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CONSUMERS PERCEPT ION OFRELATEDNESS IN MENTAL

REPRESENTAT IONS OF PRODUCTSvalentin gattol

Valentin Gattol 2013

Cover design by Milene Guerreiro GonalvesTypesetting by Gabriel A.D. LopesPrinted by CPI Whrmann Print Service

ISBN 978-94-6203-500-3

Consumers perception of relatedness inmental representations of products

Proefschrift

ter verkrijging van de graad van doctoraan 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, Universitt Wien, Oostenrijkgeboren te Bad Ischl, Oostenrijk.

Dit proefschrift is goedgekeurd door de promotor:Prof.dr. J.P.L. Schoormans

Copromotor:Dr. M. Sksjrvi

Samenstelling promotiecommissie:

Rector Magnificus, voorzitter

Prof.dr. J.P.L. Schoormans, Technische Universiteit Delft, promotor

Dr. M. Sksjrvi, 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

Dr. T. Gill, Wilfrid Laurier University, Canada

CONTENTS

list of figures viiilist of tables xiacknowledgments xiii1 introduction 1

1.1 Grounding the notion of relatedness in concept the-ories . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31.1.1 Concepts and mental representations . . . . 41.1.2 Earlier concept theories: From defining at-

tributes to prototypes and exemplars . . . . 41.1.3 Frame theory and perceptual symbol sys-

tems theory . . . . . . . . . . . . . . . . . . . 61.2 Relatedness in mental representations of products . 101.3 Purpose of the thesis . . . . . . . . . . . . . . . . . . 121.4 Outline of the thesis . . . . . . . . . . . . . . . . . . 13

2 feature relations and their effects on prod-uct value and learning costs 172.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 172.2 Features and feature relations . . . . . . . . . . . . . 19

2.2.1 Study 1 . . . . . . . . . . . . . . . . . . . . . . 212.2.2 Results . . . . . . . . . . . . . . . . . . . . . . 21

2.3 A priori relatedness in products: Differences be-tween incrementally and radically new features . . 232.3.1 Pre-Studies . . . . . . . . . . . . . . . . . . . 242.3.2 Study 2 . . . . . . . . . . . . . . . . . . . . . . 272.3.3 Results . . . . . . . . . . . . . . . . . . . . . . 28

2.4 General discussion . . . . . . . . . . . . . . . . . . . 332.4.1 Limitations and suggestions for further re-

search . . . . . . . . . . . . . . . . . . . . . . 352.4.2 Conclusions and managerial implications . . 35

3 adding to or deleting features from new prod-ucts? then consider both goal congruenceand goal relatedness 393.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 39

v

vi contents

3.2 Theoretical background and propositions . . . . . . 423.2.1 Consumption goals . . . . . . . . . . . . . . . 423.2.2 Goal congruence and goal relatedness . . . . 423.2.3 Feature addition versus feature deletion . . 46

3.3 Study 1: Adding features to a product . . . . . . . . 483.3.1 Methodology . . . . . . . . . . . . . . . . . . 483.3.2 Results . . . . . . . . . . . . . . . . . . . . . . 50

3.4 Study 2: Deleting features from a product . . . . . . 533.4.1 Methodology . . . . . . . . . . . . . . . . . . 533.4.2 Results . . . . . . . . . . . . . . . . . . . . . . 54

3.5 General discussion . . . . . . . . . . . . . . . . . . . 573.5.1 Theoretical implications . . . . . . . . . . . . 593.5.2 Managerial implications . . . . . . . . . . . . 603.5.3 Limitations and suggestions for further re-

search . . . . . . . . . . . . . . . . . . . . . . 613.5.4 Appendix A . . . . . . . . . . . . . . . . . . . 623.5.5 Appendix B . . . . . . . . . . . . . . . . . . . 633.5.6 Appendix C . . . . . . . . . . . . . . . . . . . 633.5.7 Appendix D . . . . . . . . . . . . . . . . . . . 65

4 its time to take a stand: depicting crosshairscan indeed promote violence 67

5 evaluating new product concepts under lowversus high cognitive loadsevidence for abrand effect? 715.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 715.2 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 . 745.2.3 Hypotheses . . . . . . . . . . . . . . . . . . . 76

5.3 The treadmill study . . . . . . . . . . . . . . . . . . . 765.3.1 Method . . . . . . . . . . . . . . . . . . . . . . 765.3.2 Results . . . . . . . . . . . . . . . . . . . . . . 81

5.4 General Discussion . . . . . . . . . . . . . . . . . . . 885.5 Limitations and suggestions for future research . . 90

6 extending the implicit association test (iat):assessing consumer attitudes based on mul-tidimensional implicit associations 93

contents vii

6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . 936.2 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 manifestationsof attitudes . . . . . . . . . . . . . . . . . . . 96

6.2.3 Design of the IAT . . . . . . . . . . . . . . . . 976.3 The multi-dimensional Implicit Association Test (md-

IAT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 996.4 Materials and methods . . . . . . . . . . . . . . . . . 100

6.4.1 Study 1 . . . . . . . . . . . . . . . . . . . . . . 1006.4.2 Study 2 . . . . . . . . . . . . . . . . . . . . . . 108

6.5 Results . . . . . . . . . . . . . . . . . . . . . . . . . . 1096.5.1 Study 1 . . . . . . . . . . . . . . . . . . . . . . 1096.5.2 Study 2 . . . . . . . . . . . . . . . . . . . . . . 116

6.6 General discussion . . . . . . . . . . . . . . . . . . . 1207 general discussion 125

7.1 Main findings . . . . . . . . . . . . . . . . . . . . . . 1267.2 Theoretical contribution . . . . . . . . . . . . . . . . 131

7.2.1 Positive effects of relatedness on perceptionsof product value . . . . . . . . . . . . . . . . 132

7.2.2 Relatedness is relevant for feature additionsand feature deletions . . . . . . . . . . . . . . 133

7.2.3 Relatedness is more relevant for radicallythan 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 ImplicitAssociation Test (IAT) . . . . . . . . . . . . . 135

7.3 Managerial implications . . . . . . . . . . . . . . . . 1367.4 Limitations and suggestions for future research . . 138

references 143summary 161samenvatting 163curriculum vit 165

L I ST OF F IGURES

Figure 1.1 Example of the concept bird as representedby several attributes, attribute values andframes. Adapted from Frames, concepts, andconceptual 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 fortransportation. Adapted from Frames, con-cepts, and conceptual fields (p. 38), by L. W.Barsalou, 1992, Hillsdale, NJ: Lawrence Erl-baumAssociates. Copyright 1992 by LawrenceErlbaum Associates Inc. . . . . . . . . . . . 9

Figure 2.1 Ease of seeing a relation between existingand new features . . . . . . . . . . . . . . . 26

Figure 2.2 Learning costs . . . . . . . . . . . . . . . . . 30Figure 2.3 Product value . . . . . . . . . . . . . . . . . 30Figure 2.4 Overview of the variables in the mediation

analyses . . . . . . . . . . . . . . . . . . . . . 31Figure 3.1 The effect of goal congruence and goal re-

latedness on the incremental value of addedfeatures (Study 1) . . . . . . . . . . . . . . . 52

