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Innovation Perception from a Customer Perspective Recognition, Assessment, and Comprehension of Innovations

D I S S E R T A T I O N of the University of St. Gallen,

School of Management, Economics, Law, Social Sciences and

International Affairs to obtain the title of

Doctor of Philosophy in Management

submitted by

Dennis Vogt

from

Germany

Approved on the application of

Prof. Dr. Torsten Tomczak

and

Prof. Dr. Sven Henkel

Dissertation no. 4189

Rosch-Buch, Schesslitz 2013

The University of St. Gallen, School of Management, Economics, Law, Social Sciences, and International Affairs hereby consents to the printing of the present dissertation, without hereby expressing any opinion on the views herein expressed. St. Gallen, May 17, 2013 The President: Prof. Dr. Thomas Bieger

Für Ines und Thomas

Vorwort Die Promotion an der Universität St. Gallen stellt für mich eine ganz besondere Erfahrung dar. Die vorliegende Dissertation ist das Ergebnis dieser unglaublich spannenden und lehrreichen Zeit. Gerne möchte ich denjenigen Menschen danken, die mich hierbei unterstützt haben.

In erster Linie möchte ich meinem Doktorvater, Prof. Dr. Torsten Tomczak danken. Seine persönliche und fachliche Unterstützung haben das Gelingen dieser Dissertation überhaupt erst möglich gemacht. Weiterhin möchte ich meinem Ko-Referenten Prof. Dr. Sven Henkel danken, der mich als akademischer Mentor beim Entstehen dieser Arbeit intensiv begleitet hat. Die konstruktiven Diskussionen mit ihm waren eine grosse Bereicherung.

Dank möchte ich auch meinen Kollegen an der Forschungsstelle für Customer Insight aussprechen: Simon Brösamle, Fabian Heuschele, Dr. Christian Hildebrand, Philipp Scharfenberger und Miriam van Tilburg. Ganz besonderen Dank möchte ich in diesem Zusammenhang Prof. Dr. Wibke Heidig aussprechen, die stets ein offenes Ohr für mich hatte und mir jederzeit mit Rat und Tat zur Seite stand.

Spezieller Dank geht auch an meine Freundin, Paulina Jacob. Sie war in den vergangenen Jahren immer für mich da und hat mir Rückhalt gegeben, wenn es schwierig wurde. Ich bin sehr froh, einen so besonderen Menschen gefunden zu haben. Ihre optimistische Lebenseinstellung hat mir in vielerlei Hinsicht die Augen geöffnet.

Der größte Dank gebührt meiner Mutter, Ines Vogt und meinem Vater, Dr. Thomas Vogt. Ihr unerschütterlicher Glaube in meine Fähigkeiten, ihre grenzenlose Unterstützung und ihre unermessliche Liebe haben mir immer wieder Kraft gegeben, meine Ziele zu verfolgen und meine Träume zu verwirklichen. Ich bin sehr stolz, diese beiden besonderen Menschen als Eltern zu haben. Ihnen widme ich diese Arbeit.

St. Gallen, im Mai 2013 Dennis Vogt

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Abstract Every year, firms invest billions of dollars in the creation of innovations with the aim of conquering new markets and securing future revenue streams. Even though most innovations are technically and functionally superior to existing practice, the majority fails in the market. This is surprising, given that the world’s best minds stand behind many of these failed innovations. The reason for this phenomenon stems from the fact that, at the end of the day, customers decide whether an innovation succeeds or not. However, experts and customers view innovations different: while experts are primarily concerned with technical and functional aspects of innovations, customers are driven by the superior experiences innovations provide. Thus, in order to turn a greater number of innovations into a success, it is necessary to delve deep into the minds of customers and develop a comprehensive understanding of how they form perceptions of innovations, and how they decide whether to accept them or not.

This dissertation addresses this issue by providing a comprehensive investigation of customer perception of innovations. First of all, the term innovation is defined in terms of the customer’s perspective, and key dimensions of innovation perception are derived. Thereby, it is also examined how these dimensions influence the market success of innovations. Secondly, an extensive literature review is undertaken in which core processes of innovation perception are identified and further analyzed. Two processes of innovation perception are distinguished: innovation recognition and innovation assessment. Innovation recognition refers to initial interactions with an innovation, in which customers become aware of the innovation, and develop an initial understanding of what the innovation is and does. The purpose of innovation recognition is to encourage interest in the innovation, and motivate customers to collect further information about it. This is where innovation assessment, the second process of innovation perception, begins. During the innovation assessment process, customers elaborate on the personal consequences an innovation has on their everyday lives. The innovation assessment process culminates with customers’ accepting or rejecting an innovation. Thirdly, this dissertation introduces innovation comprehension as a previously neglected aspect of innovation perception. While innovation recognition and innovation assessment are concerned with ‘what’ customers think about an innovation, innovation comprehension details ‘how’ customers think about an innovation. Generally, customers can think about an innovation in a creative, more abstract manner, or in an analytic, more detail-oriented manner. These different ways of thinking have the potential to profoundly change the perception of innovations.

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Zusammenfassung Jedes Jahr investieren Unternehmen Milliarden von Dollar in die Entwicklung von Innovationen, mit dem Ziel neue Märkte zu erschliessen und zukünftige Umsätze zu sichern. Obwohl die meisten dieser Innovationen bestehenden Produkten technisch und funktionsmässig deutlich überlegen sind, scheitert die Mehrheit von ihnen am Markt. Dies ist überraschend, wurden sie doch von den klügsten Köpfen der Welt entwickelt. Der Grund für das Scheitern der meisten Innovationen liegt darin begründet, dass am Ende des Tages der Kunde über den Erfolg oder Misserfolg von Innovationen entscheidet. Experten und Kunden haben jedoch eine sehr unterschiedliche Sicht auf Innovationen. Während Experten ihr Augenmerk vor allem auf technische und funktionale Aspekte legen, beschäftigen sich Kunden in erster Linie mit den positiven oder negativen Erlebnissen, die Innovationen bieten. Um also die Erfolgsaussichten von Innovationen zu steigern, ist es notwendig tief in die Psyche der Kunden einzutauchen und ein fundiertes Verständnis dafür zu entwickeln, wie genau Eindrücke von Innovationen entstehen und warum sich Kunden schliesslich für oder gegen eine bestimmte Innovation entscheiden.

Hier setzt die vorliegende Dissertation an und führt eine umfassende Untersuchung der Wahrnehmung von Innovationen durch. Erstens wird der Begriff Innovation aus Kundensicht definiert und wesentliche Dimensionen der Wahrnehmung von Innovationen abgeleitet. Dabei wird auch aufgezeigt, inwiefern diese Dimensionen auf den Markterfolg von Innovationen einwirken. Zweitens werden zentrale Prozesse der Innovationswahrnehmung identifiziert und analysiert. Zwei Prozesse werden unterschieden: Innovation Recognition und Innovation Assessment. Bei Innovation Recognition geht es darum, das Interesse für eine Innovation zu wecken. Kunden sollten sich also einer Innovation bewusst werden und verstehen, worum es sich dabei handelt. Darauf aufbauend setzt Innovation Assessment an. Im Rahmen von Innovation Assessment setzen sich Kunden intensiv mit einer Innovation auseinander und treffen eine Entscheidung für oder gegen die Innovation. Drittens wird im Rahmen dieser Dissertation Innovation Comprehension, ein bislang vernachlässigter Aspekt von Innovationswahrnehmung beleuchtet. Während sich Innovation Recognition und Innovation Assessment darauf beziehen, was Kunden über Innovationen denken, bezieht sich Innovation Comprehension auf die Art und Weise, mit der Kunden über Innovationen nachdenken. Kunden können sich grundsätzlich auf kreative Weise oder auf analytische Weise mit Dingen auseinandersetzen. Diese Denkweisen haben massgeblichen Einfluss darauf, wie sie Innovationen wahrnehmen.

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Table of Contents Abstract .......................................................................................................................... V

Zusammenfassung ........................................................................................................ VI

Table of Contents ........................................................................................................ VII

List of Tables ................................................................................................................ XI

List of Figures ............................................................................................................. XII

1 Introduction ............................................................................................................. 1 1.1 Problem Orientation ........................................................................................... 1 1.2 Research Questions and Structure of the Dissertation ....................................... 4

2 Conceptual Background of Innovation Perception ............................................. 7 2.1 Defining the Innovation Construct ..................................................................... 7 2.2 Determinants of Innovation Perception .............................................................. 8

2.2.1 Perception of Newness of Innovations ......................................................... 9 2.2.1.1 Newness as an Experience ..................................................................... 9 2.2.1.2 Classification of Innovations based on Perceived Newness ................ 10

2.2.2 Perception of Meaningfulness of Innovations ........................................... 12 2.2.2.1 Perceived Desirability of Innovations .................................................. 13 2.2.2.2 Perceived Feasibility of Innovations .................................................... 15

2.2.3 Determinants of Innovation Perception and Market Success .................... 15

3 Processes of Innovation Perception ..................................................................... 17 3.1 Processes-Oriented View of Innovation Perception ......................................... 17 3.2 Innovation Recognition .................................................................................... 19

3.2.1 Identification of Innovations ...................................................................... 19 3.2.1.1 Formation of Awareness of Innovations .............................................. 19 3.2.1.2 Identification of Category Membership of Innovations ....................... 22

3.2.2 Learning of Innovations ............................................................................. 25 3.2.2.1 Customer Knowledge Transfer ............................................................ 25 3.2.2.2 Encouragement of Customer Knowledge Transfer .............................. 30 3.2.2.3 Single vs. Multiple Category Inferencing ............................................ 31

3.2.3 Perceived Differentiation of Innovations ................................................... 33 3.2.3.1 Feature Based Differentiation of Innovations ...................................... 33

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3.2.3.2 Perception of Utilitarian versus Hedonic Differentiation ..................... 36 3.2.3.3 Assimilation/Contrast Effects in Innovation Perception ...................... 38

3.2.4 Perceived Incongruity of Innovations ........................................................ 41 3.2.4.1 Perceived Incongruity and the Moderate Incongruity Effect ............... 41 3.2.4.2 Coping with Extreme Levels of Incongruity ........................................ 45

3.3 Innovation Assessment ................................................................................... 47 3.3.1 Determinants of Intention and Behavior .................................................... 47

3.3.1.1 Model of Reasoned Action ................................................................... 47 3.3.1.2 Formation of Intentions towards Behavior ........................................... 49

3.3.2 Determinants of Innovation Acceptance .................................................... 52 3.3.2.1 Performance Expectancy ...................................................................... 53 3.3.2.2 Effort expectancy ................................................................................. 55 3.3.2.3 Social Influence .................................................................................... 56 3.3.2.4 Facilitating Conditions ......................................................................... 58 3.3.2.5 Hedonic Motivation .............................................................................. 61 3.3.2.6 Price Value ........................................................................................... 64 3.3.2.7 Habit ..................................................................................................... 64

3.3.3 Risk Associated with Innovation Acceptance ............................................ 66 3.3.3.1 Types of Risks associated with Innovation Acceptance ...................... 66 3.3.3.2 Regulatory Focus and Risk Perception ................................................ 68 3.3.3.3 Information Acquisition and Risk Perception ...................................... 69

3.3.4 Formation of Mental Scenarios about Innovations .................................... 73 3.3.4.1 The Simulation Heuristic and Innovation Perception .......................... 73 3.3.4.2 Mental Simulation and Innovation Perception ..................................... 74 3.3.4.3 Difficulty of Imagination and Innovation Perception .......................... 77

3.3.5 Resolution of Trade-Offs in Evaluation of Innovations ............................. 78 3.3.5.1 Resolution of Trade-Offs between Capability and Usability ............... 78 3.3.5.2 Resolution of Trade-Offs between Functional and Hedonic Benefits . 79

3.3.6 Expectations about Usage of Innovations .................................................. 81 3.3.6.1 Formation of Expectations about Innovations ...................................... 81 3.3.6.2 (Dis)Confirmation of Expectations about Innovations ........................ 81 3.3.6.3 Insight and Discontinuous Learning in Innovation Usage ................... 83

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4 Innovation Comprehension .................................................................................. 86 4.1 Introduction to Innovation Comprehension ..................................................... 86 4.2 Conceptual Development Innovation Comprehension .................................... 89

4.2.1 Specification of Global/Local Processing .................................................. 89 4.2.1.1 Distinguishing Global and Local Processing ....................................... 89 4.2.1.2 Inducing Global and Local Processing ................................................. 90

4.2.2 Implications of Global/Local Processing for Innovation Perception ......... 91 4.2.2.1 Assimilation/Contrast Effects ............................................................... 92 4.2.2.2 Creative and Analytical Problem Solving Performance ....................... 93

4.3 Experimental Analysis Innovation Comprehension ................................... 96 4.3.1 Overview over the Experimental Studies ................................................... 96 4.3.2 Experiment 1 .............................................................................................. 98

4.3.2.1 Hypothesis Development ..................................................................... 98 4.3.2.2 Design, Participants, and Procedure ..................................................... 99 4.3.2.3 Manipulation of Independent Variables ............................................... 99 4.3.2.4 Selection of Measures ........................................................................ 101 4.3.2.5 Results ................................................................................................ 102 4.3.2.6 Discussion .......................................................................................... 105

4.3.3 Experiment 2 ............................................................................................ 106 4.3.3.1 Hypothesis Development ................................................................... 106 4.3.3.2 Design, Participants, and Procedure ................................................... 107 4.3.3.3 Manipulation of Independent Variables ............................................. 107 4.3.3.4 Selection of Measures ........................................................................ 109 4.3.3.5 Results ................................................................................................ 110 4.3.3.6 Discussion .......................................................................................... 114

4.3.4 Experiment 3 ............................................................................................ 115 4.3.4.1 Hypothesis Development ................................................................... 115 4.3.4.2 Design, Participants, and Procedure ................................................... 116 4.3.4.3 Manipulation of Independent Variables ............................................. 117 4.3.4.4 Selection of Measures ........................................................................ 119 4.3.4.5 Results ................................................................................................ 120 4.3.4.6 Discussion .......................................................................................... 123

4.4 General Discussion ....................................................................................... 125 4.4.1 Summary of Results ................................................................................. 125 4.4.2 Theoretical Contributions ........................................................................ 126

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4.4.3 Managerial Contributions ........................................................................ 126 4.4.4 Limitations ............................................................................................... 128 4.4.5 Future Research ........................................................................................ 129

5 Conclusion ........................................................................................................... 131

6 References ............................................................................................................ 134

7 Appendices ........................................................................................................... 157 7.1 Appendix 1: Navon-Letters used in Experiment 1 ......................................... 157 7.2 Appendix 2: Navon-Letters used in Experiment 2 ......................................... 163 7.3 Appendix 3: Navon-Letters used in Experiment 3 ......................................... 169

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List of Tables Table 2-1: Types of Customer Benefits ....................................................................... 14 Table 3-1: Congruent and Incongruent Additions of Functionalities (Gill, 2008) ...... 37 Table 3-2: Core Constructs UTAUT (Venkatesh et al., 2012) .................................... 53 Table 3-3: Performance Expectancy Scale (Venkatesh et al., 2012) ........................... 54 Table 3-4: Effort Expectancy Scale (Venkatesh et al., 2012) ...................................... 55 Table 3-5: Social Influence Scale (Venkatesh et al., 2012) ......................................... 57 Table 3-6: Facilitating Conditions Scale (Venkatesh et al., 2012) .............................. 59 Table 3-7: Hedonic Motivation (Venkatesh et al., 2012) ............................................ 62 Table 3-8: Price Value Scale (Venkatesh et al., 2012) ................................................ 64 Table 3-9: Habit Scale (Venkatesh et al., 2012) .......................................................... 65 Table 3-10: Extrabrand Attributes for Selected Innovations (Boyd & Mason, 1999) . 72 Table 3-11: Types of Process and Outcome Simulation (Zhao et al., 2011) ............... 75 Table 4-1: Measures Employed in Experiment 1 ...................................................... 102 Table 4-2: Results of the ANOVAs in Experiment 1 ................................................ 105 Table 4-3: Mean Values for the Dependent Variables in Experiment 1 .................... 105 Table 4-4: Measures Employed in Experiment 2 ...................................................... 110 Table 4-5: Results of the ANOVAs in Experiment 2 ................................................ 114 Table 4-6: Mean Values for the Dependent Variables in Experiment 2 .................... 114 Table 4-7: Measures Employed in Experiment 3 ...................................................... 120 Table 4-8: Results of the ANOVAs in Experiment 3 ................................................ 123 Table 4-9: Mean Values for the Dependent Variables in Experiment 3 .................... 123 Table 4-10: Primes of Global/Local Processing (Förster, 2012) ............................... 127

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List of Figures Figure 1-1: Structure of the Dissertation ....................................................................... 6 Figure 2-1: Types of Innovations (Chandy & Tellis, 1998, 2000) .............................. 12 Figure 3-1: Car Segments and Associated Prototypes (Landwehr et al. 2011) ........... 24 Figure 3-2: The Knowledge Transfer Process (Gregan-Paxton et al., 2002) .............. 26 Figure 3-3: Model of Knowledge Transfer (Gregan-Paxton & Roedder John, 1997) . 29 Figure 3-4: Congruent and Incongruent Functionality Additions (Gill, 2008) ........... 38 Figure 3-5: Comparisons and Innovation Evaluation (Ziamou & Ratneshwar, 2003) 40 Figure 3-6: The Moderate Incongruity Effect (Meyers-Levy & Tybout, 1989) .......... 43 Figure 3-7: Perception of Product Incongruity (Jhang et al., 2012) ............................ 44 Figure 3-8: Model of Reasoned Action (Ajzen, 1991) ................................................ 49 Figure 3-9: Determinants of Behavior and Associated Beliefs ................................... 50 Figure 3-10: Basic Concept of Innovation Acceptance (Venkatesh et al., 2003) ........ 52 Figure 3-11: The Affect Circumplex (Armstrong & Detweiler-Bedell, 2008) ........... 63 Figure 3-12: Expectations and Innovation Evaluation (Wood & Moreau, 2006) ....... 82 Figure 3-13: Learning to Use Innovations (Lakshmanan & Krishnan, 2011) ............. 84 Figure 4-1: Broad vs. Narrow Categorization Primes (Ulkumen et al., 2010) ............ 87 Figure 4-2: Example Item of the Kimchi-Figures task (Kimchi & Palmer, 1982) ...... 90 Figure 4-3: Sample-Item Navon-Letter task ................................................................ 91 Figure 4-4: Overview over the Experimental Studies ................................................. 97 Figure 4-5: Manipulation of Innovations in Experiment 1 ........................................ 100 Figure 4-6: Example of a Navon-Letter (Prime for Processing Style) ...................... 101 Figure 4-7: Product Evaluation Interaction (Experiment 1) ...................................... 103 Figure 4-8: Purchase Intention Interaction (Experiment 1) ....................................... 104 Figure 4-9: Manipulation of Innovations in Experiment 2 ........................................ 108 Figure 4-10: Product Evaluation Interaction (Experiment 2) .................................... 111 Figure 4-11: Purchase Intention Interaction (Experiment 2) ..................................... 112 Figure 4-12: Usefulness Interaction (Experiment 2) ................................................. 113 Figure 4-13: Manipulation of Innovations in Experiment 3 ...................................... 118 Figure 4-14: Product Evaluation Interaction (Experiment 3) .................................... 121 Figure 4-15: Purchase Intention Interaction (Experiment 3) ..................................... 122

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1 Introduction

1.1 Problem Orientation

If a man can write a better book, preach a better sermon, or make a better mousetrap than his neighbor, though he builds his house in the woods, the world will make a beaten path to his door.

Ralph Waldo Emerson (1803-1882)

This quotation from the iconic American essayist, lecturer, and poet, Ralph Waldo Emerson, implies that an innovation which improves existing practice will inevitably enjoy success. However, this happy picture does not seem to hold true in today’s fiercely competitive marketplace (Chiesa & Frattini, 2011). Every year, firms right across the spectrum of industry spend billions of dollars on their research and development activities, with the purpose of creating successful innovations (Henard & Dacin, 2010). However, despite being technically and functionally superior to existing alternatives, a large number of these innovations fail (Chiesa & Frattini, 2011; Cierpicki, Wright, & Sharp, 2000; Wilke & Sorvillo, 2005). In only a few cases do firms successfully commercialize their innovations (Gourville, 2006; Hartley, 2005; Schilling, 2005), thereby generating promising new revenue streams as a basis for future growth and profitability (Danneels & Kleinschmidt, 2001).

First and foremost, it is the customer who decides whether an innovation ultimately succeeds. Only if a customer becomes aware of an innovation and perceives it as attractive will he or she respond favorably to it. However, a customer-centric perspective on innovation represents a considerable challenge (Kunz, Schmitt, & Meyer, 2011). Most firms have an expert-based view, which is diametrically opposed to the way that customers perceive an innovation (Rogers & Shoemaker, 1971). While experts take a technical or functional perspective, customers think about an innovation primarily in terms of the experiences it provides (Danneels & Kleinschmidt, 2001). Specifically, experiences such as ease, comfort, and safety represent important determinants of the success of many innovations (Schmitt, 2003). Take the car industry for example: innovations such as power steering, air-conditioning, and the antilock-breaking system enjoyed success because they provided these positive experiences to customers.

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Despite repeated calls for the development of a broad-based customer-centric approach to innovation perception (Danneels & Kleinschmidt, 2001; Rogers & Shoemaker, 1971), such an approach remains largely absent from the literature (Kunz et al., 2011). During the last decades different research streams have emerged, with each of them addressing a different aspect of innovation perception. Numerous lines of research have investigated when customers take notice of an innovation and how they learn what the innovation does. For example, various researchers have investigated the way with which customers compare an innovation to familiar concepts stored in memory (e.g., Jhang, Grant, & Campbell, 2012; Mandler, 1982; Meyers-Levy & Tybout, 1989; Zheng Zhou & Nakamoto, 2007). Such categorization processes were found to play an important role in determining the nature of an innovation and how it differs from existing alternatives (Selinger, Dahl, & Moreau, 2006). Going a step further, different researchers have also investigated how customers transfer prior knowledge that they have gleaned in other domains in order to develop an initial understanding of an innovation (e.g., El Houssi, Morel, & Hultink, 2005; Goode, Dahl, & Moreau, 2010; Gregan-Paxton, Hibbard, Brunel, & Azar, 2002; Gregan-Paxton & Roedder John, 1997; Moreau, Markman, & Lehmann, 2001; Roehm & Sternthal, 2001). When customers first encounter an innovation like a personal digital assistant, for example, their understanding of the device can be significantly enriched by drawing on completely different, yet comparable, concepts such as the office secretary. That is, similar to a secretary, a personal digital assistant organizes one’s appointments, informs one of when a friend or colleague celebrates his or her birthday, and provides relevant contact details when one wishes to make a call (Gregan-Paxton et al., 2002).

Further, various research streams have addressed how customers cope with the potential consequences of innovation adoption (e.g., Kulviwat, Bruner, Kumar, Nasco, & Clark, 2007; Rogers, 2003; Venkatesh, Thong, & Xu, 2012). When customers evaluate an innovation, they do not only evaluate the innovation itself, but also the consequences associated with adoption. That is, even though an innovation might appear attractive at the first glance, a more detailed analysis may reveal that it is difficult to use or that its use presents considerable risk to health. A variety of different models have been proposed which attempt to explain how these and other factors influence innovation perception (Venkatesh, Morris, Davis, & Davis, 2003). Thereby, it was also investigated how customers learn to make use of an innovation. Such learning represents a considerable hurdle for innovation adoption. If this process is too confusing, customers are likely to respond very unfavorably (e.g., Lakshmanan & Krishnan, 2011; Wood & Moreau, 2006; Ziamou, Gould, & Venkatesh, 2012).

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Finally, researchers have recently begun to investigate how innovation perception is influenced by the way in which customers think about an innovation (e.g., Förster & Dannenberg, 2010; Förster, Liberman, & Shapira, 2009; Förster, Marguc, & Gillebaart, 2010). Literature refers to these ways of thinking as processing styles. Two different processing styles are distinguished: global processing and local processing (Förster, 2012). As an old proverb says, people can either attend to the forest or the trees when looking at a stimulus. When they look at the forest, they are in global processing, when they look at the trees, they are in local processing. Global and local processing represent two different ways of thinking that profoundly change the way in which one perceives the world (Förster & Dannenberg, 2010). Recent findings indicate that processing styles may also significantly influence customer perception of innovation. An individual is likely to perceive the same innovation completely different depending on his or her current processing style (Förster, Liberman, et al., 2009; Förster et al., 2010; Ulkumen, Chakravarti, & Morwitz, 2010).

This prior discussion underlines the importance of understanding of how customers perceive innovations. To date, a wide range of literature streams have examined individual aspects of innovation perception, with each of them taking a different perspective. Thereby, the various research results have not yet been integrated into a comprehensive view of innovation perception. Only if different aspects of innovation perception are captured holistically is it possible to design and communicate innovations so that they are broadly accepted in the marketplace.

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1.2 Research Questions and Structure of the Dissertation

The main purpose of this dissertation is to provide a comprehensive analysis of different aspects of innovation perception. Key customer perception processes are examined, and it is investigated how these processes may be influenced to encourage favorable responses to a given innovation. Based on an extensive literature review, two core processes of innovation perception are identified and further specified. One of these processes concerns innovation recognition. Prior to the evaluation of an innovation, customers need to become aware of its existence. Consequently, a customer’s first interaction with an innovation should provide an initial understanding of what that innovation does. This first interaction should also motivate the customer to further experiment with the innovation. That is, an innovation should pique a customer’s interest rather than confusing him or her. The second innovation perception process analyzed in the literature review concerns innovation assessment. This process represents an intensive examination of an innovation in which customers make inferences about its consequences. That is, customers ask themselves how well an innovation satisfies specific needs, and whether the fulfillment of these needs is associated with reasonable sacrifices. If these sacrifices are too high, customers are likely to reject an innovation, even if it provides considerable benefits.

Building on the literature review, innovation comprehension is introduced as a new aspect of innovation perception. Past research has tended to suggest that, for a specific individual, innovation perception always follows the same pattern. However, recent findings have cast doubt on this hypothesis: innovation perception may also be determined by global and local processing, which represent different ways of thinking about everyday things (i.e. ‘looking at the forest’ vs. ‘looking at the trees’). This dissertation investigates the influence of global/local processing on innovation perception. More formally, the following research questions are addressed:

Research Questions 1:

What determines an innovation from a customer perspective, and how do different dimensions of innovation perception influence market success?

Research Question 2:

What kinds of innovation perception processes can be distinguished, and how do these processes influence the perception of innovations?

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Research Question 3:

How does global/local processing of innovations (i.e. innovation comprehension) influence the perception of innovations?

The dissertation is structured as follows. Chapter 1 provides an introduction to the topic of the dissertation and describes the research questions. Chapter 2 develops a theoretical conceptualization of innovation perception from a customer perspective. Furthermore, this chapter provides an overview over key determinants of innovation perception and reveals how these influence market success of an innovation. Chapter 3 examines innovation recognition and innovation assessment as core processes of innovation perception. Innovation recognition is the process by which customers become aware of an innovation and gain an initial understanding of what the innovation does. Innovation assessment is the process by which customers actively analyze an innovation and draw inferences about the consequences of that innovation. Chapter 4 introduces innovation comprehension as a new aspect of innovation perception. More precisely, this part contains an empirical analysis of the influence of global/local processing on innovation perception. Chapter 5 concludes with a summary of the dissertation’s findings. Figure 1-1 provides an overview over this structure.

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Figure 1-1: Structure of the Dissertation

Introduction Problem orientation of the dissertation and formulation of

research questions pp. 1-6

Chapter 1

Conceptual Background Innovation Perception Definition of innovation from a customer perspective

pp. 7-16

Chapter 2

Literature Analysis Processes of Innovation Perception Analysis of innovation recognition and innovation

assessment as core processes of innovation perception pp. 17-85

Chapter 3

Empirical Investigation Innovation Comprehension Empirical analysis of innovation comprehension as new

aspect of innovation perception pp. 86-130

Chapter 4

Conclusion Summary and concluding remarks on

innovation perception pp. 131-133

Chapter 5

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2 Conceptual Background of Innovation Perception

2.1 Defining the Innovation Construct

Research provides numerous definitions for the term innovation, with each of them viewing innovation from a different perspective. Firstly, from a strategic perspective, innovation is defined as “the initial introduction of a new product or process whose design departs radically from past practice. It is derived from advances in science, in its introduction makes existing knowledge in that application obsolete. It creates new markets, supports freshly articulated user needs in the new functions it offers, and in practice demands new channels of distribution and aftermarket support. In its wake it leaves obsolete firms, practices, and factors of production, while creating a new industry (Abernathy & Clark, 1985, pp. 6-7).”

