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Valentin Gattol
Valentin Gattol
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R E L AT E D N E S S I N M E N TA L
R E P R E S E N TAT I O N S O F P R O D U C T S
Cover design by Milene Guerreiro Gonçalves Typesetting by Gabriel A.D. Lopes
Printed by CPI Wöhrmann Print Service ISBN 978-94-6203-500-3
mental representations of products
Proefschrift
ter verkrijging van de graad van doctor aan de Technische Universiteit Delft,
op gezag van de Rector Magnificus prof.ir. K.C.A.M. Luyben, voorzitter van het College voor Promoties,
in het openbaar te verdedigen op vrijdag 20 december 2013 om 10:00 uur
door
Valentin GATTOL
Magister rerum naturalium, Universität Wien, Oostenrijk geboren te Bad Ischl, Oostenrijk.
Prof.dr. J.P.L. Schoormans Copromotor:
Dr. M. Sääksjärvi
Samenstelling promotiecommissie:
Rector Magnificus, voorzitter
Prof.dr. J.P.L. Schoormans, Technische Universiteit Delft, promotor
Dr. M. Sääksjärvi, Technische Universiteit Delft, copromotor
Prof.dr. H. Robben, Nyenrode Business Universiteit
Prof.dr.ir. J. Hellendoorn, Technische Universiteit Delft
Prof.dr. H. de Ridder, Technische Universiteit Delft
Prof.dr. H.J. Hultink, Technische Universiteit Delft, reservelid
list of figures viii
list of tables xi
acknowledgments xiii
1 introduction 1
1.1 Grounding the notion of relatedness in concept
the-ories . . . 3
1.1.1 Concepts and mental representations . . . . 4
1.1.2 Earlier concept theories: From defining at-tributes to prototypes and exemplars . . . . 4
1.1.3 Frame theory and perceptual symbol sys-tems theory . . . 6
1.2 Relatedness in mental representations of products . 10 1.3 Purpose of the thesis . . . 12
1.4 Outline of the thesis . . . 13
2 feature relations and their effects on prod-uct value and learning costs 17 2.1 Introduction . . . 17
2.2 Features and feature relations . . . 19
2.2.1 Study 1 . . . 21
2.2.2 Results . . . 21
2.3 A priori relatedness in products: Differences be-tween incrementally and radically new features . . 23
2.3.1 Pre-Studies . . . 24
2.3.2 Study 2 . . . 27
2.3.3 Results . . . 28
2.4 General discussion . . . 33
2.4.1 Limitations and suggestions for further re-search . . . 35
2.4.2 Conclusions and managerial implications . . 35
3 adding to or deleting features from new prod-ucts? then consider both goal congruence and goal relatedness 39 3.1 Introduction . . . 39
3.2 Theoretical background and propositions . . . 42
3.2.1 Consumption goals . . . 42
3.2.2 Goal congruence and goal relatedness . . . . 42
3.2.3 Feature addition versus feature deletion . . 46
3.3 Study 1: Adding features to a product . . . 48
3.3.1 Methodology . . . 48
3.3.2 Results . . . 50
3.4 Study 2: Deleting features from a product . . . 53
3.4.1 Methodology . . . 53
3.4.2 Results . . . 54
3.5 General discussion . . . 57
3.5.1 Theoretical implications . . . 59
3.5.2 Managerial implications . . . 60
3.5.3 Limitations and suggestions for further re-search . . . 61
3.5.4 Appendix A . . . 62
3.5.5 Appendix B . . . 63
3.5.6 Appendix C . . . 63
3.5.7 Appendix D . . . 65
4 “it’s time to take a stand”: depicting crosshairs can indeed promote violence 67 5 evaluating new product concepts under low versus high cognitive loads—evidence for a brand effect? 71 5.1 Introduction . . . 71
5.2 Theoretical framework and hypotheses . . . 73
5.2.1 Two cognitive systems: automatic versus con-trolled processing . . . 73
5.2.2 Brands in consumer knowledge structures . 74 5.2.3 Hypotheses . . . 76
5.3 The treadmill study . . . 76
5.3.1 Method . . . 76
5.3.2 Results . . . 81
5.4 General Discussion . . . 88
5.5 Limitations and suggestions for future research . . 90 6 extending the implicit association test (iat):
assessing consumer attitudes based on
6.1 Introduction . . . 93
6.2 Indirect versus direct measures . . . 95
6.2.1 Attitude measurement and the Implicit As-sociation Test (IAT) . . . 95
6.2.2 Conscious and less conscious manifestations of attitudes . . . 96
6.2.3 Design of the IAT . . . 97
6.3 The multi-dimensional Implicit Association Test (md-IAT) . . . 99
6.4 Materials and methods . . . 100
6.4.1 Study 1 . . . 100 6.4.2 Study 2 . . . 108 6.5 Results . . . 109 6.5.1 Study 1 . . . 109 6.5.2 Study 2 . . . 116 6.6 General discussion . . . 120 7 general discussion 125 7.1 Main findings . . . 126 7.2 Theoretical contribution . . . 131
7.2.1 Positive effects of relatedness on perceptions of product value . . . 132
7.2.2 Relatedness is relevant for feature additions and feature deletions . . . 133
7.2.3 Relatedness is more relevant for radically than incrementally new features . . . 133
7.2.4 Visual cues can be powerful in priming re-latedness in mental representations . . . 134
7.2.5 Multi-dimensional extension of the Implicit Association Test (IAT) . . . 135
7.3 Managerial implications . . . 136
7.4 Limitations and suggestions for future research . . 138
references 143
summary 161
samenvatting 163
Figure 1.1 Example of the concept bird as represented by several attributes, attribute values and frames. Adapted from Frames, concepts, and conceptual fields (p. 53), by L. W. Barsalou, 1992, Hillsdale, NJ: Lawrence Erlbaum As-sociates. Copyright 1992 by Lawrence
Erl-baum Associates Inc. . . 7
Figure 1.2 Example of constraints in the frame for
transportation. Adapted from Frames, con-cepts, and conceptual fields (p. 38), by L. W. Barsalou, 1992, Hillsdale, NJ: Lawrence Erl-baum Associates. Copyright 1992 by Lawrence
Erlbaum Associates Inc. . . 9
Figure 2.1 Ease of seeing a relation between existing
and new features . . . 26
Figure 2.2 Learning costs . . . 30
Figure 2.3 Product value . . . 30
Figure 2.4 Overview of the variables in the mediation
analyses . . . 31
Figure 3.1 The effect of goal congruence and goal
re-latedness on the incremental value of added features (Study 1) . . . 52
Figure 3.2 The effect of goal congruence and goal
re-latedness on the incremental value of added features (Study 1) . . . 52
Figure 3.3 The effect of goal congruence and goal
re-latedness on the decrease in value from deleted features (Study 2) . . . 56
Figure 3.4 The effect of goal congruence and goal
re-latedness on the decrease in value from deleted features (Study 2) . . . 56
Figure 3.5 Product scenario used in one of the con-ditions in Study 1 (namely, a hedonic base with a congruent and related feature added) 62
Figure 3.6 Product scenario used in one of the
condi-tions in Study 2 (namely, a utilitarian base with a congruent and non-related feature removed) . . . 63
Figure 4.1 “Crosshairs map” of the Dutch province
of Utrecht . . . 68
Figure 4.2 “Plain circles map” of the Dutch province
of Utrecht . . . 68
Figure 5.1 Examples of the stimuli in the product
eval-uation task for two of the four conditions on the treadmill . . . 80
Figure 5.2 The effect of cognitive load and type of
product presentation on overall evaluation (MP3 player) . . . 86
Figure 5.3 The effect of cognitive load and type of
product presentation on overall evaluation (Smartphone) . . . 86
Figure 5.4 The effect of cognitive load and type of
product presentation on overall evaluation (Pocket camera) . . . 87
Figure 5.5 The effect of cognitive load and type of
product presentation on overall evaluation (E-reader) . . . 87
Figure 6.1 Images used to represent the brands AUDI,
BMW, and FORD, varying according to the factor BRAND CUE. . . 104
Figure 6.2 Study 1 ("AUDI vs. FORD”): D measure
