UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2010 – 2011

Customer Co-creation

&

Customer Experience Management

Masterproef voorgedragen tot het bekomen van de graad van

Master in de Toegepaste Economische Wetenschappen

Lorenzo Van Doorslaer

onder leiding van

Prof. dr. Patrick Van Kenhove

en Prof. dr. Deva Rangarajan

UNIVERSITEIT GENT

FACULTEIT ECONOMIE EN BEDRIJFSKUNDE

ACADEMIEJAAR 2010 – 2011

Customer Co-creation

&

Customer Experience Management

Masterproef voorgedragen tot het bekomen van de graad van

Master in de Toegepaste Economische Wetenschappen

Lorenzo Van Doorslaer

onder leiding van

Prof. dr. Patrick Van Kenhove

en Prof. dr. Deva Rangarajan

I

PERMISSION

Undersigned declares that the content of this master thesis may be made public and/or

reproduced, with acknowledgement.

Ondergetekende verklaart dat de inhoud van deze masterproef mag geraadpleegd worden

en/of gereproduceerd worden, mits bronvermelding.

Lorenzo Van Doorslaer

II

Preface

I have written this thesis to complete my studies in Applied Economics, specialization

Marketing, at Ghent University. I would like to thank all the people that supported me during

my entire education and helped me, both directly and indirectly, to accomplish this master

dissertation.

First of all, I wish to thank my promoter Partick Van Kenhove and co-promoter Deva

Rangarajan for giving me the opportunity to write my master thesis about co-creation, for the

positive feedback and advice during the realization of this paper. They also provided useful

information concerning authors (Prahalad & Ramaswamy, Vargo & Lusch…) and recent

literature about CC to get a better insight in the topic.

As doctoral student in CC, I am grateful to Katrien Verleye for giving me her ideas about co-

creation and information concerning the set-up of the experiment.

I also would like to thank assistant Hendrik Slabbick who was willing to answer my questions

about the data analysis.

I thank Ann De Boeck for correcting spelling and grammar errors.

A word of thanks to all the people that participated in the experiment. Without their voluntary

time and efforts, I could not have finished this paper.

Finally, I would like to thank my girlfriend, Jolien De Baerdemaeker, and my friends and

parents, who encouraged me during this last phase of my studies.

With utmost sincerity,

Lorenzo Van Doorslaer

May 24th, 2011

III

Table of contents

PERMISSION ......................................................................................................................... I

Preface .................................................................................................................................. II

Table of contents ...................................................................................................................III

List of abbreviations .............................................................................................................. V

List of figures ........................................................................................................................ VI

List of tables ........................................................................................................................ VII

Nederlandstalige samenvatting .......................................................................................... VIII

Abstract ................................................................................................................................. 1

Introduction ........................................................................................................................... 2

Part I: Literature review ......................................................................................................... 4

1. Customer Experience ........................................................................................................ 4

1.1 Definitions and concepts ......................................................................................... 4

1.2 Meaningful experiences .......................................................................................... 8

1.3 Evolution of the Experience Economy ..................................................................... 9

1.4 Relevance of experiences ......................................................................................11

2. Co-creation .......................................................................................................................14

2.1 Definitions and concepts ...........................................................................................15

2.2 Relevance from customer‟s perspective ...................................................................18

2.2.1 The changing role of the customer .........................................................................19

2.2.2 From GDL to SDL ..................................................................................................21

2.3 Building blocks of CC: the DART model ....................................................................22

2.4 Customer motives/benefits for CC/CP ......................................................................24

2.5 Cost-benefit analysis ................................................................................................28

2.6 Where and with whom does CC occur in the value chain? ........................................30

Part II: Empirical study ..........................................................................................................33

3. Purpose of the study .........................................................................................................33

3.1 Management question .............................................................................................33

3.2 Research questions .................................................................................................33

3.3 Investigative questions ............................................................................................34

3.4 Measurement questions...........................................................................................37

4. Data analysis ....................................................................................................................38

4.1 Sample & procedure ..............................................................................................38

4.2 Results ..................................................................................................................40

IV

4.3 Partial conclusions experiment - discussion ...........................................................49

4.4 Post hoc analysis ...................................................................................................52

4.5 Partial conclusions post hoc analysis - discussion .................................................54

4.6 General conclusion ................................................................................................55

5. Limitations and further research .......................................................................................57

5.1 Limitations of the research .....................................................................................57

5.2 Directions for further research and managerial implications ...................................58

References .......................................................................................................................... XII

Figures ............................................................................................................................... XIX

Appendices ..................................................................................................................... XXIX

V

List of abbreviations

B2C: Business-to-Consumer

CC: co-creation / co-creatie

CE: customer experience

CEM: Customer experience Management

CP: co-production

CRM: Customer Relationship Management

EE: experience economy / ervaringseconomie

GDL: goods-dominant logic

NPD: new product development

SDL: service-dominant logic

SST: self-serving technology

WTP: willingness to pay

VI

List of figures

Figure 1: The process of experiencing………………………………………………………...XIX

Figure 2: Motives of people. .............................................................................................. XX

Figure 3: Actors in the creation of a meaningful experience. ............................................. XXI

Figure 4: Progression of economic value. ......................................................................... XXI

Figure 5: The coffee progression. .................................................................................... XXII

Figure 6: CC matrix. ........................................................................................................ XXII

Figure 7: GDL versus SDL on value creation. ................................................................. XXIII

Figure 8: Building blocks of the DART model combined. ................................................ XXIII

Figure 9: Motive categories for engaging in virtual CC projects. .................................... XXIV

Figure 10: Proposed impact of personal characteristics on consumer motives. ................ XXV

Figure 11:Relationship between types of adopters classified by innovativeness and their

location on the adoption curve. ......................................................................................... XXV

Figure 12: Classification of experimental designs. ........................................................... XXVI

Figure 13: Interaction effect degree of CC and technology on customer enjoyment. ....... XXVI

Figure 14: Interaction effect degree of CC and technology on cognitive effort and ability.

....................................................................................................................................... XXVII

Figure 15: Interaction effect degree of CC and technology on WTP Garment. ................ XXVII

Figure 16: Types of motivation and regulation within SDT ............................................. XXVIII

VII

List of tables

Table 1: Hypothesis H1a……………………………………………………………………………33

Table 2: Hypothesis H2a…………………………………………………………………………....33

Table 3: Hypothesis H2b……………………………………………………………………………33

Table 4: Hypothesis H3a……………………………………………………………………………34

Table 5: Hypothesis H3b……………………………………………………………...…………….34

Table 6: Hypotheses H1c / H1d…………………………………………………...……………….34

Table 7: Hypotheses H1e / H1f…………………………………………………………………….35

Table 8: Hypotheses H2d / H2e…………………………………………………………...……….35

Table 9: Hypotheses H2f / H2g…………………………………………………….………………35

Table 10: Hypotheses H3d / H3e…………………………………………….……………………36

Table 11: Hypotheses H3f / H3g…………………………………………….…………………….36

Table 12: Set up experiment………………………………………………………………….……37

Table 13: N-values per condition in the experiment…….………………………………………37

Table 14: Summary variables experiment………………………………………………………..38

Table 15: Cronbach alpha interval scaled items………………………………………………….41

Table 16: Output dependent variable „enjoyment‟……………………………………………….42

Table 17: Output dependent variable „cognitive effort and ability‟…………………………..…44

Table 18: Output dependent variable „WTP garment‟………………………………………..….45

Table 19: Output dependent variable „WTP computer‟………………………………………....47

Table 20: Output dependent variable „WTP ball pen‟………………………………..…………..47

Table 21: Output dependent variable „WTP car‟………………………………..………………..48

Table 22: Output moderating variable: High product involvement garment…………………..51

Table 23: Output moderating variable: High product involvement computer………………….51

Table 24: Output moderating variable: High product involvement ball pen…………...………52

Table 25: Output moderating variable: High product involvement car………………….……..52

Table 26: Means WTP all respondents versus high product involved respondents…………53

Table 27: Means Enjoyment all respondents versus high product involved respondents…...53

VIII

Nederlandstalige samenvatting

Geïnspireerd door de actuele relevantie van het concept CC en zijn toekomstige belang,

startte ik deze thesis om mijn studies in Toegepaste Economische Wetenschappen te

beëindigen. CC pioniers Prahalad en Ramaswamy voorzien in hun boek De toekomst van de

competitie: unieke waarde co-creëren met klanten nieuwe opportuniteiten om waarde te

creëren. In een zeer recent boek (2010) over CC, De kracht van CC, beschrijft coauteur

Gouillart de theorie van CC als een proces waarbij waarde gecreëerd wordt tussen een

bedrijf en een heel scala aan andere mensen gaande van klanten, tot werknemers, partners

en zelfs leveranciers. Vandaag is iedereen is betrokken bij de waarde creërende processen.

CC wordt in deze thesis beperkt tot de klantenzijde omwille van interesse en meer

inlevingsvermogen.

Onderdeel 1 van de literatuurstudie gaat over klantenervaring (customer experience). Dit

concept kan beschreven worden als een voort vloeiing uit een reeks van interacties tussen

een klant en een product, een bedrijf, of een deel van haar organisatie. Deze ervaring is

strikt persoonlijk en impliceert de betrokkenheid van de klant op verschillende niveaus

(rationeel, emotioneel, zintuiglijk, lichamelijk en geestelijk). De evaluatie van de

klantenervaring is afhankelijk van de vergelijking tussen de verwachtingen van de klanten en

de prikkels die voortvloeien uit de interactie-momenten met het bedrijf. Doorheen de jaren

werd klantenervaring gezien als een multidimensionale structuur bestaande uit verschillende

componenten. Gentile et al. (2007) stellen op basis van de bestaande literatuur zes

dimensies zijn van klantenervaring voor: zintuiglijke, cognitieve, emotionele, pragmatische,

levensstijl- en relatiecomponent. Boswijk, Thijssen en Peelen (2007) beschrijven

klantenervaring als een proces - zintuigelijke perceptie, emotie, ervaring, betekenisvolle

belevenis en tot slot betekenis geven - in plaats van bestaande uit verschillende

componenten. Er wordt dieper ingegaan op het concept van de betekenisvolle belevenis

daar er een behoefte is om een persoonlijke interactie te hebben met het bedrijf die een

waarde propositie vooropstelt die betekenisvol en specifiek is voor de klant.

Vervolgens wordt de evolutie in de EE van eerste tot derde generatie besproken. Midden de

jaren 90 rees een ervaringsgerichte benadering van het concept klantenervaring waarbij de

rol van emoties, klanten als voelers, denkers en doeners en de nood aan plezier opnieuw

overwogen werden. Pine & Gilmore zijn voorbeelden van vertegenwoordigers van de eerste

generatie van de EE. Zij stellen dat de ervaring van de klant de nieuwe bron van

waardecreatie is. In de tweede generatie van de EE zijn ervaringen als memorabele

IX

geënsceneerde evenementen volgens Pine en Gilmore niet langer beschouwd als primaire

bron van waardecreatie. Het is het in staat stellen om alle momenten in de relatie tussen het

bedrijf en de klant te beleven op een uitstekende manier, boven zijn verwachtingen, dat het

meeste bijdraagt tot waardecreatie. Prahalad en Ramaswamy verwoorden het als de CC van

unieke ervaringen met het bedrijf. De derde generatie van de EE gaat nog een stap verder

waarbij een individu zijn eigen betekenisvolle belevenis creëert en richting geeft zonder

tussenkomst van het bedrijf. Dit wordt communicatieve zelf-directie genoemd. In de laatste

sectie van deel 1 wordt de relevantie van ervaringen besproken door de evolutie van de

inhoud van economische waarde te bespreken gaande van grondstoffen, goederen, services

tot de huidige ervaring van de klant.

Onderdeel 2 van de literatuurstudie gaat over CC. De link naar dit concept werd gelegd in

deel 1. Eerst wordt de lezer opgewarmd met enkele voorbeelden (Threadless, LEGO en

ReDesignMe) over hoe bedrijven vandaag CC toepassen. Net zoals in onderdeel 1 gaan we

van start met een overzicht van de inhoud van het begrip doorheen de jaren. Prahalad en

Ramaswamy beschrijven CC als de gezamenlijke creatie van waarde door het bedrijf en de

klant. Het is de creatie van een ervaringsomgeving waarin klanten kunnen beschikken over

een actieve dialoog en mee de gepersonaliseerde ervaring opbouwen. Het product kan

hetzelfde zijn (bv. LEGO) maar elke ervaring is uniek. Het betekent niet dat de klant koning

is, de klant willen plezieren of een variëteit aan producten aanbieden. CC wordt ook

gekaderd naast enkele gerelateerde begrippen zoals massa customisatie en co-productie.

De invloed die klanten vandaag uitoefenen op waardecreatie is ongekend hoog door de

veranderende rol van de consument. Deze beschikt mede door de opmars van het internet

over vijf krachten: toegang tot informatie, globale visie, netwerken, experimenteren en

activisme. Daarnaast wordt ook de verschuiving van het wereldbeeld van marketing van een

goederen dominante logica naar service dominante logica besproken waarbij service staat

voor de toepassing van competenties zoals vaardigheden en kennis. Vervolgens wordt

dieper ingegaan op de bouwstenen van CC: dialoog, toegang, risicomanagement en

transparantie. Daarna wordt een overzicht gegeven van de motieven en voordelen die

klanten ertoe kan aanzetten om deel te nemen aan CC. Deze worden geklasseerd onder

pragmatisch, economisch, persoonlijk, cognitief, sociaal en affectief. Füller stelt dat

engagement in CC een functie is van intrinsieke motivatie en zelf-gedetermineerde

extrinsieke motivatie. Daarnaast zijn er ook een aantal kosten verbonden aan CC,

economisch en niet-economisch, waarbij het verschil tussen de motieven/voordelen en de

kosten een nettoresultaat oplevert dat determineert of iemand al dan niet deelneemt aan een

X

CC activiteit. Tot slot wordt CC voor persoonlijk gebruik besproken naast CC in nieuwe

product ontwikkeling en de personen die voor deze laatste vorm van CC in aanmerking

komen.

Op basis van de literatuurstudie wordt verantwoording voor de keuze van de onafhankelijke

variabelen afgelegd en hypothesen opgesteld over enkele relevante afhankelijke variabelen

voor het experiment.

Deel 2 van de thesis gaat over het experimenteel onderzoek. De onafhankelijke variabelen

die gebruikt worden zijn de graad van CC (hoog versus laag) en de technologie (online

versus offline) wat resulteert in 4 scenario‟s. Het experimenteel opzet is bijgevolg een 2*2

volledig factorieel opzet dat geklasseerd kan worden onder statistisch waarachtige opzetten.

De afhankelijke variabelen zijn plezier (interval-geschaald), cognitieve inspanning en

vermogen (interval-geschaald), en bereidheid tot betalen (ratio-geschaald). Het doel van de

studie is om na te gaan wat de invloed is van de graad van CC (i.e. niveau van

betrokkenheid van de klant) en technologie op het plezier van de klant, zijn bereidheid tot

betalen en de inspanningen die nodig zijn. Daarnaast wordt product betrokkenheid

opgenomen als onafhankelijke (modererende) variabele om na te gaan welke invloed deze

variabele uitoefent op de relatie tussen de onafhankelijke variabelen enerzijds en de

afhankelijke variabele plezier en bereidheid tot betalen anderzijds. Daarvoor werd elk

scenario opgesplitst in CC van 4 producten: kledingstuk, computer, balpen en auto.

Via de managementvraag wordt overgegaan tot de onderzoekvragen, vervolgens tot

specifieke onderzoeksvragen (i.e. de hypothesen) om de uiteindelijke meetvragen te

bekomen die gesteld werden in een online vragenlijst waarbij elke respondent willekeurig 1

scenario voorgeschoteld kreeg. Alvorens de vragenlijst te activeren werd een offline pre-test

uitgevoerd bij 10 personen waarbij het onderscheid tussen hoge en lage graad van CC

duidelijk moest zijn. De vragenlijst bestaat uit vragen afkomstig van bestaande, geteste

meetschalen zodat enkel een cronbach alpha analyse uitgevoerd werd. Hieronder bevindt

zich een overzicht van de hypothesen met bijhorend resultaat na uitvoering van twee-wegs

analyses van de variantie met significantieniveau gelijk aan 0.05.

1. Plezier

H1A: Ongeacht de technologie, klanten die beschikken over een hoge graad van CC hebben

een hoger plezier dan klanten met een laag niveau van co-creatie (hoofdeffect): aanvaard.

H1b: Er is een interactie-effect tussen de mate van CC en technologie aan de ene kant, en

plezier van de klant aan de andere kant: aanvaard.

XI

2. Cognitieve inspanning en vermogen

H2A: Ongeacht de technologie, klanten die beschikken over een hoge graad van CC hebben

meer cognitieve inspanning en vermogen nodig dan klanten met een laag niveau van CC

(hoofdeffect): aanvaard

H2B: Ongeacht de mate van CC, klanten die online co-creëren hebben meer behoefte aan

cognitieve inspanning en vermogen dan de klanten die offline co- creëren (hoofdeffect): niet

aanvaard.

H2C: Er is een interactie-effect tussen de mate van CC en technologie aan de ene kant, en

de cognitieve inspanning en vermogen aan de andere kant: aanvaard.

3. Bereidheid tot betalen

H3A: Ongeacht de technologie, klanten die beschikken over een hoog niveau om te co-

creëren zijn bereid meer te betalen dan klanten die een laag niveau beschikken om te co-

creëren. (hoofdeffect)

H3B: Ongeacht de mate van CC, klanten die offline doen aan CC zijn meer bereid om te

betalen dan klanten die online co-creëren. (hoofdeffect)

H3C: Er is een interactie-effect tussen de mate van CC en technologie aan de ene kant, en

de bereidheid tot betalen aan de andere kant.

Kledingstuk: H3A: aanvaard; H3B: niet aanvaard; H3C: aanvaard.

Computer: H3A: niet aanvaard, H3B: niet aanvaard; H3C: niet aanvaard.

Balpen: H3A: niet aanvaard, H3B: niet aanvaard; H3C: niet aanvaard.

Auto: H3A: niet aanvaard, H3B: niet aanvaard; H3C: niet aanvaard.

Als het interactie-effect significant is, werden ook hypothesen betreffende de interactie-

effecten getest op basis van de grafiek van de interactie-effecten en de 95%

betrouwbaarheidsintervallen. Nadien werd een post hoc analyse uitgevoerd die voor een

verfijning van het eerste experiment moet zorgen. De invloed van product involvement op de

bereidheid tot betalen en plezier wordt onderzocht.

Conclusies worden geformuleerd op basis van de resultaten van het onderzoek: bij een hoge

graad van co-creatie beleven klanten meer plezier en hebben ze meer cognitieve inspanning

en vermogen nodig hebben. De bereidheid tot betalen is hoger bij een hoge graad van co-

creatie, maar dit zal vooral afhangen van de product betrokkenheid van de klant. De

toekomst van co-creatie ligt online omdat we offline meer inspanning nodig hebben en dit

ook in het laatste geval vanuit bedrijfsperspectief te duur zou zijn. Tot slot volgt een

algemeen besluit dat bedrijven het concept van co-creatie moeten omarmen omdat het

onontbeerlijk is om een duurzaam competitief voordeel te behouden.

1

Abstract

Purpose – This paper aims to understand the impact of customer participation on customer

experience. Today a lot of mass customization examples are present. But is it useful to go one step

further in the direction of co-creation that is more demand-driven? In a 2*2 full factorial design a high

(co-creation on itself) and low (mass customization) degree of co-creation in an online and offline

environment (degree of co-creation and technology as independent variables) are manipulated to

reveal whether there is a difference in customer experience regarding enjoyment, cognitive effort and

ability needed, and on top of that the customer‟s willingness to pay (dependent variables).

Furthermore, a customer‟s product involvement was introduced to check its effect on the relationship

between degree of co-creation and technology on the one hand, and customer experience regarding

enjoyment and willingness to pay on the other hand.

Methodology – An online survey was conducted for data collection. 10 people completed an offline

pre-test, while 149 respondents were collected to fill in the final survey online.

Findings – Co-creation opportunities result in a higher customer enjoyment, a higher cognitive effort

and ability needed, and a higher willingness to pay than mass customization opportunities. However,

customer product involvement is an important factor in a customer‟s willingness to pay. Interestingly,

customers need more cognitive effort and ability in an offline environment instead of an online

environment. The future of co-creation lies online. Furthermore, low involvement products are not

suitable for co-creation.

Research limitations – Co-creation is limited to B2C side from a customer‟s point of view. Due to

the research methodology and language of execution, only people with an internet connection and

knowledge of Basic English were able to be part of the study. As I describe two forms of CC, the

experiment is limited to co-creation/ mass customization for personal use.

Originality/value – The comparison between co-creation and mass customization on the one hand,

and online versus offline environment on the other hand regarding customer experience has never

been conducted before.

Keywords – Customer experience; Experience economy; Co-creation; Co-creation motives

2

Introduction

Inspired by the actual relevance of the concept of CC and its future importance, I started my

master thesis to complete my studies in Applied Economics. The Marketing Science Institute

has listed involving customers into CC or innovation processes as one of the top priorities for

2008-2010 (Hoyer et al., 2010). Although neither of my marketing courses put emphasis on

this future of competition, it appealed to me because of earlier work on CC and how

companies today make use of it. Among others, the book The Experience Economy – A New

Perspective (Boswijk, Thijssen, Peelen, 2007) caught my attention because of its focus on

experiences instead of products. It encouraged my motivation to take a closer look and learn

more about CC. Also CC pioneers Prahalad and Ramaswamy (2004) reveal in their book

The Future of Competition: Co-Creating Unique Value with Customers new opportunities to

create value and thus serves as a guide for managers how to search for new strategic

capital, illustrated with many examples. In an interview about the most recent book about

CC, The Power of CC (2010), co-author Gouillart talks about the theory of CC which states

that value is co-created between a company and a whole host of other people from

customers, to employees, to partners and even to suppliers. Now everybody is involved in

the value CC process. Customers are involved in marketing, employees are involved in

human resources processes, and suppliers get involved in the definition of vendor

management. This master thesis focuses on the B2C side, because of interest and more

empathy.

The structure of this paper is as follows. Part one is a literature review which elaborates on

the two concepts of the title: CC and CE. Section one of the literature review deals with CE:

definitions of CEM, CE, and different approaches to the components of CE are discussed.

Boswijk et al. (2007) describe CE as a process instead of consisting of different components.

The link from experiences to meaningful experiences is made as there is a need for entering

into a personal interaction with the company for the creation of a value proposition that is

meaningful to and specific for the customer. Next, I describe the evolution of the EE from first

to third generation in which I touch the concept of CC. Finally, the relevance of experiences

is explained as the source of value creation is shifted from goods and services to

experiences.