Figure 3.2 The effect of goal congruence and goal re-latedness on the incremental value of addedfeatures (Study 1) . . . . . . . . . . . . . . . 52

Figure 3.3 The effect of goal congruence and goal re-latedness on the decrease in value fromdeleted features (Study 2) . . . . . . . . . . 56

Figure 3.4 The effect of goal congruence and goal re-latedness on the decrease in value fromdeleted features (Study 2) . . . . . . . . . . 56

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List of Figures ix

Figure 3.5 Product scenario used in one of the con-ditions in Study 1 (namely, a hedonic basewith 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 basewith a congruent and non-related featureremoved) . . . . . . . . . . . . . . . . . . . . 63

Figure 4.1 Crosshairs map of the Dutch provinceof Utrecht . . . . . . . . . . . . . . . . . . . . 68

Figure 4.2 Plain circles map of the Dutch provinceof Utrecht . . . . . . . . . . . . . . . . . . . . 68

Figure 5.1 Examples of the stimuli in the product eval-uation task for two of the four conditionson the treadmill . . . . . . . . . . . . . . . . 80

Figure 5.2 The effect of cognitive load and type ofproduct presentation on overall evaluation(MP3 player) . . . . . . . . . . . . . . . . . . 86

Figure 5.3 The effect of cognitive load and type ofproduct presentation on overall evaluation(Smartphone) . . . . . . . . . . . . . . . . . 86

Figure 5.4 The effect of cognitive load and type ofproduct presentation on overall evaluation(Pocket camera) . . . . . . . . . . . . . . . . 87

Figure 5.5 The effect of cognitive load and type ofproduct presentation on overall evaluation(E-reader) . . . . . . . . . . . . . . . . . . . . 87

Figure 6.1 Images used to represent the brands AUDI,BMW, and FORD, varying according to thefactor BRAND CUE. . . . . . . . . . . . . . 104

Figure 6.2 Study 1 ("AUDI vs. FORD): D measuremeans for every single IAT (N = 26) result-ing from combinations of the two factorsATTRIBUTE DIMENSION and BRANDCUE.114

x List of Figures

Figure 6.3 Study 2 (AUDI vs. BMW): D measuremeans for every single IAT (N = 26) result-ing from combinations of the two factorsATTRIBUTE DIMENSION and BRANDCUE.119

L I ST OF TABLES

Table 3.1 Changes in perceived product value whenadding and deleting features . . . . 65

Table 5.1 Results of the ANOVA for the dependentvariable overall evaluation index for eachof 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 fourproducts. . . . . . . . . . 85

Table 6.1 Word stimuli for each category of the sixbipolar attribute dimensions, translated intoEnglish (original German terms used inthe study are given in parentheses). . . 102

Table 6.2 Adapted D measure algorithm relying onthe dynamic outlier criterion. . . . 110

Table 6.3 Study 1: Summary of all 18 single IATswith factors ATTRIBUTE DIMENSION andBRAND CUE (6 3). . . . . . . 112

Table 6.4 Split-half estimates of reliability for eachof 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 IATswith factors ATTRIBUTE DIMENSION andBRAND CUE (6 3). . . . . . . 117

Table 6.7 Study 2: Estimates of convergent validity(simple linear regressions for all six dimen-sions). . . . . . . . . . . 120

xi

ACKNOWLEDGMENTS

Relatedness is not only an important notion to consider in men-tal representations of products. Most evidently, it shows itself inthe many relationships we cultivate with people. In the last fewyears working on this thesis, I benefited greatly from relating to anumber of people.First and foremost, I would like to express my greatest appre-

ciation to my supervisors Jan Schoormans and Maria Sksjrvi.Jan: as my promotor you have taught me the virtues of focusingon promotion rather than prevention. Your optimistic, humorousand witty attitude towards life, the many discussions we had ontopics related and unrelated to my thesis, your creative mind andyour sharp senses, have all been a great inspiration to me. Dankje wel! Maria: as your first PhD student I would like to thankyou wholeheartedly for entrusting me with that former topic ofyours. Your continuous support and encouragement, your manyvaluable suggestions and constructive comments, have all greatlybenefited the quality of this thesis. Mnga tack!I would also like to thank two bright minds that I had the

opportunity to work with during the last years. Claus-ChristianCarbon: your enthusiasm and devotion to research never cease toimpress me. Tripat Gill: your grasp of complex issues and yourability 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 interestsSwedish cuisine and engaging in manly conversation, amongotherswe struck up an exciting collaboration at work, whichI continue to enjoy to this day. I am also a proud graduate of theOscar School of Design. Jaap: thank you further for the Dutchtranslation work.Moreover, I would like to thank my colleagues and friends

from the department. You have all made my time in Delft veryworthwhile! Special thanks go to Milene Guerreiro Gonalvesand Ana Valencia Cardona: I thoroughly enjoyed your company;

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xiv acknowledgments

it will be tough (read: impossible) to find better officemates ever!Milene: on top of that, thank you for the most beautiful cover Icould 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 andbarhapje with my next-door officemates Janneke Blijlevens, Fer-nando Del Caro Secomandi, Nik Shahman, and Silje Dehli. Manythanks also to Agnes Tan and her wonderful team at the PEL.Snia da Silva Vieira (special return hug), Gabriel A.D. Lopes,

Emilie Yane Lopes, Annegien Tijssen, Vessela Chakarova, ArturoTejada, Tnia 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 powersthank 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 2013Valentin Gattol

1INTRODUCT IONTodays mature markets place huge demands on companies to in-novate and improve their products (Cooper, 2011). A commonlyadopted 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 marketwhich allserve the purpose of increasing or maintaining their share of themarket. This strategy, however, does not always bring about goodproducts 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 bythe 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 somethingdifferent and new, it seems, is not enough.

Software giant Microsoft, for example, has fallen prey to suchunintended and unwanted consequences when it introduced itsnew operating system Windows Vista in 2007. With every newversion of Windows, Microsoft ended up packing more and morefeatures into the same program, while seeking greatest compati-bility with previous versions of software and hardware native tothe Windows environment. Over time, while becoming more andmore 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 intendedto offer an enhanced visual experience through its lightweightand 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 stillstruggling to get it right with its newest operating system Win-dows 8, which this time around comes as a muddled mishmashof two operating systems in one, designed for use both with

1

2 chapter 1

a mouse and keyboard and for touch screens (Pogue, 2012, p.B1)1. While the design and programming behind Windows 8 issupposedly 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 StartScreen that replaced the old Start menu now boasts large tile-likeicons designed for touch interfaces, yet not all of the programsand 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 orlinked with one another, has received scant attention both amongresearchers and practitioners involved in the development andmarketing 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 howconsumers view products. Utilizing both Barsalous frame the-ory (1992) and Barsalous theory 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 theirminds; and how these representations in consumer knowledgestructures may influence the inferences consumers draw about aproduct, 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 freshapproach, a new Windows. Something bold, fluid and finger-friendly. Well, hold on,someone must have countered. We cant forget the 600 million regular mouse-drivenPCs. We also need to update Windows 7 for them! And then things went terriblywrong. Hey, I know! somebody piped in. Lets combine those two Windows versionsinto one. One OS for all machines. Everybodys happy! (Pogue, 2012, p. B1).