means for every single IAT (N = 26) result-ing from combinations of the two factors ATTRIBUTE DIMENSION and BRAND CUE.114
Figure 6.3 Study 2 (“AUDI vs. BMW”): D measure means for every single IAT (N = 26) result-ing from combinations of the two factors ATTRIBUTE DIMENSION and BRAND CUE.119
Table 3.1 Changes in perceived product value when
adding and deleting features . . . . 65
Table 5.1 Results of the ANOVA for the dependent
variable overall evaluation index for each
of the four products. . . 84
Table 5.2 Means (standard deviations in
parenthe-ses) based on the dependent variable over-all evaluation index for each of the four
products. . . 85
Table 6.1 Word stimuli for each category of the six
bipolar attribute dimensions, translated into English (original German terms used in
the study are given in parentheses). . . 102
Table 6.2 Adapted D measure algorithm relying on
the dynamic outlier criterion. . . . 110
Table 6.3 Study 1: Summary of all 18 single IATs
with factors ATTRIBUTE DIMENSION and
BRAND CUE (6 ⇥ 3). . . 112
Table 6.4 Split-half estimates of reliability for each
of the 6 x 3 IATs in Study 1 and Study 2. 113
Table 6.5 Study 1: Estimates of convergent validity
(simple linear regressions for all six
dimen-sions). . . 115
Table 6.6 Study 2: Summary of all 18 single IATs
with factors ATTRIBUTE DIMENSION and
BRAND CUE (6 ⇥ 3). . . 117
Table 6.7 Study 2: Estimates of convergent validity
(simple linear regressions for all six
dimen-sions). . . 120
Relatedness is not only an important notion to consider in men-tal representations of products. Most evidently, it shows itself in the many relationships we cultivate with people. In the last few years working on this thesis, I benefited greatly from relating to a number of people.
First and foremost, I would like to express my greatest appre-ciation to my supervisors Jan Schoormans and Maria Sääksjärvi. Jan: as my promotor you have taught me the virtues of focusing on promotion rather than prevention. Your optimistic, humorous and witty attitude towards life, the many discussions we had on topics related and unrelated to my thesis, your creative mind and your sharp senses, have all been a great inspiration to me. Dank je wel! Maria: as your first PhD student I would like to thank you wholeheartedly for entrusting me with that former topic of yours. Your continuous support and encouragement, your many valuable suggestions and constructive comments, have all greatly benefited the quality of this thesis. Många tack!
I would also like to thank two bright minds that I had the opportunity to work with during the last years. Claus-Christian Carbon: your enthusiasm and devotion to research never cease to impress me. Tripat Gill: your grasp of complex issues and your ability to always see the wood for the trees are admirable.
My gratitude extends also to another set of bright minds: Os-car Person and Jaap Daalhuizen. Besides many shared interests— Swedish cuisine and engaging in manly conversation, among others—we struck up an exciting collaboration at work, which I continue to enjoy to this day. I am also a proud graduate of the Oscar School of Design. Jaap: thank you further for the Dutch translation work.
Moreover, I would like to thank my colleagues and friends from the department. You have all made my time in Delft very worthwhile! Special thanks go to Milene Guerreiro Gonçalves and Ana Valencia Cardona: I thoroughly enjoyed your company;
it will be tough (read: impossible) to find better officemates ever! Milene: on top of that, thank you for the most beautiful cover I could wish for and the drawings of stimuli and figures in Chap-ters 1 and 5, all testaments to your wicked design skills. Moreover, I have fond memories of coffee breaks or the occasional beer and barhapje with my next-door officemates Janneke Blijlevens, Fer-nando Del Caro Secomandi, Nik Shahman, and Silje Dehli. Many thanks also to Agnes Tan and her wonderful team at the PEL.
Sónia da Silva Vieira (special return hug), Gabriel A.D. Lopes, Emilie Yane Lopes, Annegien Tijssen, Vessela Chakarova, Arturo Tejada, Tânia Veiga, Mathieu Gerard: thank you for your friend-ship, many great dinners, and fun evenings. Gabriel: I am beer-normously indebted to you for lending me your magical typeset-ting powers—thank you dearly!
Finally, I wish to thank my girlfriend and my family for their love, support, and encouragement during the years: Karo; Mama, Papa, Iris, Oliver; Christoph; Karen; David, Greta, and Fynn. Vienna, November 2013
1
I N T R O D U C T I O N
Today’s mature markets place huge demands on companies to in-novate and improve their products (Cooper, 2011). A commonly adopted strategy is to add different and new features to estab-lished products (Levitt, 1980; Porter, 1985). Through such a strat-egy companies hope to gain or secure a competitive advantage, bring in new customers, or fill a gap in the market—which all serve the purpose of increasing or maintaining their share of the market. This strategy, however, does not always bring about good products or satisfied consumers. Adding new features to a previ-ously successful product may sometimes cause consumers’ per-ceptions and evaluations to change in directions unintended by the company. Consumers may question the value of a new feature (Simonson, Carmon, & Ocurry, 1994) or fear that it may compli-cate usage (Mukherjee & Hoyer, 2001). Just making something “different” and “new”, it seems, is not enough.
Software giant Microsoft, for example, has fallen prey to such unintended and unwanted consequences when it introduced its new operating system Windows Vista in 2007. With every new version of Windows, Microsoft ended up packing more and more features into the same program, while seeking greatest compati-bility with previous versions of software and hardware native to the Windows environment. Over time, while becoming more and more advanced, it has also become bulky, cluttered, incoherent, and in general, more complex in its use (Pogue, 2009). For ex-ample, the Windows Vista Aero appearance, which was intended to offer an enhanced visual experience through its lightweight and translucent windows, actually ended up slowing down com-puters for many users due to its high demands on the hardware (Kingsley-Hughes, 2009). Fast-forward to 2012, Microsoft is still struggling to get it right with its newest operating system Win-dows 8, which this time around comes as a “muddled mishmash” of two operating systems in one, designed for use both with
a mouse and keyboard and for touch screens (Pogue, 2012, p.
B1)1. While the design and programming behind Windows 8 is
supposedly outstanding, there still remain problems in usability (Garfinkel, 2013). Users are torn between the old and the new en-vironment of interacting with the hybrid OS and have trouble un-derstanding how the two are related. For example, the new Start Screen that replaced the old Start menu now boasts large tile-like icons designed for touch interfaces, yet not all of the programs
and all of the functionality can be accessed from it.2
As the example above illustrates, companies do not always con-sider (the effects of) relatedness in consumers’ perception of prod-ucts. Relatedness, described as a state where features (or prop-erties) in consumers’ mental representations are connected or linked with one another, has received scant attention both among researchers and practitioners involved in the development and marketing of (new) products.
