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The Psychology of Category Labels, Category Organizations and Their

Interplay:

Empirical Essays on the Socio-Economic Effects of Type-Based and Goal-Based

Similarity in Mass Customization Contexts

D I S S E R T A T I O N

of the University of St. Gallen,

School of Management,

Economics, Law, Social Sciences

and International Affairs

to obtain the title of

Doctor of Philosophy in Management

submitted by

Marcel Mazur

from

Germany

Approved on the application of

Prof. Dr. Andreas Herrmann

and

Prof. Dr. Torsten Tomczak

Dissertation no. 4357

Rosch-Buch, Scheβlitz 2014

The University of St. Gallen, School of Management, Economics, Law, Social

Sciences and International Affairs hereby consents to the printing of the present

dissertation, without hereby expressing any on the views herein expressed.

St. Gallen, October 22, 2014

The President:

Prof. Dr. Thomas Bieger

In memory of my grandfathers

Mieczyslaw Mazur & Helmut Buchecker

Preface

This PhD thesis was written during my time as a research associate at the Center for

Customer Insight at the University of St. Gallen (CCI-HSG). What began with the

comparison of opportunities for meaningful category labels and category organizations

of identical product attributes within car configurators quickly developed into a

promising research topic. More than three years passed before the completion of this

cumulative dissertation with an introductory essay and three academic essays. I would

like to take this opportunity to thank the people who have supported me on my journey

through the various stages of the PhD process with their highs and lows.

Special thanks are due first and foremost to my supervisor Prof. Dr. Andreas

Herrmann and my co-supervisor Prof. Dr. Torsten Tomczak for helping me to fulfil

my wish to do a doctorate at CCI-HSG, for creating the conditions needed to make this

doctoral thesis a success and for their specialist and personal support. I would also like

to thank Prof. Dr. Michael Gibbert for his constructive comments that continuously

steered me in the right direction. Likewise, thanks are due to my colleagues at CCI-

HSG for the agreeable working relationship. I would in particular like to highlight the

contribution of Christian Hauner, who made me laugh even in stressful situations and

never let me down when it came to method-related questions. My thanks also go to

Zijian Pu, Lucas Beck, Dr. Philipp Scharfenberger and Dr. Kai Kruthoff for their

willingness to listen and their valuable advice.

I dedicate this PhD thesis to those people who are closest to me. Above all, I would

like to thank my parents Claudia and Marek, who laid the foundation for my education

through the care and unfailing support they have given me throughout my life. Sylwia

Kucharczyk, my future wife and best friend, also deserves my most heartfelt thanks,

for giving me the backing needed with her patience, confidence and constant feeling of

security in all stages of the PhD process and creating the balance required for

mastering everyday life. I would also like to thank my brother Michael for numerous

encouraging conversations and the unforgettable time we spent living together in

Munich during my PhD studies as well as my soon-to-be parents-in-law Bożena and

Marian for their loving support.

St. Gallen, October 2014 Marcel Mazur

In Gedenken an meine Grossväter

Mieczyslaw Mazur & Helmut Buchecker

Vorwort

Diese Doktorarbeit entstand während meiner Zeit als wissenschaftlicher Mitarbeiter

am Center for Customer Insight der Universität St. Gallen (CCI-HSG). Was mit dem

Vergleich von Möglichkeiten für die sinnvolle Kategorie-Benennung und -Anordnung

gleicher Produktattribute in Fahrzeug-Konfiguratoren begann, entwickelte sich schnell

zu einem vielversprechenden Forschungsthema. Mehr als drei Jahre vergingen bis zur

Fertigstellung dieser kumulierten Dissertation bestehend aus einem einleitenden

Dachbeitrag und drei wissenschaftlichen Aufsätzen. Ich möchte diese Gelegenheit

nutzen und den Personen danken, die mich auf meinem Weg durch die zahlreichen

Promotionsphasen mit ihren Höhen und Tiefen begleitet und unterstützt haben.

Mein besonderer Dank gebührt in erster Linie meinem Doktorvater Prof. Dr. Andreas

Herrmann und meinem Korreferenten Prof. Dr. Torsten Tomczak für die Erfüllung

meines Wunsches, am CCI-HSG zu promovieren, die Schaffung der Voraussetzungen

für das Gelingen dieser Doktorarbeit sowie ihre fachliche und persönliche

Unterstützung. Ein spezieller Dank gilt auch Prof. Dr. Michael Gibbert für seine

konstruktiven Anmerkungen, die mich stets in die richtige Richtung leiteten. Ebenfalls

bedanke ich mich bei meinen Kolleginnen und Kollegen am CCI-HSG für die

angenehme Zusammenarbeit. Hervorheben möchte ich Christian Hauner, der mich

selbst in stressigen Situationen zum Lachen brachte und mich bei methodischen

Fragen nie im Stich liess. Für ihre stets offenen Ohren und wertvollen Ratschläge

danke ich Zijian Pu, Lucas Beck, Dr. Philipp Scharfenberger und Dr. Kai Kruthoff.

Ich widme diese Doktorarbeit jenen Personen, die mir persönlich am nächsten stehen.

Allen voran danke ich meinen Eltern Claudia und Marek, die mit ihrer fürsorglichen

Erziehung sowie ihrer ausnahmslosen Unterstützung und Förderung meines bisherigen

Lebensweges den Grundstein für meine Ausbildung gelegt haben. Mein herzlichster

Dank gilt ebenfalls meiner zukünftigen Ehefrau und besten Freundin Sylwia

Kucharczyk, die mir mit ihrer Geduld, Zuversicht und dem ständigen Gefühl von

Geborgenheit in allen Phasen der Dissertation den nötigen Rückhalt gab und mir die

nötige Balance zum Arbeitsalltag verschaffte. Danken möchte ich ebenfalls meinem

Bruder Michael für die vielen aufmunternden Gespräche und die unvergessliche WG-

Zeit in München während des Doktorats sowie meinen baldigen Schwiegereltern

Bożena und Marian für ihre liebevolle Unterstützung.

St. Gallen, im Oktober 2014 Marcel Mazur

Summary

Companies can choose from several methods to determine meaningful category labels

and category organizations for the same product information at their various touch

points. Whereas some marketers opt for organizing and labeling their products in a

type-based or taxonomic way by shared attributes but different benefits (e.g.,

organizing all creams beneath the category label “Creams”), others prefer a goal-based

or thematic categorization by different attributes but shared benefits (e.g., organizing

all anti-aging products beneath the category label “Anti-Aging”).

Following the growing need-orientation of consumers, practitioners are increasingly

implementing such a goal-based customer communication at their various touch points

to establish a holistic customer experience and build long-lasting relationships. What

seems to be intuitively promising in practice has not been sufficiently investigated in

science: although evidence from psychology points to differing behavioral effects of

type-based and goal-based similarity, the conditions under which each type of

similarity is more promising remain unclear. This PhD thesis closes this research gap

via three academic essays by using mass customization contexts. The replication of car

configurators as widely used mass customization systems enables to examine the

impact of type-based and goal-based category labels, category organizations and their

interplay on socio-economic parameters in large assortment contexts.

The introductory essay defines the research questions and describes the interlinking of

the three essays. Essay I explores the effects of type-based and goal-based category

labels on economic parameters for constant product information using mental

accounting as a theoretical lever. The results reveal a budgeting-attenuating effect for

goal-based category labels with a moderating impact of the preference for budget

tracking, which can be partially explained by choice uncertainty. Essay II disentangles

category labels and category organizations to examine their interplay for similar and

dissimilar forms of similarity. The results not only indicate that the disentanglement

matters but also that knowledge moderates the interplay. Based on Essays I and II,

Essay III adds several socio-economic variables to the conceptual model and provides

a roadmap to practitioners for the stepwise establishment of need-based touch points

without depleting the customers.

In summary, this research provides new insights at the theoretical intersection of

similarity, categorization and mass customization along with practical implications for

the optimal design of customer touch points and customer segmentation decisions.

Zusammenfassung

Unternehmen nutzen unterschiedliche Wege für die Kategorie-Benennung und

-Anordnung ihrer Produkte an den Kontaktpunkten. Während die Einen ihre Produkte

attributspezifisch bzw. taxonomisch nach gleichen Attributen und verschiedenen

Nutzeneigenschaften anordnen und benennen (z.B. Anordnung aller Cremen mit der

Benennung „Cremen“), bevorzugen die Anderen nutzenspezifische bzw. thematische

Kategorien mit verschiedenen Attributen und gleichen Nutzeneigenschaften (z.B.

Anordnung aller Anti-Age Produkte mit der Benennung „Anti-Aging“).

In Zeiten der Bedürfnisorientierung nutzen Praktiker vermehrt eine nutzenspezifische

Kommunikation an den Kontaktpunkten, um ein holistisches Kundenerlebnis zu

realisieren und langfristige Kundenbeziehungen aufzubauen. Was für Praktiker

vielversprechend klingt, ist wissenschaftlich noch unerforscht: Obwohl Ergebnisse aus

der Psychologie divergierende Verhaltenseffekte durch attribut- und nutzenspezifische

Ähnlichkeitsformen zeigen, ist unklar, wann welche Form vorzuziehen ist. Diese

Dissertation schliesst die Forschungslücke mit drei wissenschaftlichen Aufsätzen. Der

Fokus auf Fahrzeug-Konfiguratoren ermöglicht die Analyse der Effekte von attribut-

und nutzenspezifischen Kategorie-Benennungen und -Anordnungen sowie ihrem

Wechselspiel auf sozio-ökonomische Parameter für grosse Sortimente.

Der Dachbeitrag beschreibt die Forschungsfragen und den Zusammenhang der drei

Aufsätze. Aufsatz I nutzt die Mental Accounting Theorie, um die Effekte von attribut-

und nutzenspezifischen Kategorie-Benennungen auf ökonomische Parameter zu

untersuchen. Die Ergebnisse zeigen einen abnehmenden Mental Accounting Effekt für

nutzenspezifische Kategorie-Benennungen sowie einen moderierenden Einfluss durch

die Präferenz für Mental Accounting, der partiell durch die Unsicherheit erklärt wird.

Aufsatz II differenziert zwischen Kategorie-Benennungen und -Anordnungen, um ihr

Wechselspiel für gleiche und ungleiche Ähnlichkeitsformen zu untersuchen. Die

Ergebnisse zeigen die Wichtigkeit der Differenzierung und, dass das Wechselspiel von

dem Produktwissen moderiert wird. Aufsatz III ergänzt das Forschungsmodell um

sozio-ökonomische Faktoren und bietet Praktikern einen Leitfaden für die schrittweise

Umsetzung von nutzenorientieren Kontaktpunkten ohne ihre Kunden zu überfordern.

Zusammenfassend liefert die vorliegende Dissertation neue Erkenntnisse an der

theoretischen Schnittstelle zwischen Ähnlichkeitsformen, Kategorisierung und Mass

Customization sowie praktische Implikationen für das optimale Design von

Kundenkontaktpunkten sowie Entscheidungen bezüglich der Kundensegmentierung.

Table of Contents

Preface.......................................................................................................................... VI

Vorwort ..................................................................................................................... VIII

Summary.......................................................................................................................IX

Zusammenfassung ..........................................................................................................X

A. Introductory Essay ................................................................................................. 1

Mazur, M. (2014). The Psychology of Category Labels, Category Organizations

and Their Interplay. Unpublished Manuscript.

B. Essay I .................................................................................................................... 43

Mazur, M. and Herrmann, A. (second round). The Power of Category Labels:

Exploring the Moderating Role of Budget Tracking in Spending and Payment

Decisions. Journal of Economic Psychology.

C. Essay II................................................................................................................... 91

Mazur, M., Herrmann, A., & Gibbert, M. (submitted). The Beauty of Moderately

Incongruent Similarity: How the Disentanglement of Category Labels and

Category Organizations Drives Satisfaction with Mass Customization Decisions.

Psychology & Marketing.

D. Essay III ............................................................................................................... 117

Mazur, M. (2013). Bedürfnisorientierte Gestaltung von Kontaktpunkten [Need-

Based Design of Customer Touch Points]. Marketing Review St. Gallen, 30(6), 34-

49.

E. Curriculum Vitae ................................................................................................ 139

-1-

A. Introductory Essay

Mazur, M. (2014). The Psychology of Category Labels, Category Organizations and

Their Interplay. Unpublished Manuscript.

-2-

The Psychology of Category Labels, Category Organizations

and Their Interplay

Marcel Mazur (1)

(1) Marcel Mazur is a Doctoral Candidate of Management, Center for Customer

Insight, University of St. Gallen, Switzerland ([email protected]).

A. INTRODUCTORY ESSAY

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1 Forms of Similarity as a Categorization Principle

Starting at birth, consumers acquire rules (Schmitt & Zhang, 1998) and expectations

(Sujan, 1985) for categorizing information (Alba & Hutchinson, 1987; Barsalou, 1992;

Rosch & Lloyd, 1978; Rosch & Mervis, 1975; Rosch, Simpson, & Miller, 1976; Sujan

& Dekleva, 1987; Sujan & Tybout, 1988). Information is typically organized into

various categories according to its similarity, either implicitly by consumers (e.g.,

mental representation in the brain) or explicitly by practitioners (e.g., product

presentation), and labeled accordingly. Individuals are exposed to methods of

categorizing information that reoccur more frequently and ultimately construct specific

choice heuristics in line with their future expectations (Barsalou, 1983, 1985; Biehal &

Chakravarti, 1982; Hutchinson, Raman, & Mantrala, 1994; Morales, Kahn, McAlister,

& Broniarczyk, 2005; Ratneshwar & Shocker, 1991). Expectations refer to the

strengths of beliefs regarding the future based on prior experience (Alloy &

Tabachnik, 1984; Bettman, 1979), are incorporated into multi-attribute choice models

as reference points against which observed information is compared (Bettman, 1978;

Chiesi, Spilich, & Voss, 1979; Mandler & Parker, 1976; Oliver & Winer, 1987) and

thus serve as benchmarks for congruency judgments (Stayman & Alden, 1992).

Research on categorizing information is directly connected to cognitive science related

to similarity (Alloy & Tabachnik, 1984; Medin, Goldstone, & Gentner, 1993; Murphy

& Medin, 1985; Ratneshwar, Barsalou, Pechmann, & Moore, 2001; Rips, 1989; Rosch

& Mervis, 1975; Smith & Medin, 1981; Tversky, 1977). Research on similarity

distinguishes two major mechanisms with different degrees of expectation by which

the same information can be related to each other: expected feature comparison (i.e.,

taxonomic or type-based similarity) and unexpected benefit integration (i.e., thematic

or goal-based similarity) (Estes, 2003a; Estes, Golonka, & Jones, 2011; Estes & Jones,

2009; Golonka & Estes, 2009; Lin & Murphy, 2001; Wilkenfeld & Ward, 2001;

Wisniewski, 1997; Wisniewski & Bassok, 1999; Wisniewski & Love, 1998). Type-

based similarity can be traced to the structural alignment model (Gentner & Markman,

1997; Markman & Gentner, 2000) and the contrast model (Tversky, 1977) and is

considered scenario-independent as well as internal due to shared properties and roles

of the considered information within the same attribute (e.g., pizza and spaghetti share

the same role as food but do not complement one another). By contrast, goal-based

similarity describes the context-dependent beneficial, functional or need-based

relationships between different attributes (e.g., spaghetti and tomato sauce are

associated with each other only in the eating context and complement one another) and


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Forms of Similarity

Type-Based Taxonomic

Goal-Based Thematic

• Attribute-specific Within-category comparison

• Feature-based • Concrete (How?) • Congruity • Alignable • Internal • Constant • Context-independent • Similarity-focused

• Benefit-specific • Between-category

integration • Associative • Abstract (Why?) Incongruity

• Non-Alignable • External • Temporal • Context-dependent • Dissimilarity-focused

is therefore characterized by externality. While the consideration of the same attributes

within type-based relationships prompts individuals to focus on dissimilarities among

items, associatively connected attributes based on shared benefits or goals prompt

individuals to focus on similarities. Furthermore, goal-based similarity is characterized

by complementarity (Estes et al., 2011) because it occurs between at least two things

with different roles in a scenario (e.g., cows and milk do not have the same roles but

complement each other). Finally, whereas type-based similarity is constant over time

(e.g., cats and dogs are always related), goal-based similarity is characterized by

spatial and temporal relationships among objects (e.g., popcorn and movies are only

related in the cinema context) (Ji, Zhang, & Nisbett, 2004). Figure 1 summarizes the

differences between type-based and goal-based forms of similarity.

Figure 1

Type-Based versus Goal-Based Similarity

The higher expectation of type-based similarity is reflected by its dominance in the

marketplace, where it constitutes widely used assortment principles (Ratneshwar,

Pechmann, & Shocker, 1996) or mass customization decisions (Levav, Heitmann,

Herrmann, & Iyangar, 2010) by organizing the same attributes together and labeling

them accordingly (e.g., organizing all shampoos together and labeling them


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“Shampoo”). Likewise, present research on categorization research generally and

specifically examining category organization (Mogilner, Rudnick, & Iyengar, 2008;

Ratneshwar et al., 1996) as well as category labels (Bettman, 1979; Lucy, 1992;

Ratneshwar & Shocker, 1991) entirely focuses on type-based information relationships

as a categorization principle for information (Farjoun & Lai, 1997; Moreau et al.,

2001; Tversky, 1977), thereby implicitly assuming that other forms of similarity do not

alter the influence exerted on relevant decision-making variables.

By categorizing information based on common properties, type-based categorization

represents a concrete and product-oriented “attribute-centric” communication that is

characterized by a technical language. Therefore, type-based similarity does not

directly address the different implied benefits or needs of products, which are the

result of large investments in market research to define tailor-made solutions that best

reflect consumers’ needs and clearly communicate the benefits of the product offer

compared to those of competitors. Thus, a dilemma has emerged that is inherent to

type-based similarity because it limits the information presented at various customer

touch points to sheer attributes and does not meet the expectations of increasingly

pluralistic customers, thus failing to properly address their needs, goals and

preferences through a series of personalized decisions. Thus, research has increasingly

emphasized the limited similarity judgments of type-based similarity resulting from a

focus on commonalities between similar attributes and has suggested that attributes be

related based on specific benefits or goals that enable complementary judgments

regarding participation in the same event (Estes, 2003a; Estes et al., 2011; Golonka &

Estes, 2009; Huffman & Houston, 1993; Lin & Murphy, 2001; Ratneshwar et al.,

1996; Ratneshwar et al., 2001; Simmons & Estes, 2008; Wilkenfeld & Ward, 2001;

Wisniewski & Bassok, 1999). Such a goal-based method of categorizing information

reflects a more promising “consumer-centric” communication that better reflects the

continuous trend toward an increasing need-based orientation in the marketplace and

can thus be expected to be better suited to the generation of compelling web

experiences in optimal mental states (Csikszentmihalyi, 1990; Hoffman & Novak,

1996; Novak, Hoffman, & Duhachek, 2003). As a result, to better address the growing

need-orientation among their customers, practitioners are increasingly replacing

product-oriented “attribute-centric” communication with more benefit-oriented

“consumer-centric” communication (e.g., by organizing different anti hair-loss

products together and labeling them “Anti Hair-Loss”).


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Although recent research in consumer behavior indicates the advantages of goal-based

similarity (Ratneshwar et al., 2001; Estes, 2003a; Golonka & Estes, 2009; Poynor &

Wood, 2010), type-based and goal-based forms of similarity have rarely been

considered jointly in marketing research, making it difficult to deduct clear theoretical

implications and determine which form of similarity is more promising in practice.

This lack of research is surprising because the two forms are activated in different

parts of the brain (Davidoff & Roberson, 2004; Lupyan, 2009; Sass, Sachs, Krach, &

Kircher, 2009), based on different underlying cognitive processes (Estes, 2003a,b) and

influenced by various behavioral parameters, such as information search, memory,

inference, choice and the perceived complexity of different categories (Alba &

Hutchinson, 1987; Cohen & Basu, 1987; Huber & McCann, 1982; Sujan & Dekleva,

1987). This is evidence that thematic information processing complements rather than

challenges present findings, thereby providing a more holistic view of cognition. The

lack of joint comparison of type-based and goal-based similarity in the marketing

literature is aggravated by the fact that although the limited existing research points to

significant differences between the two forms of similarity, results suggesting context-

dependent effects have been inconsistent due to different levels of analysis and

inconclusive due to several shortcomings.

Detrimental effects of goal-based similarity were observed at the superordinate brand

level in research on brand extensions. Whereas some brand extensions occur in a type-

based manner because they internally share features with the original product that

enable practitioners to utilize the advantages of the original product (Aaker & Keller,

1990), others occur in an ambiguous, goal-based manner via associative relationships

in common situations (Park, Lawson, & Milberg, 1991). In a recent article, Estes,

Gibbert, Guest, and Mazursky (2012) reported counterintuitive results indicating that

goal-based brand extensions are processed faster than type-based brand extensions in

which the new product is in the same category. The authors explain this finding via the

creation of noun compounds that combine brand names and the extension product

(e.g., “Budweiser chips”). Whereas the name “Budweiser chips” is thematically (i.e.,

goal-based) related to beer due to the joint consumption, the meaning “Budweiser

cola” is taxonomically (i.e., type-based) related to beer due to the shared feature,

liquid. To interpret their findings, Estes et al. (2012) referred to Estes (2003b), who

found that thematically related noun compounds are processed faster than thematically

related noun compounds and Labroo, Dhar, and Schwarz (2008), who found that

processing fluency amplifies product evaluations.


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Research at the more subordinate product level has extended the previously

demonstrated constraints of goal-based similarity at the brand level. Research by

Ratneshwar et al. (2001) indicated that consumers consider both sets of products from

the same category that are grouped together based on shared attributes and product

attributes from different categories that are grouped together based on shared benefits

or goals in case this grouping is in line with the consumption goals of the consumers.

Furthermore, Felcher, Malaviya, and McGill (2001) demonstrated that goal-based

similarity positively impacts consumer perceptions. They empirically identified a

positive correlation between positive product evaluations for a new product (e.g., a

chocolate bar) and the ratio of associatively categorized goal-based information (e.g.,

“cinema” scenario for nachos and popcorn) to type-based categorized information

(e.g., “supermarket” scenario for energy bars and cereal bars) when the new product

was described in a congruent context (e.g., availability of the new product in “single

and multi-packs”). The authors conclude that goal-based information relationships

improve product evaluations when provided in a congruent context. Further

detrimental effects of goal-based similarity at the product level have been identified by

Gibbert and Mazursky (2009) in their work on hybrid products, which they consider as

new products that contain features of initially separate products. The authors compared

taxonomically (i.e., type-based) similar and thematically (i.e., goal-based) dissimilar

hybrids consisting of products from same product categories (e.g., sofa bed) with

taxonomically dissimilar and thematically similar hybrids consisting of associatively

connected products from different product categories (e.g., refrigerator TV). The

results revealed a clear preference for taxonomically over thematically similar hybrids.

The most subordinate level of analysis is the attribute level, which differs only in the

method by which the same product information is categorized while keeping all other

information (e.g., products, brands) constant. The best-known essay on the attribute

level was written by Poynor and Wood (2010), who focused on the category format

(i.e., category organization) and investigated whether and how presenting the same

product information (i.e., type-based versus goal-based) impact satisfaction ratings for

a transaction. In their restaurant study, they distinguished between a type-based and a

goal-based organized restaurant menu based on the same dishes. Whereas the type-

based menu was organized by assigning all soups, sandwiches, finger foods, and

salads together, respectively, the goal-based menu was organized by assigning the

same dishes thematically to the regions “Mexican”, “American”, “Italian”, or

“Chinese”. The results revealed a higher satisfaction and a greater invested effort in

the goal-based condition in the case of higher prior knowledge. The results were


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Brand Level

Product Level

Attribute Level

• Estes, Gibbert, Guest, and Mazursky (2012)

• Ratneshwar, Barsalou, Pechmann, and Moore (2001)• Felcher, Malaviya, and McGill (2001)• Gibbert and Mazursky (2009)

• Poynor and Wood (2010)• Poynor Lamberton and Diehl (2013)Subordinate

Level

Superordinate Level

reversed for consumers with lower prior knowledge, who invested greater effort in and

were more satisfied by type-based organized product information.

In the most recent study comparing type-based and goal-based information

relationships at the attribute level, Poynor Lamberton and Diehl (2013) investigated

how forms of similarity vary the strength of preference for different product items for

constant presented information. The findings revealed that the mere selection of items

from different category organizations led to different construal levels, with a more

abstract construal in the goal-based condition. Thus, the authors identified the

construal level as a major parameter that influences the general perception of the

relationship between items. The findings indicated that consumers are more satisfied

with their most preferred option and tend to select lower-priced items when confronted

with goal-based category organizations compared to type-based category

organizations. In summary, the authors empirically demonstrated that the way in

which the same attribute information is organized influences not only satisfaction

levels, as previously demonstrated by Poynor and Wood (2010), but also economic

variables.

Taken together, the existing limited research on similarity in consumer behavior

indicates differential and inconsistent effects caused by type-based and goal-based

similarity throughout the different levels of analysis (i.e., brand level, product level

and attribute level). Figure 2 summarizes this brief outline by assigning the major

contributions to their different levels of analysis.

Figure 2

Levels of Analysis of Research on Similarity in Consumer Behavior


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2 Research Gaps and Scope of the Dissertation Project

Following the brief introduction to the research context and the outline of the limited

existing research on similarity in consumer behavior, the present dissertation project

aims at further elucidating the effects of type-based and goal-based similarity by

addressing the shortcomings of the existing research and accounting for aspects that

might influence consumer perception. Next, two interlinked research gaps are

identified, followed by a brief description of research on mass customization that

serves as tool for operationalizing the underlying research question and applying the

research to real-world phenomena.

2.1 Disentanglement of Category Labels and Category Organizations

The detection of several moderating variables in existing research demonstrated that

type-based and goal-based similarity can have a varied impact on consumer

perceptions depending on the context. Following this and the inconsistency of previous

research due to different levels of analysis, the goal of the present dissertation project

is to further shed light on the role of type-based and goal-based similarity in

categorization decisions and their influence on major socio-economic variables. To

derive clear theoretical and practical contributions, the dissertation aims to investigate

the type-based and goal-based similarity as the basis for categorization decisions at the

most subordinate attribute level. This aim is reflected by the consideration of constant

product information throughout conditions and adequately narrows the comparison of

the forms of similarity to category decisions. Previous research on the attribute level

has been significantly flawed by being entirely based on how (i.e., type-based versus

goal-based) the same product is organized, thereby neglecting changes in the category

labels caused by changes in the category organizations.

In contrast to the existing literature, this research aims to further subdivide the impact

of type-based and the goal-based categorization of the same information at the

attribute level into different aspects of a category, namely the manner in which product

items are arranged (i.e., category organization) and the name of the group of arranged

items (i.e., category label). Thus, the present dissertation is the first research on

similarity in marketing that disentangles the effects caused by different forms of

similarity of category labels and category organizations. This timely disentanglement

not only addresses major shortcomings within existing literature on the attribute level

but also reveals two interlinked research gaps within existing literature on the attribute

level, which are subsequently described.


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Brand Level

Product Level

Attribute Level(Category Organizations)

Attribute Level(Category Labels)

• Estes, Gibbert, Guest, and Mazursky (2012)

• Ratneshwar, Barsalou, Pechmann, and Moore (2001)• Felcher, Malaviya, and McGill (2001)• Gibbert and Mazursky (2009)

• Poynor and Wood (2010)• Poynor Lamberton and

Diehl (2013)

Research GapSubordinate Level

Superordinate Level

Interplay

Research Gap

First, the disentanglement of category labels and category organizations provides a

timely counterbalance to existing research on the attribute level that focusses on

category organizations (e.g., Poynor & Wood, 2010) but is flawed by not using

constant category labels across conditions. For example, when changing the

assortment of a restaurant menu from a type-based menu organized by dishes that are

attribute-related (e.g., all salads together) to a goal-based menu organized by dishes

that are thematically related by geographic regions (e.g., all Italian dishes together),

the category labels do not remain constant across conditions (e.g., “Salads” versus

“Italian”). This results in an uncontrolled change of category labels and category

organizations across conditions, which hinders the unambiguous ascription of the

observed effects to either one of the aspects. Second, as a result of the

disentanglement, the present research provides the first investigation of the interplay of

category labels and category organizations for similar (i.e., pure conditions) and

dissimilar (i.e., hybrid conditions) information relationships. This results in the

comparison of highly congruent, moderately incongruent and highly incongruent

information relationships based on the expected (i.e., congruent) type-based standard.

By focusing on the two interlinked research gaps, the present research not only

provides significant theoretical contributions to research on similarity and

categorization but also offers practitioners a concrete roadmap for implementing the

results. Figure 3 visualizes the two research gaps as the basis for the present research.

Figure 3

Visualization of the Research Gaps


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2.2 Operationalization of Type-Based and Goal-Based Similarity

Considering that the discrepancies in the conclusions drawn from the limited prior

research on similarity without a clear preference for any of the two forms appear to be

a function of the different domains investigated (Gibbert & Mazursky, 2009;

Noseworthy, Finlay, & Islam, 2010), this research aims at applying the present object

of investigation to a naturalistic context with high theoretical and practical relevance.

Mass customization decisions are chosen to operationalize the underlying research

questions because they are common in today’s dynamic marketplace and are the

results of active participation in the purchase process of complex products (e.g.,

automobiles) via several attribute decisions using mass customization systems (Franke,

Keinz, & Schreier, 2008; Franke & Schreier, 2010; Lancaster, 1966; Levav et al.,

2010; Rosen, 1974).