Secondly, from a process-oriented perspective, innovation is defined as “an iterative process initiated by the perception of a new market and/or service opportunity for a technology based invention which leads to development, production, and marketing tasks striving for commercial success of the invention (Garcia & Calantone, 2002, p. 112).” This definition emphasizes that an invention does not constitute an innovation until it has progressed through production and marketing, and is diffused into the marketplace. In line with this, Smith & Barfield (1996) argue that “the solution to a basic scientific puzzle or the invention of a new product only in a laboratory setting makes no direct economic contribution. Innovation not only includes basic and applied research but also product development, manufacturing, marketing, distribution, servicing, and later product adaptation and upgrading (p. 1).”

Thirdly, from a product-oriented perspective, innovation refers to discontinuities in product benefits, technological capability, and/or consumption patterns (Danneels & Kleinschmidt, 2001; McNally, Cavusgil, & Calantone, 2010; Veryzer, 1998a). Changes in product benefits are based on the new capabilities an innovation provides in terms of customer need fulfillment (Veryzer, 1998a). In other words, innovations offer greater functionality relative to existing offerings (Ali, Krapfel, & LaBahn, 1995). Changes in technological capability are based on the degree to which an innovation expands technological capabilities beyond existing boundaries (Veryzer, 1998a). That is, innovations improve technical performance compared to other products (Colarelli O’Connor, 1998). Changes in consumption patterns denote the degree to which customers need to adapt their thinking and behavior to utilise an

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innovation (Veryzer, 1998a). Research suggests that innovations frequently involve significant learning, as well as considerable behavioral change (McNally et al., 2010).

Finally, in a sociological context, innovation may be defined as an idea, practice, or object that the members of a social system perceive as new. Thereby, an innovation needs to initiate a communication process in which the members of the social system exchange information about the innovation and develop a mutual understanding of it. As a consequence, the innovation results in some kind of social change. That is, the innovation alters the structure or function of a social system (Rogers, 2003).

From these different definitions it follows that innovation is not restricted only to new products and services, but also includes any other kind of innovative output. More precisely, innovations may manifest themselves in product and service innovations, “design innovations (e.g., the styling of a car, the look and feel of a MP3 player, the lifestyle services of a mobile phone service), process innovations (e.g., a new delivery process), marketing innovations (e.g., new communication campaigns or a new web site), and in broad based business innovations (e.g., selling directly to consumers) (Kunz et al., 2011, p. 817).” Hence, from a customer perspective, innovation refers to any kind of a firm’s innovative output. However, to be perceived as an innovation, this output needs to have market impact (Henard & Dacin, 2010; Kunz et al., 2011).

Even though innovation perception may refer to all kinds of innovative outputs, this dissertation solely examines perception of product innovations. The restriction to product innovations is primarily done for comprehensibility reasons. However, many of the findings may also be generalized to other kinds of a firm’s innovative outputs.

2.2 Determinants of Innovation Perception

In line with the previous discussion, research suggests that innovation perception is determined by the extent to which an innovation is perceived as being different from existing alternatives in a way that is meaningful to customers (Sethi, Smith, & Park, 2001). Two determinants of innovation perception are distinguished: perceived newness and perceived meaningfulness. Newness denotes the degree to which an innovation differs from established practice (Amabile, 1983; Andrews & Smith, 1996; Jackson & Messick, 1965; Szymanski, Kroff, & Troy, 2007). Meaningfulness refers to the degree to which customers perceive an innovation as desirable and feasible (Andrews & Smith, 1996; Im & Workman, 2004; Jackson & Messick, 1965).

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2.2.1 Perception of Newness of Innovations

2.2.1.1 Newness as an Experience

In general, research refers to newness as ‘not previously experienced’ or ‘lack of familiarity’. Newness is typically related to different characteristics such as unexpectedness, atypicality, obscurity, ambiguity, complexity, and uncertainty (Förster et al., 2010). It seems that perceived newness is independent of valence. That is, something new may be perceived as an opportunity, and evoke interest and curiosity, or it may be perceived as a potential threat, and evoke safety concerns (Förster et al., 2010). Loewenstein (1994), for example, argues that new things may trigger positive and negative affect at once. On the one hand, new things represent knowledge gaps that may be perceived as unpleasant in the first instance. However, by exploring new things, based on a desire to know, people may experience positive affect from developing new knowledge. Similarly, Scherer (2001) argues that the experience of something new involves both a novelty check and a pleasantness check. A novelty check determines whether someone further attends to a stimulus. At the same time, the pleasantness check determines whether to avoid or approach the stimulus.

Furthermore, research suggests that perceived newness represents a subjective experience (Radford & Bloch, 2011; Rogers, 2003) which is highly dependent on decision context (Förster et al., 2010). That is, perceived newness is not only determined by the objective features of a stimulus itself, but also by a number of other variables (Förster et al., 2010). These include contextual factors such as framing, priming, categorization, and motivational orientation (Förster, Liberman, et al., 2009). Building on Heraclit’s well known aphorism, ‘you could never step twice in the same river; for other waters are ever flowing onto you’, Förster et al. (2010) argue that almost any event can be experienced as new. More precisely, one may perceive a familiar event as new simply by adopting a different perspective on that event (Gati & Ben-Shakhar, 1990). At the same time, it is suggested that almost anything can be perceived as familiar, just by adopting a ‘been there, done that’ attitude. That is, when taking a trip to a foreign country, one can refer to this experience as just another adventure (Förster et al., 2010). This implies that perceived newness may not only vary from individual to individual, but also from situation to situation: the same individual may judge newness of the same stimulus in one situation as very low and in another situation as very high.

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2.2.1.2 Classification of Innovations based on Perceived Newness

From a customer perspective, research distinguishes two broad types of innovations: incrementally new products and really new products (e.g., Dahl & Hoeffler, 2004; Zhao, Hoeffler, & Dahl, 2009, 2012). Incrementally new products represent minor changes to established ideas or concepts (Dahl & Hoeffler, 2004). Research also refers to these innovations as evolutionary or continuous innovations, which do not seem very new to customers (Veryzer, 1998b). Incrementally new products neither involve dramatically new science nor provide very new benefits. They represent improvements, upgrades, or line-extensions rather than groundbreaking departures from existing practice (Zhao et al., 2009). Put differently, incrementally new products are based on mere adaptations, refinements, or enhancements of established products and/or associated systems of production and distribution (Song & Montoya-Weiss, 1998). When trying to make sense of incrementally new products, customers can draw on prior experiences (Zhao et al., 2012). Thus, incrementally new products are easy to understand and involve only small learning costs (Veryzer, 1998a).

In comparison, really new products are perceived as very new from a customer perspective (Veryzer, 1998a). This perceived newness may be based on technological newness and/or newness of customer benefits (Chandy & Tellis, 1998, 2000). Technological newness is the degree to which an innovation involves technologies that depart from prior technologies (Chandy & Tellis, 1998). In other words, technological newness is based on the extent to which an innovation is based on advanced technological capabilities (Veryzer, 1998a). Generally, a technology is defined as “a design for instrumental action that reduces the uncertainty in the cause effect relationship involved in achieving a desired outcome (Rogers, 2003, p. 13).” A technology involves both a hardware aspect which refers to the tool that embodies the technology as a material or physical object, and a software aspect which refers to the information base for the tool (Rogers, 2003). Technological newness may be based on changes to the core design concepts of a technological system. In such case, research speaks of modular innovations (Henderson & Clark, 1990). An example of a modular innovation is runflat tires that provide emergency running properties for cars. In contrast to established tires, runflat tires either involve reinforced sidewalls or a support ring in the rim. In addition to modular innovation, technological newness may be based on architectural innovation. This type of innovation is the configuration of new and/or established design concepts in a new architecture (Henderson & Clark, 1990). The engine of an electric car is an instance of architectural innovation. The architecture of electric engines differs markedly from traditional combustion engines.

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Newness of customer benefits denotes the degree to which an innovation fulfills customer needs substantially better than existing alternatives (Chandy & Tellis, 1998). A high level of newness of benefits occurs if an innovation has the “potential to produce one or more of the following: an entirely new set of performance features, improvements in known performance features of five times or greater, a significant (30% or greater) reduction in cost (Leifer, McDermott, Colarelli O’Connor, G., Peters, & Veryzer, 2000, p. 5).” More generally, innovations characterized by a high level of newness of benefits go beyond previously recognized demand (Garcia & Calantone, 2002) and provide customers with totally different ways of doing things, sometimes even allowing them to do things they have not been able to do before (Lehmann, 1997). Consequently, these innovations often involve a steep learning curve for customers. That is, customers have to alter their thinking and behavior to understand and make use of these innovations (Veryzer, 1998a). This is because customers lack prior experience with completely new benefits and are likely to feel uncertain about associated consumption utilities (Hoeffler, 2003).

Arising from this discussion, three types of really new products can be distinguished: technological breakthroughs, market breakthroughs, and radically new products. Technological breakthroughs are innovations that “adopt a substantially different technology than existing alternatives but do not provide new customer benefits (Chandy & Tellis, 1998, p. 476).” Research also refers to technological breakthroughs as technologically discontinuous innovations (Veryzer, 1998b). Market breakthroughs denote innovations that “are based on core technology that is similar to existing alternatives but provides substantially higher customer benefits (Chandy & Tellis, 1998, p. 476).” Research also refers to market breakthroughs as commercially discontinuous innovations (Veryzer, 1998b). Radically new products refer to innovations “that involve substantially new technology and provide completely new customer benefits (Chandy & Tellis, 1998, p. 476).” Research also refers to these innovations as revolutionary innovations (Veryzer, 1998b).

Figure 2-1 summarizes the different types of innovations. These include: incrementally new products characterized by low levels of technological newness and newness of customer benefits. Conversely, really new products refer to innovations with a high level of technological newness and/or a high level of newness of customer benefits. Three sub-types of really new products are distinguished: (1) technological breakthroughs characterized by a high level of technological newness and a low level of newness of customer benefits; (2) market breakthroughs characterized by a low level of technological newness and a high level of newness of customer benefits; and

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(3) radically new products characterized by a high level of technological newness and a high level of newness of customer benefits.

Figure 2-1: Types of Innovations (Chandy & Tellis, 1998, 2000)

2.2.2 Perception of Meaningfulness of Innovations

According to its formal definition, perceived meaningfulness comprises the desirability of an innovation as well as its feasibility (Arts, Frambach, & Bijmolt, 2011; Trope & Liberman, 2003). The higher an innovation scores in these dimensions, the higher is its perceived meaningfulness and the more favorable is customers’ response. However, if customers perceive a lack of desirability and/or feasibility, they won’t regard an innovation as meaningful and reject it (Rogers, 2003). An example for such a case represents the Dvorak keyboard which was introduced in the 1930s as an alternative to the conventional QWERTY keyboard. Even though the Dvorak keyboard increased efficiency of typewriting, typists rejected this innovation. More precisely, the Dvorak keyboard required typists to learn a new way of typing. From the perspective of typists, the effort associated with this learning overweighed the perceived benefits of the Dvorak keyboard. As a consequence, typists did not perceive it as meaningful and responded unfavorably to this innovation (Rogers, 2003).

Incrementally New Products

Technological Breakthroughs

Market Breakthroughs

Radically New Products

Low

High

Low High

New

ness

of B

enef

its

Technological Newness

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2.2.2.1 Perceived Desirability of Innovations

The desirability of an innovation refers to the benefits that arise when putting an innovation into use. It is based on the specific advantages an innovation provides over existing alternatives (Sethi et al., 2001). Research distinguishes three types of benefits: functional benefits, hedonic benefits, and symbolic benefits (Smith & Colgate, 2007). Functional benefits refer to the degree “to which a product (good or service) has desired characteristics, is useful, or performs a desired function (Smith & Colgate, 2007, p. 10).” Firstly, functional benefits arise from superior features, functions, attributes, and/or characteristics of an innovation (Woodruff, 1997). In other words, functional benefits may be based on superior quality (Sethi & Sethi, 2009). Secondly, an innovation provides functional benefits if it enhances performance compared to existing practice (Dahl & Hoeffler, 2004; Norman, 2004; Zhao et al., 2012; Zhao, Hoeffler, & Zauberman, 2007). Thus, the better an innovation fulfills its instrumental or physical purpose, the higher its functional benefits (Sheth, Newman, & Gross, 1991). In extreme cases, an innovation may even offer “a completely new way of doing something, or a completely new thing to do, something that was not possible before (Norman, 2004, p. 77).” Thirdly, functional benefits relate to favorable outcomes and consequences. That is, an innovation has high functional benefits if it solves current problems or helps to prevent future problems (Smith & Colgate, 2007).

Hedonic benefits refer to the degree to which a product “creates appropriate experiences, feelings, and emotions (Smith & Colgate, 2007, p. 10).” Firstly, hedonic benefits may be based on the special sensory experiences an innovation provides (Rafaeli & Vilnai-Yavetz, 2004). Typically, these derive from an innovation’s formal attributes such as color, shape, proportions, materials, or craftsmanship (Rindova & Petkova, 2007). Secondly, hedonic benefits may arise from the emotional experiences an innovation provides. Examples of such emotional experiences include pleasure, fun, enjoyment and excitement (Smith & Colgate, 2007). Thirdly, hedonic benefits relate to the social-relational experiences an innovation provides (Ulaga & Eggert, 2005). Facebook is a prominent example of an innovative firm that primarily provides social-relational benefits. Through its social platform, Facebook allows customers to interact with friends and family in completely new ways. Finally, hedonic benefits may be based on epistemic experiences (Sheth et al., 1991) which involve a “sudden expansion, recombination, or ordering of previously adopted information (Csikszentmihalyi & Robinson, 1990, p. 18).” Examples of epistemic experiences include knowledge emotions, such as interest, curiosity, or fantasy (Silvia, 2005).

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Symbolic benefits refer to the degree to which “customers attach or associate psychological meaning to a product (Smith & Colgate, 2007, p. 10).” Firstly, symbolic benefits may address customers’ self-identity and self worth. That is, an innovation provides symbolic benefits if it contributes to the way that customers would like to see themselves. Secondly, symbolic benefits relate to social acceptance. Put differently, symbolic benefits may be based on the messages customers can convey to others by their use of an innovation (Norman, 2004). Frequently, customers adopt innovations so that they will be associated or disassociated with specific socio-cultural groups (Sheth et al., 1991). Thirdly, symbolic benefits involve personal and conditional associations. Personal associations are events that are relevant for a particular customer only. Conditional associations refer to sociocultural-ethnic events and traditions (Smith & Colgate, 2007). Generally, symbolic benefits arise from the deeper meaning of an innovation (Verganti, 2008). Such meaning includes the signs that distinguishes an innovation from existing practice, the general significance of an innovation, as well as an innovation’s relations to other objects, concepts, or ideas (Krippendorff, 1989). Verganti (2008) argues that such meaning arises particularly from specific design languages, consisting of signs, symbols, and icons incorporated into an innovation. The Bang & Olufsen Beosound 4000 stereo, for example, represented an innovation that markedly changed the meaning of music players by transforming them from electronic devices to items of furniture.

Table 2-1 provides a summarizing overview of the different types of customer benefits as a basis for the perception of meaningfulness of innovations.

Table 2-1: Types of Customer Benefits

Functional Hedonic Symbolic

Correct/accurate attributes

Appropriate performances

Appropriate outcomes

Appropriate consequences

Sensory experience

Emotional experience

Social/relational experience

Epistemic experience

Self-identity/ self-worth

Social Acceptance

Personal Associations

Conditional Associations

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2.2.2.2 Perceived Feasibility of Innovations

Feasibility refers to the cost and other sacrifices that may be involved in the purchase, ownership, and use of an innovation (Arts et al., 2011; Smith & Colgate, 2007). It includes economic costs, psychological costs, and risk (Smith & Colgate, 2007). Firstly, an innovation reduces economic costs if it provides an existing functionality at a lower price. An example of this would be Netbooks which provide similar functionalities as Notebooks, only at a much lower price (Rogers, 2003). Secondly, an innovation may reduce psychological costs by increasing the usability of existing alternatives, that is, by making usage easier and more intuitive, thereby decreasing learning costs (Norman, 2004). Specifically, innovations by the Apple brand provide a high level of usability. Finally, many innovations reduce the risks associated with existing practice (Rogers, 2003). An example of this is the air bag in cars, which significantly reduced the risk of severe injuries in car accidents.

2.2.3 Determinants of Innovation Perception and Market Success

As the previous discussion suggests, newness and meaningfulness represent key determinants of innovation perception. This invites the question whether each of these dimensions influences innovation perception in the same way. Important implications of the influence of determinants of innovation perception may be derived from studies which examined the impact of newness and meaningfulness on market success. Research indicates that market success/failure of innovations represents a key indicator of favorable/unfavorable innovation perception (Kunz et al., 2011).

In a recent study, Im & Workman (2004) examined the impact of the newness and meaningfulness of new products and associated marketing programs on new product performance. 312 new product project managers were surveyed in the study. Respondents indicated the newness and meaningfulness of new products, and associated marketing programs that were introduced into the market within 6 months prior to the study. New product success was measured on the basis of relative sales, relative market share, relative return on investment, and relative profits of the new products. The study revealed that new product performance was driven more by meaningfulness than by newness of new products and associated marketing programs.

Similar results are provided by Szymanski, Kroff, & Troy (2007) who conducted a meta-analysis in which they examined the impact of newness and meaningfulness of new products on new product success. The authors distinguished between studies that solely used newness as predictor of new product success, and studies which used

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newness and meaningfulness as predictors of new product success. The analysis revealed that newness only does not represent an appropriate predictor of new product success. For some studies, the meta-analysis even showed a negative relationship between newness and new product success. Hence, Szymanski et al. (2007) suggest that new products will be successful only if they are characterized by a sufficient degree of meaningfulness in addition to newness.

These findings also appear to confirm the findings of a study by Moldovan, Goldenberg, & Chattopadhyay (2011) which examined the impact of the newness and meaningfulness of new products on word of mouth. The authors distinguished between valence of word of mouth and amount of word of mouth. Valence refers to whether word of mouth is positive or negative. Positive word of mouth creates favorable attitudes towards products, while negative word of mouth creates negative attitudes. Amount of word of mouth depends on how many people talk about a product. It refers to the total buzz generated in a given market. The study demonstrated that newness significantly increased the amount of word of mouth, but had no impact on the valence of word of mouth. The valence of word of mouth was primarily determined by meaningfulness. At a high level of meaningfulness, word of mouth was positive, whereas, at low levels of meaningfulness, word of mouth even became negative.

Together, these findings imply that perceived newness represents a necessary, yet not sufficient driver of innovation perception (Henard & Szymanski, 2001). As a matter of fact, newness itself is unlikely to evoke positive reactions to an innovation. Only if customers perceive an innovation as considerably meaningful they will respond favorably to it (Im & Workman, 2004). It could be concluded that newness solely draws attention to an innovation. However, whether customers perceive an innovation as attractive or not primarily depends on meaningfulness (Moldovan et al., 2011).

Now that the term innovation has been specified from a customer perspective, key processes of innovation perception are discussed in the next chapter (chapter 3). The main emphasis of this chapter will be to reveal how processes of innovation perception can be influenced so that innovations are perceived as favorable.

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3 Processes of Innovation Perception

3.1 Processes-Oriented View of Innovation Perception

Literature indicates that innovation perception does not represent an instantaneous act, but a process that involves a variety of different interactions with an innovation. Research distinguishes five stages through which individuals pass when adopting an innovation. These stages include a knowledge stage, a persuasion stage, a decision stage, an implementation stage, and a confirmation stage (Rogers, 2003). The knowledge stage occurs, when individuals are exposed to an innovation and develop an understanding of what the innovation does. In the persuasion stage, individuals deal with potential consequences of innovation adoption and form a favorable or unfavorable attitude towards the innovation. The decision stage takes place when individuals engage in activities that lead to the adoption or rejection of an innovation. During the implementation stage, individuals attempt to put an innovation to use. In the confirmation stage, individuals seek reinforcement of a previous adoption decision. However, they may also reverse their decision in this last stage (Rogers, 2003).

Following the five stages of adoption, two primary processes of innovation perception may be distinguished: innovation recognition and innovation assessment. Innovation recognition is a person’s taking note of an innovation and becoming interested in it (Ziamou & Gregan-Paxton, 1999). It relates to the knowledge stage of the adoption process and involves both exposure to as well as learning of what an innovation does (Rogers, 2003). Innovation recognition may arise from an internal or external change of one’s situation. Perception of external change is typically based on the communication of innovations via advertising, personal selling, publicity, or word of mouth. Furthermore, perception of external change may occur if individuals come across an innovation by engaging in unplanned activities such as exploratory shopping. Conversely, perception of internal change is the internal recognition of a specific problem or need (Bagozzi & Lee, 1999). In the case of internal change, an individual actively searches for an innovation that suits his or her problem or need (von Hippel, 1988). Innovation recognition may result either in initial resistance or openness to an innovation (Bagozzi & Lee, 1999).

In comparison, innovation assessment is the persuasion, decision, implementation, and confirmation stages of the adoption process. In case of innovation assessment, individuals become intensively involved with an innovation (Rogers, 2003). During this process, individuals repeatedly interact with an innovation and actively seek

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information about it. They analyze key attributes and characteristics of the innovation and make inferences about potential favorable and unfavorable consequences that may result from adoption (Bagozzi & Lee, 1999). A key aspect of innovation assessment refers to the effort required for effective use of an innovation. Individuals will only form positive attitudes toward an innovation if adoption is associated with reasonable effort (Venkatesh et al., 2003, 2012). Furthermore, individuals will only react favorably to an innovation as long as adoption is not perceived as too risky (Castaño, Sujan, Kacker, & Sujan, 2008; Ostlund, 1974).

As Innovation Recognition and Innovation Assessment have now been specified, chapters 3.2 and 3.3 will provide a more detailed discussion of these processes.

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3.2 Innovation Recognition

3.2.1 Identification of Innovations

3.2.1.1 Formation of Awareness of Innovations

Before individuals can develop a favorable attitude towards an innovation in the recognition phase, they must become aware of its existence. Research indicates that individuals form a high awareness of an innovation if they are repeatedly exposed to that innovation, and if they develop strong associations with relevant purchase or consumption cues (Keller & Lehmann, 2006; Keller, 1993). This relationship follows from the way in which information is structured in human memory. Specifically, human memory consists of a network of interlinked nodes in which information is stored (Anderson, 1983; Srull & Wyer, 1989). Thereby, the probability of information retrieval from a specific node is a function of how often that node is activated. Such activation can either occur directly, or indirectly through activation of other nodes to which a link was previously established (Ratcliff & McKoon, 1988). Thus, in order to build awareness of an innovation - especially a highly novel innovation - a new node needs to be created and interlinked with other nodes. This particularly occurs if individuals come across an innovation repeatedly and are thereby able to relate the innovation to important aspects of their lives. In other words, the more often individuals experience an innovation by seeing it, hearing about it, or thinking about it in a way that is personally relevant to them, the more likely these individuals are to strongly register that innovation in memory (Keller, 2007).

Repeated exposure is not only likely to increase awareness of an innovation, but also to evoke a favorable attitude towards it. Research found that repeated presentation of a stimulus generally enhances its liking because of an experience of perceptual fluency (Reber, Winkielman, & Schwarz, 1998). Perceptual fluency may be defined as the ease of identifying the physical identity of a stimulus (Reber, Schwarz, & Winkielman, 2004). If an individual encounters a specific stimulus, he/she will form a mental image of that stimulus. Through repeated exposure, the vividness of this mental image is enhanced. The more vivid the mental image, the easier it is to identify the physical identity of the stimulus. This fluency experience evokes a positive feeling, which is attributed to the stimulus and, as a consequence, leads to a favorable attitude (Bornstein, 1989). Thus, if an individual encounters an innovation repeatedly,

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perceptual fluency of this innovation is likely to increase. This in turn results in a more favorable attitude towards that innovation.

Perceptual fluency is not only a function of quantity of exposure, but also of quality of exposure (Reber et al., 2004). More precisely, perceptual fluency of an innovation is likely to increase as people become more intensively involved with that innovation (Gordon & Holyoak, 1983). Typically, this involvement depends on an individual’s motivation, ability, and opportunity to process information about the innovation (Maclnnis & Jaworski, 1989; Petty & Cacioppo, 1986). Motivation may be defined as the degree to which an individual is interested and willing to put effort in processing information about an innovation. Ability denotes the degree to which an individual possesses the necessary knowledge to interpret that information appropriately. Opportunity may be defined as the degree to which situational factors facilitate processing that information (Chandy, Tellis, MacInnis, & Thaivanich, 2001).

Following this, research identified different types of message cues that promote or prevent involvement with an innovation. These include appeal mode, appeal prominence, and appeal frame (Chandy et al., 2001). Appeal mode refers to the degree to which a message is argument-based or emotional (Chandy et al., 2001). Argument-based messages emphasize the technological superiority of an innovation over its competitors. That is, they clearly emphasize what an innovation does for the individual (Golden & Johnson, 1983). Argument-based messages convince customers by means of functional appeals such as quality, economy, or value (Lee & Colarelli O’Connor, 2003). Conversely, emotional messages evoke positive feelings associated with the product and its use (Friestad & Thorson, 1986). In other words, the purpose of emotional messages is to create some kind of emotional experience that people associate with an innovation (Ray & Batra, 1983). With emotional messages, customers are convinced by appeal to such feelings as joy, humor, love, or pride (Lee, Lin, Wong, & Calantone, 2011).

For really new products, argument-based messages are likely to be more effective than emotional messages. Because of their inherent newness, really new products create an unwanted information gap (Loewenstein, 1994) which motivates customers to collect further information of what the product does (Schumann, Petty, & Clemons, 1990). Accordingly, in such a situation, customers are more willing to consider factual information. Factual information reduces purchase risks and differentiates the product more effectively from existing alternatives (Talke & Colarelli O’Connor, 2011). Conversely, emotional messages are counterproductive for really new products. Emotional messages neither provide a compelling reason for buying a product nor

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change fundamental beliefs about it (Chandy et al., 2001). Sometimes, emotional messages may even distract customers from critical information (Moore & Hutchinson, 1983).

In contrast, with incrementally new products, emotional messages are more effective than argument-based ones (Chandy et al., 2001). Incrementally new products do not create information gaps. Individuals can easily make sense out of these products by drawing on prior experiences (Zhao et al., 2009). Thus, individuals are less responsive to argument-based messages when encountering incrementally new products. In extreme cases, they may even respond negatively to these kinds of messages because of satiation, boredom, or irritation (Batra & Ray, 1986). Things are different for emotional messages. They draw attention to incrementally new products by stimulating the visualization of concrete self-related consumption experiences (Adaval & Wyer, 1998; Shiv & Huber, 2000).

The second message cue, appeal prominence, refers to the prominence of key attributes of an innovation. Messages may be prominent by virtue of their size (e.g. a large font as opposed to a small font), duration on screen, or the number of times they are shown (Stewart & Furse, 1986). As individuals are less familiar with really new products, they experience difficulties in assimilating key message information into memory. Relative to incrementally new products, customers will require more time to assimilate key message information for really new products. Thus, anything that increases attention to and assimilation of key message information should increase effectiveness of messages about really new products (Chandy et al., 2001).

The third message cue, appeal frame, may be defined in terms of the positive and negative goal framing of a message. Generally, research refers to positive goal frames as messages that are concerned with obtaining favorable consequences. In contrast, negative goal frames refer to messages that are concerned with avoiding unfavorable consequences (Levin, Schneider, & Gaeth, 1998). In the marketing context, a positive goal frame highlights a product’s ability to provide gains or obtain benefits, whereas a negative goal frame highlights a product’s ability to avoid losses or solve problems (Chandy et al., 2001). Negative goal frames are more effective for really new products. These products allow individuals to do things they have not been able to do before and provide solutions to previously unsolved problems (Dahl & Hoeffler, 2004). Messages that contain information for avoiding or eliminating unsolved problems are likely to motivate individuals to process these messages (Maheswaran & Meyers-Levy, 1990). Conversely, positive goal frames are more effective for incrementally new products. For these products, individuals are already aware of the problems they solve. To

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motivate product use, individuals should be provided with information about how the product fulfills appetitive and positive states (Chandy et al., 2001).