Most of the work in this thesis derives from the idea that re-latedness can explain an important part of the variability in how consumers view products. Utilizing both Barsalou’s frame the-ory (1992) and Barsalou’s thethe-ory of perceptual symbol systems (1999) as a theoretical basis, it deals with relatedness from a mul-titude of perspectives: how consumers perceive the various prop-erties or qualities inherent to products to be related; how related-ness may change the way consumers represent products in their minds; and how these representations in consumer knowledge structures may influence the inferences consumers draw about a product, which in turn are known to influence consumers’ evalu-ations and choices.
The remainder of this introductory chapter is structured as
fol-1 David Pogue, personal-tech columnist for the New York Times, whimsically imag-ined the following conversation taking place at Microsoft: “PC sales have slowed,” some executive must have said. “This is a new age of touch screens! We need a fresh approach, a new Windows. Something bold, fluid and finger-friendly.” “Well, hold on,” someone must have countered. “We can’t forget the 600 million regular mouse-driven PCs. We also need to update Windows 7 for them!” And then things went terribly wrong. “Hey, I know!” somebody piped in. “Let’s combine those two Windows versions into one. One OS for all machines. Everybody’s happy!” (Pogue, 2012, p. B1). 2 In reaction to the mounting criticism, and more than half a year after its initial
release, Microsoft recently confirmed that it will bring back the start button in its next update to Windows 8.1 (Warren, 2013).
lows: first, I will ground the notion of relatedness in theory from psychology on mental representations of concepts; second, I will show how the notion of relatedness can be applied to consumers’ mental representations of products (mainly in the context of new products); third, I will state the purpose of the thesis; and last, I will provide an outline of the thesis, describing how each chapter incorporates some aspect of relatedness.
1.1 grounding the notion of relatedness in concept theories
According to Murphy (2002, p. 1) “concepts are the glue that holds our mental world together.” They are the mental equiva-lent of real-world categories—representations of classes of objects or entities in people’s minds (Eysenck & Keane, 2005; Murphy, 2002). The notion of relatedness, at its core, describes a state where things are connected or linked to one another in a particular man-ner rather than being unconnected or independent. Relatedness can be observed in many situations, people, objects, and prod-ucts. It is part of the physical world around us but extends also to the mental world inside of us, to our thinking. In this mental world, concepts are the basic building blocks, along with features, and relations.
In this section I review the extensive literature on concepts in order to provide a theoretical grounding for the notion of re-latedness. First, I will introduce some key terms that are used throughout this thesis (such as concepts, categories, mental repre-sentations, and features) and explain why concepts are so impor-tant in our thinking; second, I will review earlier concept theories (such as classical, prototype, and exemplar theory); third, I will review more recent work that merges conceptual with perceptual accounts of concepts (such as frame theory and perceptual sym-bol systems). The notion of relatedness will be grounded in this more recent work.
1.1.1 Concepts and mental representations
Concepts are the mental equivalent of real-world categories. For example, based on our observations of the world around us we might develop concepts for (countless) categories such as various foods, animals, people, and things. Concepts themselves consist of sets of features (Murphy, 2002). Such features may represent both concrete (e.g., size or color) and abstract properties of a con-cept (e.g., quality or complexity, Tversky, 1977). Other terms that are used interchangeably in the literature (and also throughout this thesis) are attribute, characteristic, part, and property. For ex-ample, the concept cake, which itself belongs to the superordinate category of food, may include features such as flour, sugar, eggs, butter, milk, and water. Concepts are important because they help us to interpret newly encountered objects, to relate them to sim-ilar objects in memory, and ultimately to draw inferences about those objects. For instance, we do not have to analyze each new exemplar of a tomato just to know that it is edible (Murphy, 2002). Relatedness is found in the various relations between features. For example, we know about the particular relation between color and ripeness—that a green tomato is not ready-to-eat, whereas a red tomato is. Features and relations are part and parcel of concepts—once we have formed a mental representation of an object (like the tomato), this representation can be accessed from knowledge and guide our understanding (see Murphy, 2002). 1.1.2 Earlier concept theories: From defining attributes to prototypes
and exemplars
Theories about how concepts organize our thinking have a long history. They date as far back as to Aristotle, who was a repre-sentative of the so-called classical view, which assumed that con-cepts are mentally represented as definitions (defining attributes); these definitions, in theory, should allow for clear-cut and unam-biguous interpretations of word meaning and category member-ship (Eysenck & Keane, 2005; Murphy, 2002). One problem with the view of concepts as defining attributes is that it does not cope well with category fuzziness (Murphy, 2002): if a dog is defined
as “things that have four legs, bark, have fur, eat meat...” (p. 17), what if you come across an exemplar that has lost one of its legs in an accident or one that does not bark? Is it still a dog? An-other problem of the classical view is that it does not distinguish between more or less typical exemplars: rather, once an object or entity fulfills the necessary and sufficient criteria of category membership, all members are viewed as similarly good exam-ples of the category (Murphy, 2002). The empirical data, however, leave no doubt that category members differ in typicality. For ex-ample, people generally agree that a robin is a better example of the category bird than an ostrich or a penguin (see Table A1, Rosch, 1975, p. 232).
In the 1970s, the so-called similarity-based views, prototype theory (Rosch, 1975; Smith & Medin, 1981) and exemplar theory (Medin & Schaffer, 1978), quickly superseded the constricted clas-sical view for its lack of explaining basic phenomena in rization. Similarity-based views assume that items can be catego-rized by means of their similarity to a conceptual representation (Medin, Goldstone, & Gentner, 1990; Morel, 2000; Murphy, 2002). In prototype theory this similarity is determined by comparing a particular instance to a “prototype” (a kind of summary rep-resentation of a category), whereas exemplar theory compares a particular instance to “stored instances” known to belong to the same category (Murphy, 2002). While the similarity-based views are well suited for describing concepts in isolation, accounting both for category fuzziness and typicality, they fall short once one considers concepts as part of a larger conceptual space. For example, the similarity-based views cannot explain goal-derived categories such as “things to take on a picnic”, which are con-structed ad hoc and consist of items that bear little resemblance in their features (e.g., a blanket, a basket, drinking cups...) but that are all connected to a specific goal (Barsalou, 1983, 1985; Rat-neshwar, Barsalou, Pechmann, & Moore, 2001). Only the more recent knowledge-based views consider relations that exist be-tween concepts (Barsalou, 1992; Murphy & Medin, 1985). One such knowledge-based approach that can account for relatedness is frame theory (Barsalou, 1992). As we will see in the next sub-section, frame theory forms a suitable theoretical basis also for
the work in this thesis, and is therefore described in more detail along with its extension of perceptual symbol systems theory (Barsa-lou, 1999).
1.1.3 Frame theory and perceptual symbol systems theory
Frames are structured mental representations of concepts. They
consist of attribute–value sets3 and relations. According to
Barsa-lou (1992), an attribute is defined as “a concept that describes an aspect of at least some category members”; for example, the concept “color becomes an attribute when viewed as an aspect of [the concept] bird” (p. 30). Values are defined as “subordinate concepts of an attribute” (p. 31) that are more specific than their respective parent attributes; for example, the above-mentioned at-tribute color in a bird can take on values such as red, green, or blue. According to Barsalou (1992, p. 32), “because values are concepts, they in turn can be attributes having still more specific values.” Thus, the color blue may itself become an attribute when one con-siders possible attribute values of lightness, for example a light blue or a dark blue. What sets frame theory apart from the earlier concept theories is that it explicitly accounts for relations between attributes and attribute values. Moreover, it also accounts for re-lations across frames because a frame itself can be composed of other, more specific, frames. Figure 1.1, provides an example of such an interrelated frame-structure.
