This introductory essay discusses how companies can choose between type-based or taxonomic categorization, which organizes products by shared attributes, and goal-based or thematic categorization, which organizes products by shared benefits. It notes that while type-based categorization aligns with consumer expectations formed through repeated exposure, goal-based categorization may better reflect consumers' growing need-orientation. The three subsequent essays will examine the effects of type-based and goal-based category labels, category organizations, and their interplay on consumer decisions in mass customization contexts.

1. 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

2. 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

3. In memory of my grandfathers
Mieczyslaw Mazur & Helmut Buchecker

4. 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

5. In Gedenken an meine Grossväter
Mieczyslaw Mazur & Helmut Buchecker

6. 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

7. 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.

8. 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.

9. 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

11. -1-
A. Introductory Essay
Mazur, M. (2014). The Psychology of Category Labels, Category Organizations and
Their Interplay. Unpublished Manuscript.

12. -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 (Marcel.Mazur@unisg.ch).

13. A. INTRODUCTORY ESSAY
-3-
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

14. A. INTRODUCTORY ESSAY
-4-
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

15. A. INTRODUCTORY ESSAY
-5-
“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”).

16. A. INTRODUCTORY ESSAY
-6-
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.

17. A. INTRODUCTORY ESSAY
-7-
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

18. A. INTRODUCTORY ESSAY
-8-
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

19. A. INTRODUCTORY ESSAY
-9-
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.

20. A. INTRODUCTORY ESSAY
-10-
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

21. A. INTRODUCTORY ESSAY
-11-
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

22. A. INTRODUCTORY ESSAY
-12-
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).

23. A. INTRODUCTORY ESSAY
-13-
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.

24. A. INTRODUCTORY ESSAY
-14-
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

25. A. INTRODUCTORY ESSAY
-15-
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

26. A. INTRODUCTORY ESSAY
-16-
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

27. A. INTRODUCTORY ESSAY
-17-
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.

28. A. INTRODUCTORY ESSAY
-18-
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.

29. A. INTRODUCTORY ESSAY
-19-
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),

30. A. INTRODUCTORY ESSAY
-20-
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.

31. A. INTRODUCTORY ESSAY
-21-
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

32. A. INTRODUCTORY ESSAY
-22-
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.

33. A. INTRODUCTORY ESSAY
-23-
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

34. A. INTRODUCTORY ESSAY
-24-
Practical RelevanceLow High
TheoreticalRigor
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

35. A. INTRODUCTORY ESSAY
-25-
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

36. A. INTRODUCTORY ESSAY
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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

37. A. INTRODUCTORY ESSAY
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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

38. A. INTRODUCTORY ESSAY
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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

39. A. INTRODUCTORY ESSAY
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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

40. A. INTRODUCTORY ESSAY
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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.

41. A. INTRODUCTORY ESSAY
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52. A. INTRODUCTORY ESSAY
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53. -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.

54. -44-
The Power of Category Labels: Exploring the Moderating
Role of Budget Tracking in Spending and Payment Decisions
Marcel Mazur (1)
Andreas Herrmann (2)
(1) Marcel Mazur is a Doctoral Candidate of Management, Center for Customer
Insight, University of St. Gallen, Switzerland (Marcel.Mazur@unisg.ch).
(2) Andreas Herrmann is a Professor of Marketing, Center for Customer Insight,
University of St. Gallen, Switzerland (Andreas.Herrmann@unisg.ch).

55. 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

56. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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

57. B. ESSAY I: THE POWER OF CATEGORY LABELS
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Category
Labels
(X)
Choice
Uncertainty
(M)
Budget
Tracking
(W)
Estimation
Bias
(Y)
Budget
Deviation
(Y)
Category
Labels
(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

58. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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

59. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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).

60. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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.

61. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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.

62. B. ESSAY I: THE POWER OF CATEGORY LABELS
-52-
Type-Based
vs.
Goal-Based
Category Labels
Type-Based
Category Labels
Goal-Based
Category Labels
Decreased
Choice
Uncertainty
Decreased
Choice
Uncertainty
Increased
Choice
Uncertainty
Increased
Choice
Uncertainty
Budget
Trackers
Non-Budget
Trackers
Budget
Trackers
Non-Budget
Trackers
Increased
EstimationBias
and Budget
Deviation
Increased
EstimationBias
and Budget
Deviation
Decreased
EstimationBias
and Budget
Deviation
Decreased
EstimationBias
and Budget
Deviation
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

63. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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

64. B. ESSAY I: THE POWER OF CATEGORY LABELS
-54-
Condition
Type-based category label
a
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 label
a
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
a
Within-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.

65. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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

66. B. ESSAY I: THE POWER OF CATEGORY LABELS
-56-
Upholstery
1
€0
Upholstery
2
€650
Upholstery
3
€650
Upholstery
4
€650
Upholstery
5
€1,440
Upholstery
6
€1,440
Upholstery
7
€1,750
Upholstery
8
€1,750
Upholstery
9
€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
Rims
1
€0
Rims
2
€480
Rims
3
€1,150
Rims
4
€1,710
Rims
5
€2,030
Rims
6
€2,030
Rims
7
€2,330
Rims
8
€2,330
Rims
9
€2,330
Rims Upholstery
Color
1
€0
Color
2
€0
Color
3
€450
Color
4
€450
Color
5
€450
Color
6
€450
Color
7
€450
Color
8
€850
Color
9
€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).

67. B. ESSAY I: THE POWER OF CATEGORY LABELS
-57-
Category
∆P1
a
(%)
Correct
ranking (%)
∆P2
a
(%)
Correct
ranking (%)
∆P3
a
(%)
Correct
ranking (%)
Model 0 70 10 93 20 99
Color 0 75 10 75 20 75
Rims 0 59 10 59 20 83
Upholstery 0 68 10 68 20 68
Average 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

68. B. ESSAY I: THE POWER OF CATEGORY LABELS
-58-
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:

69. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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.

70. B. ESSAY I: THE POWER OF CATEGORY LABELS
-60-
Events New Budgets
Mental Budgeting
Effect (MBE)
Budget with
Type-Based
Labels
Budget with
Goal-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 =

71. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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 0.062
SE 85.11 160.37 42.60 82.89 2.000*
MBE
a
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 €)
a
MBE = PE - IE - SE.
*p < .05. **p < .01. ***p < .001.

72. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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).

73. B. ESSAY I: THE POWER OF CATEGORY LABELS
-63-
Image
Upholstery
1
Image
Upholstery
2
Image
Upholstery
3
Image
Upholstery
4
Image
Upholstery
5
Image
Upholstery
6
Image
Upholstery
7
Image
Upholstery
8
Image
Upholstery
9
Image
Model
1
Image
Model
2
Image
Model
3
Image
Model
4
Image
Model
5
Image
Model
6
Image
Model
7
Image
Model
8
Image
Model
9
Component IIComponent I
Image
Rims
1
Image
Rims
2
Image
Rims
3
Image
Rims
4
Image
Rims
5
Image
Rims
6
Image
Rims
7
Image
Rims
8
Image
Rims
9
Component III Component IV
Image
Color
1
Image
Color
2
Image
Color
3
Image
Color
4
Image
Color
5
Image
Color
6
Image
Color
7
Image
Color
8
Image
Color
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

74. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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.

75. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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
Color
3
Color
4
Color
5
Color
6
Color
7
Color
8
Color
9
Colors & Rims
Rims
1
Rims
2
Rims
3
Rims
4
Rims
5
Rims
6
Rims
7
Rims
8
Rims
9
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
Color
3
Color
4
Color
5
Color
6
Color
7
Color
8
Color
9
Exterior Design
Rims
1
Rims
2
Rims
3
Rims
4
Rims
5
Rims
6
Rims
7
Rims
8
Rims
9
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.

76. B. ESSAY I: THE POWER OF CATEGORY LABELS
-66-
Category
Labels
Budget
Tracking
Labels ×
Budgeting
Estimation
Bias
Budget
Deviation
c‘1
c‘2
c‘3
B
Choice
Uncertainty
b
Category
Labels
Budget
Tracking
Labels ×
Budgeting
Estimation
Bias
Budget
Deviation
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

77. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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).

78. B. ESSAY I: THE POWER OF CATEGORY LABELS
-68-
Outcome
PredictorpCoefficientpCoefficientpCoefficientpCoefficientp
Constant1,092.809.0001,326.624.0004.206.0001,151.554.0001,370.074.000
(354.439)(363.972)(0.139)(320.785)(347.039)
c11,343.417.0081,576.719.002a1-0.179.360c'11,146.329.0121,430.947.004
(498.674)(512.087)(0.196)(452.155)(489.161)
c2-1,882.555.000-1,682.39.000a21.056.006c'2-722.221.015-824.174.010
(258.552)(265.506)(0.101)(293.681)(317.717)
b-1,099.020.000-812.867.000
(168.196)(181.961)
c33,573.741.0003,611.062.000a3-2.046.000c'31,324.840.0071,947.706.000
(375.743)(385.849)(0.147)(483.747)(523.338)
ModelR
2
.345.000.340.000.509.000.467.000.404.000
InteractionΔR
2
.318.000.306.000.503.000.022.000.044.000
Note.Presentedaretheunstandardizedregressioncoefficients.
X:Independentvariable.Y:Dependentvariables.M:Mediatiorvariable.W:Moderatorvariable.
Categorylabelswasdummy-coded.Budgettracking(Model1-3)andchoiceuncertainty(Model3)weremean-centered.
Choice
Uncertainty(M)
Model1Model2Model3
Labels×Budget
Tracking(X×W)
Choice
Uncertainty(M)
Budget
Tracking(W)
Category
Labels(X)
Coefficient
Estimation
Bias(Y)
Budget
Deviation(Y)
Estimation
Bias(Y)
Budget
Deviation(Y)
Table 5
Regression-Based Path Analysis Coefficients for Models 1-3 (Standard Errors in
Parentheses)