3.2.1.2 Identification of Category Membership of Innovations

An important aspect during identification of innovations involves categorization of an innovation. Categorization research is concerned with how people organize knowledge in memory, and how this knowledge influences perception of novel objects such as innovations (e.g., Goode, Dahl, & Moreau, 2012; Ozanne, Brucks, & Grewal, 1992; Ratneshwar & Shocker, 1988). This line of research argues that knowledge is organized around partially integrated knowledge structures in memory. Each of these structures contains information about a group of objects that are perceived as alike in important respects. Research refers to these entities as categories (J. Cohen & Basu, 1987). Every time an individual encounters a novel object, a categorization process is initiated. During this process, the object is compared to categories already stored in memory (Piaget, 1969; Rosch, 1978). The purpose of categorization is to make information processing more efficient, thereby decreasing cognitive load (Bruner, Goodnow, & Austin, 1956; Lingle, Altom, & Medin, 1984).

Identification of category membership is determined by how similar an object is perceived to other objects within a given category (Creusen & Schoormans, 2005; Loken & Ward, 1990). Research identified analytical and non-analytical categorization processes as a basis for the identification of an object’s category membership (J. Cohen & Basu, 1987). In case of an analytical categorization process, perceived similarity is based on a set of core attributes that jointly determine the membership of a category. The more of these critical category defining features a novel object possesses, the more similar it will be perceived to the associated category. However, if a novel object does not possess any of the critical features that define a category, it will be perceived as highly dissimilar to that category (Dominowski, 1974).

In comparison, non-analytical categorization processes are not concerned with a set of category defining attributes, but with an object’s overall representativeness of specific category exemplars or its general prototypicality (Rosch & Mervis, 1975). Non-analytical categorization is based on knowledge abstracted from prior experience. The overall representativeness of specific category exemplars is determined by the degree to which an object is representative of integral representations of available exemplars of a specific category (Cantor & Mischel, 1979). Similarly, prototypicality is defined in terms of a set of features commonly associated with members of a category. It is

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determined by an object’s departure from the average values of these features (J. Cohen & Basu, 1987; Langlois & Roggman, 1990; Veryzer & Hutchinson, 1998).

Accordingly, the prototypicality of an innovation’s appearance depends on how far its design deviates from the means of characteristic feature points in a given product category. Drawing on this insight, Landwehr, Labroo, & Herrmann (2011) developed a methodology for determining the prototypicality of car designs. Specifically, they defined 50 characteristic features of car designs, including the vertex of headlights, grill, and windshield. For each of the features the mean position was calculated across different models within a given segment (see exemplars of different car segments in Figure 3-1). This procedure yielded a segment-specific prototype for each segment (see prototypes of different car segments in Figure 3-1). This prototype was then used to determine the prototypicality of the designs in the segments on the basis of the Euclidian distances of each of the 50 feature points from the corresponding feature points in the respective prototypes.

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Figure 3-1: Car Segments and Associated Prototypes (Landwehr et al. 2011)

Exemplars Compact Segment Exemplars Premium Segment

Prototype Compact Cars Prototype Compact Cars

Research demonstrates that the identification of category membership plays an important role in innovation perception. Goode, Dahl, & Moreau (2012) provide empirical evidence that the certainty of identifying an innovation’s superordinate product category leads to more favorable response to that innovation. Specifically, they find that certainty of identification results in more positive evaluation of an innovation. This is because the successful identification of an appropriate category provides individuals with a clear point of reference from which to compare and evaluate an innovation with confidence. If such a point of reference is missing due to uncertainty of an innovation’s category membership, people are likely to conduct more conservative evaluations.

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3.2.2 Learning of Innovations

3.2.2.1 Customer Knowledge Transfer

Besides identification of an innovation, innovation recognition is also concerned with the development of an initial understanding of what an innovation does. In the course of such a learning process, people often appeal to prior knowledge. The literature refers to this process as knowledge transfer. It occurs when people make something unfamiliar intelligible by relating it to something familiar. More precisely, knowledge transfer „is concerned with the use of a familiar domain (the base) to understand a novel domain (the target) (Gregan-Paxton & Roedder John, 1997, p. 267).“ For example, when cars were first introduced, many people made sense out of this new technology on the basis of their prior knowledge of horse carriages.

In the process of knowledge transfer, representations of the target domain and the base domain consist of systems of objects, attributes, and relations. Objects may refer to clear entities such as a car as a whole, individual parts of a larger object such as a door of a car, or even combinations of entities such as a complete model range of cars (Gentner, 1983). Attributes are independent properties or components of objects. They can be concrete or abstract. An example of a concrete attribute is the steering wheel of a car. Reliability provides an example of an abstract attribute of a car. In comparisons, relations refer to the links between the attributes of a domain. For a car, such a relation exists between the gas pedal and the car’s velocity. Specifically, by pushing the gas pedal, the velocity increases (Gregan-Paxton & Roedder John, 1997).

Gregan-Paxton et al. (2002) suggest that knowledge transfer occurs in a three-stage process. The stages of this process comprise: (1) accessing the base domain, (2) mapping the elements from the target onto the base, and (3) transfer of knowledge from the base to the target (see Figure 3-2). When people first encounter an innovation, they typically follow this process in order to learn what the innovation does. In each stage of the process a variety of factors need to be considered so that customers can develop an adequate understanding of an innovation.

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Figure 3-2: The Knowledge Transfer Process (Gregan-Paxton et al., 2002)

Accessing the Base Domain

In the access stage, the target should activate an appropriate base domain in the mind of an individual. This base domain should represent a potential source of information that is suitable for making sense out of the target domain. Thereby, access may either occur spontaneously or on the basis of a prompt provided by an external source (Gregan-Paxton et al., 2002). If the access stage is successful, a mental representation of the base domain is evoked. In doing so, knowledge potentially relevant for understanding the target domain becomes active in memory (Gregan-Paxton & Roedder John, 1997). In case of the car example, the access stage is completed if a mental representation of a horse carriage is evoked.

The success of the access stage is strongly determined by the degree to which the target domain and the base domain share common attributes (Nisbett & Ross, 1980). Put differently, a base domain is more likely to be activated if it is characterized by a high degree of overlap to the target domain (Gentner, 1983). Research provides considerable empirical evidence for this relationship. For example, Gentner et al. (1993) conducted a study in which they presented participants with sets of stories that varied in the degree to which they contained common attributes such as characters. They found that an increase in attribute overlap of the stories resulted in a higher number of participants who would retrieve a base story.

Mapping from the Base to the Target

The purpose of the mapping stage is to relate the base domain to the target domain so that knowledge about the base domain can be transferred. Therefore, individuals need to construct one-to-one correspondences between the mental representations of the base domain and the target domain (Falkenhainer, Forbus, & Gentner, 1989). Put differently, in the mapping stage, individuals create paths between the base and the target. Across these paths, they can transport relevant knowledge (Gregan-Paxton & Roedder John, 1997). Mapping occurs in the car example by identifying

Access Mapping Transfer

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commonalities between cars and horse carriages. Such commonalities might include the transportation of heavy objects or comfortable traveling of large distance.

In the mapping stage, individuals prefer relation-based matches over attribute-based matches. Research provides considerable empirical evidence to support this claim (Gregan-Paxton & Roedder John, 1997). For example, Gentner et al. (1993) conducted a study in which they presented participants with different stories of varying degrees of matching attributes and relations. They found that the addition of matching relations increased the perceived soundness of a match between stories, whereas the addition of common attributes had no effect on the perceived soundness of a match between stories. Furthermore, in a study by Spellman & Holoyak (1992), participants were asked to compare the Persian Gulf War and World War II. The study showed that participants tended to generate mappings that preserved the relation between a country and its leader, in spite of mappings that separated the two.

Research suggests that individuals prefer relation-based matches in the mapping stage, because relation-based matches allow them to derive causal principles (Gentner, 1983). In line with this, Gregan-Paxton & Roedder John (1997) argue that comparisons building on relation-based matches are more informative than comparisons building on attribute-based matches. They use the example of laundry detergents to illustrate this point. Specifically, they argue that, when customers compare a store-brand laundry detergent (target) with a laundry-detergent of an established brand like Ariel (base), customers are likely to be more concerned about common relations than about common attributes. This is because the store-brand is more likely to clean properly if it has an identical chemical configuration (i.e. common relation) as the established brand, Ariel. Whether or not the store-brand is packaged in white and green boxes (common attribute) is irrelevant.

Transferring Knowledge from the Base to the Target

In the transfer phase, the actual transfer of knowledge takes place. By moving knowledge from the base to the target, people make sense of the target. This stage is driven by the belief that domains which seem similar in certain respects may also be similar in other respects. With the car example, the fact that cars and horse carriages allow the transportation of heavy objects may encourage individuals to assume that both devices share more characteristics. Relying on their experience of horse carriages, they may conclude, for instance, that cars require horses as drive (Gregan-Paxton & Roedder John, 1997).

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Research distinguishes different kinds of knowledge transfer. Knowledge transfer can occur in a schema-based process, which builds on more abstract knowledge structures (Gick & Holyoak, 1983; Spencer & Weisberg, 1986). Additionally, knowledge transfer can occur in a similarity-to-exemplar process, which is entirely based on the similarity between the target domain and a specific exemplar of the base domain (Alba & Hutchinson, 1987; Beattie, 1982; Carey, 1985; Klein, Loftus, Trafton, & Fuhrman, 1992; Rumelhart, 1989). In general, schema-based knowledge transfer leads to better results than similarity-to-exemplar transfer. This is because schema-based knowledge transfer concentrates on the essential matters and prevents the transfer of inappropriate information. In contrast, similarity-to-exemplar transfer represents more of a backup-strategy. It is used primarily when an appropriate schema is absent or the schema is insufficient to guide knowledge transfer (Gregan-Paxton & Roedder John, 1997).

It must be stressed that similarities between different domains are limited (Gregan-Paxton & Roedder John, 1997). In general, only a subset of information relating to a base is appropriate for the transfer of knowledge to a specific target (Ortony, 1975). This implies that errors may occur in the knowledge transfer process (Novick, 1988).

Figure 3-3 provides a summarizing overview over the knowledge transfer model. As this model indicates, the knowledge transfer process begins with the access stage in which individuals search for common attributes between a target domain and appropriate base domain. If a base domain is activated, the individual proceeds to the mapping stage. In this stage, individuals create one-to-one correspondences between the target and the base. Thereby, they prefer relation-based mappings over attribute-based mappings. However, relation-based mappings will occur only if individuals possess sufficient knowledge about the target and the base domain. If this is not the case, attribute-based mapping will occur. After the mapping stage, actual knowledge transfer occurs in the transfer phase. In this phase, an individual transfers knowledge from the base to the target, thereby developing a deeper understanding of the target domain (Gregan-Paxton & Roedder John, 1997).

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Figure 3-3: Model of Knowledge Transfer (Gregan-Paxton & Roedder John, 1997)

Identification of Commmon Attributes

Accessing the Base Domain

Perceive Common Relations

Don’t Perceive Common Relations

Map Relations Map Attributes

Schema-Based Transfer

Similarity-to-Exemplar Transfer

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Research identified knowledge transfer as a key aspect of innovation recognition. It may help people to develop a much better understanding of what an innovation does (e.g., El Houssi et al., 2005; Hoeffler, 2003). Thereby, knowledge transfer may be based on closely related knowledge structures as well as more disparate knowledge structures. Take a personal digital assistant (PDA), for example. People could make sense out of such a device by transferring prior knowledge they have developed about organizers. In this case, people would draw on a closely related knowledge structure. In comparison, people could also draw on a more disparate knowledge structure when learning about a PDA. Specifically, they could make sense out of a PDA by building on prior knowledge they have gained with secretaries. That is, people can conclude that a PDA may be similar to a secretary in that it performs many routine tasks such taking a dictation or setting up appointments (Gregan-Paxton et al., 2002).

It is suggested that extensive knowledge transfer is likely to provide a comprehensive understanding of an innovation and, as a consequence, lead to more favorable response to an innovation (Hoeffler, 2003). Given the high relevance for innovation recognition, it is next discussed how knowledge transfer can be encouraged.

3.2.2.2 Encouragement of Customer Knowledge Transfer

Research demonstrates that knowledge transfer is effective only in certain situations. Specifically, individuals need the ability to map attribute relations from a base domain to understand the benefits of a target domain, and they need to allocate substantial resources to complete this mapping. If one of these conditions is absent, sufficient knowledge transfer is unlikely to occur. However, research has identified a variety of devices that help to overcome the challenge of mapping relations, thereby improving the understanding of a target domain (Roehm & Sternthal, 2001).

One of these devices represents expertise in the base domain. This is likely to promote extensive relation-based mappings. The advantage associated with such expertise is the difference in base knowledge structures between experts and novices (Roehm & Sternthal, 2001). Specifically, the knowledge structures of novices contain some attribute information, but little information about structural relations. In contrast, the knowledge structures of experts contain extensive attribute information as well as considerable abstract information about structural relations (Alba & Hutchinson, 1987). In line with this, Reidenbach & Grimes (1984) showed that individuals with prior knowledge in the portable phone category were able to develop a much more comprehensive understanding of an innovative portable phone system. Similarly,

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Dickerson & Gentry (1983) demonstrated that prior experience with computer-related products significantly improved appreciation of home computers.

If expertise in the base domain is lacking, relation-based mapping may be alternatively promoted through training individuals how to process base information (Roehm & Sternthal, 2001). A positive relationship between training and relation-based mappings was identified by studies examining the impact of knowledge transfer on negotiation performance of individuals (Loewenstein, Thompson, & Gentner, 1999; Thompson, Gentner, & Loewenstein, 2000). In one of these studies, management students had to read two case studies on the topic of negotiation and were subsequently asked to perform a face-to-face negotiation task. While one group of students had to study the case studies separately (control condition), another group of students had to compare the two cases and derive an overall negotiation principle (training condition). It was found that students in the training condition were almost three times more likely to make relation-based mappings when transferring knowledge from the cases to the subsequent negotiation task (Loewenstein et al., 1999). Following this, Roehm & Sternthal (2001) demonstrated that instructing individuals to focus their attention on structural relations among attributes of a base domain is likely to prompt relation-based mappings, thereby significantly improving knowledge transfer.

3.2.2.3 Single vs. Multiple Category Inferencing

To develop an adequate understanding of a target domain, it is often necessary to transfer knowledge from more than one base domain. Specifically, when learning about innovations, people frequently need to make inferences from multiple product categories. Understanding the benefits of a camera phone, for example, requires inferences from both the mobile phone category as well as the digital camera category. The literature distinguishes multiple category inferencing (transferring knowledge from two or more categories) and single category inferencing (transferring knowledge from only one category) (e.g., Gregan-Paxton et al., 2002; Moreau, Markman, et al., 2001).

Research indicates that learning about innovations is characterized by a disposition to make single category inferencing. Specifically, Moreau, Markman, et al. (2001) showed that people tend to draw inferences about an innovation on the basis of the first category cue that becomes available. A subsequent second category cue was found to have hardly any effect. The tendency for single category inferencing may be overcome, however, by pointing out specific correspondences between different

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category cues and a target domain. Take digital cameras for example: to learn about such an innovation, it might be helpful to draw inferences from both film based cameras as well as computer scanners. Individuals can be focused on specific correspondences to these categories in the following way: First of all, a digital camera manufacturer could point out that a digital camera resembles a film based camera in that one can take pictures with it. The manufacturer can also point out that the digital camera is like a computer scanner in that one can process pictures with it (Moreau, Markman, et al., 2001). This approach was applied when Steve Jobs presented the first Apple iPhone. He started his presentation by informing the audience that he was about to present three separate devices, including a media player, a mobile phone, and an internet communications device. Only later in his presentation did he reveal that these three devices were part of a single product, namely, the iPhone.

Research suggests that multiple category inferencing may be evoked though a specific priming approach derived from literature on conceptual combinations in psycholinguistics. This research examines primarily how people interpret novel noun-noun combinations such as a book-magazine. People predominantly adopt one of two different strategies when interpreting such novel combinations: relational interpretations and property interpretations (Wisniewski & Love, 1998). In case of a relational interpretation, a relation is formed between the categories such that a book-magazine is interpreted as a magazine about books. In comparison, property interpretation occurs if a particular property or attribute of one category maps to the second category such that a book-magazine is interpreted as a magazine that is as thick as a book. Following these considerations, Rajagopal & Burnkrant (2009) conducted a study in which they primed participants with the aim of evoking relational and property interpretations. Specifically, examples of relational priming in the study included ‘An ink pen is a pen that writes with ink,’ while examples for property priming in the study included ‘A pencil pen is a pen and a pencil together in a single product.’ The study revealed that multiple category inferencing was more likely to occur under property priming relative to relational priming.

Furthermore, multiple category inferencing depends on the propensity to adapt prior category knowledge. When individuals apply multiple category inferencing, they frequently need to change or update at least one of the categories involved in that process. An example of multiple category inferencing might be a product that combines a personal digital assistant (PDA) and a cell phone. If individuals make predictions about the performance of this ambiguous device, they are likely to make inferences that involve discrepant attribute values. Specifically, if they make

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inferences about the scheduling capability of a PDA/cell phone, the PDA category is likely to prompt a favorable response, whereas the cell phone category is likely to prompt an unfavorable response. In contrast, if individuals make inferences about the device’s capability to make phone calls, the PDA category is likely to prompt an unfavorable response, whereas the cell phone category is likely to prompt a favorable response. Following this, Gregan-Paxton, Hoeffler, & Zhao (2005) argue that in such a situation individuals either need to update the PDA category to accommodate the new value for making phone calls, or they need to update the cell phone category to accommodate the new value for scheduling.

Gregan-Paxton, Hoeffler, & Zhao (2005) show that category updating is in part determined by the familiarity of category cues. Individuals are more likely to update low-familiarity categories than high-familiarity categories (Elliott & Anderson, 1995). The authors further argue that category updating is determined by the nature of category cues. Research distinguishes between perceptual and conceptual category cues. Perceptual category cues arise from visual depiction of categories, whereas conceptual category cues arise from the provision of category labels. Generally, individuals perceive perceptual category cues as more diagnostic of a specific functionality than conceptual category cues (Matan & Carey, 2001). Gregan-Paxton, Hoeffler, & Zhao (2005) demonstrate that, when an ambiguous product is described in terms of conflicting perceptual and conceptual category cues, multiple category inferencing is applied when the perceptually cued category is as familiar as or less familiar as the conceptual cued category. Where these conditions do not obtain, individuals are more likely to apply single category inferencing.

3.2.3 Perceived Differentiation of Innovations

3.2.3.1 Feature Based Differentiation of Innovations

Furthermore, innovation recognition is determined by how different an innovation is perceived from existing alternatives. Differentiation of an innovation occurs where individuals are able to identify one or more features that distinguish the innovation from existing alternatives (Carpenter, Glazer, & Nakamoto, 1994). Specifically, features on which an innovation is perceived as being about equal with existing alternatives will result in unsuccessful differentiation. Features on which an innovation is perceived to be different will result in successful differentiation (Fuchs & Diamantopoulos, 2012).

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Research distinguishes between unique and enhanced features as a basis of perceived differentiation. Unique features are defined in terms of the extent to which an innovation is differentiated vis-ä-vis existing alternatives by virtue of perceptions unique to that innovation (Chaturvedi & Carroll, 1998). Unique features are typically new and unexpected, often causing considerable levels of attention (Kardes & Kalyanaram, 1992). Owing to their high level of newness, unique features are typically perceived as highly differentiating (Ravi Dhar & Sherman, 1996). At the same time, however, unique features are likely to raise performance concerns, because individuals lack prior experience with them (Hsee, 1996; Nowlis & Simonson, 1996). For the same reason, unique features may also evoke negative learning-cost inferences. This is particularly the case, if unique features are added to high-complexity products such as personal computers or programmable cameras as opposed to low-complexity products such as refrigerators or washing machines (Mukherjee & Hoyer, 2001).

Enhanced features, on the other hand, are based on performance improvements along existing product characteristics (Zheng Zhou & Nakamoto, 2007). They refer to the degree to which an innovation outperforms competing offerings along familiar attributes and functionalities (Rijsdijk, Langerak, & Hultink, 2011). On account of their lack of newness, enhanced features are likely to be perceived as less differentiating (Carpenter et al., 1994). Nevertheless, enhanced features have a crucial advantage: enhanced features allow individuals to compare an innovation to established products on the basis of a common dimension (Hsee, 1996). In doing so, they do not cause the same level of performance uncertainty as unique features (Zhang & Markman, 1998; Zheng Zhou & Nakamoto, 2007). Furthermore, enhanced features are unlikely to evoke negative learning-cost inferences. When encountering an enhanced feature, customers can easily make sense of it by drawing on prior experiences (Mukherjee & Hoyer, 2001).

Research suggests that already the mere existence of a distinguishing feature may lead to successful differentiation, even if the feature fails to provide actual value. Literature refers to this phenomenon as meaningless differentiation. More formally, it may be defined as the successful differentiation of a product on the basis of a feature that does not provide any benefit. A series of studies have revealed that people are likely to respond favorably towards products to which a distinguishing but irrelevant feature was added. This effect even persists when individuals are informed that the distinguishing feature has no value (Carpenter et al., 1994).

In line with this, it has also been shown that specifications may change product preferences even if experiences are available, and specifications provide little or no

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consumption relevant information (Hsee, Yang, Gu, & Chen, 2009). This occurs because, similar to meaningless differentiation, specifications represent an illusion of advantage over an otherwise not advantageous option (Hsee, Yu, Zang, & Zhang, 2003). This implies that providing appropriate specifications can significantly increase the success of new products. Where a quantitative specification does not exist, a new one should be invented. If a well-known quantitative specification is already established, it can be converted to accentuate the advantage of a product. By way of example, the size of a television could be stated in square inches rather than by conventional diagonal length (Hsee et al., 2009).

However, it needs to be accentuated that meaningless differentiation is only effective under certain circumstances. If one of these circumstances is not apparent, the addition of a distinguishing but irrelevant feature is likely to result in very negative reactions. Firstly, meaningless differentiation is only effective if people have difficulty in evaluating the differentiating feature. This is especially the case where people do not care about, or are not able to judge the true value of a feature. An example of such a case is shampoo that contains silk. Such a shampoo suggests that users’ hair will be silky. The truth is, however, that silk has no benefits for hair (Carpenter et al., 1994).

A second factor is price. Specifically, meaningless differentiation will have an effect only if the underlying product is priced appropriately. Specifically, individuals will evaluate an irrelevant feature favorably only as long as the price of the associated product corresponds to higher priced market offerings. If this is not the case, either because the price corresponds to low-priced competitive offerings or is much higher than existing alternatives, individuals are likely to respond unfavorably (Carpenter et al., 1994).

A third factor is the attractiveness of the irrelevant feature’s label. An attractive label will increase evaluation whereas an unattractive label will decrease evaluation (Carpenter et al., 1994). Broniarczyk & Gershoff (1997) provide empirical evidence for this relationship. They examined how individuals evaluate jackets that were differentiated by their down fill. Specifically, participants were asked to evaluate jackets that had down fills from different kinds of birds. Prior to the study, they were informed, however, that for down fill the age of the bird matters but not the type of bird. Results showed that jackets with an attractive down fill label of ‘goose’ received positive evaluations. Even more positive evaluations were garnered by replacing ‘goose’ with the even more attractive label ‘alpine’. By contrast, jackets with the unattractive down fill label ‘duck’ received negative evaluations. These evaluations

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became even more negative when ‘duck’ was replaced with the even less attractive label ‘regular’.

Hence, even though meaningless differentiation needs to be handled carefully, this research indicates that the addition of distinguishing but irrelevant features may be applied strategically in certain cases, specifically, to evoke a more positive response to an innovation which hardly differs from existing alternatives. Firstly, awareness and familiarity may be increased by distinctiveness. Secondly, perceived quality may be enhanced by setting premium prices especially. Thirdly, positive associations may be evoked if the irrelevant feature is labeled attractively. However, to build a long-term competitive advantage through meaningless differentiation, it is necessary to make sure that a distinguishing feature cannot be duplicated (Carpenter et al., 1994).

3.2.3.2 Perception of Utilitarian versus Hedonic Differentiation

When differentiating innovations from existing market offerings, it is necessary to distinguish whether the differentiation is based on utilitarian or hedonic functionalities. Generally, the addition of a new functionality may be congruent or incongruent with the base (see Table 3-1). Where a utilitarian (hedonic) functionality is added to a utilitarian (hedonic) base product, one speaks of an added functionality that is congruent to the base. However, if a hedonic (utilitarian) functionality is added to a utilitarian (hedonic) base product, one speaks of an added functionality that is incongruent to the base. Consequently, the addition of congruent functionalities is likely to lead to diminishing marginal utility (Gill, 2008). Generally, the addition of congruent functionalities is believed to decrease with the overall value of a product (Nowlis & Simonson, 1996).

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Table 3-1: Congruent and Incongruent Additions of Functionalities (Gill, 2008)

Base Product Added Functionality Nature of Addition

Personal Digital Assistant (utilitarian)

Handheld Email Device (utilitarian)

MP3 Music Player (hedonic)

Television (hedonic)

Global Positioning System (utilitarian)

MP3 Music Player (hedonic)

Video Capability (hedonic)

Internet Access (utilitarian)

CONGRUENT

INCONGRUENT

CONGRUENT

INCONGRUENT

However, adding incongruent functionalities does not necessarily lead to favorable evaluation of innovations. The addition of utilitarian and hedonic functionalities fulfills different consumption goals. Hedonic functionalities serve goals associated with pleasure and excitement, whereas utilitarian functionalities serve practical and instrumental goals (Hirschman & Holbrook, 1982). Adding a hedonic functionality to a utilitarian base product normally provides more pleasure in using this product. Such pleasure is likely to have a stronger effect on overall evaluation than established utilitarian functionalities of the base (Keller & McGill, 1994). By contrast, adding a utilitarian functionality to a hedonic base product is often perceived as a loss in hedonic value. Such a loss is likely to be weighted more heavily than the gain in utilitarian value (Dhar & Wertenbroch, 2000; Tversky & Kahneman, 1991).

Gill (2008) conducted a study in which he examined how participants evaluated products with added functionalities. He distinguished between utilitarian and hedonic base products to which either a utilitarian or hedonic functionality was added. The study demonstrated that the addition of congruent functionality led principally to diminishing marginal utility. Furthermore, the study showed that the addition of an incongruent hedonic functionality to a utilitarian base significantly increased product evaluation compared to addition of a congruent utilitarian functionality. This was because participants thought that a hedonic functionality provided more pleasure/excitement to the utilitarian base product, resulting in a gain of hedonic value. Conversely, the addition of an incongruent utilitarian functionality to a hedonic base

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significantly decreased evaluation compared to the addition of a congruent hedonic functionality. Participants regarded the addition of such functionality as less pleasurable/exciting, and also weighted this loss in hedonic value more heavily than the gain in utilitarian value. Figure 3-4 provides an overview over this relationship.

Figure 3-4: Congruent and Incongruent Functionality Additions (Gill, 2008)

3.2.3.3 Assimilation/Contrast Effects in Innovation Perception

Moreover, perceived differentiation of innovations strongly depends on whether an innovation is contrasted away from or assimilated to existing alternatives. Research identified these so called assimilation/contrast effects as a robust psychological phenomenon, which occurs every time a novel object is encountered. While

Utilitarian Product

Hedonic Product

New Functionality from Another Category

Diminishing Marginal Utility

Loss in Hedonic Value

Gain in Hedonic Value

Diminishing Marginal Utility

Converged Product with a Utilitarian Base

Converged Product with a Hedonic Base

Goal Congruence

Goal Congruence

yes no yes no

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contrasting promotes perceived differentiation, assimilation decreases perceived differentiation (Gill, 2008). It was found that pioneering products are contrasted away from existing products, and are evaluated more positively than follower products which are assimilated to the pioneer, and therefore do not provide sufficient differentiation (Carpenter & Nakamoto, 1989). The application of assimilation versus contrast depends on the degree to which a novel object is perceived as similar to a specific category. High levels of similarity are likely to lead to assimilation, whereas low levels of similarity are likely to lead to contrast (Herr, Sherman, & Fazio, 1983).