As can be seen from the figure, various relations exist between the frames, attributes, and values. For example, type relations ex-ist between the frame for the concept bird and its increasingly spe-cific subordinate concepts: a duck is a type of water fowl, a water fowl is a type of fowl, and a fowl is a type of bird; aspect relations exist between each concept and its various attributes, such as size, color, beak, locomotion, and neck; and again, more type relations ex-ist between these attributes and specific attribute values: small and large are two types of size, brown and white are two types
3 To align Barsalou’s terminology with the one used up to now: frames, attributes, and values translate into concepts, features, and feature values, respectively.
BIRD FOWL DUCK SIZE SIZE COLOR COLOR BEAK BEAK LOCOMOTION LOCOMOTION NECK NECK a spec t aspect asp ect aspect asp ect small large brown white small large paddles runs short long type type type type type type type type type type aspect aspect aspect WATER FOWL SIZE COLOR BEAK aspect aspe ct aspect aspect LOCOMOTION SIZE COLOR BEAK aspect aspect aspect aspec t aspect type type type type type ty pe
Figure 1.1: Example of the concept bird as represented by several at-tributes, attribute values and frames. Adapted from Frames, concepts, and conceptual fields (p. 53), by L. W. Barsalou, 1992, Hillsdale, NJ: Lawrence Erlbaum Associates. Copyright 1992 by Lawrence Erlbaum Associates Inc.
of color, and so on. Relations differ from simple correlations in that they include conceptual information that goes beyond sim-ple co-occurrence (Barsalou, 1992). For examsim-ple, we know that the concepts driver and car are correlated, but additionally our mental representations include more specific information in the form of relations. Many different types of relations are conceiv-able: some of this information may be represented in the form of spatial relations (that the driver sits behind the wheel), other information may be represented in the form of causal relations (that the driver operates the car), and so on (example adapted from Barsalou, 1992, p. 35).
Two particularly important properties of frames are structural invariants and constraints (see Barsalou, 1992). Structural invari-ants refer to a common set of relations that hold up invariably across most exemplars of a concept. For example, the fact that the driver of a car sits either to the left or a row before his passen-gers is an example of spatial relations that hold up in most exem-plars. Exceptions that break with this invariable structure would be, for instance, F1 racing cars (where there is typically only one seat) or cars made for markets with left-handed traffic (where the driving wheel is located on the right). Constraints refer to yet another characteristic of relational structure, namely when values of attributes constrain other attributes. Consider the fol-lowing constraints in the transportation frame depicted in Figure 1.2. The transportation frame includes the attributes cost, speed and duration, which are all related to each other by mutual con-straints: faster speeds, for example, will raise the costs but lower the duration of travel.
Frame theory provides a suitable basis for examining the no-tion of relatedness. However, it rests on the critical assumpno-tion that concepts are represented by amodal symbols that operate in a cognitive system detached from the brain and its percep-tual modalities (i.e., a system of its own that relies on abstract symbols that are unrelated to the perceptual experiences that caused them). This assumption has been continuously contested in recent years, especially as new empirical evidence points to-wards close ties between cognition and perception (Barsalou, Sim-mons, Barbey, & Wilson, 2003). Barsalou, a fierce proponent of the
COST SPEED DURATION TRANSPORTATION
low high slow fast long short
type typ
e type type type type
aspec t aspec t aspec t + + –
Figure 1.2: Example of constraints in the frame for transportation. Adapted from Frames, concepts, and conceptual fields (p. 38), by L. W. Barsalou, 1992, Hillsdale, NJ: Lawrence Erlbaum Asso-ciates. Copyright 1992 by Lawrence Erlbaum Associates Inc. grounded cognition view (see Barsalou, 2008), extended frame theory in his later work on perceptual symbol systems (Barsalou, 1999), in which he gives a detailed account of how conceptual rep-resentations (concepts or frames) come into being through men-tal simulators or simulation in the brain’s perceptual modalities— most felicitously described by Barsalou himself:
Consider the process of storing perceptual symbols while viewing a particular car. As one looks at the car from the side, selective attention focuses on various aspects of its body, such as wheels, doors, and win-dows. As selective attention focuses on these aspects, the resulting memories are integrated spatially, per-haps using an object-centered reference frame. Sim-ilarly, as the perceiver moves to the rear of the car, to the other side, and to the front, stored perceptual records likewise become integrated into this spatially organized system. As the perceiver looks under the hood, peers into the trunk, and climbs inside the pas-senger area, further records become integrated. As a result of organizing perceptual records spatially, per-ceivers can later simulate the car in its absence. They
can anticipate how the car would look from its side if they were to move around the car in the same di-rection as before; or they can anticipate how the car would look from the front if they were to go around the car in the opposite direction. Because they have integrated the perceptual information extracted ear-lier into an organized system, they can later simulate coherent experiences of the object (Barsalou, 1999, p. 586).
In essence, the theory of perceptual symbol systems extends frame theory by grounding it in previous perceptual experiences. Frame theory on its own cannot explain how conceptual struc-ture emerges. One of the advantages of merging the two theories is that, together, they can account for influences in categorization and categorical inference that originate from the environment (e.g., affordances) or from bodily states (e.g., arousal). As such, it is possible to explain, for example, how certain visual design cues lead to the selective activation and processing of only cer-tain subsets of a conceptual frame, rather than the frame in its entirety (Barsalou, 1999).
1.2 relatedness in mental representations of prod-ucts
Based on both frame theory (Barsalou, 1992) and perceptual sym-bol systems theory (Barsalou, 1999), we can make the following assumptions for relatedness in products:
First, relatedness matters as much in products as it does else-where. Just like other objects and entities, products are also rep-resented in our minds as concepts. Based on their constituting parts, mental representations of products are no different from mental representations of other objects and entities: they, too, con-sist of (specific) features and relations between features. As men-tioned earlier, features may represent both concrete and abstract properties of a concept (Tversky, 1977). Applied to mental rep-resentations of products, concrete features usually describe an aspect of a product’s appearance (e.g., shape, size, color) or
func-tionality (e.g., in a car: 180 horse power engine, automatic trans-mission, 4-wheel drive...), while abstract features describe more general aspects of a product (e.g., consumers’ inferences about the quality, usability, different use-scenarios, consumption goals, costs and benefits, attitudes, brand-related information...). Such features are often related with one another in a particular man-ner, rather than unrelated or independent. For example, in a car consumers may perceive the engine to be related to its fuel econ-omy: the more powerful the engine, the less mileage one gets. This information in turn may be related to consumers’ consumption goals (e.g., “saving on fuel”), consumer’s brand-related informa-tion (e.g., “Brand X strikes the perfect balance between power and economy”), and so on.
