982) provides support for the second condition (J = .18, p = .91).


67

the contract R&D organization’s order book in particular and for the aerospace industry

in general.8 Because of unobserved heterogeneity, these observations may not be

independent of each other. We control for unobserved heterogeneity by randomizing

the intercepts.

Third, if unobserved factors explain variation in both the customer’s and the

supplier’s evaluations of task performance, the error terms are correlated. Seemingly

unrelated regression (SUR) accounts for potentially correlated errors across the two

equations by estimating them simultaneously.9

3.5 RESULTS

Descriptive Insights

The average budget of the 140 projects in our sample is approximately $250k, with

a total amounting to $35 million. The sample projects have an average duration of 22

months, with a maximum of 75 months. This is not unusual in technology development:

technology exploration followed by the development of technology applications and

extensive testing takes time (Rosenberg 1990).

Customers participate in 132 out of 140 projects, in line with our field-interview

observation that customers are pushing suppliers for increased participation in their

outsourced NPD projects. In these 132 projects, customers participate in, on average, 73%

of the activities of the project and their depth of participation in these activities is, on

average, above the scale midpoint (4.5 on a seven-point scale). Customer participation

differs dramatically between projects: the number of activities in which customers

participate ranges from 9% to 100% and the depth of participation ranges from 1 to 7.

Multiplex relationships are commonplace: 101 projects are executed for customers that

simultaneously play a self-initiated partner role in one or more other projects, 87

projects are executed for customers that simultaneously play an engineered partner

8 Executing multiple projects for a single customer is also not uncommon in other business markets, including automotive (Liker and Choi 2004), telecommunications (Wu and Choi 2005), and pharmaceuticals (Wuyts, Dutta, and Stremersch 2004). 9 As we specify random effects in both equations, the specification of the equations is not identical. Hence, SUR is more efficient than OLS (Singh and Ullah 1974).


68

TA

BL

E 3

.1:

De

sc

rip

tiv

e S

tati

sti

cs

an

d C

or

re

lati

on

s

Va

ria

ble

M

ea

n

SD

M

in

Ma

x

1

2

3

4

5

6

7

8

9

10

1

1

12

1

3

14

1

STP

(cu

stom

er p

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on

)

4.2

0

.56

2

.0

5.0

1

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STP

(su

pp

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tio

n)

4

.16

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1

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tici

pat

ion

.0

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1.3

.0

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Self

-in

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par

tner

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5.1

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38

-.

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-.

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1

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9

.49

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4

-.0

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.4

3

.49

1

7

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sal t

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3

.91

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.19

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.03

-.

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.1

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9

.46

1

8

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ject

siz

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.47

3

.18

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(tw

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(aft

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ean

-cen

teri

ng.


69

role,10 and 54 out of 140 projects are executed for customers that are also competitors

to the contract R&D organization. Role-reversal ties occur in 79 out of 140 projects.

Table 3.1 provides descriptive statistics.

Hypotheses Testing

The results are reported in Table 3.2. None of the VIF statistics exceed four,

suggesting that multicollinearity is not an issue. In presenting the results, we use ‘c’ to

signify the effect on the customer’s evaluation of STP, and ‘s’ to signify the effect on the

supplier’s self-reported STP. When differences across the dyad occur, we take up the

implications of these findings in the discussion section.

Conform H1, we find that customer participation positively affects the customer’s

evaluation of STP (c = .19, p < .05). However, and contrary to expectations, customer

participation does not significantly influence the supplier’s evaluation of STP (s = -.15,

p > .10). We thus find partial support for H1.

Overall, the results indicate that the relationship between customer participation

and STP strongly depends on relationship multiplexity. Consistent with H2a, we find that

the effect of customer participation on STP increases when the number of self-initiated

partner ties with the participating customer increases, both from the customer’s and the

supplier’s perspective (c = .21, p < .01; s = .16, p < .05). Thus, H2a is supported. Simple

slope analyses reveal that customer participation has no effect on the customer’s

perception of STP when customers share no self-initiated partner ties with the supplier

(cABSENCE = -.08, n.s.), but increases STP for high levels of self-initiated partner ties (cHIGH

= .40, p = .01), as reflected in one standard deviation above the mean. From the

perspective of the supplier, on the other hand, customer participation lowers STP in

case self-initiated partner ties are absent (sABSENCE = -.20, p < .01), whereas this negative

effect disappears for high levels of self-initiated partner ties (sHIGH = .15, n.s.).

Contrary to H2b, we do not find a significant interaction effect between customer

participation and engineered partner ties (c = .04, n.s.; s = -.03, n.s.). Tests on the

parameter distances (β7 – β8 and γ7 – γ8 ) reveal that the effect of customer participation

10 In our sample, self-initiated and engineered partnerships coexist in the relationship: 68 projects are executed for customers that simultaneously play a self-initiated partner role and an engineered partner role.


70

on STP is more positive for multiplex relationships consisting of self-initiated rather

than engineered partner ties (p < .05), lending statistical support for H3. In sum, the

beneficial effect of customer participation on supplier task performance only pertains to

self-initiated partner ties, and not to engineered partner ties.

H4 poses that customer-as-competitor multiplexity weakens the effect of customer

participation on STP. Consistent with H4, we find a negative interaction effect between

customer participation and competitor tie from the perspective of the customer (c = -.29,

p < .05). Everything else equal, in the absence of a competitor tie, customer participation

positively affects STP as perceived by the customer (cABSENCE = .19, p < .10), but this

positive effect disappears if the participating customer is also a competitor to the

supplier (cPRESENCE = -.10, n.s.). Regarding the supplier’s self-reported STP, the results

paint a different picture. In contrast to H4, we find a positive but nonsignificant

interaction effect between customer participation and competitor tie (s = .22, n.s.). Thus,

H4 is only partially supported.

We find support for H5 that role-reversal ties weaken the effect of customer

participation on STP (c = -.12, p < .05; s = -.13, p < .05). Simple slope analyses show that

the effect of customer participation on the customer’s evaluation of STP is positive for

relationships without role-reversal ties (cABSENCE = .31, p < .01), but not significant for

relationships with high levels of role-reversal ties (cHIGH = .07, n.s.). Turning to the

perspective of the supplier, customer participation does not significantly affect the

supplier’s self-reported STP in the absence of role-reversal ties (sABSENCE = .28, n.s.), but

lowers the supplier’s self-reported STP for high levels of role-reversal ties (sHIGH = -.14, p

< .10).

Although not the focus of our study, we also find that some of the multiplexity

variables exert a significant main effect on STP. We find a negative main effect of self-

initiated partner ties on STP (c = -.14, p < .05; s = -.13, p < .10). Further, whereas a

competitor tie positively affects the customer’s perception of STP (c = .31, p = .05), it has

no impact on the supplier’s self-reported STP (s = .06, n.s.).

Turning to the control variables, we find that perceptions of supplier task

performance increase as project innovativeness increases (c = .13, p < .01; s = .12, p

< .01). Further, we find a negative effect of customer involvement in project-plan


71

development on the customer’s perception of STP (c = -.09, p < .05), but not on the

supplier’s self-reported STP (s = -.02, n.s.). Project size, time pressure, customer size,

and ex-ante customer investments have no significant impact (p > .10). Finally, and

somewhat surprisingly, the interaction effect between customer participation and

concurrent projects is positive and significant in the supplier model (s = .12, p < .05), but

negative and significant in the customer model (c = -.11, p < .05). We will return to this

finding in the discussion section.

TABLE 3.2: Model Estimates

Hyp.

sign

STP – customer

perception

STP – supplier

perception

Variables β t γ t

Main effects

Intercept 4.02 19.44 ††† 3.77 17.09 †††

Customer participation H1: + .19 1.83 ** -.15 -1.39

Self-initiated partner ties -.14 -2.14 †† -.13 -1.82 †

Engineered partner ties -.04 -.81 -.02 -.31

Competitor tie .31 2.06 †† .06 .38

Role-reversal ties .01 .21 .03 .49

Interaction effects

Customer participation x Self-intiated partner ties H2a: + .21 2.79 *** .16 1.93 **

Customer participation x Engineered partner ties H2b: + .04 .94 -.03 -.76

Customer participation x Competitor tie H4: – -.29 -1.71 ** .22 1.21

Customer participation x Role-reversal ties H5: – -.12 -1.78 ** -.13 -1.88 **

Control variables

Project characteristics

Project size -.03 -.54 -.03 -.56

Innovativeness .13 3.38 ††† .12 3.06 †††

Time pressure .04 1.45 .03 .93

Customer characteristics

Customer size -.02 -.47 -.03 -.93

Ex ante customer investments -.04 -1.13 -.00 -1.44

Customer involvement in project-plan development -.09 -2.38 †† -.02 -.55

Concurrent projects

Concurrent projects -.01 -.30 .02 .56

Customer participation x Concurrent projects -.11 -2.24 †† .12 2.29 ††

Significant effects are indicated in bold.

*** p < .01, ** p < .05, * p < .10 (one-sided); ††† p < .01, †† p < .05, † p < .10 (two-sided).


72

Robustness Checks

To further validate our results, we perform several robustness checks.

Including additional interaction effects. Partner ties (self-initiated or engineered),

competitor ties, and role-reversal ties are not necessarily mutually exclusive but can

occur in combination, in which case they can reinforce or obstruct one another. To

explore this formally, we re-estimated our model including all two-way interactions

between these four types of ties. None of the interactions were significant (p > .10), nor

did they substantially change the significance of the hypothesized effects reported in

Table 3.2. In a similar vein, we re-estimated our model including higher-order

interactions between customer participation and partner ties, competitor ties, and role-

reversal ties. None of these higher-order interactions were significant (p > .10).

Using an alternative measure for self-reported supplier task performance. The

measurement items that we obtained from the quality assurance officers for (i) STP as

perceived by the customer and (ii) STP as perceived by the supplier were similarly

although not identically worded. We tested the robustness of our findings to an

alternative measure of STP as perceived by the supplier. Specifically, in the survey that

we administered to the project managers, we measured their perception of STP using

the same items that the company had used to measure the customer’s perception of STP.

We re-estimated our model using this alternative self-reported STP measure. Results

remained substantively the same. In our focal model, we preferred using the non-

identical measures that were provided to us by the quality assurance officers over the

identical measures that were included in the project-manager survey, since the former

were administered by the company immediately following project completion whereas

the latter were administered up to eight years after project completion.

Using alternative time windows to operationalize the multiplexity variables. Conform

with our conceptualization, the multiplexity variables were measured based on the focal

project period. For example, the self-initiated partner ties variable is measured as the

number of self-initiated technology development projects in which the contract R&D

organization and the customer collaborate during the focal project period. We re-

estimated our model with measures based on alternative time windows, starting one,


73

two, or three years prior to the project start date, and ending at project completion. The

results were robust to these alternative time windows.

Breadth versus depth of customer participation. Following precedence (e.g., Fang,

Palamtier, and Evans 2008), our measure of customer participation captures both

breadth (number of activities in the supplier’s NPD process in which the customer

participates) and depth (how deeply the customer participates in each of these

activities). To investigate the robustness of our findings to the measurement of

customer participation, we re-estimated the model specified in Eqs. 1-2 twice, replacing

our two-dimensional measure of customer participation with i) the breadth dimension

only (operationalized as the ratio of the number of activities in which the customer

participated to the number of activities present in the project), and ii) the depth

dimension only (operationalized as the average depth across the activities the customer

participated in). Overall, the results of these two models are substantively the same to

those reported in Table 2. There are two notable differences. In the customer evaluation

equation, the significant positive main effect of customer participation and the

significant negative interaction effect between customer participation and competitor

tie only hold for the breadth measure but not for the depth measure. For the depth

measure, the parameters are of the correct signs. This further attests to the robustness

of our findings.

3.6 DISCUSSION

Does customer participation in outsourced NPD increase supplier task performance,

and how does this effect vary according to other relationship roles customers may play?

Using a unique database combining several sources of archival data with survey data,

and testing effects at both sides of the dyad, we show in which types of multiplex

relationships customer participation helps or hurts supplier task performance. We

conclude with a discussion of the essay’s contributions to marketing theory,

implications for managerial practice, and limitations and opportunities for further

research.

Theoretical Implications

The essay’s findings extend the literature on customer participation. Specifically,

our study helps explain why the previous literature has not unequivocally established


74

that customer participation is beneficial (e.g., Stump, Athaide, and Joshi 2002).

Moreover, we find that whether customer participation helps or hurts is contingent on

relationship multiplexity.