In addition, research suggests that assimilation/contrast effects depend on the context in which a novel object is presented. In a series of studies of electronic agent recommendations, the evaluation of unfamiliar recommendations was examined. It was found that individuals tended to react negatively to unfamiliar recommendations. However, these negative reactions were overcome by embedding the unfamiliar recommendation among a set of familiar recommendations that individuals were known to like. Although it was unlikely that they would purchase the familiar recommendations (because they might have already owned these products), the presence of the familiar recommendations increased the attractiveness of the unfamiliar recommendations (Cooke, Sujan, Sujan, & Barton, 2002). By embedding unfamiliar recommendations within a set of familiar recommendations, individuals were provided with a set of appropriate category exemplars with which the unfamiliar recommendation could be contrasted. This resulted in more favorable evaluations.

Similarly, it is possible to influence assimilation/contrast effects by making explicit comparisons between an innovation and an existing product category. In marketing practice, innovations are often explicitly compared to existing alternatives. By making such comparisons, typical products on which innovation evaluation is based are evoked in memory. Thereby, the perception of the innovation may either shift towards existing alternatives or away from them (Buchanan, Simmons, & Bickart, 1999; Herbert, Schwarz, & Bless, 1998; Schwarz & Bless, 1992). Whether assimilation or contrast occurs in such circumstances depends on whether the new functionality is offered in a product design that is typical of an existing functionality, or a design that is atypical of an existing functionality (Ziamou & Ratneshwar, 2003).

Empirical evidence demonstrates that explicit comparisons are only effective when a new functionality is offered in a product with an atypical product design. In this case, the new product is contrasted with established products because individuals perceive a mismatch between the salient features of the new product and the typical products evoked in working memory. However, if a new functionality is offered in a product

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with a typical product design, explicit comparisons are counter-productive because they lead to assimilation. Customers will perceive a high overlap between the salient features of the new product and existing products evoked in working memory (Ziamou & Ratneshwar, 2003). Figure 3-5 summarizes the effects of explicit comparisons on product evaluation depending on typicality of design.

Figure 3-5: Comparisons and Innovation Evaluation (Ziamou & Ratneshwar, 2003)

Explicit comparisons of a new functionality with an

existing functionality

Customers mentally access the goal-derived category that corresponds

to the existing functionality

Customers evoke in working memory products that are typical of the goal-derived

category that corresponds to the existing functionality

High feature overlap between stimulus and the product(s) evoked in working memory

Assimilation of new functionality to existing

functionality

Contrast of new functionality to existing functionality

Low feature overlap between stimulus and the product(s) evoked in working memory

New functionality is offered in a product that is typical of the existing functionality

New functionality is offered in a product

that is atypical of the existing functionality

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3.2.4 Perceived Incongruity of Innovations

3.2.4.1 Perceived Incongruity and the Moderate Incongruity Effect

As the previous discussion implies, an innovation needs to be clearly differentiated from existing alternatives so that customers respond favorably. From this arises the question, whether an innovation is also evaluated positively if it is perceived as very different from existing alternatives. The so-called moderate incongruity effect sheds light on this.

In psychology and consumer behavior literature, incongruity refers to „the extent that structural correspondence is achieved between the entire configuration of attribute relations associated with an object, such as a product, and the configuration specified by an associated schema (Meyers-Levy & Tybout, 1989, p. 40).“ Two extreme cases of incongruity are distinguished. A complete match between an object and an activated category schema refers to congruity. In contrast, a complete mismatch between multiple features of an object and an activated category schema refers to incongruity. Between these two extreme cases, different levels of incongruity may occur involving both congruent and incongruent attributes (Mandler, 1982).

An innovation that is perceived as highly congruent with existing alternatives is unlikely to result in a particularly positive response. Even though people appreciate things that are predictable and correspond to their expectations, they evaluate high congruity unfavorably, because they do not perceive it as exciting (Meyers-Levy & Tybout, 1989). This is supported by research on curiosity which argues that events characterized by a lack of unknown information are likely to evoke feelings of boredom and disinterestedness (Min Jeong et al., 2009).

However, as an innovation becomes more incongruent with existing alternatives, people begin to respond more favorably (Meyers-Levy & Tybout, 1989). This is because increasing levels of incongruity represent knowledge gaps that are perceived as interesting (Loewenstein, 1994) and motivate learning and exploration. Typically, people engage in such processes in order to develop new knowledge, skills, and experiences. If they are successful, they will experience positive affect (Silvia, 2008). Thereby, incongruity will only yield positive affect as long as people expect they can handle it. In contrast, if people think that they lack the ability to resolve incongruity, they will be confused and respond unfavorably (Silvia, 2010).

Generally, confusion arises when the “environment is giving insufficient or contradictory information (Keltner & Shiota, 2003, p. 82).” Research suggests that

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such a situation is likely to occur at extreme levels of incongruity. Specifically, if people are confronted with extreme levels of incongruity, they can only resolve it by making fundamental changes to established knowledge structures (Jhang et al., 2012). Such a process typically results in mental overload and unfavorable evaluation of an innovation (Mandler, 1982). Support for a general aversion against extreme levels of incongruity is provided by brand extension research. Specifically, this line of research demonstrated that customers will only evaluate brand extensions as favorable as a long as they perceive a certain fit between the extending product and the existing product portfolio of a brand. If this is not the case, customers are likely to react unfavorably towards the extending product (Aaker & Keller, 1990; Maoz & Tybout, 2002; Park, Milberg, & Lawson, 1991).

Meyers-Levy & Tybout (1989) examined how individuals evaluate innovations with different levels of incongruity. In line with the previous discussion, they identified an inverted U-shaped relationship between perceived incongruity and innovation evaluation (see Figure 3-6). Specifically, at high levels of perceived congruity, people get bored and respond unfavorably to an innovation. However, as perceived incongruity increases, curiosity is evoked, and people begin to find innovations interesting and respond favorably. Increasing levels of incongruity will further enhance innovation evaluation until moderate incongruity is reached. Beyond that point, innovation evaluation will decrease again, because resolution of incongruity becomes more and more difficult. At extreme levels of perceived incongruity, people will become confused and respond unfavorably (Kashdan & Silvia, 2009).

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Figure 3-6: The Moderate Incongruity Effect (Meyers-Levy & Tybout, 1989)

While research consistently defines incongruity as the function of a mismatch between an innovation and an associated category schema, it less clear what exactly distinguishes moderate incongruity from extreme incongruity. Jhang et al. (2012) have proposed that different levels of incongruity may be distinguished on the basis of associations individuals need to make in order to resolve incongruity. According to this view, congruent innovations provide attributes that are directly related to pre-existing category knowledge. An example of this is vitamin-fortified orange juice that is congruent with the orange juice schema, because vitamins represent an existing association of the orange juice category (see A in Figure 3-7). In comparison, moderately incongruent innovations provide attributes that are not part of prior category knowledge, but can be resolved by activating preexisting shared associations between a category and the attribute. An example of this is vitamin-fortified coffee (see B in Figure 3-7). Vitamins do not represent a common association for coffee. However, vitamin and coffee are both associated with a “good start to the day”. By activating this shared association, incongruity can be resolved. By contrast, extremely incongruous innovations neither include attributes that are part of established category associations nor hold any associations that they share with a category. An example is vitamin-fortified vodka (see C in Figure 3-7). Vitamins are neither associated with vodka, nor do they share a common association with it.

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Figure 3-7: Perception of Product Incongruity (Jhang et al., 2012)

A: Congruent: Vitamin-Fortified Orange Juice

B: Moderately Incongruent: Vitamin-Fortified Coffee

C: Extremely Incongruent: Vitamin-Fortified Vodka

Enhancing New Product Acceptance 249

products provide an attribute that is not part of product cate-gory knowledge but can be resolved when a preexistingshared association is activated between the category and theattribute. For example, vitamin-fortified coffee is moderatelyincongruent because although vitamins are not an associa-

tion for coffee, a “good start to the day” is a preexistingassociation that coffee and vitamins share; activating thisshared association can resolve the incongruity. Extremelyincongruent new products include an attribute that neither ispart of the preexisting category associations nor holds any

Figure 1DEFINITION OF PRODUCT INCONGRUITY




ORANGE

JUICE

Beverage

VITAMIN

Good

start to

the day

Tropicana

Healthy

Tablet

Centrum

Good start to the day

Beverage

VITAMIN

Orange juice

Starbucks

Hot

Healthy

Tablet

Centrum

COFFEE

Coffee beans

VODKA

Alcohol

Hangover

Party


VITAMIN

Orange juice

Healthy

Tablet

Centrum

Tonic

Grey Goose

?








ORANGE

JUICE

Beverage

VITAMIN

Good

start to

the day

Tropicana

Healthy

Tablet

Centrum


Beverage

VITAMIN

Orange juice

Starbucks

Hot

Healthy

Tablet

Centrum

COFFEE

Coffee beans

VODKA

Alcohol

Hangover

Party


VITAMIN

Orange juice

Healthy

Tablet

Centrum

Tonic

Grey Goose

?








ORANGE

JUICE

Beverage

VITAMIN

Good

start to

the day

Tropicana

Healthy

Tablet

Centrum


Beverage

VITAMIN

Orange juice

Starbucks

Hot

Healthy

Tablet

Centrum

COFFEE

Coffee beans

VODKA

Alcohol

Hangover

Party


VITAMIN

Orange juice

Healthy

Tablet

Centrum

Tonic

Grey Goose

?

45

It needs to be accentuated that the moderate incongruity effect does not apply, if incongruity-based affect is overwhelmed by other sources of affect. Specifically, an innovation may relate to a category schema, which evokes strong affective reactions. These reactions are typically based on associations to specific attributes of other products in that category (Peracchio & Tybout, 1996). That is, previous experiences with products of a category are likely to influence affective reactions to new products in that category (Boush et al., 1987). This relationship is supported by research on brand extensions which found that extending products evoke affective impressions previously developed with other products of a brand (Aaker & Keller, 1990).

3.2.4.2 Coping with Extreme Levels of Incongruity

Research suggests that the perception of extreme incongruity does not always lead to negative evaluation of an innovation. In certain situations, people may be able to resolve extreme incongruity. If this is the case, even extremely incongruent new products are likely to be evaluated favorably. Literature provides different possibilities of extreme incongruity resolution. Firstly, extreme incongruity can be resolved, if individuals are provided with comprehensive knowledge about an innovation and associated ideas. More developed knowledge structures generally enable people to cope with both schema-consistent and schema-inconsistent information (Fiske & Taylor, 1991). Accordingly, research found that building extensive and well-interconnected knowledge structures, allows people to resolve extreme incongruity with reasonable cognitive effort (Peracchio & Tybout, 1996).

Secondly, resolution of extreme incongruity may be enabled by prompting people to consider multiple perspectives and alternatives when learning about an innovation. In other words, people are likely to resolve extreme incongruity if they think flexibly about an innovation (Murray, Sujan, Hirt, & Sujan, 1990). This was shown in a study by Jhang et al. (2012) in which participants were asked to evaluate an extremely incongruent new product. Prior to evaluation, however, participants were either primed with flexible or with non-flexible thinking. Therefore, they received a short story in which a can of cola explodes inside a car on a hot day. After they had read the story, participants in the flexible thinking condition were asked to think of as many explanations for the explosion as they could. In contrast, participants in the non-flexible thinking condition were asked to think of only one explanation for the explosion of the can. The results of the study showed that participants in the flexible thinking condition evaluated extremely incongruent new products significantly more favorably than participants in the non-flexible thinking condition.

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Thirdly, extreme incongruity may be resolved, if a strong benefit rationale is apparent. Empirical evidence for this relationship is provided by a recent experimental study, in which participants were asked to evaluate an extremely incongruent new product, vitamin-fortified vodka. In this study, each participant was assigned to one of two conditions: a benefit rationale-absent condition and a benefit rationale-present condition. Prior to evaluation, participants in the benefit rational-absent condition only received a short description of the vitamin-fortified vodka. Participants in the benefit rational-present condition were additionally informed that vodka is dehydrating, and that replacing lost vitamins can help people feel better. The study showed that the presence of a strong benefit rationale significantly improved evaluation of the extremely incongruent new product. This implies that a strong benefit rationale generally helps people to make sense out of extremely incongruent new products and, as a consequence, is likely to result in positive responses (Jhang et al., 2012).

Now that all important aspects of innovation recognition have been discussed, the second process of innovation perception, innovation assessment, is examined next.

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3.3 Innovation Assessment

3.3.1 Determinants of Intention and Behavior

3.3.1.1 Model of Reasoned Action

In case of innovation assessment, people get intensively involved with an innovation and decide whether to accept it or not. Accordingly, innovation acceptance represents the ultimate measure for favorable perception of an innovation in the innovation assessment process. Thereby, innovation acceptance is strongly determined by the formation of mental scenarios about an innovation’s impact on everyday life (Ziamou, 2002). In other words, innovation acceptance represents an informed decision about the behavioral consequences arising from the usage of an innovation (Bagozzi, Davis, & Warshaw, 1992). A model that provides valuable insights about innovation acceptance represents the Model of Reasoned Action (MRA). MRA is a general model which predicts human behavior across different domains. It represents an important starting point for the examination of innovation acceptance.

MRA assumes that most behavior is strongly determined by people’s intention to perform that behavior. Specifically, people are more likely to perform a specific action if they have formed strong intentions towards it (Fishbein & Ajzen, 2010). Intentions capture the motivational factors which determine whether to engage or not engage in a behavior. In other words, intentions indicate how hard people are willing to try and how much effort they plan to exert (Ajzen, 1991). Even though there will not always be perfect congruence between intention and behavior, people will normally act according to their intentions, as long as no unforeseen events occur (Ajzen & Fishbein, 1980). This is also the case for innovation acceptance.

Davis, Bagozzi, & Warshaw (1989) provide empirical evidence for the positive relationship between intention and innovation acceptance. In their study, they collected data from 107 full-time MBA students on their acceptance of the word-processing program WriteOne. At the beginning of their first semester, students were introduced to the program and asked to answer a questionnaire containing measures of intention towards its usage. After 14 weeks, at the end of the first semester, the students were asked to answer a second questionnaire with intention measures and a 2-item self-reported usage measure. It was shown that intentions measured directly after introduction correlated at 0.35 with behavior 14 weeks later. Intentions and usage measured contemporaneously at the end of the semester correlated at 0.63.

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The notion that intentions predict behavior is intuitively understandable and does not yet provide valuable insight. To actually explain behavior, the motivational factors of intention must be specified. MRA distinguishes attitude toward behavior and subjective norm as key motivational factors towards behavior. Attitude toward behavior refers to “the individual’s positive or negative feelings (evaluative affect) about performing the target behavior’ (Fishbein & Ajzen, 1975, p. 216).” It refers to the evaluation of an action as good or bad. Subjective norm concerns “the person’s perception that most people who are important to him think he should or should not perform the behavior in question (Fishbein & Ajzen, 1975, p. 302).” It refers to the social pressures a person associates with the action.

However, motivational factors will only predict behavior sufficiently if a people can decide at will whether or not to engage in the behavior in question. This is often not the case. As a matter of fact, numerous actions depend on the availability of requisite opportunities and resources. These aspects refer to people’s behavioral control over actions. Research suggests that behavior is normally jointly determined by motivational factors (attitude toward behavior and subjective norm) and ability (behavioral control). Motivation influences performance where a person has behavioral control, and behavioral control increases performance if the person is motivated to try. Accordingly, MRA identified behavioral control as another predictor of intention and behavior. Perceived behavioral control refers to the perceived ease or difficulty associated with an action. It reflects anticipated impediments and obstacles as well as past experiences (Ajzen, 1991).

MRA points out that attitude toward behavior and subjective norm have a different impact on behavior than perceived behavioral control (see overview of MRA Figure 3-8). Specifically, attitude toward behavior and subjective norm only influence behavior indirectly via intention. In contrast, perceived behavioral control additionally influences behavior directly. Research assumes a direct influence of perceived behavioral control on behavior, because perceived behavioral control increases people’s confidence towards performing a behavior effectively. Specifically, it is argued that if two persons have strong intentions towards engaging in some type of behavior, the person with higher confidence in his ability to master this challenge is more likely to succeed than the person who has doubts in his ability (Ajzen, 1991).

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Figure 3-8: Model of Reasoned Action (Ajzen, 1991)

The question which arises, then, concerns the relative weight of attitude toward behavior, subjective norm, and perceived behavioral control in influencing intention and behavior. MRA argues that the relative weights of these factors are situation specific. That is, relative weights must be determined with regard to the respective behavior in question. In some situations, one or two of the factors may have priority. In many other situations, all three may be equally important (Fishbein & Ajzen, 1975).

3.3.1.2 Formation of Intentions towards Behavior

MRA suggests that people form intentions towards a behavior on the basis of beliefs about that behavior. Three kinds of beliefs are distinguished: behavioral beliefs, normative beliefs, and control beliefs (Fishbein & Ajzen, 2010). As Figure 3-9 indicates, behavioral beliefs determine attitudes towards behavior, normative beliefs determine subjective norm, and control beliefs determine perceived behavioral control.

Attitude toward the behavior

Subjective norm

Perceived behavioral control

Intention Behavior

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Figure 3-9: Determinants of Behavior and Associated Beliefs

Behavioral Beliefs

Behavioral beliefs are concerned with the positive or negative consequence that people might experience when they perform the behavior. People will form more positive attitudes towards the behavior if they expect more positive and less negative outcomes to follow from that behavior (Fishbein & Ajzen, 2010). Thus, people are more likely to accept an innovation if they believe that its use will provide many favorable consequences and only a few unfavorably consequences (Davis et al., 1989).

The relationship between behavioral beliefs and attitude towards behavior has been expressed using a mathematical equation. Attitude towards behavior results from the sum of the product of behavioral beliefs (bi) about outcomes and the evaluation (ei) of these outcomes (Davis et al., 1989).

Attitude towards behavior =

i ib e∑

Behavioral beliefs

Normative beliefs

Control beliefs

Attitude toward behavior

Subjective norms

Perceived behavioral control

Beliefs about Behavior Determinants of Behavior

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Normative Beliefs

Normative beliefs refer to perceived social pressure to engage or not engage in the behavior. Normative beliefs are based on expectations that important others would approve or disapprove of the behavior. If many others think that the behavior should be carried out, and if the majority of them actually perform the behavior, people are likely to feel high social pressure to engage in that behavior (Fishbein & Ajzen, 2010). Accordingly, people are more likely to accept an innovation, if they think that important others will appreciate acceptance (Davis et al., 1989).

Research formulated a mathematical equation of the relationship between normative beliefs and subjective norm. According to this equation, subjective norm associated with the behavior results from the sum of the product of normative beliefs (nbi) and the motivation to comply (mci) with these beliefs (Davis et al., 1989):

Subjective norm =

It must be emphasized that a belief about performing a behavior that involves a referent does not automatically represent a normative belief. In many cases, one may have a belief that engaging in some type of behavior is likely to please another person. Such a belief represents more of a behavioral belief, and therefore influences attitude towards behavior rather than subjective norm. According to MRA, normative beliefs refer only to situations in which another person thinks one should or should not engage in the behavior in question (Ajzen & Fishbein, 1980).

Control Beliefs

Control beliefs refer to expectations of personal and environmental factors that promote or impede people’s attempts to engage in that behavior (Fishbein & Ajzen, 2010). They deal with the presence or absence of resources and opportunities required for performing an action effectively. The more resources and opportunities people believe they possess, and the fewer obstacles or impediments they anticipate, the greater should be their perceived behavioral control (Ajzen, 1991). Hence, people are more likely to accept an innovation, if they think they are capable of using the innovation effectively (Davis et al., 1989).

i inbmc∑

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A mathematical equation of the relationship between control beliefs and perceived behavioral control has been formulated. According to this equation, perceived behavioral control results from the sum of products of control beliefs (ci) and the perceived power (pi) of the control beliefs (Ajzen, 1991):

Perceived behavioral control =

3.3.2 Determinants of Innovation Acceptance

Even though MRA provides a good starting point for explaining innovation acceptance, it only represents a general model to predict behavior. In this way, it treats important aspects of innovation acceptance rather superficially. Following this, research derived a series of models which predict innovation acceptance more specifically (Venkatesh et al., 2012). These models include the Technology Acceptance Model (TAM), the Motivational Model (MM), the Theory of Planned Behavior (TPB), the Model of PC Utilization (MPCU), Innovation Diffusion Theory (IDT), and the Social Cognitive Theory (SCT). All of these models are based on the same basic concept of innovation acceptance depicted in Figure 3-10. In accordance with MRA, these models assume that innovation acceptance is strongly determined by the behavioral consequences arising from the use of an innovation. Each of the models suggests a different set of distinct factors as determinants of innovation acceptance. These were intensively examined during the last decades (Venkatesh et al., 2003).

Figure 3-10: Basic Concept of Innovation Acceptance (Venkatesh et al., 2003)

From the different models of innovation acceptance, Venkatesh et al. (2003) derived the Unified Theory of Acceptance and Use of Technology (UTAUT). It represents a comprehensive synthesis of existing models. Initially, UTAUT identified four key factors that influence innovation acceptance. These factors include: performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh et al., 2003). To fit the consumer context, research identified three additional factors

i ic p∑

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that complement the initial factors of UTAUT. These include: hedonic motivation, price value, and habit (Venkatesh et al., 2012).

Table 3-2 provides an overview over key factors of UTAUT. Each of these factors will now be discussed in more detail.

Table 3-2: Core Constructs UTAUT (Venkatesh et al., 2012)

Construct Definition

Performance Expectancy

Effort Expectancy

Social Influence

Facilitating Conditions

Hedonic Motivation

Price Value

Habit

Degree to which one perceives that using an innovation provides benefits in performing certain activities

Degree to which one perceives the use of a particular innovation as easy or difficult

Degree to which one perceives that important others (e.g. friends and family) believe one should use an innovation

Degree to which one perceives that resources and support is available to facilitate the use of an Innovation

Degree to which one perceives the use of an innovation to be associated with fun or pleasure

Cognitive tradeoff between perceived benefits of using an innovation and monetary costs associated with its use

Degree to which individuals tend to perform automatically during innovation usage because of learning

3.3.2.1 Performance Expectancy

Performance expectancy is defined as the degree to which using a technology will provide benefits to potential customers in performing certain activities (Venkatesh et al., 2012). Generally, people are more likely to perform behaviors they believe will result in valued outcomes than those which they do not expect to provide favorable consequences (Compeau & Higgins, 1995). Accordingly, performance expectancy is related to task accomplishment and productivity (Venkatesh et al., 2003). This implies that it is primarily based on functional or instrumental considerations (Smith & Colgate, 2007). Performance expectancy has consistently been demonstrated to be the strongest predictor of innovation acceptance (Venkatesh et al., 2003). Following these

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considerations, Venkatesh et al. (2012) developed a measurement scale for performance expectancy which is depicted in Table 3-3.

Table 3-3: Performance Expectancy Scale (Venkatesh et al., 2012)

1. I find […] useful in my daily life.

2. Using […] helps me accomplish things more quickly.

3. Using […] increases my productivity.

Performance expectancy is closely related to relative advantage, a construct initially introduced by IDT (Tornatzky & Klein, 1982). Relative advantage denotes the degree to which an innovation is perceived as being better than the idea it supersedes. Subdimensions of relative advantage include economic profitability, low initial cost, decrease in discomfort, savings in time and effort, and immediacy of reward (Rogers, 2003). To be effective, an innovation must add significant value in at least one of these subdimensions. However, if an innovation adds little or no improvements to existing alternatives, people will react unfavorably (Simonson, Carmon, & O’Curry, 1994).

Performance expectancy and relative advantage particularly arise from an innovation’s perceived usefulness. Perceived usefulness is defined as the perception that an innovation provides benefits in the performance of some task (Davis, 1989; Karahanna & Straub, 1999; Kulviwat et al., 2007). People form usefulness judgments by cognitively comparing what an innovation is capable of doing with what they need to get done. This process is based on so-called cognitive instrumental determinants of behavior. These comprise goal relevance, output quality, result demonstrability, and perceived ease of use. Goal relevance refers to the degree to which an innovation is perceived as applicable to the achievement of one’s goals. The more goal relevant tasks an innovation is capable of supporting, the more useful it is. Over and above the considerations of what tasks an innovation is capable of, and the degree to which these tasks match one’s goals, people will take into account how well the innovation performs those tasks, which is referred to as perceptions of output quality. Furthermore, result demonstrability refers to the tangibility of the results of using an innovation. If an innovation produces goal relevant results desired by customers, but does so in an obscure manner, customers are unlikely to understand how useful the innovation really is. Finally, perceived ease of use has a positive impact on usefulness

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such as the less effortful an innovation is to use, the more it can increase task performance (Venkatesh & Davis, 2000).

3.3.2.2 Effort expectancy

Effort expectancy is defined as the degree of ease associated with the use of a technology (Venkatesh et al., 2012). It derives from the difficulty of learning how to make use of an innovation effectively, the propensity to make errors when using it, and inefficiencies in using it (McLaughin & Skinner, 2000). The time required to perform a task, the ratio of favorable to unfavorable interactions, and the number of errors are typical operationalizations of effort expectancy (Nielsen, 1993). Effort expectancy is constructed rather than retrieved. It may be based on prior knowledge, the observable experiences of others, and marketing messages (Wood & Moreau, 2006). Following these considerations, Venkatesh et al. (2012) developed a measurement scale for effort expectancy which is depicted in Table 3-4.

Table 3-4: Effort Expectancy Scale (Venkatesh et al., 2012)

1. Learning how to use […] is easy for me.

2. My interaction with […] is clear and understandable.

3. I find […] easy to use

4. It is easy for me to become skillful at using […].

Effort expectancy strongly relates to the easy-of-use construct which was originally introduced by TAM (Davis, 1989; Venkatesh & Davis, 1996). Ease-of-use is formally defined as “the degree to which a person believes that using a particular system would be free of effort (Davis, 1989, p. 320).” This definition was derived from the word ‘ease’, which means ‘freedom from difficulty or great effort’ (Davis, 1989). The construct is based on the consideration that effort is a limited resource, and therefore one needs to deploy it economically (Radner & Rothschild, 1975). Perceived ease-of-use is associated with people’s self-efficacy beliefs and procedural knowledge about how to effectively make use of an innovation (Venkatesh & Bala, 2008). Thereby, ease-of-use involves both mental and physical effort. Mental effort is determined by the degree to which the operation and benefits of an innovation are difficult to learn (Veryzer, 1998a). By comparison, physical effort is determined by the degree to which

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using an innovation feels uncomfortable or requires high bodily strain (Mugge & Schoormans, 2012).

Effort expectancy and perceived ease-of-use particularly arise from an innovation’s usability. Usability refers to the convenience associated with the use of an innovation (Mugge & Schoormans, 2012). Thereby, usability is viewed as a multidimensional construct which consists of the following attributes: Learnability, efficiency, memorability, errors, and satisfaction. Learnability is the ease with which people can learn to use an innovation. The more rapidly people can get some work done with an innovation, the higher is its learnability. Efficiency is the level of productivity people can achieve with an innovation. Memorability is the ease with which people can remember how to effectively make use of an innovation. Memorability is high when people can return to an innovation after some period of time and use it effectively without having to learn everything all over again. Errors refer to negative consequences resulting from faulty operation or misuse of an innovation. Thus, an innovation should be designed in such a way that errors are unlikely to occur. If, nonetheless, an error does occur, an innovation should rapidly recover from the error. Satisfaction is the degree to which an innovation is pleasant to use. Put differently, people should be satisfied when using an innovation (Nielsen, 1993).

3.3.2.3 Social Influence

Social influence is the extent to which potential customers perceive that important others (e.g. friends, family, or colleagues) will evaluate their usage of an innovation favorably (Venkatesh et al., 2012). It represents one of the most important motivations to adopt an innovation (Rogers, 2003). The construct follows from the assumption that the behavior of potential customers is influenced by the way that others will view them as a result of innovation usage (Venkatesh et al., 2003). Following these considerations, Venkatesh et al. (2012) developed a measurement scale for social influence which is depicted in Table 3-5.