2 In reaction to the mounting criticism, and more than half a year after its initialrelease, Microsoft recently confirmed that it will bring back the start button in itsnext update to Windows 8.1 (Warren, 2013).

introduction 3

lows: first, I will ground the notion of relatedness in theory frompsychology on mental representations of concepts; second, I willshow how the notion of relatedness can be applied to consumersmental representations of products (mainly in the context of newproducts); third, I will state the purpose of the thesis; and last, Iwill provide an outline of the thesis, describing how each chapterincorporates some aspect of relatedness.

1.1 grounding the notion of relatedness in concepttheories

According to Murphy (2002, p. 1) concepts are the glue thatholds our mental world together. They are the mental equiva-lent of real-world categoriesrepresentations of classes of objectsor entities in peoples minds (Eysenck & Keane, 2005; Murphy,2002). The notion of relatedness, at its core, describes a state wherethings are connected or linked to one another in a particular man-ner rather than being unconnected or independent. Relatednesscan be observed in many situations, people, objects, and prod-ucts. It is part of the physical world around us but extends alsoto the mental world inside of us, to our thinking. In this mentalworld, 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 usedthroughout 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 willreview more recent work that merges conceptual with perceptualaccounts of concepts (such as frame theory and perceptual sym-bol systems). The notion of relatedness will be grounded in thismore recent work.

4 chapter 1

1.1.1 Concepts and mental representations

Concepts are the mental equivalent of real-world categories. Forexample, based on our observations of the world around us wemight develop concepts for (countless) categories such as variousfoods, animals, people, and things. Concepts themselves consistof sets of features (Murphy, 2002). Such features may representboth concrete (e.g., size or color) and abstract properties of a con-cept (e.g., quality or complexity, Tversky, 1977). Other terms thatare used interchangeably in the literature (and also throughoutthis thesis) are attribute, characteristic, part, and property. For ex-ample, the concept cake, which itself belongs to the superordinatecategory of food, may include features such as flour, sugar, eggs,butter, milk, and water. Concepts are important because they helpus to interpret newly encountered objects, to relate them to sim-ilar objects in memory, and ultimately to draw inferences aboutthose objects. For instance, we do not have to analyze each newexemplar of a tomato just to know that it is edible (Murphy, 2002).Relatedness is found in the various relations between features. Forexample, we know about the particular relation between colorand ripenessthat a green tomato is not ready-to-eat, whereasa red tomato is. Features and relations are part and parcel ofconceptsonce we have formed a mental representation of anobject (like the tomato), this representation can be accessed fromknowledge and guide our understanding (see Murphy, 2002).

1.1.2 Earlier concept theories: From defining attributes to prototypesand exemplars

Theories about how concepts organize our thinking have a longhistory. 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 withthe view of concepts as defining attributes is that it does not copewell with category fuzziness (Murphy, 2002): if a dog is defined

introduction 5

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 legsin 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 distinguishbetween more or less typical exemplars: rather, once an objector entity fulfills the necessary and sufficient criteria of categorymembership, 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 ofthe 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 catego-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 comparinga particular instance to a prototype (a kind of summary rep-resentation of a category), whereas exemplar theory compares aparticular instance to stored instances known to belong to thesame category (Murphy, 2002). While the similarity-based viewsare well suited for describing concepts in isolation, accountingboth for category fuzziness and typicality, they fall short onceone considers concepts as part of a larger conceptual space. Forexample, the similarity-based views cannot explain goal-derivedcategories such as things to take on a picnic, which are con-structed ad hoc and consist of items that bear little resemblancein their features (e.g., a blanket, a basket, drinking cups...) butthat are all connected to a specific goal (Barsalou, 1983, 1985; Rat-neshwar, Barsalou, Pechmann, & Moore, 2001). Only the morerecent knowledge-based views consider relations that exist be-tween concepts (Barsalou, 1992; Murphy & Medin, 1985). Onesuch knowledge-based approach that can account for relatednessis frame theory (Barsalou, 1992). As we will see in the next sub-section, frame theory forms a suitable theoretical basis also for

6 chapter 1

the work in this thesis, and is therefore described in more detailalong 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. Theyconsist of attributevalue sets3 and relations. According to Barsa-lou (1992), an attribute is defined as a concept that describesan aspect of at least some category members; for example, theconcept color becomes an attribute when viewed as an aspectof [the concept] bird (p. 30). Values are defined as subordinateconcepts of an attribute (p. 31) that are more specific than theirrespective 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 bluemay itself become an attribute when one con-siders possible attribute values of lightness, for example a lightblue or a dark blue. What sets frame theory apart from the earlierconcept theories is that it explicitly accounts for relations betweenattributes and attribute values. Moreover, it also accounts for re-lations across frames because a frame itself can be composed ofother, more specific, frames. Figure 1.1, provides an example ofsuch 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 waterfowl is a type of fowl, and a fowl is a type of bird; aspect relationsexist 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: smalland large are two types of size, brown and white are two types

3 To align Barsalous terminology with the one used up to now: frames, attributes,and values translate into concepts, features, and feature values, respectively.

introduction 7

BIRD

FOWL

DUCK

SIZE

SIZE

COLOR

COLOR

BEAK

BEAK

LOCOMOTION

LOCOMOTION

NECK

NECK

a

spec

t

aspect

aspect

aspect

aspect

small

large

brown

white

small

large

paddles

runs

short

long

type

type

type

type

type

type

type

type

type

type

aspect

aspect

aspect

WATERFOWL

SIZE

COLOR

BEAK

aspect

aspec

t

aspect

aspect

LOCOMOTION

SIZE

COLOR

BEAK

aspect

aspec

t

aspect

aspec

t

aspect

type

type

type

type

typetype

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 1992by Lawrence Erlbaum Associates Inc.