The continuous trend of customization has tremendously increased the number of

available product options and the complexity of assortments. Mass customization

systems can address this issue and the previously identified shortcomings because they

are best suited to analyzing the decision-making processes of individuals in a

naturalistic context (Pine, Peppers, & Rogers, 1995) and are automatically concerned

with categorizing (i.e., organizing and labeling) tremendous amounts of information

into meaningful categories by structuring, facilitating and individualizing the purchase

process. As a result, mass customization systems have evolved to a major customer

touch point in the purchase process that has been widely considered in science. Mass

customization systems are best suited to fit complex products to the heterogeneous

needs of customers (Dellaert & Stremersch, 2005; Franke et al., 2008, 2010; Randall,

Terwiesch, & Ulrich, 2005) and are drivers of major competitive advantages

(Fogliatto, da Silveira, & Borenstein, 2012). In addition to their substantial practical

relevance, mass customization systems are ideally suited to examine decision

processes and behaviors resulting from the direct participation of consumers as co-

producers of products. To date, this literature stream is largely concerned with the

determination of personalized measures in the customization process (e.g., assistance,

product recommendations, pricing, promotion) to decrease customer confusion (Berry,

Seiders, & Grewal, 2002; Srinivasan, Anderson, & Ponnavolu, 2002) and to improve

the quality of purchasing decisions (Häubl & Trifts, 2000). Mass customization

research has only recently begun to address questions related to similarity by

investigating the customization of decisions based on the information presented

(Thirumalai & Sinha, 2011). Taken together, mass customization systems are a


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promising tool for investigating the effects caused by the different forms of similarity

for organizing and labeling the same information on major socio-economic

parameters, including the willingness to pay or the satisfaction with the product, and

are thus used to organize and label product information in several empirical

experiments in the subsequently described essays.

To determine a specific domain that enables the replication of a mass customization

system in a naturalistic field setting within this research, six criteria were used: (1) a

well-established type-based market standard, (2) basic awareness of the product

category, (3) the configurability of the product, (4) categorization of product attributes

on the basis of plausible category labels and category organizations, (5) high

assortment sizes, and (6) high variability in the levels of product knowledge. As a

result, car configurators as major mass customization systems in the pre-purchase

phase of cars were chosen by replicating the essential configuration steps (i.e., model,

color, rims, and upholstery) of an online configurator from a German car

manufacturer. The purpose of online configurators is to organize thousands of options

into dozens of sequential categories (i.e., configuration steps) to facilitate the

configuration process for customers. The importance of online configurators within the

car industry is illustrated by Capgemini’s “Cars Online 2014” survey of more than

10,000 participants (Capgemini, 2014). The results revealed that 97% of the

participants use the internet for vehicle research and that 70% consider the car

configurator the most important website feature. Next, the three essays composing this

dissertation project are outlined.

3 Empirical Essays

Across three interlinked empirical essays, this dissertation aims to stepwise examine

category labels, category organizations and their interplay to provide greater insight

into the conditions under which consumers prefer type-based or goal-based category

labels and organizations to categorize the same underlying information. Mass

customization systems are used to address major shortcomings of existing research on

similarity by replicating the major selection steps of an online car configurator from a

German car manufacturer, which serves as basis for mass customization decisions.

Following the addressed shortcomings and previous findings, a general research

question is defined for each essay (see Table 1).


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Table 1

Underlying Research Questions of the Three Essays

Research question

Essay I

Are mental budgets strict constraints or can they be

manipulated by switching from type-based to goal-based

category labels for the same underlying information for

budget trackers and non-budget trackers?

Research question

Essay II

How does the interplay of category labels and category

organizations impact product satisfaction for novices and

experts?

Research question

Essay III

Which combination of type-based and goal-based similarity

of category labels and category organizations is most

promising for a successful touch point management in the

marketplace?

Based on the proven influence of mass customization decisions by individual-level

variables (Fogliatto et al., 2012), the present research also considers moderating

variables to examine under what conditions type-based and goal-based category labels

and category organizations influence the dependent variables of interest and a

mediating variable to understand the underlying process. The previously defined

research questions can be directly transferred into an organizing framework that

summarizes the overall scope of the dissertation project (see Figure 4).

To examine the interplay of category labels and category organizations, different

conceptual models are used in Essays II and III. Whereas the intended comparison of

pure and hybrid conditions for different levels of product knowledge in Essay II

requires moderation analysis, Essay III merely focuses on the direct effects of type-

based and goal-based similarity of category labels and category organizations on

various socio-economic parameters.


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Independent Variables

Category Label

(Essays I, II and III)

Category Organization

(Essay III)

Dependent Variables

Estimation Bias

Budget Deviation

(Essay I)

Product Satisfaction

(Essay II)

Willingness to Pay

Purchase Probability

Duration Configuration Process

Product Satisfaction

Mental Reflection

Expectancy Category Organization

(Essay III)

Moderators

Budget Tracking

(Essay I)

Product Knowledge

(Essay II)

Category

Organization

(Essay II)

Mediator

Choice Uncertainty

(Essay I)

Figure 4

Organizing Framework of the Dissertation Project

3.1 Essay I: The Power of Category Labels

Building on previous research demonstrating that consumers use resources based on

how they are labeled (Henderson & Peterson, 1992; Kahneman & Tversky, 1984;

Thaler, 1985), Essay I focuses on category labels with mental accounting as a

theoretical lever. Consumers partition their money into different categories depending


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on their plans to better control their running costs (Heath, 1995; Kahneman &

Tversky, 1984). If consumers assign expenses to a previously defined mental account,

the amount of money still available in the mental account and the probability of future

expenses within the same category decrease. Although the process of mental

accounting can influence and change consumer decisions (Heath & Soll, 1996), the

theory has not been applied to research in cognitive science on similarity at the

attribute level, and has instead focused on comparing the budgeting process of several

expenses with varied typicality ratings compared to generally used mental budgets

(Heath & Soll, 1996; Soman & Gourville, 2001). This is surprising because mental

accounting theory aims to explain the deviation of consumer behavior from that

dictated by economic theory (Duxbury, Keasey, Hao, & Shue Loong, 2005; Thaler,

1999). The theory of mental accounting is built on prospect theory, which implies that

the expected utility of an outcome is determined by the manner in which an outcome is

framed by individuals (Kahneman & Tversky, 1979). A classical and easy applicable

example illustrating the mental accounting theory and its relevance to research on

similarity is the cinema ticketing problem (Kahneman & Tversky, 1984). The authors

found that the reaction of an individual toward the purchase of a cinema ticket depends

on how the problem is framed, even though the monetary loss is constant (i.e., a lost

cinema ticket worth $10 versus the loss of the same monetary amount in cash). They

used this result to argue that individuals take efforts to mentally label their money for

different purposes and associatively assign it to different categories. This violates the

assumption of fungibility and leads to results that deviate from the standard economic

model, assuming rational decision making to maximize utility.

Theory suggests that individuals create expectations about choice architectures that

serve as reference points (Simmons & Estes, 2008) and that research on similarity,

with its high relevance for categorization decisions, provides an explanation for mental

budgeting because general categorization principles are in line with ordinary budgeting

categories (Heath & Soll, 1996; Henderson & Peterson, 1992). Comparing the

expected type-based and the unexpected goal-based forms of similarity in the context

of mental budgeting decisions appears promising because individuals regularly

categorize their expenses into several accounts to better track them against previously

set budgeting constraints for the respective budget. By building upon Heath and Soll

(1996), who reported that typical expenses are most subject to budgeting constraints,

Essay I intends to further differentiate these findings by assuming that not only the

typicality of an expense with a budgeting category, but also the form of similarity used

to label this expense matter. Thus, Essay I suggests that the form of similarity of


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category labels in the marketplace used to provide context for the information

organized beneath is of high relevance for budgeting decisions because category labels

provide evidence on how to evaluate (i.e., budget) the costs for a selected product.

In the ideal situation, mental accounting can be described as an expense-tracking

device and self-control mechanism that helps individuals to do what they rationally

should (e.g., saving money) instead of what they irrationally want (e.g., spending

money) (Antonides, de Groot, & van Raaij, 2011; Thaler, 1988; Thaler & Shefrin,

1981). The adequate accounting process requires individuals to unambiguously decide

how to budget (i.e., categorize) expenses and thus provides a rigid self-control

mechanism. Research has shown that the typicality (i.e., relevance) of an expense with

the mental account of individuals increases the probability of classification within this

category due to the activation of self-control mechanisms, which hinders the

justification for expenses, leads to more conservative spending and thus amplifies

underconsumption of future expenses (Antonides et al., 2011; Heath & Soll, 1996;

Felcher et al., 2001; Rajagopal & Rha, 2009). By contrast, failing to implement or

difficulty following a promising self-control strategy can result in overconsumption.

The unambiguous ascription of expenses to budget categories is occasionally

problematic because expenses can have a graded membership and thus be assigned to

two or more mental accounts at the same time in a justifiable way (Henderson &

Peterson, 1992; Rosch, Mervis, Gray, Johnson, & Boyes-Braem, 1976). This

ambiguity is amplified by the fact that individuals look for loopholes within financially

constraining and inflexible mental budgets to justify preferred future choices or

judgments and to engage in “creative bookkeeping” to incur a certain expense without

violating their general budgeting constraints (Cheema & Soman, 2006). Controlling

expenses is facilitated when individuals are easily able to track expenses and assign

them to different mental accounts (Rajagopal & Rha, 2009) but is significantly

hindered when the monitoring of budget categories and consumption goals is hindered

(Krishnamurthy & Prokopec, 2010). Thus, if expenses have a low typicality or can be

interpreted in several ways due to graded membership, individuals are more likely to

interpret these expenses in a way that justifies spending (Kunda, 1990). The ambiguity

associated with graded membership results in a malleable mental accounting process

that facilitates the justification of expenses because it gives individuals the flexibility

to construct new mental accounts or to assign the same expenses to different mental

accounts (Heath & Soll, 1996; Read, Loewenstein, & Rabin, 1999; Soman &

Gourville, 2001). In addition, the malleability makes it more difficult for individuals to


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keep track of the expenses. By contrast, in absence of malleability, individuals are

constrained by their determined mental budgets, which prevent them from employing

loopholes (Cheema & Soman, 2006). Thus, in addition to the cognitive component,

mental budget decisions are driven by a motivational component when assessing

expenses to mental accounts that impacts economic decisions (Heath & Soll, 1996).

Pairing these findings with research on similarity generally and category labels

specifically suggests that mental budgeting decisions for the same transaction are

subject to change depending on the underlying form of similarity. Category labels play

an important role in mental budgeting decisions because they provide specific contexts

that guide individuals in the selection and evaluations of information organized

beneath the label and are used by consumers as cognitive anchors and reference points

when confronted with complex assortments in the marketplace. By assigning different

names (i.e., labels) to the organized information, each of the sequential selection steps

within a mass customization system represents a budget category with a specific level

of typicality with the used mental budgets that resembles a reference point about how

to evaluate a transaction. Considering the differences between type-based and goal-

based similarity, the typicality can be varied by changing the contextual information

provided by the category labels while keeping the organized information constant.

Because type-based information relationships are predominant in the marketplace, they

are more expected by individuals (Poynor & Wood, 2010). The concrete and narrow

nature of type-based similarity, which eliminates overlap, increases the typicality with

budget categories, leading to the unambiguous activation of the mental account and

facilitating the overall budgeting process. By contrast, goal-based category labels can

seduce consumers to (partly) forgo budgeting constraints because they provide

individuals greater flexibility in assigning expenses to mental budgets and thus

promote the activation of more than one mental account. Thus, simply replacing type-

based with goal-based category labels while keeping the underlying product

information constant increases the malleability (i.e., ambiguity) of the budgeting

process, which decreases the need for justification and increases the budgeting options

for consumers. This results in a lower typicality with general budget categories and an

increased flexibility to activate several accounts for the same expense, thereby

amplifying economic parameters. By contrast, if the purchase steps within a mass

customization system contain type-based category labels that resemble more typical

budget categories of individuals, the probability of remaining below the indicated

budget is amplified, leading to a negative impact on economic parameters.


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Building on this, Essay I presumes that the different typicality levels of type-based and

goal-based category labels influence economic parameters such as the willingness to

pay and actual spending. To ascribe differences in economic parameters caused by

type-based and goal-based category labels to mental budgeting, relative measures for

spending and payment decisions are computed. The relative spending measure is

named “estimation bias” and calculated as the difference between the actual monetary

spending for a configured car and the initially indicated budget for a car purchase prior

to the configuration process. Accordingly, the term “budget deviation” refers to the

relative payment measure, expressed as the difference between the willingness to pay

for a configured car and the initially indicated budget for a car. Following the varied

preferences for a rigid mental accounting strategy among individuals (Thaler, 1985),

budget tracking is included as a moderating variable for the direct relationship between

category labels (i.e., type-based versus goal-based) and the two relative measures.

Furthermore, the perceived choice uncertainty is expected to partly explain the

underlying process and is thus included as a mediating variable in the conceptual

model.

A literature review is followed by two empirical studies. The results support the

hypotheses by demonstrating that the narrow type-based similarity promotes rigid

budget tracking, which mitigates economic parameters and thus leads to a negative

estimation bias and budget deviation. Importantly, the converse is true for the broader

and more ambiguous goal-based category labels, which amplify economic parameters.

The results further indicate that the direct relationship between the forms of similarity

of category labels and the relative measures is moderated by budget tracking. The

results from the main effect remain stable for budget trackers because narrow,

unambiguous and concrete type-based category labels do not provide any loopholes

and prevent shifting of budgets between categories, whereas broader, ambiguous and

malleable goal-based category labels provide room for interpretation (i.e., loopholes)

and enable budget trackers to engage in creative bookkeeping. Interestingly, the

converse is true for non-budget trackers, who are overtaxed by the complexity of goal-

based category labels and are thereby restrained from spending and paying more.

Finally, a moderated mediation analysis with choice uncertainty as the mediating

variable reveals that the moderating impact of budget tracking can be partly explained

by the perceived choice uncertainty. The partially moderated mediation can be

attributed to the different levels of justification among budget trackers (Gupta & Kim,

2000) and the different levels of expectancy among non-budget trackers (Poynor &

Wood, 2010) caused by type-based and goal-based forms of similarity.


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3.2 Essay II: The Beauty of Moderately Incongruent Similarity

Building on Essay I, Essay II extends the analysis to category organizations and

addresses two major shortcomings of existing research on similarity. First, existing

research is entirely related to category organizations and neglects the role of category

labels, which are not held constant across groups with different category organizations.

This results in an uncontrolled change of category labels and category organizations

and the inability to clearly attribute effects to either aspect. The disentanglement of

these two aspects is important because category labels contain context-specific

information and provide guidance to consumers that influence their decision behavior

(Barsalou, 1982; Bettman & Sujan, 1987). Second, both traditional research (e.g.,

Felcher et al., 2001; Simmons & Estes, 2008) and the recently emerged applied

research on similarity are limited to pairwise comparisons of attributes (e.g., dog and

cat versus dog and bone) or small assortment sizes (e.g., four dishes per category in the

restaurant study of Poynor and Wood (2010)), primarily for the sake of convenience

(Iyengar & Lepper, 2000). Such small assortment sizes lower the explanatory power of

the results and do not reflect increasing assortment sizes in the marketplace (Chernev,

2003; Gourville & Soman, 2005; Iyengar & Lepper, 2000; Schwartz, 2004).

Examining type-based and goal-based forms of similarity in a high assortment context

is highly relevant because research has long suggested that assortment size is a major

driver of choice overload, mental depletion and lower satisfaction levels (Dellaert &

Stremersch, 2005; Diehl & Poynor, 2010; Galli & Gorn, 2011; Gregan-Paxton & John,

1997; Felcher et al., 2001).

The underlying assumption of Essay II is that organizations and labels are distinct

aspects of a category whose influence on decision-making processes must be

considered individually to derive reliable theoretical and practical implications.

Furthermore, the motivational effect of goals for information encoding (Bettman,

1979) and the positive influence of informative category labels for judgments

(Mogilner et al., 2008) provide evidence for the necessary distinction between type-

based and goal-based forms of similarity of category labels and category

organizations. The resulting disentanglement enables the analysis of the interplay of

category labels and category organizations for similar (i.e., pure conditions) or

dissimilar (i.e., hybrid conditions) forms of similarity. This reveals two pure (i.e., same

similarity of labels and organizations) and two hybrid (i.e., different similarity of

labels and organizations) conditions with congruent (i.e., pure type-based),


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incongruent (i.e., pure goal-based) and two moderately incongruent (i.e., hybrid

conditions) forms of similarity based on the expectations of individuals.

Research on congruity has suggested a positive impact of moderate changes from an

established standard on perceived satisfaction due to sufficient mental challenge,

which prevents complacency caused by congruence and depletion as a result of

incongruence (Meyers-Levy & Tybout, 1989; Poynor & Wood, 2010). Thus, perceived

satisfaction is included as a dependent variable in the conceptual model to compare

pure and hybrid conditions as a result of the disentanglement. Satisfaction is

considered a generally good measure of the underlying psychological processes (Diehl

& Poynor, 2010) that best captures evaluations of products (Oliver, 2009) and serves

as a specific measure of the perceived experience with the customization process

(Thirumalai & Sinha, 2011). The impact of the disentanglement is examined by

including category organizations as a moderating variable for the direct effect of type-

based or goal-based category labels on satisfaction. Finally, the conceptual model

further accounts for prior knowledge, which serves as a measure of the willingness to

invest resources in processing information (Alba & Hutchinson, 1987; Peracchio &

Tybout, 1996; Poynor & Wood, 2010; Whitmore, Shore, & Smith, 2004) and is

relevant to the preferred design of mass customization systems (Da Silveira et al.,

2001; Randall et al., 2005). Furthermore, prior knowledge has been widely used as a

moderating variable in research on categorization and similarity because of varying

mental representations of domain-specific information (Alba, 1983; Brucks, 1985;

Chaffin, 1997; Cohen & Basu, 1987; Herr, 1989; Johnson & Russo, 1984;

Maheswaran & Sternthal, 1990; Ratneshwar & Shocker, 1991; Sujan, 1985). This

setting further improves the informative power of the results and enables the deduction

of more specific theoretical and practical implications.

The results of the two empirical studies reveal that both the assortment size (Study 1)

and disentanglement of category labels and category organizations affect satisfaction

(Study 2) for type-based and goal-based forms of similarity. Study 1 replicates the

restaurant study of Poynor and Wood (2010) in a high assortment size context and

confirms the assumed impact of an increased assortment size for the different forms of

similarity across different knowledge levels. Specifically, whereas larger assortments

attenuate the risk of complacency in the type-based condition and the perception as

newness cue in the goal-based condition among experts, they do not change the

direction of the effects shown by Poynor and Wood (2010) among novices due to the

further amplified choice overload in the goal-based condition.


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Built on the first study, Study 2 examines the interplay (i.e., interaction) between

category labels and category organizations, which enables the analysis of moderately

incongruent hybrid conditions with dissimilar forms of similarity of category labels

and category organizations. The results confirm a two-way interaction between

category labels and category organizations, thereby indicating that the two aspects

should be individually considered (i.e., disentangled) due to varied satisfaction levels

for different combinations of category labels and category organizations across

different knowledge levels. Specifically, the results suggest amplified satisfaction

levels under moderately incongruent hybrid conditions with different forms of

similarity of category labels and category organizations compared to the pure

conditions. The results further reveal that the two-way interaction between category

labels and category organizations on satisfaction is moderated by prior knowledge,

with varied preferences for the moderately incongruent conditions across knowledge

levels. Whereas novices are most satisfied by the less complex hybrid condition with

incongruent goal-based category labels and congruent type-based category

organizations, the satisfaction of experts is maximized by the more complex hybrid

condition consisting of congruent type-based category labels and incongruent goal-

based category organizations.

Taken together, the results indicate that the perceived complexity that impacts

satisfaction ratings is not only largely driven by the assortment size (Dellaert &

Stremersch, 2005) and the underlying form of similarity (Poynor & Wood, 2010) but

is also a function of the interaction between category labels and category

organizations, which is further moderated by prior knowledge, thereby resulting in a

three-way interaction. Moreover, subdividing the interplay into similar and dissimilar

information relationships for category labels and category organizations provide

further insights into previous findings, supporting the relevance of the present

research.

3.3 Essay III: Need-Based Design of Customer Touch Points

Following Essay II, Essay III disentangles category labels and category organizations

to compare similar and dissimilar information relationships, while spanning the gap

between science and practice. The conceptual model comprises socio-economic

variables of high practical relevance (e.g., willingness to pay, purchase probability,

product satisfaction, and mental reflection). Starting from the dominance of type-based

similarity within the currently used car configurators, Essay III intends to define the


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best compromise of category labels and category organizations for consumers and

practitioners that can be reached for the considered variables in the short and long

term.

In accordance with Essay II, the results from an empirical study reveal that the hybrid

condition with goal-based category labels and type-based category organizations leads

to the best results, as expressed by the highest willingness to pay, product satisfaction,

and purchase probability. Furthermore, the highest levels of mental reflection and a

significantly higher duration of the selection process indicate that these promising

effects are accompanied by an amplified attention due to the stimulating influence of

moderately incongruent changes. Importantly, while replicating the direct effects

described in Essays I and II, the plurality of analyzed variables reveal that the

detrimental effects of goal-based information relationships can be traced to the lower

expectancy of the goal-based similarity, which leads to choice overload. This effect is

critical because companies increasingly use goal-based category organizations (e.g., by

bundling products with shared benefits) to address the need-orientation of their

customers at the various touch points. To avoid depleting customers with goal-based

category organizations and to improve major socio-economic parameters, Essay III

suggests a roadmap that practitioners can use to successfully establish need-based

touch points by incrementally familiarizing their customers with goal-based category

labels and category organizations. Based on the type-based status quo, the first step

involves implementing goal-based category labels in the short-term, while keeping the

category organization constant, followed by the addition of more complex goal-based

category organizations as a second step in the long-term. If this purely incongruent

condition with goal-based category labels and category organizations is still perceived

as too complex after some time, practitioners should first familiarize their customers

with goal-based category organizations along with type-based category labels as an

intermediate step after making them familiar with goal-based category labels in the

first step.

3.4 Summary: Outline of the Interlinked Empirical Essays

Figure 5 outlines the three interlinked empirical essays, including the contributing

author(s), titles, research areas, conducted empirical studies and their current

publication status.


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Figure 5

Outline of the Interlinked Empirical Essays

Essay I Mazur, Herrmann

Title The Power of Category Labels: Exploring the Moderating Role of Budget Tracking in

Spending and Payment Decisions

Essay II Mazur, Herrmann, Gibbert

Essay III Mazur

Title The Beauty of Moderately

Incongruent Similarity: How the Disentanglement

of Category Labels and Organizations Drives

Satisfaction with Mass Customization Decisions

Title Bedürfnisorientierte

Gestaltung von Kontaktpunkten

[Need-Based Design of Customer Touch Points]

Publication status Accepted by the Marke-ting Review St. Gallen

Empirical studies

1. Type-Based and goal-based category labels and the mental budgeting effect

2. Type-Based and goal-based category labels and their impact on economic parameters for different levels of budget tracking

Empirical studies

1. Type-Based and goal-based categorization in a large assortment context

2. Interplay between type-based and goal-based category labels and category organiza-tions; interaction between the interplay and knowledge

Empirical study

1. Interplay of category labels and category organizations and its impact on socio-economic parameters

Publication status

2nd round at the Journal of Economic Psychology

Publication status Submitted to

Psychology & Marketing

Research area Category labels, organiza-tions, and their interplay

Research area Category labels, organiza-tions, and their interplay

Research area

Category labels


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Practical RelevanceLow High

Th

eore

tica

l Rig

or

Low

High

Death Valley

Boring story tellers

Exciting story tellers

Esoterics

Promised Land

Dissertation Project

4 Theoretical Rigor and Practical Relevance of this

Dissertation Project

Assessing the quality of scientific work based on rigor (i.e., theoretical contributions)

and relevance (i.e., practical contributions) has received considerable attention in

previous research (Varadarajan, 2003). With reference to Zmud (1996), Varadarajan

(2003) defines rigor as “(…) soundness in theoretical and conceptual development,

methodological design and execution, interpretation of findings, and use of findings in

extending theory or developing new theory” (p. 368). By contrast, Ruback and Innes

(1988) describe the relevance of psychological research as a function of “(…) (1) the

number of policy variables used as predictor variables and (2) the extent to which the

dependent variables are of interest to practitioners” (p. 683). Depending on their

theoretical rigor and practical relevance, scientific work can be positioned along four

different dimensions (see Figure 6).

Figure 6

Rigor and Relevance Dimensions and Positioning of the Dissertation Project

Note. Adapted from “Einführung in die Wissenschaftstheorie und -methodik: Forschungskonzeption“

by T. Tomczak and T. Dyllick, 2011, Doctoral Seminar, p. 32.

Figure 4 derives four different dimensions, three of which can be hyperbolically

described as a “Death Valley” with regard to the likelihood of publication success.

First, contributions with a low rigor and relevance are characterized by low theoretical

and practical implications, and can thus be described as “boring story tellers” from the


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scientific perspective. Furthermore, Figure 4 presumes that high rigor or high

relevance alone is no longer sufficient to keep away from “Death Valley”.

Specifically, scientific work with high relevance but low rigor (“exciting story tellers”)

adds an interesting and important component to the “boring story tellers” simply by

providing examples without a thorough empirical and theoretical investigation. By

contrast, research projects with a high rigor but low relevance (“esoterics”) extend

existing or develop new theories by conducting thorough experiments or controlling

for most variables but are limited in the number of considered predictor variables and

concern a niche topic without practical relevance.

By examining the psychology of category labels, category organizations and their

interplay via a promising conceptualization of similarity at the attribute level and

conducting several empirical studies in a naturalistic environment, the present

dissertation enriches applied research on similarity and is thus characterized by high

rigor. Furthermore, because the underlying research questions are operationalized

through mass customization decisions and all findings are directly applicable to real-

world phenomena beyond mass customization decisions, the present research provides

a highly relevant counterbalance to the dominance of type-based similarity in the

marketing literature and among practitioners that can be easily implemented.

Therefore, the present dissertation is well-positioned along the rigor and relevance

dimensions, hyperbolically described as a “Promised Land”, because it provides a

multitude of theoretical and practical contributions. The theoretical rigor and the

practical relevance of this dissertation are subsequently described.

4.1 Theoretical Rigor of this Dissertation Project

The utility derived through mass customization systems follows differentiated product

models that are based on the notion that the utility of the customized product is the

sum of the utilities derived from each selected attribute throughout the customization

process (Lancaster, 1966; Rosen, 1974). The results presented in the three essays

indicate that such traditional models fall short because they presume that the manner in

which product information is presented (i.e., category organization) and named (i.e.,

category label) is not relevant. This dissertation project contributes to product utility

optimization in two different aspects: firstly by identifying moderating variables for

the effects of type-based and goal-based forms of similarity of category labels and

category organizations; secondly by examining the previously neglected interplay of

category labels and category organizations. In this respect, the findings further


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enhance traditional product models, which have thus far ignored the underlying form

of similarity for determining product utility.

Essay I enhances the theory of mental accounting by connecting it to research on

similarity, identifying the preference for budget tracking as a moderating variable for

the relationship between forms of similarity of category labels and economic decisions

and demonstrating that this process can be partially explained by the perceived choice

uncertainty. Following the contribution “Mental Accounting Matters” by Thaler

(1999), the results presented in Essay I demonstrate that both mental accounting and

how the same information is labeled (i.e., type-based versus goal-based) influence

economic parameters. As such, the results contribute to the invariance axiom of

general decision-making theory with its assumption of rationality among decision

makers (Tversky & Kahneman, 1986). Invariance is violated in framing effects

because extensionally equivalent descriptions of the same problem lead to different

choices by emphasizing different aspects of a problem (e.g., the number of people

killed versus the number of survivors). Furthermore, the results build on previous

research on mental accounting by Heath and Soll (1996), who found that the rigidity of

the mental accounting process (i.e., the mental budgeting effect) depends on the

typicality of expenses with the budget categories, thereby ignoring the underlying form

of similarity (i.e., type-based versus goal-based) used to label the same information

(i.e., expenses). The results indicate that economic parameters are mitigated for the

same product information for narrow type-based category labels compared to the

broader goal-based category labels, which allow individuals to relocate expenses more

freely with a lower necessity for self-justification, resulting in the amplification of

economic parameters. Thus, the mental accounting process should not be considered

detached from contextual information provided by category labels because category

labels influence the manner in which problems are framed and thus significantly

influence mental budgeting decisions and, consequently, economic parameters. Taken

together, by successfully applying previous research in mental accounting to cognitive

research on similarity and providing evidence for expense tracking in the context of

consumer decisions, the results presented in Essay I add predictive power to the mental

accounting literature. Specifically, the results prove that the extent of irrational

decision behavior within mass customization systems is a function of the form of

similarity used for labeling the same categorized (i.e., organized) product information.

Essay II presents the first scientific contribution to disentangle category labels and

category organizations to investigate their interplay. In addition, Essay II addresses a


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major shortcoming of existing research, which has focused on small assortments or

pairwise comparisons of the type-based and the goal-based forms of similarity.

Building on research on congruity, investigating the interaction between category

labels and category organizations further enables the comparison of different

congruence levels based on the congruent (i.e., expected) type-based similarity.