Section two of the literature review deals with CC. After a warm up of CC examples, I start

with the evolution of the content of CC and how CC can be compared with related concepts

3

such as mass collaboration, mass customization, user generated content and CP. The

relevance of CC from a customer‟s perspective is dealt with as the role of the customer has

changed over time and a shift in the worldview of marketing from GDL to SDL is recognized.

Next, an overview is given of the building blocks of CC, better known as the DART model.

Furthermore, I provide seven motives why customers may engage in a CC process. One‟s

motive(s) to co-create minus the cost(s) associated with engaging in CC equals a net result

that determines whether the customer will participate in a CC activity. As consumers want to

be involved in every part of the business system, we discuss CC for personal use next to CC

in NPD. Also the question who will be involved into a CC process will be answered.

Based on the literature review around CE and CC, justifications for the independent variables

are formed along with hypotheses concerning some relevant dependent variables.

Part two of this master dissertation is the empirical part in which an online survey was set up

to find an answer on the hypotheses of the scenario based experiment.

In a last section, limitations of the research and directions for further research are discussed.

4

Part I: Literature review

1. Customer Experience

In the first part of this master thesis, a synopsis is given about the literature around CE.

Definitions and classifications of CE are discussed as it is important to have a good

understanding of (the evolution in) CE. Next, the concept of CE is used to describe

developments in the EE. Here we link CE to the main topic in this dissertation: CC. Finally,

the relevance of the topic of CE will be explained.

1.1 Definitions and concepts

The goal of CEM is to understand the CE from a customer‟s perspective and take steps to

optimize that experience in order to maximize value for both the company and the customer.

Meyer and Schwager (2007) note that CEM captures and distributes what a customer thinks

about a company. What are a customer‟s subjective thoughts about a particular company? It

is direct response of a customer to the „touch points‟ with the company. Conversely, CRM

captures and distributes what a company knows about a customer. It tracks the actions of

customers post purchase. This can include product returns, service requests, inquiries…

The literature in marketing, retailing and service management rather put the focus on

measuring customer satisfaction and service quality rather than CE. However, this doesn‟t

mean that CE has always been ignored. Holbrook and Hirschmann (1982) stated that CE

has experiential aspects. Schmitt (1999) researched how companies create experiential

marketing. He identified five strategic experiential modules: sensory experiences (sense);

affective experiences (feel); creative cognitive experiences (think); physical experiences,

behaviors and lifestyle (act) and social identity- experiences that result from relating to a

reference or culture (relate). Barry, Carbone, and Haeckel (2002) suggest that companies

have to arrange all the “clues” that customers detect in a purchase process in order to

achieve a satisfactory experience.

Based on these insights, I have formulated two recent definitions of CE. The concept of CE

arises from a set of interactions between a customer and a product, a company, or part of its

organization, which arouses a reaction (LaSalle & Britton, 2003; Shaw & Ivens, 2005). This

experience has two characteristics: it‟s strictly personal and implies the customer

involvement at different levels (rational, emotional, sensorial physical and spiritual) (LaSalle

5

& Britton, 2003; Schmitt, 1999). The evaluation of CE depends on the comparison between

the expectations of the customer and the stimuli resulting from the interaction with the

company and its offering in correspondence of the different moments of contact or touch-

points (LaSalle & Britton, 2003; Shaw & Ivens, 2005). A related definition originates from

Meyer and Schwager (2007) as they define CE as “Customer Experience is the internal and

subjective response customers have to any direct or indirect contact with a company. Direct

contact generally occurs in the course of purchase, use, and service and is usually initiated

by the customer. Indirect contact most often involves unplanned encounters with

representatives of a company‟s products, service or brands and takes the form of word-of

mouth recommendations or criticisms, advertising, news reports, reviews and so forth.”

(Meyer & Schwager 2007,p.118).

Over time, the concept of CE was conceived as a multidimensional structure composed of

elementary components. You have to keep in mind that customers hardly ever recognize

such a structure. They perceive an experience as a complex but unitary feeling, where each

component is hard to distinguish from another (Gentile, Spiller, Noci, 2007). In 2006

Fornerino, Helme-Guizon and Gaudemaris analyzed CE as an immersive consumption

experience that consists of five distinct dimensions: sensorial-perceptual, affective and

physical-behavioral (components) and social and cognitive (facets). More recently, Gentile et

al. (2007) state that, based on the extant literature of CE, there are six dimensions of CE.

They researched the role of different experiential features that lead to a good customer

experience by some well-known products.

1. Sensorial component

The stimulation of this component affects our senses. The goal of a company‟s offer can be

providing good sensorial experiences. These experiences include smell, touch, sight, hearing

and taste to arouse pleasure, satisfaction, and excitement. Jamba Juice bars is a good

example that focuses on the senses of customers as they work with natural ingredients, zero

grams trans-fat, no high-fructose corn syrup…

2. Cognitive component

This component is related to mental processes or thinking. Companies can engage

customers in using their creativity or problem solving skills. Besides this, a company can lead

a consumer to revise the usual idea of a product or some common mental assumptions. This

happened for example with Barbie, the first doll with the image of a young woman.

6

3. Emotional component

This is a component of CE that involves the affective system through creating moods,

feelings and emotions. An offering can be directed to generate an emotional experience in

order to create an affective relation with the company, its brands or products. Kinder Surprise

is an example.

4. Pragmatic component

The fourth component is derived from the practical act of doing something. It includes the

concept of usability. The Apple Imac is a good illustration of an astonishing practical

experience by its design based on usability standards. It doesn‟t only refer to the use of the

product post purchase, but extends to all product life cycles. A good example is the company

Whirlpool and its subsidiary KitchenAid that came up with the initiative of the Insperience

Studio.

5. Lifestyle component

This component comes from the adoption of a lifestyle and the behavior of a person that is

formed through the affirmation of the system of values and individual‟s beliefs. The product

and its usage become means of devotion to certain values that company and brand

impersonate and values that customers share. Consumption of products without logo is an

example.

6. Relational component

The relational component involves the person, the relationship of the person with others, his

social context and on top of this his ideal self. An offering can empower the relational

dimension by means of a product that stimulates its use together with other people. Are you

willing to spend your time in Disneyland Paris alone or would you rather go with your kids,

family or friends? The product can be the core of a common passion from which a

community can be created like the Ducati community. People can identify themselves

belonging to or distancing themselves from a social group which refers to a social identity.

This model of dimensions of CE is in contrast with Schmitt‟s (1999) model in the sense that

Gentile et al. (2007) distinguish the physical aspects from the values. On the other hand,

they join the physical part with the sensorial dimension.

Boswijk et al. (2007) describe an experience as either a professional skill or a

sensation/feeling. In the literature of CE it is the latter definition that matters. Sensation or

7

feeling implies the act of undergoing something, for example letting a potential customer test-

drive a car. Here the rational choice for buying a car is supported by the emotional

experience of driving it. Boswijk et al. (2007) explain CE as a process rather than a concept

composed of different elements.

[Insert Figure 1]

Starting point is the sensory perception that can be compared with sensory

experiences (Schmitt, 1999), the sensorial-perceptual dimension (Fornerino et al., 2006) and

the sensorial component (Gentile et al., 2007). Sensory perceptions lead to emotions (Frijda,

1986). It‟s a way of processing information. “Emotions are an involuntary, unintended, non-

deliberate way of dealing with the outside world (…)” (Boswijk et al., 1986, p.21-22). An

experience is often a complex of emotions that occur either simultaneously or one after the

other. Boswijk et al. (2007, p.22) describe it as “an immediate, relatively isolated occurrence

with a complex of emotions that make an impression and represent a certain value for the

individual within the context of a specific situation.” A service complaint, for example, leads to

a variety of combined emotions and feelings that form the experience. The customer is either

treated well or not, with satisfaction or dissatisfaction as result. The next step in the process

concerns a meaningful experience. The next paragraph will elaborate more on this.

Finally, giving meaning is the last step in the process of experiencing. Sometimes people find

themselves in situations where a meaningful experience takes place. Coincidence plays a

role, but the individual also gives direction to the situation. What are the needs and motives?

The pyramid of Maslow (1962) is the most famous example where a hierarchy of needs is

distinguished. In the pyramid people will only strive to fulfill the needs of a certain level, once

the needs of lower levels are accomplished. Frijda (1986) also distinguishes different

motives, although his model is less well known. Frijda states that there are four motives for

people: not wanting to be alone, wanting to be recognized, wanting to maintain control over

the environment that one is familiar with and wanting to experience something new. These

motives could be put on two axes as in the figure below.

[Insert Figure 2]

It is a graphic representation of the world that people move around in. People can

move or be pushed from one quadrant to another. For example, a movement from left to right

can occur when a person climbs up his career ladder, leaving his colleagues and maybe

8

friends. As a result, he can feel lonely and try to search for friendship of other colleagues,

which means a movement to the right of the figure.

1.2 Meaningful experiences

The transition from an experience to a meaningful experience as the fourth step in the

process of experiencing (see Figure 1) (Boswijk et al., 2007) concerns an important learning

component, an aspect of awareness. Take also into account the difference in language

between experience and meaningful experience. In Dutch, the difference between these two

concepts is clear due to the use of „ervaring‟ for „experience‟ and „betekenisvolle belevenis‟

for „meaningful experience‟. Unfortunately, in English the same word is used – experience –

but there is a clear distinction between these two concepts. In the framework of a meaningful

experience a person wonders what a particular experience means to him. He asks himself

several questions: Why do I find myself in situations like this? How should I deal with them?

What does it say about me? Do I want this? A meaningful experience gives the individual

insight into himself and into the way in which he might change or transform himself.

In an interview (conversation with Boswijk and Peelen by Pieterse P., 22/08/2008), Boswijk

and Peelen explain their views about meaningful experiences. People are nowadays

searching for meaningful experiences because in their view people search for meaning in

their lives. They are against the idea to convince people and get people‟s attention with

entertainment as a marketing tool. The focus has to be on the individual. According to

Boswijk and Peelen, there are three types of meaningful experiences:

1. A strictly personal experience that is a turning point in life or full of emotion and

meaningful in the progress of your life: the birth of your child, a marriage, a passed

exam. Those are real meaningful experiences.

2. Experiences that you share with others: social/ social-cultural experiences.

3. Paid experiences. Someone organizes it and directs it for you, for example Rock

Werchter festival. In this case, you pay an amount of money for the experience.

Man is put in the center of things and he is in search of meaning, sometimes this can be

entertaining or relaxing. He can buy this to have more excitement, more experiences in life.

It‟s more than just pleasure or an even better party. It is doing something that makes sense.

What really has value for someone? That is the question companies affiliate with. They

should step out of their product domain and get access to the private world of their customer.

9

How can they help people? Let‟s take insurance companies. A lot of insurances are adjusted

automatically in your life. Instead of this, companies can put their customer in the center and

ask him the question whether their current insurance is still what they really want and need.

When you‟re pregnant or retired, you probably have other insurance needs. They state that if

the company does not adapt to and focuses on what the customer really wants, it will make

less profit in the future.

1.3 Evolution of the Experience Economy

The EE is as old as the oldest profession in the world. The Greeks and Romans knew how to

make money with experiences. Today, under more prosperous circumstances, the system,

developed to fulfill material needs, is transformed to an economy that addresses the

psychological needs. Toffler (1970) talks about the dematerialization of the economy in his

book Future Shock in a chapter called…The Experience Makers. Management consultants

and economists didn‟t pay a lot of attention to this process of „psychologizing‟ because they

focused on the growing service sector. We had to wait until the mid-nineties for a boost of the

concept of CE when a new experiential approach offered an original view to consumer

behavior. Different variables that were neglected are reconsidered because of their

importance: the role of emotions in behavior; consumers are feelers, thinkers and doers,

consumer‟s need for fun and pleasure… This perspective grew along with the mainstream

approach in consumer behavior that viewed customers1 as rational decision makers (Addis &

Holbrook, 2001). In the late 1990s Pine and Gilmore launched the bestseller The Experience

Economy (1999).The authors describe experiences as a new source of value creation. It is a

„fourth economic offering‟ next to commodities, goods and services in what they call the

progression of economic value. It has always been there, but unnoticed. When you buy an

experience, you pay to spend time enjoying a series of meaningful events that a company

stages to engage you in a personal way. According to Pine, an experience is a distinguishing

economic advantage for which one can ask a premium.

According to another pioneer Wolf and his Entertainment Economy (1999), the entertainment

character will become the key differentiator in the economy where products without this

characteristic will not succeed in the future. This view is different from Pine and Gilmore who

talk more about „engaging‟ the customer at a personal level. Danish author Ralph Jensen

(1999) claims in his book The Dream Society that the biggest part of future growth in

1 I use the terms “consumer” and “customer” interchangeably throughout the paper, meaning the end user of the

firm‟s offering.

10

consumption will have a higher intangible character and that the story surrounding the

product will play an important role in the purchasing decision. These authors in the late

1990s can be considered as representatives of the first generation of the EE.

After the publications of these pioneers, the popularity of the subject quickly grew and

different approaches focused their attention on CE for leveraging value. The basis of these

contributions is a revised way to consider the concept of consumption: it becomes a holistic

experience which affects a person as a whole at different levels and in every touch point

between company or its offer and a person as mentioned in the definition of CE above.

Notice the use of the term person as opposed to customer. As both parties (customer and

company) play an active role, this is called the second generation of EE. Experiences as

memorable „staged‟ events according to Pine and Gilmore are no longer regarded as primary

focus. Enabling the customer to have an excellent life-long relationship with a company, even

beyond his expectation, is what contributes most to the creation of value (LaSalle & Briton,

2003). Prahalad and Ramaswamy (2004) describe it as co-creating unique experiences with

the company. In other words, selling or staging experiences is converted into providing

artifacts and contexts that are favorable of experiences and which can be correctly applied

by consumers to co-create their own, unique experiences (Caru & Cova, 2003).

In an interview (Boswijk, Thijssen, Peelen, 2007), Ramaswamy talks about experiences as

the new source of value creation. How this became the new source of value, will be

explained in the next section. It is no longer the company that provides a good customer

experience by executing the sequence of activities in the value chain and thereby creating a

good; now value is more routed in the experience of the customer. If you put the CE as the

source of value at the center of doing business, then it allows consumers to be part of the

value creation process. In order to create a good CE, companies now have to involve

customers by definition, because the experience is after all routed in the individual. If you

become experience-centric, this implies a process of CC. Why? Customers and companies

need to start converging around the experiences and this implies a very different process of

interaction, a process that is as much focused on how the consumer wants to interact with

the company rather than just how the company wants to interact with the consumer (see also

section 2.2.2: from GDL to SDL). This is a process of joint value creation on both sides, not

the company deciding what is good for the customer.

Schmitt (1999) supported this view by stating that marketers need to provide the right

customer setting and environment for the desired customer experiences to appear. In 2007,

11

a major extension was added by Caru and Cova in their book Consuming Experience in

which the authors identify a “continuum of consuming experience”. This continuum ranges

from experiences, like staging experiences according to Pine and Gilmore that are mainly

constructed by companies (first generation of EE) to experiences that are largely developed

by consumers. In the middle of these two extremes experiences that are co-created by

companies and consumers (second generation of EE) are situated as per Prahalad and

Ramaswamy in which the company provides the customer with a basic platform and raw

materials that are then being used by the consumer to bring in and reach his/her own

experience.

As mentioned above, there‟s an extreme where experiences are largely developed by

consumers. The individual creates and directs even more his own meaningful experience

without interference of the supplier. This is the third generation of EE, which is opposed to

the second generation. In this generation we are moving to communicative self-direction

(Cornelis, 1988). It‟s not the company that decides what the customer can buy and what he

will experience. In that sense, people are directed from the outside. The two parties will be

directing themselves from the inside and communicate with each other: dialogue arises

between company and customer. It‟s a kind of cooperation where people communicate with

companies about what they want to experience. Figure 3 reproduces a summary of the

evolution in the EE.

[Insert Figure 3]

The evolution of the EE will be understood better when we talk about the shift in the

dominant logic: from GDL to SDL.

1.4 Relevance of experiences

Why am I talking about experiences and not about goods or services? Pine proposes a

framework of how things work in the business world and how economic value has changed

over time (Boswijk et al., 2007; Pine II & Gilmore, 1998). A graphical representation is given

in the figure below.

[Insert Figure 4]

12

In the beginning there were commodities. Commodities are things that grow out of the

ground and pulled out of the ground: minerals and vegetables. You collect them out of the

ground and sell them in an open market place. These are the basis of the Agrarian economy

that lasted for four millennia. It was the primary economic offer. Commodity output increased

over time due to higher productivity and better technology. Less people were needed so

employees moved out of farms into factories where physical goods were manufactured.

Goods are physically tangible things that we touch and feel using commodities as raw

material. Goods are the basis of the Industrial economy. Output of goods sky rocketed too

and again fewer people were needed to produce more and more goods. People moved out

of factories and into service jobs like restaurants, hotels, offices where they deliver an

intangible activity on behalf of an individual person. Today 80 percent of employment is in

services and what customers want is services, not the products. Goods become

commoditized, treated like a commodity. People don‟t care about the brand, the producer or

the features because they‟re all more or less the same. They only care about the price.

Internet is the greatest force of commoditization because people can compare prices of

different vendors instantly. Consequently, we push prices down to the lowest possible price.

But now services are being commoditized as well. For example in financial serviceswhere

you can buy a block a shares with a full service broker for € 700, it only costs you € 10 with

an Internet based broker. Another example is the travel industry. Internet is pushing the

prices down with sites such as priceline.com that connects you directly to airlines and hotels.

This way no commissions need to be paid.

It is now time to move to a new level of economic value. Goods and services are no longer

enough. Today Customers want experiences. Experiences are a distinct economic offering

that engage each customer in a personal way and thereby create a memory. If there‟s no EE,

all jobs will disappear because of technology and automation. Experiences are replacing

them, becoming the predominant economic offering. As an example of this progression of

economic value, Pine describes the coffee progression (Boswijk et al., 2007).

[Insert figure 5]

Raw coffee beans are a commodity that is a practically negligible value. Once roasted

and packaged, the coffee beans become goods that can be bought in the supermarket

resulting in a homemade cup of coffee with a cost price of € 0.20. Serve the same coffee

beans in a local dinner or corner coffee shop and they become part of a service where you

13

pay depending on the environment €1.5 – €10 for one cup. Serving an espresso or

cappuccino at Café Florian on Piazza San Marco in Venice providing a heightened ambiance

is the ultimate coffee experience for which you will pay €17. This example shows that it‟s not

just about the product itself, but also the experience surrounding the product and/or service.

14

2. Co-creation

I mentioned already once the concept of CC (supra, p.10) when talking about the second

generation of the EE. In the second part of this dissertation, the concept of CC will be

investigated starting with an introduction of examples to get an idea of applications of the

concept in the marketplace. The developments of the concept of CC will be reproduced

along with an overview of how CC can be distinguished from related concepts. Its relevance

from a customer‟s point of view will be discussed as the role of consumers changed over

time and the traditional model of value creation will be questioned as I explain the shift from a

GDL to SDL. Furthermore, an answer will be given to the question why people CC, in which

part of the value chain they have an influence and who will be involved in a CC process. On

the basis of the literature review around CC, hypotheses are formulated for the research

study.

You have probably already bought several T-shirts in your life, searching and choosing from

the tons of different finished products that are available in clothing stores. But have you ever

dreamt of making your own T-shirt starting from the very beginning? Well, within a few

minutes, you can get started. The company „Threadless‟ offers customers the opportunity to

submit, inspect and approve T-shirt graphic designs (Elofson & Robinson, 2007). Surf to

www.threadless.com, push „participate‟, download the submission kit to start creating your

own T-shirt and off you go! The mission of the LEGO Company, the world‟s fifth-largest

manufacturer of toys, is to “inspire and develop the builders of tomorrow”. In your childhood

or as a parent you probably have come in contact with LEGO, playing with the six shapes of

bricks and following the guidelines until you get the end result that looks the same as on the

picture of the packaging2. But aren‟t you bored of making obvious creations like a car and do

you wonder if you could ever make your own electric guitar because playing the guitar is your

passion? No problem. Nowadays people can join the so called „LEGO factory‟ (LEGO

Factory, 2007) where users are invited to download the Digital Designer software. This

provides you with a platform to begin designing and building with virtual LEGO bricks. The

point is that you can create whatever you want. Afterwards you can submit your virtual model

to LEGO through its website. An employee of LEGO will analyze your creation and based on

how many and what type of LEGO bricks you used, he will charge you a certain price for a

manufactured version. You will be given the option to order your own piece directly from the

website. On top of that, you have the chance to share your idea with other members of the

2 Visit <www.lego.com>. Accessed 16/03/2011.

15

LEGO factory „workers‟3 community. You can comment on the work of someone else, make

copies, propose changes and create adaptations. In fact, a lot of the designed products by

customers are appropriated by LEGO for general production and sale. The LEGO sample

shows the involvement of the customer in the product development and how one can create

his own LEGO experience (Zwick, Bonsu & Darmody, 2008). RedesignMe B.V. is a company

that describes themselves as “the world‟s largest creative marketplace4”. On their website,

the company uploads a creative assignment, called a „challenge‟. This can include logo

designs, marketing ideas, product designs…. You can participate to any of the challenges

you like and afterwards RedesignMe rewards the winning entries with cash. The company

also consists of an offline part „RedesignMe Live‟5. Face-to-face brainstorm sessions are

organized where a select group of professionals and consumers join together with

companies to realize new and refreshing ideas. Indeed, you can be part of such project that

a company prepares together with RedesignMe.

The three examples - Threadless, LEGO and RedesignMe – all have the same message:

Welcome to the world of CC. This introduction leads straight to the first independent variable

for the experiment. CC can happen in an online (Threadless, LEGo and RedesinMe) or

offline (RedesignMe Live) environment.

2.1 Definitions and concepts

In the late 1990s, a first definition of CC emerged. Kambil, Friesen and Sundaram (1999)

defined CC as a partnership with the customers where value is created by both the firm and

the customer and “engaging customers directly in the production or distribution of value.

Customers, in other words, can get involved at just about any stage of the value chain”

(Kambil et al., 1999, p.38). In every definition the word „value‟ will return. Customer value can

be defined as the difference between perceived benefits (product benefits, service value,

image value and personal interaction value) of a company‟s offering and perceived costs

3 Obviously, these workers are not working in actual Lego factories in Asia, Europe, and soon Mexico, nor are

they employed by LEGO.