Second, due to the ubiquity of products in our lives, mental representations of products form an important part of our gen-eral knowledge about the world. As consumers, we continuously rely on information held in our mental representations of prod-ucts. This information is used to (1) interpret new products (e.g., assign them to a category), (2) compare them to other, similar, products in memory (e.g., previous generations of a product, or products that belong to the same category or brand), and (3) draw inferences about those products (e.g., about their features, price, quality, potential value, and benefits...). Relatedness in mental representations (e.g., between features or consumption goals) is particularly important for new products that often introduce new performance and thereby cause uncertainty in estimating their potential usefulness and value (Hoeffler, 2003).
Third, when accessing this knowledge about products, we reen-act previous perceptual experiences with a product via mental simulation (Barsalou, 1999, 2008). This last assumption is impor-tant because it can explain why we do not always attend to the same pieces of information in mental representations of products. Rather, we engage in selective processing and activation of only certain subsets of a mental representation, also with regard to re-lational structure. Which type of information we attend to may be influenced, among others, by certain cues in the visual ap-pearance of a product (Berkowitz, 1987; Blijlevens, Mugge, Ye, & Schoormans, 2013; Creusen & Schoormans, 2005; Crilly, Moultrie,
& Clarkson, 2004), by product labels or categories (Gill & Dube, 2007; Moreau, Markman, & Lehmann, 2001), or by the type of cognitive processing (Kahneman, 2003; Stanovich & West, 2000). 1.3 purpose of the thesis
The main purpose of this thesis is to show that gathering a deeper understanding of the effects of relatedness in mental representa-tions of products is not an end in itself or only of interest in the area of concept theories, but quite the contrary, that it will feed back both to the theory and practice in new product develop-ment and marketing. When consumers evaluate a product, they evaluate not just the features but also the (product as a) whole. Relations between features provide much of this additional in-formation: for example, how feature A relates to feature B, and how both features together may or may not satisfy a certain goal. For consumers, such knowledge about relatedness can be just as valuable as knowledge about the features themselves. Sim-ilarly, for companies such knowledge about the relatedness in consumers’ mental representations can be valuable as well. The relational structure in consumers’ mental representations is not static or immutable and should thus be considered as a potential target in the context of new product development and marketing activities. In other words, being cognizant about relatedness pro-vides companies with opportunities to influence the information consumers attend to, how they interpret a product offering, and what inferences and conclusions they make.
The following questions are addressed in the original research chapters of this thesis:
What is the role of relatedness in new products? How does relat-edness affect perceived product value? Does relatrelat-edness matter more for incremental or more for radical feature additions? Does relatedness matter in consumers’ consumption goals? How do vi-sual signs influence people’s thinking? Can cognitive load make consumers think more intuitively and less deliberatively? How do brands shape the information consumers attend to in mental
representations? Is it possible to indirectly (implicitly) measure relatedness within mental representations of brands?
1.4 outline of the thesis
This thesis consists of five empirical chapters followed by a gen-eral discussion in a final chapter. All empirical chapters address aspects of relatedness in consumers’ knowledge structures. Chap-ters 2 and 3 address relatedness more directly (by manipulating relatedness between features and between consumption goals), whereas Chapters 4, 5, and 6, address relatedness more indirectly (by showing how consumers’ mental models are influenced by signs, bodily states, or implicit associations with a brand). Even though relatedness is not directly manipulated in the latter chap-ters, they still focus on the idea that relatedness plays a central role in mental representations. The empirical chapters were orig-inally devised as articles, some of which are already published in peer-reviewed journals. As a result, there is some repetition of terms and definitions in the individual chapters. The benefit for the reader is that the chapters can be read independently of each other, according to one’s interests. In the following I will provide a brief overview of the contents in each of the empirical chapters: Chapter 2—Feature relations and their effects on product value and learning costs.
This chapter addresses the effects of relatedness between newly added and existing features in new products. The results of two studies show that product value increases when new features are easy to relate with the existing features of a product. This effect occurs in addition to the impact of main product benefits (Study 1) (i.e., the benefits most commonly linked with a product) and is prevalent in product variants that introduce either an incre-mentally or radically new feature (Study 2). The results further show no significant effect on learning costs when radically new features are easy to relate (Study 2).
Then consider both goal congruence and goal relatedness. This chapter addresses the effects of relatedness between con-sumers’ consumption goals in new products. Previous research has examined the effects of goal congruence (i.e., similar goals be-tween a new feature and its base) on the perceived incremental value in a hedonic and a utilitarian base product. A second source of fit in consumers’ consumption goals is examined in this chap-ter: goal relatedness (i.e., degree to which a new feature is linked to the goals of its base). The results of two studies show that (1) goal relatedness adds value to a base independently from and in ad-dition to goal congruence, (2) goal relatedness matters more for a hedonic than for a utilitarian base product, and (3) goal related-ness can overcome the negative effects of goal (in)congruence of added features. These results are observed both for feature addi-tions (Study 1) and for feature deleaddi-tions (Study 2).
Chapter 4—”It’s time to take a stand”: Depicting crosshairs can indeed promote violence.
This chapter shows the importance of (visual) signs and how they influence people’s perceptions and attitudes. More specifi-cally, it shows how crosshairs (as opposed to neutral markers) can be viewed as representing violence when used in a map to inform participants about areas afflicted by a fictive fox plague. This study does not involve products in the experimental setup and is intended as a transition to Chapters 5 and 6, which include signs (in the form of brands) in their manipulations.
Chapter 5—Evaluating new product concepts under low versus high cognitive loads—evidence for a brand effect?
This chapter shows how consumers’ perceptions and evaluations of products are influenced by cognitive load. More specifically, we tested the idea that consumers will rely more on brands when put under high (as opposed to low) cognitive loads in an experi-ment in which cognitive load is manipulated by the speed of the treadmill (slow or fast) and by inducing stress (little time
pres-sure or increased time prespres-sure).
Chapter 6—Extending the Implicit Association Test (IAT): Assess-ing consumer attitudes based on multi-dimensional implicit as-sociations.
This chapter continues with the study of brands in consumer knowledge structures. A procedural extension of the Implicit As-sociation Test (IAT; Greenwald, McGhee, & Schwartz, 1998) is in-troduced that allows for indirect measurement of brand attitudes on multiple dimensions (e.g., safe–unsafe; young–old; innovative– conventional, etc.) rather than on a single evaluative dimension only (e.g., good–bad). Indirect measures are useful for measur-ing attitudes consumers may not be consciously aware of, able to express, or willing to share with the researcher (Brunel, Tietje, & Greenwald, 2004; Friese, Wanke, & Plessner, 2006). The results of two within-subjects studies that measured attitudes towards three automobile brands provide strong evidence for the reliabil-ity, validreliabil-ity, and sensitivity of this multi-dimensional extension of the IAT.
2
F E AT U R E R E L AT I O N S A N D T H E I R E F F E C T S O N
P R O D U C T VA L U E A N D L E A R N I N G C O S T S1
2.1 introduction
In their attempts to increase or maintain market share within a product category, many companies adhere to a simple formula: adding new features to an existing product in anticipation that the new features will provide added value. Indeed, new features are often beneficial to the success of a new product (Sen & Mor-witz, 1996). Particularly in the case of mature markets, one way to increase market share is by introducing new features that are relevant to the target market (Tholke, Hultink, & Robben, 2001). For example, by introducing the new feature “touch screen” to their devices, mobile phone manufacturer Samsung was able to grow its market share by 4.6 % in 2009, with sales reaching 51.4 million units worldwide in an otherwise declining market (Eten-goff, 2009; Stevens & Pettey, 2009).