Furthermore, our findings extend the literature on multiplex relationships (e.g.,

Lilien et al. 2010; Rindfleisch et al. 2010; Ross and Robertson 2007; Tuli, Bharadwaj,

and Kohli 2010). First, we offer empirical evidence that relationship multiplexity

matters in the context of customer participation. Our findings indicate that the various

ties shared between a supplier and a customer affect the effectiveness of customer

participation. Second, we extend the framework devised by Ross and Robertson (2007)

to analyze relationship multiplexity by distinguishing between self-initiated and

engineered partnerships.

In addition, our approach to incorporate both the evaluations of the customer and

the supplier in the analysis uncovered systematic differences between the customer and

the supplier – what is beneficial in the eyes of the customer is not necessarily beneficial

in the eyes of the supplier, and vice versa. Thus, our overall pattern of results paints a

more complex picture than is currently expressed in the literature.

Specifically, the customers in our study evaluate supplier task performance more

favorably when they participate more in the outsourced NPD project, whereas customer

participation has no direct effect on the supplier’s self-reported task performance. A

possible explanation of these perceptual differences across the dyad is that the

customer’s evaluation is colored. This corresponds with the “I designed it myself effect”

reported by Franke, Schreier, and Kaiser (2010), in which they show that customer self-

design leads to higher outcome appraisals because of feelings of accomplishment. In a

similar vein, Troye and Supphellen (2012) report an “I made it myself effect”; consumer

participation in co-production improves outcome evaluations, via the creation of

linkages between the outcome and the self.


The essay’s findings, which offer evidence of interactions between customer

participation and relationship multiplexity, translate into guidelines for managers. We

discuss the managerial implications that follow from our findings by answering three

questions.


75

Does customer participation help or hurt in case of customer-as-partner multiplexity?

The managers we interviewed thought the answer was a straightforward ‘yes.’ However,

the empirical evidence indicates otherwise. Involving a customer that is also a partner is

only synergistic when the participating customer and the supplier share self-initiated

partner ties, but not so when they share engineered partner ties. Our findings thus

underscore the need to distinguish between these two types of partner ties, a

distinction that has been largely overlooked in the marketing literature but that is

prominent in industrial markets. Interestingly, the beneficial effect of involving

customers in case of self-initiated partner ties was found at both sides of the dyad: both

the customer and the supplier report increased supplier task performance in this

situation.

Somewhat surprisingly, we also found a negative main effect of self-initiated

partner ties on supplier task performance, as perceived by both the customer and the

supplier. The fact that partner ties sometimes work counter-productively is commonly

referred to as the dark side of partnerships. One explanation for this dark side is that

customers develop higher expectations whereas suppliers invest less effort (Grayson

and Ambler 1999). The negative effect of self-initiated partner ties on supplier task

performance may reflect a spillover of such dark side effects to the focal customer-

supplier tie. When discussing our results with the marketing director of the contract

R&D organization that supplied the data for our study, he acknowledged that partnering

customers developed increased expectations: “Our organization and this partner

collaborate as equals. Over time, we have learned each other’s weaknesses and

strengths, which is helpful in joint projects. However, I have also experienced that this

insider knowledge may backfire on us – in its customer role, our partner tends to

increase its expectations.”

Does customer participation help or hurt in case of customer-as-competitor

multiplexity? As our field interviews indicated, the answer to this question is not

obvious, with some interviewees preferring to keep competitors at a distance and

others acknowledging the positive aspects of involving competitors. Our empirical test

confirms that the answer is not a simple ‘yes’ or ‘no’: we find different effects at both

sides of the dyad. Conform expectations, we find that from the perspective of the

customer, customer participation is less beneficial in case of customer-as-competitor


76

multiplexity. Thus, in the eyes of the customer, the potential benefits of customer-as-

competitor multiplexity appear to be overshadowed by role conflict. From the

perspective of the supplier, however, it does not matter whether the participating

customer is also a competitor. Neither the main effect of a competitor tie nor the

interaction with customer participation is significant. The lack of support for our

hypothesis at the supplier side questions our initial argument that the conflict between

the participating-customer role (sharing knowledge) and the competitor role

(protecting knowledge) hinders effective resource sharing. Then, how can we explain

why the role conflict leads to a negative appraisal of the outcome by the customer, and

not by the supplier? Although firm conclusions require additional evidence, we offer an

alternative explanation rooted in the nature of the outsourcing relationship, where the

supplier agrees to develop a new product for the customer in exchange for payment.

The customer, on the one hand, may consider resource sharing with a competitor as an

important cost, which devalues the project outcome. The supplier, on the other hand,

may not view resource sharing with a competing customer as a cost because it is

financially compensated according to the terms of the outsourcing contract.

Does customer participation help or hurt in case of customer-as-supplier multiplexity?

Our theorizing and empirical analyses indicate that role-reversal ties lower the

effectiveness of customer participation. A situation where the customer is also a

supplier to its supplier, and the supplier is also a customer to its customer, creates role

ambiguity. When the customer is unsure about what is appropriate behavior, the

participating-customer role is likely to be played in an ineffective way. The same holds

for the supplier. As a result, supplier task performance decreases.

In addition to the hypothesized effects, we find that when the supplier executes

more concurrent projects for a customer, the supplier’s self-reported task performance

improves with customer participation. We speculate that executing concurrent projects

offers a supplier access to additional customer knowledge, which the supplier can use to

perform better in the focal project (cf. Tuli, Bharadwaj, and Kohli 2010). In contrast, the

participating customer’s perception of supplier task performance declines when the

supplier executes more concurrent projects for the customer. Possibly, when multiple

concurrent projects are executed, the customer expects that the supplier is sufficiently

familiar with its situation to meet customer demands without customer participation.


77

Limitations and Opportunities for Further Research

The study suffers from a number of limitations, which offer interesting avenues for

future research. First, although our sample involves multiple NPD projects for multiple

customers, we only considered outsourced NPD projects executed by one supplier firm

active in a single industry, which warrants caution regarding the generalizability of the

findings. However, focusing on one firm allowed us to collect very detailed and rich

information about the relationships between the firm and the multiple customers

included in the sample. In addition, we achieved greater comparability of key variables

by controlling for organizational and environmental factors. To gain confidence in the

broader applicability of our findings, it would be desirable for follow-up studies to

collect information from multiple companies and across multiple industries.

Nevertheless, it is comforting that the qualitative insights that we obtained in the

roundtables and in-depth interviews with executives and project managers from three

different industries showed great similarity across these industries.

Second, future research could investigate the specific reasons for differences across

the dyad. Why do customers evaluate customer participation more positively than

suppliers? Why is customer-as-competitor multiplexity not an issue in the eyes of the

supplier, but detrimental in the eyes of the customer? We offered possible explanations

for the differential effects, but firm conclusions require additional evidence.

Third, we focused on the effects of customer participation on supplier task

performance. Although this is an important outcome variable in the context of

outsourced NPD (see e.g. also Carson 2007), future research could study other

outcomes, such as knowledge development, innovativeness of the end product, costs of

the NPD process, repeat orders, and customer lifetime value.


78

AP

PE

ND

IX:

Me

asu

rem

en

ts

Co

nst

ruct

O

pe

rati

on

ali

za

tio

n

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ta s

ou

rce

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pp

lie

r ta

sk p

erf

orm

an

ce

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pli

er t

ask

per

form

ance

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sto

mer

p

erce

pti

on

) (S

TP

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ST)

(α =

.78

)

Ho

w s

atis

fied

are

yo

u w

ith

▪ t

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pro

du

ct d

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op

ed

▪

th

e en

d r

esu

lt in

gen

eral

? (5

-po

int

scal

e w

ith

1 =

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ry d

issa

tisf

ied

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d 5

= “

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luat

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med

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pli

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pp

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p

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akes

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of

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79

AP

PE

ND

IX:

Me

asu

rem

en

ts –

co

nti

nu

ed

Co

ntr

ol

va

ria

ble

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roje

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get

in $

/ 1

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og-

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dm

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cord

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nes

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NO

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▪

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the

star

t o

f th

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roje

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as e

xpec

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at a

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tio

nar

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in

tech

no

logy

wo

uld

be

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essa

ry.

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its

star

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was

exp

ecte

d t

hat

th

is p

roje

ct w

ou

ld m

eet

cust

omer

req

uir

emen

ts in

a

sup

erio

r w

ay.

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ey a

mo

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pro

ject

m

anag

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ject

tim

e p

ress

ure

(T

IME

_PR

ESS

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his

pro

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, th

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sto

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pu

t u

s u

nd

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reat

tim

e p

ress

ure

. Su

rvey

am

on

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sto

mer

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(α =

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sto

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had

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vest

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ly in

th

e te

chn

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efo

re w

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bro

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t in

. ▪

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e cu

sto

mer

had

alr

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evel

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a la

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bef

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ecu

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efo

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tsou

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LA

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(α

= .8

1)

▪

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of t

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pla

n c

om

po

nen

ts w

ere

set

in c

lose

co

op

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wit

h t

he

cust

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▪

Bef

ore

th

e st

art

of t

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pro

ject

, all

pro

ject

ph

ases

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e d

iscu

ssed

in d

etai

l wit

h t

he

cust

om

er.

Surv

ey a

mo

ng

pro

ject

m

anag

ers

Co

ncu

rren

t p

roje

cts

(CO

NC

UR

R)

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mb

er o

f pro

ject

s o

ther

th

an t

he

foca

l pro

ject

th

e su

pp

lier

car

ries

ou

t fo

r th

e cu

sto

mer

du

rin

g th

e fo

cal p

roje

ct p

erio

d, l

og-

tran

sfo

rmed

P

roje

ct a

dm

inis

trat

ion

re

cord

s

No

te: A

ll it

ems

are

mea

sure

d o

n a

7-p

oin

t sc

ale,

wit

h 1

= c

om

ple

tely

dis

agre

e an

d 7

= c

om

ple

tely

agr

ee, u

nle

ss o

ther

wis

e in

dic

ated

.

81

At last the Dodo said,

“Everybody has won, and all must have prizes.”


Chapter 4

Managing the Crowd:

Prize Structure and Creativity

In Online Idea Generation Contests11

4.1 INTRODUCTION

Fueled by developments in Web 2.0 technologies, many firms are now using online

idea generation contests, a form of crowdsourcing in which individuals external to the

firm generate ideas (Howe 2006, 2008; Poetz and Schreier 2012). In an online idea

generation contest, a company (contest sponsor) posts an idea generation challenge,

which is open to the general public. The disclosure of the challenge as an open call

reaches many individuals with diverse knowledge bases and skills (Girotra, Terwiesch,

and Ulrich 2010; Jeppesen and Lakhani 2010; Terwiesch and Xu 2008). The challenge

specifies the ideation task and the prize structure. At the end of the contest, the contest

sponsor awards prizes to one (or more) contestant(s).

Online idea generation contests are growing in popularity across diverse industries

(Lowry 2011). For example, since 2008, Frito-Lay has organized multiple ‘Do Us A

Flavor’ contests to generate ideas for flavors for potato chips in the United States,

11

We thank the senior executives of the online idea generation contest platform company for insightful discussions and for providing access to confidential data. We also thank seminar participants at McCombs Business School for useful feedback on the research. Further, we acknowledge useful inputs from Sandeep Arora, Gary Lilien, Raj Raghunathan, Gaia Rubera, and Debika Sihi on a previous version of this article.

Chapter 4 – Managing the Crowd: Prize Structure and Creativity in Online Idea Generation Contests

82

Australia, Spain, The Netherlands, and the United Kingdom, which generate between

300,000-800,000 submissions per contest. In 2012, Titan Inc., a Tata Group company in

India, organized an online contest for their jewelry brand ‘Tanishq’ which resulted in

over 3,000 product design ideas within just six weeks. In 2011, Schiphol Airport in

Amsterdam, Netherlands, organized an online contest to generate ideas for their new

terminal, which resulted in 125 entries.

A McKinsey and Company survey of companies using online innovation contests

indicated that motivating contestants is the key challenge in effective contest design

(McKinsey 2009, p. 53). As contestants of online contests do not receive any upfront or

guaranteed payment for their efforts, they are motivated by the possibility of winning a

prize (Afuah and Tucci 2012). Hence, a key design element of an online idea generation

contest is its prize structure. Comparing the prize structures of various online idea

generation contests, we observe that prize structures vary in terms of three

characteristics: the total prize value, the number of prizes, and the prize spread (i.e., the

variance in the values of the different prizes). In this essay, we study the effects of these

three prize structure characteristics of online idea generation contests on idea creativity.

Insights on increasing creativity in online idea generation contests have high

managerial relevance. Idea creativity is considered a core element of the fuzzy front end

of the innovation process (Im and Workman 2004). Historically, firms have relied

primarily on their employees as a source of new product ideas (Burroughs et al. 2011).