8 chapter 1

of color, and so on. Relations differ from simple correlations inthat they include conceptual information that goes beyond sim-ple co-occurrence (Barsalou, 1992). For example, we know thatthe concepts driver and car are correlated, but additionally ourmental representations include more specific information in theform of relations. Many different types of relations are conceiv-able: some of this information may be represented in the formof spatial relations (that the driver sits behind the wheel), otherinformation may be represented in the form of causal relations(that the driver operates the car), and so on (example adaptedfrom 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 invariablyacross most exemplars of a concept. For example, the fact thatthe 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 wouldbe, for instance, F1 racing cars (where there is typically only oneseat) or cars made for markets with left-handed traffic (wherethe driving wheel is located on the right). Constraints refer toyet another characteristic of relational structure, namely whenvalues of attributes constrain other attributes. Consider the fol-lowing constraints in the transportation frame depicted in Figure1.2. The transportation frame includes the attributes cost, speedand duration, which are all related to each other by mutual con-straints: faster speeds, for example, will raise the costs but lowerthe duration of travel.Frame theory provides a suitable basis for examining the no-

tion of relatedness. However, it rests on the critical assumptionthat concepts are represented by amodal symbols that operatein a cognitive system detached from the brain and its percep-tual modalities (i.e., a system of its own that relies on abstractsymbols that are unrelated to the perceptual experiences thatcaused them). This assumption has been continuously contestedin 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

introduction 9

COST SPEED DURATION

TRANSPORTATION

low high slow fast long short

type

type type

type type

type

aspect

aspect

aspe

ct

+

+

Figure 1.2: Example of constraints in the frame for transportation.Adapted from Frames, concepts, and conceptual fields (p. 38), byL. W. Barsalou, 1992, Hillsdale, NJ: Lawrence Erlbaum Asso-ciates. Copyright 1992 by Lawrence Erlbaum Associates Inc.

grounded cognition view (see Barsalou, 2008), extended frametheory 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 brains perceptual modalitiesmost felicitously described by Barsalou himself:

Consider the process of storing perceptual symbolswhile viewing a particular car. As one looks at the carfrom the side, selective attention focuses on variousaspects 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 perceptualrecords likewise become integrated into this spatiallyorganized system. As the perceiver looks under thehood, peers into the trunk, and climbs inside the pas-senger area, further records become integrated. As aresult of organizing perceptual records spatially, per-ceivers can later simulate the car in its absence. They

10 chapter 1

can anticipate how the car would look from its sideif they were to move around the car in the same di-rection as before; or they can anticipate how the carwould look from the front if they were to go aroundthe car in the opposite direction. Because they haveintegrated the perceptual information extracted ear-lier into an organized system, they can later simulatecoherent experiences of the object (Barsalou, 1999, p.586).

In essence, the theory of perceptual symbol systems extendsframe 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 theoriesis that, together, they can account for influences in categorizationand 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 designcues lead to the selective activation and processing of only cer-tain subsets of a conceptual frame, rather than the frame in itsentirety (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 followingassumptions 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 constitutingparts, mental representations of products are no different frommental 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 abstractproperties of a concept (Tversky, 1977). Applied to mental rep-resentations of products, concrete features usually describe anaspect of a products appearance (e.g., shape, size, color) or func-

introduction 11

tionality (e.g., in a car: 180 horse power engine, automatic trans-mission, 4-wheel drive...), while abstract features describe moregeneral aspects of a product (e.g., consumers inferences aboutthe quality, usability, different use-scenarios, consumption goals,costs and benefits, attitudes, brand-related information...). Suchfeatures are often related with one another in a particular man-ner, rather than unrelated or independent. For example, in a carconsumers may perceive the engine to be related to its fuel econ-omy: the more powerful the engine, the less mileage one gets. Thisinformation in turn may be related to consumers consumptiongoals (e.g., saving on fuel), consumers brand-related informa-tion (e.g., Brand X strikes the perfect balance between powerand 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 continuouslyrely 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, orproducts that belong to the same category or brand), and (3) drawinferences about those products (e.g., about their features, price,quality, potential value, and benefits...). Relatedness in mentalrepresentations (e.g., between features or consumption goals) isparticularly important for new products that often introduce newperformance and thereby cause uncertainty in estimating theirpotential usefulness and value (Hoeffler, 2003).Third, when accessing this knowledge about products, we reen-

act previous perceptual experiences with a product via mentalsimulation (Barsalou, 1999, 2008). This last assumption is impor-tant because it can explain why we do not always attend to thesame pieces of information in mental representations of products.Rather, we engage in selective processing and activation of onlycertain subsets of a mental representation, also with regard to re-lational structure. Which type of information we attend to maybe 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,

12 chapter 1

& Clarkson, 2004), by product labels or categories (Gill & Dube,2007; Moreau, Markman, & Lehmann, 2001), or by the type ofcognitive processing (Kahneman, 2003; Stanovich & West, 2000).

1.3 purpose of the thesis

The main purpose of this thesis is to show that gathering a deeperunderstanding of the effects of relatedness in mental representa-tions of products is not an end in itself or only of interest in thearea of concept theories, but quite the contrary, that it will feedback both to the theory and practice in new product develop-ment and marketing. When consumers evaluate a product, theyevaluate 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, andhow both features together may or may not satisfy a certain goal.For consumers, such knowledge about relatedness can be justas valuable as knowledge about the features themselves. Sim-ilarly, for companies such knowledge about the relatedness inconsumers mental representations can be valuable as well. Therelational structure in consumers mental representations is notstatic or immutable and should thus be considered as a potentialtarget in the context of new product development and marketingactivities. In other words, being cognizant about relatedness pro-vides companies with opportunities to influence the informationconsumers attend to, how they interpret a product offering, andwhat inferences and conclusions they make.

The following questions are addressed in the original researchchapters of this thesis:

What is the role of relatedness in new products? How does relat-edness affect perceived product value? Does relatedness mattermore for incremental or more for radical feature additions? Doesrelatedness matter in consumers consumption goals? How do vi-sual signs influence peoples thinking? Can cognitive load makeconsumers think more intuitively and less deliberatively? Howdo brands shape the information consumers attend to in mental

introduction 13

representations? Is it possible to indirectly (implicitly) measurerelatedness 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 addressaspects of relatedness in consumers knowledge structures. Chap-ters 2 and 3 address relatedness more directly (by manipulatingrelatedness between features and between consumption goals),whereas Chapters 4, 5, and 6, address relatedness more indirectly(by showing how consumers mental models are influenced bysigns, bodily states, or implicit associations with a brand). Eventhough relatedness is not directly manipulated in the latter chap-ters, they still focus on the idea that relatedness plays a centralrole in mental representations. The empirical chapters were orig-inally devised as articles, some of which are already publishedin peer-reviewed journals. As a result, there is some repetition ofterms and definitions in the individual chapters. The benefit forthe reader is that the chapters can be read independently of eachother, according to ones interests. In the following I will providea brief overview of the contents in each of the empirical chapters:

Chapter 2Feature relations and their effects on product valueand learning costs.

This chapter addresses the effects of relatedness between newlyadded and existing features in new products. The results of twostudies show that product value increases when new features areeasy to relate with the existing features of a product. This effectoccurs in addition to the impact of main product benefits (Study1) (i.e., the benefits most commonly linked with a product) andis prevalent in product variants that introduce either an incre-mentally or radically new feature (Study 2). The results furthershow no significant effect on learning costs when radically newfeatures are easy to relate (Study 2).

Chapter 3Adding to or deleting features from new products?

14 chapter 1

Then consider both goal congruence and goal relatedness.