4 Visit <http://www.redesignme.com/>. Accessed 29/03/2011.

5 Visit <http://www.rdmlive.nl/about>. Accessed 29/03/2011.

Independent variable 1: Technology

Online versus Offline

16

(financial, time, energy and psychic cost) (Sanchez & Heene, 2004; Kambil et al., 1999). This

is a broad definition that is difficult to apply when we have to understand how value is

created. Kambil et al. (1999) determines value in terms of the interaction of three variables:

specific customer needs, the attributes of the firm‟s offering and the overall cost for the

customer (sum of price, risk and effort). Value is created when attributes of the offering

match specific customer needs arising from any of the five processes that customers take

part in – buying, using, selling/disposing of the offering, integrating multiple products to fulfill

needs, and CC – at a cost considered reasonable by the customer. The greater the fit, the

greater the customer value created.

The concept of CC was further evolved by Prahalad and Ramaswamy (2004) in the sense of

firms creating value with customers producing a unique customer experience. They describe

CC as: “Co-creation is about joint creation of value by the company and the consumer. It is

not the firm trying to please the customer.” and “Co-creation is […] creating an experience

environment in which customers can have an active dialogue and co-construct personalized

experiences; product may be the same (e.g. Lego Mindstorms) but customers can construct

different experiences” (Prahalad & Ramaswamy,2004,p.8). It is not about “Customer is king

or customer is always right. Delivering good customer service […]. Mass customization of

offerings that suit the industry‟s supply chain. Transfer of activities from the firm to the

customer as in self-service. Customers as product manager […]. Product variety. Staging

experiences” (Prahalad & Ramaswamy, 2004, p.8).

The descriptions of CC have been gradually extended toward autonomous individual

initiatives (Zwass, 2010). The LSE Enterprise team (2009) made an in-depth study of CC by

order of Promise, the world‟s leading CC Consultancy Company6. To frame the concept of

CC, they start by introducing two dimensions: the role of the firm and the type of value

created. The former indicates whether the process is more consumer-led or producer-led. Is

the process mainly user-driven or rather initiated and orchestrated by the firm? The latter

dimension reproduces whether the value is standardized (benefiting all customers),

customized (as in mass customization for example) or personalized (as in CP). On basis of

these two dimensions a CC matrix is developed as in the figure below to illustrate where CC

sits as opposed to related concepts.

[Insert Figure 6]

6 Visit <www.promisecorp.com>. Accessed 21/03/2011.

17

LSE Enterprise describes CC as “Co-creation is an active, creative and social

process, based on collaboration between producers and users that is initiated by the firm to

generate value for customers” (LSE Enterprise, 2009, p.9). CC differs from mass

collaboration and user generated content on the one hand as the latter are more consumer

led and from mass customization and personalization as those two concepts are more

producer led.

Mass collaboration is a collaboration model based on collective actions that are conducted

independently by a large amount of contributors or participants, but in collaboration on a

single modular project. Projects typically take place on the World Wide Web by means of

web based collaboration tools. For example Wikipedia, the world‟s largest online

encyclopedia that consists of articles fully written by Internet users (Ghazawneh, 2008).

User generated content implies that content is made publicly available based on

technologies like the Internet, whereby a certain level of creativity and effort is present and

this is created outside of professional practices or routines (Wunsch-Vincent & Vickery,

2007). The best example here is Youtube, where you can upload your own videos, share

them and watch videos generated by other users.

Mass customization refers to companies that offer product variety and customization through

flexibility and quick responsiveness. It‟s different from mass production as mass production is

aimed at standardized products, while there is a lot more variety in the mass customized

products. People can find almost exactly what they want at a fair price (Kotha, 1995). Dell

provides its customers with a site where you can configure the specifications of your own

personal computer and Nike ID is an initiative to build your own pair of shoes. Mass

customization is more producer-led as customers have to choose from a menu dictated by

the company. “Customization ultimately is a matter of what can be built and delivered to suit

the efficient operation of a company‟s value chain” (Prahalad & Ramaswamy, 2002, p.6). It is

clear that mass customization offers variety, but it is the company itself that decides what can

be customized. Consequently, I would describe mass customization as a low degree of CC.

This lays against a high degree of CC, CC on itself, where the process is more user-driven

and customers don‟t face those limitations set by the company. Here the company provides

an experience environment where individual consumers can create their own unique

personalized experience where the building blocks of CC are present (infra, section 2.3). It

induces individualized interactions and experience outcomes. A personalized CC experience

is a reflection of how the individual chooses to interact with the experience environment that

18

the firm facilitates (Prahalad & Ramaswamy, 2004b). This leads to the second independent

variable in the experiment:

CP implies that the customer is an active participant in the production and delivery of a

service, which gives him the chance to customize his own world (Bernapudi & Leone, 2003).

According to Vargo and Lusch (2008) CP is a component of CC relating to the specific tasks

performed by customers that may occur before or during consumption, usage or experience.

The customer is consequently always a co-creator but not always a co-producer. An example

of this recently became a trend in a lot of Belgian department stores: self-scans. Customers

can scan their own articles during the shopping taking away the job of the cashier and thus

CP this service.

In a very recent paper, Zwass (2010) defines CC as “Co-creation is the participation of

consumers along with producers in the creation of value in the marketplace” (Zwass, 2010,

p.13). As opposed to LSE Enterprises analysis, he states that the CC process can be

initiated by the firm or the consumers themselves. He distinguishes sponsored CC from

autonomous CC. The former consists of activities performed by consumer communities or

individuals by order of the firm, for example P&G „Connect+Develop‟ initiative in the search

for innovative ideas resulting from external people. The latter refers to consumer

communities or individuals that produce value independently of any organization and

voluntary. However, they might provide platforms that benefit economically.

2.2 Relevance from customer’s perspective

“Product variety has not necessarily resulted in better customer experiences.”

(Prahalad & Ramaswamy, 2004, p.1)

As mentioned already in the relevance of experiences (supra, p.11) we have more choices of

products and services today than ever before. The question is whether this results in a better

customer experience. Prahalad and Ramaswamy (2004) state that the increasing variety

yields less satisfaction and call it the paradox of the twenty-first-century economy. The march

of the Internet played a major role in this. Stimulated by the consumer-centric culture of the

Independent variable 2: Degree of CC

High degree of CC versus Low degree of CC

19

Internet, characterized by interactions, speed, individuality and openness, the impact of the

customer on value creation is increasingly growing. The role of customers has changed from

isolated to connected, from unaware to informed, from passive to active. This will be

explained in the next section. About.com is one of the most popular consumer word-of-mouth

sites, where people can discuss more than 50.000 subjects. Consumer to consumer

recommendations, comments, ideas, etc. have a powerful influence on choice. It is clear that

nowadays the consumer‟s influence on value creation is enormous and companies should

listen, learn and absorb their valuable intelligence (Prahalad & Ramaswamy, 2004). Thanks

to the Internet, consumers can actively define the way they see value – as experiences – and

force their visions towards the companies. The market is becoming a forum for interactions

between consumers, consumer communities and firms. These dialogues, access,

transparency and understanding of risk-benefits are the core of the next step in value

creation. Prahalad and Ramaswamy (2004a) call them the building blocks of CC. This topic

will be explored as well further-on.

2.2.1 The changing role of the customer

CC of value begins with the changing role of the consumer in the industrial system.

Companies should welcome the five powers of the connected consumer.

1. Information access

Consumers nowadays have access to enormous amounts of information. We use this

information to make better, more informed choices. For companies that usually prevent the

customer of flows of information, this shift is radical. They can no longer take control over

value creation. For example, consumers make use of the Internet to learn about diseases,

treatments, the latest drug trials and share their personal experiences with others. In this way

they can question their physicians more aggressively and have a greater share of

participation in choosing their own treatment modalities (Prahalad & Ramaswamy, 2002,

2004a).

2. Global View

Consumers can search on the Internet every moment, they have the ability to check what is

happening in the world. They can access information on products, technology, firms,

performance, prices... There are still some geographical limits on information, but they erode

fast. This changes the rules for how companies compete (Prahalad & Ramaswamy, 2002,

20

2004a). Due to this evolution, arbitrage7 for example – asking a different price for the same

product on another market – is a practice that hardly can be carried out nowadays by

multinational firms.

3. Networking

People have a natural desire to coalesce around interests, needs and experiences. The

boom of the Internet and developments in messaging and telephony strengthens this by

creating an unparalleled ease and openness of communication among consumers. They are

„thematic consumer communities‟ where individuals can share ideas and feelings without

geographical constraints and few social barriers. Consumers share the same interest, but

may know nothing more about each other. The power of these consumer networks is their

independence from the firm and that they are based on real consumer experiences, not the

companies story what they will experience. Consumer networking turns the traditional

company controlled marketing communications upside down. Rather than making

advertisements on television and billboards for their next movie The Lord of the Rings that

will be launched, the New Line Cinema works together with fan sites to help spread word of

mouth and create buzz (Prahalad & Ramaswamy, 2002, 2004a). Now, people can share

their experiences through all kinds of new social media and communication technologies

and therefore force companies to engage them in the creation of mutual value.

4. Experimentation

The Internet can also be used for experiments with or developments of products, especially

digital ones. For example, technology-savvy people began to experiment with an MP3, a

compression standard for encoding digital audio, and this caused challenges to the music

industry. The skills of individual software users bundled together also enabled the co-

development of popular products such as the Linux operating system and Apache Web

server. Of course, experimentations go beyond digital spheres as well: people that improved

their homes can share their projects with others, or passionate gardeners can share tips on

how to grown organic vegetables. Companies can benefit from these creative ideas for the

development of products and services (Prahalad & Ramaswamy, 2002, 2004a).

5. Activism

By learning people can better discriminate when making choices and by networking people

encourage each other to speak out and act. Consumers provide companies and each other

with unsolicited feedback. AOL Watch is a website where posts about former and current

7 Visit <http://www.britannica.com/EBchecked/topic/32311/arbitrage>. Accessed 08/04/2011

21

AOL customers are written. The web is also a powerful tool for group discussions on issues

of the same interest. When individual powers join together in a community, share of voice is

greater. People that find it important to protect the animals can affiliate with the World Wildlife

Fund (WWF) to promote reforms and to get governmental attention.

So what is the result of the changing role of the customer? Companies have to interact with

customers and thereby co-create value because customers seek to exercise their influence

in every part of the business system. Companies can no longer design products on their

own, develop production processes, craft marketing messages or put simply act,

autonomously.

2.2.2 From GDL to SDL

"Any customer can have a car painted any color he wants so long as it is black."

(Ford, 1922, p.71-72)

This quote from the founder of the Ford Motor Company reflects the way companies

traditionally created value: unilaterally from the firm to the consumer and controlled by the

firm. Companies provided a good customer experience themselves. The market, viewed as a

locus of exchange or as a whole consisting of different segments of consumers, was

separated from the value creation process. Consumers were the target of the firm‟s offerings;

passive, seen as a „prey‟. They had no role in value creation; they were only involved at a

single point of exchange where value extraction takes place from the customer for the

company. Firms acted autonomously in the design of products, development of production

processes, control of sales and craft of marketing messages without involvement of the

customer. The traditional concept of the market is company-centric as well as the process of

value creation (Prahalad & Ramaswamy, 2004b). Thus, marketing used a model of economic

exchange, which had a dominant logic based on the exchange of operand resources,

resources where an act or operation is performed on, such as goods. Consequently, this

dominant logic is called the GDL (Vargo & Lusch, 2004). A firm‟s production process embeds

value or utility into a good, and the value of the good is represented by the market price or

what the consumer is willing to pay. From this perspective, maximum efficiency and

maximum profit is achieved by standardization and economies of scale. In other words, the

traditional model is based on the value-in-exchange meaning of value. As an example of

these thoughts, Ford‟s quote tells us that a firm constructs the automobile (Ford Model T) out

22

of raw materials, arranges them and packages them together. Value is created during the

activities performed in the production process.

This concept is being challenged due to the changing role of the customer as discussed

above (section 2.2.1); the march of the informed, connected, active and empowered

consumers. This has led to a revision of the traditional dominant logic, now focused on

intangible resources, CC of value and relationships referred to as the SDL. Service refers to

the application of competences such as skills and knowledge by one entity for the benefit of

another. These skills and knowledge are called operant resources, employed to act on

operand resources (and other operant resources); they are often invisible and intangible. All

exchange is based on service and when goods are involved, they are tools for the delivery

and application of resources (Vargo & Lusch, 2004). If we go back to the example of the

automobile, firms use knowledge, skills and capabilities to transform raw materials into a car.

But according to the SDL, the manufactured automobile is only an input into the value

creation that occurs as the customer uses the car: transport, self-identity… The car would not

have any value if the customer does not know how to drive, has no access to fuel and

maintenance or even does not belong to a social group where the car has a particular

meaning. In this way, value is co-created: companies apply their skills and knowledge in the

production and branding of the car while customers use their operant resources in the

context of their own lives. “There is no value until an offering is used- experience and

perception are essential to value determination” (Vargo & Lusch, 2006, p.44). Consequently,

in SDL the value driver is value-in-use. A summary of the differences between GDL and SDL

are given in the figure below.

[Insert Figure 7]

2.3 Building blocks of CC: the DART model

As the locus of value creation are the interactions between the customer and company, one

needs to understand the CC process through its key building blocks: Dialogue, Access, Risk

Assessment and Transparency. Along with the discussion about these key elements the

example of Summerset, the world‟s largest builder of houseboats8, will be applied to have a

better understanding of each building block as they manage to fit all the pieces of the CC

model together (Prahalad & Ramaswamy, 2002, 2004a/b/c).

8 Visit <http://www.sumerset.com/models-custom.php>. Accessed 27/03/2011.

23

1. Dialogue

Dialogue is not just listening to your customers. It is creating a shared meaning; people listen

and learn from each other. It means interactivity, deep engagement and a propensity to act

on both sides (company and customer). What do consumers really experience? The

company needs to recognize and understand the social, emotional and cultural context that

shapes the experiences. A loyal community can be created and maintained through

dialogue.

If you want to create your own unique houseboat together with the Summerset Company,

you can contact the Summerset‟s development group and discuss your ideas for the boat -

size, furnishings, budget ... – so that over several conversations you and the engineers co-

developed the specifications according to your needs and preferences.

2. Access

Traditionally, companies created and transferred ownership of products to consumers. Now,

customers do not necessarily want the ownership, but access to desirable experiences. You

do not need to own something to access an experience.

Once your houseboat specifications are made, you can access the manufacturing plant

through the Web. Here you can watch your boat being built and track its progress. You can

also get access to a community of house boaters. When joining this, you can obtain new

ideas about how to design and accessorize your boat.

3. Risk assessment

Risk refers in this third building block to the probability of harm to the consumer (Prahalad &

Ramaswamy, 2004a). In the past firms managed the risks of their products. When

communicating with potential customers they gave prominence to the benefits, largely

ignoring the risks. As consumers are now more involved in CC experiences with companies,

should they also take more responsibility for managing those risks if the company reveals

more information about the risks associated with the product? One thing is for sure; the

customer can help the company through continuous dialogue to reduce risk. Problems can

be identified and corrected proactively.

In the case of Summerset, motor exhausts are very toxic. After the death of two people due

to carbon monoxide poisoning, Summerset redesigned the exhaust system so that the

carbon dioxide now escapes via a top-mounted stack nine feet above the deck. Or, while

designing your houseboat with Summerset, employees can inform you about the risks

associated with a certain preference. For example, placing a whirlpool in the front of the boat

24

can cause serious balance problems. Thus, proactive risk communications and management

is an opportunity for companies to differentiate themselves.

4. Transparency

Earlier, companies benefited from the information asymmetry between the firm and

consumers. As consumers now have more access to information about products,

technologies and business systems, the asymmetry is fading. If companies make important

business-process information visible to consumers, they cede control of the value creation

before the traditional point of exchange.

At Summerset, customers can follow the product development process of their own boat so

that they can intervene more often and intensely than normal. Transparency is needed on

both sides: the customer has to know what is happening at all times and why while

Summerset must know the customers‟ desires, concerns, and requirements.

In the example of Summerset, not only a houseboat is co-created. Experiences are created,

next to the physical good. Even before one is the owner of a new houseboat, an emotional

attachment is formed to their boat while building their stake in the output of the value creation

process. As Summerset is able to combine all building blocks of CC, they can better engage

customers as collaborators. Some companies can be outstanding for one of the CC building

blocks, but if the DART blocks are combined, new and important capabilities can emerge as

shown in the figure below (Prahalad & Ramaswamy, 2004c).

[Insert Figure 8]

2.4 Customer motives/benefits for CC/CP

Why do customers participate in a CC process? As mentioned already (supra, section 2.2),

one can take part in CC because of dissatisfaction with existing products (Prahalad &

Ramaswamy, 2004; Ernst et al., 2010). When engaging in CC, offerings will be developed

that are more aligned to the consumer needs (Hoyer et al, 2010; Van Der Wal, 2005).

Successful NPD depends on a deep understanding of consumer needs and development

efforts that meet those needs. New product ideas can be generated that have a bigger

chance to be valued by consumers and thereby increasing the likelihood of product success

(Hoyer et al., 2010).

Secondly, consumers can engage in CC to reduce risks associated with receiving

inappropriate products or service failures or to increase control over products and services

25

(Etgar, 2008). These risks include physical, financial, psychological, performance, social, and

time-related risks (Stone & Gronhaug, 1993). Physical risk is the risk whereby the body gets

harmed, for example when a consumer allergic to peanuts consumes a product that contains

traces of peanuts while you can read on the product itself that it does not contain any traces

of peanuts. Financial risks refer to the possibility that a product needs to be repaired or left

unused. Performance risk happens when the product does not fulfill your needs. This can be

the case when you bought a new computer that is not as fast as you expected. Social and

psychological risks are related to instances where the self-esteem of the consumer can be

harmed as in „Will my friends laugh at me with my new T-shirt?‟ Last, time risk is associated

with the devaluation of the product over time.

Thirdly, consumers can be motivated to CC because of the expected benefits, such as

distinct different services and better service quality (Etgar, 2008). These three motives –

dissatisfaction with existing products, risk reduction, and expected benefits – relate to the

use of co-creating products and are therefore called pragmatic motives.

A second reason for the consumer to co-create has to do with economic motives. Financial

rewards can be given to the customer for the effort made in the CC process. These rewards

can be direct, like receiving a monetary prize as it is the case with RedesignMe (supra, p.14)

or profit sharing from the firm that engages in CC with them. A very recent example is the

Lays action Create your own taste. Until today there is no such thing as the real Belgian

chips taste. From 10 January 2011 on, every Belgian had the ability to be creative and

develop a taste that can become part of the Lays product range in Belgium. A jury of three

famous Belgian cooks chooses the two best flavors and those will be launched in October

2011. Again consumers are involved to vote for their favorite taste and the winning taste will

be on the market as Limited Edition from the beginning of 2012. The winning participant will

receive a € 25.000 prize and 1 % of the turnover9.

Financial rewards can also be indirectly through the intellectual property they might receive

from engaging and especially winning in CC competitions (Hoyer et al., 2010). Next,

consumers can expect cost reductions when co-creating as consumers perform an activity

(Etgar, 2008). For the organization of your holiday, you can go to a travel agent who is

concerned with the purchase of your airline tickets and booking of your hotel room. Instead of

paying these rather expensive resources, customers can replace this service by making use

of lower cost resources. The customer organizes the holiday himself by buying the airline

tickets and hotel room directly on an Internet-based web site.

9 Visit <www.lays.be/press>. Accessed 05/04/2011.

26

The desire for a better status, recognition and social esteem can be classified as personal

motives (Etgar, 2008; Hoyer et al., 2010; Nambisan & Baron, 2009). In online CC customers

can expose their knowledge of products and problem-solving skills. If they contribute to

product support, an increase in their expertise-related status and reputation among peer

customers as well as with the manufacturer is possible. They can influence peer customers‟

products usage behavior and the product improvement plans of the vendor. As a

consequence, a sense of self-efficacy may be realized (Kollock, 1999). A firm can give a

form of recognition on individual, valuable contributors. For example, Amazon.com‟s Top 100

reviewer and other formal recognitions can give a feeling of pride to many of their receivers,

because they get a visible symbol of their uniqueness relative to other consumers.

Cognitive motives are found among customers motivated to co-create if they can gain

technology, product or service knowledge through participating in development groups and

forums run by the manufacturer (Hoyer et al., 2010). They can also acquire new skills

(Ramaswamy & Gouillart, 2010). Also strengthening of the understanding of the environment

can be seen as a cognitive motive (Nambisan & Baron, 2009). For example, Blackberry has

a forum where consumers can be part in all stages of the CC process. By exchanging ideas

and inputs from others in the community, gains in technology knowledge can be achieved.

Social motives refer to the expectation of enjoyment of sharing activities with people of

similar interests and desires, referred to as social contact values (Berthon & John, 2006).

Consumers can join actual or virtual CC communities and social networks, such as Harley-

Davidson bike riders. These are non-commercial communities organized around particular

experiences characterized by a collective consciousness, rituals and traditions, and a sense

of moral responsibility (Etgar, 2008). Social motives also include the advantages derived

from the social and relational ties that are formed over time among customers.

Enhancements of a sense of belongingness or social identity are examples (Kollock, 1999).

Customers‟ CC motives can also be based on hedonic or affective motives as they long for

pleasure, play and fun. Interactions between consumers can be a source of highly

pleasurable as well as mentally stimulating experiences (Nambisan & Baron, 2009). Studies

on brand communities show that customers derive an amount of pleasure when conversing

about the product, its features and peculiarities of the usage context (Muniz & O‟Guinn,

2001). Next, the problem solving that is the basis of product support CC can be a source of

mental or intellectual stimulation that forms another aspect of hedonic benefits.

27

Füller (2010) also recently elaborated on consumer expectations from online CC and how

their motivations and personalities influence those expectations. He states that consumer

motives for CC may be heterogeneous and depend on their personality. With self-

determination theory as starting point - a macro-theory of human motivation recently

developed in the psychology (Vansteenkiste, Niemiecc & Soenens, 2010) - consumers‟

engagement in CC is a function of intrinsic motivation and self-determined extrinsic

motivation.

A consumer is intrinsically motivated when he values an activity because of the gratification

of the experience. Motivation is present inside. For example, you are a teacher because you

like teaching and working with children. The second source of motivation, extrinsic

motivation, implies that people will engage in CC activities not because of gratification and

joy, but because a certain results needs to be obtained. For example, as a teacher you

prepare the lessons well in order to get a positive evaluation at the end of the year. As

intrinsic motivation originates from the person himself, extrinsic motivation is controlled from

outside. Self-determination states that a part of the extrinsically initiated activities and

behaviors can be internalized so that people will undertake extrinsic motivated actions and

behaviors by themselves. In this way, autonomous or self-determined extrinsic motivation

arises.

It is a combination of intrinsic, internalized extrinsic and extrinsic motivation that drives

people to engage in online CC activities. A summary of the ten motivation categories

identified – intrinsic playful, curiosity, self-efficacy, skill development, information seeking,

recognition (visibility), community support, making friends, personal need (dissatisfaction),

and compensation (monetary reward) - are represented in the figure below.