However, using employees for idea generation has some limitations. People tend to

interpret information and develop new ideas in terms of existing frames (Reeves and

Weisberg 1993). Employees tend to explore familiar rather than novel paths as they are

constrained by their experiences with their company’s technologies, products, and

resource constraints. In addition, the employees’ potential involvement in the

implementation of the idea may also constrain their creativity. In contrast, participants

in online idea generation contests are free agents, unaffiliated with the contest sponsor,

and may be able to generate very creative ideas.

The study’s findings also have the potential to extend the marketing literature.

While idea generation is central to the “fuzzy front end” of the new product

development process (Toubia 2006), there are only limited insights on idea generation


83

by sources external to the firm. In a recent conceptual paper, Afuah and Tucci (2012)

identify conditions under which crowdsourcing outperforms internal sourcing and

designated contracting (with suppliers) in idea generation. There is also some emergent

evidence of the benefits of crowdsourcing. Bayus (2013) reports that online community

members contribute high quality new product ideas and Poetz and Schreier (2012)

report that user-generated ideas are on par (in terms of novelty and customer benefits)

with those generated by professionals. Yet, to the best of our knowledge, there is no

evidence of how prize structures affect the creative outcomes of idea generation by

external sources, the issue we focus on in this essay.

We define creativity as the degree to which something is novel, unusual, or original,

and differs from existing solutions to a problem (Marsh, Ward, and Landau 1999;

Oldham and Cummings 1996). Whereas other conceptualizations of creativity have also

included a meaningfulness dimension (e.g., Amabile 1996; Burroughs, Moreau, and Mick

2008; Im and Workman 2004), we focus on the novelty dimension of creativity for two

reasons. First, prior research (Shalley and Zhou 2008) has argued that novelty is the

primary dimension of creativity, especially in the idea generation process (Shah, Smith,

and Vargas-Hernandez 2003). Second, the main objective of organizing an online idea

generation contest is to avoid the myopic focus of the company’s employees and

develop really novel ideas (Jeppesen and Lakhani 2010; Terwiesch and Xu 2008).

Extending developments in organizational theory (e.g., Malhotra 2010), psychology

(e.g., Amabile 1996; Deci and Ryan 1985), and economics (e.g., Lazear and Rosen 1981),

we examine the effects of a contest’s total prize value, number of prizes, and prize

spread on the creativity of the ideas submitted in the contest. We hypothesize that the

total prize value and number of prizes of an online idea generation contest will increase

idea creativity, while prize spread will decrease idea creativity. Furthermore, we

hypothesize that the three prize structure characteristics of the contest will interact

with each other to affect idea creativity.

The empirical context is an online contest platform used by companies (i.e., contest

sponsors) to field idea generation challenges to a community of about 10,000

contestants. The contest platform is used for idea generation for products, packaging,

brand identity, and advertising copy. We collected data on 106 online contests using the


84

contest briefs provided by the contest sponsors and contestants’ submissions. We take

into account the possibility that the prize structure of the contest may not only increase

idea creativity, but also attract more contestants. We address this endogeneity concern

by using a system estimation procedure to model idea creativity and the number of

contestants. The results strongly support the hypotheses. Further, the results are robust

to alternative measures of idea creativity and explanatory variables, and alternative

model specifications.

We proceed as follows. We first propose theory and hypotheses that link the prize

structure characteristics of online idea generation contests to idea creativity. Next, we

discuss the data, measures, and method used to test the hypotheses. We then present

the results and additional robustness analyses. We conclude with a discussion of the

essay’s managerial implications, theoretical contributions, and limitations and

opportunities for future research.

4.2 THEORY AND HYPOTHESES

Although some early scholars (Vinacke 1953; Wertheimer 1945) considered

creativity to be a mysterious process, subsequent work has generated a large body of

insights on the factors influencing creative behaviors. Motivation is considered to be a

key factor that affects creative performance (Amabile 1996; Deci and Ryan 1985;

Vallerand 1997). Contextual factors of the task environment, such as rewards, can

increase or decrease motivation and thereby, affect creative performance (Amabile,

Hennessey, and Grossman 1986; Burroughs et al. 2011; Eisenberger and Armeli 1997).

Zhou and Shalley (2003) provide a review of the past literature on the effects of

rewards on creativity in noncompetitive contexts.

Motivation increases the time and effort that contestants may be willing to invest in

the idea generation task (cf. Shalley 1995), which will, ceteris paribus, increase idea

creativity. We identify four sources of motivation in online idea generation contests.

First, a contest’s prize structure may increase contestants’ efforts because of the

monetary value at stake in the contest (Eisenberger and Armeli 1997). Second, the heat

of the competition in the contest (i.e., rivalry among contestants) may also be another

motivator (Malhotra 2010). Third, the prospect of getting social recognition by winning

a prize in the contest may also motivate contestants (Amabile, Hennessey, and


85

Grossman 1986). In addition, prizes in contests offer contestants opportunities for

positive self-confirmation of their creative competency (Deci and Ryan 1985), which

should increase their motivation to invest time and effort in the idea generation task.

Applying these arguments, we propose main effects of the total value of all prizes,

the number of prizes, and prize spread among the prizes on idea creativity, as well as

interaction effects between the three prize structure characteristics on idea creativity.

Total Prize Value

We define the total prize value as the sum of the amounts across the different prizes

in the contest. We propose multiple mechanisms by which the total prize value in the

contest will increase idea creativity. First, a high total prize value has obvious monetary

advantages that may motivate contestants to work hard and long to increase their

chances of winning in the contest (Eisenberger and Armeli 1997).

Second, a high total prize value increases the heat of the competition (Malhotra

2010), which will motivate contestants to expend more effort and time in the contest

(Boudreau, Lacetera, and Lakhani 2011).

Third, the literature on creativity indicates that individuals are motivated by the

prospect of social recognition (Amabile 1996; Amabile, Hennessey, and Grossman 1986).

Developments in the Web 2.0 literature suggest that building an online reputation of

competency is an important motivation for online contributors of content (Dellarocas

2003; Yoganarasimhan 2012). Similarly, free agent contestants of online idea

generation contests may be motivated by the social recognition and reputational effects

associated with winning a prize. The total prize value of a contest reflects the contest’s

prestige, enhancing the social recognition derived from being a winner in the contest.

Thus, we expect that an increase in the total prize value of the online idea generation

contest increases the efforts invested by contestants in generating ideas.

Fourth, winning an online idea generation contest with a high total prize value

sends a strong positive signal to the contestant about her creative competency,

increasing her motivation (Deci and Ryan 1985) to invest time and effort in the idea

generation task. Hence, we offer H1:


86

H1: The higher the total prize value in the online idea generation contest, the

higher the idea creativity of the contest

Number of Prizes

A second prize structure characteristic of online idea generation contests is the

number of prizes. Holding the total prize value constant, although an increase in the

number of prizes dilutes the average value of the prizes, the probability of winning a

prize increases. We argue that there may be several advantages associated with

increasing the number of prizes with regard to idea creativity.

First, as discussed in the development of H1 above, winning a prize may enhance

contestants’ online reputations by providing them social recognition. A contest with

more prizes increases contestants’ opportunities for social recognition. As the

opportunity for social recognition motivates contestants, contestants will likely invest

more time and effort in the task, increasing their creativity.

Second, individuals prefer situations that are more likely to result in self-enhancing

outcomes (Pfeffer and Fong 2005). Since multiple prizes offer more possibilities to

contestants to confirm their creative abilities to themselves, contestants may also be

more motivated as a result of the prize structure (Deci and Ryan 1985), again increasing

their creativity. In sum, we expect that increasing the number of prizes in an online idea

generation contest will increase idea creativity. Accordingly, we hypothesize H2:

H2: The higher the number of prizes in the online idea generation contest, the

higher the idea creativity of the contest

Prize Spread

We define the prize spread as the variation in the value of the different prizes in the

contest. When multiple prizes are awarded in an online idea generation contest, this

may be through a “grouped-winner” or a “rank-order” prize structure (Clark and Riis

1998; Lazear and Rosen 1981). An example of grouped-winner prize structure is a

contest in which all (say five) winners get the same prize of $600 (zero prize spread) for

a total prize value of $3,000. An example of a rank-order prize structure is a contest

where the $3,000 is split into five unequal prizes of $1250, $750, $500, $350, and $150


87

(prize spread = .71, as measured by the coefficient of variation of the different prizes, i.e.,

standard deviation scaled by the mean).

Following Festinger’s (1954) theory of social comparison, we propose that the

value of a prize to a contestant in an online idea generation contest serves as an

indicator of the contestant’s competence relative to other contestants. In online contests

with a high prize spread, low ranked winners may receive less social recognition since a

high prize spread may result in unflattering upward social comparisons (Collins 1996).

Thus, in contests with a high prize spread, the prospect of being a runner-up or a lower

ranked winner may therefore weaken the effectiveness of prizes as motivators. Hence,

contestants may be less motivated to invest time and effort in the task, decreasing idea

creativity.

In addition, when a contest has a high prize spread, the (many) low ranked winners

may not perceive themselves as ‘true’ winners when they engage in upward social

comparisons with the high ranked winners. Hence, a high prize spread reduces

opportunities for self-confirmation of contestants’ creative competence, decreasing

their motivation. In other words, in contests with low prize spread, contestants may be

more motivated to invest time and effort, increasing idea creativity, because the

negative impact of potentially negative social comparisons is low when winning a low

ranked prize (which is close in value to the high ranked prize). Thus, we hypothesize H3:

H3: The higher the prize spread in the online idea generation contest, the lower

the idea creativity of the contest

Interactions between Prize Structure Characteristics

Total prize value and number of prizes. Recall that as we discussed in H1, a high total

prize value in the contest has a monetary advantage, increases the heat of competition,

provides high social recognition, and sends a strong signal of self-confirmation to the

winners, all of which increase idea creativity.

We propose that these four mechanisms by which total prize value affects idea

creativity are weakened as the number of prizes increases. First, an increase in the total

prize value offers less monetary advantage when this increase is to be divided across a

high number of prizes. Second, the heat of competition effect of a high total prize value


88

on idea creativity is undermined when the number of prizes is high: the large number of

prizes reduces the need to compete for a piece of the pie. Third, the social recognition

stemming from an increase in total prize value is weakened by a high number of prizes

as multiple winners have to share the prestige of winning. Fourth, for similar reasons,

the signal of self-confirmation of contestants’ creative competence associated with

winning a contest with high total prize value is weakened when the high total prize

value is to be divided among many contestants.

In sum, we expect that the motivating mechanisms of monetary advantage, heat of

competition, social recognition, and self-confirmation on idea creativity, all associated

with higher total prize value, are weakened by the number of prizes. As a result, we

expect that the positive effect of total prize value on the time and effort vested in

generating creative ideas, and thus on the creativity of the contest, is weakened as the

number of prizes increases. Thus, we propose H4:


weaker the positive effect of total prize value on idea creativity of the contest

Total prize value and prize spread. We propose that the positive effect of total prize

value on idea creativity of the contest will also be weakened by a high prize spread.

Relative to low prize spread, when the prize spread is high, increasing the total

prize value of the contest increases the difference between any given prize and the one

ranked next to it, which we illustrate using two examples. Consider moving from a

contest where the total prize value of $3,000 is split into five equal prizes of $600 (prize

spread = 0) to one where the total prize value of $4,000 is split into five equal prizes of

$800 (again prize spread = 0). The prize spread remains unchanged, at zero. Now,

consider moving from a contest where the total prize value of $3,000 is split into five

unequal prizes of $1250, $750, $500, $350, and $150 (prize spread = .71, measured as

the coefficient of variation) to one where the total prize value of $4,000 is split into five

unequal prizes of $1667, $1000, $666, $467, and $200 (again prize spread = .71). The

average difference between two adjacent prizes is larger in the latter contest ($367)

than in the former contest ($275). These examples illustrate that increasing total prize

value in an online idea generation contest with high prize spread exacerbates the

problem of negative upward social comparisons, decreasing opportunities for social


89

recognition and reducing motivation. More pronounced negative upward comparisons

also decrease the opportunity to confirm one’s creative competency. Consequently,

contestants are likely to be less motivated to invest less time and effort in the task,

reducing idea creativity. In sum, given these different arguments, we expect that the

positive effect of increasing total prize value in online contests will be weakened as the

prize spread in the contest increases. Accordingly, we hypothesize H5:

H5: The higher the prize spread in the online idea generation contest, the weaker

the positive effect of total prize value on idea creativity of the contest

Number of prizes and prize spread. Finally, we propose that the negative effect of

prize spread on idea creativity is weaker in contests with more prizes.