79. B. ESSAY I: THE POWER OF CATEGORY LABELS
-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.

80. B. ESSAY I: THE POWER OF CATEGORY LABELS
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3,601.98
182.12
1,093.59
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
EstimationBias(€)
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
ConditionalEffectofCategoryLabels
onEstimationBias
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)

81. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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.

82. B. ESSAY I: THE POWER OF CATEGORY LABELS
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-15,000
-10,000
-5,000
0
5,000
10,000
15,000
1.0 2.5 4.0 5.5 7.0
ConditionalEffectofCategoryLabels
onBudgetDeviation
A
B
3.73 4.33
3,569.00
332.71
1,327.32
2,902.54
-914.35
5,472.37
-2,000
-1,000
0
1,000
2,000
3,000
4,000
5,000
6,000
Type-Based Labels Goal-Based Labels
BudgetDeviation(€)
Non-Budget Trackers (-1 SD)
Moderate Budget Trackers (mean)
Budget Trackers (+1 SD)
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
Johnson-Neyman Regions of Significance for the Conditional Effect of Category
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)

83. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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).

84. B. ESSAY I: THE POWER OF CATEGORY LABELS
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-6
-4
-2
0
2
4
6
1.0 2.5 4.0 5.5 7.0
ConditionalEffectofCategoryLabels
onChoiceUncertainty
3.94 4.79
2.79
5.34
4.20 4.02
5.61
2.71
1
2
3
4
5
6
7
8
9
Type-Based Labels Goal-Based Labels
ChoiceUncertainty
Non-Budget Trackers (-1 SD)
Moderate Budget Trackers (mean)
Budget Trackers (+1 SD)
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
Johnson-Neyman Regions of Significance for the Conditional Effect of Category
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)

85. B. ESSAY I: THE POWER OF CATEGORY LABELS
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-6'000
-4'000
-2'000
0
2'000
4'000
6'000
2.8
10th
percentile
3.8
25th
percentile
4.6
50th
percentile
5.4
75th
percentile
6.0
90th
percentile
ConditionalIndirectEffectofCategoryLabels
onEstimationBiasThroughChoiceUncertainty
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]).

86. B. ESSAY I: THE POWER OF CATEGORY LABELS
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Category
Labels
Estimation
Bias
Choice
Uncertainty
Budget
Tracking
-.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 UL
3.1571 (-1 SD ) -2,800.373 588.376 -4,010.125 -1,750.216
4.4896 (mean) 196.151 219.804 -202.125 670.890
5.8220 (+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 -2,301.225
3.8000 (25th percentile) -1,354.656 352.998 -2,093.55 -730.510
4.6000 (50th percentile) 444.467 230.499 39.064 947.781
5.4000 (75th percentile) 2,243.591 459.435 1,434.005 3,241.274
6.0000 (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
Uncertainty for Different Values of Budget Tracking
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.

87. B. ESSAY I: THE POWER OF CATEGORY LABELS
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-6'000
-4'000
-2'000
0
2'000
4'000
6'000
2.8
10th
percentile
3.8
25th
percentile
4.6
50th
percentile
5.4
75th
percentile
6.0
90th
percentile
ConditionalIndirectEffectofCategoryLabels
onBudgetDeviationThroughChoiceUncertainty
Point Estimate
95% CI Lower Limit
95% CI Upper Limit
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
Uncertainty for Different Values of Budget Tracking
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).

88. B. ESSAY I: THE POWER OF CATEGORY LABELS
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Category
Labels
Budget
Deviation
Choice
Uncertainty
Budget
Tracking
-.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 UL
3.1571 (-1 SD ) -2,071.237 578.557 -3,325.165 -1,078.431
4.4896 (mean) 145.079 169.484 -144.721 536.923
5.8220 (+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 -1,374.251
3.8000 (25th percentile) -1,001.943 316.236 -1,707.208 -475.496
4.6000 (50th percentile) 328.741 185.966 40.796 793.599
5.4000 (75th percentile) 1,659.425 468.309 840.542 2,684.259
6.0000 (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
Uncertainty for Different Values of Budget Tracking
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.

89. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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

90. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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.

92. B. ESSAY I: THE POWER OF CATEGORY LABELS
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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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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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101. -91-
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.

102. -92-
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)
(1) Marcel Mazur is a Doctoral Candidate of Management, Center for Customer
Insight, University of St. Gallen, Switzerland (Marcel.Mazur@unisg.ch).
(2) Andreas Herrmann is a Professor of Marketing, Center for Customer Insight,
University of St. Gallen, Switzerland (Andreas.Herrmann@unisg.ch).
(3) Michael Gibbert is a Professor of Marketing, Institute for Marketing and
Communication, Università della Svizzera italiana, Switzerland
(Michael.Gibbert@usi.ch).

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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).

117. C. ESSAY II: THE BEAUTY OF MODERATELY INCONGRUENT SIMILARITY
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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).

118. C. ESSAY II: THE BEAUTY OF MODERATELY INCONGRUENT SIMILARITY
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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

120. C. ESSAY II: THE BEAUTY OF MODERATELY INCONGRUENT SIMILARITY
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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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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.

127. -117-
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.

128. 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 (Marcel.Mazur@unisg.ch).

129. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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

130. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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

131. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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.

132. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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

133. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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).

134. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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

135. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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 Anordnung
Gruppe 3:
Taxonomische Bezeichnung/
Thematische Anordnung
Gruppe 4:
Thematische Bezeichnung
und Anordnung
5 Farben
4 Felgen
9 Modelle
4 Farben
5 Felgen
Motoren Farben & Felgen
5 Farben
4 Felgen
9 Modelle
4 Farben
5 Felgen
Motoren Sportive & Elegance
Bezeichnung
Anordnung
Gemeinsame
Manipulation
von Bezeichnung
und Anordnung
Status quo
und
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.

136. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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).

137. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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M SD M SD t
Zahlungsbereitschaft (€) 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 t
Zahlungsbereitschaft (€) 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 -1.135
Konfigurationsdauer (sec.) 128.59 55.30 -6.154*** 121.25 52.79 -4.653***
Produktzufriedenheit 5.64 1.54 -1.974* 4.89 1.45 0.801
Mentale 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 Ähnlichkeitsform
Mittelwert - Statistiken
Gruppe 1
Taxonomische Bezeichnung
und Anordnung
Gruppe 2
Thematische Bezeichnung/
Taxonomische Anordnung
Gruppe 3
Taxonomische Bezeichnung/
Thematische Anordnung
Gruppe 4
Thematische Bezeichnung
und Anordnung
20'957.74
22'942.00
19'313.79
18'690.36
18'000
20'000
22'000
24'000
Taxonomische
Ähnlichkeitsform
Thematische Bezeichnung / Taxonomische
Anordnung
Taxonomische Bezeichnung / Thematische
Anordnung
Thematische
Ähnlichkeitsform
Zahlungsbereitschaft (€)
Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4
43.58
56.12
47.93
52.07
40
45
50
55
60
65
Taxonomische
Ähnlichkeitsform
Thematische Bezeichnung / Taxonomische
Anordnung
Taxonomische Bezeichnung / Thematische
Anordnung
Thematische
Ähnlichkeitsform
Kaufwahrscheinlichkeit (%)
Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4
83.22
111.96
128.59
121.25
80
100
120
140
160
Taxonomische
Ähnlichkeitsform
Thematische Bezeichnung /
Taxonomische Anordnung
Taxonomische Bezeichnung /
Thematische Anordnung
Thematische
Ähnlichkeitsform
Konfigurationsdauer (sec.)
Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4
5.14
5.90
5.64
4.89
4.5
5.0
5.5
6.0
6.5 Produktzufriedenheit
Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4
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.

138. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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4.04
4.75
4.53
3.18
3.0
3.5
4.0
4.5
5.0
5.5 Mentale Anstrengung
Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4
4.37
4.32
3.85
3.75
3.5
4.0
4.5
5.0 Erwartung Produktanordnung
Gruppe 1 Gruppe 2 Gruppe 3 Gruppe 4
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.

139. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
-129-
Abbildung 4
Ergebniszusammenfassung und –analyse sowie Praxisumsetzung

140. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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

141. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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

142. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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.

143. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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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.

144. D. ESSAY III: NEED-BASED DESIGN OF CUSTOMER TOUCH POINTS
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149. -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

150. E. CURRICULUM VITAE
-140-
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