This chapter addresses the effects of relatedness between con-sumers consumption goals in new products. Previous researchhas examined the effects of goal congruence (i.e., similar goals be-tween a new feature and its base) on the perceived incrementalvalue in a hedonic and a utilitarian base product. A second sourceof fit in consumers consumption goals is examined in this chap-ter: goal relatedness (i.e., degree to which a new feature is linked tothe goals of its base). The results of two studies show that (1) goalrelatedness adds value to a base independently from and in ad-dition to goal congruence, (2) goal relatedness matters more fora hedonic than for a utilitarian base product, and (3) goal related-ness can overcome the negative effects of goal (in)congruence ofadded features. These results are observed both for feature addi-tions (Study 1) and for feature deletions (Study 2).

Chapter 4Its time to take a stand: Depicting crosshairs canindeed promote violence.

This chapter shows the importance of (visual) signs and howthey influence peoples 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 toinform participants about areas afflicted by a fictive fox plague.This study does not involve products in the experimental setupand is intended as a transition to Chapters 5 and 6, which includesigns (in the form of brands) in their manipulations.

Chapter 5Evaluating new product concepts under low versushigh cognitive loadsevidence for a brand effect?

This chapter shows how consumers perceptions and evaluationsof products are influenced by cognitive load. More specifically,we tested the idea that consumers will rely more on brands whenput under high (as opposed to low) cognitive loads in an experi-ment in which cognitive load is manipulated by the speed of thetreadmill (slow or fast) and by inducing stress (little time pres-

introduction 15

sure or increased time pressure).

Chapter 6Extending 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 consumerknowledge 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 attitudeson multiple dimensions (e.g., safeunsafe; youngold; innovativeconventional, etc.) rather than on a single evaluative dimensiononly (e.g., goodbad). Indirect measures are useful for measur-ing attitudes consumers may not be consciously aware of, ableto express, or willing to share with the researcher (Brunel, Tietje,& Greenwald, 2004; Friese, Wanke, & Plessner, 2006). The resultsof two within-subjects studies that measured attitudes towardsthree automobile brands provide strong evidence for the reliabil-ity, validity, and sensitivity of this multi-dimensional extensionof the IAT.

2FEATURE RELAT IONS AND THE IR EFFECTS ONPRODUCT VALUE AND LEARNING COSTS 1

2.1 introduction

In their attempts to increase or maintain market share within aproduct category, many companies adhere to a simple formula:adding new features to an existing product in anticipation thatthe new features will provide added value. Indeed, new featuresare often beneficial to the success of a new product (Sen & Mor-witz, 1996). Particularly in the case of mature markets, one wayto increase market share is by introducing new features that arerelevant to the target market (Tholke, Hultink, & Robben, 2001).For example, by introducing the new feature touch screen totheir devices, mobile phone manufacturer Samsung was able togrow its market share by 4.6 % in 2009, with sales reaching 51.4million 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 thenumber of features in new products to be excessive or too muchof 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 thebenefits of the new feature are unknown (Hoeffler, 2003). Past re-search indeed indicates that features may have a negative effecton product evaluation when consumers see them as irrelevant(Meyvis & Janiszewski, 2002) or trivial (Broniarczyk & Gershoff,2003). Evidently, adding new features to existing products canhave 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 the34th Annual Global Conference on Product Innovation Management, Orlando,Florida.

17

18 chapter 2

knowledging this fact, it is therefore essential to examine the po-tential risks of such a strategy along with the potential 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 notconfined to the feature itself but may transcend itthat is, influ-ence the value of other features or the product as a whole. Asfor the example with the new touch screen feature, the addedvalue may not only be due to the new screen itself, but also dueto 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 screento create growth in the mobile phone market can be attributedto the fact that the new feature itself added performance value,but also to the fact that its introduction positively influenced theperformance 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 offeatures as experienced by consumers. They are important be-cause they can increase value and help companies to mitigate therisks and maximize the chances for product success. This is par-ticularly the case in the development of radical products. In twostudies 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 showthat product value is enhanced when consumers connect a newlyadded 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 themarket; and, ultimately, that such estimates will help companiesin making the necessary changes, for example in the design ormarketing of a product. In short: feature relations are beneficialbecause the value associated with a new product becomes morepronounced.

product value and learning costs 19

2.2 features and feature relations

Most commonly, the term feature is used synonymously withterms such as attribute, characteristic, and property. Our defi-nition of (product) features follows that of Tversky (1977) whoassumed that any given object (or product) is represented by aset 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 featurefuel economy. Moreover, consumers are typically aware of therelation between the engine and its fuel economy (i.e., the morepowerful 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 muchattention in the literature, although feature relations are likelyto have an impact on product value (Goldstone, Medin, & Gen-tner, 1991; Medin, Goldstone, & Gentner, 1993; Sloman, Love, &Ahn, 1998). The concept of feature relations denotes the fact thatfeatures in products are interdependent rather than independent.Because of the relations features share, they can influence eachother; by changing the feature value of one feature, the valueof another feature may also change (Barsalou, 1992). This is par-ticularly relevant to consider when adding a new feature to aproduct, 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 canalso become enhanced. This was the case when Apple introducedthe iPhone featuring a touch screen interface, which improvedthe performance of many features that were previously ratherdisregarded on other devices (e.g., internet browsing, location-based services, photo slideshow...). Second, another way in whichfeature relations can enhance value is by triggering each other(Markman & Gentner, 1993). When using one feature, consumerscan come to think about another feature and start using it aswellor in other words, feature relations facilitate the adoption

20 chapter 2

of new product features. For example, when taking a picture ona 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 eachother, consumers gain deeper knowledge about a particular prod-uct; that is, they become more like experts, characterized not justby more knowledge, but generally, more richly interconnectedknowledge (Alba & Hutchinson, 1987; Gregan-Paxton, Hibbard,Brunel, & Azar, 2002, p. 535). Our first hypothesis pertains to thisknowledge. Deeper and more interconnected knowledge tendsto 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 onproduct value.

The contribution of feature relations to product value should bein addition to the other benefits already provided by the newproduct. If feature relations contribute to product value (as sug-gested in H1), they do so by providing additional benefits thatshould impact product value over and above the products mainbenefits. 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 areno substitute for that. Instead, feature relations provide informa-tion to consumers that cannot be captured by the main benefitsalone. As the linkages are not included in a products main ben-efits (e.g., a touch screen is often marketed as a touch screen andnot as an enhancement to other features), emphasizing or high-lighting (some of the) feature relations should provide additionalvalue over and above the main benefits of a product. Thus, wepropose:

H2: The number of feature relations contributes to product valueover and above a products main benefits.

product value and learning costs 21

2.2.1 Study 1

Study 1 examines how feature relations contribute to productvalue. To establish that people account for feature relations whenthinking about products, we conducted a qualitative study with40 consumers between the ages of 2545, 19 women and 21 men.In a thought-listing task consumers were asked to talk out loudwhile evaluating three new products/services for which the valuewas unknown or uncertain: a new mobile phone, a PDA watch,and an Internet service accessible through mobile phones. For allproducts, feature relations were examined by coding the thoughtlistings. A feature relation was coded in the data if consumersexplicitly linked two features to each other. For example, if con-sumers said, a larger screen improves the functionality of thecamera, which makes the phone well suited for taking pictures,a feature relation was considered to exist between the featuresscreen and camera (screen! camera! good pictures).Two coders (one nave to the purpose of the study) coded the par-ticipants responses. The interrater reliability was .74. The codingof the feature relations was not always straightforward. Someconsumers mentioned relations only implicitly, for example, bymentioning the end state (good pictures) of the relations, butno relations per se. To ensure a robust procedure, we system-atically coded only the relations that were explicitly mentionedby consumers (i.e., the co-occurrence of two features and theirexpected outcome). Main benefits were examined by asking con-sumers to list all of the products benefits, and counting the num-ber of benefits listed. To assess the impact on product value, weasked howmuch consumers agreed with the following statement:This product could give me real value (on a scale from 17).