We propose that controlling for the total prize value, increasing the number of

prizes in contests with high prize spread is likely to decrease the salience of the

difference between any given prize and the one ranked next to it. As an illustration,

compare a contest where the total prize value of $3,000 is split into five unequal prizes

of $1250, $750, $500, $350, and $150 (prize spread = .71) with a contest where the total

prize value of $3,000 is split into six unequal prizes of $1050, $725, $550, $375, $225,

and $75 (prize spread = .71). In the contest with five prizes, the average difference

between two adjacent prizes is $275, whereas in a contest with six prizes, the average

difference between two adjacent prizes is lower at $195. We propose that the social

recognition problem associated with an increased prize spread hypothesized in H3 is

weakened when the number of prizes in the contest increases, because the upward

social comparisons for contestants are more favorable when the distance to the next

higher prize in the contest is smaller. As a result, contestants’ motivation is higher in

contests with many prizes and high prize spread, compared to contests with few prizes

and high prize spread.

Similarly, more favorable upward social comparisons for contestants may increase

the opportunities for confirmation of their creative competence, thereby increasing

their motivation to expend time and effort in the contest, increasing idea creativity. In

sum, given these different arguments, we expect that the negative effect of increasing

prize spread in online contests will be weakened as the number of prizes increases.

Accordingly, we propose H6:


90


weaker the negative effect of prize spread on idea creativity of the contest

4.3 METHOD

Empirical Context

We obtained access to a sample of idea generation contests from an online contest

platform. The online idea generation contest platform was launched in early 2008 and

had about 10,000 international contestants in their community by December 2011, the

end of the observation period. Using data from a single contest platform rules out

potential biases caused by unobserved differences across contest platforms.

The contest platform hosts online idea generation contests for product innovation,

new packaging design, brand identity, and advertising copy, for contest sponsors from

many countries including the United States, Canada, Australia, Russia, the United

Kingdom, Germany, Italy, The Netherlands, Sweden, and South Africa. For example, in a

contest for new diabetic monitors, the content sponsor sought product ideas for

integrated sensors (e.g., in clothes or glasses). In a contest for new packaging for an

existing food product, contestants developed ideas for new packaging including wrap,

box, etc. We provide a fictionalized example of a contest and a submission for the

contest in the Appendix.12

The contest sponsor sets the contest’s prize structure and task constraints, which

are then posted on the contest platform’s website. A contestant works independently

and submits his or her idea(s) to the contest platform before the contest deadline.

Intellectual property rights are assigned to the contest sponsor when the ideas are

submitted to the contest. Following the contest deadline, the contest sponsor awards

(one or more) prizes to the contestants.

The majority of contestants are male (68% vs. 32% female; with 12% missing data).

They are on average 33.2 years old (standard deviation (s.d.) = 11.2; with 11% missing

data) and come from various countries including the United States, Canada, Mexico,

Australia, several European countries, and Asian countries including India and China. 23%

12 We show a representative, fictionalized example of the contest as our confidentiality agreement with the online contest platform company precludes disclosure of contest details.


91

of the contestants disclose their profession; 54% of these contestants are professionally

active in design/art/creativity. 38% of the contestants submitted ideas for more than

one contest.

Sample and Measures

We use a sample of 106 online idea generation contests, executed between July

2008 and December 2011. The sample includes 84% of all contests in this period on the

contest platform.13 We have access to all submissions (n = 5,299) for the 106 contests;

623 of these submissions were judged as winners by the contest sponsors. We use data

from the contest briefs and submissions for the measures. Table 4.1 provides an

overview of the variables and measures in the study. Table 4.2 contains the descriptive

statistics and the correlation matrix of the measures.

The contest briefs include details of the idea generation task, the start and end

dates of the contest, and the number and value of the prizes. Total prize value

(TOTAL_PRIZE_VALUE; mean = 3,990, minimum = 500, maximum = 21,500, s.d. = 4,252)

is the sum of the value of all prizes as measured in points which can be redeemed for

cash through the contest platform for an exchange rate of 1 point = $.20. Thus, the mean

value of the total prize value is $798, which is comparable to those on other contest

platforms such as Brandsupply ($565, for design ideas), 99designs ($411, for design

ideas), Crowdspring ($1112, for website ideas), and CreatAd (€1753, for ad ideas).

Number of prizes (NO_PRIZES; mean = 6.09, s.d. = 6.53) is a count variable. Prize spread

(PRIZE_SPREAD; mean = .67, s.d. = .42) is calculated as the coefficient of variation: the

standard deviation of the values of all prizes, divided by the average prize value, which

controls for any scale effects. We log-transform total prize value and number of prizes

to account for skewedness.

As many of the ideas generated in contests may be mediocre, the success of an online

idea generation contest depends on the most creative idea and not on the mean

creativity of all ideas generated in the contest (Dahan and Mendelson 2001; Girotra,

13 Twelve contests fielded on the platform in the time period are not included in the study as they pertained to a very simple task of new brand name generation. We were unable to obtain data on the dependent variable (idea creativity) for eight idea generation contests because of a technical malfunction in the contest platform’s database. There were no significant differences for total prize value, number of prizes and prize spread between the 106 contests in our sample and the excluded twenty contests.


92

TA

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93

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94

Terwiesch, and Ulrich 2010). Hence, we conceptualize idea creativity as the creativity of

the most creative idea generated in the contest.

Following prior research (e.g., Amabile 1982; Dahl, Chattopadhyay, and Gorn 1999),

we measure the creativity of the ideas using the ratings of ten creative judges, who hold

bachelor degrees in creative domains, such as art, photography, and graphic design. The

judges were not aware of the study’s hypotheses. They received an honorarium of $150

for their rating services. As the total number of submissions is very large (n = 5,299), we

restricted the judges’ rating activities to the subset of 623 winning submissions (judged

as such by the contest sponsor) across the 106 contests. As the task descriptions

included on average 2.2 mentions of phrases stimulating creativity (e.g., “think out of

the box,” “looking for innovative designs,” and “we are open to new ideas”), the data

support our premise that contest sponsors seek creative ideas. Consequently, the

winning ideas are likely more creative than the submissions that did not receive a

prize.14 We randomized the submissions and the order of the measurement items across

judges (Amabile 1982). The judges independently completed the task over a period of

three weeks. For each submission, each judge completed three seven-point scales

measuring the winning submissions’ novelty (not at all novel/very novel), originality

(not at all original/very original) and unusualness (not at all unusual/very unusual)

(Burroughs et al. 2011; Dahl, Chattopadhyay and Gorn 1999).15 To examine the

consistency of ratings across the different judges, we computed the inter-judge

reliability, i.e., the intraclass reliability coefficient (Shrout and Fleiss 1979) between the

different judges’ ratings for each of the three items; the correlations were above .60

(‘originality’ (.62), ‘novelty’ (.63), ‘unusualness’ (.66)) and statistically significant (p

< .001). The reliability scores are similar in magnitude to the reliability scores reported

14 Because we focus on the ideas that are identified as winners (by the contest sponsor) among the idea submissions, they are presumably considered “meaningful” from the contest sponsor’s perspective. 15 We also measured the winning submissions’ meaningfulness by having the expert judges complete two additional seven-point scales measuring the winning submissions’ usefulness (not at all useful/very useful) and appropriateness (not at all appropriate/very appropriate). To verify the existence of the two dimensions of novelty and meaningfulness, we performed a confirmatory factor analysis. We compared a first-order factor structure of creativity, with the five items as indicators, with a second-order factor structure, incorporating the two latent factors of novelty (three items) and meaningfulness (two items). The analysis supported the higher order structure: the second-order factor model of creativity outperformed the first-order factor model (second-order factor model: χ2(4) = 40.6, Goodness of Fit Index [GFI] = .88, Root Mean Square Residual [RMR] = .06, Akaike’s Information Criterion [AIC] = 32.6, Consistent Akaike’s Information Criterion [CAIC] = 17.9, Schwarz’s Bayesian Criterion [BIC] = 21.9; first-order factor model: χ2(5) = 651.5, GFI = .33, RMR = .58, AIC = 641.5, CAIC = 623.1, BIC = 628.1).


95

by Dahl, Chattopadhyay, and Gorn (1999), Holbrook, Lacher, and Latour (2006), and

Kristensson, Gustafsson, and Archer (2004). The intraclass reliability coefficient

between the different judges’ ratings of the total creativity scale is .91, indicating high

internal consistency of our measure.

For each submission, we averaged the total creativity scales across judges to arrive

at our measure of the submission’s creativity. Subsequently, we selected the most

creative submission to arrive at our measure of idea creativity at the contest level. We

verified and found high concurrence between the evaluations of the judges and the

contest sponsors. 86.5% of submissions that received the highest creativity rating from

our judges were also selected by the contest sponsor as either first prize or second prize

winners. Thus, the ratings of our judges are generally consistent with the contest

sponsors’ evaluations, increasing our confidence in the measure of idea creativity.

FIGURE 4.1: Distribution of Idea Creativity Across Contests

Figure 4.1 shows the distribution of idea creativity (CREATIVITY) across the

contests. The relatively high mean (mean = 4.52) reflects our focus on the most creative

idea in the contest rather than on the average creativity of all ideas. Importantly, there

is variation in the creativity of the most creative idea across contests (s.d. = .55). The

0

5

10

15

20

25

Fr

eq

ue

nc

y

Idea Creativity of Most Creative Idea in the Contest

n = 106mean = 4.52s.d. = .55kurtosis = -.142skewness = .012Jarque-Bera = .091 (p = .96)


96

Jarque-Bera test for the assumption of normality could not be rejected, indicating that

the dependent variable is normally distributed.

Control variables. In addition to our focal variables, we also include contest and

contest sponsor characteristics as control variables. First, we control for the number of

contestants in the contest (NO_CONTESTANTS; mean = 24.92, s.d. = 18.00) as increased

competition may increase idea creativity (log-transformed). Second, we use a dummy

variable (NEWTASK_DUM; mean = .71, s.d. = .46) to control for the nature of the task in

terms of idea generation for a new product, package, or brand (NEWTASK_DUM = 1) or

“redo” idea generation for improving an existing product, package, or brand

(NEWTASK_DUM = 0). Third, we control for the length of the contest brief, measured by

the number of words (LENGTH; mean = 159.00, s.d. = 94.30) (log-transformed).

Furthermore, we control for the level of instructional guidance pertaining to the use

of specific materials, textures, and colors, among others, in the creative task (Moreau

and Dahl 2005) as input constraints limit exploratory thinking and decrease creativity

(Dahl and Moreau 2007). We also control for the number of requirements that the

target outcome must fulfill (e.g., the product must be environmentally friendly to

produce) as output constraints provide task clarity and free cognitive resources to

increase creativity (Amabile 1998; Dahl and Moreau 2007). Two graduate marketing

students, not aware of the study’s hypotheses, independently coded task descriptions in

the contest briefs to develop these two measures of the creative task constraints:

number of items of instructional guidance (INSTRUCT; mean = 1.38, s.d. = 1.85) and the

number of target outcome specifications (TARGET; mean = 2.42, s.d. = 2.42). The two

coders agreed on the coding in 89% of the contests. A third coder (also a graduate

marketing student) helped resolve inconsistencies in the remaining 11% of the contests.

Finally, we control for the reputation of the contest sponsor, as reputed contest

sponsors may increase contestants’ effort in the idea generation task. Given the online

context and the international character of the contest sponsors and contestant base, we

proxy the contest sponsor’s reputation by using the (log-transformed) number of hits

on Google on the final day of the contest (REPUTATION; mean = 148,994, s.d. = 602,960).


97

Bivariate Analysis

In Figure 4.2, we plot the bivariate relationships between each prize structure

characteristic and idea creativity, using mean splits of the prize structure variables.16

FIGURE 4.2: Idea Creativity by Prize Structure Characteristics (Mean Splits)

16 We obtain similar results when using median splits of the prize structure variables.

4.0

4.2

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4.6

4.8

5.0

Low total prize value High total prize value

Ide

a C

re

ati

vit

yPanel A. Idea Creativity × Total Prize Value

4.0

4.2

4.4

4.6

4.8

5.0

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Ide

a C

re

ati

vit

y

Panel B. Idea Creativity × Number of Prizes

4.0

4.2

4.4

4.6

4.8

5.0

Low prize spread High prize spread

Ide

a C

re

ati

vit

y

Panel C. Idea Creativity × Prize Spread


98

Panel A shows that idea creativity is higher (t = 3.48, p < .01) in contests with high

total prize value (CREATIVITY = 4.80) than in contests with low total prize value

(CREATIVITY = 4.41). In Panel B, we see that idea creativity is higher (t = 2.36, p < .05) in

contests with more prizes (CREATIVITY = 4.71) than in contests with fewer prizes

(CREATIVITY = 4.45). Panel C indicates that idea creativity is higher (t = 1.78, p < .10) in

contests with low prize spread (CREATIVITY = 4.62) than in contests with high prize

spread (CREATIVITY = 4.42). Thus, the model-free evidence is consistent with the

hypothesized relationships in H1-3 between prize structure characteristics and idea

creativity.