[Insert Figure 9]

Dependent variable 1: Enjoyment

H1a: Regardless of the technology, customers that dispose of a high level to co-

create have a higher enjoyment than customers with a low level to co-create. (Main

effect)

H1b: There is an interaction effect between degree of CC and technology on the

one hand, and customer enjoyment on the other hand.

28

A cluster analysis grouped these motives to four different consumer types based on

the degree of web-exploration and innovation behavior.

[Insert Figure 10]

Reward oriented consumers are very motivated to engage in online CC. They have a

high interest in innovation activities and for their knowledge input they desire monetary

rewards. Need-driven consumers engage in CC mainly because of dissatisfaction with

existing products solutions available on the market. Curiosity interested consumers

participate in CC because of… curiosity. Finally, intrinsically motivated consumers yield high

on emotional aspects connected with the innovation activity and are not interested in

monetary rewards.

2.5 Cost-benefit analysis

As the benefits of CC/CP for the customer are defined, they need to be weighed against the

costs that are associated with engaging in a CC process. The end result is a cost-benefit

analysis that determines whether the customer will engage in such CC process or rather

avoid the involvement.

In the definition of customer value (supra, p.15) financial, time, energy and psychic costs

were distinguished. Etgar (2008) handles a similar classification making a distinction

between economic and non-economic costs. Economic costs are the cost of using their

operand material resources and the time customers have to spend on the CC process. Time

is evaluated subjectively while material resources can be sometimes objectively compared

with the market price.

The non-economic costs concern the psychological and social losses a customer can

experience in their CC effort. First of all, there is a loss of freedom as customers cannot

choose between the different brands and suppliers that result from linking up with particular

production partners. Another non-economic cost is the risks of misperformance. CC

involvement demands cognitive effort and ability of the customers. If they are unskilled so

that the customers do not know how to handle the experience environment provided by the

company, no desired output can be obtained (Etgar, 2008). Hilton and Hughes (2008) state

that the cognitive resources available to the individual are an important factor in using

technological interfaces and thus in online CC. Some are more enthusiastic and capable in

29

using these interfaces, some are technologically anxious or lack experience in technology. It

is clear that (online) CC asks physical effort of the customer, and maybe it is too exhaustive

for the customer to take part in such a process. Prahalad and Ramaswamy (2004a) suggest

that access to computer and electronic communications technology skills are nowadays

important to dialogue with firms and to engage in CC processes. This leads to the following

proposition.

Another interesting question about these costs is how much a customer wants to pay for a

product he made based on CC. Does he want a lower price because of the time and effort

made in the CC process or does he want to pay more for the creation because the

involvement gives him more value (McGraw & Tetlock, 2005)?

Dependent variable 2: Cognitive effort and ability

H2a: Regardless of the technology, customers that dispose of a high level to co

create need more cognitive effort and ability than customers with a low level to co-create.

(Main effect)

H2b: Regardless of the degree of CC, customers that co-create online need more

cognitive effort and ability than customers that co-create offline. (Main effect)

H2c: There is an interaction effect between degree of CC and technology on the

one hand, and customer cognitive effort and ability on the other hand.

Dependent variable 3: WTP

H3a: Regardless of the technology, customers that dispose of a high level to co-

create are willing to pay more than customers that dispose of a low level to co-create.

(Main effect)

H3b: Regardless of the degree of CC, customers that co-create offline are more

WTP than customers that co-create online.

H3c: There is an interaction effect between degree of CC and technology on the

one hand, and customer WTP on the other hand.

30

2.6 Where and with whom does CC occur in the value chain?

Consumers now seek to exercise their influence in every part of the business system, not

only at the point of exchange (Prahalad & Ramaswamy, 2004b). I would like to make a

distinction between CC for personal use and CC in NPD for commercialization.

Concerning CC for personal use, such as the introduction examples of CC (supra, p.13),

anyone can be part of a CC activity, based upon the net result between motives and benefits

minus costs associated with engaging in a CC process as discussed above. Looking at the

building blocks of CC (supra, section 2.3) some prefer not to dialogue at length about the

product. If a person decides to engage in a CC activity, it does not necessarily mean that the

CC experience will be positive. For example, if the customer believes that the interactions

with the company did not go as they should, because the company dialogued unilaterally,

behaved unfairly, or did not discuss the risks overtly, then the CC experience may be very

negative.

An interesting question is whether CC is the future for every single product (Interview with

Mr. Goedertier, 10/02/2011, Vlerick Leuven Management School; Van Der Wal, 2007). How

does CE differ from CC in a high involvement product as opposed to a low involvement

product? High product involvement has been researched to lead to greater perception of

differences in attributes, perception of greater product importance and greater commitment to

brand choice (Howard & Seth, 1969). Bauer et al. (2006) set up a measurement model for

product involvement and came to the conclusion that product involvement is a function of

sign value, importance and pleasure of the product to a particular person. This leads to the

last variable. In the experiment this variable can be used to check its influence on the

hypotheses as stated above.

Next, the area of NPD is a context where consumer CC is essential (Hoyer et al., 2010). Now

consumers have the opportunity and willingness to brainstorm on and discuss ideas for new

goods and services that may fulfill needs that not yet have been met by the market or

ameliorate existing offerings (Ernst et al., 2010). In the context of NPD, O‟Hern and

Rindfleisch (2009, p.4) define CC as “A collaborative new product development (NDP)

activity in which consumers actively contribute and select various elements of a new product

Moderating variable: Product involvement

31

offering.” The customer is in other words an active player in the NPD process. A great

example in which customers are involved in all steps of a NPD process - idea generation,

screening and investigation, specification of features, product development, beta testing or

field testing, product launch and evaluation (Dwyer & Tanner, 2009) - is the collaboration

between customer groups and the Volvo Company concerning the XC90 NPD process. A

group of people was invited for several meetings over time. Expectations and opinions were

extracted about SUVs in general to be used in the concept development phase. Focus

groups around interior and exterior design were organized and ultimately a test drive was

offered in the final version of the XC90. Every participant received $ 50 but this was less of

an incentive than the social value that was derived from the meetings and the opportunity to

be heard. But who will take part in this form of CC, CC as innovation? Not everybody is

interested or able to be part of a CC process in NPD. In firms that possess a huge customer

base, only a relative small amount of people will be willing to be fully engaged in or have the

skills to be useful in the product development and launch processes (Etgar, 2008; O‟Hern &

Rindfleisch, 2009). Investigators have recently identified segments of consumers that might

be especially willing and able to engage in these CC activities.

1. Innovators

Rogers (1995) set up a theory in his book Diffusion of Innovations about how, why, and at

what rate new ideas and technologies spread through cultures. He defines an adopter

category as the classifications of members of a social system on the basis of innovativeness.

A total of five categories of adopters in order to standardize the usage of adopter categories

in diffusion research were distinguished: innovators, early adopters, early majority, late

majority, and laggards. An S-curve is the reproduction of the innovation adoptions when

plotted over a length of time as shown in the figure below.

[Insert Figure 11]

Innovators are the consumers who are the earliest to adopt new products. They have

many characteristics: very young, risk loving, highest social class, great financial lucidity,

very social, closest contact to scientific sources and interaction with other innovators

(Rogers, 1995).

32

2. Lead users

A term developed by Von Hippel (1986). These people face needs that will eventually be

general in a marketplace. They face these needs before the majority of the marketplace

recognizes them and are therefore well positioned to solve these needs themselves.

3. Emergent consumers

These are consumers that are capable of applying judgment and intuition for the

improvement of product concepts that mainstream consumers will find pleasing and helpful

(Hoffman et al., 2010).

4. Market mavens

The last segment of consumers that especially will be willing and able to engage in NPD CC

activities are the market mavens. These people possess a lot of information about a wide

range of products, shopping places and other facets of the market. They also have a high

propensity to set up discussions and respond to information requests from other consumers

(Feick & Price, 1987).

33

Part II: Empirical study

3. Purpose of the study

A research study starts with the preparation of a management question. Through collecting

appropriate information, this question should be answered by the research study with a high

degree of certainty. Starting from the management question, a hierarchy of other questions

can be derived. First of all, the management question needs to be translated to the research

question. Next, the research question is translated into investigative questions, a more

specific description of the different needs questions that need to be answered. Finally, the

investigative questions are converted into measurement questions, specific questions and

examination procedures by which the information will be collected (De Pelsmacker & Van

Kenhove, 2006).

3.1 Management question

The purpose of this study is to understand the impact of customer participation on customer

experience. The management question can be formulated as follows:

What is the effect of evolving from mass customization (referred to as low degree of

CC) to CC (referred to as high degree of CC) concerning a customer‟s experience? Is

it useful to go one step further?

3.2 Research questions

The research questions will determine the type of information which the research must

provide to answer the management question (De Pelsmacker & Van Kenhove, 2006).

1. What is the impact of the degree of CC and technology on customer enjoyment?

2. What is the impact of the degree of CC and technology on customer WTP?

3. What is the impact of the degree of CC and technology on customer cognitive effort

and ability?

4. What is the impact of customer product involvement on the relationship between

degree of CC on the one hand, and WTP and customer enjoyment on the other

hand?

34

3.3 Investigative questions

The investigative questions are in fact the implicit hypotheses that support the model and will

be tested during the research (De Pelsmacker & Van Kenhove, 2006). All these hypotheses

were developed based on a literature study in the previous section. A summary of the

developed hypotheses for each dependent variable is given together with tables for

clarification.

1. Enjoyment

H1a: Regardless of the technology, customers that dispose of a high level to co-

create have a higher enjoyment than customers with a low level to co-create. (Main effect)

Degree of CC High Low

Customer enjoyment Customer enjoyment

Table 1: H1a

H1b: There is an interaction effect between degree of CC and technology on the one

hand, and customer enjoyment on the other hand.

2. Cognitive effort & ability

H2a: Regardless of the technology, customers that dispose of a high level to co-

create need more cognitive effort and ability than customers with a low level to co-create.

(Main effect)

Degree of CC High Low

Cognitive effort & ability Cognitive effort & ability

Table 2: H2a

H2b: Regardless of the degree of CC, customers that co-create online need more

cognitive effort and ability than customers that co-create offline. (Main effect)

Technology Online Offline

Cognitive effort & ability Cognitive effort & ability

Table 3: H2b

H2c: There is an interaction effect between degree of CC and technology on the one

hand, and customer cognitive effort and ability on the other hand.

35

3. WTP

H3a: Regardless of the technology, customers that dispose of a high level to co-

create are willing to pay more than customers that dispose of a low level to co-create. (Main

effect)

Degree of CC High Low

Cognitive effort & ability Cognitive effort & ability

Table 4: H3a

H3b: Regardless of the degree of CC, customers that co-create offline are more WTP

than customers that co-create online. (Main effect)

Technology Online Offline

Cognitive effort & ability Cognitive effort & ability

Table 5: H3b

H3c: There is an interaction effect between degree of CC and technology on the one

hand, and customer WTP on the other hand.

If there is indeed an interaction effect as hypotheses H1b, H2c and H3c state, the following

hypotheses are set up for the interaction effect.

1. Enjoyment

H1c: Customers that dispose of a high level to co-create in an online environment

have a higher enjoyment than customers that dispose of a low level to co-create in an online

environment.

H1d: Customers that dispose of a high level to co-create in an offline environment

have a higher enjoyment than customers that dispose of a low level to co-create in an offline

environment.

Technology/Degree of CC High Low

Online Customer enjoyment Customer enjoyment

Offline Customer enjoyment Customer enjoyment

Table 6: H1c/H1d

36

H1e: Customers that dispose of a high level to co-create in an online environment

have a higher enjoyment than customers that dispose of a high level of CC in an offline

environment.

H1f: Customers that dispose of a low level to co-create in an online environment have

a higher enjoyment than customers that dispose of a low level of CC in an offline

environment.

Degree of CC/Technology Online Offline

High Customer enjoyment Customer enjoyment

Low Customer enjoyment Customer enjoyment

Table 7: H1e/H1f

2. Cognitive effort and ability

H2d: Customers that dispose of a high level to co-create in an online environment

need more cognitive effort and ability than customers with a low level to co-create in an

online environment.

H2e: Customers that dispose of a high level to co-create in an offline environment

need more cognitive effort and ability than customers with a low level to co-create in an

offline environment.

Technology/Degree of CC High Low

Online Cognitive effort & ability Cognitive effort & ability

Offline Cognitive effort & ability Cognitive effort & ability

Table 8: H2d/H2e

H2f: Customers that dispose of a high level to co-create in an online environment

need more cognitive effort and ability than customers with a high level to co-create in an

offline environment.

H2g: Customers that dispose of a low level to co-create in an online environment

need more cognitive effort and ability than customers with a low level to co-create in an

offline environment.

Degree of CC/Technology Online Offline

High Cognitive effort & ability Cognitive effort & ability

Low Cognitive effort & ability Cognitive effort & ability

Table 9: H2f/H2g

37

3. WTP

H3d: Customers that dispose of a high level to co-create in an online environment

have a higher WTP than customers that dispose of a low level of co- creation in an online

environment.

H3e: Customers that dispose of a high level to co-create in an offline environment

have a higher WTP than customers that dispose of a low level of co- creation in an offline

environment.

Technology/Degree of CC High Low

Online WTP WTP

Offline WTP WTP

Table 10: H3d/H3e

H3f: Customers that dispose of a high level to co-create in an offline environment

have a higher WTP than customers that dispose of a high level to co-create in an online

environment.

H3g: Customers that dispose of a low level to co-create in an offline environment

have a higher WTP than customers that dispose of a low level to co-create in an online

environment.

Degree of CC/Technology Online Offline

High WTP WTP

Low WTP WTP

Table 11: H3f/H3g

3.4 Measurement questions

These are the specific survey questions that can be found in appendix A.

38

4. Data analysis

4.1 Sample & procedure

To find an answer to these questions an online survey was used for data collection. After an

offline pre-test with 10 participants, data collection with the final, adjusted questionnaire was

conducted within 2 weeks. As manipulation check, respondents had to perceive a difference

between high and low degree of CC. Both high and low degree was presented in the pre-test

and scenarios were adjusted until a difference in the independent variable „degree of CC‟

was clear. Starting point for the experimental design (i.e. the online survey) was a 2* 2 full

factorial design, derived from the figure below.

[Insert Figure 12]

Two variables – Degree of CC and Technology – were manipulated. Each variable was

measured on two levels; degree of CC: high versus low, and technology: online versus

offline. This resulted in four scenarios (X1, X2, X3, X4):

Degree of CC/ Technology Online Offline

High X1 X2

Low X3 X4

Table 12: Set up experiment

Each respondent was presented one scenario randomly (X1, X2, X3 or X4). To analyze the

data with a sufficient statistical certainty, a minimum of 2*2*30 or 120 observations were

needed in total. Consequently, at least 30 observations per condition were collected as

shown in table 12.

Degree of CC/ Technology Online Offline

High degree of CC N=34 N=42

Low degree of CC N=30 N=43

Table 13: N-values per condition in the experiment

A total of 149 people filled in the questionnaire completely. 26 were excluded. Possible

reasons are lack of time and/or Basic English to fill in the survey. A non-restrictive sample

was taken.

39

Within the total sample, 56 % of the respondents were male, 44 % female. On average,

participants were 36.19 years old (SD=13.00 years) and well educated: 49 % held a college

degree.

In each questionnaire, the respondent was presented one of the following combinations of

independent variables: High degree of CC + Online (X1); High degree of CC + Offline (X2);

Low degree of CC + Online (X3); or Low degree of CC + Offline (X4). The survey started with

an introduction in which people were encouraged to carefully read the scenario they would

be given. Next, some demographics were asked: gender, age, education and profession.

Then a first scenario was shown (accompanied by a picture for the clarity and empathy) with

a specific product to co-create: a garment. Starting question for this scenario had to do with

the first dependent variable „Enjoyment‟ regarding CC for a garment. The following questions

concerned the second dependent variable „WTP‟. Then, questions for the third dependent

variable „Cognitive effort and ability‟ were presented. The last questions consisted of the

fourth dependent variable „Product involvement‟. After the first scenario 3 similar scenarios

were presented, each scenario with another product to co-create: a computer, a ball pen and

a car. For each of these scenarios identical questions as the ones above were presented

except some questions about cognitive effort and ability as this depends on the independent

variables (X1, X2, X3 or X4). The purpose of the presentation of these four products for CC

is to collect both high involvement and low involvement products. Finally, respondents were

thanked for their contribution to the experiment.

Summarized, each respondent was presented 1 scenario X1, X2, X3 or X4 as a combination

of 2 independent variables as explained above; within this scenario, 4 small scenarios where

only the product to co-create changed (garment, computer, ball pen and car) were

presented. In total, 4 dependent variables were used: enjoyment, WTP, cognitive effort and

ability and product involvement.

Independent variables Dependent variables Moderating variables

Degree of CC Enjoyment Product involvement

Technology WTP

Cognitive effort and ability

Table 14: Summary variables experiment

40

4.2 Results

All questions for the 4 dependent variables were adapted from existing, tested

questionnaires (consult appendix B):

1. Enjoyment: 7-point interval scaled (1= strongly disagree, 7= strongly agree).

The following questions were combined to form the dependent variable „Enjoyment‟:

According to the scenario you just read (X1, X2, X3 or X4), this will be…

A. Enjoyable

B. Entertaining

C. Fun

D. Interesting

2. WTP: ratio scaled.

The following technique was used to measure the WTP:

Suppose that the average market price in a shopping store for the product you will co-create

is Y euros.

A. Would you pay Y + 20% euros for your creation according to the scenario? (=

medium price increase)

B. If answer is „yes‟ on question A: Would you pay Y + 40%? (= high price increase)

C. If answer is „no‟ on question A: Would you pay Y + 10%? (= low price increase)

D. What is the maximum amount you want to pay (in €) for your creation according to

the scenario?

The last question (D) was used for the analysis.

3. Cognitive effort and ability: 7-point interval scaled (1= strongly disagree, 7= strongly

agree).

The following questions were taken together to form the dependent variable „Cognitive effort

and ability‟:

In case of the online scenarios X1(internet software) and X3 (option menu):

A. I would become confused when I use the internet software/option menu.

B. I would find it cumbersome to use the internet software/option menu.

C. I find it easy to get the internet software/option menu to do what I want it to do.

D. I would find the internet software/option menu rigid and inflexible to interact with.

E. Interacting with the internet software/option menu will be frustrating.

F. I will find it easy to learn how to work with the internet software/option menu.

41

G. Interacting with the internet software/option menu will be easy for me to

understand.

H. Overall, I find internet software/option menus easy to use.

Questions C, E, F and G were converted into the same direction as the other questions.

In case of the offline scenarios X2 and X4:

A. I would find it cumbersome to interact with the employee.

B. I would it hard to create the garment together with the employee exactly the way I

want it to look.

C. It will be easy to interact with the employee about what the garment should look

like.

D. Creating a garment together with an employee will be frustrating.

E. It will cost me a lot of effort to create the garment piece with the employee.

Question C was converted into the same direction as the other questions.

4. Product involvement: 7-point interval scaled (1= strongly disagree, 7= strongly

agree):

The following questions were taken together to form the dependent variable „Product

involvement‟:

In the scenario you were asked to create Y (garment, computer, ball pen or car). The next

questions are general questions about Y. Y…

A. Helps me express my personality.

B. Tells other people something about me.

C. Is part of my self-image

D. Does not reflect my personality

E. Is not relevant to me.

F. Matters to me

G. Is of no concern to me.

H. Is important to me.

I. Is fun.

J. Is fascinating.

K. Is exciting.

L. Is interesting.

Questions D, E and G were converted into the same direction as the other questions.

42

First a data control was conducted. In the questions for WTP, some people did not fill in a

price because they did not know what amount they would be willing to pay in the scenario

they were presented. Some people also filled in an amount of “zero” in the WTP questions.

As these are outliers and reflected that they were not interested (reasons like no time or low

product involvement) rather than really willing to pay nothing, these answers were excluded

from data analysis.

As the questions were adapted from existing questionnaires, only a Cronbach Alpha analysis

was conducted to measure whether the interval scaled items (questions) form the same

construct (dependent variable). In other words, are the items internally consistent? As an

example, Cronbach Alpha is represented in table 15 below per construct (interval scaled

dependent variable) for scenario X1 and the products that were presented for CC.

SCENARIO X1 Enjoyment Cognitive effort and ability

Product involvement

Garment, computer, ball pen and car

0.94 0.91 0.93

Table 15: Cronbach alpha interval scaled items

From the table above I can conclude that the internal consistency of the three constructs –

Enjoyment, Cognitive effort and ability, and Product involvement are reliable. See appendix

C for more detail. The internal consistency in the other scenarios for the different constructs

is also reliable. For an overview, consult appendix D.

43

For the full factorial design, is it appropriate to analyze the data with the General Linear

Univariate Model. The advantage of this two-way analysis of variance is that next to the main

effect of one independent variable on a dependent variable, possible interaction effects of the

two independent variables on a dependent variable can be retrieved. In other words, a two-

way analysis of variance executes two one-way analysis of variance tests and a test for

interaction all put together in one single table. The goal is to check whether the means of

different groups are equal. Consequently, the hypothesis that the means are equal is refuted

when the p-value < 0.05. First, the effect of degree of CC and technology on customer

enjoyment will be investigated. The following table shows the results.

DF F p

Intercept 1 2865.63 <.01

DegreeCC 1 4.13 .04

Technology 1 .20 .65

DegreeCC * Technology

1 8.41 <.01

Table 16: Output dependent variable „enjoyment‟

The explanatory power of this model is 7.3%. Table 16 shows that the degree of CC has a

significant effect on customer enjoyment (F(1) = 4.13; p = 0.04). When you dispose of a high

degree to co-create (M = 4.82), people have a higher enjoyment than when you dispose of a

low degree to co-create (M = 4.53) which is in line with hypothesis H1a. Technology does

not have a significant effect on customer enjoyment (F(1) = 0.20; p = 0.65). There is an

interaction effect between degree of CC and technology, and customer enjoyment (F(1) =

8.41; p < .01), which confirms hypothesis H1b. Where the interaction occurs can be derived

from figure 13 below supported by the 95% confidence intervals.

44

People with a high degree of CC in an online environment (M = 5.14) have a higher

enjoyment than people with a low degree to co-create in an online environment (M = 4.28),

which confirms hypothesis H1c. People with a high degree to co-create in an offline

environment (M = 4.56) enjoy it less than people with a low degree to co-create in an offline

environment (M = 4.71). If we compare the 95% confidence intervals, there is an overlap

between the confidence interval of „high degree of CC + offline‟ and „low degree of CC +

offline‟. Consequently, this difference is not significant so that hypothesis H1d is refuted.