2.2.2 Results

The participants listed on average 0.89 feature relations (range:04), and 2.17 main benefits (range: 06). The most common rela-tions were screencamera, buttonsSMS, cameraMMS, rotating-screencamera for the mobile phone; screentyping, buttonsoft-

22 chapter 2

ware, screensize for the PDA watch; and linksnavigation, loca-tion-sensitivityticket-ordering, built-in-payment-functionalityticket-delivery for the Internet service. Some illustrative quotesabout feature relations are provided below:

Having a rotating screen really makes it easier totake pictures. It gives you flexibility that a regularscreen does not provide (male, 35) (rotating screen! taking pictures! improved flexibility)

A large screen really makes all the difference whenwatching videos...I like this phone (female, 34) (largescreen! watching videos! product liking)

The backlight is nice...makes it easier to view thedate and time ...This PDAwatch is pretty cool...(male,35) (backlights! date and time! product liking)

H1 proposed that feature relations have a positive impact onproduct 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 featurerelations contributes to product value over and above the prod-ucts main benefits. This hypothesis was examined by conductingtwo regressions: the first regression examined the impact of mainbenefits on product value (Y = 0 + 1) and the second one con-sidered the simultaneous impact of main benefits and additionalbenefits (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 animpact on perceived product value. The second regression wasalso significant, both for additional benefits ( = .274, t = 3.21, p< .01) and main benefits ( = .277, t = 3.25, p < .01), which meansthat additional benefits derived from feature relations have an im-pact on product value over and above the effect of main productbenefits.

product value and learning costs 23

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 newproducts, features can be classified according to their innovative-ness. For example, in a car, a new feature may come in the formof (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, sincethey build on the value trajectories of existing products (Chris-tensen, 1997). In contrast, radical product innovations involve theintroduction of completely new features that bring new perfor-mance to the product (Hoeffler, 2003). They involve much greaterlearning-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 productvalue, while at the same time reducing learning costs. In productswith incrementally new features (where the learning costs aretypically low), feature relations will mainly affect perceptions ofproduct value. Yet, in products with radically new features, fea-ture relations should affect both perceptions of learning costs andproduct value. Further, given that the value-enhancing potentialis greater for radically new features, additional benefits derivedfrom feature relations should have a greater impact on productvalue 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 existingfeatures in a product. We propose the following hypotheses:

H3: In products with radically new features, a priori feature re-latedness will lead to reduced learning costs for the product.

24 chapter 2

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 valuewill 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 valueand 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 valuewill mediate the effect of a priori feature relatedness on productevaluation.

H7: In products with radically new features, product value andlearning 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 forStudy 2: the first pre-study assessed the a priori relatedness be-tween existing and new product features; the second pre-studytested 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 bothby short life and innovation cycles offering ample opportunityfor adding new features (both incrementally and radically newfeatures).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

product value and learning costs 25

years; 19% female).Procedure and design. Both studies involved participants filling

in a three-page questionnaire about mobile phone features, whichtook about 510 minutes to complete. For the first pre-study, a 5(existing feature) x 3 (new feature) mixed factorial design waschosen to test for all combinations of an existing feature witheach 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 evaluatedthe 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-videoplayer 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 theyagreed or disagreed with various statements. A priori related-ness between features was measured by the following item ona 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 secondpre-study participants rated the three new features in regard totheir perceived newness. Perceived newness was measured withthe following two items on 7-point Likert-scales (range: - 3 to +3): Considering the type of product, this feature seems reallynew. / 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 feature 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, anda significant interaction between conditions of the new featureand conditions of the existing feature, F(8, 78) = 3.54, p < .01.Specifically, a post hoc pairwise comparison between the newfeature longer lasting battery (10x) and the portable pocketbeamer module showed that participants rated the first to be

26 chapter 2

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Note. Lower scores correspond with disagreement, higher scores withagreement.

Figure 2.1: Ease of seeing a relation between existing and new features

significantly easier to relate to the five existing features than thesecond (p < .01). However, within levels of the existing feature(e.g., camera, SMS, audio-video player, etc.) this was moderatedby the type of new feature. For example, there was no signifi-cant difference between the battery and the beamer withinthe 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 oftheir perceived newness. A repeated measures ANOVA with per-ceived newness (consisting of two items; Cronbach = .88) as thedependent variable revealed a significant main effect, F(2, 58) =7.90, p < .01. The battery was judged significantly less new thanboth the beamer (p < .01) and the taser (p < .01). However,there was no significant difference between the beamer and thetaser (p = .65).

Based on the results of the two pre-tests, the new feature audio-

product value and learning costs 27

video player seemed particularly interesting, because it showedto be easy to relate to both the beamer and the battery. Thisvery fact served a crucial part in the experimental manipulationsof 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 lackof understanding the experimental manipulations.Procedure and design. Each participant randomly received one

out of five versions of a two-page questionnaire. Each versioncorresponded 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 suitablenew features for the study. The two features differed in regard totheir 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 toserve a crucial part in the manipulation because it had shownto be easy to relate to both the incrementally new feature bat-tery and the radically new feature beamer in pre-study 1. Allconditions involved participants reading a product descriptionfor 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 newfeature differed between conditions: battery was used in con-dition 1 and beamer in conditions 2, 3, 4, and 5. The crucialmanipulation in the beamer conditions centered on the easy-to-relate existing feature audio-video-player. In condition 2 itwas deliberately left out (thus deemphasized), that is, it was notone of the existing features listed. In conditions 3, 4, and 5, it

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replaced one of the three existing features. Thus, in the latterconditions the relation between audio-video player and thebeamer was emphasized (or made explicit). In order to controlfor 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 peoples inferences can be attributed solely to thepresence of the audio-video-player. Based on our hypotheseswe expected learning costs to be lowest and perceptions of prod-uct value to be highest in condition 1. In addition, we expectedlearning costs to decrease and inferences about product value toincrease in the three beamer conditions with the easy-to-relateexisting 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 / Itis easy to see how the new feature influences the existing fea-tures / It is easy to see how the existing features influence thenew 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 newfeature 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 usinga semantic differential (range: - 3 to + 3): not usefuluseful /badgood / I dislike itI like it / undesirabledesirable.