While introducing new features often brings value to a prod-uct, it also entails certain risks. Consumers might consider the number of features in new products to be excessive or “too much of a good thing” (Thompson, Hamilton, & Rust, 2005) and pre-fer “going back to basics” (Dua, Hersch, & Sivanandam, 2009). This is particularly prevalent in radical products in which the benefits of the new feature are unknown (Hoeffler, 2003). Past re-search indeed indicates that features may have a negative effect on product evaluation when consumers see them as irrelevant (Meyvis & Janiszewski, 2002) or trivial (Broniarczyk & Gershoff, 2003). Evidently, adding new features to existing products can have far-reaching consequences, both positive and negative.
Ac-1 This chapter is an adaptation of Gattol, V., Saaksjarvi, M., & Schoormans, J. P. L. (2010). Feature relationships and their effects on product value. Paper presented at the 34th Annual Global Conference on Product Innovation Management, Orlando, Florida.
knowledging this fact, it is therefore essential to examine the po-tential risks of such a strategy along with the popo-tential benefits.
One way to do so involves capitalizing on feature relatedness, that is, the relations or connections among product features. Un-derlying the idea of feature relatedness is the fact that, more of-ten than not, the value associated with a (new) feature is not confined to the feature itself but may transcend it—that is, influ-ence the value of other features or the product as a whole. As for the example with the new touch screen feature, the added value may not only be due to the new screen itself, but also due to changes in other features that are connected with it. For exam-ple, the touch screen increases ease-of-use by altering the perfor-mance value of other features like “internet browsing”, “text mes-saging”, or “voice calls”. Thus, the capability of the touch screen to create growth in the mobile phone market can be attributed to the fact that the new feature itself added performance value, but also to the fact that its introduction positively influenced the performance value of already existing features in the phone.
Feature relations (i.e., connections among product features) are crucial in gaining a deeper insight into the complex interplay of features as experienced by consumers. They are important be-cause they can increase value and help companies to mitigate the risks and maximize the chances for product success. This is par-ticularly the case in the development of radical products. In two studies we show that feature relations influence product value (Study 1) and that this is particularly so when radically new fea-tures are added to a product (Study 2). In this chapter we show that product value is enhanced when consumers connect a newly added feature with the existing features of the product. We ar-gue that accounting for feature relations in NPD provides com-panies with better estimates of how a new product will fare in the market; and, ultimately, that such estimates will help companies in making the necessary changes, for example in the design or marketing of a product. In short: feature relations are beneficial because the value associated with a new product becomes more pronounced.
2.2 features and feature relations
Most commonly, the term feature is used synonymously with terms such as attribute, characteristic, and property. Our defi-nition of (product) features follows that of Tversky (1977) who assumed that any given object (or product) is represented by a set of features or attributes that may entail either concrete (e.g., size or color) or abstract properties (e.g., quality or complexity). For example, in a car consumers typically consider, among others, the more concrete feature “engine” and the more abstract feature “fuel economy”. Moreover, consumers are typically aware of the relation between the engine and its fuel economy (i.e., the more powerful the engine, the less mileage one gets).
The fact that product features are interrelated (as opposed to unrelated and independent of each other) has not received much attention in the literature, although feature relations are likely to have an impact on product value (Goldstone, Medin, & Gen-tner, 1991; Medin, Goldstone, & GenGen-tner, 1993; Sloman, Love, & Ahn, 1998). The concept of feature relations denotes the fact that features in products are interdependent rather than independent. Because of the relations features share, they can influence each other; by changing the feature value of one feature, the value of another feature may also change (Barsalou, 1992). This is par-ticularly relevant to consider when adding a new feature to a product, as it can influence the value of other features in it.
Feature relations are likely to influence product value in two main ways. First, feature relations enhance the value of features, and thereby bring additional value to a product. In other words, by enhancing one feature, the performance of another feature can also become enhanced. This was the case when Apple introduced the iPhone featuring a touch screen interface, which improved the performance of many features that were previously rather disregarded on other devices (e.g., internet browsing, location-based services, photo slideshow...). Second, another way in which feature relations can enhance value is by triggering each other (Markman & Gentner, 1993). When using one feature, consumers can come to think about another feature and start using it as well—or in other words, feature relations facilitate the adoption
of new product features. For example, when taking a picture on a smart phone featuring a “camera” and “internet access”, a con-sumer may start thinking about sending it to a friend (“camera” $ “internet” ! “send by email”). By linking features to each other, consumers gain deeper knowledge about a particular prod-uct; that is, they become more like experts, characterized not just by “more knowledge”, but generally, “more richly interconnected knowledge” (Alba & Hutchinson, 1987; Gregan-Paxton, Hibbard, Brunel, & Azar, 2002, p. 535). Our first hypothesis pertains to this knowledge. Deeper and more interconnected knowledge tends to improve product assessments because people have a richer un-derstanding of the product benefits. As such, we propose:
H1: The number of feature relations has a positive impact on
product value.
The contribution of feature relations to product value should be in addition to the other benefits already provided by the new product. If feature relations contribute to product value (as sug-gested in H1), they do so by providing additional benefits that should impact product value over and above the product’s main benefits. Main benefits (i.e., the benefits most commonly asso-ciated with a product) are often the key driving force why con-sumers buy new products (Dua et al., 2009)—feature relations are no substitute for that. Instead, feature relations provide informa-tion to consumers that cannot be captured by the main benefits alone. As the linkages are not included in a product’s main ben-efits (e.g., a touch screen is often marketed as a touch screen and not as an enhancement to other features), emphasizing or high-lighting (some of the) feature relations should provide additional value over and above the main benefits of a product. Thus, we propose:
H2: The number of feature relations contributes to product value
2.2.1 Study 1
Study 1 examines how feature relations contribute to product value. To establish that people account for feature relations when thinking about products, we conducted a qualitative study with 40 consumers between the ages of 25–45, 19 women and 21 men. In a thought-listing task consumers were asked to talk out loud while evaluating three new products/services for which the value was unknown or uncertain: a new mobile phone, a PDA watch, and an Internet service accessible through mobile phones. For all products, feature relations were examined by coding the thought listings. A feature relation was coded in the data if consumers explicitly linked two features to each other. For example, if con-sumers said, “a larger screen improves the functionality of the camera, which makes the phone well suited for taking pictures”, a feature relation was considered to exist between the features “screen” and “camera” (“screen” ! “camera” ! “good pictures”).
Two coders (one naïve to the purpose of the study) coded the par-ticipants’ responses. The interrater reliability was .74. The coding of the feature relations was not always straightforward. Some consumers mentioned relations only implicitly, for example, by mentioning the end state (“good pictures”) of the relations, but no relations per se. To ensure a robust procedure, we system-atically coded only the relations that were explicitly mentioned by consumers (i.e., the co-occurrence of two features and their expected outcome). Main benefits were examined by asking con-sumers to list all of the product’s benefits, and counting the num-ber of benefits listed. To assess the impact on product value, we asked how much consumers agreed with the following statement: “This product could give me real value” (on a scale from 1–7).