Building on previous findings about the rewarding influence of mass customizing

tailor-made products through the “I designed it myself effect” (Franke, Schreier, &

Kaiser, 2010), Essay II not only provides insights about how (i.e., type-based versus

goal-based) attributes must be organized but also how assortments must be labeled to

maximize satisfaction. In addition, Essay II contributes to both the rewarding and

detrimental effects of mass customization, such as complacency in the case of

congruence (Poynor & Wood, 2010) and mental depletion in the case of incongruence

(Dellaert & Stremersch, 2005). Building on the research of Meyers-Levy and Tybout

(1989) on the positive impact of moderate incongruence on satisfaction, the negative

effects caused by complacency and complexity are mitigated by using hybrid forms of

similarity of category labels and category organizations consisting of type-based (goal-

based) category labels and goal-based (type-based) category organizations. The beauty

of such moderately incongruent mass customization systems is further enriched by

detecting knowledge as a moderating variable in the interaction between category

labels and category organizations, leading to a three-way interaction. Specifically, the

results indicate a preference for the hybrid condition with type-based (goal-based)

category labels and goal-based (type-based) category organizations among experts

(novices). Taken together, examining the interplay of category labels and category

organizations based on the disentanglement of these two aspects reveals promising

results that contribute to the previous counter-intuitive finding that experts do not

always perform better than novices.

Building on the results presented in the first two essays, Essay III extends the

knowledge about the optimal design of touch points with regard to labeling and

organizing product information by considering several socio-economic parameters

(i.e., willingness to pay, purchase probability, product satisfaction, mental reflection,

expectancy of the category organization). Importantly, the results further build on the

findings from Essays I and II by showing that the detrimental effects of goal-based

information relationships, which can be traced to the perceived degree of mental

reflection resulting from different levels of expectancy of type-based and goal-based

information relationships, affect the plurality of the considered variables. Whereas

mental reflection is mitigated in the expected purely type-based condition due to


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complacency and the unexpected purely goal-based condition due to choice overload,

mental reflection is amplified in both moderately incongruent hybrid conditions. Thus,

the results presented in Essay III support the notion that the unexpected goal-based

similarity provides a timely counterbalance to the expected type-based similarity.

4.2 Practical Relevance of this Dissertation Project

The growing acceptance of thematic information relationships (i.e., goal-based

similarity) in psychology and marketing is not limited to science but involves

substantial managerial implications for category management, segmentation strategies

and customer communication at various touch points throughout the customer lifecycle

to help companies prosper at minimal additional costs. Building on previous research

about the optimal design of mass customization systems (e.g., Dellaert & Stremersch,

2005; Franke et al., 2010), the present findings provide a roadmap to practitioners for

choosing category labels and category organizations for the same product information

that optimize major socio-economic parameters (e.g., willingness to pay, purchase

probability, product satisfaction). Focusing on category labels, category organizations

and their interplay at the attribute level, while keeping the product information

constant across studies, enable the findings to be easily and cost-efficiently

implemented.

Companies are increasingly responding to the growing need-orientation of customers

by directly interacting with them via an increasing number of touch points throughout

the different stages of the customer lifecycle (i.e., interest, purchase, ownership,

repurchase). The results presented in the three essays provide valuable guidelines for

practitioners to facilitate the implementation of a holistic customer experience at the

various touch points that goes beyond the mere purchase process using goal-based

category labels and/or category organizations to directly address the needs (i.e.,

preferences) of their consumers while retaining the same content. Such goal-based

touch point management goes beyond the considered mass customization decisions

and is generalizable to any online and offline touch points with category labels and

category organizations, such as the layout of a website or social media channel, the

structure of consultation processes, the design of brochures or the general client

communication at the point of sale.

By detecting a moderating impact of budget tracking (Essay I), category organizations

and product knowledge (Essay II), the results are also highly relevant for market

segmentation questions and competitive differentiation because they provide


-29-

practitioners with clear guidelines of how best to use differently designed customer

touch points during the different stages of the purchase process. Segmentation

approaches lead to the perception of customer-centric one-to-one interactions that

prioritize the needs of the customer and increase the probability for lock-ins that help

companies to develop long-lasting customer relationships. If practitioners successfully

enrich their touch points with goal-based elements, they will be better able to

differentiate themselves from competitors, thereby reducing their exposure to the

volatility caused by the ambiguous markets. Specifically, whereas category labels and

category organizations are, by definition, narrowly determined in the case of type-

based similarity, the more abstract goal-based similarity provides practitioners with

greater flexibility due to its beneficial relationships, which enable them to label and

organize attributes according to the benefits that they intend to address. Interestingly,

considering the relatively small assortment size throughout the studies compared to the

continuous increase in assortment size observed in the marketplace, the detected

effects can be expected to be even stronger for more complex real purchase decisions

for both type-based and goal-based similarity. However, following the theoretical

implications and previous research (e.g., Gibbert & Mazursky, 2009), the results also

reveal detrimental effects of goal-based similarity under certain conditions, primarily

due to its lower expectancy, which amplifies the perceived complexity, overstrains

consumers and, consequently, negatively impacts socio-economic parameters. This

result indicates that practitioners should refrain from unconditionally implementing

goal-based relationships at the various touch points.

The findings presented in Essay I reveal that the creation of a “tailor-made” account

for each customer would be most promising because it minimizes the need for

consumer justification. Because this strategy cannot be easily implemented for

technical and financial reasons, companies should identify compromise solutions that

maintain ambiguity as well as the need for justification at low levels and provide

customers with sufficient loopholes. Moreover, the moderating impact of budget

tracking suggests the implementation of at least two differently designed touch points

to optimize major economic parameters by implementing unexpected goal-based

category labels for budget trackers to fully utilize their budgets, and expected type-

based category labels for non-budget trackers to prevent them from experiencing

depletion and forgoing consumption.

The results presented in Essay II demonstrate that dissimilar information relationships

for category labels and category organizations (i.e., hybrid conditions) maximize


-30-

product satisfaction because they are perceived as moderately incongruent. The

detected moderating impact of product knowledge suggests that practitioners aiming at

building long-lasting relationships with existing clients and attracting new clients (i.e.,

virtually all companies) should provide their customers with two differently designed

touch points. Specifically, to avoid overstraining their consumers, practitioners should

implement the more complex hybrid condition with type-based category labels and

goal-based category organizations for experts and the less complex hybrid condition

with goal-based category labels and type-based category organizations for novices.

Finally, Essay III emphasizes the promising paradigm shift from the routinely used

type-based similarity to the proactive implementation of goal-based designed touch

points without changing the underlying product information. Although the purely

incongruent goal-based condition provides the highest degree of need-orientation at

the various touch points, the results of the empirical study are not promising. This

counter-intuitive finding is a function of the simultaneous change of category labels

and category organizations from expected type-based to unexpected goal-based

information relationships, which cannot be adequately processed by consumers, leads

to choice overload and negatively impacts socio-economic parameters. To successfully

implement the recommended paradigm shift toward need-based customer

communication, Essay III provides practitioners with a roadmap for the stepwise and

time-lagged establishment of the goal-based similarity of category labels and category

organizations as a new standard at the various touch points in the marketplace without

conducting a costly market or customer segmentation.


-31-

References

Aaker, D. A., & Keller, K. L. (1990). Consumer evaluations of brand extensions.

Journal of Marketing, 54(1), 27-41.

Alba, J. W. (1983). The effects of product knowledge on the comprehension, retention,

and evaluation of product information. Advances in Consumer Research, 10,

577-580.

Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal

of Consumer Research, 13, 411-454.

Alloy, L. B., & Tabachnik, N. (1984). Assessment of covariation by humans and

animals: The joint influence of prior expectations and current situational

information. Psychological Review, 91, 112-149.

Antonides, G., de Groot, I., & van Raaij, W. (2011). Mental budgeting and the

management of household finance. Journal of Economic Psychology, 32, 546-

555.

Barsalou, L. W. (1982). Context-independent and context-dependent information in

concepts. Memory & Cognition, 10, 82-93.

Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11, 211-227.

Barsalou, L. W. (1985). Ideals, central tendency, and frequency of instantiation as

determinants of graded structure in categories. Journal of Experimental

Psychology: Learning, Memory, and Cognition, 11, 629-654.

Barsalou, L. W. (1991). Deriving categories to achieve goals. In G. H. Bower (Ed.),

The psychology of learning and motivation: Advances in research and theory

(Vol. 27, pp. 1-64). San Diego, CA: Elsevier Academic Press.

Barsalou, L. W. (1992). Cognitive psychology: An overview for cognitive scientists.

Hillsdale, NJ: Lawrence Erlbaum Associates.

Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience.



-32-

Bettman, J. R. (1978). Consumer information acquisition and search strategies. In A.

Mitchell (Ed.), The effects of information on consumer and market behavior

(pp. 35-48). Chicago, MI: American Marketing Association.

Bettman, J. R. (1979). Issues in research on consumer choice. Advances in Consumer

Research, 6, 214-217.

Bettman, J. R., & Sujan, M. (1987). Effects of framing on evaluation of comparable

and noncomparable alternatives by expert and novice consumers. Journal of

Consumer Research, 14, 141-154.

Biehal, G., & Chakravarti, D. (1982). Information-presentation format and learning

goals as determinants of consumers' memory retrieval and choice processes.

Journal of Consumer Research, 8, 431-441.

Brucks, M. (1985). The effects of product class knowledge on information search

behavior. Journal of Consumer Research, 12, 1-16.

Capgemini. (2014). Cars online 2014. Generation connected. Retrieved from

http://www.capgemini.com/sites/default/files/annual-

report/768636/img/Cars_Online_2014_Final_Web_Group.pdf.

Chaffin, R. (1997). Associations to unfamiliar words: Learning the meanings of new

words. Memory & Cognition, 25, 203-226.

Cheema, A., & Soman, D. (2006). Malleable mental accounting: The effect of

flexibility on the justification of attractive spending and consumption

decisions. Journal of Consumer Psychology, 16, 33-44.

Chernev, A. (2003). Product assortment and individual decision processes. Journal of

Personality and Social Psychology, 85, 151-162.

Chiesi, H. L., Spilich, G. J., & Voss, J. F. (1979). Acquisition of domain-related

information in relation to high and low domain knowledge. Journal of Verbal

Learning and Verbal Behavior, 18, 257-273.

Cohen, J. B., & Basu, K. (1987). Alternative models of categorization: Toward a

contingent processing framework. Journal of Consumer Research, 13, 455-472.

Csikszentmihalyi, M. (1990). Flow: The psychology of optimal experience. New York,

NY: Harper and Row.


-33-

Davidoff, J., & Roberson, D. (2004). Preserved thematic and impaired taxonomic

categorization: A case Study. Language and Cognitive Processes, 19, 137-174.

Dellaert, B. G. C., & Stremersch, S. (2005). Marketing mass-customized products:

Striking a balance between utility and complexity. Journal of Marketing

Research, 42, 219-227.

Diehl, K., & Poynor, C. (2010). Great expectations?! Assortment size, expectations,

and satisfaction. Journal of Marketing Research, 47, 312-322.

Duxbury, D., Keasey, K., Hao, Z., & Shue Loong, C. (2005). Mental accounting and

decision making: Evidence under reverse conditions where money is spent for

time saved. Journal of Economic Psychology, 26, 567-580.

Estes, Z. (2003a). A tale of two similarities: Comparison and integration in conceptual

combination. Cognitive Science, 27, 911-921.

Estes, Z. (2003b). Attributive and relational processes in nominal combination.

Journal of Memory and Language, 48, 304-319.

Estes, Z., Gibbert, M., Guest, D., & Mazursky, D. (2012). A dual-process model of

brand extension: Taxonomic feature-based and thematic relation-based

similarity independently drive brand extension evaluation. Journal of Consumer

Psychology, 22, 86-101.

Estes, Z., Golonka, S., & Jones, L. L. (2011). Thematic thinking: The apprehension

and consequences of thematic relations. In B. H. Ross (Ed.), The psychology of

learning and motivation: Advances in research and theory (Vol. 54., pp. 249-

294). San Diego, CA: Elsevier Academic Press.

Estes, Z., & Jones, L. L. (2009). Integrative priming occurs rapidly and uncontrollably

during lexical processing. Journal of Experimental Psychology. General, 138,

112-130.

Farjoun, M., & Lai, L. (1997). Similarity judgments in strategy formulation: Role,

process and implications. Strategic Management Journal, 18, 255-273.

Felcher, E. M., Malaviya, P., & McGill, A. L. (2001). The role of taxonomic and goal-

derived product categorization in, within, and across category judgments.

Psychology & Marketing, 18, 865-887.


-34-

Fogliatto, F. S., da Silveira, G. C., & Borenstein, D. (2012). The mass customization

decade: An updated review of the literature. International Journal of

Production Economics, 138, 14-25.

Franke, N., Keinz, P., & Schreier, M. (2008). Complementing mass customization

toolkits with user communities: How peer input improves customer self-design.

Journal of Product Innovation Management, 25, 546-559.

Franke, N., & Schreier, M. (2010). Why customers value self-designed products: The

importance of process effort and enjoyment. Journal of Product Innovation

Management, 27, 1020-1031.

Franke, N., Schreier, M., & Kaiser, U. (2010). The "I designed it myself" effect in

mass customization. Management Science, 56, 125-140.

Galli, M., & Gorn, G. (2011). Unconscious transfer of meaning to brands. Journal of

Consumer Psychology, 21, 215-225.

Gentner, D., & Markman, A. B. (1997). Structure mapping in analogy and similarity.

American Psychologist, 52, 45-56.

Gibbert, M., & Mazursky, D. (2009). How successful would a phone-pillow be: Using

dual process theory to predict the success of hybrids involving dissimilar

products. Journal of Consumer Psychology, 19, 652-660.

Golonka, S., & Estes, Z. (2009). Thematic relations affect similarity via

commonalities. Journal of Experimental Psychology: Learning, Memory, and

Cognition, 35, 1454-1464.

Gourville, J. T., & Soman, D. (2005). Overchoice and assortment type: When and why

variety backfires. Marketing Science, 24, 382-395.

Gregan-Paxton, J., & John, D. R. (1997). Consumer learning by analogy: A model of

internal knowledge transfer. Journal of Consumer Research, 24, 266-284.

Gupta, S., & Kim, H. (2010). Value-driven Internet shopping: The mental accounting

theory perspective. Psychology & Marketing, 27, 13-35.

Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping

environments: The effects of interactive decision aids. Marketing Science, 19,

4-21.


-35-

Heath, C. (1995). Escalation and de-escalation of commitment in response to sunk

costs: The role of budgeting in mental accounting. Organizational Behavior

and Human Decision Processes, 62, 38-54.

Heath, C., & Soll, J. B. (1996). Mental budgeting and consumer decisions. Journal of


Henderson, P. W., & Peterson, R. A. (1992). Mental accounting and categorization.

Organizational Behavior and Human Decision Processes, 51, 92-117.

Herr, P. M. (1989). Priming price: Prior knowledge and context effects. Journal of


Hoffman, D. L., & Novak, T. P. (1996). Marketing in hypermedia computer-mediated

environments: Conceptual foundations. Journal of Marketing, 60(3), 50-68.

Huber, J., & McCann, J. (1982). The impact of inferential beliefs on product

evaluations. Journal of Marketing Research, 19, 324-333.

Huffman, C., & Houston, M. J. (1993). Goal-oriented experiences and the

development of knowledge. Journal of Consumer Research, 20, 190-207.

Hutchinson, J. W., Raman, K., & Mantrala, M. (1994). Finding choice alternatives in

memory: Probability models of brand name recall. Journal of Marketing

Research, 31, 441-461.

Iyengar, S. S., & Lepper, M. R. (2000). When choice is demotivating: Can one desire

too much of a good thing? Journal of Personality and Social Psychology, 79,

995-1006.

Ji, L., Zhang, Z., & Nisbett, R. E. (2004). Is it culture or is it language? Examination

of language effects in cross-cultural research on categorization. Journal of

Personality and Social Psychology, 87, 57–65.

Johnson, E. J., & Russo, J. E. (1984). Product familiarity and learning new

information. Journal of Consumer Research, 11, 542-550.

Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under

risk, Econometrica, 47, 263-291.


-36-

Kahneman, D., & Tversky, A. (1984). Choices, values, and frames. American

Psychologist, 39, 341-350.

Krishnamurthy, P., & Prokopec, S. (2010). Resisting that triple-chocolate cake: Mental

budgets and self-control. Journal of Consumer Research, 37, 68-79.

Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108, 480-

498.

Labroo, A. A., Dhar, R., & Schwarz, N. (2008). Of frog wines and frowning watches:

Semantic priming, perceptual fluency, and brand evaluation. Journal of


Lancaster, K. J. (1966). A new approach to consumer theory. Journal of Political

Economy, 74, 132-157.

Levav, J., Heitmann, M., Herrmann, A., & Iyengar, S. S. (2010). Order in product

customization decisions: Evidence from field experiments. Journal of Political

Economy, 118, 274-299.

Lin, E. L., & Murphy, G. L. (2001). Thematic relations in adults' concepts. Journal of

Experimental Psychology: General, 130, 3-28.

Lucy, J. A. (1992). Language diversity and thought: A reformulation of the linguistic

relativity hypothesis. Cambridge, England: Cambridge University Press.

Lupyan, G. (2009). Extracommunicative functions of language: Verbal interference

causes selective categorization impairments. Psychonomic Bulletin & Review,

16, 711-718.

Maheswaran, D., & Sternthal, B. (1990). The effects of knowledge, motivation, and

type of message on ad processing and product judgments. Journal of Consumer

Research, 17, 66-73.

Mandler, J. M., & Parker, R. E. (1976). Memory for descriptive and spatial

information in complex pictures. Journal of Experimental Psychology: Human

Learning and Memory, 2, 38-48.

Markman, A. B., & Gentner, D. (2000). Structure mapping in the comparison process.

The American Journal of Psychology, 113, 501-538.


-37-

Medin, D. L., Goldstone, R. L., & Gentner, D. (1993). Respects for similarity.

Psychological Review, 100, 254-278.

Mervis, C. B., & Rosch, E. (1981). Categorization of natural objects. Annual Review of


Meyers-Levy, J., & Tybout, A. M. (1989). Schema congruity as a basis for product

evaluation. Journal of Consumer Research, 16, 39-54.

Mogilner, C., Rudnick, T., & Iyengar, S. S. (2008). The mere categorization effect:

How the presence of categories increases choosers' perceptions of assortment

variety and outcome satisfaction. Journal of Consumer Research, 35, 202-215.

Morales, A., Kahn, B. E., McAlister, L., & Broniarczyk, S. M. (2005). Perceptions of

assortment variety: The effects of congruency between consumers' internal and

retailers' external organization. Journal of Retailing, 81, 159-169.

Moreau, C. P., Markman, A. B., & Lehmann, D. R. (2001). 'What is it?' Categorization

flexibility and consumers' responses to really new products. Journal of


Murphy, G. L., & Medin, D. L. (1985). The role of theories in conceptual coherence.


Noseworthy, T. J., Finlay, K., & Islam T. (2010). From a commodity to an experience:

The moderating role of thematic positioning on congruity-based product

judgment. Psychology & Marketing, 27, 465-486.

Novak, T. P., Hoffman, D. L., & Duhachek, A. (2003). The influence of goal-directed

and experiential activities on online flow experiences. Journal of Consumer


Oliver, R. L. (2009). Satisfaction. A behavioral perspective on the consumer (2nd ed.).

New York, NY: McGraw-Hill.

Oliver, R. L., & Winer, R. S. (1987). A framework for the formation and structure of

consumer expectations: Review and propositions. Journal of Economic



-38-

Park, C. W., Lawson, R., & Milberg, S. (1991). Evaluation of brand extensions: The

role of product feature similarity and brand concept consistency. Journal of


Peracchio, L. A., & Tybout, A. M. (1996). The moderating role of prior knowledge in

schema-based product evaluation. Journal of Consumer Research, 23, 177-192.

Pine, B. J., II, Peppers, D., & Rogers, M. (1995). Do you want to keep your customers

forever? Harvard Business Review, 73(2), 149-160.

Poynor, C., & Wood, S. (2010). Smart subcategories: How assortment formats

influence consumer learning and satisfaction. Journal of Consumer Research,

37, 159-175.

Poynor Lamberton, C., & Diehl, K. (2013). Retail choice architecture: The effects of

benefit- and attribute-based assortment organization on consumer perceptions

and choice. Journal of Consumer Research, 40, 393-411.

Rajagopal, P., & Rha, J. (2009). The mental accounting of time. Journal of Economic


Randall, T., Terwiesch, C., & Ulrich, K. T. (2005). Principles for user design of

customized products. California Management Review, 47(4), 68-85.

Ratneshwar, S., Barsalou, L. W., Pechmann, C., & Moore, M. (2001). Goal-derived

categories: The role of personal and situational goals in category

representations. Journal of Consumer Psychology, 10, 147-157.

Ratneshwar, S., Pechmann, C., & Shocker, A. D. (1996). Goal-derived categories and

the antecedents of across-category consideration. Journal of Consumer

Research, 23, 240-250.

Ratneshwar, S., & Shocker, A. D. (1991). Substitution in use and the role of usage

context in product category structures. Journal of Marketing Research, 28, 281-

295.

Read, D., Loewenstein, G., & Rabin, M. (1999). Choice bracketing. Journal of Risk

and Uncertainty, 19, 171-197.


-39-

Rips, L. J. (1989). Similarity, typicality, and categorization. In S. Vosniadou & A.

Ortony (Eds.), Similarity and analogical reasoning (pp. 21-59). New York, NY:

Cambridge University Press.

Rosch, E., & Lloyd, B. B. (1978). Cognition and categorization. Oxford, England:

Lawrence Erlbaum.

Rosch, E., & Mervis, C. B. (1975). Family resemblances: Studies in the internal

structure of categories. Cognitive Psychology, 7, 573-605.

Rosch, E., Mervis, C. B., Gray, W., Johnson, D., & Boyes-Braem, P. (1976). Basic

objects in natural categories. Cognitive Psychology, 8, 382-439.

Rosch, E., Simpson, C., & Miller, R. S. (1976). Structural bases of typicality effects.

Journal of Experimental Psychology: Human Perception and Performance, 2,

491-502.

Rosen, S. (1974). Hedonic prices and implicit markets: Product differentiation in pure

competition. Journal of Political Economy, 82, 34-55.

Ruback, R., & Innes, C. A. (1988). The relevance and irrelevance of psychological

research: The example of prison crowding. American Psychologist, 43, 683-

693.

Sass, K., Sachs, O., Krach, S., & Kircher, T. (2009). Taxonomic and thematic

categories: Neural correlates of categorization in an auditory-to-visual priming

task using fMRI. Brain Research, 1270, 78-87.

Schmitt, B. H., & Zhang, S. (1998). Language structure and categorization: A Study of

classifiers in consumer cognition, judgment, and choice. Journal of Consumer

Research, 25, 108-122.

Schwartz, B. (2004). The paradox of choice: Why more is less. New York, NY:

HarperCollins Publishers.

Simmons, S., & Estes, Z. (2008). Individual differences in the perception of similarity

and difference. Cognition, 108, 781-795.

Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA:

Harvard University Press.


-40-

Soman, D., & Gourville, J. T. (2001). Transaction decoupling: How price bundling

affects the decision to consume. Journal of Marketing Research, 38, 30-44.

Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-

commerce: An exploration of its antecedents and consequences. Journal of

Retailing, 78, 41-50.

Stayman, D. M., & Alden, D. L. (1992). Some effects of schematic processing on

consumer expectations and disconfirmation judgments. Journal of Consumer

Research, 19, 240-255.

Sujan, M. (1985). Consumer knowledge: Effects on evaluation strategies mediating

consumer judgments. Journal of Consumer Research, 12, 31-46.

Sujan, M., & Dekleva, C. (1987). Product categorization and inference making: Some

implications for comparative advertising. Journal of Consumer Research, 14,

372-378.

Sujan, M., & Tybout, A. M. (1988). Applications and extensions of categorization

research in consumer behavior. Advances in Consumer Research, 15, 50-54.

Thaler, R. (1985). Mental accounting and consumer choice. Marketing Science, 4,

199-214.

Thaler, R. H. (1988). Anomalies: The ultimatum game. Journal of Economic

Perspectives, 2(4), 195-206.

Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision

Making, 12, 183-206.

Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. Journal of

Political Economy, 89, 392-406.

Thirumalai, S., & Sinha, K. K. (2011). Customization of the online purchase process in

electronic retailing and customer satisfaction: An online field Study. Journal of

Operations Management, 29, 477-487.

Tomczak, T., & Dyllick, T. (2011, October). Einführung in die Wissenschaftstheorie

und -methodik: Forschungskonzeption [PowerPoint slides]. Doctoral Seminar.

Lecture conducted from University of St. Gallen, Switzerland.


-41-

Tversky, A. (1969). Intransitivity of preferences. Psychological Review, 76, 31-48.

Tversky, A. (1977). Features of similarity. Psychological Review, 84, 327-352.

Tversky, A., & Kahneman, D. (1986). Rational choice and the framing of decisions.

Journal of Business, 59, 251-278.

Varadarajan, P. (2003). Musings on relevance and rigor of scholarly research in

marketing. Journal of the Academy of Marketing Science, 31, 368-376.

Whitmore, J. M., Shore, W. J., & Smith, P. H. (2004). Partial knowledge of word

meanings: Thematic and taxonomic representations. Journal of Psycholinguistic

Research, 33, 137-164.

Wilkenfeld, M. J., & Ward, T. B. (2001). Similarity and emergence in conceptual

combination. Journal of Memory & Language, 45, 21-38.

Wisniewski, E. J. (1997). When concepts combine. Psychonomic Bulletin & Review, 4,

167-183.

Wisniewski, E. J., & Bassok, M. (1999). What makes a man similar to a tie? Stimulus

compatibility with comparison and integration. Cognitive Psychology, 39, 208-

238.

Wisniewski, E. J., & Love, B. C. (1998). Relations versus properties in conceptual

combination. Journal of Memory and Language, 38, 177-202.

Zmud, B. (1996). Editor's Comments. MIS Quarterly, 20.


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-43-

B. Essay I

Mazur, M., Herrmann, A. (second round). The Power of Category Labels: Exploring

the Moderating Role of Budget Tracking in Spending and Payment Decisions. Journal

of Economic Psychology.

-44-

The Power of Category Labels: Exploring the Moderating

Role of Budget Tracking in Spending and Payment Decisions

Marcel Mazur (1)

Andreas Herrmann (2)



(2) Andreas Herrmann is a Professor of Marketing, Center for Customer Insight,

University of St. Gallen, Switzerland ([email protected]).

B. ESSAY I: THE POWER OF CATEGORY LABELS

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Abstract

This paper investigates whether and how goal-based category labels mitigate the

detrimental impact on economic parameters of rigid type-based category labels. Our

budgeting-attenuating model is based on the low expectancy and malleability of goal-

based similarity, which encourages the use of loopholes to circumvent budget

constraints. The results confirm attenuated mental budgeting of goal-based labels that

is directly transferable to the economic parameters of budget trackers. We further

examine the indirect effects via uncertainty of forms of similarity of category labels

and of preferences for budget tracking on economic parameters. The results indicate a

positive indirect effect of loophole-providing goal-based labels among budget trackers

through a reduction in uncertainty and stimulation of economic parameters. By

contrast, among non-budget trackers, unexpected goal-based labels are found to

stimulate negative emotions that increase uncertainty, thus mitigating economic

parameters. The results indicate the reverse for type-based labels among budget

trackers and non-budget trackers, revealing no superiority of either type-based or goal-

based forms of similarity. This analysis confirms partially moderated mediation in

which the moderating role of budget tracking on economic parameters is partly

explained by uncertainty and is also a function of the underlying form of similarity of

labels. The detrimental effect of mental budgeting when narrow type-based labels are

used is limited to budget trackers and can be resolved by replacing such labels with

malleability-inducing and uncertainty-decreasing goal-based labels.

Keywords: similarity, category label, mental budgeting, spending, willingness to pay


-46-

1 Introduction

Consumers establish rules for assigning different expenses to associatively linked

categories instead of organizing all expenses into one superordinate category and

integrating all decisions into a single optimization problem (Prelec & Loewenstein,

1998; Thaler & Shefrin, 1981). For example, when selecting the different components

of a car purchase, consumers mentally assign their available budgets to distinct

categories labeled by the type of expense (e.g., “Engine,” “Color,” “Rims,” and

“Upholstery”).

Mental budgets serve as self-control devices to maintain accountability (Hsee, 1995;

Kivetz & Simonson, 2002; Kunda, 1990), better anticipate future expenses, and avoid

exceeding previously established budget constraints (Antonides, de Groot, & van

Raaij, 2011; Heath & Soll, 1996). However, consumers frequently seek loopholes that

enable funds to be transferred among budgeting categories (Cheema & Soman, 2006).

To prevent consumers from such creative bookkeeping that enables them to

circumvent budgeting constraints and overcome self-control barriers, mental budgets

must serve as rigid and consequential self-control devices (Cheema & Soman, 2006;

Heath & Soll, 1996; Krishnamurthy & Prokopec, 2010; Shefrin & Thaler, 1988).

However, loopholes are frequently exploited in various decisions because expenses

cannot always be assigned unambiguously to specific budgets but rather might apply

to several budgets (Barsalou, 1982; Heath & Soll, 1996; Henderson & Peterson, 1992;

Sussman & Alter, 2012; Thaler & Shefrin, 1981). For example, a visit to the theatre is

typical for the entertainment category, whereas a dinner at a restaurant can be assigned

to both the nutrition and entertainment categories.

We argue that using predetermined category labels to describe categorized product

information in the marketplace can create self-serving loopholes and hinder the self-

control efforts of consumers. Category labels provide contextual information that is

used as reference points for typicality judgments in different mental budgets (Cheema

& Soman, 2008). Furthermore, category labels guide consumers in the decision-

making process (Barsalou, 1982; Henderson & Peterson, 1992) and serve as means of

framing contexts by ascribing different labels to otherwise similar information or

labeling goods as relevant to certain mental accounts (Barsalou, 1982; Henderson &

Peterson, 1992). Building on research on the impact of typicality ratings on the

malleability of the budgeting process (Barsalou, 1982, 1985), which affects similarity

decisions (Goldstone 1994; Tversky, 1977; Simmons & Estes, 2008), we argue that the

similarity of predetermined category labels used in the marketplace provides a lever


-47-

CategoryLabels

(X)

ChoiceUncertainty

(M)

Budget Tracking

(W)

EstimationBias(Y)

BudgetDeviation

(Y)

CategoryLabels

(X)

Mental Budgeting

Effect(Y)

A

B

that affects a consumer’s budgeting decisions. Building on research on mental

budgeting that investigated the impact of different levels of typicality for expenses

(e.g., dinner), using superordinate categories (e.g., food) (Heath & Soll, 1996), we

examine whether and how different forms of similarity of category labels for the same

underlying information attenuate or amplify mental budgeting decisions and thus

affect spending and payment decisions.