Hypothesis H1e which states that customers‟ online enjoyment in a high degree of CC (M =

5.14) will be higher than customers‟ offline enjoyment in a high degree of CC (M = 4.56), is

refuted as there is again an overlap in the 95% confidence intervals. Finally, hypothesis H1f

is refuted as customers that dispose of a low level to co-create in online environment (M =

4.28) have a lower enjoyment than customers that dispose of a low level to co-create in an

offline environment (M = 4.71) and this difference is not significant. More detail of the results

can be found in appendix E.

45

The second analysis is also a two-way analysis of variance that examines the effect of

degree of CC and technology on customer cognitive effort and ability.

DF F p

Intercept 1 1310.31 <.01

DegreeCC 1 6.31 .01

Technology 1 4.12 .04

DegreeCC * Technology

1 4.95 .03

Table 17: Output dependent variable „cognitive effort and ability‟

The explanatory power of this model is 10.6%. Table 17 shows that the degree of CC has a

significant effect on customer cognitive effort and ability (F(1) = 6.31; p = 0.01). People who

dispose of a high degree to co-create (M = 4.04), have to put in more effort than when they

dispose of a low degree to co-create (M = 3.47) which aligns with hypothesis H2a.

Technology also has a significant effect on customer enjoyment (F(1) = 4.12; p = 0.04).

When people co-create online (M = 3.53) they need less cognitive effort and ability than

people that co-create offline (M = 3.94), regardless of the degree of CC so that hypothesis

H2b is disproved. Hypothesis H2c is confirmed as there is also an interaction effect between

degree of CC and technology, and a customer‟s cognitive effort and ability needed (F(1) =

4.95; p = 0.03). The occurrence of interaction can be derived from figure 14 below.

46

People with a high degree of CC in an online environment (M = 3.56) need more cognitive

effort and ability than people with a low degree to co-create in an online environment (M =

3.50). As this difference is not significant, hypothesis H2d is refuted. People with a high

degree to co-create in an offline environment (M = 4.43) need more effort than people with a

low degree to co-create in an offline environment (M = 3.46), which confirms hypothesis H2e.

Hypothesis H2f which states that customers‟ online cognitive effort and ability in a high

degree of CC (M = 3.56) will be higher than customers‟ offline enjoyment in a high degree of

CC (M = 4.43), is refuted. Finally, hypothesis H2g who says that customers that dispose of a

low level to co-create in online environment (M = 3.50) need more effort than customers that

dispose of a low level to co-create in an offline environment (M = 3.46), is refuted as there is

a strong overlap in the 95% confidence intervals of „low degree of CC + online‟ and „low

degree of CC + offline‟. More detail of the results of the second dependent variable can be

found in appendix F.

A third analysis examines the effect of degree of CC and technology on customer WTP.

Here, the effect of degree of CC and technology on the WTP for a garment, computer, ball

pen and car is investigated.

1. WTP garment

DF F p

Intercept 1 499.61 <.01

DegreeCC 1 11.03 <.01

Technology 1 .11 .74

DegreeCC * Technology

1 4.95 .03

Table 18: Output dependent variable „WTP garment‟

The R square of this model is 9.0%. Table 18 shows that the degree of CC has a significant

effect on customer WTP (F(1) = 1.03; p = <.01). When you dispose of a high degree to co-

create (M = 45.26), people are willing to pay more than when you dispose of a low degree to

co-create (M = 34.51) which aligns with hypothesis H3a. Technology has no significant

effect on customer WTP (F(1) = 0.11; p = 0.74). Consequently, hypothesis H3b is disproved.

There is an interaction effect between degree of CC and technology, and the WTP of

customers (F(1) = 4.95; p = 0.03) which confirms hypothesis H3c. Where the interaction

occurs can be derived from figure 15 below.

47

People with a high degree of CC in an online environment (M = 50.29) are willing to pay

more than people with a low degree to co-create in an online environment (M = 30.53) so

hypothesis H3d is confirmed. People with a high degree to co-create in an offline

environment (M = 41.19) have a higher WTP than people with a low degree to co-create in

an offline environment (M = 37.28). If we look at the confidence intervals, this difference is

not significant so hypothesis H3e is refuted. Hypothesis H3f which states that customer‟s

online WTP in a high degree of CC (M = 50.29) will be lower than a customer‟s offline WTP

in a high degree of CC (M = 41.19), is refuted as this difference is not significant. Finally,

hypothesis H3g who says that customers that dispose of a low level to co-create in online

environment (M = 30.53) have a lower WTP than customers that dispose of a low level to co-

create in an offline environment (M = 37.28), is also refuted as there is an overlap in the 95%

confidence intervals of „low degree of CC + online‟ and „low degree of CC + offline‟. More

detail of the results of WTP of a garment can be found in appendix G.

48

2. WTP computer

DF F p

Intercept 1 1904.75 <.01

DegreeCC 1 2.28 .13

Technology 1 1.14 .29

DegreeCC * Technology

1

.66 .42

Table 19: Output dependent variable „WTP computer‟

The explanatory power of this model is only 3.3%. Table 19 shows that the degree of CC

does not have a significant effect on customer WTP for a computer (F(1) = 2.28; p = 0.13) so

hypothesis H3a is refuted. Hypothesis H3b is also refuted as technology has no significant

effect on the WTP (F(1) = 1.14; p = 0.29). Hypothesis H3c together with H3d-g are refuted as

there is no interaction effect between degree of CC and technology, and WTP for a computer

(F(1) = 0.66; p = 0.42). More details of the results of WTP of a garment can be found in

appendix H.

3. WTP ball pen

DF F p

Intercept 1 1.01 .32

DegreeCC 1 1.87 .18

Technology 1 .01 .92

DegreeCC *

Technology

1 1.01 .32

Table 20: Output dependent variable „WTP ball pen‟

The explanatory power of this model is only 2.7%. Table 20 shows that the degree of CC

does not have a significant effect on customer WTP for a ball pen (F(1) = 1.87; p = 0.18) so

hypothesis H3a is refuted. Hypothesis H3b is also refuted as technology has no significant

effect on the WTP (F(1) = 0.01; p = 0.92). Hypothesis H3c together with H3d-g are refuted as

there is no interaction effect between degree of CC and technology, and WTP for a ball pen

(F(1) = 1.01; p = 0.32). More details of the results of WTP of a ball pen can be found in

appendix I.

49

4. WTP car

DF F p

Intercept 1 130.02 <.01

DegreeCC 1 3.59 .06

Technology 1 .13 .72

DegreeCC * Technology

1 .22 .64

Table 21: Output dependent variable „WTP car‟

The explanatory power of this model is only 2.9%. Table 21 shows that the degree of CC

does not have a significant effect on customer WTP for a car (F(1) = 3.59; p = 0.06) so

hypothesis H3a is refuted. Hypothesis H3b is also refuted as technology has no significant

effect on the WTP (F(1) = 0.13; p = 0.72). Hypothesis H3c together with H3d-g are refuted as

there is no interaction effect between degree of CC and technology, and WTP for a car (F(1)

= 0.22; p = 0.64). More details of the results of WTP of a car can be found in appendix J.

A summary of all the hypotheses with results can be found in appendix K.

4.3 Partial conclusions experiment - discussion

As I suspected, the results proof that people have a higher enjoyment when they dispose of

a high level to co-create (H1a: main effect), especially in an online environment (H1c:

interaction effect). Nowadays you can find a lot of low degree of CC examples, referred to as

mass customization, in which people are limited to the choices a company provides. It is the

company that decides what can be customized in order to suit the supply chain. In this case

the degree of freedom of a customer is limited. A high degree of CC along with the

technologies used as a platform for experience environments has enabled the realization of a

basic human need, which is the need to be creative, the need to be socially involved, the

need to participate which results in a higher enjoyment. If you give someone the means to

interact with the company and other people co-creatively, people will utilize this (Interview

with Francis Gouillart about the power of CC, 2010). The Self-Determination Theory as

discussed above (supra, p.26), reveals that autonomous motivation is determined by the

extent to which people‟s three basic psychological needs are fulfilled. These needs are the

extent to which people feel competent, feel related to others and have the feeling that they

50

can act autonomously (Vansteenkiste, Ryan, Deci, 2008). People with a high degree of CC

will have a higher autonomy, because a high degree of CC fosters a sense of freedom.

Customers are no longer limited to the customization options of a company. Secondly, the

need for relatedness is more fulfilled due to the existence of the building blocks of CC:

dialogue, access, risk-assessment and transparency. The need for competence has

increased also as people can now fully on what the co-created product must look like;

customers have more control over the situation. The better fulfillment of these three needs

enhances the autonomous motivation of people. As enjoyment is the purest form of intrinsic

motivation (see Figure 9) and intrinsic motivation is part of autonomous motivation (see

figure below), the higher enjoyment of people with a high degree of CC can also be

explained from a Self-Determination Theory perspective.

[Insert Figure 16]

As I suspected, there is no difference in enjoyment between online and offline CC. A possible

reason may be that some people like to chat with other unknown people, but have a social

anxiety or dislike to communicate with strangers face to face. Others may be very social and

prefer to co-create offline. Therefore, the choice between online and offline CC can depend

on the person‟s personality.

I expected that people who have a high degree of CC need more cognitive effort and ability

(H2a: main effect) and this hypothesis is confirmed. When you dispose of a low degree of

CC, this does not really require a lot of effort as people just have to pick their choices from

the option menu provided by the company. When you dispose of a high level to co-create,

people need more physical and psychological effort. Especially in online CC, it will depend

upon the cognitive resources available to the individual and his capabilities to use new

technologies. Some people may be technologically fearful while others may be more willing

to experiment with new technologies (Hilton, Hughes, 2008). There must be a match

between the cognitive resources available to the individual and the amount of cognitive

resources a new technology demands from the customer. It is the company‟s task to reduce

this cost associated with CC to a minimum. Companies will have to build an experience

environment in which people need a minimum of effort to co-create a unique personalized

experience. An important factor in the realization of this goal is to educate the customer by

giving him support and training. Also in a world in which the population ages this is an

interesting challenge. Apparently, and this was unexpected, people need more cognitive

effort and ability in an offline environment (H2b: main effect). A logical explanation for this

51

result is the high education of the respondents (49.0% held a college degree) and the fact

that 36.2 % of the respondents can be classified under „Generation Y‟, which is twice the

percentage of the population belonging to that age group (18.6% based on figures for

200810). People from generation Y are now between 16 and 30 years old and are

characterized by being technological savvy (De Pelsmacker, Geuens, Van den Bergh).

Consequently, they are familiar with different technologies so that their online effort will be

smaller than the offline effort needed for CC.

Concerning the WTP for each of the co-created products – garment, computer, ball pen and

car – the results only show a significant higher WTP for a garment when customers dispose

of a high degree of CC as opposed to a low degree of CC (H3a: main effect), especially in an

online environment (H3d). Nor for a computer, ball pen or car, the results show a significant

higher WTP. A post hoc analysis should try to reveal the reason for this ambiguity. A factor

that can affect the dependent variable „WTP‟ is the income of the respondent and this

variable was not taken into account. For example, people with a high income may willing to

pay a lot more for co-creating a product, although they have a low product involvement.

Conversely, people with a very high product involvement will have a high interest for CC but

their low income does not allow them to pay more for a co-created product than market price.

Technology has no influence on the WTP (H3b: main effect). A possible reason for this may

be that some people prefer not to buy products on the web because they do not trust online

purchases. People may be afraid of giving their credit card number, thinking that their data

will be abused or they will never receive their order. Other people might prefer online

purchases because of convenience, speed and time savings. These two effects may cancel

each other out so that no difference in technology is recognized. Technology only has an

impact on the cognitive effort and ability (not on enjoyment and WTP) where people need

more effort an offline environment (H2b: main effect). As it will be too expensive for

companies to pay their employees for letting a customer co-create products with them for

personal use, and as people need more effort for offline CC (technological skills seem to be

higher than social skills nowadays), I conclude that the future of CC lies in the World Wide

Web. Companies must try to build an online experience environment where people can co-

create a unique personalized experience.

10

Visit <http://economie.fgov.be/nl/statistieken/cijfers/bevolking/structuur/leeftijdgeslacht/pyramide>.

Accessed 10/05/2011

52

4.4 Post hoc analysis

The fact that customers are only willing to pay more for a garment in a high degree of CC

and the WTP for a computer, ball pen and car is the same in both high and low degree of CC

raises the question as to what causes this determination. A factor that can have an important

influence in the relationship between degree of CC and technology on the one hand, and the

WTP on the other hand is a customer‟s product involvement.

To start the analysis, some variables are recoded. The construct „Product involvement‟ is

interval scaled on a 7-point scale. Product involvement for a garment, computer, ball pen and

car were recoded. All values equal or above a 5 mean a high product involvement; with a

value less than 5 we talk about low product involvement. A two-way analysis of variance

checks the effect of degree of CC and technology on the WTP for people who have a high

product involvement for a garment, computer, ball pen and car.

WTP garment DF F P

Intercept 1 315.72 <.01

DegreeCC 1 6.69 .01

Technology 1 .50 .48

DegreeCC * Technology

1 2.61 .11

Table 22: Output moderating variable: High product involvement garment.

WTP computer DF F P

Intercept 1 1034.47 ,<.01

DegreeCC 1 4.55 .04

Technology 1 .45 .50

DegreeCC * Technology

1 .25 .62

Table 23: Output moderating variable: High product involvement computer.

53

WTP ball pen DF F P

Intercept 1 6.04 .09

DegreeCC 1 .14 .73

Technology 1 .14 .73

DegreeCC * Technology

1 .14 .73

Table 24: Output moderating variable: High product involvement ball pen.

WTP car DF F P

Intercept 1 70.75 <.01

DegreeCC 1 4.42 .04

Technology 1 .15 .70

DegreeCC * Technology

1 .43 .51

Table 25: Output moderating variable: High product involvement car.

If you take another look at the results of the dependent variable WTP now for people who

have a high product involvement, there is not only a significant difference in the WTP for a

garment (F(1) = 6.69; p = 0.01) depending on the degree of CC, but also for a computer (F(1)

= 4.55; p = 0.04) and a car (F(1) = 4.42; p = 0.04). Hypothesis H3a which states that people

with a high degree of CC are willing to pay more than people with a low degree of CC can

now not only be confirmed for a garment (M high degree of CC = 47.55; M low degree of CC

= 36.48), but also for a computer (M high degree of CC = 943.33; M low degree of CC =

823.24) and car (M high degree of CC = 52628.21; M low degree of CC = 31962.50) if we

take into account only people with a high product involvement. For a ball pen, the difference

between high and low degree of CC is not significant (F(1) = 0.14; p = 0.73). The reason for

this is that only 7 out of the 149 respondent classify a ball pen as high involvement product

so that generally this product is perceived as a low involvement product. More detail can be

found in appendices L, M, N and O.

What is also remarkable in the post hoc analysis is the fact that people with a high product

involvement are generally willing to pay more. The following table shows the differences in

the means of the WTP of all the respondents for a garment, computer and car versus the

mean in WTP for a garment, computer and car for people with a high product involvement.

54

WTP (in €) Degree of CC All Respondents High product involved

respondents

WTP Garment High 45.26 47.55

Low 34.51 36.48

WTP Computer High 847.13 943.33

Low 787.54 824.24

WTP Car High 40971.01 52628.21

Low 35425.37 31962.50

Table 26: Means WTP all respondents versus high product involved respondents.

Also the enjoyment of people with a high product involvement is higher than the mean of all

respondents. For more detail, see appendix P.

Degree of CC All respondents High product involved

respondents

Enjoyment (7-point

scale)

High 4.82 5.63

Low 4.53 5.50

Table 27: Means Enjoyment all respondents versus high product involved respondents.

The WTP for a ball pen as low involvement product does not show any differences for a high

versus low degree of CC. This raises the question whether there are any differences in the

enjoyment for the CC of a ball pen. In the previous analysis the construct enjoyment was

analyzed for the four products together. Now, I want to what the effect on the enjoyment is

for a ball pen only. An independent samples T-test checks the impact of the degree of CC on

the enjoyment of co-creating a ball pen. People with a high degree of CC (M = 3.87) do not

have a significant higher enjoyment than people with a low degree to co-create a ball pen (M

= 3.49) (t(147) = 1.38; p = 0.17). Consult appendix Q for more detail.

4.5 Partial conclusions post hoc analysis - discussion

The determination in the previous analysis of the WTP was that there is no difference in WTP

depending on the degree of CC. This is due to the fact that in the previous analysis people

with both high and low product involvement cancelled each other out so that there was no

significant difference. Now that only people with a high degree of CC are taken into account,

significant differences in the WTP for a garment, computer and car are clear. Consequently, I

55

can state that there is a higher WTP for a product when a person disposes of a high degree

of CC as opposed to a low degree of CC if that person has a high product involvement. In

this way the previous analysis of the WTP is refined.

People with a high product involvement are the potential customers. Those people have a

high interest in a particular product and therefore have a higher likelihood to co-create that

product. As high product involved people show even a higher WTP and enjoyment for CC

than in the previous where both high and low involved people were taken into account, this

strengthens the results: CC was proved to lead to a higher enjoyment and WTP (WTP after a

post hoc analysis) and this is even higher for high product involved people.

The fact that a ball pen does not show any differences between high and low degree of CC

concerning the WTP and enjoyment, this means that it is not useful as a company to take the

opportunity to let customers co-create a ball pen for personal use. Mass customization is

enough. This is in line with Etgar (2008) who states that CP will occur mainly in product

categories “where there are large and noticeable differences of product attributes among

different items or brands, whether physical or perceived” (Etgar, 2008, p. 100). Similarly, a

ball pen has not a lot of attributes so that Etgars conclusions can be extended from CP to

CC. Other products like detergents or toilet paper also do not have many different attributes.

Consequently, I can conclude that low involvements products will not be the future of CC.

4.6 Main conclusion

In general, I can conclude that the answer on the management question whether it is useful

to go from mass customization towards CC is a definite “Yes”. Indeed, it is a necessity.

Companies do not dictate any longer how value is created so they have to learn co-creating

value together with their customers. It is becoming a competitive imperative. As the role of

consumers changed from passive to active and their influence on value creation has never

been greater than today, companies should listen to them by embracing the concept of CC.

Thanks to the Internet word-of-mouth, dialogues, comments, ideas… reveal what consumers

value and this information is freely available in information networks. If you, as a company,

do not take into account this intelligence to create an even better customer experience, you

will be passed by your competitors who will. For the customer, CC enhances the enjoyment

as opposed to mass customization and the positive outcome for a company is the higher

WTP. However, the latter should be refined and state that a high product involvement leads

to a higher WTP. But this is not a problem as people with a high product involvement are

56

most likely to be interested in co-creating that particular product. Consequently, low

involvement products are not appropriate for CC. Applications of CC can happen online or

offline, but the future will be online as people nowadays have more technological skills and

would need more effort to co-create in an offline customer-employee relationship. Put simply,

companies should build online experience environments where people can co-create their

own unique personalized experience.

57

5. Limitations and further research

5.1 Limitations of the research

In this master dissertation, CC was approached by a B2C side from a customer‟s

perspective. As mentioned under section 2.6, people can co-create for personal use and in

NPD. The experiment only captures CC for personal use.

The questionnaire was solely completed by Belgian respondents. As this master thesis is

written in English and thus the survey too, an understanding of Basic English to fill out the

survey was required. As a consequence, people with little or no acquaintance of this

language were excluded from participation.

A third limitation is about the online fulfilment of the questionnaire. In the first trimester of

2010, Eurostat calculated that 73% of the Belgian households dispose of an internet

connection11. Consequently, 27% of the households were prevented from participating.

A last limitation is the fact that the scenario based experiment talks about making your own

garment, computer, ball pen and car in general. There were no existing brands for each of

the products mentioned or taken into account. Etgar (2008) indicates the importance of

product linked factors as a prior condition for consumers to be willing to co-create. The

existence of powerful brands such as designer clothing (for example Armani) have

developed brand personalities to convince consumers with a specific product attribute matrix

to fit the customer needs best (Ries & Trout, 2000; Aaker, 1996). In this case, consumers will

not be interested in changing or customizing a famous brand because it will lose its social or

psychological benefits of using or wearing such a brand. In the experiment, people were

asked to imagine creating their own garment. As a starting position they could make the

association with their own clothing brands or car brand. These can include such brand

personalities and may therefore have an influence on the results.

11

< http://datanews.rnews.be/nl/ict/nieuws/nieuwsoverzicht/2010/12/15/driekwart-belgische-gezinnen-heeft-

toegang-tot-internet/article-1194888897273.htm>. Accessed 10/05/2011.

58

5.2 Directions for further research and managerial implications

The book The Power of Co-Creation: Build It With Them to Boost Growth, Productivity, and

Profits (Ramaswamy & Gouillart, 2010) is an illustration for managers how CC of value is the

next business paradigm for all enterprises in the 21st century, and how the next generation

of organizations entails building capabilities for a co-creative enterprise12. The question is

whether the company is ready to embrace the new framework of CC. Traditional roles of the

firm are being challenged (supra, p.20) as the points of interaction between company and

consumer increase. These contact points provide opportunities for collaboration and

negotiation between company and consumer as well as opportunities for those processes to

break down (Prahalad & Ramswamy, 2004c). I think that a part of the answer may be found

in the culture of an organization. Schein (2010, p. 373-374) defines it as “A pattern of shared

basic assumptions that the group learned as it solved its problems of external adaptation and

internal integration, that has worked well enough to be considered valid and, therefore, to be

taught to new members as the correct way you perceive, think, and feel in relation to those

problems.” If everything in the organization is directed from top management, better known

as top-down approach, the need to change from a company-centric view to a customer-

centric view might not be visible or if it is recognized by lower levels, not accepted by the

institutional leaders of the firm. Lower levels of management are closer to the consumers,

communicate more with them and thus know more about changes in the marketplace. Put

simply, how does organization culture affect the ability to adapt the new EE along with the

concept of CC?

Many of the current mass customization and emerging CC applications occur online. At this

moment almost 2 billion people out of a worldwide population of 6.85 billion people have an

internet connection13. This penetration amount will still grow in the future. As the web can

provide an unprecedented number of touch-points between firm and end-consumer, so it will

be interesting for companies to have the ability to co-create on a global scale in order to

boost profits.

CC/CP takes place in mature economies and not in emerging or growing markets

(Johansson, 2006). In emerging markets, the focus is on the consumption of basic products

to fulfill the needs of people. In the early stages of a country‟s economic development,

customers are not that interested in customizing products but rather survive with existing

12

Visit <http://www.powerofcocreation.com/content/about-book>. Accessed 09/04/2011.

13 Visit <http://www.internetworldstats.com/stats.htm>. Accessed 11/04/2011.

59

regular products. In growth markets CC is not an issue. Growth economies provide

developments that create mass markets. Customers want to improve their living standards

by buying low cost, mass produced and standardized goods (Etgar, 2008). Brazil, Russia,

India and China - better known as the BRIC countries - are examples of such fast growing

markets. Once they reach the phase of maturity, it is just a question of how companies will

respond to these evolutions and try to get a first mover advantage by leveraging the concept

of CC.