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Table 3-5: Social Influence Scale (Venkatesh et al., 2012)

1. People who are important to me think that I should use […].

2. People who influence my behavior think that I should use […].

3. People whose opinions I value prefer that I use […].

Social influence arises on the one hand from subjective norm. It is defined as the degree to which one believes that most people who are important to oneself believe one should or should not use an innovation (Venkatesh & Bala, 2008; Venkatesh & Davis, 2000). People may choose to adopt an innovation, even if they themselves do not have a favorable attitude towards that innovation (Venkatesh & Davis, 2000). A social influence mechanism that is very similar to subjective norm is compliance. This refers to a situation in which one performs a certain behavior with the purpose of attaining reward or avoiding punishment (Miniard & Cohen, 1979; Venkatesh & Davis, 2000). Compliance occurs if one believes that a referent wants him or her to perform a specific behavior, and the referent has the ability to reward behavior or punish non-behavior (French & Raven, 1959; Kelman, 1961; Venkatesh & Davis, 2000; Warshaw, 1980).

Subjective norm may influence innovation acceptance directly based on compliance (Venkatesh & Bala, 2008). Additionally, subjective norm may influence innovation acceptance indirectly via usefulness. This is because subjective norm may stimulate internalization. The concept of internalization is the process by which an individual perceives that important others belief that he or she should use an innovation and begin to integrate that belief into his or her own belief structure. Over time, the individual actually begins to belief that the innovation is useful. Following this, if important others suggest that a specific innovation might be useful, one may come to the conclusion that it is actually useful, and one may develop an intention to use that innovation (Venkatesh & Bala, 2008).

Research suggests that the effects of subjective norm decline over time. It was found that the opinions of others primarily play a role when direct experience with an innovation is lacking (Hartwick & Barki, 1994). However, after implementation, when an individual has experienced the strengths and weaknesses of an innovation first hand, influence of subjective norm begins to diminish (Venkatesh & Davis, 2000).

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Similarly, Agarwal & Prasad (1997) demonstrated that subjective norm first and foremost motivates people to overcome the hurdle of initial use.

Besides subjective norm, social influence may also arise from image considerations. Image is understood as the degree to which the adoption of an innovation is perceived to improve one’s image or social status (Moore & Benbasat, 1991). For some innovations, image enhancement is believed to be almost the sole benefit provided. This is particularly the case for innovations in fashion. Trends in fashion typically gain momentum within a very short period of time. As soon as a fashion trend is broadly established, however, it does not improve image anymore and vanishes. Arising from these considerations, it is argued that status-conferral plays an important role in any innovation that is highly visible, such as new cars or hairstyles (Rogers, 2003).

Research shows that image considerations may even result in an overadoption of innovations. In case of overadoption, an individual adopts an innovation despite the fact that it does not make sense from a rational point of view. Mobile devices and laptops represent two product classes that are often overadopted. Many people buy high-speed computers and solely use these for word processing or other tasks for which a much less powerful computer would be sufficient. Similarly, many people buy multifunctional smart phones and then use them only for making phone calls or for text messaging (Rogers, 2003).

Similar to subjective norm, image has a direct effect on innovation acceptance. In addition, image influences innovation acceptance indirectly via usefulness (Venkatesh & Bala, 2008). The influence of image on perceived usefulness is based on the fact that modern society is characterized by a high degree of interdependence between the individual and other social actors in carrying out one’s duties. Social status is a source of power and influences processes such as social exchange, coalition formation, and resource allocation. Power and influence resulting from status gains often provide an important basis for greater productivity in a given task (Venkatesh & Davis, 2000).

3.3.2.4 Facilitating Conditions

Facilitating conditions are potential customers’ perceptions of the resources and support available to perform a behavior (Venkatesh et al., 2012). All of these concepts reflect aspects of the technological and/or organizational environment with the purpose of removing barriers to adoption (Venkatesh et al., 2003). Following these considerations, Venkatesh et al. (2012) developed a measurement scale for facilitating conditions which is depicted in Table 3-6.

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Table 3-6: Facilitating Conditions Scale (Venkatesh et al., 2012)

1. I have the resources necessary to use […].

2. I have the knowledge necessary to use […].

3. […] is compatible with other technologies I use.

4. I can get help from others when I have difficulties using […].

Facilitating conditions arise from an innovation’s compatibility with potential customers. Compatibility is the degree to which an innovation is perceived as consistent with existing values, past experiences, and the needs of customers. It allows customers to give meaning to an innovation, so that it is regarded as more familiar. Naming an innovation and positioning it relative to previous ideas are important means of making innovations more compatible (Rogers, 2003).

Research distinguishes between two interpretations of the compatibility construct. The first interpretation refers to normative compatibility. This depends on an innovation’s correspondence to the values and norms of customers. Normative compatibility is an innovation’s compatibility with what people feel or think (Tornatzky & Klein, 1982). It is concerned with the compatibility of an innovation with previously adopted ideas. Old ideas represent an important means of assessing innovations and give these innovations meaning. Customers cannot make sense of a new idea other than by reference to something familiar. Previous ideas provide a standard that makes it easier to draw inferences about an innovation (Rogers, 2003).

A study by Hawley (1946) demonstrates the important role of normative compatibility. The study examines the adoption of Roman Catholicism by Native Americans. It finds that Eastern Pueblo Indians in Arizona and New Mexico readily accepted the religion. By contrast, Western Pueblo Indians rejected the religion, and even killed the Spanish priests that proselytized it. Hawley concludes that different reactions to Roman Catholicism may be best explained by normative compatibility. Specifically, the family structure of Eastern Pueblo Indians was heavily patrilineal and father-oriented, and therefore corresponded to a religion in which god is described as a male figure. For Western Pueblo Indians, however, this compatibility was not apparent because their family structure was mother-centered.

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Normative compatibility also concerns whether an innovation addresses current, latent or future needs. Current needs are customers’ expressed needs. Latent needs are those that customers cannot articulate. Future needs are those of which customers are not yet aware of (Narver, Slater, & MacLachlan, 2004). If an innovation appeals to current needs, normative compatibility is high. However, if an innovation addresses latent and future needs, normative compatibility is low. In such a situation, normative compatibility needs to be increased first. This may be done by revealing the specific needs that are addressed by the innovation.

The second interpretation of compatibility refers to operational compatibility. This is defined as the extent to which an innovation is consistent with existing practices. Operational compatibility is determined by how smoothly customers can integrate an innovation into their daily routines (Tornatzky & Klein, 1982). For adoption of electric cars, for example, operational compatibility is very important. Electric cars have a significantly lower range than cars with petrol engine. Furthermore, charging times of electric cars are relatively long, and a widespread network of charging stations does not exist yet. Thus, for many potential customers, particularly those who have to travel long distances on a daily basis, the adoption of electric cars does not make sense.

Operational compatibility depends on whether an innovation disrupts existing complementarities between a product category and the context in which it is used. Two types of complementarities are distinguished: product-user complementarities and product-product complementarities. Product-user complementarities refer to interrelationships between a product category and the user, whereas product-product complementarities refer to interrelationships between a product category and other products with which it is used. If an innovation disrupts one or more of these established complementarities, the rate of adoption is likely to decrease significantly (Dhebar, 1995).

Disruption of product-customer complementarities may occur in three ways: firstly, an innovation may be a disruption in terms of its touch and feel. Touch and feel are highly important: they often represent the defining traits of established product categories. Secondly, an innovation may disrupt the way in which customers are informed about the state and performance of a given product category. Many products contain devices such as gauges that keep the customer informed about the current state of the product. Typically, customers become familiar with the way they are informed about the state and performance of a specific product category. Often, this familiarity is neither easy to attain nor easily forgotten. An example is the speedometer in cars, which is typically very similar across manufacturers. Thirdly, an innovation may

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disrupt the way in which the customer interacts with the underlying product category. In many cases, customers have to make substantial investments in learning how to operate products. Once these skills are developed, it is difficult to unlearn them. An example is the placement of keys on a computer keyboard (Dhebar, 1995).

Furthermore, disruption of product-product complementarities may occur in different ways. A large number of products are used along with other products as part of a multicomponent system. To function effectively, the different system components must be compatible with each other. Industrial-organization economics literature has invested considerable effort in the examination of intercomponent complementarity, that is, the standardization of the method by which different components interface with each other. Consequently, a distinction should be drawn between technical and physical complementarity. Disruption of technical complementarity occurs if a product lacks a standardized interface that is necessary for the product to work effectively with other products. In contrast, disruption of physical complementarity occurs if a product is designed to be physically connected with other products but the new product does not support these connections (Dhebar, 1995).

3.3.2.5 Hedonic Motivation

Hedonic motivation’ is the positive feelings and emotions derived from using a technology (Agarwal & Karahanna, 2000; Heijden, 2004; Venkatesh et al., 2012). So far, only a few studies have addressed the influence of hedonic motivation on innovation acceptance. This is surprising, given the fact that it was repeatedly found to play an important role in acceptance and use of technology (Brown & Venkatesh, 2005; Childers, Carr, Peck, & Carson, 2001). Childers et al. (2001), for example, conducted a study in which they identified enjoyment as a core predictor of attitude towards interactive shopping. Similarly, Dabholkar & Bagozzi (2002) found that fun significantly affects technology-based self-service acceptance. Finally, Bruner & Kumar (2005) showed that fun has a direct effect on actual usage of a new product. Following these considerations, Venkatesh et al. (2012) developed a measurement scale for performance expectancy which is depicted in Table 3-7.

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Table 3-7: Hedonic Motivation (Venkatesh et al., 2012)

1. Using […] is fun.

2. Using […] is enjoyable.

3. Using […] is very entertaining.

Besides fun and enjoyment, many other emotions may influence hedonic motivation. On the positive side, people may be pleasantly surprised, excited, or confident. Conversely, they may be annoyed, worried, or scared (Kulviwat et al., 2007). In order to capture the full range of emotions that people may experience when interacting with an innovation, Kulviwat et al. (2007) applied emotion theory to explain innovation adoption more effectively. Emotion theory suggests that any kind of emotion can be described in terms of two core dimensions: pleasure and arousal (Armstrong & Detweiler-Bedell, 2008). Pleasure is the degree to which one experiences an enjoyable reaction. Arousal refers to a combination of mental alertness and physical activity (Kulviwat et al., 2007; Nasco, Kulviwat, Kumar, & Bruner, 2008).

Thereby, pleasure ranges from unpleasant to pleasant emotional states and arousal ranges from deactivating to activating emotional states. Together, pleasure and arousal open a two-dimensional space. Within this space, different kinds of emotional states arrange themselves along a circle around the intersection of the pleasure and arousal dimensions of emotion (see Figure 3-11). Research refers to this circle as the affect-circumplex (Russell & Barrett, 1999). An example for a pleasant deactivating emotional state is tranquility, whereas an example for a pleasant activating emotional state is elation. In comparison, an example for an unpleasant activating emotional state is anger, while an example for an unpleasant deactivating emotional state is sadness (Armstrong & Detweiler-Bedell, 2008).

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Figure 3-11: The Affect Circumplex (Armstrong & Detweiler-Bedell, 2008)

It was found that pleasure and arousal represent important predictors of innovation acceptance. More precisely, the more pleased or excited and the less frustrated or confused people are about an innovation, the more likely it is that they will form a favorable attitude toward the innovation and adopt it. Thus, when designing and communicating an innovation, marketers should not only focus on its functionality, but also on the full range of emotional reactions that it may evoke. That is, strong positive emotional reactions should be created, while negative emotional reactions should be avoided (Kulviwat et al., 2007; Nasco et al., 2008). Positive emotions can be particularly evoked by designing an innovation in such a way that it is aesthetically appealing (Creusen & Schoormans, 2005; Goode et al., 2012; Radford & Bloch,

High Arousal

Low Arousal

Unp

leas

ant Pleasant

e.g. elation

e.g. tranquility

e.g. anger

e.g. sadness

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2011). At the same time, negative reactions can be prevented by making innovations highly comfortable to use (Billeter, Kalra, & Loewenstein, 2011; Lakshmanan & Krishnan, 2011; Mugge & Schoormans, 2012; Wood & Moreau, 2006).

3.3.2.6 Price Value

Price value is defined as potential customers’ cognitive tradeoff between the perceived benefits of the applications and the monetary costs of using them. Potential customers perceive price value as positive as long as the perceived benefits associated with acceptance and use of technology are perceived as greater than the monetary costs (Venkatesh et al., 2012). Following these considerations, Venkatesh et al. (2012) developed a measurement scale for performance expectancy which is depicted in Table 3-8.

Table 3-8: Price Value Scale (Venkatesh et al., 2012)

1. […] is reasonably priced.

2. […] is good value for money.

3. At the current price, […] provides good value.

Specifically, it is argued that many promising innovations are rejected because they are too expensive for potential customers. To succeed, an innovation must be offered at a reasonable price. This was the case with the personal digital assistant, PalmPilot. At a selling price of only a few hundred dollars, it provided a real bargain to customers by combining a digital address book, a digital date book, and a digital notebook in a small portable handheld device. In fact, 65% of PalmPilot customers purchased the device because they heard of it from other people. In many cases, these people mentioned the attractive price of the device as a factor in their decision to purchase the device (Rogers, 2003).

3.3.2.7 Habit

Habit is the extent to which potential customers tend to perform automatically because of learning. This implies that habit is determined by the degree of interaction and familiarity with a certain technology. Consequently, prior use represents a strong predictor of habit. However, research suggests that habit extends beyond mere

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experience, which solely reflects the passage of time from the initial use of the technology by potential customers (Venkatesh et al., 2012). Following these considerations, Venkatesh et al. (2012) developed a measurement scale for habit which is depicted in Table 3-9.

Table 3-9: Habit Scale (Venkatesh et al., 2012)

1. The use of […] has become a habit for me.

2. I am addicted to using […].

3. I must use […].

Research demonstrates that acceptance and use of innovation is likely to increase considerably if their use corresponds to the existing habits of customers. The Nintendo Wii game console represents an innovation which is strongly based on existing habits. When playing with the Wii, people can rely on body movements that feel natural to them, such as swinging a tennis racket or rolling a bowling ball. This represents a key success factor of this innovative game console (Billeter et al., 2011). Another successful innovation that is strongly based on existing customer habits represents the Apple iPad. On the iPad, all eBooks are visualized on a wooden bookshelf. This corresponds to the way with which customers have stored their books in the past.

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3.3.3 Risk Associated with Innovation Acceptance

3.3.3.1 Types of Risks associated with Innovation Acceptance

Innovations generally represent a risk to customers, and pose potential unfavorable side effects that customers cannot anticipate (Ram & Sheth, 1989). Risks associated with innovations may be defined as the degree to which risks are perceived as associated with an innovation (Ostlund, 1974). Research identified a constellation of such risks (Rijsdijk & Hultink, 2003), including:

− performance risk − financial risk − social risk − psychological risk − physical risk − risk of time loss.

Performance risk is the risk that there is something faulty with an innovation, or that the innovation will not work correctly (Jacoby & Kaplan, 1972). It also includes concerns about the expected utility of an innovation (Castaño et al., 2008). Many customers develop performance concerns for an innovation because they lack prior experience. They may be uncertain about the need for or benefit of an innovation. Furthermore, they may have difficulty in assessing the technological advancement of an innovation (Veryzer, 1998a). Performance risk is often based on the perception that an innovation has not been fully tested prior to market introduction (Ram & Sheth, 1989).

Financial risk is the chance that the adoption of an innovation will have unfavorable financial consequences for potential customers (Jacoby & Kaplan, 1972). The more expensive an innovation is, the higher customers’ perceived financial risk. If an innovation is very expensive, many customers may even postpone its purchase and wait until a more advanced generation of the product is offered at a lower price (Ram & Sheth, 1989). Besides the purchase price, financial risk may also arise from implementation costs. Take electric cars, for example. For fast charging, customers need to buy a so-called wallbox which shortens charging times significantly. In such a case, financial risk involves both the purchase price of the car as well as the price of the wallbox.

Social risk refers to the chance that others will judge the adoption of an innovation unfavorably (Jacoby & Kaplan, 1972). Owing to a lack of prior experience, customers

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may encounter difficulty in assessing whether an innovation is socially undesirable or inappropriate (Castaño et al., 2008). If this is actually the case, adoption of the innovation may have unfavorable social consequences, such as ostracism or peer ridicule (Ram & Sheth, 1989). Social risk is based on concerns regarding the social values of self-esteem and status. If potential customers feel uncertain towards these values, they are likely to respond unfavorably to an innovation (Mick & Fournier, 1998). Social risk plays an important role if usage of an innovation is visible to others (Thompson & Norton, 2011).

Psychological risk denotes the possibility that the adoption of an innovation will not cohere with one’s self-image or self-concept (Jacoby & Kaplan, 1972). In many cases, innovations are rejected because of emotional attachment to established technology or traditional products that have become a central part of everyday life. Potential customers may experience difficulty in letting go of these devices (Castaño et al., 2008). An example represents automatic shifting in cars. Even though automatic shifting is much more convenient than manual shifting, many customers reject this technology. For them, manual shifting represents an important aspect of driving which they do not want to give up.

Physical risk is the chance that the adoption of an innovation may be harmful or injurious to the adopter’s health (Jacoby & Kaplan, 1972). Physical risk represents a major issue for adoption of many innovations. Innovations based on highly advanced technologies often evoke safety concerns (Veryzer, 1998a). In line with this, research showed that physical risk is particularly evoked if an innovation is characterized by a high level of autonomy. Autonomy refers in this context to the degree to which an innovation is able to operate in an independent and goal-directed way without interference of the user (Rijsdijk et al., 2011).

Risk of time loss refers to the chance that the adoption of an innovation is associated with a waste of time, convenience, or effort (Roselius, 1971). Lack of experience using the product necessitates that customers make significant investments of time and effort before they can make use of an innovation effectively. Accordingly, many customers are concerned with the risk of time loss and the associated difficulty to switch from an old to a new technology (Castaño et al., 2008).

Research commends a variety of different strategies for reducing perceived risk. On the one hand, perceived risk may be reduced by making the results of an innovation as visible as possible (Rogers, 2003). The easier it is for customers to observe the results of an innovation, the more likely they are to form a favorable opinion of that

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innovation (Tornatzky & Klein, 1982). Personal channels, such as trade shows, customer shows and personal selling demonstrations represent important means of increasing the visibility of the results of an innovation. Personal channels are an effective means of revealing benefits and relating them to specific usage situations. Furthermore, personal channels show how to effectively make use of the benefits of an innovation, and overcome obstacles related to changes in consumption patterns (Guiltinan, 1999).

Another way to reduce perceived risk is to provide potential customers with an opportunity to experiment with an innovation. Research demonstrates that a new idea that can be tried on an installment plan will be adopted more quickly than an innovation that is not divisible. By trying out an innovation, people can give meaning to it, and find out how the innovation works under their own conditions. Thus, an innovation should be designed in a way that allows potential customers to easily experiment with it. Trying an innovation may also involve its re-invention; customers can customize it more closely to their individual preferences (Rogers, 2003).

3.3.3.2 Regulatory Focus and Risk Perception

It is emphasized that perception of risk represents a subjective experience, which is determined by people’s self-regulatory system. The self-regulatory system is guided by the fundamental instinct to seek pleasure and avoid pain (i.e. the hedonic principle). Research across different domains provides considerable evidence for this proposition. Biological models, for example, have identified an appetitive system associated with approach and an aversive system associated with avoidance (Lang, 1995). Similarly, personality and social psychology literature has identified a motivational system that prompts people to move toward desirable end-states, and a motivational system that prompts people to move away from undesirable end-states (Carver & Scheier, 1981).

Building on the hedonic principle, research suggests that the self-regulatory system may be based either on a desirable or an undesirable end-state as a reference point guiding behavior. Depending on decision context, the system motivates people to move as close as possible to desirable end-states and as far away as possible from undesirable end-states. Research refers to the motivation towards desirable end-states as the approach system and the motivation away from undesirable end-states as the avoidance system (Carver & Scheier, 1981, 1990). The motivation to achieve the same goal may be guided by the approach system in some cases and the avoidance system in other cases (Higgins, Roney, Crowe, & Hymes, 1994). Take the decision to adopt an

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electric car, for example. Some people may adopt this innovation to achieve low fuel consumption (approach motivation), whereas others adopt this innovation to avoid high fuel consumption (avoidance motivation).

Furthermore, research suggests that the self-regulatory system not only distinguishes approach and avoidance motivation, but also ideal self-regulation and ought self-regulation. Ideal self-regulation concerns people’s hopes, whishes, and aspirations. Ought self-regulation refers to people’s duties, obligations, and responsibilities (Higgins, 1987). It was found that ideal self-regulation is concerned with the presence or absence of positive outcomes (Higgins, 1989). In contrast, ought-self-regulation was found to be concerned with the presence or absence of negative outcomes (Rotter 1982). Furthermore, it was found that ideal-self regulation relates to a predilection for approach means to achieve desirable end-states associated with positive outcomes, whereas ought self-regulation relates to a predilection for avoidance means of obtaining desired end-states associated with negative outcomes (Higgins et al., 1994).

Following these considerations, research identified two types of regulatory focus: promotion focus and prevention focus. Promotion focus concerns advancement, growth, and accomplishment. In contrast, prevention focus concerns security, safety, and responsibility (Crowe & Higgins, 1997). These definitions imply that a prevention focus is likely to increase risk perception in innovation adoption, while a promotion focus is likely to decrease risk perception. In line with this, it was found that people in a prevention focus associate more risks with innovations because they are more concerned with the avoidance of unfavorable consequences that may result from adoption. In contrast, people in a promotion focus associate less risk with innovations, because they are more concerned with favorable consequences or opportunities associated with adoption Thus, potential customers of an innovation are likely to perceive less risk in situations in which innovation communication addresses advancement and achievement goals as opposed to security and responsibility goals (Herzenstein, Posavac, & Brakus, 2007).

3.3.3.3 Information Acquisition and Risk Perception

Typically, customers reduce perceived risk of an innovation by collecting extensive information about the innovation (Rogers, 2003). This is supported by (Jacoby et al., 1994) who examined how information acquisition reduces risk perception. They found an accelerating power function for the relationship between information acquisition and risk reduction. According to this relationship, risk remains relatively high until a

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considerable amount of information has been collected. This occurs, because people want to reduce risk to a minimum and, thus try to avoid closure (Kruglanski, 1989). Accordingly, when collecting information about an innovation, customers look for information that specifically addresses their risk-related concerns. If these risk-related concerns are not addressed sufficiently, customers are likely to respond unfavorably towards an innovation (Castaño et al., 2008; Herzenstein et al., 2007).

Generally, the information acquisition process is determined by so-called contextual cues that function as risk reduction mechanisms. Contextual cues are risk relevant pieces of information that are salient in the decision context (Zhu, Billeter, & Inman, 2012). Research distinguishes two specific types of contextual cues: product cues (Bearden & Shimp, 1982) and extrabrand attributes (Boyd & Mason, 1999).

Product Cues

Product cues refer to information about an innovation that indicates its quality and performance. Research distinguishes between intrinsic and extrinsic product cues (Bearden & Shimp, 1982). Intrinsic cues represent cues which, if changed, would result in a change in physical product characteristics (Kaplan, Szybillo, & Jacoby, 1974). They refer to product composition characteristics such as taste, aroma, color, style, size, or touch and feel (Jacoby, Olson, & Haddock, 1971). Intrinsic cues result from different interactions with a product. These so called product experiences (Brakus, Schmitt, & Zarantonello, 2009) can be direct when an individual has physical contact with a product (Hoch & Ha, 1986), or indirect when an individual interacts with a product virtually or experiences it in an advertisement (Hoch & Ha, 1986; Kempf & Smith, 1998).

Extrinsic cues go beyond physical product characteristics. They are particularly important if intrinsic cues have low confidence and predictive value. In other words, people turn to extrinsic cues when they have problems in determining how well a product will perform, how safe it is, or how socially acceptable it might be (Bearden & Shimp, 1982). Price, for example, represents one such extrinsic cue. People view price information with much confidence, because it is concrete and measurable (Jacoby et al., 1971). Furthermore, warranties represent an effective extrinsic cue. Warranties assure customers that redress is possible if a product does not perform satisfactorily. Reputation or credibility of the manufacturer represents another extrinsic cue of high importance. Research provides empirical evidence for the relationship between these extrinsic cues and perceived risk of innovations. Specifically, a series of studies demonstrates that high prices increase perceived risks associated with innovations,

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while warranties and a favorable manufacturer reputation decrease perceived risks associated with innovations (Bearden & Shimp, 1982).

Extrabrand Attributes

Extrabrand attributes (EBAs) go beyond a specific product and reflect considerations at the product category level. The key difference between product cues and EBAs is that product cues refer to a specific model, whereas EBAs reflect all models available in a given product category. Research shows that customers use EBAs to reduce the risks associated with adoption (Boyd & Mason, 1999). Table 3-10 provides an overview over different EBAs, and their importance ratings for selected product categories.

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Table 3-10: Extrabrand Attributes for Selected Innovations (Boyd & Mason, 1999)

Overall Digital Camera

High-Def Television

Photo CD

Video-phone

Cost

Key benefits

Manufacturers’ reputations

Accessories and service

Poor reviews

Product complexity

Relative advantage

Favorable reviews

Level of standardization

Alternatives

Variety of features/models

Time firms in business

Future enhancements

Future price trends

Number of model choices

How many others own it

How many friends own it

Future sales expectations

Number of stores selling

Advertising expenditures

Size of competing firms

6.28

6.21

5.94

5.77

5.72

5.64

5.64

5.42

5.29

5.15

4.91

4.64

4.37

4.08

3.95

3.88

3.66

3.64

3.57

2.57

2.49

6.28

6.21

5.92

5.96

5.82

5.96

5.82

5.75

4.96

5.14

4.85

4.21

4.28

3.85

3.75

3.85

3.96

3.89

3.32

2.53

2.07

6.44

6.36

5.84

6.24

5.44

5.28

5.72

5.04

5.28

5.48

4.92

5.08

4.80

3.88

4.12

3.48

2.52

3.48

3.16

2.20

2.20

6.38

6.46

5.84

5.76

6.11

5.61

5.92

5.69

5.23

5.15

4.96

4.34

4.57

4.11

4.19

3.69

3.19

3.46

3.57

2.53

2.80

6.03

5.85

6.14

5.17

5.53

5.67

5.14

5.21

5.71

4.89

4.92

4.96

3.89

4.46

3.78

4.46

4.82

3.71

4.21

2.96

2.89

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3.3.4 Formation of Mental Scenarios about Innovations

3.3.4.1 The Simulation Heuristic and Innovation Perception

The evaluation of innovations is strongly determined by the simulation heuristic, a well-established phenomenon from psychological research (Tversky & Kahneman, 1974). It builds on the notion that the probability of an outcome depends on mental scenarios associated with that outcome. The more readily these scenarios are apparent, the more likely it seems that the outcome will occur (Kahneman & Miller, 1986; Kahneman, 1997; Tversky & Kahneman, 1974). Hence, people will place a high likelihood that an innovation performs unsatisfactorily if different scenarios are evoked in which the innovation malfunctions (Ziamou & Ratneshwar, 2002).

Unfavorable mental scenarios associated with new product use are often evoked by innovations that incorporate a new interface and a preexisting functionality. An example of such an innovation is a cell phone with speech recognition as a new interface that provides familiar functionalities such as dialing a telephone number. Research shows that customers evaluate these kinds of innovations negatively: innovations with a new interface and a preexisting functionality focus customers’ attention on the interface, which is the sole differentiator relative to existing product categories (Hastie, 1981; Lynch & Srull, 1982). As a consequence, customers are more likely to generate failure scenarios about the performance of the interface which they associate with considerable learning costs (Ziamou & Ratneshwar, 2002).

Potential customers generate more favorable scenarios if a new product incorporates a new interface and a new functionality. An example of such an innovation is a cell phone with speech recognition as a new interface that provides new functionalities such as local news on demand and directions to the nearest parking lot. In this case, the new functionality offers a considerable advantage over existing products. Competitive offerings are unlikely to provide similar benefits (Gregan-Paxton & Roedder John, 1997). Accordingly, customers will focus their attention on the functionality and put less emphasis on the learning costs associated with the new interface (Shneiderman, 1998). In line with this, research shows that customers evaluate positively innovations with a new interface and a new functionality (Ziamou, 2002).