Research on similarity distinguishes between type-based (i.e., taxonomic) and goal-

based (i.e., thematic) similarity, which differ in their familiarity (expected versus

unexpected), width (narrow versus broad) and rigidity (unambiguous versus

ambiguous). Based on the same underlying information, we argue that expected,

narrow and unambiguous type-based labels (e.g., “Rims”) promote rigid bookkeeping,

whereas unexpected, broader and ambiguous goal-based labels (e.g., “Exterior

Design”) promote creative bookkeeping. We suggest a two-stage conceptual model

and test our hypotheses using two laboratory experiments (see Figure 1).

Figure 1

Hypothesized Conceptual Models Tests in Study 1(A) and Study 2(B)

Note. X: Independent variable. Y: Dependent variables. M: Mediator variable. W: Moderator variable


-48-

Study 1 examines the direct impact of similarity of category labels on mental

budgeting decisions, and Study 2 intends to explain the underlying process via three

interlinked sub-models in a more naturalistic, multi-attribute mass customization

context. In Study 2, the preference for budget tracking is expected to mitigate or

amplify the impact of different forms of similarity of category labels on spending and

payment decisions through perceived choice uncertainty.

1.1 Similarity and Mental Budgeting Decisions

Research in economic and consumer psychology has recently started to examine the

effects of type-based (i.e., attribute-based) and goal-based (i.e., alternative-based)

ways of information processing on (Pizzi, Scarpi, & Marzocchi, 2014; Poynor &

Wood, 2010). Type-based similarity is concrete and context-independent because it is

formed based on naturally occurring relationships between objects (Felcher, Malaviya,

& McGill, 2001; Markman & Gentner, 2000). By contrast, goal-based similarity is

abstract and context-dependent because information is grouped according to external

associations between objects, shared benefits or their participation in the same event

(Estes, Golonka, & Jones, 2011; Wisniewski & Bassok, 1999).

Unlike existing literature, which compared type-based and goal-based strategies for

information presentation, the present research focuses on labels used to provide a

context for categorized information. We suggest that the form of similarity of category

labels determines the malleability of the mental accounting process. A central

assumption of the mental accounting process is the unambiguous coupling of costs and

benefits, described as “narrow framing” (Kahneman & Lovallo, 1993), “narrow

bracketing” (Read, Loewenstein, & Rabin, 1999) or “narrow grouping” (Sussman &

Alter, 2012). This unambiguity increases the probability of classification within the

same category and amplifies rigid budget tracking (Antonides et al., 2011; Felcher et

al., 2001; Heath & Soll, 1996; Kivetz & Simonson, 2002; Markman & Gentner, 2000;

Rajagopal & Rha, 2009). By contrast, ambiguity amplifies malleability, which

increases the number of associated mental accounts (Cheema & Soman, 2006),

provides loopholes through which to move expenses among different budgets (Hsee,

1995, Prelec & Loewenstein, 1998) and influences previously established reference

points (Cheema & Soman, 2008).

Considering that the underlying principles of mental accounting coincide with

common categorization principles (Henderson & Peterson, 1992) and that type-based

similarity is more expected (Poynor & Wood, 2010), individuals generally use narrow


-49-

type-based mental accounts that enable them to better track expenses (Krishnamurthy

& Prokopec, 2010; Thaler, 1999; Van Ittersum, Pennings, & Wansink, 2010; Van

Ittersum, Wansink, Pennings, & Sheehan, 2013). Type-based category labels provide

unambiguous reflections of categorized information and do not enable the transfer of

money between accounts without violating self-imposed budgeting rules. Thus, type-

based labels amplify rigidity and inhibit creative bookkeeping by activating only one

account, thereby increasing the mental budgeting effect (MBE). For example, the type-

based label “Rims” unambiguously activates the budget for rims. By contrast, goal-

based labels are broader and are thus ambiguously connected to categorized

information. This ambiguity amplifies loopholes in the budgeting process and leaves

room for interpretation regarding which budget to activate. For example, the goal-

based label “Exterior Design” is associatively connected to colors and rims in the car

context. Thus, we argue that ambiguous goal-based labels induce malleability that

provides loopholes for creative bookkeeping and mitigates the MBE. Thus, we posit

Hypothesis 1.

H1: Holding the underlying information constant, the MBE will be

significantly higher for expected type-based labels compared with

unexpected goal-based labels.

1.2 Similarity and Economic Decisions

Mental accounts are reference points that serve as a basis for a successful self-control

strategy. This strategy determines whether specific budgets are depleted and thus

causes individuals to pay attention to economic parameters (Antonides et al., 2011;

Baumeister, 2002; Heath, 1995). Thus, our second conceptual model is based on the

assumption that type-based and goal-based labels have different impacts on spending

and payment decisions such that previously established budgets are exceeded in some

cases but are not completely exhausted in others (see Figure 1B). Rigid mental budgets

serve as self-control devices that urge individuals to employ safety margins and

prevent them from exceeding economic constraints (Soman & Cheema, 2011; Thaler

& Shefrin, 1981). This decreases mental stress, but amplifies underconsumption

(Heath & Soll, 1996; Read et al., 1999; Thaler, 1985). By contrast, ambiguity in

assigning expenses to mental accounts provides consumers with more justifiable

loopholes, enabling them to deviate from pre-established budgets, diminishing their

self-control and thus amplifying their overconsumption (Cheema & Soman, 2008;

Heath, 1995; Krishnamurthy & Prokopec, 2010).


-50-

To ascribe intergroup differences of economic parameters to mental budgeting, we

calculate two relative measures. The first, the estimation bias, is the difference

between the price of the selected product and the indicated budget prior to shopping.

The second, the budget deviation, is the difference between the willingness to pay

(WTP) for a selected product and the indicated budget prior to shopping. While the

estimation bias is a spending measure, the budget deviation serves as a direct payment

measure. Building on Hypothesis 1, we argue that goal-based labels evoke creative

bookkeeping, which leads consumers to exceed previously set budgets. By contrast,

type-based labels promote rigid bookkeeping, which motivates consumers to spend

and pay without exceeding previously determined budgets. We therefore formulate

Hypothesis 2.

H2: Holding the underlying information constant, type-based labels will lead

to a lower estimation bias and budget deviation compared with goal-

based labels.

1.3 The Moderating Impact of Budget Tracking

The preference for budget tracking is subject to interpersonal processes that vary

between individuals (Thaler, 1985). Budget trackers follow their predefined budgets to

avoid negative consequences; they do not exchange budgets between different

categories, and they constantly experience high mental stress from comparing

budgeting constraints with market prices (Stilley, Inman, & Wakefield, 2010; Van

Ittersum et al., 2010). To mitigate the attention toward self-control and concerns about

exceeding budget constraints, budget trackers employ safety margins in their shopping

(Pennings & Wansink, 2004; Van Ittersum et al., 2013). We argue that broader goal-

based labels cause the safety margin to disappear. Goal-based labels lower the

perceived effort, improve the shopping experience and create loopholes that enable

budget trackers to escape rigid budget constraints.

By contrast, non-budget trackers consume according to their intrinsic desires rather

than their rational choices and base their economic decisions on parameters such as

their familiarity with choice contexts. Based on the higher expectancy of type-based

similarity (Poynor & Wood, 2010), we assume that type-based labels amplify spending

and the WTP. We also expect the reverse effect of goal-based labels, which create

negative emotions (e.g., depletion) and cause non-budget trackers to reject automatic

behavior. In view of these expectations, budget tracking is expected to moderate the

relationship between category labels and both estimation bias and budget deviation.


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H3: Holding the underlying information constant, budget tracking will

moderate the relationship between category labels and both estimation

bias and budget deviation such that unexpected goal-based labels will

a) result in a higher estimation bias and budget deviation than expected

type-based labels among budget trackers; and

b) result in a lower estimation bias and budget deviation than expected

type-based labels among non-budget trackers.

Because consumers have difficulties in depicting their spending and payment

behaviors within specific categories, their decisions in the marketplace are subject to

uncertainty. Rigidity in mental accounting amplifies a tendency toward stressing about

not exceeding budget constraints and thus increases uncertainty (Bénabou & Tirole,

2004; Thaler & Shefrin, 1981). Building on the variable malleability of type-based and

goal-based similarity, we assume that budget tracking further moderates the

relationship between category labels and choice uncertainty.

Budget trackers are constantly concerned about not exceeding their budget and thus

experience high uncertainty (Van Ittersum et al., 2010). Malleability increases the

number of associated mental budgets and enables different interpretations in a self-

serving and justifiable way (Gourville & Soman, 1998; Klein & Kunda, 1992; Soman

& Gourville, 2001). Therefore, goal-based labels decrease thoughts regarding

budgeting constraints and consequently uncertainty for budget trackers. Non-budget

trackers rely on their familiarity with the choice context, which is inversely related to

uncertainty. Uncertainty increases in unexpected contexts because of unpleasant

feelings of discomfort (Kahneman & Tversky, 1982), whereas it decreases in expected

contexts because of easily accessible cues (Van Horen & Pieters, 2013). We argue that

familiar type-based labels provide decision-aiding cues that decrease uncertainty,

whereas unfamiliar goal-based labels overstrain non-budget trackers and thus increase

uncertainty. Thus, we posit Hypothesis 4.

H4: Holding the underlying information constant, budget tracking will

moderate the relationship between category labels and choice uncertainty

such that unexpected goal-based labels will

a) lead to a lower choice uncertainty than expected type-based labels

among budget trackers; and

b) lead to a higher choice uncertainty than expected type-based labels

among non-budget trackers.


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Type-Basedvs.

Goal-BasedCategory Labels

Type-BasedCategory Labels

Goal-BasedCategory Labels

DecreasedChoice

Uncertainty

DecreasedChoice

Uncertainty

IncreasedChoice

Uncertainty

IncreasedChoice

Uncertainty

Budget Trackers

Non-BudgetTrackers

Budget Trackers

Non-BudgetTrackers

IncreasedEstimation Bias

and Budget Deviation

IncreasedEstimation Bias


DecreasedEstimation Bias


DecreasedEstimation Bias


1.4 The Mediating Impact of Choice Uncertainty

Empirical evidence supports the relationship between choice uncertainty and economic

parameters (Van Schie, Donkers, & Dellaert, 2012) and the role of mental accounting

in explaining behaviors under uncertainty (Gupta & Kim, 2010; Stilley et al., 2010;

Van Ittersum et al., 2010; Van Ittersum et al., 2013). We assume that uncertainty

mediates the moderation of budget tracking between category labels and both

estimation bias and budget deviation, a pattern known as moderated mediation (see

Figure 1B; Hayes, 2013; Preacher, Rucker, & Hayes, 2007). Higher (lower) choice

uncertainty in the type-based (goal-based) condition leads to a lower (higher)

estimation bias and budget deviation among budget trackers, whereas the reverse

effects are expected among non-budget trackers (see Figure 2).

Figure 2

Expected Effects for the Hypothesized Moderated Mediation Model

We predict that uncertainty only partially mediates the moderation due to the expected

direct relationship between category labels and both estimation bias and budget

deviation. Additionally, we expect the existence of further mediating factors that are

beyond the scope of this article. Thus, we establish Hypothesis 5.

H5: Holding the underlying information constant, choice uncertainty will

partially mediate the relationship between category labels and both


-53-

estimation bias and budget deviation for budget and non-budget trackers

such that unexpected goal-based labels, in contrast to expected type-based

labels, will

a) have a positive conditional indirect effect of category labels on

estimation bias and budget deviation through choice uncertainty among

budget trackers; and

b) have a negative conditional indirect effect of category labels on

estimation bias and budget deviation through choice uncertainty among

non-budget trackers.

1.5 Overview of the Empirical Studies

Building on the idea that consumers use mental budgets while shopping (Gupta &

Kim, 2010; Stilley et al., 2010), we use mass customization systems to test Hypotheses

1-5. Mass customization systems are prevalent tools for replicating shopping contexts

and are widely used in the marketplace to create unique products based on various

attribute decisions (Franke, Schreier, & Kaiser, 2010). They subdivide billions of

attribute combinations into several categories with clear boundaries and category

labels. Thus, mass customization systems are best suited for measuring the MBE of

predetermined category labels in the marketplace and their impact on economic

parameters. Given this tool and considering that consumers often refrain from

assigning small and routine expenses to their mental accounts (Thaler, 1999), we

replicated a car purchase process via configurators to test our hypotheses. Following a

discussion of three Pre-Studies to test the necessary requirements, we present Study 1

(Hypothesis 1) and Study 2 (Hypotheses 2-5).

2 Pre-Studies

2.1 Pre-Study 1: Expectancy of Category Labels

We assume that the higher expectancy of type-based similarity (Poynor & Wood,

2010) depends on the choice context. Thus, Pre-Study 1 was used to identify expected

and unexpected category labels in a mass customization context through an analysis of

type-based and goal-based category labels of more than 70 online configurators in the

marketplace. We created screenshots of the entry pages in the configuration process

and compared the labels of the essential selection steps. In agreement with our

theoretical prediction, more than 90% of the selection steps involved type-based


-54-

Condition

Type-based category labela

M SD M SD t (31)Model 4.06** 1.105 Performance 2.50*** 1.016 5.506***Colors & Rims 4.03** 0.967 Exterior Design 2.69** 1.491 4.207***Upholstery 4.13* 1.408 Interior Design 2.28*** 1.276 6.183***

Goal-based category labela

labels, indicating that type-based labels are the expected market standard in the

configuration process of a car.

2.2 Pre-Study 2: Typicality of Category Labels

To ensure that different typicality ratings provided a basis for the intended

manipulations, Pre-Study 2 was designed to identify type-based and goal-based labels

with different typicality ratings. Two designs of a car configurator with different

category labels were presented to 32 students at the University of St. Gallen (52%

female, Mage = 27.20, SDage = 3.70). To ensure differences in expectancy, we used

type-based labels from the marketplace (i.e., “Model,” “Colors & Rims,” and

“Upholstery”) and goal-based labels that were not used in any of the analyzed

configurators in Pre-Study 1 (i.e., “Performance,” “Exterior Design,” and “Interior

Design”). Using the following wording, participants were asked to evaluate the

typicality of the category labels with their mental budgets:

“Please indicate the typicality of the following category labels based on the

category labels that you would normally expect to encounter in the process of

purchasing a car.”

The answers were tracked on a 6-point Likert scale anchored by 1 (highly non-typical)

and 6 (highly typical). Participants were not asked to rate the typicality of the

presented labels compared to their generally used mental budgets, as not all

individuals are budget trackers. The results revealed significantly higher typicality

ratings for type-based labels (see Table 1).

Table 1

Typicality Ratings of Category Labels

aWithin-column significance tests were based on a pairwise comparison between each level and the

scale midpoint of 3.50. *p < .05. **p < .01. ***p < .001.


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The typicality rating of the type-based label “Model” was significantly higher (M =

4.06, SD = 1.11) than the scale midpoint of 3.50 (t(31) = 2.88, p = .007) and the

corresponding goal-based label “Performance” (M = 2.50, SD = 1.02, t(31) = 5.51, p <

.001), which was significantly lower than the scale midpoint of 3.50 (t(31) = -5.57, p <

.001). Similarly, the typicality rating of the type-based label “Colors & Rims” (M =

4.03, SD = 0.97) was significantly higher than the scale midpoint of 3.50 (t(31) = 3.11,

p = .004) and its corresponding goal-based label “Exterior Design” (M = 2.69, SD =

1.49, t(31) = 4.21, p < .001), which was significantly lower than the scale midpoint of

3.50 (t(31) = -3.08, p = .004). Finally, the typicality rating of the type-based label

“Upholstery” (M = 4.13, SD = 1.41) was significantly higher than the scale midpoint

of 3.50 (t(31) = 2.51, p = .018) and the corresponding goal-based label “Interior

Design” (M = 2.28, SD = 1.28, t(31) = 6.18, p < .001), which was significantly lower

than the scale midpoint of 3.50 (t(31) = -5.40, p < .001). The significant differences in

typicality ratings indicate that the selected labels are an effective basis for the intended

manipulations in Studies 1 and 2.

2.3 Pre-Study 3: Relative Price Knowledge

Although perfect price knowledge is an underlying assumption of economic theory,

this assumption has been strongly challenged (e.g., Monroe & Lee, 1999). To allay

concerns regarding the effectiveness of our hypothesis testing, which omits prices,

Pre-Study 3 was used to ascertain the relative price knowledge of specific car

components (Vanhuele & Drèze, 2002). Forty students (51% female, Mage = 25.50,

SDage = 3.76) from the University of St. Gallen participated in a survey. A research

assistant briefed the participants and accompanied each of them into a room with a

laptop and direct access to the study. Participants were provided with 36 product items

from the car components – models, colors, rims, and upholsteries (i.e., nine items

each) – of a German car manufacturer. They were asked to rank the items by dragging

and dropping them according to their actual market prices, starting with the most

expensive item (see Figure 3).

The nine items of each component were presented sequentially in a randomized order

with the corresponding type-based label (e.g., “Model”) and an image with the name

of the item. Randomization was employed to prevent participants from forming

conclusions regarding the real order of prices based on historically conditioned

expectations. Because some of the items were identically priced, participants were

allowed to place multiple items in the same rank position. Participants were provided


-56-

Upholstery1

€0

Upholstery2

€650

Upholstery3

€650

Upholstery4

€650

Upholstery5

€1,440

Upholstery6

€1,440

Upholstery7

€1,750

Upholstery8

€1,750

Upholstery9

€1,750

Model 1

€15,550

Model 2

€16,850

Model 3

€18,450

Model 4

€19,550

Model 5

€21,250

Model 6

€22,790

Model 7

€23,650

Model 8

€24,650

Model 9

€29,500

ColorModel

Rims1

€0

Rims2

€480

Rims3

€1,150

Rims4

€1,710

Rims5

€2,030

Rims6

€2,030

Rims7

€2,330

Rims8

€2,330

Rims9

€2,330

Rims Upholstery

Color 1

€0

Color 2

€0

Color3

€450

Color4

€450

Color5

€450

Color6

€450

Color7

€450

Color8

€850

Color9

€850

with the lowest and highest price of each component. Upon completing the study,

participants were given a chocolate bar as compensation. Eight participants were

excluded; four did not complete the study, and the remaining four assigned the items

in the wrong order, starting with the lowest priced item.

Figure 3

Items Used for the Test of Price Knowledge, Including Their List Prices as of March

2013 (in €)

Note. The nine items for the four attributes were presented in a randomized order. Any price

information was removed from the items prior to the study.

The correct ranking was determined based on market prices as of March 2013. The

results showed that on average, participants ranked 68% of the items correctly. This

was further qualified by 74% (81%) correctly ranked items within a 10% (20%)

deviation from the actual price (see Table 2).


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Category

∆P1a

(%)Correct ranking (%)

∆P2a


∆P3a


Model 0 70 10 93 20 99Color 0 75 10 75 20 75Rims 0 59 10 59 20 83Upholstery 0 68 10 68 20 68Average 0 68 10 74 20 81

Test 3Test 2Test 1

Table 2

Determination of Price Knowledge for Items Used in Study 1 and Study 2 as

Percentages of Correctly Ranked Items for Different Acceptable Price Deviations

a∆P1, ∆P2 and ∆P3 denote the maximum acceptable deviation between the list price of the correct item

and the list price of the ranked item for each price point.

A Spearman correlation analysis indicated a positive correlation between the actual

price rank and the ranked items for models (r = .96), colors (r = .67), rims (r = .83)

and upholsteries (r = .72) (all ps < .001). Furthermore, the mean correlation for the

pooled items was also significant (r = .93, p < .001). Taken together, the results

confirmed relative price knowledge, thereby approving the suitability of the items for

testing our hypotheses.

3 Study 1: Category Labels and the MBE

Study 1 was designed to test whether type-based and goal-based labels influence the

mental budgeting process differently (Hypothesis 1; see Figure 1A).

3.1 Sample

The participants consisted of 137 individuals (46% female, Mage = 42.30, SDage =

11.43) from an online survey panel (www.innofact.com) that were recruited through

an email notification that included a brief description of the research and a link to the

study. The participants were paid €5 upon completion of the study, and the study

materials were written in German.

3.2 Method and Experimental Framework

Participants were presented with images of nine rims of a German car manufacturer

without any category labels and were then asked to indicate their budgets for a set of


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four rims as part of a car purchase. To account for the lower product knowledge of

some participants, the lowest (€0) and highest prices (€2,350) were indicated. The

following wording was used:

“Imagine you own a [brand name] hatchback and you are about to replace

some components. Please indicate your budget for the component presented

below. Your answer should be between €0 (lowest list price) and €2,350

(highest list price).”

Next, participants were randomly assigned to a type-based or a goal-based condition

and were again asked to indicate their budget for the same rims. This time, however,

the type-based label “Rims” in Group 1 or the goal-based label “Exterior Design” in

Group 2 was included. The wording for Group 1 (Group 2) was:

“You now see the same images as before, but this time, they are accompanied

by the label ‘Rims’ (‘Exterior Design’). Please indicate your budget for rims

(exterior design) for your [brand name] hatchback. Please note that your

answer should be between €0 (lowest list price) and €2,350 (highest list price).”

To ascribe intergroup differences to budgeting decisions, we computed the MBE. The

MBE represents the unbiased degree of underconsumption that can be traced back to

mental budgeting and is computed by subtracting the income effect (IE) and the

satiation effect (SE) from the purchase effect (PE) (Heath & Soll, 1996). To compute

these effects, participants were randomly confronted with three events and then asked

to indicate their new budget for the same items labeled “Rims” (Group 1) or “Exterior

Design” (Group 2), respectively. To make the initial budget indication consequential,

participants were told to imagine that they had shared the initial budget with close

friends. The following wording was used:

“Imagine you have shared your initial budget of [amount in €] with close

friends. Next, you will be presented with three events; after each event, you will

be asked to indicate your new budget.”

The first event was used to determine the PE by computing the difference between the

initial budget and the amount a person would spend after a previous purchase in the

same category. The wording for Group 1 (Group 2) was as follows:


-59-

“Imagine you have just had an accident with your [brand name] hatchback and

spent €200 to repair the rims (exterior design). Please indicate your new budget

for a new set of rims (exterior design) for your [brand name] hatchback.”

Next, the IE, which equals the difference between the initial budget and the new

budget after unexpected spending in a dissimilar category, was computed. The

wording for Group 1 (Group 2) was as follows:

“Imagine that you received an unexpected wedding invitation and spent €200

on a wedding gift. Please indicate your new budget for the set of rims (exterior

design) for your [brand name] hatchback.”

For example, if consumers spend €800 for the presented rims labeled “Exterior

Design” after the unexpected expense of €200, the IE would not explain why

consumers would spend less than €800 on the same rims labeled “Rims” after

incurring the same unexpected expense of €200.

To assess the SE, a third event was used to compute the difference between the initial

budget and the new budget after receiving a gift in the same category, using the

following wording for Group 1 (Group 2):

“Imagine that [brand name] is currently running a promotion and offers you a

voucher worth €200 for rims (exterior design) that can be redeemed with your

upcoming car purchase. Please indicate your new budget for the set of rims

(exterior design) for your [brand name] hatchback.”

For example, if consumers spend €800 for the presented rims labeled “Rims” after

redeeming the voucher worth €200, the SE would not explain why consumers would

spend less than €800 on the same rims labeled “Exterior Design” after redeeming the

same voucher worth €200. Figure 4 presents the experimental framework of Study 1.


-60-

Events New BudgetsMental Budgeting

Effect (MBE)

Budget withType-Based

Labels

Budget withGoal-Based

Labels

Event 1:Purchase Effect

(PE)

Event 2:Income Effect

(IE)

Event 3:Satiation Effect

(SE)

Events 1-3:Type-Based

Labels

Events 1-3:Goal-Based

Labels

Budget without Labels

Random Assignment

MBE =PE - IE - SE

Initial Budget

Figure 4

Experimental Framework for Study 1

3.3 Results

The average budget increased by more than 30% after adding the goal-based label

“Exterior Design” based on the same underlying information (Mno label = 726.67 versus

Mgoal-based label = 948.73; t(74) = -10.78, p < .001). There was no change in the type-

based condition (Mno label = 751.03 versus Mtype = 735.83; t(62) = 1.29, p = .202). The

results also revealed a significant main effect of a lower budget for rims when the

type-based label “Rims” was included than when the goal-based counterpart “Exterior

Design” was included (Mtype-based label = 735.83 versus Mgoal-based label = 948.73; F(1; 136)

= 4.57, p = .034). The proportion of participants indicating a higher budget after

adding labels was significantly lower in the type-based (43%) than in the goal-based

condition (79%; 2(1, N = 137) = 18.70, p < .001).

To determine whether these results were attributable to mental budgeting decisions,

the MBE was computed, as exemplified in the following scenario. After indicating a

budget of €800, Mr. A was randomly assigned to the type-based condition (Group 1).

After the first event, the initial budget decreased to €650, yielding a PE of 150 (PE =

800 - 650 = 150). To determine whether this lower budget was caused by mental

budgeting, the IE and the SE had to be subtracted. The unexpected expense of €200 for

the gift decreased the budget to €750, leading to an IE of 50 (IE = 800 - 750 = 50). The

receipt of a voucher worth €200 resulted in a new budget for rims of €720, yielding an

SE of 80 (SE = 800 - 720 = 80). This revealed an MBE of 20 (MBE = PE - IE - SE =


-61-

Condition Type-based Goal-based 2

MBE > 0 44 4 32.16***MBE < 0 27 79 36.96***

Proportion (%)

Condition

M SD M SD t (136)

PE 188.21 95.43 37.93 97.20 9.121***IE 64.63 63.46 63.47 137.17 0.062SE 85.11 160.37 42.60 82.89 2.000*

MBEa 38.46 147.90 -68.13 139.69 4.347***

Type-based Goal-based

150 - 50 - 80 = 20), indicating that Mr. A. did not fully exploit his budget for reasons

attributable to mental budgeting.

The results indicated a significantly higher proportion of positive MBEs in the type-

based condition (44%) than in the goal-based condition (4%; 2(1, N = 137) = 32.16, p

< .001) (see Table 3).

Table 3

Proportions of Positive and Negative MBEs (in %)

*p < .05. **p < .01. ***p < .001.

In monetary terms, the MBE was significantly higher in the type-based condition (M =

38.46, SD = 147.90) than in the goal-based condition (M = -68.13, SD = 139.69)

(t(136) = 4.35, p < .001) but was significantly different from zero in both the type-

based condition (t(62) = 2.06, p = .044) and the goal-based condition (t(74) = -4.22, p

< .001). Heath and Soll (1996) analyzed the three effects for different levels of

typicality between expenses and budgets. Following their research, the PE and SE are

expected to be significantly higher for typical type-based labels, whereas no such

difference is assumed for the IE. Table 4 supports this reasoning and Hypothesis 1 in

indicating that the significantly higher MBE in the type-based condition is caused by

the PE (p < .001) and the SE (p = .048) but not the IE (p = .951).

Table 4

Components of the MBE across Conditions (in €)

aMBE = PE - IE - SE. *p < .05. **p < .01. ***p < .001.


-62-

3.4 Summary and Conclusion

Study 1 built on previous research and demonstrated that the MBE depends not only

on the typicality of expenses with budget categories (e.g., Heath & Soll, 1996) but also

on the similarity of category labels, with a positive (negative) MBE for type-based

(goal-based) labels. These results are reasonable because similarity differs in its level

of expectancy (Poynor & Wood, 2010), activates different parts of the brain (Davidoff

& Roberson, 2004; Sass, Sachs, Krach, & Kircher 2009) and varies with respect to

how attribute information is evaluated (Huffman & Houston, 1993). Importantly, the

varied algebraic signs of the MBE (see Table 4) provide a first indication of the

expected impact of category labels on the economic parameters investigated in Study

2.

4 Study 2: Budgeting Decisions in a Mass Customization

Context

Study 2 illuminated the process that underlies the previous findings in a mass

customization context by directly measuring the impact of category labels on

economic parameters and accounting for the moderating role of budget tracking and

the mediating impact of choice uncertainty (Hypotheses 2-5; see Figure 1B).

4.1 Sample

The participants were recruited from an online survey panel (www.innofact.com) via

an email notification containing a brief description and the link to the study. Again, all

study materials were written in German, and participants were compensated with €5 in

cash upon completion of the study. A total of 198 individuals participated, of which

six provided incomplete answers and were removed from the sample (60% female,

Mage = 42.78, SDage = 11.07).

4.2 Method and Experimental Framework

First, the participants’ preferences for budget tracking were measured using a 7-point

Likert scale (Homburg, Koschate, & Totzek, 2010) (see Table A1 in the Appendix).

Next, participants were presented with a scenario and were asked to indicate their car

budget based on the pretested items. Neutral category labels were used to avoid

priming the participants toward any one condition (see Figure 5).