As CC initiatives in NPD for market launch may rise to successful new ideas and outputs,

one can ask oneself whether the company or the consumer as co-creator/co-innovator has

the right of ownership of intellectual property. Some people may be co-creating because of

motives discussed in section 2.4 where little or no importance is given to economic motives.

Other people might be co-creating only because of extrinsic motivation, in particular financial

rewards, and this can cause some potential problems. Suppose your submission for a

famous company has become a great success and you are rewarded only € 500. Is this

sufficient to motivate people to generate successful ideas? This can create perceptions of

unfairness among contributing consumers. Do they want to retain full ownership over

intellectual property? Should there be a policy of consistency in intellectual property? As

firms and CC contributors want the intellectual property rights for themselves, this issue

should be carefully investigated so that it may not become a serious impediment for

engaging in CC activities (Interview with Mr. Goedertier, 10/02/2011, Vlerick Leuven

Management School; Hoyer, 2010).

XII

References

Aaker, D., 1996, Building strong brands, The Free Press, New York.

Addis, M., & Holbrook, M.B., 2001, On the conceptual link between mass customization and

experiential consumption: An explosion of subjectivity, Journal of Consumer Behaviour 1(1),

pp. 50-66.

Bauer, H.H., Sauer, N.E., & Becker, C., 2006, Investigating the relationship between product

involvement and consumer decision-making styles, Journal of Consumer Behaviour, Vol. 5,

pp. 350.

Bendapudi, N., & Leone, R., 2003, Psychological Implications of Customer Participation in

Co-Production, Journal of Marketing, 67(1), pp. 14-15.

Berthon, P., & John, J., 2006, From Entities to Interfaces, M.S. Sharpe, New York.

Boswijk, A., Thijssen, T., & Peelen, E., 2007, The Experience Economy – A new perspective,

Pearson Education Benelux, Amsterdam.

Boswijk, A., Thijssen, T., & Peelen, E., 2007, The Experience Economy – A new perspective:

DVD: Presentation Prof. Joseph Pine – The progression of economic value + Interview: the

Experience Economy by Venkat Ramaswamy, Pearson Education Benelux, Amsterdam.

Boswijk, A., & Peelen, E., Albert Boswijk & Ed Peelen over „Experiences, Interview, URL:

<http://www.managementenliteratuur.nl/925/albert_boswijk_en_ed_peelen_over_%E2%80%

98experiences%E2%80%99 >. (15/02/2011)

Bruggeman, W., Slagmulder, R., & Hoozée, S., 2010, Handboek beheerscontrole:

doelgericht sturen van bedrijfsprestaties, Intersentia, Antwerpen, Oxford.

Caru, A., & Cova, B., 2003, Revisiting consumption experience: A more humble but complete

view of the concept, Marketing Theory 3(2), pp.267–286.

Caru, A., & Cova, B., 2007, Consuming experience, Routledge, London.

XIII

Cornelis, A., 1988, Logica van het gevoel. Stabiliteitslagen in de cultuur als nesteling der

emoties, Stichting Essence, Diemen.

Dabholkar, P.A., 1996, Consumer evaluations of new technology-based self-service

options: An investigation of alternative models of service quality, International Journal of

Research in Marketing, Vol. 13, pp. 39.

Davis, F.D., 1989, Perceived Usefulness, Perceived Ease of Use, and User Acceptance of

Information Technology, Management Information Systems Research Center, Vol. 13, No. 3,

pp. 319-340.

De Pelsmacker, P, Geuens, M., Van den Bergh, J., 2010, Marketing communications: A

Europen Perspective (4th edition), Pearson Education, Harlow.

De Pelsmacker, P. & Van Kenhove, P., 2006, Marktonderzoek: Methoden en Toepassingen,

Pearson Education, Benelux.

Dwyer, F.R , & Tanner, J.F., 2009, Business Marketing: Connecting Strategy, Relationships

and Learning, McGraw-Hill/Irwin, New York.

Elofson, G., & Robinson, W., 2007, Collective customer collaboration impacts on supply-

chain performance, International Journal of Production Research, 45(11), pp. 2567-2594.

Ernst, Holger, Hoyer, W.D., Krafft, M., & Soll, J.H., 2010, Consumer Idea Generation,

working paper, WHU, Vallendar.

Etgar, M., 2008, A Descriptive Model of the Consumer Co-Production Process, Journal of the

Academy of Marketing Science, 36 (Spring), pp. 97-108.

Feick, L., & Linda Price, 1987, The Market Maven: A Diffuser of Marketplace Information,

Journal of Marketing, Vol. 51 (1), pp. 83-97.

Ford, H. & Crowther, S., 1922, My Life and Work, Doubleday, Page & Company, pp. 71-72.

XIV

Foreit, K.F, & Foreit, J.R., 2004, Willingness to pay surveys for setting prices for reproductive

health products and services; A user‟s manual, U.S. Agency for International Development

(USAID).

Fornerino, M.,Helme-Guizon, A., & de Gaudemaris, C., 2006, Mesurer L’immersion dans une

experience de consommation: Premiers developpements, Proceedings of the XXIIth

Congress de l‟AFM, Nantes, May 2006.

Frijda, N.H., 1986, The emotions: Studies in emotion and social interaction, Cambridge

University Press, New York.

Füller, J., 2010, Refining Virtual Co-Creation from a consumer perspective, California

Management Review, Vol. 52, No. 2, pp. 98-122.

Gentile, C., Spiller, N., & Noci, G., 2007, How to sustain the Customer Experience: An

overview of experience components that co-create value with the customer, European

Management Journal, pp. 395-410.

Ghazawneh, A., 2008, Managing Mass Collaboration: Toward a Process Framework, PhD

Thesis, University of Lund, Sweden,

URL:<http://biblioteket.ehl.lu.se/olle/papers/0003116.pdf>.(20/03/2011)>

Gouillart, 2010, Francis Gouillart about The Power of Co-creation, Youtube video, URL:

<http://www.youtube.com/watch?v=wGA-f0TLJ-c&feature=player_embedded>. (11/04/2011)

Hoffman, Donna, L., Kopalle, P.K., & Novak, T.P., 2010, The „„Right‟‟ Consumers for Better

Concepts: Identifying and Using Consumers High in Emergent Nature to Further Develop

New Product Concepts, Journal of Marketing Research, forthcoming.

Howard, John A., & Jagdish N. Sheth, 1969, The Theory of Buyer Behavior, John Wiley, NY.

Hoyer, W.D., Chandy, R., Dorotic, M., Krafft, M., & Singh, S.S., 2010, Consumer Co-creation

in New Product Development, Journal of Service Research, Vol. 13, pp. 283-296.

Jensen, R., 1999, The Dream Society: How the coming shift from information to imagination

will transform your business, McGraw-Hill, New York.

XV

Johansson, J., 2006, Global Marketing, McGraw-Hill International Edition, Boston.

Kambil, A., Friesen, G.B., & Sundaram, A., 1999, Co-creation: A new source of value,

Accenture Outlook, Nr. 2, pp. 38–43

URL: <https://ap2ps.com/NR/rdonlyres/63EBA8BD-5931-49D5-9752

8A0C65FF8049/0/cocreation2.pdf>. (20/03/2011)

Kollock, P., 1999, The Economies of Online Cooperation, Routledge, London, pp. 220-242.

Kotha, S., 1995, Mass Customisation – Implementing the Emerging Paradigm for

Competitive Advantage, Strategic Management Journal, p.22.

LaSalle, D., & Britton, T.A., 2003, Priceless: Turning ordinary products into extraordinary

experiences, Harvard Business School Press, Boston.

LEGO Factory, 2007

URL: <http://designbyme.lego.com/en-us/default.aspx>. (22/03/2011)

LSE Enterprise, Promise, 2009, Co-creation: New pathways to value. An overview

URL: <www.promisecorp.com/newpathways>. (10/02/2011)

Maslow, A.H., 1962, Towards a Psychology of Being, Van Nostrand, New York.

McGraw, A.P., & Tetlock, P.E., 2005, Taboo trade-offs, relational framing, and the

acceptability of exchanges, Journal of Consumer Psychology, Vol. 15(1), pp. 2-15.

Meyer, C. & Schwager, A., 2007, Understanding Customer Experience, Harvard Business

Review, pp. 1-11.

Nambisan, S., & Baron, R.A., 2009, Virtual Customer Environments: Testing a Model of

Voluntary Participation in Value Co-creation Activities, Journal of product Innovation

Management, Vol. 26, pp. 288-406.

Needham, A., & Medeiros, A.C.M., 2008, The ‘Co-creation Revolution’, Paper presented at

INNOVATE 2008 – ESOMAR World Research Conference.

XVI

Website, URL:

<http://www.facegroup.co.uk/up_video/learn/AFN.AM%20Esomar%20Paper%20Final.pdf>.

(19/03/2011)

Pine II, B.J., & Gilmore, J.H., 1999, The Experience Economy, Harvard Business School

Press, Boston.

Porter, M.E., 1998, Competitive advantage: creating and sustaining superior performance:

with a new introduction, Simon & Schuster, Free Press, New York.

Prahalad, C.K., & Ramaswamy, V., 2002, The Co-creation Connection, Strategy & Business,

issue 27.

Prahalad, C.K., & Ramaswamy, V., 2004a, The Future of Competition: Co-Creating Unique

Value with Customers, Harvard Business School Press, Boston.

Prahalad, C.K., & Ramaswamy, V., 2004b, Co-creation Experiences: The next practice in

value creation, Journal of interactive Marketing, Vol.18, No.3.

Prahalad, C.K., & Ramaswamy, V., 2004c, Co-creating unique value with customers,

Strategy & Leadership, Vol. 32, No. 3, pp. 4-9.

Ramaswamy, V., 2005, Co-creating Experiences with Customers: New Paradigm of Value

Creation, The TMTC Journal of Management, pp.6-14.

Ramaswamy, V., & Gouillart, F., 2010, The Power of Co-Creation: Build It with Them to

Boost Growth, Productivity, and Profits, Simon & Schuster, Free Press, New York.

Ries, A., & Trout, J., 2000, Positioning: The battle for your mind, Mc Graw-Hill, New York.

Rogers, E., 1995, Diffusion of Innovations (4th Edition), Simon & Schuster, Free Press, New

York.

Sanchez, R., & Heene, A., 2004, The new Strategic Management: Organization, Competition

and Competence, R.R. Donnelly & Sons, New York.

XVII

Schein, E.H., 2010, Organizational Culture and Leadership (4th Edition), Jossey Bass, San

Francisco.

Schmitt, B.H., 1999, Experiential Marketing, The Free Press, New York.

Shaw, C., & Ivens, J., 2005, Building Great Customer Experiences, MacMillan, New York.

Stone, R. N., & Gronhaug, K., 1993, Perceived risk: Further considerations for the marketing

discipline, European Journal of Marketing, 27(3), pp. 39–50.

Toffler, A., 1970, Future Shock, Bantam Books, New York.

Van Der Wal, 2007, Wordt het nog wat met mass customization en co-creation?

URL: < www.product-online.nl/document_download.php>. (13/04/2011)

Vansteenkiste, M., Niemiecc, C.P., & Soenens, B., 2010, The development of the five mini-

theories of self-determination theory: an historical overview, emerging trends, and future

directions, Advances in motivation and achievement, Vol.16A, pp.105-165.

Vansteenkiste, M., Ryan, R.M., & Deci, E.L., 2008, Self-determination theory and the

explanatory role of psychological needs in human well-being, In L. Bruni, F. Comim, & M.

Pugno (Eds.), Capabilities and happiness (pp 187-223), Oxford, UK : Oxford University

Press.

Vargo, S.L., & Lusch. R.F., 2008, Service dominant logic: continuing the evolution, Journal

of the Academy of Marketing Science, pp.1-10.

Verhoef, P.C., Lemon, K.N., Parasuraman, A., Roggeveen, A., Tsiros, M., Schlesinger, L.A.,

2009, Customer Experience Creation: Determinants, Dynamics and Management strategies,

Journal of Retailing 85 (1), pp. 31-41.

Von Hippel, E., 1986, Lead Users: A Source of Novel Product Concepts, Journal of

Management Science, Vol. 32, pp. 791-805.

Wolf, M.J., 1999, The entertainment economy. The mega media forces that are reshaping

our lives, Penguin Group, London.

Wunsch-Vincent, S., & Vickery, G., 2007, Participative Web: User-Generated Content.

XVIII

Directorate for Science, Technology and Industry, p.4

URL: <www.oecd.org/dataoecd/57/14/38393115.pdf>

Zwass, V., 2010, Co‑Creation: Toward a Taxonomy and an Integrated Research

Perspective, International Journal of Electronic Commerce. Vol. 15, No. 1, pp. 11-48.

Zwick, D., Bonsu S.K., & Darmody, A., 2008, Putting Consumers to Work: „Co-creation‟ and

new marketing govern-mentality, Journal of Consumer Culture, Vol 8(2):pp. 163–196,pp.

1469-5405.

XIX

Figures

XX

Figure 1: The process of experiencing. (Boswijk et al., 2007)

Figure 2: Motives of people. (Boswijk et al., 2007)

XXI

Figure 3: Actors in the creation of a meaningful experience. (Boswijk et al., 2007)

Degree of direction

100%

Staging

Staging of First generation

experience

CC

setting by

Second

generation

supplier

Self-

direction

Third generation

0%

0%

100%

Staging of experience setting by

individual

Figure 4: Progression of economic value. (Pine & Gilmore, 1999)

XXII

Figure 5: The coffee progression. (Pine & Gilmore, 1999)

Figure 6: CC matrix. (LSE Enterprise, 2008)

XXIII

Figure 7: GDL versus SDL on value creation. (Vargo, Maglio & Akaka, 2008)

Figure 8: Building blocks of the DART model combined. (Prahalad & Ramaswamy,

2004c)

Combined building blocks/ Effect New capabilities

Dialogue & risk assessment Debate and co-develop public and private

choices

Access & dialogue Develop and maintain thematic communities

Risk assessment & Transparency Co-develop trust

Transparency & access Make informed choices

XXIV

Figure 9: Motive categories for engaging in virtual CC projects. (Füller, 2010)

XXV

Figure 10: Proposed impact of personal characteristics on consumer motives. (Füller,

2010)

Figure 11: Relationship between types of adopters classified by innovativeness and

their location on the adoption curve. (Rogers, 1995)

XXVI

Figure 12: Classification of experimental designs. (Based on De Pelsmacker & Van

Kenhove, 2006)

Natural classical design True classical design True statistical design

Time series or trend: Before-after design with

control group

Completely randomized design

- After only design Four group six study

design

Full factorial design

- Before - after design After only design with

control group

Fractional factorial design

- Panel Randomized block design

- Ex post facto Latin square

Cross sectional design

Figure 13: Interaction effect degree of CC and technology on customer enjoyment.

XXVII

Figure 14: Interaction effect degree of CC and technology on cognitive effort and

ability.

Figure 15: Interaction effect degree of CC and technology on WTP Garment.

XXVIII

Figure 16: Types of motivation and regulation within SDT. (Vansteenkiste, Ryan &

Deci, 2008)

XXIX

Appendices

XXX

Two independent variables: Degree of CC (High/Low) * Technology (Online/Offline) results in

four scenarios.

Online Offline

High CC degree X1 X2

Low CC degree X3 X4

In all scenarios (X1, X2, X3 and X4) the following socio-demographics were asked: gender,

age, education and profession.

In the two online scenarios (X1 and X3) respondents were asked if they have already bought

once something online, if they possess a credit card and frequency of internet usage.

Scenario X1: High degree of CC + Online

Imagine that you're sitting at your desk behind your computer connected to the World Wide

Web and you want to buy a garment. You search for the site

www.makeyourowngarment.com where you have the chance to create a garment.

You start from a white screen. To support the process of creating your own piece, the

website provides you with a software package that contains everything you need to make a

garment (you can create any garment you like: T-shirt, trousers...). You decide FULLY about

the shape, colors, design, materials... while making your creation.

It is not just an option menu from which you choose the colors or designs. You imagine

how your ideal garment looks like and with the support of the software you can draw, design,

shape... until your desired result is obtained. In other words, there are NO limitations on

your creation! You can make your own garment look like whatever you want.

You can dialogue with other people‟s creation, give comments, ideas,… You can also check

the progress of your creation on the manufacturing plant‟s website and dialogue with

employees about the eventual risks associated with your creation.

Appendix A: Survey scenario based experiment

XXXI

After you made your own garment, you get the option to let the company manufacture it for

you and buy it.

Please keep this information in mind when answering the next questions.

A. Enjoyment (Dabholkar,1996)

On a 7-point scale, according to the scenario (create garment online, no limitations, support

of software) this will be:

1. Enjoyable.

2. Entertaining.

3. Fun.

4. Interesting.

B. WTP (McGraw&Tetlock,2005)

Suppose that the average market price in a shopping store for the type (T-shirt, trousers...) of

garment you want to create is €25.

1. Would you pay €30 for your creation according to the scenario? (= medium price

increase of 20%)

2. If answer is „yes‟ on question 1: Would you pay €35? (= high price increase of 40%)

3. If answer is „no‟ on question 1: Would you pay €27.5? (= low price increase of 10%)

4. What is the maximum amount you want to pay (in €) for your creation according to the

scenario?

C. Cognitive effort and ability (Davis,1989)

On a 7-point scale, according to the scenario (creating your garment online with the support

of a software package…

1. I would become confused when I use the internet software.

2. I would find it cumbersome to use the internet.

3. I find it easy to get the internet software to do what I want it to do.

4. I would find the internet software rigid and inflexible to interact with.

5. Interacting with internet software will be frustrating.

6. I will find it easy to learn how to work with the internet software.

7. Interacting with internet software will be easy for me to understand.

8. Overall, I find internet software easy to use.

D. Involvement (Bauer, Sauer, Becker, 2006)

In the scenario you were asked to create any garment you like. The next questions are

general questions about garments. On a 7-point scale, a garment...

XXXII

1. Tells other people something about me.

2. Helps me express my personality.

3. Does not reflect my personality.

4. Is part of my self-image.

5. Is not relevant to me.

6. Does not matter to me.

7. Is of no concern to me.

8. Is important to me.

9. Is fun.

10. Is fascinating.

11. Is exciting.

12. Is interesting.

After these questions, the survey (except the questions under C. Cognitive effort and ability)

is repeated for the following products:

Computer (WTP: Suppose average market price is €700)

Ball pen (WTP: Suppose average market price is €2)

Car (WTP: Suppose average market price is € 25.000)

XXXIII

Scenario X2: High degree of CC + Offline

Imagine that you go outside to a clothing shop A that provides a service to create your own

garment together with an employee. You sit together with the employee at a desk with a

white page in front of you and supporting materials to make drawings. You give your ideas to

the employee and create together with the employee your own garment piece.

You decide FULLY about the shape, colors, design, materials and so on.

It is not just an option menu from which you choose the colors or designs. You imagine

how your ideal garment looks like and with the support of the software you can draw, design,

shape... until your desired result is obtained.

In other words, there are NO limitations on your creation! You make your own garment look

like whatever you want. You can dialogue with the employee about eventual risks associatd

with your creation. After you made your own garment, you get the option to let the company

manufacture it for you and buy it.

Please keep this information in mind when answering the following questions.

A. Enjoyment (Dabholkar,1994)

On a 7-point scale, according to the scenario (create garment together with an employee, no

limitations, supporting materials) this will be:

1. Enjoyable.

2. Entertaining.

3. Fun.

4. Interesting.

XXXIV

B. WTP (McGraw&Tetlock,2005)

Suppose that the average market price in a shopping store for the type (T-shirt, trousers...) of

garment you want to create is €25.

1. Would you pay €30 for your creation according to the scenario? (= medium price

increase of 20%)

2. If answer is „yes‟ on question 1: Would you pay €35? (= high price increase of 40%)

3. If answer is „no‟ on question 1: Would you pay €27.5? (= low price increase of 10%)

4. What is the maximum amount you want to pay (in €) for your creation according to the

scenario?

C. Cognitive effort and ability (Davis,1989)

On a 7-point scale, according to the scenario (creating your garment together with an

employee, no limitations…

1. I would find it cumbersome to interact with the employee.

2. I would it hard to create the garment together with the employee exactly the way I

want it to look like.

3. I will be easy to interact with the employee about how the garment must look like.

4. Creating a garment together with an employee will be frustrating.

5. It will cost me a lot of effort to create the garment piece with the employee.

D. Product involvement (Bauer,Sauer,Becker,2006)

In the scenario you were asked to create any garment you like. The next questions are

general questions about garments. On a 7-point scale, a garment...

1. Tells other people something about me.

2. Helps me express my personality.

3. Does not reflect my personality.

4. Is part of my self-image.

5. Is not relevant to me.

6. Does not matter to me.

7. Is of no concern to me.

8. Is important to me.

9. Is fun.

10. Is fascinating.

11. Is exciting.

12. Is interesting.

XXXV

After these questions, the survey (except the questions under C. Cognitive effort and ability)

is repeated for the following products:

Computer (WTP: Suppose average market price is €700)

Ball pen (WTP: Suppose average market price is €2)

Car (WTP: Suppose average market price is € 25.000)

Scenario X3: Low degree of CC + Online

Imagine that you're sitting at your desk behind your computer connected to the world wide

web and you want to buy a garment. You search for the site www.makeyourowngarment.com

where you have the chance to create a garment.

There are limitations on your creation. The website provides you with an option menu from

which you can choose between X different types of garments, X different colors, X different

shapes, X different materials, X different designs... to customize your own garment.

For example: you could choose between 20 colors and chose dark blue, you picked design

number 14, shape 2....

After you made your own garment by choosing from the option menu, you get the option to

let the company manufacture it for you and buy it.

Please keep this information in mind when answering the next questions.

The same questions as in scenario X1 were asked, putting emphasis on the characteristics

of this scenario: creation with limitations instead of no limitations and support of option menu

instead of software package.

XXXVI

Scenario X4: Low degree of CC + Offline

Imagine that you go outside to a clothing shop A that provides a service to create your own

garment together with an employee. You sit together with the employee at a desk to create

the garment.

There are limitations on your creation. The employee gives you an option menu from

which you choose between X different types of garments, X different colors, X different

designs, X different shapes… to customize your own garment.

For example: you could choose between 20 colors and chose dark blue, you picked design

number 14, shape 2....

After you made your own garment, you get the option to let the company manufacture it for

you and buy it.

Please keep this information in mind when answering the following questions.

The same questions as in scenario X2 were asked, putting emphasis on the characteristics

of this scenario: creation with limitations instead of no limitations and support of option menu

instead of software package.