Estimation

To test the hypotheses, we specify a model of idea creativity as a function of the

contest’s prize structure characteristics, related interactions, and control variables. One

of the included variables, the number of contestants, may itself be influenced by the

prize structure characteristics of the contest (e.g., high total prize value may attract

more contestants). To address this potential endogeneity issue, we specify a model of

the (log-transformed) number of contestants; as explanatory variables, we include the

contest’s prize structure characteristics, the dummy variable identifying a new (versus

redo) task, the length of the contest brief, the contest sponsor’s reputation, and task

constraints. In addition, for identification purposes, we include instruments in the

model of number of contestants that are not included in the model of idea creativity: (1)

the number of pictures in the contest brief (PICTURES; mean = 2.60, s.d. = 2.16), (2) the

duration of the contest in days (DURATION; mean = 44.66, s.d. = 48.18) (log-

transformed), and (3) the community size on the start date of the contest

(COMMUNITY_SIZE; mean = 5,708, s.d. = 2,181) (log-transformed), all of which may

affect the number of contestants. We thus specify the following equations for idea

creativity and the log of the number of contestants:

(1) CREATIVITYi = β0 + β1 * TOTAL_PRIZE_VALUEi + β2 * NO_PRIZESi

+ β3 * PRIZE_SPREADi + β4 * TOTAL_PRIZE_VALUEi * NO_PRIZESi

+ β5 * TOTAL_PRIZE_VALUEi * PRIZE_SPREADi + β6 * PRIZE_SPREADi *

NO_PRIZES + β7* NO_CONTESTANTSi + β8 * TASKNEW_DUMi

+ β9 * LENGTHi + β10 * INSTRUCTi + β11 * TARGETi + β12 * REPUTATIONi + εi


99

(2) NO_CONTESTANTSi = γ0 + γ1 * TOTAL_PRIZE_VALUEi + γ2 * NO_PRIZESi

+ γ3* PRIZE_SPREADi + γ4* TASKNEW_DUMi + γ5* LENGTHi

+ γ6* INSTRUCTi + γ7* TARGETi + γ8* REPUTATIONi + γ9 * PICTURESi

+ γ10 * DURATIONi + γ11* COMMUNITY_SIZEi + νi

where i denotes the online idea generation contest. We note that we mean-centered the

prize structure variables to facilitate interpretation of the interaction effects.

Since the number of contestants is included as an explanatory variable in the

equation of idea creativity, we jointly estimate the two equations using the full

information maximum likelihood method (Maddala 1977). A possible drawback of full

information methods is that misspecification in one equation may bias the parameter

estimates in both equations. The RESET specification test (Ramsey 1969) did not

indicate misspecification (parameters for additional paths and alternative functional

forms were neither statistically significant, nor did they improve model fit).

4.4 RESULTS

Model Selection

We provide the results of the simultaneous estimation of the models for idea

creativity and for the number of contestants in Table 4.3. Column 1 and Column 2

contain the results with only the main effects of the contest’s prize structure

characteristics (i.e., total prize value, number of prizes, and prize spread) and the

control variables. Column 3 and Column 4 contain the results for the models that also

include the hypothesized interaction effects. Minus two times the difference between

the log-likelihoods of both models is distributed as Chi-square with 3 degrees of

freedom. The ��-test statistic (10.88) indicates that the interaction effects significantly

improve model fit (p = .01). In both equations, none of the Variance Inflation Factors

exceeds four, suggesting that multicollinearity is not a threat to the validity of the

estimation.

We first discuss the results of the model of the number of contestants. As expected

(Column 3 of Table 4.3), the total prize value increases the number of contestants (b

= .19, p < .05). With respect to other contest characteristics, new idea generation tasks

relative to redo tasks (b = .43, p < .01), specification of target outcomes (b = .04, p < .10)

and contest duration (b = .37, p < .05) increase the number of contestants, whereas


100

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101

contest brief length decreases the number of contestants (b = -.40, p < .01). Furthermore,

the contest sponsor’s reputation (b = .04, p < .10) and community size (b = .31, p < .05)

increase the number of contestants.

Hypotheses Testing

Next, we discuss the tests of hypotheses (Column 4 of Table 4.3). We find support

for the three hypotheses of the main effects of the prize structure characteristics.

Supporting H1 and H2 respectively, total prize value (b = .34, p < .01) and number of

prizes (b = .37, p < .01) of the online contest both increase idea creativity, and in support

of H3, prize spread of the online contest decreases (b = -.41, p < .01) idea creativity.

We find support for H4, that the number of prizes of the online contest weakens the

positive effect of total prize value (b = -.23, p < .01) on idea creativity. We also find

support for H6, that the number of prizes of the online contest weakens the negative

effect of prize spread (b = .36, p <. 05) on idea creativity. However, the results do not

support the hypothesized negative interaction effect (H5) between total prize value and

prize spread (b = .07, p = .31) of the online contest on idea creativity.17

With respect to the control variables, the length of the contest brief (b = -.34, p < .05)

and instructional guidance (b = -.05, p < .10) decrease idea creativity, whereas

specification of target outcomes (b = .06, p < .05) increases idea creativity. Before

discussing these results, we examine their robustness and report additional analyses.

Additional Analyses

Idea creativity of top three ideas in the contest. As discussed earlier, the success of an

online idea generation contest is judged by the creativity of the most creative idea

generated in the contest. We next estimate the system of equations replacing the

original idea creativity measure (most creative idea) with an alternative measure: the

mean idea creativity of the top three creative ideas. To this end, we selected the three

ideas per contest with the highest creativity ratings for 92 contests with three or more

17 Although we did not hypothesize a three-way interaction among the three prize structure characteristics, we estimated a model including a three-way interaction term. The results (not reported in the essay in the interest of brevity) indicate that the three-way interaction term is not significant (b = -.10, p = .31). In addition, we examined quadratic effects to account for the possibility that the effects of the prize variables decrease and perhaps turn negative at high levels, but we do not observe any quadratic effects within the boundaries of the data.


102

prizes. To avoid losing observations, we retained contests with only two prizes (8

contests, dependent variable is measured as the mean creativity of the two winning

ideas) and contests with only one prize (6 contests, dependent variable is unchanged).

We present the results of the system estimation using this alternative dependent

variable in Model 3 (reported in Columns 1 and 2 of Table 4.4). The results, which are

consistent with the model estimating the idea creativity of the single most creative idea,

indicate that the findings are robust to this broader definition of idea creativity.

Alternative measure of prize spread. In the hypothesis tests reported in Model 2 of

Table 3, we use the coefficient of variation as the measure of prize spread of the contest.

We examine the robustness of the results to an alternative measure, namely the relative

mean difference (RMD). This measure averages the differences in prize values for each

prize pair, relative to the average prize value. More formally, the relative mean

difference is computed as follows: �� !" ∑ ∑ |$ % $�

!��

!�� |& /(, where n is the

number of prizes, each combination of xi and xj represents a prize pair, and μ is the

mean prize value.

We present the results of the system estimation using this alternative measure of

prize spread of the online contest in Model 4 (reported in Columns 3 and 4 of Table 4.4).

The results reported are generally consistent with those measuring prize spread using

the coefficient of variation (reported in Column 3 and 4 of Table 4.3), showing that our

results are robust to the choice of measure for prize spread.

Dependent variable of idea meaningfulness. As discussed earlier, we focus on novelty

as the primary dimension of creativity, as the focus of online idea generation contests is

to generate novel ideas. Nonetheless, we also estimate a model with the (main and

interaction) effects of prize structure characteristics and control variables on two

measures of idea meaningfulness: meaningfulness of the most meaningful idea and

meaningfulness of the most creative (i.e., novel) idea. As expected, the results indicate

that prize structure characteristics of the online contest have no effect on either

measure of meaningfulness of ideas generated (the results are not reported in the essay

in the interest of brevity, but are available upon request from the authors).

Selection of contestants. We developed the hypotheses based on the theoretical

premise that a contest’s prize structure characteristics influence contestants’


103

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104

motivation, which affects idea creativity. Alternatively, it is possible that the effects of

prize structure characteristics of online idea generation contests on idea creativity are

driven by a selection effect. That is, the prize structure characteristics of online idea

generation contests could attract more (or less) creative contestants to a contest, which

directly influences idea creativity. Although we are unable to measure the creativity of

the contestants, we are able to examine the “winning histories” of contestants.

Assuming that contestants who have won in prior contests have superior creative

abilities, we would find evidence of selection if the proportion of winners systematically

differs across high and low levels of the prize structure characteristics of online contests.

Mean split analyses indicate that the proportion of contestants who had previously won

contests does not differ between contests with a high or a low total prize value (t = -.63,

p = .53), number of prizes (t = -1.03, p = .30), and prize spread (t = .14, p = .89). This

evidence, albeit indirect, does not support the alternative explanation that prize

structure characteristics of online idea generation contests attract contestants with

different creative abilities.

Repeater contestants. The different idea generation contests are organized by one

contest platform, which allows contestants to compete in multiple contests. To rule out

the possibility that “repeater contestants” (i.e., contestants who have entered prior

contests) systematically affect idea creativity of the online contest, we re-estimate the

system of equations including a control variable measuring the percentage of repeater

contestants in the contest. The results (not reported in the essay in the interest of

brevity) indicate that the proportion of repeater contestants in online contests has no

effect on idea creativity (b = .06, p = .82) and its inclusion does not improve model fit.

Professional contestants. A number of the contestants have disclosed a professional

background in design, art, or creativity. To control for the effect of the share of

professionals competing in the contest, we re-estimate the system of equations

including a control variable measuring the share of professional contestants in the

contest, based on the disclosed data. The results show that inclusion of the variable does

not improve model fit and that the share of professional contestants does not affect idea

creativity (b = .02, p = .93).


105

Multiple submissions per contestant. Contestants are allowed to submit multiple

ideas to a single contest. 49.99 submissions per contest on average (5,299 submissions

in total for 106 contests in the sample) are generated by an average of 24.92 contestants,

implying that each contestant submits 1.98 ideas on average. To rule out the theoretical

possibility that the contestants who submit multiple submissions systematically affect

the idea creativity of the online contest, we re-estimate the model using a control

variable measuring the average number of submissions per contestant in the contest.

The results indicate that the number of submissions per contestant has no effect on idea

creativity (b = -.11, p = .14), and its inclusion does not improve the fit of the model.

Effect of demanding creative ideas. It is possible that contest sponsors who require

very creative ideas may set prize structures accordingly. To control for this effect, we

re-estimated the system while controlling for the frequency of phrases to stimulate

creativity (e.g., “Think out of the box”) in the contest brief. The control variable is

marginally significant (b = .04, p = .06) and the hypothesized effects are retained. The

results indicate the model is robust to stimulating creativity.

Overall, the findings strongly support the hypotheses and are robust to alternative

measures of the variables and model specifications.

4.5 DISCUSSION

Online idea generation contests are an increasingly popular crowdsourcing

approach in business practice to generate new ideas. Yet, very little is known about the

effective design of these contests in the marketing literature. In this essay, we develop

theory and report evidence on the effects of prize structure characteristics of online

idea generation contests on the creativity of the ideas generated in these contests. We

conclude with a discussion of the essay’s implications for managerial practice,

contributions to marketing theory, and limitations and opportunities for further

research.


The study’s findings, which point to interactions among the prize structure

characteristics, provide guidelines for contest sponsors and platform managers for the

effective design of online idea generation contests to improve idea creativity. We report


106

simple slope analysis to better understand the effects of prize structure characteristics

on idea creativity.

FIGURE 4.3: Interaction Effect Plots

The positive effect of total prize value of online contests on idea creativity is

weakened as the number of prizes increases. A simple slope analysis (at 2 s.d.

above/below the mean) indicates that the total prize value only increases creativity for

contests with few prizes (bLOW = .67, p < .01); this effect disappears for contests with

many prizes (bHIGH = .00, p = .97) (Figure 4.3, Panel A). The nature of the interaction

effect suggests that contest sponsors who are unable to offer a high total prize value can

increase idea creativity by splitting up the low total prize value into many prizes.