The evaluation of an innovation that combines a new interface with either a preexisting or new functionality may be influenced by providing customers with information about the interface. For an innovation that incorporates a new interface with a preexisting functionality, customers automatically focus on the interface and

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generate unfavorable scenarios. Several of these possible failure scenarios can be rejected by providing specific information about the interface which allows customers to assess their plausibility (Folkes, 1988; Kahneman & Miller, 1986; Kahneman, 1997). Conversely, information about the interface is counter-productive for innovations that incorporate a new interface and new functionality. When customers are confronted with these innovations, they automatically focus on the new functionality and generate favorable usage scenarios. If they receive information on the new interface, however, they are distracted from the new functionality, and their focus shifts on the interface itself. As a consequence, unfavorable usage scenarios are generated (Ziamou & Ratneshwar, 2002).

3.3.4.2 Mental Simulation and Innovation Perception

The formation of mental scenarios about an innovation may be influenced by encouraging potential customers to imagine specific scenarios involving the usage of that innovation. In such a case, research speaks of mental simulation, which occurs when people are prompted to cognitively construct real or hypothetical events in memory. Mental simulation enables people to re-experience past events and to generate different versions of future events (Escalas, 2004). Research distinguishes between different types of mental simulation. Depending on decision context, these may lead to the generation of favorable or unfavorable mental scenarios of a given event. For innovation adoption, the following types of mental simulation are of particular importance: process versus outcome simulation, memory-focused versus imagination-focused simulation, and self-related versus other-related simulation. Each of these different types of mental simulation is discussed next.

Process versus Outcome Focused Simulation

Process simulation focuses people on the step-by-step process of reaching a goal. In case of process simulation, people set a goal and they then actively mentally rehearse the steps needed to reach that goal (Taylor, Pham, Rivkin, & Armor, 1998). Process simulation may be evoked through advertisements that encourage people to think of the process of using a product, and of how they would incorporate the product into their daily routine (Escalas & Luce, 2003). By contrast, outcome simulation focuses people on the desirable outcome of fulfilling a goal. It helps people to envision the outcome associated with goal attainment, thereby facilitating efforts to achieve that goal and improve perceptions of self-efficacy (Pham & Taylor, 1999). Outcome

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simulation may be evoked through advertisements that encourage people to imagine the benefits they would receive by using a product (Escalas & Luce, 2003).

Process and outcome simulation may be based either on a cognitive processing mode or on an affective processing mode. Generally, cognitive processing refers to cold, deliberate, and analytic thinking, whereas affective processing refers to hot, rapid, and emotional feelings. Following these considerations, research distinguishes two types of process simulation - cognitive process simulation and affective process simulation - and two types of outcome simulation - cognitive outcome simulation and affective outcome simulation. Table 3-11 summarizes the different types of process and outcome simulation of product use (Zhao, Hoeffler, & Zauberman, 2011).

Table 3-11: Types of Process and Outcome Simulation (Zhao et al., 2011)

Cognitive process simulation Focus is on the specific features of a product, the process of how to use the product, and how to incorporate it into one’s daily routine.

Affective process simulation Focus is on the emotions one may feel during the process of using a product, and how one may feel when incorporating it into one’s daily routine.

Cognitive outcome simulation Focus is on the specific benefits of using a product, the outcome of using the product, and the benefits that one would receive after using it.

Affective outcome simulation Focus is on the emotions one may feel after receiving the benefits of using a product, and how one may feel about the outcome of product use.

Research demonstrates that different types of process and outcome simulations play an important role in the evaluation of innovations. Under a cognitive mode, outcome simulation leads to more favorable evaluation of utilitarian innovations, whereas process simulation leads to more favorable evaluation of hedonic innovations. Conversely, under an affective mode, process simulation leads to a more favorable evaluation of utilitarian innovations, whereas outcome simulation leads to a more favorable evaluation of hedonic innovations (Zhao et al., 2011).

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Memory versus Imagination Focused Simulation

Memory-focused simulation is thinking about an innovation in terms of consumption situations and experiences that are most readily apparent. In other words, memory-focused simulation limits mental images on consumption scenarios of established products. In contrast, imagination-focused simulation is thinking about an innovation in an imaginative focus (Zhao et al., 2009). In cases of imagination-focused simulation, people create new, never-before-experienced consumption scenarios (Dahl, Chattopadhyay, & Gorn, 1999).

A series of studies demonstrates that for really new products, imagination-focused simulation leads to more favorable evaluation than memory-focused simulation. Memory-focused simulation of a really new product is likely to focus people on the behavioral change required to integrate it into existing consumption patterns. As a consequence, people are more concerned about associated learning costs. In contrast, imagination-focused simulation is more likely to focus people on the new benefits of a really new product that may otherwise not be fully appreciated (Zhao et al., 2009).

Self versus Other Related Simulation

Self-related simulation contains images of the self when evaluating an innovation (Dahl & Hoeffler, 2004). The self-referencing literature indicates that people who are able to relate an advertisement to themselves remember the ad better, and develop more positive attitudes toward the ad and the advertised product (Bone & Pam Scholder, 1992; Burnkrant & Unnava, 1995; Debevec & Romeo, 1992). Self-referencing encourages relevance and persuasion by enabling people to link ad or product characteristics to a cognitive network of established associations (Debevec & Romeo, 1992). Following these considerations, it was found that self-related simulation leads to more favorable evaluation of incrementally new products. These innovations build on established products, and therefore provide a context in which people can draw on self-related experiences with similar products and/or consumption scenarios (Dahl & Hoeffler, 2004).

However, in case of really new products, prior experience is lacking, and self-related simulation leads to unfavorable evaluation. Specifically, self-related simulation is likely to raise concerns about how such an innovation fits into existing personal consumption patterns. This may create feelings of uncertainty. Accordingly, research shows that, for really new products, other-related simulation should be preferred which contains images of others when evaluating an innovation. Other-related simulation is less likely to evoke thoughts about changes to existing personal consumption patterns.

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Instead, it focuses people on reasons why others may value specific attributes or benefits of a really new product (Dahl & Hoeffler, 2004).

3.3.4.3 Difficulty of Imagination and Innovation Perception

Research suggests that, not only does the content of imagination influence innovation evaluation, but also the difficulty of imagination. Specifically, if the benefits of an innovation are difficult to imagine, customers are likely to respond negatively towards it. This consideration follows from a study by Wänke, Bohner, & Jurkowitsch (1997) which demonstrated that, when customers named 10 reasons for buying a BMW, the difficulty of imagination was perceived to be more diagnostic than the content of imagination, and evaluations were lower compared with when only 1 reason was named.

Ziamou (2002) provides direct evidence of the negative influence of imagination difficulty on innovation evaluation. In her study, participants were asked to imagine the use of a new product that either incorporated a new interface with a preexisting functionality or a new functionality. They subsequently had to evaluate the product. The study revealed that participants who were asked to imagine the use of the new product with a new interface and a preexisting functionality evaluated the new product favorably. This was because imagining the use of a preexisting functionality was easy for customers. Imagining product use increased the perceived likelihood that the new interface would perform as promised, and provide the particular functionality. Conversely, participants who were asked to imagine the use of a new product with a new interface and a new functionality evaluated the product negatively. In this case, imagination of product use was rather difficult, because participants lacked prior experience with the new functionality. From the difficulty of imagination, participants inferred that the new interface would not perform as expected, and its functionality would be unlikely to yield favorable results.

Following these findings, it was shown that the evaluation of innovations can be significantly improved by facilitating the imagination of new benefits. This may be achieved by focusing customers’ attention on only a limited number of benefits (Zhao et al., 2012). Another possibility is to provide customers with specific, new usage scenarios (Feiereisen, Wong, & Broderick, 2008). This may be done through interactions with sales people, TV commercials and print advertisements, and even through descriptions on the product (Zhao et al., 2012).

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3.3.5 Resolution of Trade-Offs in Evaluation of Innovations

3.3.5.1 Resolution of Trade-Offs between Capability and Usability

Adding features to products may encourage both favorable and unfavorable evaluation. People may respond favorably to added product features, because they associate more capability with these features. Perceived capability refers to beliefs about a product’s ability to perform desired functions (Thompson, Hamilton, & Rust, 2005). Research provides considerable evidence for a positive relationship between added features and perceived capability. Carpenter, Glazer, & Nakamoto (1994) have shown, for example, that added features may provide perceived advantages of a product over existing alternatives. It has also been found that people perceive a product with a greater number of features as superior in a given choice set (Brown & Carpenter, 2000).

In contrast, people may respond unfavorably to added product features, because they associate them with less usability (Thompson et al., 2005). Literature on usability and user-centered design suggests that added features reduce people’s confidence in their ability to effectively make use of a product. This relationship is believed to occur across a variety of product categories (Wiklund, 1994). It is argued that people may think of additional features as more things to learn, more things to possibly misunderstand, and more things to search through when looking for what they want (Nielsen, 1993).

Research suggests that the relative weights of capability and usability in overall product evaluation are determined by temporal distance. Specifically, capability plays a more important role in distant future considerations, whereas usability plays a more important role in near future considerations (Thompson et al., 2005; Ziamou & Veryzer, 2005). When people evaluate options for the distant future, they prefer high capability options over high usability options. When people evaluate options for the near future, they prefer high usability options over high capability options (Liberman & Trope, 1998). Similarly, it has been shown that people attribute more weight to capability and less weight to usability after product use (Thompson et al., 2005).

Furthermore, social visibility of product choice influences relative weights of capability and usability in overall innovation evaluation. People prefer high capability options over high usability options when product choice is public, but not when product use is public. Specifically, people tend to choose feature rich products (high capability and low usability products) in public choices in which they have the

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possibility to display their preferences to others. The public choice of feature rich products enables people to signal their technological skills as well as their openness to new experiences. However, if people anticipate that they have to use a product in front of others, they tend to prefer feature poor products (high usability and low capability products). In such a circumstance, people place higher weights on usability to avoid the appearance of incompetence (Thompson & Norton, 2011).

3.3.5.2 Resolution of Trade-Offs between Functional and Hedonic Benefits

When evaluating an innovation, potential customers are frequently confronted with trade-offs between functional and hedonic benefits. A considerable body of research examined how potential customers resolve such trade-offs. Chitturi, Raghunathan, & Mahajan (2007), for example, conducted a series of studies, in which they offered participants different options of a product that varied in functional and hedonic desirability. The study demonstrates that customers prefer the hedonically superior option in gain-gain situations, where all options meet or exceed desired cutoffs on both functional and experiential dimensions. However, in loss-loss situations, where all options either meet the functional cutoffs or the experiential cutoffs but not both, customers will choose the functional one.

Similar results were found in a subsequent study by Chitturi, Raghunathan, & Mahajan (2008) which examined the impact of functional versus hedonic benefits on postconsumption feelings of customer delight and satisfaction. The authors argue that functional benefits address prevention goals associated with confidence and security, whereas hedonic benefits address promotion goals associated with cheerfulness and excitement. This implies that customers regard the fulfillment of prevention goals as a necessity. A product that fails to meet prevention goals is likely to lead to highly arousing negative reactions such as anger. Conversely, a product that meets prevention goals will lead to satisfaction, which is only a low arousing positive emotion. In cases where prevention goals are already met, further functional improvements are unlikely to have an additional influence. In cases of hedonic expectations, things are different; by meeting or exceeding high arousing promotion goals, customer delight is reached. At the same time, the failure to meet promotion goals will only result in low arousing negative emotions such as sadness or disappointment. This is because promotion goals are regarded as luxury as opposed to necessity.

In another line of research, Noseworthy & Trudel (2011) examine how customers evaluate incongruent product form. Incongruent product form is frequently used to

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increase hedonic appeal of new products. The question which arises then relates to the circumstances under which customers evaluate an increase in hedonic appeal favorably. Among others, these circumstances depend on whether a product with incongruent form is functionally or hedonically positioned. An example is toning shoes which were introduced by the shoemakers Skechers and Reebok. While Skechers chose a functional product positioning with a focus on benefits for customers’ backs, cores, and muscles, Reebok chose a hedonic positioning, claiming that their shoes help customers to get better legs and glutes. It was found that functional product positioning favors the evaluation of incongruent product form. This is because incongruent form leads to functional uncertainty if functional attributes are not communicated explicitly. Thus, with hedonic positioning, customers will question functional benefits of a product with incongruent form, and evaluate it more negatively than congruent form. Conversely, if incongruent product form is functionally positioned, customers are aware of functional benefits and evaluate it more favorably than congruent form. Thus, customers must first understand a product’s functionality before they can engage in hedonic consumption.

Moreover, technological trajectory influences relative weights of capability and usability in product evaluation. Buying decisions concerning high-tech products typically follow a buying hierarchy, with performance considerations at the top, followed by convenience considerations. It was found that performance considerations will dominate choices, as long as performance plays a discriminating role. If it fails to do so, however, convenience becomes the primary choice criterion. In line with this, it was found that, at the high end of technological trajectory, people prefer dedicated products, while, at the lower end of technological trajectory, they prefer converged products (Han, Chung, & Sohn, 2009).

Together, these findings imply that an inflection point exists, up to which functional aspects play a more important role than hedonic ones, and beyond which the opposite is true. Thus, it follows that innovations generally need to meet or exceed some basic functional goals. If they fail to do so, customers are unlikely to consider them in their choice sets. However, the dominance of functional considerations only persists up to a certain functional level. If this level is met, further functional improvements are unlikely to convince customers. In such a situation, resources should be devoted to hedonic dimensions (Chitturi et al., 2007, 2008).

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3.3.6 Expectations about Usage of Innovations

3.3.6.1 Formation of Expectations about Innovations

Potential customers form expectations about innovations based on mass media information and interpersonal information. Mass media information comprises messages that involve a mass medium, such as radio, television, or newspapers (Rogers, 2003). Two types of mass media information are distinguished: customer education and anticipation creation. Customer education refers to messages describing the functionality of an innovation, thereby creating a sense of familiarity with it. The purpose of these messages is to educate potential customers about the innovation and how to use it. Anticipation creation refers to messages building momentum and demand for an innovation by heightening expectations of it (Lee & Colarelli O’Connor, 2003).

Interpersonal channels refer to messages that involve a face-to-face exchange between two or more individuals. This includes events such as trade and customer shows, as well as personal selling demonstrations. These events may either be benefit-oriented or process-oriented (Wood & Moreau, 2006). The purpose of benefit-oriented events is to reveal an innovation’s benefits and relate them to specific usage situations of potential customers. Process-oriented events are targeted towards showing potential customers how to effectively make use of an innovation, and overcome obstacles associated with changes in consumption patterns (Guiltinan, 1999).

Irrespective of whether expectations about an innovation are formed on the basis of mass media or interpersonal channels, innovation acceptance is strongly determined by whether subsequent interactions with an innovation comply with previously developed expectations. If expectations about an innovation are disconfirmed, prior attitudes toward an innovation may change completely.

3.3.6.2 (Dis)Confirmation of Expectations about Innovations

Research suggests that the creation of unrealistic expectations about innovations may have a detrimental effect on innovation evaluation. When customers have the possibility of experiencing an innovation first hand, their expectations are either confirmed or disconfirmed (Heiman & Muller, 1996). While confirmation of expectations does not have an impact on subsequent innovation evaluation, disconfirmation may either have a positive or negative impact. If an innovation performs better than expected, potential customers may be positively surprised and

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experience positive emotions. However, if an innovation performs worse than expected, potential customers are likely to be negatively surprised and experience negative emotions. Following this, the model of influence of expectations on innovation evaluation (see Figure 3-12) was developed (Wood & Moreau, 2006).

Figure 3-12: Expectations and Innovation Evaluation (Wood & Moreau, 2006)

A study by Wood & Moreau (2006) provides empirical evidence for this model. They asked participants to perform a series of tasks on a Personal Digital Assistant (PDA). Prior to the performance of these tasks, participants were asked to indicate their expectations concerning the usage complexity of the PDA. It was found that, if actual usage was easier than expected, participants experienced positive emotions and evaluated the PDA favorably. However, if actual usage was more difficult than expected, participants experienced negative emotions and evaluated the PDA unfavorably. These findings imply that promises of low complexity that create overoptimistic complexity expectations will have an unfavorable effect in the long run.

Ziamou, Gould, & Venkatesh (2012) identified three basic factors as potential causes of ineffective usage of innovations. One of these factors is prior knowledge regarding interface and functionality practices. When learning about a new product, people are often confronted with interface or functional elements that are missing or that work differently from what they are used to. Such inappropriate construals are likely to lead people down the wrong path, and consume substantial time and effort. A second factor is social influence in new product learning. In many cases, people learn how to use a new product by casually observing others using it. However, such observations are likely to provide insufficient insight into the actual details of use. A third factor that may cause ineffective usage of innovations is causal attributions generated from failure to learn satisfactorily. Sometimes people blame themselves for not being able to effectively making use of a new product. Arising from such negative self-attributions, people do not only feel less competent but also act less competent. To decrease the negative impact of these factors, innovations should incorporate some

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degree of flexibility that allow individuals to modify usage to fit their own preferences (Norman, 1998), or indicators that provide feedback about whether usage is effective (Rijsdijk & Hultink, 2003).

3.3.6.3 Insight and Discontinuous Learning in Innovation Usage

When learning how to make use of an innovation, individuals typically follow one of two different learning paths. In the first path, an individual uses a product repeatedly and gradually improves the efficiency with which the usage steps are performed. In the second path, the individual plays around with a product and explores its usage space (Lakshmanan & Krishnan, 2011). This process is likely to lead to greater conceptual learning and, as a consequence, to the discovery of optimal ways of using of the product. As a result, the individual is likely to develop a more comprehensive mental model of the product (Norman, 2002), which often results in a positive affective reaction, frequently referred to as ‘insight’ (Lakshmanan & Krishnan, 2011).

The first path refers to power law learning which improves an individual’s procedural knowledge (Cohen & Squire, 1980). Here, procedural knowledge involves the development of sensorimotor and cognitive skills associated with product use (Squire, 1986). Procedural knowledge is often associated with the development of behavioral routines such as habits. Conversely, the second path refers to discontinuous learning which improves an individual’s conceptual or declarative knowledge. This type of knowledge involves learning, representation, and use of knowledge pertaining to facts and events. Consequently, power law learning typically follows a gradual learning curve, whereas discontinuous learning typically follows a learning curve with abrupt improvements (see Figure 3-13) (Lakshmanan & Krishnan, 2011).

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Figure 3-13: Learning to Use Innovations (Lakshmanan & Krishnan, 2011)

Lakshmanan & Krishnan (2011) examined the consequences of power law learning versus discontinuous learning of innovations. They demonstrated that discontinuous learning is more likely to create a feeling of insight. This insight, in turn, was found to evoke positive affect and enhance usage intentions of innovations. Thereby, Lakshmanan & Krishnan identified exploration as key antecedent of discontinuous learning. Specifically, they found that encouraging exploration leads to the development of broader, more flexible mental models, and enhances individuals’ ability to use an innovation in different contexts. Furthermore, they found that the propensity to conduct discontinuous learning is significantly reduced if individuals are constrained by detailed usage instructions. Detailed usage instructions usually prevent the propensity for extensive exploration.

Empirical evidence by Dahl & Moreau (2007) indicates that innovations provide the most favorable usage experiences if their use is associated with a sense of autonomy. In this context, autonomy denotes the enjoyment derived from the freedom to choose the process of using an innovation. However, such autonomy has a positive effect only if individuals feel a sense of competence when using an innovation. Competence may be defined as anticipated satisfaction derived from making use of an innovation successfully. A feeling of competence in innovation usage particularly arises if

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customers are provided with clear feedback about the appropriateness of their interaction with the innovation.

Together, these findings break with the predominant perspective that initial trials of innovations always represent a hurdle which individuals must surmount to become proficient users. In addition to this learning-cost view, Lakshmanan & Krishnan (2011) propose a benefit-based view. They suggest that initial trials may actually provide value to the overall product experience of an innovation. Such value manifests itself if an innovation encourages active exploration of its usage space, thereby resulting in an experience of insight. The authors argue that the Nintendo Wii and the Apple iPhone represent two successful examples of where active discovery plays an essential role in the product experience. Specifically, the motion sensitive remote of the Wii and the touch-screen-enabled interaction of the iPhone encouraged extensive exploration, triggering an almost cultlike attachment to these products.

Chapter 3 provided a detailed overview over innovation recognition and innovation assessment as key processes of innovation perception. In the next chapter (chapter 4), innovation comprehension is addressed. Innovation comprehension represents a previously neglected, yet highly relevant aspect of innovation perception.

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4 Innovation Comprehension

4.1 Introduction to Innovation Comprehension

So far, research on innovation perception has been primarily concerned with the ‘what’ of perception. Specifically, this research has tried to discover, what makes an innovation attractive and sets it apart from existing alternatives in a way that is desirable (Venkatesh et al., 2012). Recent findings indicate, however, that, not only the ‘what’ plays a role, but also the ‘how’. This ‘how’ refers to innovation comprehension which is the way in which people process information about an innovation (Förster et al., 2010; Marguc, Förster, & Van Kleef, 2011; Marguc, Van Kleef, & Förster, 2012).

Ulkumen, Chakravarti, & Morwitz (2010) provide initial empirical evidence for the important role of different ways of processing on innovation perception. In a series of studies, these authors manipulated processing on the basis of a categorization task, and subsequently asked participants to evaluate an unrelated innovation (wireless speakers). In the categorization task, participants were told that they were shopping for a friend’s party and needed to make choices in eight different categories such as wine, cheese, beer, and music. One group of participants had to choose products from a few broad categories. The other group of participants had to choose the identical products from many narrow categories (see Figure 4-1). Even though the shopping task had nothing to do with the innovation, the study showed that participants who performed the broad categorization task evaluated the innovation significantly better than participants who performed the narrow categorization task.

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Figure 4-1: Broad vs. Narrow Categorization Primes (Ulkumen et al., 2010)

Broad Categorization Prime

Narrow Categorization Prime

Ulkumen et al. (2010) argued that this relationship occurred because the exposure to broad categories evokes processing in which individuals base their subsequent decisions on easily available information, whereas the exposure to narrow categories evokes processing in which individuals go beyond available information and base their subsequent decisions also on non-salient information. Put differently, narrow (versus broad) categories evoke more (versus less) multidimensional information processing

Next, participants responded to the dependent measure byindicating their overall attitude toward the product on a 100-point slider scale (“disliked it very much/liked it verymuch”). Then, they responded to several process measures.In two open-ended responses, they first briefly listed the keyfactors they had considered in their evaluation, and thenthey reported the number of factors they considered in theirevaluation more generally. We also measured participants’involvement and mood (across the entire experiment ses-sion); assessed their familiarity with, knowledge of, aware-ness of, and ownership of the product; and recorded studycompletion times.

Results

Dependent measure. In support of H2, compared withparticipants in the narrow condition, those in the broad con-dition had more favorable attitudes toward the new product(MBROAD = 71.40, MNARROW = 62.83; F(1, 94) = 6.41, p <.02) (for stimuli and results, see Figure 1).

Process measures. Participants previously exposed tonarrow categories reported considering a greater number offactors when evaluating the speakers (MBROAD = 2.96,MNARROW = 3.57; F(1, 93) = 4.43, p < .04). Wireless speak-ers have several attributes, such as no-clutter technology,

664 JOURNAL OF MARKETING RESEARCH, AUGUST 2010

Figure 1MANIPULATIONS, STIMULI, AND SUMMARY OF RESULTS FROM STUDY 2A

A: Decision Context 1: Shopping Study

B: Decision Context 2: New Product Evaluation

Innovation thoughts Risk thoughts

Number of Innovation–Risk Thoughts

“Broad” Shopping Web SiteExample of Screen Shot from Cheese Category

•24 cheese alternatives•2 categories (firm and soft)

“Narrow” Shopping Web SiteExample of Screen Shot from Cheese Category

•24 cheese alternatives•8 categories (firm–cow, firm–goat, …, soft–sheep)

Stimuli:Sony SRS-RF90RK Wireless Speakers

Broad NarrowNumber of Factors Considered

Results:

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Attitude Toward ProductBroad Narrow Broad Narrow

71.40

62.83

2.96

3.57

.29

.77 .73

Next, participants responded to the dependent measure byindicating their overall attitude toward the product on a 100-point slider scale (“disliked it very much/liked it verymuch”). Then, they responded to several process measures.In two open-ended responses, they first briefly listed the keyfactors they had considered in their evaluation, and thenthey reported the number of factors they considered in theirevaluation more generally. We also measured participants’involvement and mood (across the entire experiment ses-sion); assessed their familiarity with, knowledge of, aware-ness of, and ownership of the product; and recorded studycompletion times.

Results

Dependent measure. In support of H2, compared withparticipants in the narrow condition, those in the broad con-dition had more favorable attitudes toward the new product(MBROAD = 71.40, MNARROW = 62.83; F(1, 94) = 6.41, p <.02) (for stimuli and results, see Figure 1).

Process measures. Participants previously exposed tonarrow categories reported considering a greater number offactors when evaluating the speakers (MBROAD = 2.96,MNARROW = 3.57; F(1, 93) = 4.43, p < .04). Wireless speak-ers have several attributes, such as no-clutter technology,

664 JOURNAL OF MARKETING RESEARCH, AUGUST 2010

Figure 1MANIPULATIONS, STIMULI, AND SUMMARY OF RESULTS FROM STUDY 2A

A: Decision Context 1: Shopping Study

B: Decision Context 2: New Product Evaluation

Innovation thoughts Risk thoughts

Number of Innovation–Risk Thoughts

“Broad” Shopping Web SiteExample of Screen Shot from Cheese Category

•24 cheese alternatives•2 categories (firm and soft)

“Narrow” Shopping Web SiteExample of Screen Shot from Cheese Category

•24 cheese alternatives•8 categories (firm–cow, firm–goat, …, soft–sheep)

Stimuli:Sony SRS-RF90RK Wireless Speakers

Broad NarrowNumber of Factors Considered

Results:

.92

Attitude Toward ProductBroad Narrow Broad Narrow

71.40

62.83

2.96

3.57

.29

.77 .73

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(Ulkumen et al., 2010). This is supported by research on individual-level category-width which demonstrates that individuals may associate narrow equivalence ranges with a preference for greater dimensionality (Jackson & Messick, 1965; Sloane, Gorlow, & Jackson, 1963). Accordingly, Ulkumen et al. (2010) found in their study that participants exposed to broad categories considered fewer factors when evaluating the innovation. These participants focused particularly on the salient benefit-related aspects of the innovation, which led to more favorable responses. In contrast, participants exposed to narrow categories also considered the less salient risk-related aspects of the innovation, which led to less favorable responses.

Based on the findings of Ulkumen et al. (2010), the present research further examines how different ways of processing influence innovation perception. Thereby, this research draws on Novelty Categorization Theory (NCT), a recent theory from social psychology, which “attempts to predict when people perceive events as novel and how they process novel events across different domains (Förster et al., 2010, p. 736).” NCT distinguishes two distinct processing styles: global processing that is focused on the overall Gestalt of a stimulus and local processing that is focused on the constituting details of a stimulus. Thereby, NCT argues that global/local processing considerably influences the way with which people perceive novel versus familiar events. Specifically, NCT proposes that global processing improves evaluation of novel events, whereas local processing improves evaluation of familiar events (Förster et al., 2010). Following this, it could be concluded that global/local processing may also influence the perception of innovations with varying degrees of newness. More precisely, as global processing favors response to novel events, it may improve evaluation of really new products, which represent groundbreaking departures from established product categories (i.e. high level of perceived newness). Conversely, local processing may improve evaluation of incrementally new products, which solely represent improvements of established products (i.e. low level of perceived newness).

Accordingly, building on NCT, the present research examines how global/local processing influences perception of really new products as opposed to incrementally new products. For this purpose, global and local processing is specified in chapter 4.2 and implications of NCT for innovation perception are derived. Based on this theoretical foundation, the influence of processing styles on perception of incrementally new vs. really new products is empirically investigated in chapter 4.3. Further, chapter 4.4 contains a summary of the results of the empirical investigation of the influence of processing styles on innovation perception. This chapter also includes theoretical and managerial implications as well as propositions for future research.