2.2.2 Results
The participants listed on average 0.89 feature relations (range: 0–4), and 2.17 main benefits (range: 0–6). The most common rela-tions were screen–camera, buttons–SMS, camera–MMS, rotating-screen–camera for the mobile phone; screen–typing,
button–soft-ware, screen–size for the PDA watch; and links–navigation, loca-tion-sensitivity–ticket-ordering, built-in-payment-functionality– ticket-delivery for the Internet service. Some illustrative quotes about feature relations are provided below:
“Having a rotating screen really makes it easier to take pictures. It gives you flexibility that a regular screen does not provide” (male, 35) (rotating screen ! taking pictures ! improved flexibility)
“A large screen really makes all the difference when watching videos...I like this phone” (female, 34) (large screen ! watching videos ! product liking)
“The backlight is nice...makes it easier to view the date and time ...This PDA watch is pretty cool...”(male, 35) (backlights ! date and time ! product liking)
H1 proposed that feature relations have a positive impact on product value. To examine H1, we regressed the number of fea-ture relations on product value. This link was significant ( = .328, t = 3.77, p < .01). H2 suggested that the number of feature relations contributes to product value over and above the prod-uct’s main benefits. This hypothesis was examined by conducting two regressions: the first regression examined the impact of main
benefits on product value (Y = 0+ 1) and the second one
con-sidered the simultaneous impact of main benefits and additional benefits (accrued from feature relations) on product value (Y =
0 + 1+ 2). The first regression was significant ( = .330, t =
3.80, p < .01), which means that main product benefits have an impact on perceived product value. The second regression was also significant, both for additional benefits ( = .274, t = 3.21, p
<.01) and main benefits ( = .277, t = 3.25, p < .01), which means
that additional benefits derived from feature relations have an im-pact on product value over and above the effect of main product benefits.
2.3 a priori relatedness in products: differences be-tween incrementally and radically new features Having demonstrated that feature relations influence product va-lue, we now turn our attention to the role of relatedness in prod-uct value perceptions for two distinct types of features. In new products, features can be classified according to their innovative-ness. For example, in a car, a new feature may come in the form of (1) an incremental innovation (e.g., more mileage in car) or (2) a more radical innovation (e.g., an autopiloted car). Incremen-tal features improve the performance of existing features (Hoef-fler, 2003), they require little learning effort on the part of con-sumers, but also do not bring that much additional value, since they build on the value trajectories of existing products (Chris-tensen, 1997). In contrast, radical product innovations involve the introduction of completely new features that bring new perfor-mance to the product (Hoeffler, 2003). They involve much greater learning-efforts by consumers but also provide them with sig-nificantly more value than incremental products (Mukherjee & Hoyer, 2001).
As pointed out earlier, the advantages of feature relations lie in their ability to bring additional benefits and thereby raise product value, while at the same time reducing learning costs. In products with incrementally new features (where the learning costs are typically low), feature relations will mainly affect perceptions of product value. Yet, in products with radically new features, fea-ture relations should affect both perceptions of learning costs and product value. Further, given that the value-enhancing potential is greater for radically new features, additional benefits derived from feature relations should have a greater impact on product value in products with radically than incrementally new features (Mukherjee & Hoyer, 2001). In the studies that follow we manip-ulated feature relations by varying the a priori relatedness (i.e., the ease of seeing relations) between a new feature and existing features in a product. We propose the following hypotheses:
H3: In products with radically new features, a priori feature
H4: In products with radically new features, a priori feature
re-latedness will lead to greater product value.
H5: The impact of a priori feature relatedness on product value
will be greater for products with radically (as opposed to incre-mentally) new features.
Drawing on the costs-benefit framework of judgment and deci-sion making in consumer research (E. J. Johnson & Payne, 1985), Mukherjee and Hoyer (2001) showed that both additional value and reductions in learning costs will lead to higher overall evalu-ations for a new product. Thus, we propose:
H6: In products with incrementally new features, product value
will mediate the effect of a priori feature relatedness on product evaluation.
H7: In products with radically new features, product value and
learning costs will mediate the effect of a priori feature related-ness on product evaluation.
2.3.1 Pre-Studies
Two pre-studies were conducted for the product category of mo-bile phones with the purpose of selecting the right features for Study 2: the first pre-study assessed the a priori relatedness be-tween existing and new product features; the second pre-study tested for differences in the perceived newness of the new fea-tures. The category of mobile phones was selected because famil-iarity with the product can be assumed to be high for most par-ticipants and further because it is a product characterized both by short life and innovation cycles offering ample opportunity for adding new features (both incrementally and radically new features).
Participants. A total number of 74 people participated in the studies (44 in the first and 30 in the second study). All partici-pants were students at a mid-sized university (mean age of 24
years; 19% female).
Procedure and design. Both studies involved participants filling in a three-page questionnaire about mobile phone features, which took about 5–10 minutes to complete. For the first pre-study, a 5 (existing feature) x 3 (new feature) mixed factorial design was chosen to test for all combinations of an existing feature with each of the three new features. The first factor (existing feature) varied between-subjects, whereas the second factor (new feature) varied within-subjects. Thus, within participants, each evaluated the same existing feature with one of the new features at a time (three new features in total). The features tested were: (1) cam-era, (2) SMS, (3) GPS, (4) calendar-organizer, and (5) audio-video player as the existing features, and (1) electro-shock taser, (2) longer lasting battery (10X), and (3) portable pocket beamer mod-ule as the new features.
The participants’ task was to first consider a mobile phone hav-ing the two features and to indicate the extent to which they agreed or disagreed with various statements. A priori related-ness between features was measured by the following item on a 7-point Likert-scale(range: - 3 to + 3): “It is easy to see a rela-tion/connection between feature 1 and feature 2. In the second pre-study participants rated the three new features in regard to their perceived newness. Perceived newness was measured with the following two items on 7-point Likert-scales (range: - 3 to + 3): “Considering the type of product, this feature seems really new.” / “Considering the type of product, this feature seems in-novative.”
First pre-study. Several mixed ANOVAs with the existing fea-ture as the between-subjects and the new feafea-ture as the within-subjects factor were conducted. A significant effect between con-ditions of the five existing features was found, F(4, 39) = 6.52, p
<.01. The analyses also revealed a significant effect between
con-ditions of the three new features, F(2, 78) = 71.10, p < .01, and a significant interaction between conditions of the new feature and conditions of the existing feature, F(8, 78) = 3.54, p < .01. Specifically, a post hoc pairwise comparison between the new feature “longer lasting battery (10x)” and the “portable pocket beamer module” showed that participants rated the first to be
Note. Lower scores correspond with disagreement, higher scores with agreement.
Figure 2.1: Ease of seeing a relation between existing and new features significantly easier to relate to the five existing features than the second (p < .01). However, within levels of the existing feature (e.g., camera, SMS, audio-video player, etc.) this was moderated by the type of new feature. For example, there was no signifi-cant difference between the “battery” and the “beamer” within the following levels of the existing feature: “camera” (p = .31), “calendar-organizer” (p = .13), and “audio-video player” (p = .39).
Figure 2.1 summarizes the results graphically.
Second pre-study. Results from pre-study 2 confirmed the ex-pected differences between the three new features in terms of their perceived newness. A repeated measures ANOVA with per-ceived newness (consisting of two items; Cronbach ↵ = .88) as the dependent variable revealed a significant main effect, F(2, 58) = 7.90, p < .01. The “battery” was judged significantly less new than both the “beamer” (p < .01) and the “taser” (p < .01). However, there was no significant difference between the “beamer” and the “taser” (p = .65).