-63-

ImageUpholstery

1

ImageUpholstery

2

ImageUpholstery

3

ImageUpholstery

4

ImageUpholstery

5

ImageUpholstery

6

ImageUpholstery

7

ImageUpholstery

8

ImageUpholstery

9

ImageModel

1

ImageModel

2

ImageModel

3

ImageModel

4

ImageModel

5

ImageModel

6

ImageModel

7

ImageModel

8

ImageModel

9

Component IIComponent I

ImageRims

1

ImageRims

2

ImageRims

3

ImageRims

4

ImageRims

5

ImageRims

6

ImageRims

7

ImageRims

8

ImageRims

9

Component III Component IV

ImageColor

1

ImageColor

2

ImageColor

3

ImageColor

4

ImageColor

5

ImageColor

6

ImageColor

7

ImageColor

8

ImageColor

9

Figure 5

Neutral Category Labels with the 36 Type-Based Categorized Product Items as a Basis

for the Initial Budget Indication

To account for their limited price knowledge, participants were provided with the

lowest and highest prices of the four components using the following wording:

“Please imagine that you are about to buy a car. After careful consideration,

you decide to buy a [brand name] hatchback. Please indicate your budget for

this car, assuming that you can choose among the following 36 product items

from four components and that you choose exactly one item per component.

Your answer should be between €15,550 (lowest list price) and €34,450

(highest list price).”

Next, participants were randomly assigned to a type-based condition (Group 1) or

goal-based condition (Group 2) using the pre-tested category labels from Pre-Study 2.

The items for the model and upholstery attributes were categorized into two categories


-64-

of nine items each. The remaining rims and colors were assigned to one category but

categorized into two visually distinct blocks, each with nine rims and colors.

Participants were instructed to configure their preferred car by selecting one item per

component. Furthermore, given the participants’ relative price knowledge (see Pre-

Study 3), any price information was excluded to ensure that participants were not

distracted from their initial budget, which served as a reference point. Again, to make

the budgets consequential, participants were asked to imagine that they shared their

initial budgets with close friends using the following wording:

“Imagine you have shared your initial budget of [amount in €] with close

friends. Next, you are asked to configure your new [brand name] hatchback,

utilizing the four presented components. Please click ‘Continue’ to read the

following important notes before you start the configuration process:

1. Select exactly one item from each configuration step;

2. Ignore any time constraints; and

3. You can click ‘Next’ to preview your configured car and ‘Back’ to adjust the

configuration as often as desired.”

To measure spending as part of the estimation bias (i.e., the difference between

spending and the initial budget), the prices of the four selected items were determined

from the pricelist. To determine the WTP as part of the budget deviation (i.e., the

difference between WTP and the initial budget), participants were asked to indicate

their WTP for the configured car. Next, participants rated their familiarity with the

type-based (goal-based) category labels with a numbered slider (0 = not at all familiar;

100 = highly familiar):

“Please indicate how familiar you were with the category labels ‘Models,’

‘Colors & Rims,’ and ‘Upholstery’ (‘Performance,’ ‘Exterior Design,’ and

‘Interior Design’) during the configuration process.”

Finally, choice uncertainty was measured along a 9-point Likert choice confidence

scale (Heitmann, Lehmann, & Herrmann, 2007; Urbany, Bearden, Kaicker, & Smith-

de Borrero, 1997) (see Table A1 in the Appendix). Because choice uncertainty and

choice confidence are inversely related, the inverse was calculated for each scale item.

Figure 6 presents the experimental framework of Study 2.


-65-

Group 1:Type-Based Labels

Uphols-tery 1

Uphols-tery 2

Uphols-tery 3

Uphols-tery 4

Uphols-tery 5

Uphols-tery 6

Uphols-tery 7

Uphols-tery 8

Uphols-tery 9

Upholstery

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

Model

Color 1

Color 2

Color3

Color4

Color5

Color6

Color7

Color8

Color9

Colors & Rims

Rims1

Rims2

Rims3

Rims4

Rims5

Rims6

Rims7

Rims8

Rims9

Group 2:Goal-Based Labels

Interior Design

Performance

Model 1

Model 2

Model 3

Model 4

Model 5

Model 6

Model 7

Model 8

Model 9

Color 1

Color 2

Color3

Color4

Color5

Color6

Color7

Color8

Color9

Exterior Design

Rims1

Rims2

Rims3

Rims4

Rims5

Rims6

Rims7

Rims8

Rims9

Uphols-tery 1

Uphols-tery 2

Uphols-tery 3

Uphols-tery 4

Uphols-tery 5

Uphols-tery 6

Uphols-tery 7

Uphols-tery 8

Uphols-tery 9

Random Assignment

Willingness to Pay

Survey Choice Uncertainty

Familiarity Rating

Initial Budget

Survey Budget Tracking

Figure 6

Experimental Framework for Study 2

To examine the second conceptual model (Figure 1B), Hypotheses 2-5 were tested in

three interlinked sub-models (see Figure 7), using a regression-based path analysis.


-66-

CategoryLabels

BudgetTracking

Labels ×Budgeting

EstimationBias

BudgetDeviation

c‘1

c‘2

c‘3

BChoice

Uncertainty

b

CategoryLabels

BudgetTracking

Labels ×Budgeting

EstimationBias

BudgetDeviation

c1

c2

c3

A

a1

a2

a3

Model 1

Model 2

Model 3

Figure 7

Path Model for Study 2 Based on the Conceptual Model in Figure 1B

4.3 Results

Prior to the analysis, we dummy-coded category labels and mean-centered budget

tracking (α = .90) and choice uncertainty (α = .92) (Aiken & West, 1991). A category

label (type-based or goal-based) × budget tracking (mean-centered) × choice


-67-

uncertainty (mean-centered) ordinary least squares (OLS) regression on familiarity

revealed a significant main effect of category labels, β = -20.85 t(188) = -7.22, p <

.001, indicating significantly greater familiarity with type-based category labels than

with goal-based category labels (Mtype = 72.68, SD = 19.09 versus Mgoal = 51.77, SD =

20.90). More importantly, none of the other main or interaction effects were

significant (all ps > .200), showing that regardless of the preference for budget

tracking, type-based labels were more expected.

4.3.1 Test of Model 1 for Estimation Bias

Model 1 investigates the main effect of category labels on both estimation bias and

budget deviation (path c1, Hypothesis 2) and the moderation of budget tracking (path

c3, Hypothesis 3). An outlier analysis (i.e., ±3 SD from the group mean) did not reveal

any extreme values in any one condition.

An initial analysis that compared spending with the indicated budget prior to the

configuration process revealed that budget trackers spent more than 8% less than they

indicated in their initial budget in the type-based condition (Mspending = 18,865.63

versus Mbudget = 20,534.69; t(47) = 7.42, p < .001). The 2% lower spending of non-

budget trackers in the goal-based condition was insignificant (Mspending = 18,925.53

versus Mbudget = 19,336.81; t(46) = 1.15, p = .256). By contrast, both the almost 19%

higher spending of non-budget trackers in the type-based condition (Mspending =

24,101.28 versus Mbudget = 20,326.02; t(46) = -6.23, p < .001) and the more than 25%

higher spending of budget trackers in the goal-based condition (Mspending = 25,050.80

versus Mbudget = 20,014.32; t(49) = -8.53, p < .001) were significant. These results

indicate that the employment of a safety margin is limited to budget trackers and

categories with type-based labels. Thus, the results provide initial support for the

moderation of budget tracking.

Next, we calculated the estimation bias (M = 1,706.59; SD = 4,231.99) and computed a

category label (type-based or goal-based) × budget tracking (mean-centered) OLS.

Corresponding to Hypothesis 2, a significant main effect of category labels emerged

with a significantly lower estimation bias in the type-based condition than in the goal-

based condition (Mtype-based label = 1,024.44, SD = 4,140.83 versus Mgoal-based label =

2,374.68, SD = 4,234.65; t(190) = 1.98, p = .049). Furthermore, consistent with

Hypothesis 3, the analysis revealed a significant interaction between category labels

and budget tracking (β = 3,573.74, t(188) = 9.51, p < .001) that accounted for almost

93% of the explained variance (.32/.35; see Table 5).


-68-

Out

com

e

Pre

dict

orp

Coe

ffic

ient

pC

oeff

icie

ntp

Coe

ffic

ient

pC

oeff

icie

ntp

Con

stan

t1,

092.

809

.000

1,32

6.62

4.0

004.

206

.000

1,15

1.55

4.0

001,

370.

074

.000

(354

.439

)(3

63.9

72)

(0.1

39)

(320

.785

)(3

47.0

39)

c1

1,34

3.41

7.0

081,

576.

719

.002

a1

-0.1

79.3

60 c

' 11,

146.

329

.012

1,43

0.94

7.0

04

(498

.674

)(5

12.0

87)

(0.1

96)

(452

.155

)(4

89.1

61)

c2

-1,8

82.5

55.0

00-1

,682

.39

.000

a2

1.05

6.0

06 c

' 2-7

22.2

21.0

15-8

24.1

74.0

10

(258

.552

)(2

65.5

06)

(0.1

01)

(293

.681

)(3

17.7

17)

b-1

,099

.020

.000

-812

.867

.000

(168

.196

)(1

81.9

61)

c3

3,57

3.74

1.0

003,

611.

062

.000

a3

-2.0

46.0

00 c

' 31,

324.

840

.007

1,94

7.70

6.0

00

(375

.743

)(3

85.8

49)

(0.1

47)

(483

.747

)(5

23.3

38)

Mod

el R

2.3

45.0

00.3

40.0

00.5

09.0

00.4

67.0

00.4

04.0

00

Inte

ract

ion Δ

R2

.318

.000

.306

.000

.503

.000

.022

.000

.044

.000

Not

e. P

rese

nted

are

the

unst

anda

rdiz

ed r

egre

ssio

n co

effic

ient

s.X

: Ind

epen

dent

var

iabl

e. Y

: Dep

ende

nt v

aria

bles

. M: M

edia

tior

varia

ble.

W: M

oder

ator

var

iabl

e.C

ateg

ory

labe

ls w

as d

umm

y-co

ded.

Bud

get t

rack

ing

(Mod

el 1

-3)

and

choi

ce u

ncer

tain

ty (

Mod

el 3

) w

ere

mea

n-ce

nter

ed.

Cho

ice

Unc

erta

inty

(M

)

Mod

el 1

Mod

el 2

Mod

el 3

Lab

els

× B

udge

t T

rack

ing

(X ×

W)

Cho

ice

Unc

erta

inty

(M

)

Bud

get

Tra

ckin

g (W

)

Cat

egor

y L

abel

s (X

)

Coe

ffic

ient

Est

imat

ion

Bia

s (Y

)B

udge

t D

evia

tion

(Y

)E

stim

atio

n B

ias

(Y)

Bud

get

Dev

iati

on (

Y)

Table 5

Regression-Based Path Analysis Coefficients for Models 1-3 (Standard Errors in

Parentheses)


-69-

As predicted, a simple slope analysis (Aiken & West, 1991) revealed an estimation

bias that was significantly higher for budget trackers (+1 SD above the mean) (β =

6,103.71, t(188) = 8.63, p < .001) and significantly lower for non-budget trackers (-1

SD below the mean) (β = -3,419.86, t(188) = -4.84, p < .001) in the goal-based

condition. Even moderate budget trackers (mean) showed a significantly higher

estimation bias in the goal-based condition (β = 1,341.93, t(188) = 2.69, p = .008)

(Figure 8A).

The interaction was further investigated by deriving regions of significance using the

Johnson-Neyman technique (Hayes & Matthes, 2009). The conditional effect of

category labels on estimation bias (solid line) including its lower and upper limits at

the 95% confidence interval (CI) (dashed lines) are depicted in Figure 8B. The slope

of the point estimate for the estimation bias can be inferred from Table 5: c1 + (c3 ×

budget tracking) = 1,092.81 + (3,573.74 × budget tracking). This analysis revealed a

significantly negative conditional effect of category labels on the estimation bias for

non-budget trackers (budget tracking < 3.80) and a significantly positive conditional

effect on the estimation bias for budget trackers (budget tracking > 4.39). This result

indicated a significantly higher estimation bias for budget trackers (budget tracking >

4.39) and a reverse effect for non-budget trackers (budget tracking < 3.80) in the goal-

based condition. Regression analyses confirmed this finding by revealing a decreasing

estimation bias in the type-based condition (β = -1,882.56, t(93) = -7.75, p < .001) and

an increasing estimation bias in the goal-based condition (β = 1,691.19, t(95) = 5.88, p

< .001) for higher values of budget tracking.

4.3.2 Test of Model 1 for Budget Deviation

An initial analysis comparing WTP with the indicated budget revealed a 6% lower

WTP in the type-based condition among budget trackers (MWTP = 19,367.31 versus

Mbudget = 20,534.69; t(47) = 3.82, p < .001). The less than 1% lower WTP of non-budget

trackers in the goal-based condition was not different from zero (MWTP = 19,318.09

versus Mbudget = 19,336.81; t(46) = 0.07, p = .945). Both the 19% higher WTP for non-

budget trackers in the type-based condition (MWTP = 24,076.21 versus Mbudget =

20,326.02; t(46) = -6.08, p < .001) and the almost 28% higher WTP for budget

trackers in the goal-based condition (MWTP = 25,528.24 versus Mbudget = 20,014.32;

t(49) = -8.81, p < .001) were significant. This initial analysis confirmed that budget

trackers employ safety margins for spending and payment decisions but only in the

case of using type-based labels.


-70-

3,601.98

182.121,093.59

2,435.52

-1,414.79

4,688.92

-2,000

-1,000

0

1,000

2,000

3,000

4,000

5,000

6,000

Type-Based Labels Goal-Based Labels

Est

imat

ion

Bia

s (€

)

Non-Budget Trackers (-1 SD)

Moderate Budget Trackers (mean)

Budget Trackers (+1 SD)

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

1.0 2.5 4.0 5.5 7.0

Con

dit

ion

al E

ffec

t of

Cat

egor

y L

abel

s on

Est

imat

ion

Bia

s

A

B

3.80 4.39

Point Estimate

95% CI Lower Limit

95% CI Upper Limit

Budget Tracking

Figure 8

Estimation Bias as a Function of Category Labels and Budget Tracking (A) and

Johnson-Neyman Regions of Significance for the Conditional Effect of Category

Labels on Estimation Bias for Non-Budget Trackers (-1 SD below the Mean), Budget

Trackers (+1 SD above the Mean) and Moderate Budget Trackers (Mean) (B)


-71-

Next, we computed the budget deviation across participants (M = 2,057.51; SD =

4,331.83) and conducted the same category label (type-based or goal-based) × budget

tracking (mean-centered) OLS. A significant main effect of category labels emerged

with a significantly lower budget deviation in the type-based condition (Mtype-based label =

1,265.53, SD = 4,135.68 versus Mgoal-based label = 2,833.15, SD = 4,399.77; t(190) =

-2.54, p = .012). With the results for the estimation bias, Hypothesis 2 is supported.

Furthermore, the analysis revealed a significant interaction between category labels

and budget tracking (β = 3,611.06 t(188) = 9.36, p < .001) that accounted for 90% of

the explained variance (.31/.34) (see Table 5). A simple slope analysis indicated a

budget deviation that was significantly higher for budget trackers (+1 SD) (β =

6,386.72, t(188) = 8.80, p < .001) and significantly lower for non-budget trackers (-1

SD) (β = -3,236.29, t(188) = -4.46, p < .001) in the goal-based condition. A

significantly higher budget deviation in the goal-based condition (β = 1,575.22, t(188)

= 3.08, p = .002) was identified for moderate budget trackers (mean) (see Figure 9A).

The Johnson-Neyman approach revealed a significantly negative conditional effect of

category labels on the budget deviation for non-budget trackers (budget tracking <

3.73) and a significantly positive conditional effect of category labels on the budget

deviation for budget trackers (budget tracking > 4.33). This result indicated a

significantly negative budget deviation for non-budget trackers (budget tracking <

3.73) and a reverse effect for budget trackers (budget tracking > 4.33) in the goal-

based condition. The slope of the point estimate is defined by the parameter estimates

for budget deviation in Model 1 in Table 5 (c1 + (c3 × budget tracking) = 1,326.62 +

(3,611.06 × budget tracking)) (see Table 9B).

Finally, a regression analysis confirmed a decreasing budget deviation in the type-

based condition (β = -1,682.39, t(93) = -6.53, p < .001) and an increasing budget

deviation in the goal-based condition (β = 1,928.67, t(95) = 6.70, p < .001) for higher

values of budget tracking. Thus, Hypothesis 3 is supported.


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-15,000

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-5,000

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3.73 4.33

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2,902.54

-914.35

5,472.37

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4,000

5,000

6,000


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(€)




Point Estimate

95% CI Lower Limit

95% CI Upper Limit

Budget Tracking

Figure 9

Budget Deviation as a Function of Category Labels and Budget Tracking (A) and


Labels on Budget Deviation for Non-Budget Trackers (-1 SD below the Mean),

Budget Trackers (+1 SD above the Mean) and Moderate Budget Trackers (Mean) (B)


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4.3.3 Test of Model 2

Model 2 examines whether budget tracking moderates the relationship between

category labels and choice uncertainty expressed by path a3 (Hypothesis 4). A

category label (type-based or goal-based) × budget tracking (mean-centered) OLS

revealed a significant interaction (β = -2.05, t(188) = -13.88, p < .001) that accounted

for almost 99% of the explained variance (.50/.51; Table 5). A simple slope analysis

indicated a significantly lower choice uncertainty for budget trackers (+1 SD) (β =

-2.90, t(188) = -10.47, p < .001) and a significantly higher choice uncertainty for non-

budget trackers (-1 SD) (β = 2.55, t(188) = 9.20, p < .001) in the goal-based condition.

No difference between the conditions was observed for moderate budget trackers

(mean) (β = -0.18, t(188) = -0.91, p = .364) (see Figure 10A).

Using the parameters estimated from Model 2, the Johnson-Neyman technique yielded

a negative slope for the point estimate of a1 + (a3 × budget tracking) = -0.18 - (2.05 ×

budget tracking) and two regions of significance within a 95% CI (see Figure 10B).

Although choice uncertainty was significantly higher in the goal-based condition

among non-budget trackers (budget tracking < 3.94), the reverse effect was observed

among budget trackers (budget tracking > 4.79). Finally, a regression analysis

indicated that choice uncertainty increased in the type-based condition (β = 1.06, t(93)

= 11.14, p < .001) and decreased in the goal-based condition (β = -0.99, t(95) = -8.73,

p < .001) for higher values of budget tracking. Thus, Hypothesis 4 is supported.

4.3.4 Test of Model 3 for Estimation Bias

Model 3 examines the indirect effects of category labels on both estimation bias and

budget deviation through choice uncertainty for different values of budget tracking

(Hypothesis 5). A moderated mediation model with mean-centered variables was

measured using a bootstrap analysis with 10,000 re-samples and the PROCESS macro

(Hayes, 2013). This analysis yielded a conditional indirect effect estimated as (a1 + (a3

× budget tracking)) × b, with b describing the effect of choice uncertainty on the two

dependent variables (Preacher et al., 2007) (see Table 5, Model 3). Following the

finding of a significantly negative relationship between choice uncertainty and

estimation bias (b = -1,099.02, p < .001), the results revealed an upward-sloped

conditional indirect effect of category labels on estimation bias through choice

uncertainty at different values for budget tracking ((-0.18 - (2.05 × budget tracking)) ×

-1,099.02) (see Figure 11).


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3.94 4.79

2.79

5.34

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5.61

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1

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6

7

8

9


Ch

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A

B

Point Estimate

95% CI Lower Limit

95% CI Upper Limit

Budget Tracking

Figure 10

Choice Uncertainty as a Function of Category Labels and Budget Tracking (A) and


Labels on Choice Uncertainty for Non-Budget Trackers (-1 SD below the Mean),

Budget Trackers (+1 SD above the Mean) and Moderate Budget Trackers (Mean) (B)


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-2'000

0

2'000

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6'000

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percentile

3.825th

percentile

4.650th

percentile

5.475th

percentile

6.090th

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Point Estimate

95% CI Lower Limit

95% CI Upper Limit

Budget Tracking

Figure 11

Conditional Indirect Effects of Category Labels on Estimation bias through Choice

Uncertainty for Different Values of Budget Tracking

The conditional indirect effect was significantly negative for non-budget trackers (-1

SD) (mean bootstrap estimate = -2,800.37, SE = 588.38; 95% bias-corrected CI

[-4,010.13, -1,750.22]) and significantly positive for budget trackers (+1 SD) (mean

bootstrap estimate = 3,192.67, SE = 620.68; 95% bias-corrected CI [2,105.51,

4,529.06]). Thus, goal-based labels lead to higher (lower) choice uncertainty among

non-budget trackers (budget trackers), resulting in a significantly lower (higher)

estimation bias (see Table 6). The indirect effect of the product of category labels and

budget tracking can be expressed as the difference between the total effect of the

interaction in Model 1 (c3) and the direct effect of the interaction after controlling for

the mediating impact of choice uncertainty in Model 3 (c’3). This difference equals the

impact of the interaction on choice uncertainty (a3) and the effect of choice uncertainty

on the outcome accounting for the interaction (b), which is also used to test mediated

moderation (Morgan-Lopez & MacKinnon, 2006). Accordingly, we found that a3b =

c3 - c’3: -2.05(-1,099.02) ≈ 2,248 ≈ 3,573.74 - 1,324.84. We also confirmed moderated

mediation because the difference between the total and direct effect of category labels

on estimation bias differed from zero (mean bootstrap estimate = 2,248.90, SE =

431.59; 95% bias-corrected CI [1,493.24, 3,160.55]).


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CategoryLabels

EstimationBias

Choice Uncertainty

BudgetTracking

-.18 -2.05***

1,343.42***/1,146.33***

3,573.74***/1,324.84***

-1,099.02***

Budget Tracking (a 1 + a 3 BT)b Boot SE 95% CI LL 95% CI UL3.1571 (-1 SD ) -2,800.373 588.376 -4,010.125 -1,750.2164.4896 (mean) 196.151 219.804 -202.125 670.8905.8220 (+1 SD ) 3,192.674 620.683 2,105.507 4,529.055

2.8000 (10th percentile) -3,603.560 732.522 -5,131.528 -2,301.2253.8000 (25th percentile) -1,354.656 352.998 -2,093.55 -730.5104.6000 (50th percentile) 444.467 230.499 39.064 947.7815.4000 (75th percentile) 2,243.591 459.435 1,434.005 3,241.2746.0000 (90th percentile) 3,592.934 692.897 2,349.061 5,042.872

Table 6

Conditional Indirect Effects of Category Labels on Estimation Bias through Choice


Note. Unstandardized coefficients and bias-corrected confidence intervals are reported. Bootstrap

sample size = 10,000. BT = budget tracking. LL = lower limit. CI = confidence interval. UL = upper

limit.

Finally, comparing Model 3 and Model 1 showed that the coefficient for the

interaction between category labels and budget tracking was closer to zero but still

significant in Model 3 (i.e., c’3 = 1,324.84 for Model 3 versus c3 = 3,573.74 for Model

1), thereby supporting the assumed partial mediation proposed in Hypothesis 5 (see

Figure 12).

Figure 12

Impact of Choice Uncertainty as a Partial Mediator of the Relationship among

Category Labels on Estimation Bias as a Function of Budget Tracking

*p < .05. **p < .01. ***p < .001.


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Point Estimate

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Budget Tracking

4.3.5 Test of Model 3 for Budget Deviation

Regarding the budget deviation, a significantly negative relationship was found

between choice uncertainty and budget deviation (b = -812.87, p < .001). This

relationship revealed an upward-sloped conditional indirect effect of category labels

on budget deviation through choice uncertainty at different values for budget tracking

((-0.18 - (2.05 × budget tracking)) × -812.71) (see Figure 13).

Figure 13

Conditional Indirect Effects of Category Labels on Budget Deviation through Choice


Bootstrap analyses with 10,000 re-samples using PROCESS revealed a conditional

indirect effect that was significantly negative for non-budget trackers (-1 SD) (mean

bootstrap estimate = -2,071.24, SE = 578.56; 95% bias-corrected CI [-3,325.17,

-1,078.43]) and significantly positive for budget trackers (+1 SD) (mean bootstrap

estimate = 2,361.40, SE = 652.37; 95% bias-corrected CI [1,224.97, 3,753.24]). These

results indicated that goal-based labels led to higher (lower) choice uncertainty among

non-budget trackers (budget trackers). This higher (lower) choice uncertainty led to a

significantly lower (higher) budget deviation in the goal-based condition than in the

type-based condition among non-budget trackers (budget trackers) (see Table 7).


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CategoryLabels

BudgetDeviation

Choice Uncertainty

BudgetTracking

-.18 -2.05***

1,576.72***/1,430.95***

3,611.06***/1,947.71***

-812.87***

Budget Tracking (a 1 + a 3 BT)b Boot SE 95% CI LL 95% CI UL3.1571 (-1 SD ) -2,071.237 578.557 -3,325.165 -1,078.4314.4896 (mean) 145.079 169.484 -144.721 536.9235.8220 (+1 SD ) 2,361.395 652.373 1,224.974 3,753.236

2.8000 (10th percentile) -2,665.298 730.451 -4,209.067 -1,374.2513.8000 (25th percentile) -1,001.943 316.236 -1,707.208 -475.4964.6000 (50th percentile) 328.741 185.966 40.796 793.5995.4000 (75th percentile) 1,659.425 468.309 840.542 2,684.2596.0000 (90th percentile) 2,657.438 722.512 1,356.349 4,188.848

Table 7

Conditional Indirect Effects of Category Labels on Budget Deviation through Choice


Note. Unstandardized coefficients and bias-corrected confidence intervals are reported. Bootstrap

sample size = 10,000. BT = budget tracking. LL = lower limit. CI = confidence interval. UL = upper

limit.

We found that a3b = c3 - c’3: -2.05(-812.87) ≈ 1,663 ≈ 3,611.06 - 1,947.71 and again

confirmed moderated mediation (mean bootstrap estimate = 1,663.36, SE = 442.10;

95% bias-corrected CI [847.59, 2,568.53]). Finally, the coefficient of the interaction

was smaller but still significant in Model 3 (i.e., c’3 = 1,947.71) compared with Model

1 (i.e., c3 = 3,611.06), thereby supporting Hypothesis 5 (see Figure 14).

Figure 14

Impact of Choice Uncertainty as a Partial Mediator of the Relationship among

Category Labels on Budget Deviation as a Function of Budget Tracking

*p < .05. **p < .01. ***p < .001.


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4.4 Summary and Conclusion

The present results expand the findings of Study 1 by empirically showing that the

differences in the MBE for type-based and goal-based similarity impact economic

parameters (Hypothesis 2). Furthermore, the results elucidate the underlying process

by demonstrating that budget tracking moderates the main effect of category labels on

economic parameters (Hypothesis 3) and choice uncertainty (Hypothesis 4).

Furthermore, choice uncertainty partially mediates the effect of category labels on both

estimation bias and budget deviation for budget and non-budget trackers (Hypothesis

5). This shows that a lower choice uncertainty overrides commitment to previously

established budget constraints. The moderation of budget tracking on choice

uncertainty suggests that neither type-based nor goal-based similarity is always

preferred. If type-based (goal-based) category labels were preferred, budget trackers

(non-budget trackers) would spend and be willing to pay more. Budget trackers

overspend and overpay in the broader goal-based condition, which provides them with

uncertainty-decreasing loopholes that enable them to deviate from initially established

budgets and forgo any safety margins. By contrast, budget trackers underspend and

underpay in the narrow type-based condition, which leads to uncertainty-increasing

rigidity and the use of safety margins. Finally, expected type-based labels stimulate

economic parameters among non-budget trackers, whereas the effect is reversed for

unexpected goal-based labels.

5 Discussion

5.1 Theoretical Implications

The mental accounting process has often been described in mechanical terms, with

consumers characterized as perfect budget trackers. Recent research, however, has

shown that consumers continuously forgo self-controlling budget tracking and seek

strategies that justify consumption beyond budgeting constraints (Cheema & Soman,

2006; Heath & Soll, 1996; Kunda, 1990). A focus on category labels is highly

relevant, as the similarity literature has mainly examined the effects of type-based and

goal-based category organizations, thereby assuming that the form of similarity of

category labels is not relevant (e.g., Poynor & Wood, 2010).

The present research further enlightens the process of the detrimental effects of

similarity on economic parameters. In particular, when investigating problems related


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to mental accounting, expenses should not be viewed as detached from the contextual

information provided by category labels. The results reveal that consumers evaluate

economic parameters depending on the perceived familiarity with the category labels

for non-budget trackers and the malleability of the category labels for budget trackers.

This research also suggests that strict bookkeeping that is associated with narrow

groupings can be mitigated without altering any underlying product information

merely by replacing concrete, type-based labels with broad, goal-based labels. The

perception of type-based labels as rigid constraints by budget trackers limits existing

research regarding the flexibility of dividing expenses and assigning them to several

mental accounts (e.g., Soman & Gourville, 2001). By contrast, malleable goal-based

labels mitigate rigidity and serve to activate motivational creative bookkeeping by

enabling the transfer of expenses between mental accounts and amplifying economic

parameters for budget trackers.

Taken together, considering that mere changes in category labels for the same

underlying information were sufficient to impact economic variables, this contribution

challenges the research that argues that the same expenses will be ascribed to the same

mental account (Kamleitner & Kirchler, 2006) and the principle of fungibility

according to which expenses have no labels (Modigliani & Brumberg, 1954).

5.2 Managerial Implications

The widely used type-based similarity for categorizing information in the marketplace

must be reconsidered, as it does not always lead to the best possible exhaustion of

previously established budgets but rather to underconsumption among budget trackers.

Our results suggest that category labels and a preference for budget tracking provide

two promising customer segmentation criteria. To stimulate spending and WTP,

practitioners should define strategies to guide non-budget trackers to assortments with

type-based labels and budget trackers to assortments with goal-based labels.