3

4

5

6

7

Low Total Prize Value

High Total Prize Value

Ide

a C

re

ati

vit

yPanel A. Total Prize Value x Number of Prizes

High Number of Prizes

Low Number of Prizes

b = .00, n.s.

b = .67, p < .01

3

4

5

6

7

Low Prize Spread

High Prize Spread

Ide

a C

re

ati

vit

y

Panel B. Prize Spread x Number of Prizes

High Number of Prizes

Low Number of Prizes

b = .13, n.s.

b = -.84, p < .01


107

While the negative effect of prize spread on idea creativity is strong in online

contests with few prizes (bLOW = -.84, p < .01), it is not significant in contests with many

prizes (bHIGH =.13, p = .59). (Figure 4.3, Panel B). This finding suggests that the negative

effects of prize spread on idea creativity can be overcome by setting many prizes, which

decreases the distance between the different prizes.

Overall, the findings indicate that prize spread in an online idea generation contest

never increases idea creativity. Thus, contest sponsors should strive to set prizes of

equal value since prize spread decreases idea creativity, especially in contests with few

prizes. In addition, the findings indicate that setting many prizes in online idea

generation contests generally results in higher idea creativity than setting few prizes.

We also examined several characteristics of the contest brief, which generate

managerial recommendations. Contest sponsors should compose a concise contest brief

as long contest briefs reduce idea creativity. Further, contest sponsors should provide

limited instructional guidance in the contest brief, because increasing input constraints

decreases idea creativity. In contrast, they should provide detailed descriptions of the

target outcome, because specifying such output constraints increases idea creativity.

Finally, it is interesting that while the total prize value of the contest increases the

number of contestants, the number of contestants does not translate into higher idea

creativity (b = -.33, n.s., in Column 4 of Table 4.3). The null effect of the number of

contestants in online contest on idea creativity combined with the support for the

effects of prize structure characteristics on idea creativity suggests that idea creativity

in online idea generation contests can be increased by effectively managing the crowd

(through prize structure characteristics and contest brief) and not by merely increasing

the size of the crowd.

Theoretical Contributions

The essay’s findings extend the marketing literature on idea generation. Although

there is a large body of work on idea generation, past research has primarily focused on

(offline) idea generation by employees (e.g. Burroughs et al. 2011; Girotra, Terwiesch,

and Ulrich 2010; Rossiter and Lilien 1994; Toubia 2006). By examining Web 2.0 based,

online idea generation contests, we extend the nascent work in the marketing literature

on idea generation by individuals external to the firm. Recently, Bayus (2013) and Poetz


108

and Schreier (2012) provided empirical evidence that crowdsourcing can result in high

quality ideas for new product development, raising questions about mechanisms to

improve the quality of the ideas generated by crowds. In this essay, we extend this

recent work on crowdsourcing idea generation by moving beyond idea generation in

noncompetitive offline settings and developing new theory to understand the

phenomenon of crowdsourcing of ideas in the competitive setting of online idea

generation contests. We argue that in competitive settings, the nature of the prize

structure can affect the heat of competition and activate mechanisms of social

comparison, which affect creativity. We hope that the evidence we provide in this study,

that contest sponsors can enhance idea creativity by the effective design of prize

structure characteristics, inspires future theory development on the sources of

motivation in crowdsourced contests.

Further, we extend the literature on incentives and tools to stimulate creativity,

thereby addressing the recent call for research on this topic by Hauser, Tellis and Griffin

(2006). Specifically, we identify new approaches (i.e., prize structure characteristics) to

“incentivize” creative performance in online idea generation contests. The findings

related to the effects of the three prize structure characteristics indicate that total prize

value, number of prizes, and prize spread, both independently and jointly, affect idea

creativity. In particular, our findings underline the importance of discriminating

between contests with a grouped-winner design (with equal prizes) and those with a

ranked-winner design (with unequal prizes), which has not yet been distinguished in

the extant literature on the effects of incentives on creativity. The findings also offer

evidence of the dominant positive effects of setting many prizes on idea creativity; in

addition to their positive main effect on idea creativity, setting many prizes offsets the

negative effects of both a low total prize value and a high prize spread. Overall, the

findings suggest contingency effects of the prize structure characteristics on idea

creativity in competitive settings such as online idea generation contests. Future

research on whether and how these findings apply to other types of incentives (e.g.,

lottery tickets, recognition prizes, and non-monetary rewards) and in other competitive

settings (e.g., tournaments) would be useful.

Finally, the essay’s findings also generate implications for the creativity literature.

Primarily using student subjects in experimental studies, some studies (e.g., Amabile,


109

Hennessey, and Grossman 1986; Hennessey and Amabile 1988) report that rewards

decrease creativity, while others (e.g., Eisenberger and Selbst 1994; Eisenberger and

Shanock 2003) suggest the opposite. Moreover, there is limited research in the

creativity literature on whether and how competition between contestants affects the

effects of rewards on creativity (see review by Zhou and Shalley 2003). In this essay, we

provide a comprehensive picture of the effects of rewarding idea creativity via prizes in

contests in the context of new product development. Further research on creativity

contests in other domains (e.g., writing, arts) would determine the applicability of our

theory and identify its boundary conditions.

Limitations and Opportunities for Further Research

Our research approach was focused on collecting data on the most creative idea of

all ideas generated in the contest. The creativity of the most creative idea is an

important outcome variable for idea generation contests from a managerial point of

view (Dahan and Mendelson 2001; Girotra, Terwiesch, and Ulrich 2010). In brainstorm

ideations, in which the goal is to generate as many creative ideas as possible, the idea

creativity of all ideas in the contest matter. Future research could focus on this outcome,

such that it is possible to examine the effect of prize structure characteristics on the

mean creativity of all ideas and the proportion of highly creative ideas among all ideas

generated in the contest.

In this first study on the effects of prize structure characteristics on idea creativity

in crowdsourced innovation contests, we focus on idea generation in a marketing

context. However, crowdsourcing can also be used to develop new software (e.g., Netflix

Prize), movie scripts (e.g., Script Pipeline), and pharmaceutical formulations (e.g.,

InnoCentive and IdeaConnection). It would be insightful to extend the proposed theory

by incorporating the characteristics of contests (e.g., type of product), and contestants

(e.g., profiles of scientists in research and development contests), and by using

alternative performance indicators (e.g., patents).

Also, to control for characteristics of the online contest platform, we analyzed

contests from one contest platform. This precludes us from considering the effects of

platform characteristics (e.g., user interface design, other types of prizes including non-

monetary rewards). Moreover, as many sponsors use contests repeatedly (e.g., Netflix,


110

Google, Frito-Lay), an interesting avenue for future research would be to examine the

effects of prize structure on idea creativity in such repeated contests.

Finally, while our secondary data context has many advantages including external

validity, experimental studies may validate the developed theory by explicitly testing

the effects of prize structure variables on contestants’ motivation.

In conclusion, in this first study on the effects of rewards in idea generation

contests, we identify prize structure characteristics that affect idea creativity. We

anticipate that this essay’s insights are useful to contest sponsors and contest platform

owners. As the practice of crowdsourcing innovations is expected to grow in the future,

we hope that our work stimulates additional research in the area.


111

APPENDIX: Contest Brief and Submission Examples

Panel A. Fictionalized Contest Brief Text pertaining to instructional guidance and target outcomes are underlined and italicized

respectively.

Panel B. Fictionalized Submission

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Would you tell me, please, which way I ought to go from here?”, asked Alice.

“That depends a good deal on where you want to get to,” said the Cat.


Chapter 5

Conclusion

Firms increasingly open up their NPD processes to include external parties. By

crossing the boundaries of the firm, firms aim to improve the returns on investments in

innovation. Three types of external parties that are often involved in a firm’s NPD

process are suppliers, customers, and ‘the crowd’: an undefined group of individuals

that bear no relationship with the firm. The research presented in this dissertation

contributes to the literature on open innovation by studying the involvement of these

three groups of external parties in a firm’s NPD process. In the following section, the

conclusions from each chapter are summarized. Next, the chapter offers managerial

implications. The following section discusses the limitations of the studies. The final

section proposes directions for future research on involving external parties in NPD.

5.1 SUMMARY AND CONCLUSIONS

This dissertation contains three main chapters. Chapter 2 examines the role of

supplier and customer involvement in NPD by meta-analyzing prior empirical research.

Chapter 3 studies customer involvement in NPD, with special attention to the

multiplexity of the relationships that are characteristic to industrial outsourced NPD.

Chapter 4 focuses on the effects of prize structure characteristics on contestants’ idea

creativity in online idea generation contests. In the following sections, the main

conclusions are discussed per chapter.

Chapter 5 – Conclusion

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Chapter 2: The Role of Supplier and Customer Involvement in New Product

Development: A Meta-Analysis

We meta-analyze the empirical research on supplier and customer involvement in

NPD. We find that a firm’s internal resources in marketing and, to a lesser extent, in

technology, are important enablers of supplier and customer involvement. Our results

indicate that high levels of firms’ internal resources increase firms’ access to external

resources. In addition, we find that technological uncertainty stimulates firms to involve

suppliers, and, to a lesser extent, customers, in their NPD process. Market uncertainty

lowers supplier involvement.

Involving suppliers and customers affects NPD outcomes as well. Whereas supplier

involvement saves costs and time, it lowers the innovativeness of the newly developed

product. Involving customers is associated with a trade-off between higher product

innovativeness and lower speed to market. The study underscores the need to examine

multiple NPD outcomes in tandem. In addition, the analysis emphasizes the need to

distinguish supplier involvement from customer involvement in NPD.

Chapter 3: The Effect of Customer Participation in Outsourced NPD on Supplier

Task Performance: The Role of Relationship Multiplexity

Focusing on outsourced NPD, a ‘market of one’, we analyze the effect of customer

participation in NPD on the developing supplier’s task performance. Drawing on role

theory, we argue that multiplex relationships cause role synergy, role conflict, and role

ambiguity, which moderate the effect of customer participation on supplier task

performance.

Largely in line with our hypotheses, we find that involving a customer that is also

one’s partner increases supplier task performance, but the effect holds only for

partnerships that are initiated by the parties themselves and not for partnerships that

are engineered by a triggering entity. In contrast, participation of a customer that also

has a supplier role decreases supplier task performance. Participation of a customer

that is also a competitor lowers supplier task performance in the eyes of the customer,

but does not affect the supplier’s self-reported task performance.


115

Chapter 4: Managing the Crowd: Prize Structure and Creativity In Online Idea

Generation Contests

In Chapter 4, we study the involvement of individuals without a relationship with

the firm: the “crowd”. We examine how prize structures stimulate contestants’

creativity in online idea generation contests. Using arguments from motivation theory,

we argue how a contest’s total prize value, number of prizes, and prize spread affect the

idea creativity of the most creative idea, both independently and in interaction.

We find that total prize value and number of prizes increase idea creativity, while

prize spread decreases idea creativity. Furthermore, the effects of prize structure

characteristics on idea creativity are interdependent. Contest sponsors who are unable

to offer a high total prize value can increase idea creativity by having many prizes of low

value. Contest sponsors should strive to set prizes of equal value as prize spread

decreases idea creativity, especially for contests with few prizes.

5.2 IMPLICATIONS FOR PRACTICE

Managers planning for external party involvement in innovation may benefit from

the insights derived from the research in this dissertation. In the following section, the

implications for practice are discussed.

Implications for Involving Suppliers

Managers planning for involving suppliers in their internal NPD processes may

improve the supplier involvement process by following these instructions:

• Do not consider the involvement of suppliers in NPD as an alternative for lacking

internal resources. Instead, develop the internal resource bases prior to

involving suppliers in the NPD process. Supplier involvement is more attractive

to a firm as well as the involved supplier when the firm has more internal

technological and marketing resources. Especially strong internal marketing

resources are associated with high levels of supplier involvement.

• In business environments with high levels of technological uncertainty, supplier

involvement may be very beneficial. Involving suppliers may help firms to

increase the technological options available in response to technological


116

developments. In case of market uncertainty, firms tend to stay away from

supplier involvement.

• Consider the effects of supplier involvement on various NPD performance

indicators jointly. Depending on the strategic need, supplier involvement may

help or hurt the NPD process. Supplier involvement affects product

innovativeness, speed to market, and cost performance:

o NPD projects in which suppliers are involved are associated with lower

levels of product innovativeness. Thus, in case a radically innovative

product is required, supplier involvement in the NPD process is to be

avoided.

o Supplier involvement improves the speed to market of newly developed

products, and is therefore suitable in the development process for

products that need to be launched on the market quickly.

o The involvement of suppliers is also associated with improved cost

performance. Thus, NPD project managers on a tight budget could greatly

benefit from reaching out to suppliers.

Implications for Involving Customers

Managers that set out to let customers participate in NPD activities may benefit

from these guidelines:

• Customer involvement thrives when firms have strong resource positions, both

in terms of technological and marketing resources. Similar to supplier

involvement, customer involvement fares best when firms have strong internal

resource bases, and should not be considered as an alternative for insufficient

internal resources.