2.3.3 Results

Before constructing indices for each of the scales mentioned inthe previous section, we conducted analyses of internal consis-tency. Cronbach estimates of internal consistency were as fol-

product value and learning costs 29

lows: .73 for a priori feature relatedness, .89 for learning costs,.81 for product value, and .82 for product evaluation. Thus, forall 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 mainlydue to significant differences (all ps < .01) between condition1 (battery) and each of the beamer conditions (conditions2, 3, 4, and 5). In our hypotheses, H3 and H4, we specificallypredicted differences between condition 2 (lower a priori relat-edness: none of the existing features was easy to relate with thenew feature) and conditions 3, 4, and 5 (higher a priori related-ness: one of the existing features was easy to relate with the newfeature). For learning costs, H3, no significant differences (all ps> .05) were found between condition 2 and conditions 3, 4, and5. However, for product value, H4, results were more promising:condition 2 (mean = - .01, SD = 1.48) differed significantly (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, =.348, t = 1.772, p < .10), product value (path b, = .187, t = .954,

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p > .10), and product evaluation. The results for the incremen-tally new feature show that there is a direct link between a priorifeature relatedness and product evaluation. A priori feature relat-edness influences product value but not learning costs. However,the mediating role of product value between a priori feature relat-edness and product evaluation was not significant; the influenceof a priori feature relatedness was reduced from = .418 to =.348 when product value was entered into the regression. To addrobustness to the mediation results, we checked for mediationalso by using the PROCESS SPSS macro by Hayes (2013), whichprovides estimates of the indirect effects on product evaluation.The results from this analysis did not reveal a significant indirecteffect of product value, = .06 (S.E. = .06), with a 95%-confidenceinterval from - .01 to .29, or of learning costs, = - .03 (S.E. = .04),with a 95%-confidence interval from - .14 to .03. Thus, H6 receivesno support for the mediating effects of neither product value norlearning-cost inferences.Mediation analysis for the radically new feature. As before, we first

regressed a priori feature relatedness on product evaluation (pathc, = .365, t = 4.181, p < .01). Then, we regressed a priori featurerelatedness on learning costs (path a, = - .215, t = - 2.351, p< .05), and product value (path a, = .448, t = 5.352, p < .01).Next, we examined the link between a priori feature relatedness(path c, = .107, t = 1.212, p > .10), learning costs (path b, =- .177, t = - 2.251, p < .05), product value (path b, = .489, t =5.67, p < .01) and product evaluation. Following this equation,we regressed a priori feature relatedness (path c, = .336, t =3.781, p < .01) and learning costs (path b, = - .134, t = - 1.511, p> .10) on product evaluation. Finally, we conducted a regressionanalysis on the link between a priori feature relatedness (path c, = .154, t = 1.760, p < .10), product value (path b, = .470, t= 5.381, p < .01), and product evaluation. The results show thatproduct value mediates the link between feature relations andproduct evaluation; the influence of a priori feature relatednesswas reduced from = .365 to = .154 when product value wasentered into the regression. To add robustness to the mediationresults, we again checked for mediation also by using the PRO-CESS SPSS macro by Hayes (2013). The results from this analysis

product value and learning costs 33

revealed a significant indirect effect of product value, = .19 (S.E.= .06), with a 95%-confidence interval from .10 to .32, but not oflearning costs, = .03 (S.E. = .03), with a 95%-confidence intervalfrom .00 to .11. Thus, for the radically new feature, H7 receivessupport for the mediating effect of product value. Learning costs,however, again were not mediating between feature relations andproduct evaluation.

2.4 general discussion

For companies, a popular strategy to innovate entails adding newfeatures to existing products in anticipation that consumers per-ceive the new features as adding extra value. This in turn is be-lieved to benefit sales in the long run. In many mature marketsintroducing new product features is indeed a viable and reliablestrategy to increase or maintain market share (Levitt, 1980; Porter,1985; Tholke et al., 2001). The downside of this NPD strategy is,however, that due to the increasing amount of features in prod-ucts, consumers have become subject to feature fatigue (Dua etal., 2009; Thompson et al., 2005). We propose in this chapter thataccounting for relatedness between features (i.e., the relations/-connections among product features) provides companies withvaluable insights that can be applied to promote positive out-comes and avoid potential negative outcomes, which taken to-gether will increase the chances for market success in the intro-duction of new product features. This proposition was tested intwo studies.The results of the first study showed that consumers were able

to see and comment on feature relatedness in products. Further-more, the number of feature relations that consumers recognizedhad a positive influence on product value, over and above the ef-fect posited by the products main benefits. These results indicatethat consumers see more value in a product if they are framed tothink about the relations among features because these relationsbring benefits to the product that are otherwise difficult to grasp.The results from our second study, in which we manipulated

a priori feature relatedness experimentally, corroborate this find-ing further. A priori feature relatedness showed to enhance prod-

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uct value, which was true both for incremental and radical in-novations. Additionally, comparing the -coefficients for the twotypes of innovations (two types of new features) our results ap-proached significance, indicating that the effect of a priori featurerelatedness was greater in the case of radically new features.In further mediation analyses, product value showed to medi-

ate the effect of a priori feature relatedness on product evaluationfor the radically new features. These results suggest that informa-tion derived from a priori feature relatedness helps consumersto better understand the value of radically new features. This isan important result, as, especially in the case of radically newproducts, market success depends greatly on consumers under-standing of the value of these features. Thus, by deliberately em-phasizing certain relations while deemphasizing others, it maybe possible to bridge the gap between potential and perceivedproduct value, and thus maximize chances for market success.In our second study we did not find the hypothesized effect

of a priori feature relatedness on learning costs. Why exactlythis was the case remains unclear. One possible explanation maybe the nature of the experimental task, which engaged partici-pants to think about a new product from quite a distance andin rather general and abstract terms. In line with such an expla-nation, Liberman and Trope (1998) showed in a series of experi-ments that the more distant a future event (e.g., product purchaseor use) the more value is given to high-level (or abstract) as op-posed to low-level (or concrete) aspects: that is, participants putmore emphasis on the desirability of an activitys end state (ben-efits or value associated with the end state) than the feasibility ofattaining this end state (necessary investments in time and effort).Product value and learning-cost inferences may differ in the sameway in the extent that they rely on high-level versus low-level pro-cessing. Similar results were reported by Thompson et al. (2005)who found that consumers give more (less) weight to a productscapability than its usability before (after) use.

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2.4.1 Limitations and suggestions for further research

We acknowledge certain limitations of our research. First, weused an experimental setting in which products were describedusing a limited set of features. However, in many products thenumber of features is typically much higher and can amountto dozens of features (e.g., in a car). As feature fatigue will bestrongly related to the number of features in a product, more re-search is needed that addresses the effect of feature relatednessin such situations. Further, more research is also needed to es-tablish to what degree the effect that was found is due to anincrease in perceived value of the new feature or to a decreasein learning costs. If the effect is mostly due to product value,feature relatedness is likely to exert its biggest influence beforeproduct purchase, when consumers estimate the potential valueof a product; if the effect is mostly due to learning costs, featurerelatedness will exert its biggest influence after product purchase,that is, when consumers are about to use a product. In our studywe did not find an effect of feature relatedness on learning costs.As argued above, the absence of this effect may be, to a greatpart, due to consumers having difficulties imagining the amountof learning that is needed. New research that engages partici-pants in actual product use might shed some light on this, as itis likely to shift participants attention on more concrete (or low-level) aspects of the product offering that are crucial in estimatinglearning costs or product usability (Liberman & Trope, 1998).