Furthermore, combining the present results with findings of increasing preferences for

budget tracking among individuals with limited financial means (Ameriks, Caplin, &

Leahy, 2003; Thaler, 1999), segmenting customers based on income and assigning

them to differently labeled touch points is a promising strategy. To improve economic

parameters, practitioners should design their major customer touch points with goal-

based labels if targeting low-income budget trackers (e.g., discounters) and type-based

labels if targeting high-income non-budget trackers (e.g., delis).


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Furthermore, rigid, type-based labels only describe the categorized product items

without any value-added information (e.g., only the type-based label “Rims” is

suitable for describing the presented rims). By contrast, malleable, goal-based labels

not only decrease expense tracking among budget trackers but are also less

predetermined and add further meaning to categorized information (e.g., the goal-

based label “Exterior Design” adds meaning to the presented rims). Thus, practitioners

can use goal-based labels as promising tools to differentiate themselves from

competitors and to offer customers a holistic shopping experience at various touch

points.

Finally, the replicated online configurator represents only a small part of more

complex real configurators. Building on the previous research on the challenges of

accurately booking expenses to mental accounts in real life (Soman, 2001), amplified

effects are expected for complex assortments in the marketplace.

5.3 Limitations and Future Research

Despite the substantial theoretical and managerial implications, the present research is

not without limitations. First, the replicated shopping context in a laboratory setting

might limit the applicability of the findings to low and moderate emotional states.

However, this limitation is mitigated because the stimuli reflected realistic situations

and activated emotional states that are common in most everyday consumption

situations (Atakan, Bagozzi, & Yoon, 2014). Future research involving field studies

with binding transactions or analyses of secondary data with different product types or

industries could be used to further test the robustness of the findings. However, in

view of the findings of decreasing estimation accuracy (Johnson & Payne, 1985) and

increasing budget deviation (Van Ittersum et al., 2013) for higher levels of complexity,

amplified effects can be expected for more complex real purchase situations, with

increased loopholes for budget trackers and choice overload for non-budget trackers.

Second, this research only considers goal-based labels that provide feasible contextual

information and describe categorized information in a beneficial way. The results are

expected to vary depending on the intended message of the goals. Future research

should investigate goal-based labels without an adequate representation of the

underlying context because such non-beneficial labels might undermine any positive

associations and economic parameters.


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Finally, although the findings convincingly demonstrate that choice uncertainty partly

explains the category labels × budget tracking moderation, there remains much to learn

about the underlying process that impacts perceptions of type-based and goal-based

similarity. Future research that considers the interplay of category labels and budget

tracking should further explore this process and clarify its implications. Nevertheless,

the present results provide a promising foundation for further contributions in this

context.

5.4 Highlights

The mental budgeting effect depends on the form of similarity of category labels.

Type-based (goal-based) labels increase (decrease) the mental budgeting effect.

Budget tracking moderates the effect of labels on spending and payment decisions.

The results are constant for budget trackers but reverse for non-budget trackers.

Perceived choice uncertainty partially explains the moderation of budget tracking.


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References

Aiken, S. A., & West, S. G. (1991). Multiple regression: Testing and interpreting

interactions. Thousand Oaks, CA: Sage Publications, Inc.

Ameriks, J., Caplin, A., & Leahy, J. (2003). Wealth accumulation and the propensity

to plan. Quarterly Journal of Economics, 118, 1007-1047.

Antonides, G., de Groot, I. M., & van Raaij, W. F. (2011). Mental budgeting and the

management of household finance. Journal of Economic Psychology, 32, 546-

555.

Atakan, S., Bagozzi, R. P., & Yoon, C. (2014). Make it your own: How process

valence and self-construal affect evaluation of self-made products. Psychology

& Marketing, 31, 451-468.






Baumeister, R. F. (2002). Yielding to temptation: Self-control failure, impulsive

purchasing, and consumer behavior. Journal of Consumer Research, 28, 670-

676.

Bénabou, R., & Tirole, J. (2004). Willpower and personal rules. Journal of Political

Economy, 112, 848-886.

Cheema, A., & Soman, D. (2006). Malleable mental accounting: The effect of

flexibility on the justification of attractive spending and consumption decisions.

Journal of Consumer Psychology, 16, 33-44.

Cheema, A., & Soman, D. (2008). Effect of partitions on controlling consumption.

Journal of Marketing Research, 45, 665-675.


categorisation: A case study. Language and Cognitive Processes, 19, 137-174.


-84-



learning and motivation: Advances in research and theory (Vol. 54., pp. 249-





Franke, N., Schreier, M., & Kaiser, U. (2010). The "I designed it myself" effect in

mass customization. Management Science, 56, 125-140.

Goldstone, R. L. (1994). The role of similarity in categorization: Providing a

groundwork. Cognition, 52, 125-157.

Gourville, J. T., & Soman, D. (1998). Payment depreciation: The behavioral effects of

temporally separating payments from consumption. Journal of Consumer

Research, 25, 160-174.

Gupta, S., & Kim, H.-W. (2010). Value-driven Internet shopping: The mental

accounting theory perspective. Psychology & Marketing, 27, 13-35.

Hayes, A. F. (2013). Introduction to mediation, moderation, and conditional process

analysis: A regression-based approach. New York & London: The Guilford

Press.

Hayes, A. F., & Matthes, J. (2009). Computational procedures for probing interactions

in OLS and logistic regression: SPSS and SAS implementations. Behavior

Research Methods, 41, 924-936.

Heath, C. (1995). Escalation and de-escalation of commitment in response to sunk

costs: The role of budgeting in mental accounting. Organizational Behavior and

Human Decision Processes, 62, 38-54.

Heath, C., & Soll, J. B. (1996). Mental budgeting and consumer decisions. Journal of


Heitmann, M., Lehmann, D., & Herrmann, A. (2007). Choice goal attainment and

decision and consumption satisfaction. Journal of Marketing Research, 44, 234-

250.


-85-

Henderson, P. W., & Peterson, R. A. (1992). Mental accounting and categorization.

Organizational Behavior and Human Decision Processes, 51, 92-117.

Homburg, C., Koschate, N., & Totzek, D. (2010). How price increases affect future

purchases: The role of mental budgeting, income, and framing. Psychology &

Marketing, 1, 36-53.

Hsee, C. K. (1995). Elastic justification: How tempting but task-irrelevant factors

influence decisions. Organizational Behavior & Human Decision Processes,

62, 330-337.



Johnson, E. J., & Payne, J. W. (1985). Effort and accuracy in choice. Management

Science, 31, 395-414.

Kahneman, D., & Lovallo, D. (1993). Timid choices and bold forecasts: A cognitive

perspective on risk taking. Management Science, 39, 17-31.

Kahneman, D., & Tversky, A. (1982). The psychology of preferences. Scientific

American, 246(1), 160-173.

Kamleitner, B., & Kirchler, E. (2006). Personal loan users’ mental integration of

payment and consumption. Marketing Letters, 17, 281-294.

Kivetz, R., & Simonson, I. (2002). Self-Control for the righteous: Toward a theory of

precommitment to indulgence. Journal of Consumer Research, 29, 199-217.

Klein, W. M., & Kunda, Z. (1992). Motivated person perception: Constructing

justifications for desired beliefs. Journal of Experimental Social Psychology,

28, 145-168.

Krishnamurthy, P., & Prokopec, S. (2010). Resisting that triple-chocolate cake: Mental

budgets and self-control. Journal of Consumer Research, 37, 68-79.

Kunda, Z. (1990). The case for motivated reasoning. Psychological Bulletin, 108, 480-

498.

Markman, A. B., & Gentner, D. (2000). Structure mapping in the comparison process.

The American Journal of Psychology, 113, 501-538.


-86-

Modigliani, F., & Brumberg, R. (1954). Utility analysis and the consumption function:

An interpretation of cross–section data. In K. K. Kurihara (Ed.), Post-keynesian

economics (pp. 388-436). New Brunswick, NJ: Rutgers University Press.

Monroe, K. B., & Lee, A. V. (1999). Remembering versus knowing: Issues in buyers'

processing of price information. Journal of the Academy of Marketing Science,

27, 207-225.

Morgan-Lopez, A. E., & MacKinnon, D. E. (2006). Demonstration and evaluation of a

method for assessing mediated moderation. Behavior Research Methods, 38,

77-87.

Pennings, J. M. E., & Wansink, B. (2004). Channel contract behavior: The role of risk

attitudes, risk perceptions, and channel members' market structures. Journal of

Business, 77, 697-723.

Pizzi, G., Scarpi, D., & Marzocchi, G. (2014). Showing a tree to sell the forest: The

impact of attribute- and alternative-based information presentation on

consumers' choices. Journal of Economic Psychology, 42, 41-51.



37, 159-175.

Preacher, K. J., Rucker, D. D., & Hayes, A. F. (2007). Addressing moderated

mediation hypotheses: Theory, methods, and prescriptions. Multivariate

Behavioral Research, 42, 185-227.

Prelec, D., & Loewenstein, G. (1998). The red and the black: Mental accounting of

savings and debt. Marketing Science, 17, 4-28.

Rajagopal, P., & Rha, J.-Y. (2009). The mental accounting of time. Journal of

Economic Psychology, 30, 772-781.

Read, D., Loewenstein, G., & Rabin, M. (1999). Choice bracketing. Journal of Risk

and Uncertainty, 19, 171-197.





-87-

Shefrin, H. M., & Thaler, R. H. (1988). The behavioral life-cycle hypothesis.

Economic Inquiry, 26, 609-643.



Soman, D. (2001). The mental accounting of sunk time costs: Why time is not like

money. Journal of Behavioral Decision Making, 14, 169-185.

Soman, D., & Cheema, A. (2011). Earmarking and partitioning: Increasing saving by

low-income households. Journal of Marketing Research, 48, 14-22.

Soman, D., & Gourville, J. T. (2001). Transaction decoupling: How price bundling

affects the decision to consume. Journal of Marketing Research, 38, 30-44.

Stilley, K. M., Inman, J. J., & Wakefield, K. L. (2010). Spending on the fly: Mental

budgets, promotions, and spending behavior. Journal of Marketing, 74(3), 34-

47.

Sussman, A. B., & Alter, A. L. (2012). The exception is the rule: Underestimating and

overspending on exceptional expenses. Journal of Consumer Research, 39, 800-

814.

Thaler, R. H. (1985). Mental accounting and consumer choice. Marketing Science, 4,

199-214.

Thaler, R. H. (1999). Mental accounting matters. Journal of Behavioral Decision

Making, 12, 183-206.

Thaler, R. H., & Shefrin, H. M. (1981). An economic theory of self-control. Journal of

Political Economy 89, 392–406.


Urbany, J. E., Bearden, W. O., Kaicker, A., & Smith-de Borrero, M. (1997).

Transaction utility effects when quality is uncertain. Journal of the Academy of

Marketing Science, 25, 45-55.

Van Horen, F., & Pieters, R. (2013). Preference reversal for copycat brands:

Uncertainty makes imitation feel good. Journal of Economic Psychology, 37,

54-64.


-88-

Van Ittersum, K., Pennings, J., & Wansink, B. (2010). Trying harder and doing worse:

How grocery shoppers track in-store spending. Journal of Marketing, 74(2), 90-

104.

Van Ittersum, K., Wansink, B., Pennings, J. E., & Sheehan, D. (2013). Smart shopping

carts: How real-time feedback influences spending. Journal of Marketing,

77(6), 21-36.

Van Schie, R. J. G., Donkers, B., & Dellaert, B. G. C. (2012). Savings adequacy

uncertainty: Driver or obstacle to increased pension contributions? Journal of

Economic Psychology, 33, 882-896.

Vanhuele, M., & Drèze, X. (2002). Measuring the price knowledge shoppers bring to

the store. Journal of Marketing, 66(4), 72-85.

Wisniewski, E. J., & Bassok, M. (1999). What makes a man similar to a tie? Stimulus

compatibility with comparison and integration. Cognitive Psychology, 39, 208-

238.


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Appendix

Table A1

Multi-Item Measures

Variable Source Scale Items Construct α Mean

Budget Tracking

Homburg, Koschate, and Totzek (2010)

1-7 I set up a budget plan.

I compare my expenses with my budget plan.

I evaluate my financial situation regularly.

I scrutinize and evaluate my buying behavior.

I plan ahead how to use my disposable income.

.90 4.49

Choice Confidence

Heitmann, Lehmann, and Herrmann (2007); Urbany, Bearden, Kaicker, and Smith-de Borrero (1997)

1-9 It was impossible to be certain which of the product items best fits my preferences (R).

I felt confident when identifying a product item that best matches my preferences.

I was convinced I would find a product item that best fulfills my needs.

.92 4.34


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C. Essay II

Mazur, M., Herrmann, A., Gibbert, M. (submitted). The Beauty of Moderately

Incongruent Similarity: How the Disentanglement of Category Labels and Category

Organizations Drives Satisfaction with Mass Customization Decisions. Psychology &

Marketing.

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The Beauty of Moderately Incongruent Similarity:

How the Disentanglement of Category Labels and

Category Organizations Drives Satisfaction

with Mass Customization Decisions

Marcel Mazur (1)

Andreas Herrmann (2)

Michael Gibbert (3)



(2) Andreas Herrmann is a Professor of Marketing, Center for Customer Insight,

University of St. Gallen, Switzerland ([email protected]).

(3) Michael Gibbert is a Professor of Marketing, Institute for Marketing and

Communication, Università della Svizzera italiana, Switzerland

([email protected]).

C. ESSAY II: THE BEAUTY OF MODERATELY INCONGRUENT SIMILARITY

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Abstract

This paper presents two studies that examine the effect of congruent type-based and

incongruent goal-based similarity within category labels and category organizations on

satisfaction for large assortments in a mass customization context. Study 1

demonstrates that assortment size matters for comparing forms of similarity across

knowledge levels and challenges existing research with small assortments. The

disentanglement of category labels and organizations for the same assortment in Study

2 reveals two moderately incongruent hybrid conditions with co-occurring congruent

and incongruent similarity. The results show a significant interaction between category

labels and category organizations, with significantly higher satisfaction in the

moderately incongruent hybrid conditions compared to the purely congruent type-

based and the purely incongruent goal-based conditions with similar forms of

similarity for labels and organizations. Notably, this effect is further moderated by

prior knowledge, such that novices (experts) are significantly more satisfied in the

hybrid condition with unexpected category labels (category organizations) compared

to the pure conditions. Taken together, this research provides a promising contribution

to research on similarity and a cost-free tool for practitioners to increase customer

satisfaction.

Keywords: type-based similarity, goal-based similarity, congruity, category

organization, category label, mass customization


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1 Introduction

To cope with the sheer amount of information and to assist consumers in creating

individualized products, mass customization systems (MCS) have become common

(Franke & Schreier, 2010; Puligadda, Grewal, Rangaswamy, & Kardes, 2010). For

example, car manufacturers use online configurators that organize thousands of

options into sequential categories to decrease customer confusion (Berry, Seiders, &

Grewal, 2002; Srinivasan, Anderson, & Ponnavolu, 2002) and to improve the quality

of purchasing decisions (Häubl & Trifts, 2000). Recent research has begun to

investigate the effects of different ways of categorizing the same content in naturalistic

contexts (Poynor & Wood, 2010). A key challenge, which has so far been ignored,

concerns the interplay between the organization (i.e., arrangement) of the content and

the label used to describe that content in categories. In particular, prior research has

typically used labels and organizations for category content, which are expected and

learned by consumers. This finding is not surprising, as one would expect learned

organizations of category content and labels to facilitate consumer decision making

and choice by decreasing the cognitive load involved in choosing the preferred

combination of features. For instance, a self-contained comparison of 78 German car

configurators revealed that most configurators follow the same pattern for categorizing

information: each attribute (e.g., color) is organized within the same category and

labeled accordingly (i.e., “Color”). However, recent research has suggested that

unexpectedly organized category content and unexpected labels were preferred by

consumers over expected labels and organizations. Specifically, Poynor and Wood

(2010) compared expected type-based with unexpected goal-based similarity.

However, in their studies, the category labels were accurate reflections of the

organized attributes within the category (e.g., in a car configurator, the type-based

organized category colors are labeled “Color”).

What if one let go of this alignment in the form of similarity used for organizing

content into categories (i.e., category organization) and naming these categories (i.e.,

category label)? This disentanglement allows examining the impact of moderately

incongruent forms of similarity, consisting of expected (unexpected) category labels

and unexpected (expected) category organizations. Work on congruity postulates a

positive influence of moderate changes from expected standards on satisfaction as

such changes favor cognitive elaboration without causing mental depletion (Meyers-

Levy & Tybout, 1989; Poynor & Wood, 2010). This research consists of two studies in

a naturalistic setting by replicating the configurator of a German car manufacturer.


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Based on work from Poynor and Wood (2010), the first study investigates the effect of

larger (more realistic) assortments for congruent forms of similarity. For the same

content, the second study then analyzes how similar (i.e., pure conditions) and

dissimilar (i.e., hybrid conditions) forms of similarity of category labels and category

organizations influence satisfaction across knowledge levels.

2 Theoretical Background

2.1 Previous Research

Similarity is the basis for categorization with similar information (related to attributes

or goals) being categorized together (Medin, Goldstone, & Gentner, 1993;

Ratneshwar, Barsalou, Pechmann, & Moore, 2001; Smith & Medin, 1981). Individuals

are exposed to methods of categorizing information that reoccur more frequently and

that ultimately construct specific choice heuristics (Barsalou, 1983, 1985; Biehal &

Chakravarti, 1982; Morales, Kahn, McAlister, & Broniarczyk, 2005; Rosa & Porac,

2002). The most commonly used and thus expected method of categorizing

information is type-based or taxonomic similarity (Barsalou, 1982, 1985; Moreau,

Markman, & Lehmann, 2001; Poynor & Wood, 2010). Early research suggested that

type-based similarity is most promising because of the low ambiguity regarding

category membership and the easy comparability of information (Tversky, 1977).

However, type-based similarity represents a concrete, technical, and product-oriented

“attribute-centric” approach and may not adequately address the increasing need

orientation in the marketplace.

Based on the notion that knowledge is also associative, recent research has examined

ways of categorizing information based on shared benefits, goals, solutions, or

problems (Estes, Golonka, & Jones, 2011; Gibbert & Mazursky, 2009; Noseworthy,

Finlay, & Islam, 2010; Simmons & Estes, 2008). This goal-based similarity represents

a “consumer-centric” approach and is rather appropriate for generating compelling

experiences (Novak, Hoffman, & Duhachek, 2003). Few studies have investigated the

distinct effects of type-based and goal-based similarity (e.g., Poynor & Wood, 2010).

This lack of research is surprising because the two forms are activated in different

parts of the brain (Davidoff & Roberson, 2004; Sass, Sachs, Krach, & Kircher, 2009),

resulting in a varying influence of behavioral parameters, such as information search,

memory, inference, choice, and the perceived complexity of categories (Huber &

McCann, 1982; Poynor & Wood, 2010; Sujan & Dekleva, 1987).


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Previous work has suggested that congruity between expectations and choice tasks

(i.e., type-based similarity) results in a better orientation (Biehal & Chakravarti, 1982),

easier processing (Oliver & Winer, 1987), and higher satisfaction (Valenzuela, Dhar,

& Zettelmeyer, 2009). By contrast, the lack of permanent representation of goal-based

similarity in memory leads to incongruity between expectations and choice tasks and

often requires the mentally challenging ad hoc construction of processing strategies

(Barsalou, 1982, 1983). However, goal-based categorizations are not incongruent per

se but are mentally well-established and preferred in specific contexts. For example,

whereas the type-based categorization of all soft drinks is constant over time, the goal-

based categorization of popcorn and soft drinks is mentally established only in the

specific cinema context. This is particularly relevant because repeated exposure to

expected standards (i.e., type-based similarity) might lead to a feeling of knowing and

complacency (Bettman & Park, 1980; Hart, 1965; Poynor & Wood, 2010).

Poynor and Wood (2010) examined how changes in the subcategory format (i.e.,

category organization) influence satisfaction across knowledge levels. In one study,

they manipulated a restaurant menu by organizing the same dishes according to either

type-based categories by attributes (i.e., soups, sandwiches, finger foods, and salads)

or goal-based categories by their geographic origin (i.e., Mexican, American, Italian,

or Chinese). The results revealed that experts were more satisfied with the unexpected

goal-based category organization because it provides a newness cue, stimulates

processing, and helps them overcome complacency from the expected type-based

standard. However, novices were more satisfied with the type-based category

organization, as the goal-based category organization resulted in mental depletion. The

present research draws on these findings to address two major shortcomings.

First, although assortment size is a major driver for choice overload, mental depletion,

and decreased satisfaction (Dellaert & Stremersch, 2005; Felcher, Malaviya, &

McGill, 2001), research on similarity has been limited to small assortments (e.g., four

dishes per category in Poynor and Wood (2010)). Small assortments limit results in

three respects: (1) goal-based categorized information can be processed more quickly

and learned faster, (2) research on mass customization is meaningful only with an

extensive number of options, and (3) small assortments do not reflect continuously

increasing assortments in the marketplace.

Second, Poynor and Wood (2010) neglect the interplay of category labels and category

organizations in their study by changing not only the organization of the dishes but

also the labels of each menu section across the conditions (e.g., from “Soups” to


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“Mexican”). Instead of specifying the effects by disentangling category labels and

category organizations, they entirely ascribed the effects to changes in the subcategory

format (i.e., category organizations) despite varying the category labels across

conditions. This research considers labels and organizations as distinct aspects of a

category whose influence on decision making must be considered individually to

derive reliable results. Previous research not only provides evidence that category

labels influence the perceived similarity of the beneath organized information

(Ratneshwar & Shocker, 1991) but also shows that goal-based category labels are

more abstract and that they guide individuals to information relevant to their goals

(Bettman & Sujan, 1987; Poynor Lamberton & Diehl, 2013).

2.2 Conceptual Model and Hypothesis Development

MCS are used to address the shortcomings of the previous literature because they are

appropriate for analyzing consumer decision making in naturalistic contexts (Pine,

Peppers, & Rogers, 1995) and automatically involve categorizing tremendous amounts

of information in meaningful ways. Following Poynor and Wood (2010), prior

knowledge serves as a moderator to measure the willingness to devote resources for

processing information (Alba & Hutchinson, 1987; Peracchio & Tybout, 1996; Poynor

& Wood, 2010; Schwarz, 2004; Whitmore, Shore, & Smith, 2004) and satisfaction as

dependent variable to measure the underlying psychological processes and the

perceived experience with the customization process (Thirumalai & Sinha, 2011).

First, the present research intends to replicate Poynor and Wood’s (2010) study with a

large assortment in a mass customization context. In contrast to Poynor and Wood

(2010), the greater amount of mental work required amplifies satisfaction for experts,

because it leads to attenuated complacency in the type-based condition and a newness

cue in the goal-based condition. Moreover, large assortments amplify choice overload

in the goal-based condition that mitigates satisfaction for novices. Thus,

H1: With other information held constant, goal-based categorization will result

in lower satisfaction than type-based categorization among novices and

experts.

The disentanglement of category labels and category organizations not only allows

distinguishing between the purely congruent type-based and the purely incongruent

goal-based conditions but also to examine moderately incongruent forms of similarity

with co-occurring category labels and category organizations (i.e., hybrid conditions).


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Hybrid conditions lie between the congruent pure type-based condition (leading to

complacency) and the incongruent pure goal-based condition (leading to mental

depletion) and thus stimulate information processing both for novices and experts.

This is expected to result in significant differences in the satisfaction between the pure

and the hybrid conditions. Hence,

H2: With other information held constant, category labels and category

organizations will interact to predict satisfaction, such that hybrid

conditions will result in greater satisfaction than pure conditions.

Congruence levels differ between not only pure conditions but also hybrid conditions

as a function of prior knowledge, with a greater required effort for processing goal-

based category organizations compared with goal-based category labels. Experts can

better analyze the structure (i.e., organization) of information and tend to create goal-

based inferences when comparing information (Gregan-Paxton & John, 1997; Johnson

& Mervis, 1997). Thus, the hybrid condition with goal-based category organizations

and type-based category labels provides a newness cue and improves information

processing for experts. By contrast, novices are sensitive to the incongruent goal-based

similarity because they do not have predefined goals and must reconcile new

information ad hoc (Alba & Hutchinson, 1987; Bettman & Park, 1980; Ross &

Murphy, 1999). However, in a hybrid condition with goal-based category

organizations, novices use type-based category labels as expected cognitive anchors.

Thus,

H3: With other information held constant, the hybrid condition consisting of

type-based category labels and goal-based category organizations will

a) result in greater satisfaction than the pure type-based condition for

experts; and

b) result in the same satisfaction as the pure type-based condition for

novices.

Although novices are overstrained when processing incongruent goal-based

information (Bettman & Park, 1980), they use abstract decision criteria (Walker, Celsi,

& Olson, 1987). Given that category labels are less complex than category

organizations, assign a specific meaning to attributes, and form preferences (Huffman

& Houston, 1993), novices are assumed to consider goal-based category labels as

newness cues when they are used with type-based category organizations. By contrast,

experts perceive this hybrid condition as rather congruent and ignore the informative


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message from goal-based category labels because they narrow their well-established

goals with clear objectives. Hence,

H4: With other information held constant, the hybrid condition consisting of

goal-based category labels and type-based category organizations will

a) result in greater satisfaction than the pure type-based condition for

novices; and

b) result in the same satisfaction as the pure type-based condition for

experts.

Finally, both moderately incongruent hybrid conditions are expected to result in higher

satisfaction compared to the incongruent pure goal-based condition across knowledge

levels. Thus,

H5: With other information held constant, the hybrid conditions result in

greater satisfaction than the pure goal-based condition for novices and

experts.

3 Methods

To test the hypotheses, the configurator of a German car manufacturer with nine items

for each essential attribute (i.e., model, color, rims, and upholster; 36 items in total)

was replicated. This number provided sufficient freedom of choice (Mogilner,

Rudnick, & Iyengar, 2008) and helped to account for the learning effects of experts

exposed to goal-based similarity with small assortments. The configuration process

was limited to the best-selling body type (i.e., Hatch) and the manipulation to category

labels and category organizations across groups (see Figure 1).


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Figure 1

Conceptual Framework for Study 1 and Study 2

In a pre-study, configurators of 78 German car manufacturers were analyzed in March

2013 to validate the assumption that type-based similarity represents the market

standard. As expected, more than 90% of the analyzed configurators use both type-

based category labels (e.g., “Rims”) and category organizations (e.g., grouping all rims

together) for each configuration step. To increase the variability of prior knowledge,

participants without any selection criteria were acquired from an external panel and

were granted 5 EUR for completing the study. Both studies consisted of four parts (see

Figure 2).


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Figure 2

Experimental Setup for Study 1 and Study 2

After clicking on a link, the participants were asked to answer a survey about their car

knowledge using a German translation of the scale by Chang (2004). The participants

were asked to rate four statements on a 7-point Likert scale anchored by strongly

disagree/agree (see Table A1 in the Appendix). Then, the participants were presented

the following scenario:

“Please imagine that you are about to buy a car. After careful consideration of

several car brands, you decided to buy a [brand name] hatchback. In the

following configuration process, you are asked to configure your new [brand

name] hatchback out of the presented components. Please consider the

following important notes and then click ‘Continue’:

1. Ignore any budget or time constraints and configure the car according to

your preferences.

2. Select exactly one model, color, set of rims, and upholstery.

3. You can click ‘Next’ to preview your configured car and ‘Back’ to adjust

the configuration as often as desired.”

Next, the participants were randomly assigned to one condition (i.e., between-subjects

design). In contrast to the commonly used configurators with a sequential selection


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process, the four categories were aggregated on one page below one another and the

order in which they appeared on the screen was randomized. After the configuration

process, the exterior and interior views of the configured car were presented to the

participants. Finally, the participants were asked to rate their satisfaction (“Please

indicate your overall satisfaction with your customized car.”) with a numbered slider

(0 = not at all satisfied; 100 = extremely satisfied).

3.1 Study 1

To test H1, the approach of Poynor and Wood (2010) without disentangling category

labels and category organizations was replicated for large assortments.

3.1.1 Pre-Test

While the information for the type-based condition was obtained from the

marketplace, a focus group with employees from the car manufacturer was conducted

to determine the goal-based category labels and category organizations. First, the

group was asked to brainstorm about the ideal benefits of a car and received a list with

approximately 20 different benefits. Next, the group was requested to form four

realistic and disjunctive bundles of nine items from four attributes (i.e., model, color,

rims, and upholstery) and to label them based on the previously defined benefits.

Finally, the group agreed on four disjunctive bundles of nine items with the goal-based

category labels “Scene”, “Sporty”, “Classic” and “Design”. Unfamiliarity with the

goal-based condition was ensured by confirming with the group that neither the

category labels nor the category organizations have been used before.

3.1.2 Procedure

In all, 227 participants (53% female, Mage = 42.8, Rangeage = 18-72) were randomly

assigned to one of two groups (see Figure 3).


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Figure 3

Study 1: Manipulation of the Categorization (i.e., Category Labels and Category

Organizations) of a MCS


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3.1.3 Results and Discussion

Eight participants with incomplete data were eliminated from further analysis.

Knowledge was an aggregate construct based on a 7-item scale (Chang, 2004; α = .83).

To test H1, the study followed a Categorization (Type-Based or Goal-Based) × Prior

Knowledge (mean-centered) design. An analysis of outliers (i.e., ±3 SD from the

group mean) did not reveal any extreme values in the conditions. A multiple regression

analysis was conducted, predicting satisfaction with the different conditions, self-

rating on the prior knowledge scale, and the interaction term. The analysis revealed a

significant direct effect of the categorization condition (β = -5.24, t(215) = -3.76, p <

.001) and prior knowledge (β = 6.36, t(215) = 4.57, p < .001), with significantly higher

satisfaction in the type-based condition across knowledge levels. Consistent with H1,

simple slope analysis (Aiken & West, 1991) confirmed that the type-based (versus

goal-based) condition resulted in higher satisfaction for novices (-1 SD) (β = -6.12,

t(215) = -3.10, p = .002) and experts (+1 SD) (β = -4.36, t(215) = -2.21, p = .028). This

result was further qualified by the non-significant interaction between categorization

and prior knowledge for large assortments (β = 0.88, t(215) = 0.63, p = .529),

suggesting that the interaction of a positive (negative) influence of goal-based

similarity for experts (novices) observed by Poynor and Wood (2010) disappears for

large assortments (see Figure 4).