“audio-video player” seemed particularly interesting, because it showed to be easy to relate to both the “beamer” and the “battery”. This very fact served a crucial part in the experimental manipulations of Study 2.
2.3.2 Study 2
The main idea behind Study 2 was to show that when a new fea-ture is added to an existing product, its a priori relatedness (i.e., the ease of seeing relations) with other features influences infer-ences about learning costs and product value.
Participants. 156 participants (mean age = 45, SD = 9, female = 46.8 %), recruited from a consumer panel consisting of 1600 con-sumers maintained by the university, took part in the experiment. 13 participants were excluded from the analyses because of a lack of understanding the experimental manipulations.
Procedure and design. Each participant randomly received one out of five versions of a two-page questionnaire. Each version corresponded to a different experimental condition. All condi-tions were devised based on the findings of the pre-studies. First, only the “battery” and the “beamer” were selected as suitable new features for the study. The two features differed in regard to their perceived newness, with the “battery” chosen as the incre-mentally new and the “beamer” chosen as the radically new feature. Second, the existing feature “audio-video-player” was chosen to serve a crucial part in the manipulation because it had shown to be easy to relate to both the incrementally new feature “bat-tery” and the radically new feature “beamer” in pre-study 1. All conditions involved participants reading a product description for a new mobile phone, in which they were told that the manu-facturer had added a new feature. In each condition the descrip-tion listed three existing features and one new feature. The new feature differed between conditions: “battery” was used in con-dition 1 and “beamer” in concon-ditions 2, 3, 4, and 5. The crucial manipulation in the “beamer conditions” centered on the easy-to-relate existing feature “audio-video-player”. In condition 2 it was deliberately left out (thus deemphasized), that is, it was not one of the existing features listed. In conditions 3, 4, and 5, it
replaced one of the three existing features. Thus, in the latter conditions the relation between “audio-video player” and the “beamer” was emphasized (or made explicit). In order to control for possibly confounding effects caused by the other two exist-ing features present in the scenario, the “audio-video-player” re-placed a different existing-feature in each of the three conditions; thus, in case that the latter three conditions yield similar results, any effect on people’s inferences can be attributed solely to the presence of the “audio-video-player”. Based on our hypotheses we expected learning costs to be lowest and perceptions of prod-uct value to be highest in condition 1. In addition, we expected learning costs to decrease and inferences about product value to increase in the three “beamer conditions” with the easy-to-relate existing feature.
Measures. The following scales were included in the question-naire: A priori feature relatedness was measured on 7-point Lik-ert scales (range: - 3 [“strongly disagree”] to + 3 [“strongly agree”]), using the following three items: “It is easy to see a relation/con-nection between the existing features and the new feature” / “It is easy to see how the new feature influences the existing fea-tures” / “It is easy to see how the existing features influence the new feature”. Learning costs and product value were also mea-sured on 7-point Likert (range: - 3 [“strongly disagree”] to + 3 [“strongly agree”]), using three items for each construct. Partici-pants indicated to what extent they disagreed/agreed that learn-ing to use the product will take a lot of “time” / “effort” / ”en-ergy” and to what extent they disagreed/agreed that the new feature is likely to “add a lot of value” / “offer a lot of advan-tages” / “perform well” (cf., Mukherjee and Hoyer, 2001). Prod-uct evaluation was measured with the following four items using a semantic differential (range: - 3 to + 3): “not useful–useful” / “bad–good” / “I dislike it–I like it” / “undesirable–desirable”.
2.3.3 Results
Before constructing indices for each of the scales mentioned in the previous section, we conducted analyses of internal consis-tency. Cronbach ↵ estimates of internal consistency were as
fol-lows: .73 for a priori feature relatedness, .89 for learning costs, .81 for product value, and .82 for product evaluation. Thus, for all four scales, the internal consistencies can be regarded as satis-factory.
In testing H3 and H4, we conducted two one-way ANOVAs. The results revealed a significant difference for learning costs, F(4, 142) = 5.76, p < .001, and for product value, F(4, 142) = 11.69, p < .001. As indicated by pairwise comparisons, both for learn-ing costs and product value, the significant result was mainly due to significant differences (all ps < .01) between condition 1 (“battery”) and each of the “beamer conditions” (conditions 2, 3, 4, and 5). In our hypotheses, H3 and H4, we specifically predicted differences between condition 2 (lower a priori relat-edness: none of the existing features was easy to relate with the new feature) and conditions 3, 4, and 5 (higher a priori related-ness: one of the existing features was easy to relate with the new feature). For learning costs, H3, no significant differences (all ps
> .05) were found between condition 2 and conditions 3, 4, and
5. However, for product value, H4, results were more promising: condition 2 (mean = - .01, SD = 1.48) differed significantly (p < .04) from condition 3 (mean = .66, SD = 1.08). Further, there was a marginally significant difference between condition 2 and con-dition 4 (mean = .60, SD = 1.35) and concon-dition 2 and concon-dition 5 (mean = .61, SD = 1.26), with p-values of .06 and .05, respec-tively. This difference in perceptions of product value was further tested by planned contrasts. The advantage of this post hoc pro-cedure is that it can combine all three easy-to-relate conditions to one chunk of variance, which can be then compared directly with condition 2. Planned contrasts revealed that all three easy-to-relate conditions combined led to higher perceptions of product value compared to condition 2, where the easy-to-relate existing feature was absent, t(138) = 2.44, p < .01. See Figure 2.2 and 2.3 for a summary of the results on learning costs and product value.
H5 predicted a greater impact of a priori feature relatedness on product value for the radically new (as opposed to the in-crementally new) feature. To address this hypothesis, we first collapsed the radical conditions (n = 87) to be able to compare them with the incremental condition (n = 29). We then compared
Note. Lower scores correspond with disagreement, higher scores with agreement.
Figure 2.2: Learning costs
Note. Lower scores correspond with disagreement, higher scores with agreement.
a priori feature relatedness learning costs product value product evaluation a b a' b' c
Figure 2.4: Overview of the variables in the mediation analyses the two -coefficients, which we obtained from regressing the independent variable a priori feature relatedness on the
depen-dent variable product value. The two regression coefficients, =
.373 (incrementally new) and = .448 (radically new), revealed
a marginally significant difference (t = 1.39, p < .08), suggesting a trend that the impact of feature relations is greater for radical feature innovations.
To test H6, we conducted mediation analyses following the guidelines set by Baron and Kenny (1986), separately for the two levels of the variable feature type, the incrementally new and the radically new feature. Figure 2.4 provides an overview of the in-volved variables.
Mediation analysis for the incrementally new feature. First, we re-gressed a priori feature relatedness on product evaluation (path
c, = .418, t = 2.298, p < .05). Then, we regressed a priori
fea-ture relatedness on learning costs (path a, = - .292, t = - 1.529,
p > .10), and product value (path a’, = .373, t = 2.11, p < .10).
Next, we examined the link between a priori feature relatedness
(path c, = .375, t = 1.871, p < .10), learning costs (path b, =
.167, t = .821, p >.10), product value (path b’, = .246, t = 1.171,
p > .10) and product evaluation. Following this equation, we
re-gressed a priori feature relatedness (path c, = .443, t = 2.292,
p < .05) and learning costs (path b, = .086, t = .445, p > .10)
on product evaluation. Finally, we conducted a regression
analy-sis on the link between a priori feature relatedness (path c, =