2.4.2 Conclusions and managerial implications

In general, the results of our study highlight the importance ofproviding consumers with linkages across features. By showinghow features are connected, consumers are encouraged to con-sider the product more fully and consequently to make moreinformed decisions about the products functionality. Feature re-latedness is particularly relevant when learning about radicallynew features. Providing relations (or linkages) between a newfeature and existing features will help consumers to see the ben-efits regarding a radically new feature more fully.

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From a companys perspective, it is beneficial to account forfeature relatedness both in the development and realization phaseof a product. Both options are discussed below.Accounting for feature relatedness in product development. Feature

relatedness provides companies with guidance in NPD; knowl-edge about the value of different relations/linkages can be ap-plied to the design of a product already at this stage. Throughdesign it is possible to show how certain features relate to eachother, which not only changes peoples perception, but, more im-portantly, also peoples inferences and evaluations of the prod-uct, depending on the relations that are brought to attention.In essence, when a company wants to communicate a new in-novative feature, it can do so already in the design of the newproduct (Berkowitz, 1987; Creusen & Schoormans, 2005; Crilly etal., 2004), simply by highlighting certain relationsrelations thatmight otherwise go undetected and thus remain unappreciatedby consumers. An excellent example for such a missed opportu-nity marks the introduction of the Honda Civic Hybrid. Whenthe Honda Civic Hybrid was introduced to the U.S. market inspring 2002 it looked just like any regular compact car. Nothingin the design (of the chassis) would give away any of its rev-olutionary hybrid interior. Toyota, on the other hand, followeda different strategy with the introduction of their second gener-ation Prius in 2003 by opting for a design that was markedlydifferent from what consumers were accustomed to. In additionto aerodynamic advantages (less drag), the new design helpedin distinguishing the Prius from regular fuel-powered cars of itsclass. Taken together, this helped to underscore one of its mainbenefits, the superb mileage, which cumulated in a yearly dou-bling in the number of sales in the years from 2003 to 2006, whilesales for the Honda Civic Hybrid remained almost unchangedduring the same period (C. Johnson, 2011).Accounting for feature relatedness in product realization. Feature

relatedness provides companies with guidance in the marketingof new products. In addition to communicating product featuresand their relations through design, feature relations can be cap-italized on also during product launch, for example in the ad-vertising and promotion of new products. Knowledge about the

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value of different linkages can be applied strategically to comple-ment any emphasis taken already in the design of the product.In the example of the hybrid car, advertising should bring outclearly how the new electric propulsion system interacts withthe old IC-engine-based propulsion system; for instance, howenergy-savings in switching the engine on and off when acceler-ating, breaking or coasting, result in this superb mileage. Theknowledge provided need not be very detailed necessarily (e.g.,giving an accurate account of the technology behind it); what isimportant is that a new feature is not isolated from but connectedto other existing features, such that its value and its relevance be-come clear. Consequently, accounting for feature relatedness alsoin the marketing of products provides companies with an addi-tional way of maximizing the chances of product success.Clearly, the success of a new product depends on many vari-

ables. In addition to the vast knowledge available, we showedthat feature relatedness is an important variable to take into ac-count. Our research revealed that consumers resort to featurerelations when thinking about new products and that the veryrelations they recognize affect the value and the benefits theyinfer. The benefit for companies is clear: clever highlighting offeature relations enables companies to communicate a new prod-ucts value more effectively and thereby contributes to its successon the market.

3ADDING TO OR DELET ING FEATURES FROMNEW PRODUCTS ? THEN CONS IDER BOTH GOALCONGRUENCE AND GOAL RELATEDNESS 1

3.1 introduction

In current high-technology markets, new products are often theresult of adding features from different categories into a newproduct (Han, Chung, & Sohn, 2009). Products such as smart-phones or tablets combine an ever-increasing number of featuresthat were once available only in separate products (e.g., takingpictures, listening to music, getting directions were once avail-able exclusively in cameras, music players and GPS devices, re-spectively; Nielsen, 2012). Such products have now become thenorm in the consumer electronics market, lead by product manu-facturers such as Apple, Samsung, and Google, whose productsaim to become multifunctional hubs for the consumer and for thehome.For a product developer, the key dilemma related to such prod-

ucts is what features to include. There is a plethora of potentialfeatures that could be added to any given electronics product,and the product developer has to make a choice between an al-most infinite array of options. Merely providing something thatis different or new will not guarantee market success, andhence, the choice of what features to include becomes of criticalimportance.To examine what features might add value to such products,

prior research by Gill (2008) focused on goal congruence as a pre-dictor of added value. Gills results showed that the goal congru-

1 This chapter is an adaptation of Gattol, V., Sksjrvi, M., Gill, T., & Schoormans,J. (2011). To relate or not to relate How feature relatedness contributes to product value.Paper presented at the Annual North American Conference of the Associationfor Consumer Research, St. Louis, MO. It is currently under review at a peer-reviewed journal.

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ence the degree to which the base product and the added fea-ture are similar in terms of the goals they fulfill for the customer(hedonic or utilitarian)was a significant predictor of added va-lue. The effect was found to be asymmetrical: whereas addingan incongruent hedonic feature (Satellite Radio) to a utilitarianbase (PDA) increased product value, adding an incongruent util-itarian feature (Yellow Pages) to a hedonic base (MP3 Player)did not. This effect illustrates the importance of finding the rightfeatures to add to a given base product, as some types of featureshave a greater effect on product value than others. In addition,the nature of the base productutilitarian versus hedonicalsoplays a role in determining the fit of added features. That said,there might be also other sources of fit that affect product valuealongside goal congruence. For example, adding an incongruentutilitarian feature (such as Bluetooth headphones capability) toa hedonic product (MP3 player) would seem to increase productvalue as it enhances the usage of the product, despite not fittingthe effect proposed by Gill (2008). Accordingly, another source offit to consider in this context would be goal relatedness. Goal re-latedness refers to the degree to which goals are related (but notnecessarily similar on the hedonic versus utilitarian dimensions)to each other. Related goals have been shown to contribute toproduct value, and would thus seem to be an important sourceof fit (Fishbach & Zhang, 2008) to consider alongside goal con-gruence. Its inclusion is likely to have important implications forproduct developers working with new products and feature com-binations. Goal relatedness may, for instance, explain a greaterproportion of value of feature combinations and account for find-ings that cannot fully be explained by goal congruence alone.In the present research, we focus on the role of goal related-

ness as an additional source of fit in the context of new productsalongside goal congruence. We examine the role of goal related-ness and goal congruence in the context of both feature addi-tions (Study 1) and feature deletions (Study 2). Although previ-ous