XXXVII

1. Enjoyment: based on Dabholkar (1996, p. 39)

2. WTP: based on Foreit (2004, p. 6)

Appendix B: Scales used for the questionnaire

XXXVIII

3. Cognitive effort and ability: based on Davis (1989, p. 324)

4. Product involvement: based on Bauer et al. (2006, p. 350)

XXXIX

Enjoyment

Case Processing Summary

N %

Cases Valid 34 22,8

Excludeda 115 77,2

Total 149 100,0

a. Listwise deletion based on all variables

in the procedure.

Reliability Statistics

Cronbach's

Alpha N of Items

,940 16

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's

Alpha if Item

Deleted

X1 – Garment - Enjoyable 72,47 280,499 ,801 ,934

X1 – Garment - Entertaining 72,38 281,516 ,848 ,933

X1 – Garment - Fun 72,44 282,618 ,790 ,934

X1 – Garment - Interesting 72,53 281,772 ,696 ,936

X1 – Computer - Enjoyable 73,62 289,092 ,622 ,938

X1 – Computer - Entertaining 73,59 285,765 ,714 ,936

X1 – Computer - Fun 73,59 286,734 ,675 ,937

X1 – Computer - Interesting 72,94 289,390 ,611 ,938

X1 – Ball pen - Enjoyable 73,62 273,395 ,782 ,934

X1 – Ball pen - Entertaining 73,62 278,546 ,714 ,936

X1 – Ball pen - Fun 73,41 280,250 ,718 ,936

X1 – Ball pen - Interesting 74,06 284,966 ,686 ,936

X1 – Car - Enjoyable 72,15 297,644 ,575 ,939

X1 – Car - Entertaining 72,15 296,857 ,619 ,938

X1 – Car - Fun 72,21 298,714 ,571 ,939

X1 – Car - Interesting 72,15 302,735 ,489 ,940

Appendix C: SPSS output internal consistency constructs in scenario X1

XL

Cognitive effort and ability

Case Processing Summary

N %

Cases Valid 34 22,8

Excludeda 115 77,2

Total 149 100,0

a. Listwise deletion based on all variables in the

procedure.

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's

Alpha if Item

Deleted

X1 - I would become

confused when I use the

internet software

24,59 66,856 ,741 ,899

X1 - I would find it

cumbersome (lastig) to use

the internet software

24,85 63,584 ,920 ,883

X1 - I would find it hard to get

the internet software to do

what I want it to do

24,79 65,926 ,839 ,891

X1 - I would find the internet

software rigid and inflexible

to interact with

25,09 81,962 ,206 ,936

X1 - Interacting with the

internet software will be

frustrating

24,59 69,522 ,645 ,907

X1 - I will find it hard how to

work with the internet

software

25,06 67,269 ,713 ,902

X1 - Interacting with the

internet software will be hard

for me to understand

25,06 66,360 ,790 ,895

X1 - Overall, I find internet

software hard to use

25,06 65,451 ,860 ,889

Reliability Statistics

Cronbach's

Alpha N of Items

,913 8

XLI

Product involvement

Case Processing Summary

N %

Cases Valid 34 22,8

Excludeda 115 77,2

Total 149 100,0

a. Listwise deletion based on all variables in the

procedure.

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's

Alpha if Item

Deleted

X1 – Garment - helps me to express my

personality

199,62 1234,971 ,571 ,930

X1 – Garment - tells other people

something about me

199,47 1245,348 ,646 ,930

X1 – Garment - is part of my self- image 199,56 1248,799 ,499 ,930

X1 – Garment – reflects my personality 199,97 1242,272 ,405 ,931

X1 – Garment – is relevant for me 199,56 1234,739 ,584 ,930

X1 – Garment - matters to me 199,56 1219,769 ,704 ,929

X1 – Garment – is of concern to me 199,50 1219,227 ,633 ,929

X1 – Garment - is important to me 199,79 1219,017 ,670 ,929

X1 – Garment - is fun 199,88 1226,895 ,655 ,929

X1 – Garment - is fascinating 200,18 1218,150 ,633 ,929

X1 – Garment - is interesting 199,82 1233,241 ,624 ,929

X1 – Garment - is exciting 200,29 1227,305 ,543 ,930

X1 – Computer - helps me to express

my personality

202,24 1220,852 ,610 ,929

X1 – Computer - tells other people

something about me

202,26 1234,443 ,532 ,930

X1 – Computer - is part of my self-

image

202,35 1238,599 ,501 ,930

X1 – Computer – reflects my

personality

202,03 1256,817 ,233 ,932

Reliability Statistics

Cronbach's

Alpha N of Items

,932 48

XLII

X1 – Computer – is relevant for me 200,94 1237,269 ,334 ,932

X1 – Computer - matters to me 200,68 1221,619 ,448 ,931

X1 – Computer – is of concern to me 200,76 1207,882 ,564 ,929

X1 – Computer - is important to me 200,59 1231,401 ,388 ,931

X1 – Computer - is fun 200,50 1231,227 ,456 ,930

X1 – Computer - is fascinating 200,88 1234,410 ,425 ,931

X1 – Computer - interesting 200,21 1239,744 ,410 ,931

X1 – Computer – is exciting 201,15 1240,069 ,389 ,931

X1 – Ball pen - helps me to express my

personality

202,41 1265,583 ,200 ,932

X1 – Ball pen - tells other people

something about me

202,21 1251,865 ,311 ,931

X1 – Ball pen - is part of my self- image 202,56 1238,496 ,490 ,930

X1 – Ball pen – reflects my personality 202,15 1249,826 ,270 ,932

X1 – Ball pen – is relevant for me 201,68 1226,347 ,428 ,931

X1 – Ball pen - matters to me 202,06 1212,299 ,612 ,929

X1 – Ball pen – is of concern to me 201,65 1214,660 ,532 ,930

X1 – Ball pen - is important to me 201,97 1209,908 ,643 ,929

X1 – Ball pen - is fun 201,29 1272,396 ,089 ,934

X1 – Ball pen - is fascinating 201,88 1252,107 ,300 ,932

X1 – Ball pen - is interesting 201,76 1273,276 ,105 ,933

X1 – Ball pen - is exciting 202,12 1253,319 ,312 ,931

X1 – Car - helps me to express my

personality

199,97 1244,332 ,345 ,931

X1 – Car - tells other people something

about me

199,88 1232,531 ,491 ,930

X1 – Car - is part of my self- image 200,24 1238,791 ,391 ,931

X1 – Car – reflects my personality 200,29 1239,184 ,355 ,931

X1 – Car – is relevant for me 200,06 1208,663 ,651 ,929

X1 – Car - matters to me 199,94 1219,875 ,670 ,929

X1 – Car – is of concern to me 199,82 1224,574 ,606 ,929

X1 – Car - is important to me 200,15 1217,281 ,602 ,929

X1 – Car - is fun 199,50 1256,258 ,326 ,931

X1 – Car - is fascinating 199,82 1249,362 ,411 ,931

X1 – Car - is interesting 199,79 1260,653 ,306 ,931

X1 – Car - is exciting 200,00 1237,879 ,517 ,930

XLIII

SCENARIO X2 Enjoyment Cognitive effort and

ability

Product

involvement

Garment,

computer, ball pen

and car

0.88 0.86 0.93

SCENARIO X3 Enjoyment Cognitive effort and

ability

Product

involvement

Garment,

computer, ball pen

and car

0.87 0.86 0.90

SCENARIO X4 Enjoyment Cognitive effort and

ability

Product

involvement

Garment,

computer, ball pen

and car

0.91 0.85 0.93

Appendix D: Internal consistency constructs in scenarios X2, X3 and X4

XLIV

Between-Subjects Factors

Value Label N

Degree of CC 1 High 76

2 Low 73

Technology 1 Online 64

2 Offline 85

Descriptive Statistics

Dependent Variable:SSEnjoymentX1234

Degree of CC Technology Mean Std. Deviation N

High Online 5,1400 1,35811 34

Offline 4,5551 ,98736 42

Total 4,8167 1,19592 76

Low Online 4,2792 ,86334 30

Offline 4,7064 ,95553 43

Total 4,5308 ,93681 73

Total Online 4,7365 1,22340 64

Offline 4,6316 ,96858 85

Total 4,6767 1,08272 149

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSEnjoymentX1234

F df1 df2 Sig.

2,269 3 145 ,083

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix E: SPSS output dependent variable „Enjoyment‟

XLV

Parameter Estimates

Dependent Variable:SSEnjoymentX1234

Parameter B Std. Error t Sig.

95% Confidence Interval

Lower Bound Upper Bound

Intercept 4,706 ,161 29,306 ,000 4,389 5,024

[DegreeCC=1] -,151 ,228 -,662 ,509 -,603 ,300

[DegreeCC=2] 0a . . . . .

[Technology=1] -,427 ,251 -1,705 ,090 -,922 ,068

[Technology=2] 0a . . . . .

[DegreeCC=1] *

[Technology=1]

1,012 ,349 2,900 ,004 ,322 1,702

[DegreeCC=1] *

[Technology=2]

0a . . . . .

[DegreeCC=2] *

[Technology=1]

0a . . . . .

[DegreeCC=2] *

[Technology=2]

0a . . . . .

a. This parameter is set to zero because it is redundant.

Tests of Between-Subjects Effects

Dependent Variable:SSEnjoymentX1234

Source

Type III Sum

of Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 12,698a 3 4,233 3,817 ,011 ,073

Intercept 3177,891 1 3177,891 2865,628 ,000 ,952

DegreeCC 4,584 1 4,584 4,134 ,044 ,028

Technology ,227 1 ,227 ,204 ,652 ,001

DegreeCC * Technology 9,330 1 9,330 8,413 ,004 ,055

Error 160,800 145 1,109

Total 3432,301 149

Corrected Total 173,499 148

a. R Squared = ,073 (Adjusted R Squared = ,054)

XLVI

Estimated Marginal Means

Grand Mean

Dependent Variable:SSEnjoymentX1234

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

4,670 ,087 4,498 4,843

Degree of CC

Estimates

Dependent Variable:SSEnjoymentX1234

Degree

of CC Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High 4,848 ,121 4,607 5,088

Low 4,493 ,125 4,245 4,740

Pairwise Comparisons

Dependent Variable:SSEnjoymentX1234

(I)

Degree

of CC

(J)

Degree

of CC

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

High Low ,355* ,174 ,044 ,010 ,700

Low High -,355* ,174 ,044 -,700 -,010

Based on estimated marginal means

*. The mean difference is significant at the ,05 level.

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSEnjoymentX1234

Sum of Squares df Mean Square F Sig.

Contrast 4,584 1 4,584 4,134 ,044

Error 160,800 145 1,109

The F tests the effect of Degree of CC. This test is based on the linearly independent pairwise

comparisons among the estimated marginal means.

XLVII

Technology

Estimates

Dependent Variable:SSEnjoymentX1234

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Online 4,710 ,132 4,449 4,970

Offline 4,631 ,114 4,405 4,856

Pairwise Comparisons

Dependent Variable:SSEnjoymentX1234

(I)

Technolog

y

(J)

Technolog

y

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

Online Offline ,079 ,174 ,652 -,266 ,424

Offline Online -,079 ,174 ,652 -,424 ,266

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSEnjoymentX1234

Sum of Squares df Mean Square F Sig.

Contrast ,227 1 ,227 ,204 ,652

Error 160,800 145 1,109

The F tests the effect of Technology. This test is based on the linearly independent pairwise

comparisons among the estimated marginal means.

Degree of CC * Technology

Dependent Variable:SSEnjoymentX1234

Degree

of CC

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High Online 5,140 ,181 4,783 5,497

Offline 4,555 ,162 4,234 4,876

Low Online 4,279 ,192 3,899 4,659

Offline 4,706 ,161 4,389 5,024

XLVIII

XLIX

L

Between-Subjects Factors

Value Label N

Degree of CC 1 High degree of CC 76

2 Low degree of CC 73

Technology 1 Online 64

2 Offline 85

Descriptive Statistics

Dependent Variable:SSEffortX1234

Degree of CC

Technolog

y Mean Std. Deviation N

High degree of CC Online 3,5551 1,17389 34

Offline 4,4333 1,33319 42

Total 4,0405 1,33086 76

Low degree of CC Online 3,4958 1,23066 30

Offline 3,4558 1,22149 43

Total 3,4723 1,21687 73

Total Online 3,5273 1,19159 64

Offline 3,9388 1,36208 85

Total 3,7621 1,30352 149

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSEffortX1234

F df1 df2 Sig.

,249 3 145 ,862

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix F: SPSS output dependent variable „Cognitive effort and ability‟

LI

Tests of Between-Subjects Effects

Dependent Variable:SSEffortX1234

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 26,540a 3 8,847 5,703 ,001 ,106

Intercept 2032,661 1 2032,661 1310,313 ,000 ,900

DegreeCC 9,790 1 9,790 6,311 ,013 ,042

Technology 6,398 1 6,398 4,124 ,044 ,028

DegreeCC * Technology 7,678 1 7,678 4,949 ,028 ,033

Error 224,935 145 1,551

Total 2360,310 149

Corrected Total 251,476 148

a. R Squared = ,106 (Adjusted R Squared = ,087)

Estimated Marginal Means

1. Grand Mean

Dependent Variable:SSEffortX1234

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

3,735 ,103 3,531 3,939

Degree of CC Estimates

Dependent Variable:SSEffortX1234

Degree of CC Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High degree of CC 3,994 ,144 3,710 4,278

Low degree of CC 3,476 ,148 3,183 3,769

LII

Pairwise Comparisons

Dependent Variable:SSEffortX1234

(I) Degree of CC (J) Degree of CC

Mean Difference

(I-J) Std. Error Sig.a

95% Confidence Interval for

Differencea

Lower Bound Upper Bound

High degree of CC Low degree of CC ,518* ,206 ,013 ,111 ,926

Low degree of CC High degree of CC -,518* ,206 ,013 -,926 -,111

Based on estimated marginal means

*. The mean difference is significant at the ,05 level.

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSEffortX1234

Sum of Squares df Mean Square F Sig.

Contrast 9,790 1 9,790 6,311 ,013

Error 224,935 145 1,551

The F tests the effect of Degree of CC. This test is based on the linearly independent

pairwise comparisons among the estimated marginal means.

Parameter Estimates

Dependent Variable:SSEffortX1234

Parameter B Std. Error t Sig.

95% Confidence Interval

Lower Bound Upper Bound

Intercept 3,456 ,190 18,194 ,000 3,080 3,831

[DegreeCC=1] ,978 ,270 3,618 ,000 ,443 1,512

[DegreeCC=2] 0a . . . . .

[Technology=1] ,040 ,296 ,135 ,893 -,546 ,626

[Technology=2] 0a . . . . .

[DegreeCC=1] * [Technology=1] -,918 ,413 -2,225 ,028 -1,734 -,102

[DegreeCC=1] * [Technology=2] 0a . . . . .

[DegreeCC=2] * [Technology=1] 0a . . . . .

[DegreeCC=2] * [Technology=2] 0a . . . . .

a. This parameter is set to zero because it is redundant.

LIII

Technology

Estimates

Dependent Variable:SSEffortX1234

Technolo

gy Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Online 3,525 ,156 3,217 3,834

Offline 3,945 ,135 3,678 4,212

Pairwise Comparisons

Dependent Variable:SSEffortX1234

(I)

Technolo

gy

(J)

Technolo

gy

Mean Difference

(I-J) Std. Error Sig.a

95% Confidence Interval for

Differencea

Lower Bound Upper Bound

Online Offline -,419* ,206 ,044 -,827 -,011

Offline Online ,419* ,206 ,044 ,011 ,827

Based on estimated marginal means

*. The mean difference is significant at the ,05 level.

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSEffortX1234

Sum of Squares df Mean Square F Sig.

Contrast 6,398 1 6,398 4,124 ,044

Error 224,935 145 1,551

The F tests the effect of Technology. This test is based on the linearly independent

pairwise comparisons among the estimated marginal means.

Degree of CC * Technology

Dependent Variable:SSEffortX1234

Degree of CC

Technolo

gy Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High degree of CC Online 3,555 ,214 3,133 3,977

Offline 4,433 ,192 4,053 4,813

Low degree of CC Online 3,496 ,227 3,046 3,945

Offline 3,456 ,190 3,080 3,831

LIV

LV

LVI

Between-Subjects Factors

N

1= High degree of CC; 2= Low

degree of CC

1 76

2 73

1= Online; 2= Offline 1 64

2 85

Descriptive Statistics

Dependent Variable:SSWTPGarment

1= High degree of

CC; 2= Low degree of

CC

1= Online; 2=

Offline Mean Std. Deviation N

1 1 50,2941 34,04501 34

2 41,1905 14,84922 42

Total 45,2632 25,52038 76

2 1 30,5333 18,84553 30

2 37,2791 15,02272 43

Total 34,5068 16,90753 73

Total 1 41,0312 29,48552 64

2 39,2118 14,97784 85

Total 39,9933 22,32091 149

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPGarment

F df1 df2 Sig.

3,906 3 145 ,010

Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology + DegreeCC * Technology

Appendix G: SPSS output dependent variable „WTP‟ Garment

LVII

Tests of Between-Subjects Effects

Dependent Variable:SSWTPGarment

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 6669,340a 3 2223,113 4,806 ,003 ,090

Intercept 231084,897 1 231084,897 499,605 ,000 ,775

DegreeCC 5103,091 1 5103,091 11,033 ,001 ,071

Technology 50,630 1 50,630 ,109 ,741 ,001

DegreeCC * Technology 2287,604 1 2287,604 4,946 ,028 ,033

Error 67067,653 145 462,536

Total 312057,000 149

Corrected Total 73736,993 148

a. R Squared = ,090 (Adjusted R Squared = ,072)

Parameter Estimates

Dependent Variable:SSWTPGarment

Parameter B Std. Error t Sig.

95% Confidence Interval

Lower Bound Upper Bound

Intercept 37,279 3,280 11,366 ,000 30,797 43,761

[DegreeCC=1] 3,911 4,666 ,838 ,403 -5,310 13,133

[DegreeCC=2] 0a . . . . .

[Technology=1] -6,746 5,116 -1,319 ,189 -16,858 3,366

[Technology=2] 0a . . . . .

[DegreeCC=1] *

[Technology=1]

15,849 7,127 2,224 ,028 1,764 29,935

[DegreeCC=1] *

[Technology=2]

0a . . . . .

[DegreeCC=2] *

[Technology=1]

0a . . . . .

[DegreeCC=2] *

[Technology=2]

0a . . . . .

a. This parameter is set to zero because it is redundant.

LVIII

Estimated Marginal Means

1. Grand Mean

Dependent Variable:SSWTPGarment

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

39,824 1,782 36,303 43,346

Degree of CC

Estimates

Dependent Variable:SSWTPGarment

Degree

of CC Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High 45,742 2,481 40,839 50,645

Low 33,906 2,558 28,850 38,962

Pairwise Comparisons

Dependent Variable:SSWTPGarment

(I)

Degree

of CC

(J)

Degree

of CC

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

High Low 11,836* 3,563 ,001 4,793 18,879

Low High -11,836* 3,563 ,001 -18,879 -4,793

Based on estimated marginal means

*. The mean difference is significant at the ,05 level.

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPGarment

Sum of Squares df Mean Square F Sig.

Contrast 5103,091 1 5103,091 11,033 ,001

Error 67067,653 145 462,536

The F tests the effect of Degree of CC. This test is based on the linearly independent pairwise

comparisons among the estimated marginal means.

LIX

Technology

Estimates

Dependent Variable:SSWTPGarment

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Online 40,414 2,694 35,090 45,738

Offline 39,235 2,333 34,624 43,846

Pairwise Comparisons

Dependent Variable:SSWTPGarment

(I)

Technolog

y

(J)

Technolog

y

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

Online Offline 1,179 3,563 ,741 -5,864 8,222

Offline Online -1,179 3,563 ,741 -8,222 5,864

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPGarment

Sum of Squares df Mean Square F Sig.

Contrast 50,630 1 50,630 ,109 ,741

Error 67067,653 145 462,536

The F tests the effect of Technology. This test is based on the linearly independent pairwise

comparisons among the estimated marginal means.

Degree of CC * Technology

Dependent Variable:SSWTPGarment

Degree

of CC

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High Online 50,294 3,688 43,004 57,584

Offline 41,190 3,319 34,632 47,749

Low Online 30,533 3,927 22,773 38,294

Offline 37,279 3,280 30,797 43,761

LX

LXI

LXII

Between-Subjects Factors

Value Label N

Degree of CC 1 High 68

2 Low 67

Technology 1 Online 57

2 Offline 78

Descriptive Statistics

Dependent Variable:SSWTPComputer

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 807,8333 204,22133 30

Offline 878,1579 299,11451 38

Total 847,1324 262,10303 68

Low Online 781,8519 173,91503 27

Offline 791,3750 133,72852 40

Total 787,5373 150,01609 67

Total Online 795,5263 189,24116 57

Offline 833,6538 232,28352 78

Total 817,5556 215,23925 135

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPComputer

F df1 df2 Sig.

3,918 3 131 ,010

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix H: SPSS output dependent variable „WTP‟ Computer

LXIII

Tests of Between-Subjects Effects

Dependent Variable:SSWTPComputer

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 204231,332a 3 68077,111 1,485 ,221 ,033

Intercept 8,729E7 1 8,729E7 1904,746 ,000 ,936

DegreeCC 104496,720 1 104496,720 2,280 ,133 ,017

Technology 52394,263 1 52394,263 1,143 ,287 ,009

DegreeCC * Technology 30379,888 1 30379,888 ,663 ,417 ,005

Error 6003712,002 131 45829,863

Total 9,644E7 135

Corrected Total 6207943,333 134

a. R Squared = ,033 (Adjusted R Squared = ,011)

Parameter Estimates

Dependent Variable:SSWTPComputer

Parameter B

Std.

Error t Sig.

95% Confidence

Interval

Lower Bound Upper Bound Partial eta squared

Intercept 791,37

5

33,849 23,38

0

,000 724,414 858,336 ,807

[DegreeCC=1] 86,783 48,495 1,790 ,076 -9,152 182,718 ,024

[DegreeCC=2] 0a . . . . . .

[Technology=1] -9,523 53,321 -,179 ,859 -115,005 95,959 ,000

[Technology=2] 0a . . . . . .

[DegreeCC=1] * [Technology=1] -60,801 74,678 -,814 ,417 -208,533 86,930 ,005

[DegreeCC=1] * [Technology=2] 0a . . . . . .

[DegreeCC=2] * [Technology=1] 0a . . . . . .

[DegreeCC=2] * [Technology=2] 0a . . . . . .

a. This parameter is set to zero because

it is redundant.