Figure 4

Study 1: Satisfaction as a Function of Categorization and Prior Knowledge

The results from Study 1 indicate that assortment size matters for the impact of

different forms of similarity across knowledge levels. Specifically, compared with


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Poynor and Wood (2010), the present results differ for experts because larger

assortments attenuate their risk of complacency in the type-based condition and their

perception as newness cues in the goal-based condition but coincide for novices in the

goal-based condition owing to an amplified choice overload. Three explanations for

this discrepancy can be given. First, according to the expectation-disconfirmation

mechanism, the general consumer expectation for a preference match increases with

larger assortments and decreases the confidence in the decision of experts (Chernev,

2003; Diehl & Poynor, 2010). Second, novices might overestimate their abilities in

incongruent goal-based contexts with large assortments (Burson, 2007). Third, experts

can quickly familiarize themselves with incongruent goal-based contexts with small

assortments.

3.2 Study 2

To test H2-H5, the same assortment was used but this time category labels and

category organizations were disentangled to compare satisfaction across knowledge

levels for the pure and hybrid conditions.

3.2.1 Pre-Test

In a second task, the same employees as in Study 1 were asked to determine feasible

hybrid conditions. Specifically, they were exposed to the same 36 items and were

asked which two of the four attributes share similar goals. After some thought, they

agreed that color and rims as parts of the exterior design share the benefits “sporty”

and “elegant”. This choice seems plausible because neither color nor rims have

characteristics that allow an easy distinction between sporty and elegant.

3.2.2 Procedure

A category organization with 18 items was formed, consisting of two visually distinct

blocks with nine colors and nine rims for the type-based category organization or a

mix of colors and rims for the goal-based category organization among the type-based

category label “Color and Rims” or the goal-based category label “Sporty and

Elegant”. To simulate a complete configuration, the type-based selection steps for

models and upholsteries were included in each group. In contrast to Study 1, the order

of the configuration steps was specified, beginning with the model, continuing with

color and rims, and finishing with upholstery to avoid confusion. In all, 248


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participants (40% female, Mage = 42.7, Rangeage = 18-70) were randomly assigned to

one of four groups (see Figure 5).

Figure 5

Study 2: Manipulating Category Labels and/or Category Organizations of a MCS

3.2.3 Results and Discussion

Six participants did not complete the study and were eliminated from further analysis.

Again, knowledge was an aggregate construct (α = .83) based on the scale by Chang

(2004). Consistent with Study 1, the analysis did not reveal any outliers (i.e., ±3 SD

from the group mean) in the conditions. To test H2, a Category Organization (Type-

Based or Goal-Based) × Category Label (Type-Based or Goal-Based) ANOVA with

satisfaction as the dependent variable was conducted. This analysis revealed a

significant two-way interaction effect (F(3, 238) = 44.91, p < .001) (see Figure 6).


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Figure 6

Study 2: Two-Way Interaction between Category Labels and Category Organizations

on Satisfaction

The findings from Study 1 were replicated by showing that participants were

significantly more satisfied in the pure type-based condition than in the pure goal-

based condition (t(119) = -3.91, p < .001). Compared with the pure type-based

condition (M = 75.64, SD = 18.55), follow-up planned contrasts revealed significantly

higher satisfaction for the hybrid conditions with goal-based category labels ((M =

84.31, SD = 12.35), t(108) = 2.89, p = .005) and goal-based category organizations ((M

= 83.19, SD = 13.90), t(125) = 2.71, p = .008). Furthermore, compared with the pure

goal-based condition (M = 64.51, SD = 17.22), both the hybrid condition with goal-

based labels (t(109) = 6.63, p < .001) and those with goal-based organizations (t(130)

= 6.73, p < .001) showed higher satisfaction. Thus, H2 is confirmed.

To confirm that satisfaction varies across knowledge levels in the hybrid conditions, a

Category Organization (Type-Based or Goal-Based) × Category Label (Type-Based or

Goal-Based) × Prior Knowledge (mean-centered) multiple regression analysis on

satisfaction was performed. As expected, the analysis revealed a significant three-way

interaction (β = 5.60, SE = 2.12, t(234) = 2.64, p = .009) (see Figure 7).


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Figure 7

Study 2: Three-Way Interaction between Category Labels, Category Organizations and

Prior Knowledge on Satisfaction

For experts (+1 SD), the Category Organization (Type-Based or Goal-Based) ×

Category Label (Type-Based or Goal-Based) two-way interaction effect on satisfaction

was significant (β = -5.93, SE = 1.32, t(238) = -4.49, p < .001). The results from Study

1 were replicated, with significantly higher satisfaction in the pure type-based

condition (M = 79.40, SD = 14.77) than in the pure goal-based condition ((M = 68.10,

SD = 20.25), t(65) = 3.08, p = .003). Thus, neither reducing the number of manipulated

attributes nor increasing the number of product items per category organization

affected the results. Furthermore, compared with the pure type-based condition, the

hybrid conditions showed higher satisfaction from goal-based category organizations

((M = 90.48, SD = 8.33), t(67) = -3.06, p = .003) but not from goal-based category

labels ((M = 80.77, SD = 13.70), t(58) = -0.34, p = .735). Thus, the results support H3a

and H4b.

The same significant two-way interaction effect on satisfaction was observed for

novices (-1 SD) (β = -8.03, SE = 1.43, t(238) = -5.61, p < .001). Thus, the findings

from Study 2 replicated results from Study 1 with significantly higher satisfaction in

the pure type-based condition (M = 70.89, SD = 21.79) than in the pure goal-based

condition ((M = 60.42, SD = 12.01), t(64) = 2.60, p = .012). Compared with the pure

type-based condition, the hybrid conditions showed higher satisfaction from goal-

based category labels ((M = 87.99, SD = 9.73), t(62) = 4.10, p < .001) but not from


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goal-based category organizations ((M = 75.46, SD = 14.54), t(65) = -1.17, p = .246).

Thus, H3b and H4a are confirmed.

Furthermore, experts were significantly less satisfied in the pure goal-based condition

(M = 68.10, SD = 20.25) than in the hybrid conditions with goal-based category

organizations ((M = 90.48, SD = 8.33), t(66) = -6.13, p < .001) and goal-based

category labels ((M = 80.77, SD = 13.70), t(57) = -3.21, p = .002). Moreover, novices

indicated significantly lower satisfaction in the pure goal-based condition (M = 60.42,

SD = 12.01) than in the hybrid conditions with goal-based category organizations ((M

= 75.46, SD = 14.54), t(60) = -3.93, p < .001) and goal-based category labels ((M =

87.99, SD = 9.73), t(63) = -6.72, p < .001). Thus, H5 is confirmed.

The interaction between category labels and category organizations shows that existing

research on similarity is flawed (e.g., Poynor & Wood, 2010). Furthermore, the varied

preferences for the hybrid conditions resulted from differently established goals for

novices and experts. Specifically, experts have established goals and prefer hybrid

conditions with type-based category labels that provide the mental frame for the

categories (e.g., “Color and Rims”). By contrast, whereas the context-generating

function of goal-based category labels in hybrid conditions (e.g., “Sporty and

Elegant”) might restrict experts, they provide a specific meaning that assists novices in

developing choice criteria. Finally, goal-based category organizations in hybrid

conditions are too incongruent for novices but are well-suited for experts who are

better able to acquire information in less structured environments.

4 General Discussion

The present research proves, for the first time, that assortment size and category labels

influence the perceived congruence for type-based and goal-based similarity. Based on

disentangling category labels and category organizations, the hybrid conditions result

in significantly higher satisfaction than the previously considered pure conditions for

novices (experts) with unexpected goal-based category labels (organizations) and

expected type-based category organizations (labels). Hybrid conditions increase

satisfaction because they are perceived as moderately incongruent compared to the

extremely congruent (incongruent) pure type-based (goal-based) conditions. Hybrid

conditions provide promising (and virtually cost-free) tools for practitioners to present

the same product information in a more customer-oriented manner and to better

differentiate their MCS from competitors. Thus, building on the central theorem that


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individuals reward self-production via MCS (Atakan, Bagozzi, & Yoon, 2014; Franke

& Schreier, 2010), the findings provide a much-needed counterbalance to the

commonly used type-based similarity and suggest a paradigm shift toward moderately

incongruently designed MCS to increase customer satisfaction. Considering the impact

of prior knowledge, marketers who target both novices and experts (e.g., automotive

companies) should provide a MCS with type-based category labels and goal-based

category organizations for experts and a MCS with goal-based category labels and

type-based category organizations for novices.

The findings are subject to limitations. First, prior knowledge was measured rather

than manipulated, preventing control for correlations with other variables. Second, the

studies were cross-sectional and thus cannot elucidate whether satisfaction changes

over time or whether repeated exposure to hybrid or pure goal-based MCS changes

responses by generating more established processing rules in memory (Barsalou &

Ross, 1986). Future research should investigate the process of familiarization with

goal-based similarity via time series analysis. Third, this research concerned

hypothetical automobile purchases, so the findings might not reflect actual behavior,

particularly in markets for other types of products. Although future studies should

address these limitations, the present results are nonetheless promising.


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References

Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting

interactions. Thousand Oaks, CA, US: Sage Publications, Inc.

Alba, J. W., & Hutchinson, J. W. (1987). Dimensions of consumer expertise. Journal

of Consumer Research, 13, 411-454.

Atakan, S., Bagozzi, R. P., & Yoon, C. (2014). Make it your own: How process

valence and self-construal affect evaluation of self-made products. Psychology

& Marketing, 31, 451-468.



Barsalou, L. W. (1983). Ad hoc categories. Memory & Cognition, 11, 211-227.




Barsalou, L. W., & Ross, B. H. (1986). The roles of automatic and strategic processing

in sensitivity to superordinate and property frequency. Journal of Experimental


Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience.


Bettman, J. R., & Park, C. W. (1980). Effects of prior knowledge and experience and

phase of the choice process on consumer decision processes: A protocol

analysis. Journal of Consumer Research, 7, 234-248.

Bettman, J. R., & Sujan, M. (1987). Effects of framing on evaluation of comparable

and noncomparable alternatives by expert and novice consumers. Journal of


Biehal, G., & Chakravarti, D. (1982). Information-presentation format and learning

goals as determinants of consumers' memory retrieval and choice processes.

Journal of Consumer Research, 8, 431-441.


-112-

Burson, K. A. (2007). Consumer-product skill matching: The effects of difficulty on

relative self-assessment and choice. Journal of Consumer Research, 34, 104-

110.

Chang, C. (2004). The interplay of product class knowledge and trial experience in

attitude formation. Journal of Advertising, 33(1), 83-92.

Chernev, A. (2003). Product assortment and individual decision processes. Journal of

Personality and Social Psychology, 85, 151-162.


categorization: A case study. Language and Cognitive Processes, 19, 137-174.

Dellaert, B. G. C., & Stremersch, S. (2005). Marketing mass-customized products:

Striking a balance between utility and complexity. Journal of Marketing

Research, 42, 219-227.

Diehl, K., & Poynor, C. (2010). Great expectations?! Assortment size, expectations,

and satisfaction. Journal of Marketing Research, 47, 312-322.



learning and motivation: Advances in research and theory (Vol. 54, pp. 249-





Franke, N., & Schreier, M. (2010). Why customers value self-designed products: The

importance of process effort and enjoyment. Journal of Product Innovation

Management, 27, 1020-1031.

Gibbert, M., & Mazursky, D. (2009). How successful would a phone-pillow be: Using


products. Journal of Consumer Psychology, 19, 652-660.

Gregan-Paxton, J., & John, D. R. (1997). Consumer learning by analogy: A model of

internal knowledge transfer. Journal of Consumer Research, 24, 266-284.


-113-

Hart, J. T. (1965). Memory and the feeling-of-knowing experience. Journal of

Educational Psychology, 56, 208-216.

Häubl, G., & Trifts, V. (2000). Consumer decision making in online shopping

environments: The effects of interactive decision aids. Marketing Science, 19,

4-21.

Huber, J., & McCann, J. (1982). The impact of inferential beliefs on product

evaluations. Journal of Marketing Research, 19, 324-333.



Johnson, K. E., & Mervis, C. B. (1997). Effects of varying levels of expertise on the

basic level of categorization. Journal of Experimental Psychology: General,

126, 248-277.

Medin, D. L., Goldstone, R. L., & Gentner, D. (1993). Respects for similarity.


Meyers-Levy, J., & Tybout, A. M. (1989). Schema congruity as a basis for product

evaluation. Journal of Consumer Research, 16, 39-54.

Mogilner, C., Rudnick, T., & Iyengar, S. S. (2008). The mere categorization effect:

How the presence of categories increases choosers' perceptions of assortment

variety and outcome satisfaction. Journal of Consumer Research, 35, 202-215.

Morales, A., Kahn, B. E., McAlister, L., & Broniarczyk, S. M. (2005). Perceptions of

assortment variety: The effects of congruency between consumers' internal and

retailers' external organization. Journal of Retailing, 81, 159-169.

Moreau, C. P., Markman, A. B., & Lehmann, D. R. (2001). 'What is it?' Categorization

flexibility and consumers' responses to really new products. Journal of


Noseworthy, T. J., Finlay, K., & Islam T. (2010). From a commodity to an experience:


judgment. Psychology & Marketing, 27, 465-486.


-114-

Novak, T. P., Hoffman, D. L., & Duhachek, A. (2003). The influence of goal-directed

and experiential activities on online flow experiences. Journal of Consumer


Oliver, R. L., & Winer, R. S. (1987). A framework for the formation and structure of

consumer expectations: Review and propositions. Journal of Economic


Peracchio, L. A., & Tybout, A. M. (1996). The moderating role of prior knowledge in

schema-based product evaluation. Journal of Consumer Research, 23, 177-192.

Pine, B. J., II, Peppers, D., & Rogers, M. (1995). Do you want to keep your customers

forever? Harvard Business Review, 73(2), 149-160.

Puligadda, S., Grewal, R., Rangaswamy, A., & Kardes, F. R. (2010). The role of

idiosyncratic attribute evaluation in mass customization. Journal of Consumer




37, 159-175.

Poynor Lamberton, C., & Diehl, K. (2013). Retail choice architecture: The effects of

benefit- and attribute-based assortment organization on consumer perceptions

and choice. Journal of Consumer Research, 40, 393-411.

Ratneshwar, S., Barsalou, L. W., Pechmann, C., & Moore, M. (2001). Goal-derived

categories: The role of personal and situational goals in category

representations. Journal of Consumer Psychology, 10, 147-157.

Ratneshwar, S., & Shocker, A. D. (1991). Substitution in use and the role of usage

context in product category structures. Journal of Marketing Research, 28, 281-

295.

Rosa, J. A., & Porac, J. F. (2002). Categorization bases and their influence on product

category knowledge structures. Psychology & Marketing, 19, 503-531.

Ross, B. H., & Murphy, G. L. (1999). Food for thought: Cross-classification and

category organization in a complex real-world domain. Cognitive Psychology,

38, 495-553.


-115-




Schwarz, N. (2004). Metacognitive experiences in consumer judgment and decision

making. Journal of Consumer Psychology, 14, 332-348.



Smith, E. E., & Medin, D. L. (1981). Categories and concepts. Cambridge, MA:

Harvard University Press.

Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-

commerce: An exploration of its antecedents and consequences. Journal of

Retailing, 78, 41-50.

Sujan, M., & Dekleva, C. (1987). Product categorization and inference making: Some

implications for comparative advertising. Journal of Consumer Research, 14,

372-378.

Thirumalai, S., & Sinha, K. K. (2011). Customization of the online purchase process in

electronic retailing and customer satisfaction: An online field Study. Journal of

Operations Management, 29, 477-487.


Valenzuela, A., Dhar, R., & Zettelmeyer, F. (2009). Contingent response to self-

customization procedures: Implications for decision satisfaction and choice.

Journal of Marketing Research, 46, 754-763.

Walker, B., Celsi, R., & Olson, J. (1987). Exploring the structural characteristics of

consumers’ knowledge. Advances in Consumer Research, 14, 17-21.

Whitmore, J. M., Shore, W. J., & Smith, P. H. (2004). Partial knowledge of word

meanings: Thematic and taxonomic representations. Journal of Psycholinguistic

Research, 33, 137-164.


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Variable Source Items

Prior knowledge of the product class

Chang (2004) I know a lot about cars.

I would consider myself an expert in terms of my knowledge of cars.

I know more about cars than my friends do.

I usually pay a lot of attention to information about cars.

Appendix

Table A1

Scale Items for Measuring the Knowledge of the Product Class

Note. Descriptive statistics: Study 1 (M = 3.08; SD = 1.37). Study 2 (M = 3.40; SD = 1.36). All items

use 7-point Likert scales anchored by strongly disagree and strongly agree.

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D. Essay III

Mazur, M. (2013). Bedürfnisorientierte Gestaltung von Kontaktpunkten [Need-Based

Design of Customer Touch Points], Marketing Review St. Gallen, 30(6), 34-49.

D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS

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Bedürfnisorientierte Gestaltung von Kontaktpunkten

[Need-Based Design of Customer Touch Points]

Marcel Mazur (1)

(1) Marcel Mazur ist Doktorand in Betriebswirtschaftslehre, Center for Customer

Insight, Universität St. Gallen, Schweiz ([email protected]).


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Zusammenfassung

Unternehmen scheitern bei der Gestaltung ihrer Kontaktpunkte oft bereits an simplen

Dingen wie der bedürfnisorientierten Bezeichnung und Anordnung von

Produktinformationen. Der Beitrag zeigt am Beispiel eines Fahrzeug-Konfigurators

empirisch auf, wie Unternehmen Produktinformationen an ihren Kontaktpunkten

bezeichnen und anordnen sollten, um sozio-ökonomische Parameter zu optimieren.

Stichwörter: Taxonomisch, Thematisch, Kundenansprache, Kundenkontaktpunkte,

Bedürfnisorientierung


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1 Einleitung

In Zeiten sich diversifizierender Zielgruppen mit unterschiedlichen Wünschen sind

Unternehmen mehr denn je gefordert, mit ihren Sortimenten vielfältige Bedürfnisse zu

befriedigen. Die Identifikation dieser Bedürfnisse und deren Übertragung in eine

markengerechte, nachhaltige und verständliche Kundenkommunikation gehören zu

den wichtigsten Erfolgsfaktoren und Herausforderungen von Unternehmen

(Griffin/Hauser 1993, Cristiano/Liker/White 2000; Fröhling/Esch 2013;

Rawson/Duncan/Jones 2013).

2 Produkt- statt Bedürfnisorientierung an Kontaktpunkten

Unternehmen scheitern bei der Gestaltung von On- und Offline-Kontaktpunkten (z.B.

Verkaufsprozesse, Beratungsgespräche, Webseite) oft an einer adäquaten

Bedürfnisansprache, indem sie die Produkte entsprechend ihrer Eigenschaften

attributspezifisch bezeichnen und anordnen. Eine solche Produktorientierung

entspricht zwar dem gängigen Marktstandard. Sie wirkt jedoch aufgrund eines

zunehmend schwierigeren Wettbewerbsumfelds und stärkeren Kundeneinflusses

ideenlos. Unternehmen sollten vielmehr bestrebt sein, die Kundenbedürfnisse im Sinne

einer Nutzenorientierung zu adressieren, um die Marke so erlebbarer zu machen, sich

vom Wettbewerb zu differenzieren und den Customer Value zu erhöhen. So verlaufen

beispielsweise Beratungsgespräche für Hörgeräte häufig entlang technischer

Produkteigenschaften (z.B. Frequenzbereich) und wirken dadurch kompliziert, anstatt

die Nutzeneigenschaften (z.B. Komfort, Design, Multimedia) hervorzuheben und in

einer für den Kunden verständlichen Sprache zu erklären.

3 Auswahlprozesse mittels Fahrzeug-Konfiguratoren

Einen ähnlichen Ansatz findet man bei kundenindividuellen Auswahlprozessen

(sogenannten Konfiguratoren) von stark emotionalisierten bzw. erlebnisorientierten

Produkten, zum Beispiel im Automobilsektor. Ein vom Autor durchgeführter

Vergleich von 70 Fahrzeug-Konfiguratoren zeigt, dass die Produktinformationen in

Konfiguratoren nahezu ausschliesslich entlang der einzelnen Komponenten bezeichnet

(z.B. Farbe oder Felge) und angeordnet (jeweils alle Felgen und Farben zusammen)

werden. Neben dieser Gestaltungsweise wirkt auch die weit verbreitete Bezeichnung

„Konfigurator“ für diesen Kontaktpunkt ideenlos und technisch, denn er adressiert

keine Kundenbedürfnisse. Dies ist verwunderlich, zumal rund 95% aller Autokäufe im


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Internet starten und Konfiguratoren somit den Kaufprozess massgeblich beeinflussen

(Capgemini 2013).

Der Beitrag zeigt hierzu empirisch, wie Unternehmen die gleichen

Produktinformationen an Kontaktpunkten bedürfnisorientierter gestalten und dadurch

sozio-ökonomische Parameter optimieren können. Ausserdem werden die Risiken von

zu abrupt umgesetzten Änderungen dargestellt und erläutert, wie Praktiker

Kontaktpunkte als Lerntools für die schrittweise Etablierung eines

bedürfnisorientierten Standards nutzen können, ohne ihre Kunden zu überfordern.

4 Wissenschaftlicher Kontext

4.1 Taxonomische versus thematische Ähnlichkeitsform

Die theoretische Grundlage für die Relevanz dieses Beitrags bilden Arbeiten zu

Ähnlichkeitsformen von Informationen. Wissenschaftler gingen lange von der

Informationsverarbeitung nach Klassifikationen (sogenannten Taxonomien) aus

(Tversky 1977; Gentner/Markman 1997; Farjoun/Lai 1997; Markman/Genter 2000;

Moreau/Markman/Lehmann 2001). Taxonomien sind attributspezifisch, erwartet sowie

konkret und umfassen Attribute einer einzigen Produktkategorie (z.B. Klassifikation

aller Autofarben in die Taxonomie „Farbe“). Dieser Ansatz führt per Definition zu

einer technik- bzw. produktbezogenen Gestaltung der Kontaktpunkte (z.B. Anordnung

aller Farben unter der Bezeichnung „Farben“) und ermöglicht Praktikern somit keine

bedürfnisorientierte Kundenansprache.

Neuere wissenschaftliche Erkenntnisse weisen verstärkt auf die Möglichkeit einer

assoziativen bzw. thematischen Verknüpfung von Informationen hin (Lin/Murphy

2001; Golonka/Estes 2009; Estes/Golonka/Jones 2011; Poynor Lamberton/Diehl

2013). Thematisch verknüpfte Informationen sind nutzenspezifisch, unerwartet sowie

abstrakt. Sie ergeben sich aus Attributen mehrerer Produktkategorien (z.B. Assoziation

bestimmter Autofarben und Felgen mit dem Thema „Sport“). Deshalb führt dieser

Ansatz zu einer weitaus bedürfnis-, nutzen- und erlebnisorientierteren

Kundenansprache (z.B. Anordnung der mit dem Thema Sport assoziierten Autofarben

und Felgen unter der Bezeichnung „Sportpaket“). Die nachfolgende Abbildung 1 fasst

die Eigenschaften taxonomischer und thematischer Ähnlichkeitsformen illustrativ

zusammen.


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Abbildung 1

Vergleich zwischen taxonomischer und thematischer Ähnlichkeitsform

Neurowissenschaftliche Arbeiten schliessen aufgrund der Aktivierung

unterschiedlicher Teile des Gehirns durch die beiden Ähnlichkeitsformen mit

kontextspezifischen Auswirkungen auf das Entscheidungsverhalten (Ratneshwar et al.

2001; Estes 2003; Davidoff/Roberson 2004; Sachs et al. 2008; Lupyan 2009; Sass et

al. 2009). Dennoch liegen bisher kaum anwendungsorientierte Arbeiten zum

unterschiedlichen Einfluss auf sozio-ökonomische Parameter vor (Ausnahmen sind

u.a. Gibbert/Mazursky 2009; Noseworthy/Finlay/Islam 2010; Poynor/Wood 2010).

Stattdessen nehmen verschiedene wissenschaftliche Beiträge den taxonomischen

Ansatz als gegeben hin (u.a. Lancaster 1966; Rosen 1974; Franke/Schreier/Kaiser

2010). Damit unterstellen sie indirekt keine entscheidungsrelevanten Unterschiede

zwischen den beiden Ähnlichkeitsformen. Auch in der Praxis werden die vorteilhaften

Eigenschaften der thematischen Ähnlichkeitsform kaum berücksichtigt. Stattdessen

gestalten Unternehmen ihre Kontaktpunkte überwiegend taxonomisch, da diese Form

von Individuen über die Zeit erlernt wurde und deshalb von ihnen erwartet wird

(Sujan/Dekleva 1987; Whittlesea/Williams 2001; Poynor/Wood 2010). Dennoch sollte

diese Vorgehensweise kritisch hinterfragt werden. Dies gilt nicht nur hinsichtlich einer

ganzheitlichen Erlebnis- und Bedürfnisorientierung, sondern auch vor dem

Hintergrund des erheblichen Marktforschungsaufwands, den Unternehmen für die

Definition von Kundenbedürfnissen betreiben.

4.2 Unterscheidung zwischen Bezeichnungen und Anordnungen von

Informationen

Auch die begrenzten Erkenntnisse zu den Effekten beider Ähnlichkeitsformen sind

aufgrund der fehlenden Differenzierung zwischen Bezeichnungen und Anordnungen

von Informationen (u.a. Poynor/Wood 2010) in Wissenschaft und Praxis kaum


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verwendbar. Denn es bleibt dabei unklar, ob die gefundenen Effekte durch die

Bezeichnungen, Anordnungen oder beide Aspekte verursacht werden.

Ein Beispiel ist die Manipulation einer Speisekarte mit jeweils vier Suppen,

Sandwiches, Finger Foods und Salaten bei Poynor und Wood (2010). Während die

Autoren 16 Speisen in der taxonomischen Kondition entlang der jeweiligen Attribute

bezeichnen und anordnen (z.B. Anordnung aller Suppen unter der Bezeichnung

„Suppe“), greifen sie in der thematischen Kondition für die Bezeichnung und

Anordnung der gleichen Speisen auf deren geografische Herkunft zurück (z.B.

Anordnung je einer Vorspeise, Suppe, Hauptspeise, Nachspeise unter der Bezeichnung

„Italienisch“). Die gemeinsame Änderung der Bezeichnung von „Suppen“ in

„Italienisch“ und der Anordnung von vier Suppen in jeweils eine Vorspeise, Suppe,

Hauptspeise und Nachspeise ermöglicht keine eindeutige Ergebniszuweisung. Hinzu

kommt, dass Poynor und Wood (2010) die gefundenen Unterschiede, trotz der

gemeinsamen Manipulation von Bezeichnungen und Anordnungen, ausschliesslich auf

die Anordnungen zurückführen. Der vorliegende Beitrag schliesst mit der

Unterscheidung zwischen separater und gemeinsamer Manipulation von

Bezeichnungen und Anordnungen diese Lücke bestehender Forschung. Damit

ermöglicht er eine eindeutige Zuweisung der Effekte sowie die Ableitung konkreter

Handlungsempfehlungen.

4.3 Moderate versus starke Abweichungen vom erwarteten Standard

Die Unterscheidung zwischen Bezeichnungen und Anordnungen sowie ihrer separaten

und gemeinsamen Manipulation ermöglicht die vergleichende Analyse unterschiedlich

starker Abweichungen vom erwarteten Standard. Bestehende Arbeiten zeigen einen

positiven Einfluss moderater Abweichungen vom erwarteten Standard, da diese

Individuen mental herausfordern (Meyers-Levy/Tybout 1989; Peracchio/Tybout 1996;

Noseworthy/Finlay/Islam 2010) und weder demotivieren (keine Abweichung vom

Standard) noch ermüden (starke Abweichungen vom Standard).

Dieser positive Effekt kann sich jedoch bei einer zu starken kognitiven Beanspruchung

durch grössere Abweichungen vom Standard umkehren und zu mentaler Erschöpfung

sowie schliesslich zum Kaufverzicht führen (Moreau/Markman/Lehmann 2001;

Hoeffler 2003; Alexander/Lynch, Jr./Wang 2008).


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4.4 Konzeption und Design der empirischen Studie

Das Ziel der empirischen Studie war es, wesentliche Schwächen in der bestehenden

Forschung durch die Unterscheidung zwischen separater und gemeinsamer

Manipulation von Bezeichnungen und Anordnungen zu adressieren. So können die

Effekte moderater und starker Abweichungen vom Marktstandard miteinander

verglichen werden.

Konfiguratoren eignen sich in dreifacher Hinsicht für die experimentelle Untersuchung

des Einflusses unterschiedlicher Gestaltungsoptionen von Kontaktpunkten auf sozio-

ökonomische Parameter:

1. aufgrund der aktiven Beteiligung der Kunden am Auswahlprozess

(Dellaert/Stremersch 2005; Franke/Schreier/Kaiser 2010);

2. aufgrund ihrer Funktion als Lern- und Marktforschungstools und

(Pine/Peppers/Rogers 1995; von Hippel 2001; Randall/Terwiesch/Ulrich 2007); und

3. wegen der einfachen Manipulierbarkeit von Bezeichnungen und Anordnungen von

Informationen.