• When facing business environments that are affected by high levels of

technological uncertainty, firms can be unsure which technological options

should be explored in the innovation process. In such a situation, firms should

consider involving customers, as they may offer valuable market insights that

can provide technological directions.

• Balance the consequences of customer involvement in NPD carefully prior to the

NPD project execution. Whereas customer involvement increases product


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innovativeness as a result of the customer knowledge and other resources

accessed, the additional process steps required to accommodate customer

involvement decrease the speed to market of a newly developed product.

• In case of outsourced NPD, in which the customer asks an external supplier to

develop a technology, component, or product, customer participation can be

beneficial. The customer’s evaluation of a supplier’s task performance in the

outsourced NPD project becomes more positive as a result of participation, even

though the developing firm itself does not share that perception.

• Importantly, the extent to which customer participation can affect a supplier’s

task performance in the NPD project depends strongly on relationship

multiplexity: the other ties between the customer and the supplier:

o Suppliers executing outsourced NPD projects for a customer with whom

they share partner ties are advised to involve the customer. However,

customer participation will only be more effective in case of sharing self-

initiated partner ties. In case of sharing engineered partner ties, customer

involvement will not bring additional benefits.

o Suppliers that share competitor ties with their customer should avoid

customer participation as it hurts the customer’s perception of the

supplier’s task performance.

o For relationships that are characterized by reversed supplier roles,

suppliers are advised to avoid customer participation as well.

Implications for Involving the Crowd

Managers considering to source ideas using crowdsourcing techniques by

organizing online idea generation contests may improve contest outcomes as follows:

• To improve the idea creativity of the best idea submitted to the contest, carefully

design the prize structure of the contest. Take into account the total prize value,

number of prizes, and prize spread in tandem:

o Set a high total prize value, as a high total prize value will motivate

contestants to work hard on their ideas, which stimulates creativity. For

firms that cannot afford to set high total prize values, split up the (low)

total prize value into many (small) prizes.


118

o Set many prizes. Offering more prizes increases contestants’ effort by

increasing the chances of winning, which has a social effect in an online

contest, even if the total prize value is not very high.

o Avoid prize spread. A prize structure of unequal prizes can cause

unflattering social upward comparisons for the many winners of the

lower prizes, which is avoided by setting equal prizes. Especially for prize

structures with only few prizes, it is important to reduce the differences

in value between the prizes as much as possible.

• Although the prize structure will have an effect on the number of contestants

that will compete in the contest, do not focus on increasing the number of

contestants. The size of the crowd of contestants does not translate into higher

idea creativity of the online contest.

• To improve idea creativity, the contest brief delineating the idea generation task

should be as short as possible. Further, it should describe the target outcome in

detail, but it should refrain from providing detailed instructions pertaining to the

input to the creative process.

5.3 LIMITATIONS

The implications of this research must be tempered by an understanding of its

limitations. At the end of the chapters, we already pointed out a number of limitations.

We refer to these sections for a discussion of the limitations that are specific to the

studies. We now turn to a discussion of limitations that are common to all three main

chapters.

First, the three chapters were restricted to the setting of NPD. External party

involvement may also be applied in other activities. For example, suppliers are closely

involved in manufacturing (e.g., Vonderembse and Tracey 1999); customers are

increasingly involved in co-production (e.g., Bendapudi and Leone 2003) and

participate in brand promotion using consumer-to-consumer communication enabled

by social networks (e.g, Mangold and Faulds 2009); and the crowd becomes an

increasingly important source for the funding of new business initiatives (e.g., Ordanini

et al. 2011). The impact of external party involvement on activities other than NPD

requires further study.


119

Second, the theory and hypotheses development in all three chapters is based on

mechanisms that could not always be tested using the data that was available to us. In

Chapter 2, we argued that a firm’s internal resource levels influence the firm’s ability to

involve suppliers and customers in NPD. In addition, we argued that environmental

uncertainty influences the firm’s need to involve suppliers and customers in the NPD

process. Furthermore, we argued how internal resource levels and environmental

uncertainty affect the motivation of suppliers and customers to become involved in a

particular firm’s NPD process. In Chapter 3, we argued that the potential beneficial

effects of customer participation on supplier task performance may not materialize in

multiplex relationships: in customer-as-partner multiplexity, we argued for the

presence of role synergy; in customer-as-competitor multiplexity, we argued for the

presence of role conflict; in customer-as-supplier multiplexity, we argued for the

presence of role ambiguity. In Chapter 4, we argued that prize structure characteristics

affect contestants’ motivations to expend effort in an idea generation contest. Controlled

experiments in lab settings may confirm that the mechanisms in our theory cause the

effects we have observed.

Third, the two chapters with original empirical research (Chapters 3 and 4) and the

vast majority of the empirical studies that were the foundation for the meta-analysis

(Chapter 2) focused on project-level mechanisms and outcomes. It is likely that external

party involvement not only affects the NPD project at hand, but also affects the

relationship between the developing firm and the involved party. However, due to data

limitations, we were unable to provide insights on the effects of external party

involvement beyond the transactional level of the NPD project. Future investigation is

required to examine effects of external party involvement on long-term relationship-

level outcomes.

Fourth, data limitations prevented us from using objective NPD success measures.

While our empirical analysis employed data on subjective customer and project

manager evaluations (Chapter 3) and expert judge ratings (Chapter 4), it is worthwhile

to investigate the effects of external party involvement on objective NPD success

measures, such as market performance.


120

Fifth, the original empirical research in this dissertation was executed using data

supplied by a single firm (Chapter 3) and a single contest platform (Chapter 4).

Although having multiple observations from a uniform context improves comparability

and controls for industry/platform effects, the generalizability of our findings to other

contexts is potentially reduced. To increase the confidence in our results, replication of

the studies using data from different firms, from different industries, and from different

contest platforms, is warranted.

5.4 FUTURE RESEARCH DIRECTIONS

The following sections discuss several avenues for future research on involving

external parties in innovation that resulted from the studies included in this

dissertation. We start with summarizing the research opportunities on supplier

involvement that emerged from the meta-analysis. Next, we will identify future research

opportunities that are associated with the involvement of customers. We end the

section with future research directions for the examination of crowd involvement, a

nascent area of research.

Research Opportunities on Supplier Involvement

The meta-analysis in Chapter 2 of this dissertation examined the state of the

empirical literature on supplier and customer involvement. Close inspection of relevant

extant research showed that the antecedents of supplier involvement beyond the

developing firm’s internal resources and environmental uncertainty are insufficiently

studied. For example, the effects of a firm’s strategic orientation on the use of supplier

involvement are unknown. In addition, the majority of the empirical studies examined

focused on the perspective of the developing firm. The motivations of the participating

supplier to join in the developing firm’s NPD process as well as the (resource)

characteristics of the participating supplier that stimulate supplier involvement in NPD

are understudied.

With respect to the consequences of supplier involvement, a number of research

opportunities emerged. Using meta-analytic techniques, we studied linear effects of

supplier (and customer) involvement on NPD performance. These analyses have offered

interesting insights, which could be extended with more fine-grained analyses requiring

new data. Possibly, the effects of supplier involvement on NPD performance have a


121

different functional form. For example, if a firm involves its suppliers to a very high

extent, it may focus too much on technology and miss out on customer-related

opportunities. On a more general note, the benefits of both supplier and customer

involvement may have diminishing returns: as a result of very high levels of supplier

and customer involvement in the NPD process, the firm may become blind to relevant

developments in the firm itself. As the data underlying our meta-analysis did not allow

for an empirical test of quadratic effects, we leave this issue for future research.

Research Opportunities on Customer Involvement

Similar to the empirical literature on supplier involvement, the empirical literature

on the antecedents of customer involvement can be extended. Next to the firm’s internal

resources and environmental uncertainty, other aspects may influence the firm’s

decision to involve customers in the NPD process. For example, the tie strength of the

relationship between the customer and the developing firm may stimulate the firm to

invite customers to participate in NPD projects. Likewise, a strong relationship with the

firm may motivate customers to participate in the firm’s NPD project. Research into the

motivations of the customer to be involved in NPD, in conjunction with the motivations

of the developing firm to invite customers to be involved in NPD, is necessary to offer a

complete picture.

As firms increasingly reach out to external parties, the strategy of involving both

suppliers and customers becomes more prevalent. Involving both suppliers and

customers in the NPD process may result in synergistic effects. For example, involving

both types of parties allows for the verification of an involved supplier’s market insight

with the involved customer, which could improve product innovativeness. Similarly, the

technological knowledge offered by involved customers could be readily checked or

developed further by involved suppliers, which could speed up the NPD process. As a

downside, however, bringing together suppliers and customers and internal employees

may increase the costs of coordination beyond the costs required to coordinate supplier

and customer involvement separately. Thus, combining both suppliers and customers

may affect product innovativeness, speed to market, and cost performance beyond the

additive effects uncovered in the current research. Future research could explore these

synergistic effects in more detail.


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Research Opportunities on Crowd Involvement

Research on crowdsourcing is still in its infancy. The results of the current study

can be extended to other contexts. In addition, many other research questions are open

for future research efforts.

Apart from extensions of our framework to include other types of prizes, such as

non-monetary prizes, and to include other types of contests, such as R&D and creative

writing contests, the theory we developed could also be extended to include other types

of outcome variables. To verify our theoretical argument that contestants increase their

effort in response to specific prize structures, a close examination of time spent by

contestants is opportune. In addition, the measurement of the idea creativity of the total

set of submissions is necessary to examine the effects of prize structure characteristics

on the average idea creativity rather than the idea creativity of the most creative idea of

the contest.

More and more firms are using online idea generation contests to jump-start

creativity (McKinsey 2009). Firms may also use these contests for branding purposes,

which raises questions about the impact of organizing online idea generation contests

on the firm’s brand image. Prize structure characteristics may also impact the firm’s

brand image: advertising big prizes likely has a bigger branding effect than small prizes.

When firms choose to organize multiple crowdsourcing contests, they might

consider growing a community themselves. In such a situation, community managers

need to grow and maintain a community of creative individuals. One may ask what the

effects of losing and winning a contest on the participation and performance are in

future contests, as this may influence the performance of the community as an idea

generating resource. Winning a contest may lead contestants to focus on the winning

recipe which could lower the diversity of their ideas, thereby affecting the performance

of the idea generating community. Losing a contest could stimulate a contestant to

spend more effort in preparing her submission for a next submission; alternatively, she

could become demotivated. Another research question pertains to the open character of

online idea generation contests, which may cause learning effects among the

community members across contests, thereby affecting the diversity of ideas submitted

to the contest by the community.


123

Furthermore, insights on the effects of crowd composition in terms of gender

diversity (Jeppesen and Lakhani 2010), cultural diversity, variation in knowledge

background and experience on community performance help community managers to

bring together the most creative crowd. Moreover, a popular method of managing a

community is to employ social media and social networking features, such as voting and

liking, commenting, and connecting possibilities. This adds transparency to a contest

and allows contestants to discuss the contest among each other (e.g., eYeka). The

resulting combination of collaboration and competition complicates the practice of

crowdsourcing and offers a multitude of research opportunities.

Lastly, future research efforts on crowd involvement may also be focused on the

use of the crowd in other phases of the NPD process, such as concept evaluation or beta

testing. The success of these activities is measured by outcomes other than idea

creativity (e.g., precision), which may be impacted differently by prize structure

characteristics.

To Close

Collectively, the three essays in this dissertation have shed light on how to

successfully manage the participation of external parties in NPD. We hope our research

stimulates additional work in this managerially relevant and theoretically interesting

area.