LXIV

Estimated Marginal Means

1. Grand Mean

Dependent Variable:SSWTPComputer

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

814,805 18,670 777,872 851,737

Degree of CC

Estimates

Dependent Variable:SSWTPComputer

Degree

of CC Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High 842,996 26,142 791,280 894,712

Low 786,613 26,661 733,872 839,354

Pairwise Comparisons

Dependent Variable:SSWTPComputer

(I)

Degree

of CC

(J)

Degree

of CC

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

High Low 56,382 37,339 ,133 -17,484 130,248

Low High -56,382 37,339 ,133 -130,248 17,484

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPComputer

Sum of Squares df Mean Square F Sig. Partial Eta Squared

Contrast 104496,720 1 104496,720 2,280 ,133 ,017

Error 6003712,002 131 45829,863

The F tests the effect of Degree of CC. This test is based on the linearly independent pairwise comparisons among

the estimated marginal means.

LXV

Technology

Estimates

Dependent Variable:SSWTPComputer

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Online 794,843 28,395 738,671 851,014

Offline 834,766 24,248 786,799 882,734

Pairwise Comparisons

Dependent Variable:SSWTPComputer

(I)

Technolog

y

(J)

Technolog

y

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

Online Offline -39,924 37,339 ,287 -113,790 33,942

Offline Online 39,924 37,339 ,287 -33,942 113,790

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPComputer

Sum of Squares df Mean Square F Sig. Partial Eta Squared

Contrast 52394,263 1 52394,263 1,143 ,287 ,009

Error 6003712,002 131 45829,863

The F tests the effect of Technology. This test is based on the linearly independent pairwise comparisons among the

estimated marginal means.

Degree of CC * Technology

Dependent Variable:SSWTPComputer

Degree

of CC

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High Online 807,833 39,085 730,513 885,153

Offline 878,158 34,728 809,457 946,859

Low Online 781,852 41,200 700,349 863,354

Offline 791,375 33,849 724,414 858,336

LXVI

LXVII

LXVIII

Between-Subjects Factors

Value Label N

Degree of CC 1 High 56

2 Low 47

Technology 1 Online 47

2 Offline 56

Descriptive Statistics

Dependent Variable:SSWTPBallpen

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 4,5833 4,66175 24

Offline 3,4016 3,18951 32

Total 3,9080 3,89487 56

Low Online 3,6957 5,79525 23

Offline 2,6750 1,75208 24

Total 3,1745 4,22649 47

Total Online 4,1489 5,20860 47

Offline 3,0902 2,67380 56

Total 3,5733 4,04607 103

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPBallpen

F df1 df2 Sig.

2,040 3 99 ,113

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix I: SPSS output dependent variable „WTP‟ Ball pen

LXIX

Tests of Between-Subjects Effects

Dependent Variable:SSWTPBallpen

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 45,139a 3 15,046 ,917 ,436 ,027

Intercept 1303,807 1 1303,807 79,448 ,000 ,445

DegreeCC 16,486 1 16,486 1,005 ,319 ,010

Technology 30,688 1 30,688 1,870 ,175 ,019

DegreeCC * Technology ,164 1 ,164 ,010 ,921 ,000

Error 1624,670 99 16,411

Total 2984,963 103

Corrected Total 1669,809 102

a. R Squared = ,027 (Adjusted R Squared = -,002)

Parameter Estimates

Dependent Variable:SSWTPBallpen

Parameter B Std. Error t Sig.

95% Confidence Interval Partial Eta

Squared Lower Bound Upper Bound

Intercept 2,675 ,827 3,235 ,002 1,034 4,316 ,096

[DegreeCC=1] ,727 1,094 ,664 ,508 -1,444 2,897 ,004

[DegreeCC=2] 0a . . . . . .

[Technology=1] 1,021 1,182 ,863 ,390 -1,325 3,366 ,007

[Technology=2] 0a . . . . . .

[DegreeCC=1] *

[Technology=1]

,161 1,611 ,100 ,921 -3,035 3,357 ,000

[DegreeCC=1] *

[Technology=2]

0a . . . . . .

[DegreeCC=2] *

[Technology=1]

0a . . . . . .

[DegreeCC=2] *

[Technology=2]

0a . . . . . .

a. This parameter is set to zero because it is redundant.

LXX

Estimated Marginal Means

1. Grand Mean

Dependent Variable:SSWTPBallpen

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

3,589 ,403 2,790 4,388

Degree of CC

Estimates

Dependent Variable:SSWTPBallpen

Degree

of CC Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High 3,992 ,547 2,907 5,078

Low 3,185 ,591 2,013 4,358

Pairwise Comparisons

Dependent Variable:SSWTPBallpen

(I)

Degree

of CC

(J)

Degree

of CC

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

High Low ,807 ,805 ,319 -,791 2,405

Low High -,807 ,805 ,319 -2,405 ,791

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPBallpen

Sum of Squares df Mean Square F Sig. Partial Eta Squared

Contrast 16,486 1 16,486 1,005 ,319 ,010

Error 1624,670 99 16,411

The F tests the effect of Degree of CC. This test is based on the linearly independent pairwise comparisons among

the estimated marginal means.

LXXI

Technology

Estimates

Dependent Variable:SSWTPBallpen

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Online 4,139 ,591 2,967 5,312

Offline 3,038 ,547 1,953 4,124

Pairwise Comparisons

Dependent Variable:SSWTPBallpen

(I)

Technolog

y

(J)

Technolog

y

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

Online Offline 1,101 ,805 ,175 -,497 2,699

Offline Online -1,101 ,805 ,175 -2,699 ,497

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPBallpen

Sum of Squares df Mean Square F Sig. Partial Eta Squared

Contrast 30,688 1 30,688 1,870 ,175 ,019

Error 1624,670 99 16,411

The F tests the effect of Technology. This test is based on the linearly independent pairwise comparisons among the

estimated marginal means.

Degree of CC * Technology

Dependent Variable:SSWTPBallpen

Degree

of CC

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High Online 4,583 ,827 2,943 6,224

Offline 3,402 ,716 1,981 4,823

Low Online 3,696 ,845 2,020 5,372

Offline 2,675 ,827 1,034 4,316

LXXII

LXXIII

LXXIV

Between-Subjects Factors

Value Label N

Degree of CC 1 High 69

2 Low 65

Technology 1 Online 59

2 Offline 75

Descriptive Statistics

Dependent Variable:SSWTPCar

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 43822,5806 53974,77009 31

Offline 38644,7368 44473,17866 38

Total 40971,0145 48664,05577 69

Low Online 29142,8571 5811,63766 28

Offline 29837,8378 9732,57278 37

Total 29538,4615 8225,01315 65

Total Online 36855,9322 39714,61240 59

Offline 34300,0000 32475,56254 75

Total 35425,3731 35724,64175 134

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPCar

F df1 df2 Sig.

4,388 3 130 ,006

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix J: SPSS output dependent variable „WTP‟ Car

LXXV

Tests of Between-Subjects Effects

Dependent Variable:SSWTPCar

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 4,840E9 3 1,613E9 1,272 ,287 ,029

Intercept 1,649E11 1 1,649E11 130,016 ,000 ,500

DegreeCC 4,547E9 1 4,547E9 3,585 ,061 ,027

Technology 1,657E8 1 1,657E8 ,131 ,718 ,001

DegreeCC * Technology 2,843E8 1 2,843E8 ,224 ,637 ,002

Error 1,649E11 130 1,268E9

Total 3,379E11 134

Corrected Total 1,697E11 133

a. R Squared = ,029 (Adjusted R Squared = ,006)

Parameter Estimates

Dependent Variable:SSWTPCar

Parameter B Std. Error t Sig.

95% Confidence Interval

Lower Bound Upper Bound

Intercept 29837,838 5855,168 5,096 ,000 18254,089 41421,587

[DegreeCC=1] 8806,899 8225,801 1,071 ,286 -7466,864 25080,662

[DegreeCC=2] 0a . . . . .

[Technology=1] -694,981 8921,071 -,078 ,938 -18344,253 16954,292

[Technology=2] 0a . . . . .

[DegreeCC=1] * [Technology=1] 5872,824 12405,028 ,473 ,637 -18669,040 30414,689

[DegreeCC=1] * [Technology=2] 0a . . . . .

[DegreeCC=2] * [Technology=1] 0a . . . . .

[DegreeCC=2] * [Technology=2] 0a . . . . .

a. This parameter is set to zero because it is redundant.

LXXVI

Estimated Marginal Means

1. Grand Mean

Dependent Variable:SSWTPCar

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

35362,003 3101,257 29226,537 41497,469

Degree of CC

Estimates

Dependent Variable:SSWTPCar

Degree

of CC Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High 41233,659 4309,850 32707,137 49760,181

Low 29490,347 4460,536 20665,711 38314,984

Pairwise Comparisons

Dependent Variable:SSWTPCar

(I)

Degree

of CC

(J)

Degree

of CC

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

High Low 11743,311 6202,514 ,061 -527,621 24014,244

Low High -11743,311 6202,514 ,061 -24014,244 527,621

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPCar

Sum of Squares df Mean Square F Sig.

Contrast 4,547E9 1 4,547E9 3,585 ,061

Error 1,649E11 130 1,268E9

The F tests the effect of Degree of CC. This test is based on the linearly independent pairwise

comparisons among the estimated marginal means.

LXXVII

Technology

Estimates

Dependent Variable:SSWTPCar

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Online 36482,719 4642,761 27297,571 45667,867

Offline 34241,287 4112,900 26104,406 42378,169

Pairwise Comparisons

Dependent Variable:SSWTPCar

(I)

Technolog

y

(J)

Technolog

y

Mean Difference (I-

J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

Online Offline 2241,432 6202,514 ,718 -10029,501 14512,364

Offline Online -2241,432 6202,514 ,718 -14512,364 10029,501

Based on estimated marginal means

a. Adjustment for multiple comparisons: Bonferroni.

Univariate Tests

Dependent Variable:SSWTPCar

Sum of Squares df Mean Square F Sig.

Contrast 1,657E8 1 1,657E8 ,131 ,718

Error 1,649E11 130 1,268E9

The F tests the effect of Technology. This test is based on the linearly independent pairwise

comparisons among the estimated marginal means.

Degree of CC * Technology

Dependent Variable:SSWTPCar

Degree

of CC

Technolog

y Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High Online 43822,581 6396,750 31167,376 56477,785

Offline 38644,737 5777,613 27214,421 50075,052

Low Online 29142,857 6730,715 15826,943 42458,772

Offline 29837,838 5855,168 18254,089 41421,587

LXXVIII

LXXIX

LXXXI

Dependent

variable

Hypothesis Confirmed

or refuted

P-value

Enjoyment H1a: Regardless of the technology, customers that dispose of a high level to co-create have

a higher enjoyment than customers with a low level to co-create.

H1b: There is an interaction effect between degree of CC and technology on the one hand,

and customer enjoyment on the other hand.

H1c: Customers that dispose of a high level to co-create in an online environment have a

higher enjoyment than customers that dispose of a low level to co-create in an online environment.

H1d: Customers that dispose of a high level to co-create in an offline environment have a

higher enjoyment than customers that dispose of a low level to co-create in an offline environment.

H1e: Customers that dispose of a high level to co-create in an online environment have a

higher enjoyment than customer that dispose of a high level of CC in an offline environment.

H1f: Customers that dispose of a low level to co-create in an online environment have a

higher enjoyment than customer that dispose of a low level of CC in an offline environment.

0.04

< 0.01. Sign. Not sign Not sign. Not sign.

Cognitive

effort and

ability

H2a: Regardless the technology, customers that dispose of a high level to co-create need

more cognitive effort and ability than customers with a low level to co-create.

H2b: Regardless of the degree of CC, customers that co-create online need more cognitive

effort and ability than customers that co-create offline.

H2c: There is an interaction effect between degree of CC and technology on the one hand,

and customer cognitive effort and ability on the other hand.

0.01

0.04 0.03

Appendix K: Summary hypotheses with results.

LXXXII

H2d: Customers that dispose of a high level to co-create in an online environment need

more cognitive effort and ability than customers with a low level to co-create in an online

environment.

H2e: Customers that dispose of a high level to co-create in an offline environment need

more cognitive effort and ability than customers with a low level to co-create in an offline

environment.

H2f: Customers that dispose of a high level to co-create in an online environment need more

cognitive effort and ability than customers with a high level to co-create in an offline environment.

H2g: Customers that dispose of a low level to co-create in an online environment need more

cognitive effort and ability than customers with a low level to co-create in an offline environment.

Not sign. Sign. Sign.

Not sign.

WTP

garment

H3a: Regardless of the technology, customers that dispose of a high level to co-create are

willing to pay more than customers that dispose of a low level to co-create.

H3b: Regardless of the degree of CC, customers that co-create offline are more WTP than

customers that co-create online.

H3c: There is an interaction effect between degree of CC and technology on the one hand,

and customer WTP on the other hand.

H3d: Customers that dispose of a high level to co-create in an online environment have a

higher WTP than customers that dispose of a low level of co- creation in an online environment.

H3e: Customers that dispose of a high level to co-create in an offline environment have a

higher WTP than customers that dispose of a low level of co- creation in an offline environment.

H3f: Customers that dispose of a high level to co-create in an offline environment have a

higher WTP than customers that dispose of a high level to co-create in an online environment.

<0.01

0.74

0.03

Sign.

Not sign.

Not sign.

LXXXIII

H3g: Customers that dispose of a low level to co-create in an offline environment have a

higher WTP than customers that dispose of a low level to co-create in an online environment.

Not sign.

WTP

computer

H3a: Regardless of the technology, customers that dispose of a high level to co-create are

willing to pay more than customers that dispose of a low level to co-create.

H3b: Regardless of the degree of CC, customers that co-create offline are more WTP than

customers that co-create online.

H3c: There is an interaction effect between degree of CC and technology on the one hand,

and customer WTP on the other hand.

H3d-H3g

0.13

0.29

0.42

Not sign.

WTP ball

pen

H3a: Regardless of the technology, customers that dispose of a high level to co-create are

willing to pay more than customers that dispose of a low level to co-create.

H3b: Regardless of the degree of CC, customers that co-create offline are more WTP than

customers that co-create online.

H3c: There is an interaction effect between degree of CC and technology on the one hand,

and customer WTP on the other hand.

H3d-H3g

0.18

0.92

0.32

Not sign.

LXXXIV

WTP car

H3a: Regardless of the technology, customers that dispose of a high level to co-create are

willing to pay more than customers that dispose of a low level to co-create.

H3b: Regardless of the degree of CC, customers that co-create offline are more WTP than

customers that co-create online.

H3c: There is an interaction effect between degree of CC and technology on the one hand,

and customer WTP on the other hand.

H3d-H3g

0.06

0.72

0.64

Not sign.

LXXXV

Between-Subjects Factors

Value Label N

Degree of CC 1 High 53

2 Low 48

Technology 1 Online 41

2 Offline 60

Descriptive Statistics

Dependent Variable:SSWTPGarmentHighinvolv

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 53,5417 37,72092 24

Offline 42,5862 11,46584 29

Total 47,5472 27,02674 53

Low Online 33,7059 23,11051 17

Offline 38,0000 15,05324 31

Total 36,4792 18,18696 48

Total Online 45,3171 33,61059 41

Offline 40,2167 13,52598 60

Total 42,2871 23,79384 101

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPGarmentHighinvolv

F df1 df2 Sig.

4,031 3 97 ,010

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix L: SPSS output moderating variable „High product involvement‟ on WTP

Garment

LXXXVI

Tests of Between-Subjects Effects

Dependent Variable:SSWTPGarmentHighinvolv

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 4864,151a 3 1621,384 3,039 ,033 ,086

Intercept 168438,665 1 168438,665 315,718 ,000 ,765

DegreeCC 3566,525 1 3566,525 6,685 ,011 ,064

Technology 265,342 1 265,342 ,497 ,482 ,005

DegreeCC * Technology 1390,589 1 1390,589 2,606 ,110 ,026

Error 51750,522 97 533,511

Total 237223,000 101

Corrected Total 56614,673 100

a. R Squared = ,086 (Adjusted R Squared = ,058)

Estimated Marginal Means

Grand Mean

Dependent Variable:SSWTPGarmentHighinvolv

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

41,958 2,361 37,272 46,645

LXXXVII

LXXXVIII

Between-Subjects Factors

Value Label N

Degree of CC 1 High 36

2 Low 37

Technology 1 Online 33

2 Offline 40

Descriptive Statistics

Dependent Variable:SSWTPComputerHighinvolv

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 907,5000 167,63055 16

Offline 972,0000 342,66141 20

Total 943,3333 277,20029 36

Low Online 818,2353 209,44078 17

Offline 827,5000 143,44906 20

Total 823,2432 174,29308 37

Total Online 861,5152 192,76153 33

Offline 899,7500 269,41044 40

Total 882,4658 237,05825 73

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPComputerHighinvolv

F df1 df2 Sig.

1,843 3 69 ,147

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix M: SPSS output moderating variable „High product involvement‟ on WTP

Computer

LXXXIX

Tests of Between-Subjects Effects

Dependent Variable:SSWTPComputerHighinvolv

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 300914,106a 3 100304,702 1,848 ,147 ,074

Intercept 5,615E7 1 5,615E7 1034,470 ,000 ,937

DegreeCC 246905,234 1 246905,234 4,549 ,037 ,062

Technology 24584,968 1 24584,968 ,453 ,503 ,007

DegreeCC * Technology 13784,968 1 13784,968 ,254 ,616 ,004

Error 3745242,059 69 54278,870

Total 6,089E7 73

Corrected Total 4046156,164 72

a. R Squared = ,074 (Adjusted R Squared = ,034)

Estimated Marginal Means

Grand Mean

Dependent Variable:SSWTPComputerHighinvolv

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

881,309 27,401 826,645 935,973

XC

XCI

Between-Subjects Factors

Value Label N

Degree of CC 1 High 3

2 Low 4

Technology 1 Online 2

2 Offline 5

Descriptive Statistics

Dependent Variable:SSWTPBallpenHighinvolvement

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 3,0000 . 1

Offline 3,0000 ,00000 2

Total 3,0000 ,00000 3

Low Online 3,0000 . 1

Offline 5,1667 4,19325 3

Total 4,6250 3,59108 4

Total Online 3,0000 ,00000 2

Offline 4,3000 3,19374 5

Total 3,9286 2,68373 7

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPBallpenHighinvolvement

F df1 df2 Sig.

4,429 3 3 ,127

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

Appendix N: SPSS output moderating variable „High product involvement‟ on WTP

Ball pen

XCII

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPBallpenHighinvolvement

F df1 df2 Sig.

4,429 3 3 ,127

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Estimated Marginal Means

1. Grand Mean

Dependent Variable:SSWTPBallpenHighinvolvement

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

3,542 1,441 -1,043 8,127

Tests of Between-Subjects Effects

Dependent Variable:SSWTPBallpenHighinvolvement

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 8,048a 3 2,683 ,229 ,871 ,186

Intercept 70,833 1 70,833 6,043 ,091 ,668

DegreeCC 1,657 1 1,657 ,141 ,732 ,045

Technology 1,657 1 1,657 ,141 ,732 ,045

DegreeCC * Technology 1,657 1 1,657 ,141 ,732 ,045

Error 35,167 3 11,722

Total 151,250 7

Corrected Total 43,214 6

a. R Squared = ,186 (Adjusted R Squared = -,628)

XCIII

XCIV

XCV

Between-Subjects Factors

Value Label N

Degree of CC 1 High 39

2 Low 40

Technology 1 Online 36

2 Offline 43

Descriptive Statistics

Dependent Variable:SSWTPCarHighinvolv

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 58277,7778 67649,22986 18

Offline 47785,7143 58718,51740 21

Total 52628,2051 62370,57314 39

Low Online 30444,4444 6400,57187 18

Offline 33204,5455 9941,08729 22

Total 31962,5000 8544,29447 40

Total Online 44361,1111 49415,96201 36

Offline 40325,5814 41780,87449 43

Total 42164,5570 45164,06854 79

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSWTPCarHighinvolv

F df1 df2 Sig.

4,492 3 75 ,006

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix O: SPSS output moderating variable „High product involvement‟ on WTP

Car

XCVI

Tests of Between-Subjects Effects

Dependent Variable:SSWTPCarHighinvolv

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 9,576E9 3 3,192E9 1,601 ,196 ,060

Intercept 1,411E11 1 1,411E11 70,753 ,000 ,485

DegreeCC 8,811E9 1 8,811E9 4,419 ,039 ,056

Technology 2,928E8 1 2,928E8 ,147 ,703 ,002

DegreeCC * Technology 8,601E8 1 8,601E8 ,431 ,513 ,006

Error 1,495E11 75 1,994E9

Total 2,996E11 79

Corrected Total 1,591E11 78

a. R Squared = ,060 (Adjusted R Squared = ,023)

Estimated Marginal Means

Grand Mean

Dependent Variable:SSWTPCarHighinvolv

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

42428,120 5044,086 32379,786 52476,455

XCVII

XCVIII

Between-Subjects Factors

Value Label N

Degree of CC 1 High 14

2 Low 10

Technology 1 Online 8

2 Offline 16

Descriptive Statistics

Dependent Variable:SSEnjoyHighInvolv

Degree

of CC

Technolog

y Mean Std. Deviation N

High Online 6,1563 ,58863 6

Offline 5,2422 ,86179 8

Total 5,6339 ,86806 14

Low Online 3,9688 1,10485 2

Offline 5,9453 ,32977 8

Total 5,5500 ,95643 10

Total Online 5,6094 1,20302 8

Offline 5,5937 ,72744 16

Total 5,5990 ,88636 24

Levene's Test of Equality of Error Variancesa

Dependent Variable:SSEnjoyHighInvolv

F df1 df2 Sig.

1,988 3 20 ,148

Tests the null hypothesis that the error variance of the

dependent variable is equal across groups.

a. Design: Intercept + DegreeCC + Technology +

DegreeCC * Technology

Appendix P: SPSS output moderating variable „High product involvement‟ Enjoyment

XCIX

Tests of Between-Subjects Effects

Dependent Variable:SSEnjoyHighInvolv

Source

Type III Sum of

Squares df Mean Square F Sig. Partial Eta Squared

Corrected Model 9,157a 3 3,052 6,849 ,002 ,507

Intercept 495,516 1 495,516 1111,883 ,000 ,982

DegreeCC 2,404 1 2,404 5,394 ,031 ,212

Technology 1,232 1 1,232 2,763 ,112 ,121

DegreeCC * Technology 9,115 1 9,115 20,454 ,000 ,506

Error 8,913 20 ,446

Total 770,430 24

Corrected Total 18,070 23

a. R Squared = ,507 (Adjusted R Squared = ,433)

C

CI

Independent samples T-Test

Group Statistics

Degree of CC N Mean Std. Deviation Std. Error Mean

SSEnjoymentBallpen High degree of CC 76 3,8717 1,77553 ,20367

Low degree of CC 73 3,4897 1,60589 ,18796

Appendix Q: SPSS output dependent variable „Enjoyment‟ Ball pen