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4.2 Conceptual Development Innovation Comprehension

4.2.1 Specification of Global/Local Processing

4.2.1.1 Distinguishing Global and Local Processing

As previously discussed, NCT distinguishes between global and local processing. Specifically, NCT suggests that people may process the same object in two different ways: they can either zoom out and focus on the overall impression of the object, or they can zoom in and focus on the object’s constituting elements. An old proverb says that people either see the forest or the trees when attending to a stimulus set. When people see the forest rather than the trees, they are in global processing (Förster & Dannenberg, 2010). In such a situation, they attend to the overall Gestalt of a stimulus set and broaden perceptual scope. However, when people see the trees rather than the forest, they are in local processing. In this situation, they attend to the details of a stimulus set and narrow perceptual scope (Navon, 1977; Schooler, 2002).

Processing styles may be determined by the so-called Kimchi-Palmer-Figures task. In this task, people are presented with different sets of geometrical figures, such as squares or triangles, that are made up of smaller squares or triangles (Kimchi & Palmer, 1982). Each of these sets consists of a target figure and two comparison figures (see example in Figure 4-2). For each set of figures, people have to decide which of the comparison figures is more similar to the target figure. One of the comparisons figures is similar to the target figure from a global perspective (comparison figure A in Figure 4-2), whereas the other comparison figure is similar to the target figure from a local perspective (comparison figure B in Figure 4-2). The selection of a larger number of global figures in the Kimchi-Palmer-Figures task indicates a global processing style, and the selection of a larger number of local figures indicates a local processing style (Förster & Dannenberg, 2010).

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Figure 4-2: Example Item of the Kimchi-Figures task (Kimchi & Palmer, 1982)

4.2.1.2 Inducing Global and Local Processing

Global/local processing may be induced actively by evoking a carry-over of a processing style from one task to other, unrelated tasks. Research refers to this process as procedural priming (Förster, Liberman, & Friedman, 2007; Förster & Liberman, 2007). It differs from semantic priming in that the ‘how’ rather than the ‘what’ is primed. Semantic priming refers to the activation of semantic concepts in memory, which influence subsequent access to specific information (Förster & Dannenberg, 2010). For example, people become faster at identifying the word ‘car’ when they are semantically primed with the word ‘street’. A study by Smith & Branscombe (1987) illustrates the difference between procedural and semantic priming. Participants in this study were asked to rate the aggressiveness of an ambiguously aggressive person. Before this judgment, they either received a semantic prime or a procedural prime. Specifically, participants in the semantic priming condition were asked to unscramble sentences describing hostile behaviors such as ‘leg her break he’. In the procedural priming condition, participants were presented with the same sentences in an unscrambled form and had to choose matching traits for these sentences (e.g. hostile). This matching task represents a procedural prime, because participants had to follow the same procedure when they judged aggressiveness of the ambiguously aggressive person in the second phase. The study showed that procedural priming increased aggressiveness ratings for a considerably longer delay (15 min) than semantic priming (15 s).

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According to NCT, global versus local processing may be procedurally primed in various ways. One of these ways represents the so-called Navon-Letter task (Gasper & Clore, 2002). In the Navon-Letter task, people are presented with a set of global letters that consist of local letters (e.g. an H made of Ls, see Figure 4-3). These letters appear in a random order on a computer screen. In the global processing condition, people are asked to identify the global letters. The identification of global letters evokes global processing by broadening perceptual scope. Conversely, in the local processing condition people are asked to identify the local letters. The identification of local letters evokes local processing by narrowing perceptual scope (Förster & Dannenberg, 2010).

Figure 4-3: Sample-Item Navon-Letter task

L L L L L L L L L L L L L L L L L

4.2.2 Implications of Global/Local Processing for Innovation Perception

NCT argues that global/local processing does not only influence perceptual scope, but also conceptual scope (Friedman & Förster, 2008). More precisely, research indicates that mechanisms, which influence perceptual scope and let people focus visually on one percept while excluding others might be related to mechanisms that influence conceptual scope, such as the selective activation of semantic networks. Conceptual scope is determined by whether a concept such as ‘car’ spreads narrowly to concrete associates such as ‘steering-wheel’, or broadly to more abstract and remote associates such as ‘mobility’ (Förster & Dannenberg, 2010). Research identified a variety of variables that are influenced by narrowing or broadening conceptual scope through priming of global versus local processing. These include assimilation/contrast effects (e.g., Förster, Liberman, & Kuschel, 2008) as well as performance in creative and analytical problem solving tasks (e.g., Ayelet, Fishbach, Förster, & Werth, 2003). Empirical evidence on the influence of global/local processing on these variables provides important implications for innovation perception.

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4.2.2.1 Assimilation/Contrast Effects

NCT argues that global processing leads to inclusion and assimilation, thereby rendering similarities between different stimuli more accessible. In contrast, local processing leads to exclusion and contrast, thereby rendering dissimilarities between different stimuli more accessible (Förster & Dannenberg, 2010).

In a series of experiments, Förster (2009) provides empirical evidence for the relationship between processing styles and assimilation/contrast effects. In one of these experiments, participants were primed with global/local processing by means of the Navon-Letter task. Subsequently, they were asked to identify similarities and dissimilarities between two comparable TV shows. It was found that global priming promoted the identification of similarities, whereas local priming promoted the identification of dissimilarities. In the remaining experiments, Förster conceptually replicated these findings by carefully varying both independent and dependent variables. In one of these experiments, the results were replicated by manipulating global/local processing with a map task that was similar to the Navon-Letter task. In another experiment, global processing was found to cause spontaneous similarity generation, and local processing was found to cause spontaneous dissimilarity generation even without instructions to generate similarities or dissimilarities. Further, a bi-directional link was identified between global/local processing and dis/similarity generation in another experiment. In this experiment, participants were asked to focus on similarities versus dissimilarities between different stimuli. Subsequently, they were asked to perform a task similar to the Kimchi-Figures task as a measure for global/local processing. The experiment revealed that the focus on similarities led to global processing, and the focus on dissimilarities led to local processing.

The relationship between global/local processing and assimilation/contrast effects was also demonstrated for social judgment. In a series of experiments by Förster, Liberman, & Kuschel (2008), participants were primed with global and local processing styles. Subsequently, they had to compare themselves to high and low standards, and estimate how they would score on this dimension. Specifically, in one of these studies, participants had to estimate their athletic skills compared to the Formula 1 racer Michael Schumacher (i.e. high standard) or the former American president Bill Clinton (low standard i.e.). It was found that, in global processing, participants thought that they could do more push-ups when they compared themselves to Michael Schumacher and fewer push-ups when they compared themselves to Bill Clinton. Conversely, in local processing, participants thought they could do fewer

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push-ups when they compared themselves to Michael Schumacher and more push-ups when they compared themselves to Bill Clinton.

From these findings, important implications for perception of really new versus incrementally new products can be derived. As the moderate incongruity effect suggests, really new products are very dissimilar to existing alternatives and are likely to be perceived as extremely incongruent. Because of an extreme level of incongruity, customers typically respond unfavorably to really new products. In contrast, incrementally new products are very similar to existing alternatives and are likely to be perceived as highly congruent. Such congruity is also likely to result in unfavorable response (Chandy et al., 2001; Feiereisen et al., 2008; Moreau, Markman, et al., 2001). Because global processing triggers assimilation effects and local processing triggers contrast effects, it can be concluded that global processing improves evaluation of really new products and local processing improves evaluation of incrementally new products. Specifically, global processing broadens conceptual scope and prompts individuals to generate similarities between an innovation and existing alternatives. Such a process is likely to decrease perceived incongruity and, as a consequence, improve evaluation of really new products. At the same time, local processing narrows conceptual scope and prompts individuals to create dissimilarities between an innovation and existing alternatives. Such a process is likely to increase perceived incongruity and, as a consequence, improve evaluation of incrementally new products (Förster et al., 2010; Jhang et al., 2012; Meyers-Levy & Tybout, 1989).

4.2.2.2 Creative and Analytical Problem Solving Performance

NCT argues that global and local processing also has a differential effect on creative and analytical problem solving performance. Research distinguishes creative insight tasks as a measure of creative performance, and analytical reasoning tasks as a measure of analytical performance. Creative generation tasks that require people to generate unusual uses for a brick represent an example for a creative insight task. Ideas such as “grind it up and use it as make-up” indicate a high level of creative performance, whereas ideas such as “build a wall” indicate low levels of creative performance (Förster & Dannenberg, 2010). Furthermore, the following classical insight problem represents another creative insight task (Förster, Friedman, & Liberman, 2004):

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A prisoner wanted to escape from a tower. He found a rope in his cell that was half as long enough to permit him to escape safely. He divided the rope in half, tied the two parts together, and escaped. How did he do this? [Solution: He unraveled the rope lengthwise and tied the remaining strands together.]

Conversely, the Graduate Record Examination (GRE) Analytical test represents an analytical reasoning task. This test involves the evaluation of the true value of a number of propositions given an initial set of basic facts. Solving such problems requires systematic organization of the given information, analyzing it carefully to reach a conclusion about the verity of a series of logical conclusions (Friedman & Förster, 2000). The following question represents an example problem from the GRE:

Evan has four times as many books as David and five times as many as Jason. If Jason has more than 40 books, what is the least number of books that Evan could have? (A) 200 (B) 205 (C) 210 (D) 220 (E) 240 [Solution: D]

A series of studies by Förster, Epstude, & Özelsel (2009) suggests that global processing promotes creative performance, whereas local processing promotes analytical performance. In one of the studies, participants were either primed with the concepts of love or lust. Subsequently, they had to perform the Kimchi-Palmer-Figures task in order to measure global/local processing. The results showed that love led to global processing, whereas lust led to local processing. Thereby, the study also showed that love priming improved performance on creative insight problems. At the same time, lust priming improved performance in the GRE. These results indicate that global processing (love priming) relates to creative performance, while local processing (lust priming) relates to analytical performance.

In accordance with the discussion in chapter 4.2.2.1, the findings concerning the influence of processing styles on creative and analytical problem solving imply that global processing improves evaluation of really new products, while local processing improves evaluation of incrementally new products. Specifically, really new products allow customers to do things they have not been able to do before (Lehmann, 1997). Such completely new functionalities are likely to be evaluated more favorably under global processing, because it prompts creative thinking (Förster et al., 2010). That is, in a creative mind-set, individuals typically generate atypical usage scenarios which are more likely to reveal the new benefits provided by really new products (Zhao et al., 2009). In contrast, incrementally new products solely provide improvements to existing functionalities (Chandy & Tellis, 1998, 2000). Individuals are more likely to

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identify such improvements under local processing which evokes an analytical, detail-oriented mind-set (Förster & Dannenberg, 2010; Förster et al., 2010).

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4.3 Experimental Analysis Innovation Comprehension

4.3.1 Overview over the Experimental Studies

Following the previous discussion, the purpose of the empirical investigation of the present research was to examine the influence of global/local processing on the evaluation of different innovations. More specifically, three experiments were conducted in which participants were primed with global/local processing and asked to evaluate really new products (RNPs) and incrementally new products (INPs). In experiment 1, participants were primed with global/local processing and it was investigated how different processing styles influence evaluation, purchase intention, and perceived meaningfulness of RNPs and INPs. In experiment 2, the results of experiment 1 were replicated with a different sample. In addition, experiment 2 examined the influence of global/local processing on perceived usefulness of RNPs and INPs. Finally, in experiment 3, it was analyzed how global/local processing influences perception of very easy-to-use versus very difficult-to-use RNPs. Figure 4-4 provides an overview over the experimental studies.

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Figure 4-4: Overview over the Experimental Studies

Experiment 1:

Processing Styles and Perception of INPs vs. RNPs

− Design: 2 (innovation: RNP vs. INP) x (processing: global vs. local)

− Scenario: Evaluation of a digital camera depicted in a mock advertisement

− Participants: 243 adults from a representative German online panel

− Pages: 98-106

Experiment 2: Processing Styles and Perception of Usefulness of INPs vs. RNPs

− Design: 2 (innovation: RNP vs. INP) x (processing: global vs. local)


− Participants: 149 students from the University of St. Gallen

− Pages: 106-115

Experiment 3: Processing Styles and Perception of Easy-to-Use (EtU) vs. Difficult-to-Use (DtU) RNPs

− Design: 2 (innovation: EtU RNP vs. DtU RNP) x (processing: global vs. local)


− Participants: 161 adults from a representative German online panel

− Pages: 115-124

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4.3.2 Experiment 1

4.3.2.1 Hypothesis Development

According to NCT, global processing helps to make sense out of new things by activating superordinate or abstract representations in memory, and broadening conceptual scope. In doing so, global processing evokes assimilation effects, thereby, helping individuals to integrate novel events more effectively into existing knowledge structures. At the same time, global processing also enhances creativity, which promotes the development of new solutions (Förster & Dannenberg, 2010). Following this, global processing should favor perception of RNPs for two reasons. Firstly, because of their inherent newness, RNPs are likely to be perceived as extremely incongruent to previous experiences (Veryzer, 1998a). By evoking assimilation effects, global processing is likely to reduce extreme incongruity and, as a consequence, lead to more favorable perception. Secondly, RNPs provide completely new functionalities (Dahl & Hoeffler, 2004). By evoking creative thinking, global processing is more likely to help individuals to identify new benefits associated with these functionalities.

In contrast, local processing activates subordinate or concrete representations in memory and narrows conceptual scope. Such thinking is likely to lead to contrast effects. Further, local processing is likely to evoke more detail-oriented, analytical thinking. As a consequence, local processing should promote perceived differentiation (Förster & Dannenberg, 2010). Accordingly, local processing is likely to favor perception of INPs for two reasons. Firstly, because of their similarity to existing alternatives, INPs are likely to be perceived as highly congruent to prior experiences (Veryzer, 1998a). By evoking contrast effects, local processing is likely to increase perceived incongruity and, as a consequence, lead to more favorable perception. Secondly, INPs solely provide improvements to existing functionalities (Dahl & Hoeffler, 2004). By evoking detail-oriented, analytical thinking, local processing is more likely to reveal differences between INPs and existing alternatives.

A favorable perception of an innovation may best be captured by overall evaluation of the innovation (Zhao et al., 2012), intentions to purchase the innovation (Zhao et al., 2011), and perceived meaningfulness of the innovation (Rubera, Ordanini, & Griffith, 2011). Thus, these variables were used in the present study as key measures for favorable perception of an innovation.

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Arising from this discussion, the following formal hypotheses are proposed:

H1: (a) Global processing (relative to local processing) improves the evaluation of RNPs, whereas (b) local processing (relative to global processing) improves the evaluation of INPs.

H2: (a) Global processing (relative to local processing) increases the purchase intentions for RNPs, whereas (b) local processing (relative to global processing) increases the purchase intentions for INPs.

H3: (a) Global processing (relative to local processing) increases the perceived meaningfulness of RNPs, whereas (b) local processing (relative to global processing) increases the perceived meaningfulness of INPs.

4.3.2.2 Design, Participants, and Procedure

The experiment was a 2 (innovation: INP vs. RNP) x 2 (processing: global vs. local) between-subjects design. A total of 243 participants of a representative German online panel (aged 18 to 40 years) completed the study (54.7% female, 45.3% male, average age of 29.5 years). Participants were randomly assigned to either an INP condition or a RNP condition. In the INP condition, a mock advertisement of an INP was provided, and participants were asked to evaluate the INP. By contrast, in the RNP condition, a mock advertisement of a RNP was provided and participants were asked to evaluate the RNP. Prior to the presentation of the mock advertisement, participants were randomly assigned either to a global processing condition or a local processing condition. In both conditions, processing styles were primed by a version of the Navon-Letter task.

4.3.2.3 Manipulation of Independent Variables

The manipulation of innovation was derived from a recent study by Zhao, Hoeffler, & Dahl (2009) which examined the impact of visualization on evaluation of RNPs and INPs. Specifically, participants received a mock advertisement of a digital compact camera (see Figure 4-5). In the INP condition, participants received a mock advertisement of a conventional digital compact camera. In the RNP condition, participants received a mock advertisement of a new light field camera, which was not available in the German market at the time the research was conducted. The company logo, as well as all other brand identification information, was removed prior to the experiment. The product was called XR-500 in all conditions. The mock advertisements consisted of four components: a headline, a picture, a short description,

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and a set of product features (3 distinctive features and two common features). All product features were taken from consumer reports and represented real features.

Figure 4-5: Manipulation of Innovations in Experiment 1

Incrementally New Product

Really New Innovation

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Furthermore, global and local processing was manipulated by a version of the Navon-Letter task (Macrae & Lewis, 2002). Therefore, all participants were presented with a series of 12 global letters that consisted of local letters (see Appendix). Participants in the global processing condition were asked to identify the global letters and type these in a text field below. By contrast, participants in the local processing condition were asked to identify the local letters and type these in the text field below. Figure 4-6 contains an example Navon-Letter that was used in the experiment. In this example, participants in the global condition had to identify the ‘A’, while participants in the local condition had to identify the ‘C’.

Figure 4-6: Example of a Navon-Letter (Prime for Processing Style)

4.3.2.4 Selection of Measures

Participants were asked to rate product newness on a four-item scale ranging from 1 = totally disagree to 7 = totally agree (i.e. This product is out of the ordinary, …can be considered as revolutionary, …provides radical differences to industry norms, …shows an unconventional way of solving problems). Furthermore, product meaningfulness was measured on a four-item scale ranging from 1 = totally disagree to 7 = totally agree (This product is relevant to my needs and expectations, …is suitable for my desires, …is appropriate for my needs and expectations, …is useful to me). Both of these scales were derived from a study by Im & Workman (2004) which examined the influence of newness and meaningfulness on new product success.

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Product evaluation was measured on a three-item scale ranging from 1 (bad, dislike, poor) to 7 (good, like, excellent). This scale was derived from a study by Dahl & Hoeffler (2004) which initially examined the impact of self-related vs. other-related mental simulation on perception of innovations. This scale was also used in a variety of other studies on innovation perception (e.g., Zhao et al., 2012, 2011).

Purchase intention was measured by asking participants to indicate how interested they would be in purchasing the product on a scale ranging from 1 (not interested at all) to 7 (very interested). This scale was derived from a study by Zhao, Hoeffler, & Zauberman (2011) which originally investigated the impact of process and outcome simulation on perception of innovations.

Table 4-1 provides a summary of all measures and their corresponding levels of reliability.

Table 4-1: Measures Employed in Experiment 1

Measures Items Reliability Source

Dependent Variables

Product Evaluation 3 α = .91 Dahl & Hoeffler (2004)

Purchase Intention 1 - Zhao, Hoeffler, & Zauberman (2011)

Meaningfulness 4 α = .96 Im & Workman (2004)

Independent Variables

Newness 4 α = .92 Im & Workman (2004)

Processing Style 12 - Macrae & Lewis (2002)

4.3.2.5 Results

Manipulation checks. The four product newness-related items were aggregated into a newness index (α= .92). As anticipated, participants rated the RNP as significantly newer than the INP (Ms = 5.32 vs, 3.67; t(241) = 10.46, p < 0.001). Regarding processing style, all participants in the global condition correctly identified the global letters, while all participants in the local condition correctly identified the local letters.

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Product evaluation. For product evaluation, a two-way analysis of variance (ANOVA) showed no main effect of innovation (F(1, 239) = .46, p = .50) or processing (F(1, 239) = .05, p = .83). However, it did show the expected significant interaction between the two factors (F(1, 239) = 7.11, p < .01). Specifically, for the INP, local processing led to significantly better evaluation than global processing (Ms = 5.06 vs. 4.61; t(121) = 2.12, p < 0.05). For the RNP, global processing led to marginally better evaluation than local processing (Ms = 5.13 vs. 4.75; t(118) = 1.67, p < .10). The results for product evaluation are depicted in Figure 4-7.

Figure 4-7: Product Evaluation Interaction (Experiment 1)

5.06

4.75 4.61

5.13

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5.25

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INP RNP

Product Evaluation

Local processing

Global processing

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Purchase intention. For purchase intention, a 2x2 ANOVA showed no main effect of innovation (F(1, 239) = .61, p = .44) or processing (F(1, 239) = .47, p = .50), but did show a marginally significant interaction between the two factors (F(1, 239) = 2.66, p = .10). For the INP, local processing led to marginally higher purchase intention than global processing (Ms = 4.17 vs. 3.66; t(121) = 1.69, p < 0.1). For the RNP, global processing did not lead to higher purchase intention than local processing (t(121) = .65, p = .52). The results for purchase intention are depicted in Figure 4-8.

Figure 4-8: Purchase Intention Interaction (Experiment 1)

Meaningfulness. For meaningfulness, a 2x2 ANOVA showed a main effect of innovation (F(1,239) = 12.31, p < 0.01), but no main effect of processing (F(1,239) = .11, p = .74). Furthermore, this analysis did not show a significant interaction between the two factors (F(1,239) = .26, p = .61).

4.17

3.64 3.66

3.85

3.00

3.25

3.50

3.75

4.00

4.25

4.50

INP RNP

Purchase Intention

Local processing

Global processing

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Table 4-2 and Table 4-3 provide a summarizing overview over the results of experiment 1.

Table 4-2: Results of the ANOVAs in Experiment 1

Measures Dependent Variable F(1,239) p

Innovation Product Evaluation .46 p = .50

Purchase Intention .61 p = .44

Meaningfulness 12.31 p < .01

Processing Product Evaluation .05 p = .83


Meaningfulness .11 p = .74

Innovation x Processing Product Evaluation 7.11 p < .01

Purchase Intention 2.66 p = .10

Meaningfulness .26 p = .61

Table 4-3: Mean Values for the Dependent Variables in Experiment 1

INP RNP

Local processing

Global processing

Local processing

Global processing

Product Evaluation 5.06 4.61 4.75 5.13

Purchase Intention 4.17 3.66 3.64 3.85

Meaningfulness 4.13 3.97 3.39 3.42

4.3.2.6 Discussion

The purpose of this first experiment was to test the basic assumption that different processing styles influence the perception of RNPs and INPs. Specifically, the experiment provides initial evidence that global processing leads to more favorable evaluation of RNPs than global processing. Thus, hypothesis 1a is confirmed. At the same time, this experiment indicates that local processing leads to more favorable evaluation of INPs than local processing. This is in support of hypothesis 1b.

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Although the results of the first experiment are promising, the expected effects of processing styles on innovation perception could not be shown for all dependent variables. More precisely, global processing did not lead to higher purchase intention of the RNP; only local processing increased purchase intention of the INP. Thus, hypothesis 2a is not supported, whereas hypothesis is 2b supported. Furthermore, the results of the experiment did not demonstrate any influence of processing styles on perceived meaningfulness of RNPs and INPs. Accordingly, hypotheses 3a and 3b were not confirmed by the experimental results.

4.3.3 Experiment 2


The purpose of experiment 2 was to replicate the findings of experiment 1, and examine why experiment 1 showed only the effect of processing styles on evaluation and purchase intentions, but not on perceived meaningfulness of different innovations.

It could be argued that processing styles did not have an impact on perceived meaningfulness, because the meaningfulness construct involves both usefulness and ease-of-use considerations (Im & Workman, 2004; Sethi et al., 2001). More precisely, research suggests that RNPs are typically characterized by low levels of perceived ease-of-use, whereas INPs are characterized by high levels of perceived ease-of-use (Dahl & Hoeffler, 2004; Veryzer, 1998a; Zhao et al., 2009). Following this, it could be the case that effects of processing styles on perceived meaningfulness of RNPs and INPs are diminished by the inherent difficulty-of-use of RNPs and the inherent ease-of-use of INPs. In line with this, it could be concluded that different processing styles affect the perceived usefulness of RNPs and INPs instead of their perceived meaningfulness.

Arising from this, the following additional formal hypothesis is proposed.

H4: (a) Global processing (relative to local processing) increases usefulness of RNPs, whereas (b) local processing (relative to global processing) increases usefulness of INPs.

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The experiment was a 2 (innovation: INP vs. RNP) x 2 (processing mode: global vs. local) between-subjects design. A total of 149 students of the University of St. Gallen completed the study (26.2% female, 73.8% male, average age of 21.1 years).

The procedure of experiment 2 was the same as the procedure in experiment 1. Participants were randomly assigned to either an INP condition or a RNP condition. In the INP condition, a mock advertisement of an INP was provided, and participants were asked to evaluate the INP. By contrast, in the RNP condition, a mock advertisement of a RNP was provided, and participants were asked to evaluate the RNP. Prior to presentation of the mock advertisement, participants were randomly assigned either to a global processing condition or a local processing condition. In both conditions, processing styles were primed by a version of the Navon-Letter task. This procedure was the same as in experiment 1.


Similar to experiment 1, participants in the INP condition received a mock advertisement of a conventional digital compact camera, whereas in the RNP condition, participants received a mock advertisement of the new light field camera used in experiment 1 (see Figure 4-9). The company logo and all other brand identification information were removed prior to the experiment. The product was called XR-500 in all conditions. The mock advertisements consisted of four components: a headline, a picture, a short description, and a set of product features (4 distinctive features and two common features). All product features were taken from consumer reports and represented real features.

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Incrementally New Product

Really New Innovation

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Global and local processing styles were manipulated with the same version of the Navon-Letter task used in experiment 1 (Macrae & Lewis, 2002). That is, all participants were presented with a series of 12 global letters that consisted of local letters (see Appendix). Participants in the global processing condition were asked to identify the global letters and write these in a text field below. By contrast, participants in the local processing condition were asked to identify the local letters and write these in the text field below.


Participants were asked to rate product newness on a three-point scale ranging from 1 (not very innovative, not very novel, not very original) to 7 (very innovative, very novel, very original). The scale was derived from a study by Zhao, Hoeffler, Dahl (2009) which initially examined the influence of imagination-focused visualization on innovation perception.

Usefulness was measured on a three-point scale ranging from 1 = totally disagree to 7 = totally agree (This product is advantageous, …is useful, …is an improvement). Ease-of-use was measured on a three-point scale ranging from 1 = strongly disagree to 7 = strongly agree (Usage of this product may be learned with little effort, …is clear and understandable, …is easy). Both of these scales were derived from Kulviwat et al. (2007) who examined the impact of perceived usefulness and ease-of-use on innovation acceptance in the consumer context.

Product evaluation was measured by a three-point scale ranging from 1 (bad, dislike, poor) to 7 (good, like, excellent). Additionally, purchase intention was measured by asking participants to indicate how interested they would be in purchasing the product on a scale ranging from 1 (not interested at all) to 7 (very interested). Both of these scales were the same as in experiment 1. Table 4-4 provides a summary of all measures and their corresponding levels of reliability.

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Dependent Variables


Purchase Intention 1 - Zhao et al. (2011)

Usefulness 3 α = .79 Kulviwat et al. (2007)


Newness 3 α = .93 Zhao et al. (2009)

Ease-of-Use 3 α = .90 Kulviwat et al. (2007)


4.3.3.5 Results

Manipulation checks. The three product newness-related items were aggregated into a newness index (α= .93). As anticipated, participants rated the RNP as significantly newer than the INP (Ms = 4.95 vs, 2.48; t(147) = 14.99, p < .001). Further, participants rated the RNP as significantly less easy to use than the INP (Ms = 4.57 vs. 5.62; t(147) = 5.64, p < .001). Regarding processing style, all participants in the global condition correctly identified the global letters, while all participants in the local condition correctly identified the local letters.

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Product evaluation. For product evaluation, a two-way analysis of variance (ANOVA) showed a main effect of innovation (F(1, 145) = 5.99, p < .05), but no main effect of processing (F(1, 145) = .01, p = .94). Furthermore, the analysis showed the expected significant interaction between the two factors (F(1, 145) = 11.75, p < .01). Specifically, for the INP local processing led to significantly better evaluation than global processing (Ms = 4.67 vs. 4.07; t(77) = 2.52, p < 0.05). For the RNP, global processing led to significantly better evaluation than local processing (Ms = 5.07 vs. 4.50; t(68) = 2.34, p < .05). The results for product evaluation are depicted in Figure 4-10.


4.67 4.50

4.07

5.07

3.50

3.75

4.00

4.25

4.50

4.75

5.00

5.25

5.50

INP RNP

Product Evaluation

Local processing

Global processing

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Purchase intention. For purchase intention, a 2x2 ANOVA showed no main effect of innovation (F(1, 145) = 2.42, p = .12) or processing (F(1, 145) = .10, p = .75), but showed a significant interaction between the two factors (F(1, 145) = 6.86, p < .05). For the INP, local processing led to significantly higher purchase intention than global processing (Ms = 3.00 vs. 2.31; t(77) = 2.15, p < 0.05; one-tailed). For the RNP, global processing led to a marginally higher purchase intention than local processing (Ms = 3.29 vs. 2.75; t(68) = 1.59, p < 0.10; one-tailed). The results for purchase intention are depicted in Figure 4-11.