Für die Studie wurde der Konfigurator eines Automobilherstellers für ein Modell in

seinen Grundzügen mit 36 Produktinformationen (je neun Motoren, Farben, Felgen

und Polster) aus dem aktuellen Sortiment nachprogrammiert. Nach Klick auf den

Umfragelink wurden die Probanden zufällig einer der folgenden vier Gruppen

zugeordnet (siehe Abbildung 2):

Gruppe 1: Taxonomische Bezeichnung und Anordnung (Kontrollgruppe)

Gruppe 2: Thematische Bezeichnung/Taxonomische Anordnung

Gruppe 3: Taxonomische Bezeichnung/Thematische Anordnung

Gruppe 4: Thematische Bezeichnung und Anordnung


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9 Felgen 9 Polster9 Modelle 9 Farben

Motoren Farben & Felgen PolsterGruppe 1:Taxonomische Bezeichnung

und Anordnung

Gruppe 2:Thematische Bezeichnung/Taxonomische Anordnung 9 Felgen9 Modelle 9 Farben

Motoren Sportive & Elegance

Separate Manipulation

von Bezeichnung und AnordnungGruppe 3:

Taxonomische Bezeichnung/Thematische Anordnung

Gruppe 4:Thematische Bezeichnung

und Anordnung

5 Farben4 Felgen

9 Modelle4 Farben5 Felgen

Motoren Farben & Felgen

5 Farben4 Felgen

9 Modelle4 Farben5 Felgen

Motoren Sportive & Elegance

Bezeichnung

Anordnung

Gemeinsame Manipulation

von Bezeichnung und Anordnung

Status quound

Kontrollgruppe

9 Polster

Polster

9 Polster

Polster

9 Polster

Polster

Legende

Abbildung 2

Studiendesign

Die Gruppen unterschieden sich ausschliesslich in den Bezeichnungen und

Anordnungen der jeweils neun Farben und Felgen, um so die Komplexität gering zu

halten. Die Probanden erhielten die Aufgabe, ein Fahrzeug auf Basis der 36

Produktinformationen zu konfigurieren. Zuvor wurden die Probanden in ein Szenario

versetzt, das ihnen vorgab, unmittelbar vor dem Kauf des ausgewählten Modells zu

stehen und dieses nun noch gemäss ihrer Präferenzen sowie im Rahmen ihrer

finanziellen Möglichkeiten konfigurieren zu müssen. Der Konfigurationsprozess

endete mit der Darstellung der Aussen- und Innenansicht des erstellten Fahrzeugs.

Anschliessend wurden folgende Variablen mittels wissenschaftlich etablierter Skalen

abgefragt:

a. Zahlungsbereitschaft (Jones 1975): Wertmass

b. Kaufwahrscheinlichkeit (Juster 1966): Präferenzsicherheitsmass

c. Produktzufriedenheit (Srivastava/Oza 2006): Zufriedenheitsmass

d. Mentale Anstrengung (Ferraro/Shiv/Bettman 2005): Reflexionsmass

e. Erwartung Produktanordnung (Machleit/Allen/Madden 1993): Erwartungsmass

Zusätzlich wurde die Konfigurationsdauer als Komplexitätsmass automatisch

miterhoben. Die Probanden wurden durch ein externes Marktforschungsinstitut

akquiriert und nach vollständiger Durchführung der Studie mit einem Gutschein

belohnt. An der Studie nahmen 286 Personen teil, von denen 210 (47% Frauen) die

Umfrage beendeten.


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5 Studienergebnisse

5.1 Methodenwahl

Für die Auswertung wurden Mittelwertvergleiche mittels unabhängiger t-Tests

durchgeführt. Die Ergebnisse konnten durch die Berechnung von geplanten Kontrasten

bestätigt werden. Der Annahmeprüfung für den t-Test folgte jeweils der Vergleich der

Mittelwerte aller Variablen der Gruppen 2, 3 und 4 mit dem entsprechenden

Mittelwert der Kontrollgruppe.

5.2 Ergebnisse

Im Vergleich zur Kontrollgruppe (Gruppe 1) führt die Verwendung thematischer

Bezeichnungen (Gruppe 2) zu einem Anstieg der Zahlungsbereitschaft um ca. 2.000

Euro bzw. ca. 10% (p < .05) und der Kaufwahrscheinlichkeit um ca. 13 Prozentpunkte

(p < .05). Die längere Konfigurationsdauer (p < .01), stärkere Reflexion (p < .01) und

höhere Produktzufriedenheit (p < .01) verdeutlichen, dass thematische Bezeichnungen

zu einer erhöhten mentalen Anstrengung führen. Dennoch werden thematische

Bezeichnungen aufgrund der moderaten Abweichung vom taxonomischen Standard als

positive Herausforderung wahrgenommen und mit einer höheren Produktzufriedenheit

honoriert. Im Gegensatz dazu haben die Änderung der Anordnungen (Gruppe 3) sowie

die gemeinsame Änderung von Bezeichnungen und Anordnungen (Gruppe 4) einen

negativen Einfluss auf die untersuchten Variablen. Für Gruppe 3 ergeben sich eine

niedrigere Zahlungsbereitschaft (p < .05), eine längere Konfigurationsdauer (p < .001)

und erwartungsgemäss eine geringere Erwartung der Produktanordnung (p < .05).

Während sich die Kaufwahrscheinlichkeit und die Reflexion nicht von der

Kontrollgruppe unterscheiden, führt die Änderung der Anordnungen zu einer höheren

Produktzufriedenheit (p < .05). Neben den für Gruppe 3 gezeigten Effekten führt die

gleichzeitige Änderung von Bezeichnungen und Anordnungen (Gruppe 4) zu einer

schwächeren Reflexion (p < .05) und zu keinem Unterschied bei der Produkt-

zufriedenheit (siehe Abbildung 3).


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M SD M SD tZahlungsbereitschaft (€) 20.957.74 4.565.54 22.942.00 4.127.41 -2.596*Kaufwahrscheinlichkeit (%) 43.58 34.50 56.12 29.99 -2.203*Konfigurationsdauer (sec.) 83.22 29.71 111.96 67.82 -3.334**Produktzufriedenheit 5.14 1.39 5.90 0.96 -3.558**Mentale Anstrengung 4.04 1.77 4.75 1.02 2.728**Erwartung Produktanordnung 4.37 1.52 4.32 1.79 0.143

M SD t M SD tZahlungsbereitschaft (€) 19.313.79 3.052.62 2.375* 18.690.36 1.435.37 2.576*Kaufwahrscheinlichkeit (%) 47.93 37.43 -0.702 52.07 32.43 -1.135Konfigurationsdauer (sec.) 128.59 55.30 -6.154*** 121.25 52.79 -4.653***Produktzufriedenheit 5.64 1.54 -1.974* 4.89 1.45 0.801Mentale Anstrengung 4.53 1.01 1.900 3.18 1.39 -2.340*Erwartung Produktanordnung 3.85 1.07 2.193* 3.75 0.81 2.042*

Unterschiede in Anordnung, Bezeichnung und ÄhnlichkeitsformMittelwert - Statistiken

Gruppe 1Taxonomische Bezeichnung

und Anordnung

Gruppe 2Thematische Bezeichnung/Taxonomische Anordnung

Gruppe 3Taxonomische Bezeichnung/

Thematische Anordnung

Gruppe 4Thematische Bezeichnung

und Anordnung

20'957.74

22'942.00

19'313.7918'690.36

18'000

20'000

22'000

24'000

TaxonomischeÄhnlichkeitsform

Thematische Bezeichnung / TaxonomischeAnordnung

Taxonomische Bezeichnung / ThematischeAnordnung

ThematischeÄhnlichkeitsform

Zahlungsbereitschaft (€)

Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4

43.58

56.12

47.93

52.07

40

45

50

55

60

65


Thematische Bezeichnung / TaxonomischeAnordnung

Taxonomische Bezeichnung / ThematischeAnordnung


Kaufwahrscheinlichkeit (%)


83.22

111.96

128.59121.25

80

100

120

140

160


Thematische Bezeichnung /Taxonomische Anordnung

Taxonomische Bezeichnung /Thematische Anordnung


Konfigurationsdauer (sec.)


5.14

5.905.64

4.89

4.5

5.0

5.5

6.0

6.5 Produktzufriedenheit


Abbildung 3

Studienergebnisse

Anmerkung. Produktzufriedenheit, Mentale Anstrengung, Erwartung Produktanordnung gemessen mit

einer 7-Likert Skala mit 1 = sehr niedrig und 7 = sehr hoch. Alle t-Werte im Vergleich zu Gruppe 1.

*p < .05. **p < .01. ***p < .001.


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4.04

4.754.53

3.18

3.0

3.5

4.0

4.5

5.0

5.5 Mentale Anstrengung


4.37 4.32

3.853.75

3.5

4.0

4.5

5.0 Erwartung Produktanordnung


6 Analyse

Die Verbesserung der sozio-ökonomischen Parameter bei thematischen

Bezeichnungen und taxonomischen Anordnungen von Produktinformationen deckt

sich mit bestehender Forschung zum positiven Einfluss moderater Abweichungen von

einem Standard (Meyers-Levy/Tybout 1989). Bei dieser Kondition besteht aufgrund

der stärkeren Bedürfnisorientierung sowie dem relativ geringen Lernaufwand eine

optimale Balance zwischen mentalem Aufwand und Kundenerlebnis. Im Gegensatz

dazu stellen die separate Änderung von Anordnungen sowie die gemeinsame

Änderung von Anordnungen und Bezeichnungen eine zu starke Abweichung vom

Standard dar. Diese Änderungen sind zu unerwartet und führen deshalb zu mentaler

Überforderung. Die nachfolgende Abbildung 4 fasst die Analyse der

Studienergebnisse zusammen.


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Abbildung 4

Ergebniszusammenfassung und –analyse sowie Praxisumsetzung


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7 Diskussion und Implikationen der Ergebnisse

Trotz der stärkeren Bedürfnisorientierung der thematischen Ähnlichkeitsform setzen

Praktiker auf Basis von Marktforschungsergebnissen, Wettbewerbsvergleichen oder

aus eigenem Kalkül auf die taxonomische Gestaltung der Kontaktpunkte. Dies

verwundert mit Blick auf die Studienergebnisse, da Bezeichnungen und Anordnungen

zwei unterschiedliche Stellhebel für die Gestaltung diverser Kontaktpunkte darstellen

(z.B. Webseite, Social Media, Verkaufsliteratur, Werbung, Point-of-Sale), mit denen

sich das Entscheidungsverhalten der Kunden massgeblich beeinflussen lässt, ohne die

zugrunde liegenden Produktinformationen zu verändern. Ausgehend vom

taxonomischen Standard, ermöglichen thematische Bezeichnungen Praktikern,

ihre Kontaktpunkte bedürfnisorientierter zu gestalten;

sich besser vom Wettbewerb zu differenzieren; und

den Customer Value zu erhöhen.

Die Ergebnisse der empirischen Studie liefern ebenfalls interessante Ansatzpunkte für

eine weitere wissenschaftliche Auseinandersetzung, da bisherigen Arbeiten zur

Sortimentsanordnung (z.B. Ratneshwar/Pechmann/Shocker 1996) oder

Produktindividualisierung (Lancaster 1966; Rosen 1974; Franke/Schreier/Kaiser 2010;

Levav et al. 2010) die taxonomische Ähnlichkeitsform zugrunde liegt und in den

wenigen vergleichenden Arbeiten keine Unterscheidung zwischen Bezeichnungen und

Anordnungen von Informationen vorgenommen wird (Poynor/Wood 2010). Die

Studienergebnisse legen deshalb einen Paradigmenwechsel von der reaktiven

Orientierung am taxonomischen Standard zur proaktiven Etablierung bzw.

Betrachtung der thematischen Ähnlichkeitsform in Praxis und Wissenschaft nahe.

Nachfolgend wird aufgezeigt, wie Unternehmen ihre Kunden in Form von zwei

Lernschritten mit einem unterschiedlichen Zeithorizont langfristig an die thematische

Ähnlichkeitsform heranführen und diese als neuen Standard etablieren können (siehe

dazu unterer Teil von Abbildung 4).

7.1 Lernschritt 1 (kurzfristig): Thematische Bezeichnungen als neuen

Standard etablieren

Ausgehend vom Marktstandard sollten Praktiker kurzfristig thematische

Bezeichnungen für die taxonomisch angeordneten Informationen definieren. Diese

Änderung erhöht die Flexibilität der Praktiker, da Bezeichnungen nicht mehr


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entsprechend dem zu bezeichnenden Attribut gewählt werden müssen (z.B. Felge),

sondern in Abhängigkeit der zu adressierenden Bedürfnisse bzw. Markenwerte

definiert werden können (z.B. Sport, Design). Diese Änderung geht mit niedrigen

Implementierungskosten einher und ermöglicht eine bedürfnisorientiertere

Kundenansprache sowie eine bessere Wettbewerbsdifferenzierung.

7.2 Lernschritt 2 (langfristig): Die thematische Ähnlichkeitsform als

neuen Standard etablieren

Nach der Etablierung thematischer Bezeichnungen sollten Praktiker langfristig das

Erlernen der thematischen Ähnlichkeitsform, bestehend aus thematischen

Bezeichnungen und Anordnungen (z.B. Anordnung einer Farbe und einer Felge unter

der Bezeichnung „Sportpaket“), proaktiv vorantreiben. Sofern diese Kondition das

Entscheidungsverhalten negativ beeinflusst, sollten zunächst thematische

Anordnungen mittels eines Zwischenschritts mit taxonomischen Bezeichnungen

erlernt werden (z.B. Anordnung von Farben und Felgen unter der Bezeichnung

„Farben & Felgen“). Sobald thematische Anordnungen mit taxonomischen

Bezeichnungen als moderate Abweichung wahrgenommen und entsprechend honoriert

werden, sollte die thematische Ähnlichkeitsform implementiert werden. Diese Form

gibt Praktikern die grösstmögliche Flexibilität bei der Gestaltung von

unternehmensübergreifend einzigartigen Kontaktpunkten entsprechend der

Markenwerte. Dadurch schafft sie ideale Voraussetzungen für eine auf das Sortiment

sowie die Marke zugeschnittene Bedürfnisorientierung.

8 Fazit und Ausblick

Kontaktpunkte sind mehr als blosse Berührungspunkte von Kunden mit Unternehmen,

sondern müssen für das Ziel einer Bedürfnisorientierung erlebbar gemacht werden.

Ausgehend von der Theorie unterschiedlicher Ähnlichkeitsformen, wurden mit

Bezeichnungen und Anordnungen von Informationen zwei unterschiedliche Stellhebel

für die bedürfnisorientierte Gestaltung von Kontaktpunkten definiert. Trotz des

positiven Einflusses thematischer Bezeichnungen sei darauf hingewiesen, dass den

Ergebnissen ein Laborexperiment mit einer eingeschränkten Realitätsnähe zugrunde

liegt. Die Studie sollte deshalb im Rahmen eines Feldexperiments direkt am

Kontaktpunkt mit der Untersuchung realer Auswahlprozesse und Kaufabschlüsse

repliziert werden. Zudem beschränkt sich das Studiendesign auf die Messung des

direkten Einflusses unterschiedlicher Bezeichnungs- und Anordnungsformen auf


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sozio-ökonomische Parameter und verzichtet auf die Untersuchung wesentlicher

Einflussgrössen.

Zukünftige Forschungsarbeiten sollten insbesondere den Einfluss des individuellen

Produktwissens untersuchen, da Laien und Experten unterschiedlich auf die

Manipulationen reagieren sollten. Während Experten auf taxonomische

Bezeichnungen nachlässig reagieren und Abweichungen hiervon honorieren, fühlen

sich Laien mit dem taxonomischen Standard wohler und sind selbst mit thematischen

Bezeichnungen überfordert. Abschliessend ist anzumerken, dass die abgeleiteten

Lernschritte lediglich auf einer Querschnittsanalyse basieren. Der langfristige

Lernschritt 2 lässt sich deshalb im Modell empirisch nicht abbilden und sollte somit

als idealtypisch aufgefasst werden. Hinzu kommt, dass die Implementierung

thematischer Anordnungen (z.B. Bundles aus Farben und Felgen) einen hohen

Kommunikationsaufwand erfordert. Zudem decken Bundles nicht alle

Kundenbedürfnisse optimal ab und lassen sich deshalb wohl nur über eine vorherige

Bedürfnisermittlung realisieren. Zukünftige Forschungsarbeiten sollten deshalb mittels

Längsschnittanalysen validieren, ob sich Konsumenten tatsächlich entsprechend der

skizzierten Lernschritte verhalten und langfristig die bedürfnisorientierte Gestaltung

von Kontaktpunkten mit thematischen Bezeichnungen und Anordnungen als neuen

Marktstandard erachten.


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Abbildung 5

Zusammenfassung, Kernthesen und Handlungsempfehlungen

Zusammen-

fassung

Die Gestaltung von Kontaktpunkten wird einer zeitgemässen bedürfnis- und

erlebnisorientierten Kundenansprache nicht gerecht, weil Praktiker Produktinformationen

an Kontaktpunkten durchweg attributspezifisch bezeichnen und anordnen.

Das Wissen über die Effekte der attributspezifischen (sogenannten taxonomischen) und

bedürfnisorientierten (sogenannten thematischen) Ähnlichkeitsform im Allgemeinen und

die Unterscheidung zwischen Bezeichnungen und Anordnungen im Speziellen ist in der

Wissenschaft und der Praxis limitiert.

Die Ergebnisse einer empirischen Studie zeigen – ausgehend vom taxonomischen

Standard – signifikante Verbesserungen wesentlicher sozio-ökonomischer Parameter für

Kontaktpunkte mit thematischen Bezeichnungen und taxonomischen Anordnungen der

Produktinformationen.

Kernthesen Unternehmen gestalten ihre Kontaktpunkte nicht bedürfnisorientiert und lassen damit ein

erhebliches Erfolgspotenzial ungenutzt.

Bezeichnungen und Anordnungen von Produktinformationen sind zwei unterschiedliche

Stellhebel für die bedürfnisorientierte Gestaltung von Kontaktpunkten.

Kontaktpunkte mit thematischen Bezeichnungen und taxonomischen Anordnungen

weichen – ausgehend vom Status quo – leicht vom Standard ab. Sie beeinflussen das

Entscheidungsverhalten positiv.

Kontaktpunkte mit thematischen Bezeichnungen und Anordnungen weichen – ausgehend

vom Status quo – stark vom taxonomischen Standard ab. Sie beeinflussen das

Entscheidungsverhalten negativ.

Kontaktpunkte dienen als Lerntools für Kunden. Sie ermöglichen so langfristig die

Etablierung thematischer Bezeichnungen und Anordnungen als neuen Standard.

Handlungs-

empfehlungen

Der Beitrag postuliert einen Paradigmenwechsel von der taxonomischen zur thematischen

Gestaltung von Kontaktpunkten, um Kunden bedürfnisorientierter anzusprechen, sich

vom Wettbewerb zu differenzieren und den Customer Value zu erhöhen.

Praktiker sollten Kontaktpunkte als Lerntools auffassen und das individuelle

Entscheidungsverhalten nach Anpassungen der Bezeichnungen und/oder Anordnungen

laufend messen. So können sie auf negative Effekte schneller reagieren.

Kurzfristig sollten Praktiker die Produktinformationen an ihren Kontaktpunkten

thematisch bezeichnen und taxonomisch anordnen, um ihre sozio-ökonomischen

Parameter zu optimieren.

Langfristig sollten Praktiker schrittweise thematische Bezeichnungen und Anordnungen

für ihre Produktinformationen als Standard einführen. So sind sie in der Lage, die

bedürfnisorientierte Gestaltung der Kontaktpunkte aktiv voranzutreiben und sozio-

ökonomische Parameter weiter zu verbessern.

Unter Umständen sollten Praktiker nach dem Erlernen thematischer Bezeichnungen einen

Zwischenschritt einführen, indem sie die Produktinformationen an ihren Kontaktpunkten

zunächst mit den bereits erlernten taxonomischen Bezeichnungen gestalten, um den

Lernprozess der thematischen Anordnungen zu vereinfachen.


-134-

Literaturverzeichnis

Alexander, D. L./Lynch Jr., J. G./Wang, Q. (2008): As time goes by: Do cold feet

follow warm intentions for really new versus incrementally new products, in:

Journal of Marketing Research, 45, 3, pp. 307-319.

Capgemini (2013): My Car, My Way – Cars Online 12/13, (http://bit.ly/1dLFnQq,

letzter Abruf 04.09.2013).

Cristiano, J. J./Liker, J. K./White, I. C. (2000): Customer-driven product development

through quality function deployment in the U.S. and Japan, in: Journal of

Product Innovation Management, 17, 4, pp. 286-308.

Davidoff, J./Roberson, D. (2004): Preserved thematic and impaired taxonomic

categorisation: A case study, in: Language And Cognitive Processes, 19, 1, pp.

137-174.

Dellaert, B. G. C./Stremersch, S. (2005): Marketing mass-customized products:

Striking a balance between utility and complexity, in: Journal of Marketing

Research, 42, 2, pp. 219-227.

Estes, Z. (2003): A tale of two similarities: Comparison and integration in conceptual

combination, in: Cognitive Science, 27, 6, pp. 911-921.

Estes, Z./Golonka, S./Jones, L. L. (2011): The apprehension and consequences of

thematic relations, in: Ross, B. (Ed.): The Psychology of Learning and

Motivation: Advances in Learning and Research, San Diego, pp. 250-286.

Farjoun, M./Lai, L. (1997): Similarity judgments in managerial cognition: Role,

process and implications, in: Strategic Management Journal, 18, 4, pp. 255-273.

Ferraro, R./Shiv, B./Bettman, J. R. (2005): Let us eat and drink, for tomorrow we shall

die: Effects of mortality salience and self-esteem on self-regulation in consumer

choice, in: Journal of Consumer Research, 32, 1, pp. 65-75.

Franke, N./Schreier, M./Kaiser, U. (2010): The "I designed it myself" effect in mass

customization, in: Management Science, 56, 1, pp. 125-140.

Fröhling, R./Esch, F.-R. (2013): Customer-Touchpoints mit hohem Potenzial, in:

Lebensmittel Zeitung, 9, S. 52.


-135-

Gentner, D./Markman, A. B. (1997): Structure mapping in analogy and similarity, in:

American Psychologist, 52, 1, pp. 45-56.

Gibbert, M./Mazursky, D. (2009): How successful would a phone-pillow be: Using


products, in: Journal of Consumer Psychology, 19, 4, pp. 652-660.

Golonka, S./Estes, Z. (2009): Thematic relations affect similarity via commonalities,

in: Journal of Experimental Psychology: Learning, Memory, and Cognition, 35,

6, pp. 1454-1464.

Griffin, A./Hauser, J. R. (1993): The voice of the customer, in: Marketing Science, 12,

1, pp. 1-27.

Hoeffler, S. (2003): Measuring preferences for really new products, in: Journal of

Marketing Research, 40, 4, pp. 406-420.

Jones, D. (1975): A survey technique to measure demand under various pricing

strategies, in: Journal of Marketing, 39, 3, pp. 75-77.

Juster, F. T. (1966): Consumer buying intentions and purchase probability: An

experiment in survey design, in: Journal of the American Statistical

Association, 61, 315, pp. 658-696.

Lamberton, C. P./Diehl, K. (2013): Retail choice architecture: The effects of benefit-

and attribute-based assortment organization on consumer perceptions and

choice, in: Journal of Consumer Research, 40, 3, pp. 393-411.

Lancaster, K. J. (1966): A new approach to consumer theory, in: Journal of Political

Economy, 74, 2, pp. 132-157.

Levav, J. et al. (2010): Order in product customization decisions: Evidence from field

experiments, in: Journal of Political Economy, 118, 2, pp. 274-299.

Lin, E. L./Murphy, G. L. (2001): Thematic relations in adults’ concepts, in: Journal of

Experimental Psychology: General, 130, 1, pp. 3-28.

Lupyan, G. (2009): Extracommunicative functions of language: Verbal interference

causes selective categorization impairments, in: Psychonomic Bulletin &

Review, 16, 4, pp. 711-718.


-136-

Machleit, K. A./Allen, C. T./Madden, T. J. (1993): The mature brand and brand

interest: An alternative consequence of ad-evoked affect, in: Journal of

Marketing, 57, 4, pp. 72-82.

Markman, A. B./Gentner, D. (2000): Structure mapping in the comparison process, in:

American Journal of Psychology, 113, 4, pp. 501-538.

Meyers-Levy, J./Tybout, A. M. (1989): Schema congruity as a basis for product

evaluation, in: Journal of Consumer Research, 16, 1, pp. 39-54.

Moreau, C. P./Markman, A. B./Lehmann, D. R. (2001): 'What is it?' Categorization

flexibility and consumers' responses to really new products, in: Journal of

Consumer Research, 27, 4, pp. 489-498.

Noseworthy, T. J./Finlay, K. F./Islam, T. (2010): From a commodity to an experience:


judgment, in: Psychology and Marketing, 27, 5, pp. 465-486.

Peracchio, L. A./Tybout, A. M. (1996): The moderating role of prior knowledge in

schema-based product evaluation, in: Journal of Consumer Research, 23, 3, pp.

177-192.

Pine, B. J./Peppers, D./Rogers, M. (1995): Do you want to keep your customers

forever?, in: Harvard Business Review, 73, 2, pp. 103-114.

Poynor, C./Wood, S. (2010): Smart subcategories: How assortment formats influence

consumer learning and satisfaction, in: Journal of Consumer Research, 37, 1,

pp. 159-175.

Randall, T./Terwiesch, C./Ulrich, K. T. (2007): User design of customized products,

in: Marketing Science, 26, 2, pp. 268-280.

Ratneshwar, S./Pechmann, C./Shocker, A. D. (1996): Goal-derived categories and the

antecedents of across-category consideration, in: Journal of Consumer

Research, 23, 3, pp. 240-250.

Ratneshwar, S. et al. (2001): Goal-derived categories: The role of personal and

situational goals in category representations, in: Journal of Consumer

Psychology, 10, 3, pp. 147-158.


-137-

Rawson, A./Duncan, E./Jones, C. (2013): The truth about customer experience, in:

Harvard Business Review, 91, 9, pp. 90-98.

Rosen, S. (1974): Hedonic prices and implicit markets: Product differentiation in pure

competition, in: Journal of Political Economy, 82, 1, pp. 34-55.

Sachs, O. et al. (2008): Categorical and thematic knowledge representation in the

brain: Neural correlates of taxonomic and thematic conceptual relations, in:

Neuropsychologia, 46, 2, pp. 409-418.

Sass, K. et al. (2009): Taxonomic and thematic categories: Neural correlates of

categorization in an auditory-to-visual priming task using fMRI, in: Brain

Research, 1270, pp. 78-87.

Srivastava, J./Oza S. (2006): Effect of response time on perceptions of bargaining

outcomes, in: Journal of Consumer Research, 33, 2, pp. 266-272.

Sujan, M./Dekleva, C. (1987): Product categorization and inference making: Some

implications for comparative advertising, in: Journal of Consumer Research, 14,

3, pp. 372-378.

Tversky, A. (1977): Features of similarity, in: Psychological Review, 84, 4, pp. 327-

352.

Von Hippel, E. (2001): Perspective: User toolkits for innovation, in: Journal of

Product Innovation Management, 18, 4, pp. 247-257.

Whittlesea, B. W. A./Williams, L. D. (2001): The discrepancy-attribution hypothesis:

I. The heuristic basis of feelings of familiarity, in: Journal of Experimental

Psychology: Learning, Memory, and Cognition, 27, 1, pp. 3-13.


-138-

-139-

E. Curriculum Vitae

Personal Information

Name: Marcel Mazur

Date of Birth: 01 May 1984

Place of Birth: Hamburg, Germany

Education

09/2011 – 12/2014 University of St. Gallen

Doctoral Studies in Management

06/2012 – 07/2012 University of Michigan, Ann Arbor

Summer School in Quantitative Research Methods

Regression Analysis; Maximum Likelihood; Software: SPSS / R

09/2009 – 05/2011 ESCP Europe, London / Paris / Berlin

Master’s Program in International Management

Entrepreneurship; Consumer Behavior

Degrees: Diplôme de Grande École; Master of Science (M.Sc.)

05/2009 – 08/2009 University of California, Berkeley

Continuing Education in Finance

Corporate and International Finance; Business Valuation

Degree: International Diploma Certificate

10/2005 – 05/2008 Leuphana University Lüneburg

Bachelor’s Program in Empirical Economic and Social Sciences

Strategic Marketing; Organisation; Management

Degree: Bachelor of Science (B.Sc.)

08/1995 – 06/2004 Gymnasium Ohmoor, Hamburg

Degree: Abitur

Work Experience

05/2011 – 12/2014 Center for Customer Insight, St. Gallen

Project Leader and Research Associate

E. CURRICULUM VITAE

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06/2011 – 07/2013 BMW Group, Munich

External Consultant at MINI Brand Management

06/2010 – 09/2010 Grant Thornton International Ltd., Cape Town

Intern in Strategic Solutions

01/2010 – 04/2010 Commerzbank AG, Frankfurt

Intern in Fixed Income – Money Market Sales

03/2009 – 05/2009 Roland Berger Strategy Consultants GmbH, Munich

Intern in the Competence Center Consumer Goods & Retail

01/2007 – 05/2009 International Trade Marketing & Consulting, Hamburg

Managing Director

09/2008 – 02/2009 BMW Group, Munich

Intern in Product Planning and Product Strategy

04/2008 – 08/2008 Unilever Deutschland GmbH, Hamburg

Intern in Category Building Home and Personal Care

06/2004 – 08/2008 Volks1887Parfums, Hamburg

Sole Proprietor

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