125

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Center Dissertation Series

CentER for Economic Research, Tilburg University, The Netherlands

No. Author Title ISBN Published

264 Hsing-Er Lin Effects of Strategy, Context, and Antecedents and Capabilities on Outcomes of Ambidexterity– A Multiple Country Case Study of the US, China and Taiwan

978 90 5668 264 4

December 2010

265 Jeffrey Powell The Limits of Economic Self-interest: The Case of Open Source Software

978 90 5668 265 1

December 2010

266 Galla Salganik Essays on Investment Flows of Hedge Fund and Mutual Fund Investors

978 90 5668 266 8

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267 Cécile Fruteau Biological markets in the everyday lives of mangabeys and vervets: An observational and experimental study

978 90 5668 267 5

December 2010

268 Yang Zhao The Role of Directors’ Professional and Social Networks in CEO Compensation and the Managerial Labour Market

978 90 5668 268 2

December 2010

269 Mieszko Mazur Essays on Managerial Remuneration, Organizational Structure and Non-Cash Divestitures

978 90 5668 269 9

December 2010

270 Ralph Stevens Longevity Risk in Life Insurance Products 978 90 5668 270 5

February 2011

271 Cristian Dobre Semidefinite programming approaches for structured combinatorial optimization problems

978 90 5668 271 2

March 2011

272 Kenan Kalayci Essays in Behavioral Industrial Organization

978 90 5668 272 9

February 2011

273 Néomie Raassens The Performance Implications of Outsourcing

978 90 5668 273 6

March 2011

274 Muhammad Ather Elahi

Essays on Financial Fragility 978 90 5668 274 3

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275 Jan Stoop Laboratory and Field Experiments on Social Dilemmas

978 90 5668 275 0

March 2011

276 Maria Cristina Majo A Microeconometric Analysis of Health Care Utilization in Europe

978 90 5668 276 7

February 2011

277 Jérémie Lefebvre Essays on the Regulation and Microstructure of Equity Markets

978 90 5668 277 4

March 2011


278 Willem Muhren Foundations of Sensemaking Support Systems for Humanitarian Crisis Response

978 90 5668 278 1

March 2011

279 Mary Pieterse-Bloem The Effect of EMU on Bond market Integration and Investor Portfolio Allocations

978 90 5668 279 8

May 2011

280 Chris Müris Panel Data Econometrics and Climate Change

978 90 5668 280 4

April 2011

281 Martin Knaup Market-Based Measures of Bank Risk and Bank Aggressiveness

978 90 5668 281 1

April 2011

282 Thijs van der Heijden Duration Models, Heterogeneous Beliefs, and Optimal Trading

978 90 5668 282 8

May 2011

283 Titus Galama A Theory of Socioeconomic Disparities in Health

978 90 5668 283 5

June 2011

284 Hana Voňková The Use of Subjective Survey Data: Anchoring Vignettes and Stated Preference Methods

978 90 5668 284 2

May 2011

285 Frans Stel Improving the performance of co-innovation alliances: Cooperating effectively with new business partners

978 90 5668 285 9

July 2011

286 Eric Engesaeth Managerial Compensation Contracting 978 90 5668 286 6

June 2011

287 David Kroon The Post-Merger Integration Phase of Organizations: A longitudinal Examination of Unresolved Issues of Justice and Identity

978 90 5668 287 3

May 2011

288 Christian Bogmans Essays on International Trade and the Environment

978 90 5668 288 0

June 2011

289 Kim Peijnenburg Consumption, Savings, and Investments over the Life Cycle

978 90 5668 289 7

May 2011

290 Youtha Cuypers The Determinants and Performance Implications of Change in Inter-Organizational Relations

978 90 5668 290 3

June 2011

291 Marta Serra Garcia Communication, Lending Relationships and Collateral

978 90 5668 291 0

June 2011

292 Marike Knoef Essays on Labor Force Participation, Aging, Income and Health

978 90 5668 292 7

September 2011

293 Christophe Spaenjers Essays in Alternative Investments 978 90 5668 293 4

September 2011

294 Moazzam Farooq Essays on Financial Intermediation and Markets

978 90 5668 294 1

September 2011


295 Jan van Tongeren From National Accounting to the Design, Compilation, and Use of Bayesian Policy Analysis Frameworks

978 90 5668 295 8

October 2011

296 Lisanne Sanders Annuity Market Imperfections 978 90 5668 296 5

October 2011

297 Miguel Atanásio Lopes Carvalho

Essays in Behavioral Microeconomic Theory

978 90 5668 297 2

September 2011

298 Marco Della Seta Essays in Corporate Financing and Investment under Uncertainty

978 90 5668 298 9

October 2011

299 Roel Mehlkopf Risk Sharing with the Unborn 978 90 5668 299 6

October 2011

300 Roy Lindelauf Design and Analysis of Covert Networks, Affiliations and Projects

978 90 5668 300 9

October 2011

301 Viswanadha Reddy Essays on Dynamic Games 978 90 5668 301 6

November 2011

302 Pedro Duarte Bom The Macroeconomics of Fiscal Policy and Public Capital

978 90 5668 302 3

November 2011

303 Daniël Smit Freedom of Investment between EU and non-EU Member States and its impact on corporate income tax systems within the European Union

978 90 5668 303 0

December 2011

304 Juan Miguel Londoño Yarce

Essays in Asset Pricing 978 90 5668 304 7

December 2011

305 Edwin Lohmann Joint Decision Making and Cooperative Solutions

978 90 5668 305 4

January 2012

306 Verena Hagspiel Flexibility in Technology Choice: A Real Options Approach

978 90 5668 306 1

December 2011

307 Fangfang Tan Behavioral Heterogeneity in Economic Institutions: An Experimental Approach

978 90 5668 307 8

January 2012

308 Luc Bissonnette Essays on Subjective Expectations and Stated Preferences

978 90 5668 308 5

January 2012

309 Unnati Saha Econometric Models of Child Mortality Dynamics in Rural Bangladesh

978 90 5668 309 2

February 2012

310 Anne ter Braak A New Era in Retail: Private-Label Production by National-Brand Manufacturers and Premium-Quality Private Labels

978 90 5668 310 8

February 2012


311 Emiel Maasland Essays in Auction Theory 978 90 5668 311 5

February 2012

312 John Glen Essays on the UK Residential Property Market

978 90 5668 312 2

March 2012

313 Amal Elgammal Towards a Comprehensive Framework for Business Process Compliance

978 90 5668 313 9

April 2012

314 David Hollanders The Effect of Aging on Pensions 978 90 5668 315 3

June 2012

315 Salima Douhou Essays on (Small) Crime: Perception, Social Norms, Happiness, and Prevention

978 90 5668 316 0

June 2012

316 Christoph Schottmueller

Essays in Microeconomic Theory 978 90 5668 317 7

June 2012

317 Peter Cziraki Insider Trading, Shareholder Activism, and Corporate Policies

978 90 5668 318 4

September 2012

318 Philipp Geiler Essays on Executive Remuneration Contracting: Managerial Power, Corporate Payout, and Gender Discrimination

978 90 5668 319 1

September 2012

319 Ivo van Amelsfoort Firm decisions and market responses: Three accounting essays

978 90 5668 320 7

September 2012

320 Ronald Spanjers Be Patient: A Longitudinal Study on Adoption and Diffusion of IT-Innovation in Dutch Healthcare

978 90 5668 321 4

September 2012

321 Jinqiang Guo Quantitative Investment Strategies and Portfolio Management

978 90 5668 322 1

October 2012

322 Karen Poghosyan Structural and Reduced-Form Modeling and Forecasting with Application to Armenia

978 90 5668 323 8

September 2012

323 Igor Fedotenkov Pensions and Ageing in a Globalizing World: International Spillover Effects via Trade and Factor Mobility

978 90 5668 324 5

September 2012

324 Zongxin Qian Essays on Globalization, Monetary Policy and Financial Crisis

978 90 5668 325 2

September 2012

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September 2012

326 Patrick Hullegie Essays on health and labor economics 978 90 5668 327 6

October 2012

327 Mark Vroegrijk “Smart Shopping": Implications of Hard-Discounters and Multiple-Store Patronage

978 90 5668 328 3

September 2012


328 Millie Elsen Thin and Thicker Slices: How Advertising Effectiveness Depends on Exposure Duration

978 90 5668 329 0

October 2012

329 Juan-Juan Cai Estimation concerning risk under extreme value conditions

978 90 5668 330 6

October 2012

330 Fatih Cemil Özbuğday Empirical Essays on Ex Post Evaluations of Competition and Regulatory Authorities’ Decisions and Policy Reform

978 90 5668 331 3

November 2012

331 Jon Pluyter Designing Immersive Surgical Training Against Information Technology-Related Overload in the Operating Room

978 90 5668 332 0

November 2012

332 Benedikt Kratz A Model and Language for Business-aware Transactions

978 90 5668 333 7

November 2012

333 Mark Humphery-Jenner

Corporate Governance Mechanisms as a driver of Value-Destruction and Value-Creation

978 90 5668 334 4

November 2012

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978 90 5668 335 1

December 2012

335 Jaione Yabar Arriola Wait, Bond, and Buy: Consumer Responses to Economic Crisis

978 90 5668 336 8

December 2012

336 Erik von Schedvin Essays on banking, corporate bankruptcy, and corporate finance

978 90 5668 337 5

December 2012

337 Irene Gaia Becheri Limiting Experiments for Panel-Data and Jump-Diffusion Models

978 90 5668 338 2

December 2012

338 Rob Nijskens Bank risk, bailouts and ambiguity 978 90 5668 339 9

December 2012

339 Jie Zheng Essays on Pensions, Health Expectancy and Credit Insurance

978 90 5668 340 5

December 2012

340 Vincent van Kervel Competition between Stock Exchanges and Optimal Trading

978 90 5668 341 2

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341 Sara Amoroso Heterogeneity of innovative, collaborative, and productive firm-level processes

978 90 5668 342 9

January 2013

342 Aukje Leufkens Accounts of Interest and Strategies of Inter-Organizational Projects: Towards Integrative Collaboration in the Dutch Shipbuilding Industry

978 90 5668 343 6

February 2013

343 Mohammed Chahim Impulse Control Maximum Principle: Theory and Applications

978 90 5668 344 3

February 2013


344 Özge Özdemir Simulation Modeling and Optimization of Competitive Electricity Markets and Stochastic Fluid Systems

978 90 5668 345 0

February 2013

345 Dorothe Singer The Role of Institutions in International Finance

978 90 5668 346 7

March 2013

346 Jeewanie Jayasinghe Arachchige

A unified modeling framework for service design

978 90 5668 347 4

March 2013

347 Michele Aquaro Pairwise difference estimation of linear data models

978 90 5668 348 1

April 2013

348 Pedro Santos Raposo Flexibility of the labor market 978 90 5668 349 8

April 2013

349 Peter van Oudheusden Essays on Fiscal Policy 978 90 5668 350 4

May 2013

350 Gerard van der Meijden

The Macroeconomic Dynamics of Trade Liberalization, Resource Exploitation, and Backstop Technologies

978 90 5668 351 1

May 2013

351 Robert Kozarski Pricing and Hedging in the VIX Derivative Market

978 90 5668 352 8

May 2013

352 Beatrice Pataracchia Ambiguity Aversion and Heterogeneity in Financial Markets: An Empirical and Theoretical Perspective

978 90 5668 353 5

May 2013

353 Jan Bonenkamp Risk, redistribution and retirement: the role of pension schemes

978 90 5668 354 2

June 2013

354 Thomas Michielsen Essays in Environmental and Resource Economics

978 90 5668 355 9

September 2013

355 Radomir Todorov Essays on Banking and Regulation 978 90 5668 356 6

June 2013

356 Liping Lu Essays on Banking and Finance in China 978 90 5668 357 3

June 2013

357 Thijs Peeters External Knowledge Search and Use in New Product Development

978 90 5668 358 0

June 2013

358 Patrício de Alencar Silva

Value Activity Monitoring 978 90 5668 359 7

June 2013

359 Andreas Zenthöfer Essays on Development Economics 978 90 5668 360 3

June 2013

360 Po Yan Edith Leung The Influence of Reporting Standards and Inter-Firm Relationships on Financial Reporting

978 90 5668 361 0

September 2013


361 Cees Peters The faltering legitimacy of international tax law

978 90 5668 362 7

September 2013

362 Nathanaël Vellekoop Essays on Household Saving, Religion and Pay Frequency

978 90 5668 363 4

September 2013

363 Angèle Pieters Care and Cure: Compete or Collaborate? Improving Inter-Organizational Designs in Healthcare: A Case Study in Dutch Perinatal Care

978 90 5668 364 1

October 2013

364 Zeynep Burcu Ugur From Headscarves to Donation; Three Essays on the Economics of Gender, Health and Happiness

978 90 5668 365 8

October 2013

365 Consuelo Silva Buston Essays on Risk Management and Systemic Risk

978 90 5668 366 5

November 2013

366 Dinh Khoa Nguyen Blueprint Model and Language for Engineering Cloud Applications

978 90 5668 367 2

November 2013

367 Kan Ji Essays on tax policy, institutions, and output

978 90 5668 368 9

November 2013

368 Wendun Wang Essays on model averaging and political economics

978 90 5668 369 6

November 2013

369 Yaping Mao Essays on Leveraged Buyouts 978 90 5668 370 2

November 2013

370 Antonios Varvitsiotis Combinatorial Conditions for Low Rank Solutions in Semidefinite Programming

978 90 5668 371 9

November 2013

371 Johanna Slot Crossing Boundaries: Involving External Parties in Innovation

978 90 5668 372 6

December 2013

Tilburg University Crossing boundaries Slot, J.H.

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