3.00

2.75

2.31

3.29

1.50

1.75

2.00

2.25

2.50

2.75

3.00

3.25

3.50

INP RNP

Purchase Intention

Local processing

Global processing

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Usefulness. For usefulness, a 2x2 ANOVA showed a marginally significant main effect of innovation (F(1, 145) = 3.11, p < .10), but no main effect of processing (F(1, 145) = .74, p = .39). Further, this analysis showed a significant interaction between the two factors (F(1, 145) = 9.14, p < .01). For the INP, local processing led to marginally higher usefulness than global processing (Ms = 4.32 vs. 3.88; t(77) = 1.51, p < 0.10; one-tailed). For the RNP, global processing led to a significantly higher usefulness than local processing (Ms = 4.84 vs. 4.06; t(68) = 2.82, p < 0.01; one-tailed). The results for usefulness are depicted in Figure 4-12.

Figure 4-12: Usefulness Interaction (Experiment 2)

4.32

4.06

3.88

4.84

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5.00

INP RNP

Usefulness

Local processing

Global processing

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Innovation Product Evaluation 5.99 p < .05


Usefulness 3.11 p < .10



Usefulness .74 p = .39

Innovation x Processing Product Evaluation 11.75 p < .01

Purchase Intention 6.86 p < .05



INP RNP

Local processing

Global processing

Local processing

Global processing



Usefulness 4.32 3.88 4.06 4.84

4.3.3.6 Discussion

The aim of experiment 2 was to replicate the results of experiment 1, and examine why experiment 1 did not show the effect of processing styles on the perceived meaningfulness of different innovations. The results concerning hypotheses 1a and 1b could be replicated. More precisely, global processing improved evaluation of the RNP relative to local processing. At the same time, local processing improved evaluation of the INP relative to global processing. Furthermore, the results of experiment 2 also confirmed hypotheses 2a and 2b. That is, global processing led to higher purchase intentions for the RNP, whereas local processing led to a higher

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purchase intention for the INP. Finally, experiment 2 supports hypotheses 4a and 4b that global/local processing influences the perceived usefulness of different innovations. Specifically, global processing increased the perceived usefulness of the RNP, while local processing increased the perceived usefulness of the INP.

Two questions arise from the results of experiment 2. First of all, one could question whether global processing also increases evaluation of a very easy-to-use RNP. It may be the case that local processing leads only to less favorable evaluations of RNPs, because of a lower level of perceived ease-of-use. Secondly, one could question whether global processing even increases evaluation of very difficult-to-use RNPs. From the findings of experiment 2, it could be concluded that people in a global processing style generally do not mind low levels of ease-of-use.

4.3.4 Experiment 3


The purpose of experiment 3 was to examine whether global processing (relative to local processing) even increases the evaluation of RNPs that are characterized by extreme levels of ease-of-use. Specifically, experiment 3 examined the impact of processing styles on the evaluation of very easy-to-use RNPs and very difficult-to-use RNPs.

Research indicates that global processing is focused on the desirability of behavior, whereas local processing is focused on the feasibility of behavior (Liberman & Trope, 1998). In the context of innovation adoption, desirability refers to the benefits of an innovation. Feasibility denotes the obstacles associated with adoption. These obstacles particularly include learning costs (Arts et al., 2011). Following this, it may be the case that people in global processing concentrate on the benefits of an innovation, and place less emphasis on learning costs. In contrast, people in a local processing style may put more emphasis on learning costs and place less emphasis on the benefits.

From this, the following two conclusions could be drawn for the influence of global/local processing on the perception of RNPs: firstly, one may conclude that global/local processing has no influence on the perception of very easy-to-use RNPs. Specifically, as very easy-to-use RNPs are hardly associated with any learning costs, people in local processing can shift their attention from feasibility to desirability, and focus on the benefits of that innovation. In such a situation, global processing would not provide an advantage over local processing. Secondly, one may conclude that

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global processing (relative to local processing) leads to more favorable perception of RNPs, even if these are characterized by an extremely low level of ease-of-use. As people in global processing generally concentrate on the benefits of an innovation and neglect the costs associated with adoption, they may also ignore exceptionally high learning costs and, as a consequence, even perceive very difficult-to-use RNPs favorably.

However, contrary to these arguments, research indicates that global processing leads to a more favorable perception of very easy-to-use RNPs, but not of very difficult-to-use RNPs. According to NCT, local processing is associated with a general aversion to newness. This implies that people under local processing evaluate RNPs unfavorably irrespective of how easy or difficult their usage can be learned (Förster et al., 2010). Furthermore, the tendency of global processing to focus attention on the benefits of an innovation and neglect the learning costs associated with adoption does not mean that people in global processing even evaluate RNPs more favorably, if these are characterized by very high learning costs. That is, in case of very difficult-to-use RNPs, global processing is unlikely to provide an advantage (Herzenstein et al., 2007).

Arising this discussion, the following formal hypotheses are proposed:

H5: (a) Global processing (relative to local processing) increases the evaluation of easy-to-use RNPs, (b) but not of difficult-to-use RNPs.

H6: (a) Global processing (relative to local processing) increases the purchase intentions of easy-to-use RNPs, (b) but not of difficult-to-use RNPs.

H7: (a) Global processing (relative to local processing) increases the perceived usefulness of easy-to-use RNPs, but not of difficult-to-use RNPs.


The experiment was a 2 (innovation: easy-to-use RNP vs. difficult-to-use RNP) x 2 (processing: global vs. local) between-subjects design. A total of 161 participants of a representative German online panel (aged 18 to 40 years) completed the study (50.3% females, 49.7% males, average age of 29.4 years).

Participants were randomly assigned to either a easy-to-use condition or a difficult-to-use condition. In the easy-to-use condition, participants received a mock advertisement of a RNP whose usage was very easy to learn. In the difficult-to-use condition, participants received a mock advertisement of a RNP whose usage was very difficult to learn. In both conditions, participants were asked to evaluate the RNP. However,

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before participants received the mock advertisements of the RNP, they were randomly assigned to a local processing condition or a global processing condition. Similar to experiments 1 and 2, processing was primed by a version of the Navon-Letter task.


Participants received two mock advertisements of a RNP (see Figure 4-13). Similar to experiments 1 and 2, a light field camera was used as RNP. The company logo as well as all other brand identification information were removed prior to the experiment. The product was called XR-500 in all conditions. The mock advertisements included a headline, a picture, a short description, and a set of four product features. These elements were the same across conditions. The learning cost factor was manipulated by providing two types of usage information about the RNP. In the easy-to-use condition, participants were informed that all camera settings were made automatically. In contrast, in the difficult-to-use condition, participants were informed that all camera settings needed to be made manually.

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Easy-to-Use RNP Manipulation

Difficult-to-Use RNP Manipulation

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Furthermore, global and local processing was manipulated by a version of the Navon-Letter task similar to experiments 1 and 2. Therefore, all participants were presented with a series of 14 global letters that consisted of local letters (see Appendix). Participants in the global processing condition were asked to identify the global letters and type these in a text field below. By contrast, participants in the local processing condition were asked to identify the local letters and type these in the text field below.


Participants were asked to rate product newness on a three-point scale ranging from 1 (not very innovative, not very novel, not very original) to 7 (very innovative, very novel, very original). Usefulness was measured on a three-point scale ranging from 1 (totally disagree) to 7 (totally agree) (This product is advantageous, …is useful, …is an improvement). Ease-of-use was also measured on a three-point scale ranging from 1 (strongly disagree) to 7 (strongly agree) (Usage of this product may be learned with little effort, …is clear and understandable, …is easy). Product evaluation was measured by a three-point scale ranging from 1 (bad, dislike, poor) to 7 (good, like, excellent). Finally, purchase intention was measured by asking participants to indicate how interested they would be in purchasing the product on a scale ranging from 1 (not interested at all) to 7 (very interested). All scales were the same as the scales used in experiment 2. Table 4-7 provides a summary of all measures and their corresponding levels of reliability.

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Dependent Variables


Purchase Intention 1 - Zhao et al. (2011)

Usefulness 3 α = .93 Kulviwat et al. (2007)


Newness 3 α = .95 Zhao et al. (2009)

Ease-of-Use 3 α = .96 Kulviwat et al. (2007)


4.3.4.5 Results

Manipulation checks. The three product newness-related items were aggregated into a newness index (α= .95). As anticipated, participants rated the RNP as very new (M = 5.12; t(160) = 10.34, p < .001, compared with the scale midpoint). Further, participants rated the difficult-to-use RNP as significantly less easy to use than the easy-to-use RNP (Ms = 3.83 vs. 4.97; t(159) = 5.06, p < .001). Regarding global/local processing, all participants in the global condition correctly identified the global letters, while those in the local condition correctly identified the local letters.

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Product evaluation. For product evaluation, a two-way analysis of variance (ANOVA) showed no main effect of innovation (F(1, 157) = 1.08, p = .30) or processing (F(1, 157) = .06, p = .81). Furthermore, the analysis showed the expected significant interaction between the two factors (F(1, 157) = 5.34, p < .05). Specifically, the easy-to-use RNP was evaluated marginally better under global processing than under the local processing (Ms = 5.14 vs. 4.60; t(76) = 1.94, p < 0.10). In contrast, for the difficult-to-use RNP, global/local processing did not influence product evaluation (t(81) = .1.39, p = .17). The results for product evaluation are depicted in Figure 4-14.


4.60

4.87

5.14

4.43

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Easy-to-Use RNP Difficult-to-Use RNP

Product Evaluation

Local processing

Global processing

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Purchase Intention. For purchase intention, a 2x2 ANOVA showed no main effect of innovation (F(1,157) = 1.56, p = .21) but a marginally significant main effect of processing (F(1,157) = 3.56, p < .10). Furthermore, this analysis showed a marginally significant interaction between the two factors (F(1,157) = 2.82, p < .10). Specifically, for the easy-to-use RNP global processing led to higher purchase intentions than local processing (Ms = 4.43 vs. 3.44; t(76) = 2.62, p < 0.05). In contrast, for the difficult-to-use RNP, processing styles did not influence purchase intention (t(81) = .14, p = .89). The results for purchase intention are depicted in Figure 4-15.


Usefulness. For usefulness, a 2x2 ANOVA showed a marginally significant main effect of innovation (F(1,157) = 3.83, p < 0.10) but no main effect of processing (F(1,157) = .00, p = .99). Furthermore, this analysis did not show a significant interaction between the two factors (F(1,157) = 2.54, p = .11).

3.44 3.56

4.43

3.62

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3.75

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5.00


Purchase Intention

Local processing

Global processing

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Innovation Product Evaluation 1.08 p = .30





Usefulness .00 p = .99

Ease-of-Use x Processing Product Evaluation 5.34 p < .05


Usefulness 2.54 p = .11



Local processing

Global processing

Local processing

Global processing



Usefulness 4.55 4.89 4.47 4.13

4.3.4.6 Discussion

The third and final experiment of the empirical investigation examined hypotheses 5 to 7. The purpose of the experiment was to show how processing styles influence evaluation of RNPs that are either very easy to use or very difficult to use. The results of the experiment showed that global processing increases evaluation of very easy-to-use RNPs, but not of very difficult-to-use RNPs. Furthermore, the results also showed that global processing increases purchase intentions for very easy-to-use RNPs, but not of very difficult-to-use RNPs. These results support hypotheses 5 and 6. Contrary to

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expectations, the experiment did not show the expected interaction effect of processing styles on perceived usefulness. Accordingly, hypothesis 7 is not supported.

Together, the results of experiment 3 indicate that global processing even leads to a more favorable evaluation than local processing when a RNP is very easy to use. This implies that local processing relates to a general aversion to newness. Furthermore, experiment 3 demonstrates that global processing tolerates difficulty-of-use of RNPs only to a certain extent. At very high levels of difficulty-of-use, global processing does not lead to more favorable evaluation of RNPs.

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4.4 General Discussion

The following section provides a summarizing discussion of the empirical investigation of the influence of processing styles on perception of RNPs and INPs. The remainder of this chapter is structured as follows: the first section contains a brief review of the three experimental studies. The theoretical and managerial implications are drawn out in sections 2 and 3. Section 4 then discusses the limitations of the conducted studies. The purpose of the last section is to reveal potential avenues for future research.

4.4.1 Summary of Results

Research distinguishes between global and local processing as fundamental ways of perceiving the world. Global processing broadens perceptual and conceptual scope with the result that people focus on the overall impression of an object and think of it in more abstract terms. In contrast, local processing narrows perceptual and conceptual scope with the result that people focus on an object’s constitutive elements and conceive it in more concrete terms (Förster, 2012). Thereby, global processing promotes assimilation, whereas local processing promotes contrast. Further, global/local processing improves performance in different kinds of problem solving tasks. More precisely, global processing enhances creative thinking, while local processing enhances analytical, detailoriented thinking. Finally, global processing is more suitable for dealing with novel events, whereas local processing is more suitable for dealing with familiar events (Förster & Dannenberg, 2010).

Following these considerations, the present study examined the impact of processing styles on perception of different kinds of innovations. Processing styles were manipulated on the basis of simple visual primes. Specifically, global processing was evoked by focusing individuals on the overall Gestalt of a visual stimulus, whereas local processing was evoked by focusing individuals on the details of that same stimulus. The empirical results showed that these subtle manipulations are already sufficient to fundamentally change the perception of RNPs and INPs.

Specifically, global processing was found to lead to higher perceived usefulness, better evaluation, and stronger purchase intentions of RNPs. Thereby, the results indicate that global processing even tolerates a certain degree of difficulty-of-use, a typical characteristic of RNPs. In contrast, local processing was found to lead to higher perceived usefulness, better evaluation, and stronger purchase intentions of INPs. The findings further suggest that local processing is characterized by a general aversion to

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newness. More precisely, local processing will even lead to less favorable evaluation of RNPs if these are very easy to use.

4.4.2 Theoretical Contributions

The present study provides important theoretical contributions. First of all, it contributes to innovation perception theory. Previous research focused primarily on the ‘what’ of innovation perception (see chapter 3). However, recent findings suggest that innovation perception is also determined by the ‘how’. That is, customers may perceive the same innovation completely different depending on the way with which they think about that innovation (Ulkumen, Chakravarti, & Morwitz, 2010). Research refers to these ways of thinking as processing styles (Förster & Dannenberg, 2010). The present study represents the first study that examined the influence of global and local processing styles on innovation perception. The results suggest that even subtle visual primes such as Navon-Letters can influence these processing styles and fundamentally change the way people perceive these RNPs and INPs.

Secondly, the present study contributes to NCT, which evolved from psychological research on novelty perception. Research has only recently started to investigate the implications of this theory for consumers (Förster et al., 2009). Thus, this study is among the first to apply NCT to the marketing context. Thereby, the empirical findings extend NCT in an important way. So far, NCT suggests that local processing favors response to familiar stimuli, whereas global processing favors response to novel stimuli. However, the empirical results of this study show that global processing does not favor response to novel stimuli under any circumstances. A global processing style will improve evaluation of RNPs only as long as individuals do not perceive them as too difficult to use. That is, at extreme levels of perceived difficulty-of-use, global processing does not lead to more favorable evaluation than local processing.

4.4.3 Managerial Contributions

Processing styles may not only be evoked by Navon-Letters, but also by various real-life variables (see Table 4-10). With this in mind, the present study has important implications for managing innovation perception.

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Table 4-10: Primes of Global/Local Processing (Förster, 2012)

Global Processing Local Processing

Good Mood

Interoceptive Approach (e.g., Arm Flexion)

Conceptual Approach

Promotion Focus

Blue Color

Distal Events

High Power

Love

Novelty

Obstacles

Interdependent Selves

Bad Mood

Interoceptive Avoidance (e.g. Arm Extension)

Conceptual Avoidance

Prevention Focus

Red Color

Proximal Events

Low Power

Lust

Familiarity

No Obstacles

Independent Selves

Firstly, implications for innovation communication can be derived. Research suggests that global/local processing may be evoked by colors. For instance, the color blue evokes global processing, and the color red evokes local processing (Förster & Dannenberg, 2010). Hence, color schemes in innovation communication can be adjusted so that global processing is evoked for RNPs and local processing is evoked for INPs. More precisely, perception of RNPs can be improved by using blue color schemes to evoke global processing, whereas perception of INPs can be improved by using red color schemes to evoke local processing.

Secondly, implications for product positioning of innovations can be derived. Research suggests that a promotion focus evokes global processing, whereas a prevention focus evokes local processing (Förster & Dannenberg, 2010). A promotion focus is associated with advancement, growth, and accomplishment. In contrast, a prevention focus is concerned with security, safety, and responsibility (Crowe & Higgins, 1997).

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Following this, RNPs should be positioned so that people perceive them as a means for achieving their dreams and aspirations, whereas INPs should be positioned in such a way that people perceive them as means for better fulfilling their duties and obligations. Similarly, implications for the positioning of innovations may be derived from self-concepts. Research distinguishes between interdependent self-concepts which evoke global processing and independent self-concepts which evoke local processing. Interdependent self-concepts refer to groups of people. When speaking of ‘we’ and ‘us’, interdependent self-concepts are addressed. In contrast, independent self-concepts refer to the individual. When speaking of ‘I’ and ‘me’, independent self-concepts are addressed (Förster & Dannenberg, 2010). Thus, when introducing RNPs to the market, product positioning should address interdependent self-concepts. It should be revealed how RNPs consider the needs of the collective, such as lower CO2 emissions of a car. When bringing INPs into the market, product positioning should address independent self-concepts. In other words, it should be revealed how INPs consider individual needs, such as lower fuel costs of a car.

4.4.4 Limitations

As with any experimental investigation, the present study involves some limitations with regard to external validity. In academic research, external validity depends in part on whether experimental results may be replicated in other settings (Winer, 1999). One might worry that this study used digital cameras only to examine the effect of processing styles on innovation perception. Whether the use of a single product category is adequate depends on the underlying research goals (Calder, Phillips, & Tybout, 1981). That is, if the purpose of this research is to apply the findings to specific real-world situations, then the use of a single product category is not appropriate. However, if the purpose of the research is to develop scientific theories that allow a more general understanding of real-world phenomena, then the use of a single category is acceptable.

For the purpose of the present study, the sole use of innovations from a single product category seems appropriate. The primary research goal was to extend theoretical understanding of processing styles and innovation perception. Digital cameras seemed to be very well suited for such an investigation. That is, digital cameras represent familiar products that can potentially be perceived as rather new (Herzenstein et al., 2007). By using digital cameras, it was possible to use RNPs and INPs that were highly comparable and, therefore, differed primarily on the newness and ease-of-use dimensions.

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The external validity of the present study might be criticized for another reason. Research suggests that external validity depends also on whether the research context is realistic and, therefore, whether the results are also likely to hold in a more natural environment (Winer, 1999). In all three of the experiments, Navon-Letters were used as a prime for processing styles. That is, participants were required to follow detailed instructions to evoke global/local processing. Such a prime is difficult to integrate into a ‘real-live’ setting. In other words, the application of Navon-Letters in the actual communication of innovations is very unlikely to occur.

However, for the purpose of this study, the use of Navon-Letters seemed appropriate. Navon-Letters represent a prime of global/local processing that is broadly established in academic research (Förster & Dannenberg, 2010). If applied adequately, Navon-Letters solely evoke global/local processing, excluding other potential influences. Hence, this procedure was very well suited for revealing the actual effect of different processing styles on the perception of RNPs and INPs.

4.4.5 Future Research

One potential avenue for further research arises from the fact that, in addition to newness and ease-of-use, innovations may be characterized by a multitude of other kinds of variables. For example, in future research, a distinction could be made between RNPs and INPs that provide different kinds of customer benefits. Zhao, Hoeffler, & Dahl (2011) found, for example, that different kinds of mental simulation have a different impact on the evaluation of functional versus hedonic innovations. Thus, in future research on the influence of processing styles on innovation perception, a distinction could be drawn between innovations that primarily provide functional benefits and innovations that primarily provide hedonic benefits. Alternatively to customer benefits, innovations could also be distinguished on the basis of the specific customer goals these innovations address. Research distinguishes between promotion goals associated with achievement, and prevention goals associated with safety (Higgins, 1997). RNPs and INPs may address either of these goals. It could be the case, for example, that global processing particularly improves evaluation of RNPs that address promotion goals, whereas local processing particularly improves evaluation of INPs that address prevention goals.

Another potential route for further research is the use processing primes other than Navon-Letters. Processing styles are evoked by a variety of real world variables such as colors, power, or self-concepts (Förster, 2012). These variables seem to be more

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suitable for application in innovation communication rather than Navon-Letters. It could be investigated whether these variables have a similar effect on innovation perception. Thereby, it would also be interesting to investigate, whether different primes of processing styles work only prior to the presentation of an innovation, or if they also work directly in innovation presentation. More precisely, in the present study, participants were first primed with global/local processing styles via the Navon-Letter task, and then received the mock-advertisements of the innovations. In contrast, other primes of global/local processing may be integrated directly into the mock-advertisement. For example, using red color schemes in mock-advertisements may directly evoke local processing, whereas using blue color schemes in mock-advertisements may directly evoke global processing (Förster & Dannenberg, 2010).

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5 Conclusion The fundamental aim of this dissertation was to provide a comprehensive analysis of innovation perception from a customer perspective. The dissertation began with a conceptualization of customer perception of innovations. It was found that customers perceive innovations in terms of two dimensions: perceived newness and perceived meaningfulness (Sethi et al., 2001). Perceived newness is the degree to which an innovation is understood as deviating from existing practice (Amabile, 1983; Andrews & Smith, 1996; Jackson & Messick, 1965; Szymanski et al., 2007). It may arise from the technological newness of an innovation and/or the newness of the benefits an innovation provides (Chandy & Tellis, 1998, 2000). Newness is not perceived as good or bad per se. People may regard newness either as an opportunity and respond favorably to it, or they may perceive it as a potential threat and respond unfavorably to it. The question of which of these two outcomes will occur depends on people’s situation-specific motivational state. If they are driven by a need for accomplishment, they are attracted to novel events. In contrast, if people are driven by a need for safety and predictability, they avoid novel events. Research suggests that the perception of newness is also determined by people’s situation specific attitude towards newness. This attitude can, on the one hand, relate to Heraclit’s well-known aphorism, ‘you could never step twice in the same river; for other waters are ever flowing onto you’. On this view, almost everything may be perceived as new simply by adopting a slightly different perspective. In contrast, people may adopt a ‘been there, done that’ attitude in which they perceive almost anything as familiar and boring (Förster et al., 2010). Which of these attitudes predominates in a given situation depends on a variety of context factors that can be influenced through appropriate design and communication of innovations (Förster, 2012).

The second dimension of innovation perception, perceived meaningfulness, denotes the degree to which an innovation is perceived as desirable and feasible (Arts et al., 2011; Trope & Liberman, 2003). Perceived desirability arises from the benefits an innovation provides over and above those provided by existing alternatives (Rogers, 2003). An innovation can provide benefits in terms of superior functional or instrumental value, hedonic or experiential value, and/or symbolic or expressive value (Smith & Colgate, 2007). The perceived feasibility of an innovation is based on the cost and other sacrifices that may occur during its purchase, ownership, and use (Smith & Colgate, 2007). This implies that a high level of desirability is not enough for an innovation to be perceived as meaningful. Perceived meaningfulness can only be achieved if an innovation is highly desirable, and at the same time, sufficiently

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feasible. That is, people should be able to obtain the benefits of an innovation with reasonable financial and non-financial resources.

Following the conceptualization of innovation perception, this dissertation then moved to an extensive literature review of the key processes of innovation perception. These included innovation recognition and innovation assessment. Innovation recognition refers to the initial interactions with an innovation. During the innovation recognition phase, people should form a positive awareness of an innovation that motivates them to gather further information about it. The innovation recognition phase will only lead to a favorable response if an innovation is clearly differentiated from existing practice (Ziamou & Ratneshwar, 2003). For incrementally new products, this represents a considerable hurdle. Because incrementally new products provide only minor improvements, people frequently perceive them as literally the same as existing alternatives (Chandy & Tellis, 1998, 2000). Besides differentiation, innovation recognition is also determined by whether people understand what an innovation is and does. Such understandability represents a considerable hurdle for really new products, which represent groundbreaking departures from existing practice (Veryzer, 1998a). As people lack prior knowledge of really new products, they frequently overlook essential aspects of these innovations and fail to grasp them (Moreau, Lehmann, & Markman, 2001; Moreau, Markman, & Lehmann, 2001).

In comparison, in case of innovation assessment, people get actively involved with an innovation, and gather information about the personal consequences associated with adoption. During this process, people ask themselves whether an innovation is actually capable of providing desirable outcomes in their everyday lives (Venkatesh et al., 2012). More precisely, in the innovation assessment process, people try to ascertain how productive an innovation will be in their own context; how easy it will be for them to make use of the innovation; what kinds of investments they need to make before they can operate the innovation; or whether important others appreciate or depreciate their adoption of the innovation. All of these aspects represent potential risks that people need to address before they can make an informed decision about an innovation (Castaño et al., 2008). Consequently, innovation assessment is strongly determined by the degree to which people develop favorable or unfavorable mental scenarios associated with adoption of an innovation. Typically, such scenarios arise from the way in which an innovation is communicated (Ziamou & Ratneshwar, 2002). Innovation assessment is also determined by whether previously considered hypothetical scenarios about an innovation are confirmed or disconfirmed. Specifically, the disconfirmation of favorable scenarios is critical. Frequently,

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innovations are presented in a way that suggests they are easily operated. However, oftentimes this is not the case. If they encounter difficulties in operating the device, people are likely to respond very unfavorably to the innovation (Wood & Moreau, 2006). The damage arising from this situation is typically irreparable. In most of these cases, such negative experiences generate a large amount of negative word of mouth from which an innovation is unlikely to recover (Moldovan et al., 2011).

Besides innovation recognition and innovation assessment, this dissertation identified innovation comprehension as a new, highly relevant, aspect of innovation perception. While innovation recognition and innovation assessment are primarily concerned with the ‘what’ of innovation perception, innovation comprehension is concerned with the ‘how’ of innovation perception. More precisely, innovation comprehension refers to the way in which people think about an innovation. Building on social psychology literature, this dissertation identified global processing and local processing as two distinct ways of thinking that have considerable influence on innovation perception (Förster et al., 2010). Specifically, it was found that already subtle visual primes (i.e. Navon-Letters) are sufficient to evoke global and local processing, thereby, fundamentally changing the way in which people perceive really new and incrementally new products. Specifically, global processing was demonstrated to significantly improve responses to really new products, whereas local processing was found to significantly improve responses to incrementally new products.

Because social psychology literature provides a variety of variables that may evoke global and local processing, such as colors, self-concepts, and regulatory focus (Förster, 2012), innovation comprehension has extensive managerial implications for communicating and positioning innovations. Additionally, innovation comprehension is of high theoretical relevance. This dissertation represents the first empirical investigation of the influence of global/local processing on innovation perception. The promising empirical results provide numerous avenues for future research. Accordingly, this dissertation represents an important basis for further inquiry into the influence of processing styles on customer perception of innovations.

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7 Appendices

7.1 Appendix 1: Navon-Letters used in Experiment 1

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Curriculum Vitae

Name Dennis Vogt

Date of Birth 4th of October 1981 in Munich, Germany

Education

2010-2013 University of St. Gallen, Switzerland

Doctoral Candidate in Business Administration

2004-2008 Technical University Munich, Germany

Diploma Studies in Business Administration and Engineering

2002-2004 University of Bayreuth, Germany

Diploma Studies in Business Administration

1992-2001 Michaeli-Gymnasium Munich, Germany

Abitur

Work Experience

2010-2013 Center for Customer Insight, University of St. Gallen, Switzerland

Research Associate

2009-2010 Raffel GmbH Corporate Development, Munich, Germany

Junior Consultant

2009 BMW AG, Munich, Germany

Internship

2008 goetzpartners Management Consultants, Munich, Germany

Working Student

2007 goetzpartners Corporate Finance, London, United Kingdom

Internship

2004 Roland Berger Strategy Consultants, Munich, Germany

Working Student

2002 Audi AG, Ingolstadt, Germany

Internship

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