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Redefining the Logic of Value Creation:
A Global Perspective on Emerging Interaction
Patterns between Customers and Companies
DISSERTATION
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
Tobias Schlager
from
Austria and Germany
Approved on the application of
Prof. Dr. Peter Maas
and
Prof. Dr. Andreas Herrmann
Dissertation no. 4109
D-Druck Spescha, St.Gallen 2013
The University of St. Gallen, School of Management, Economics, Law, Social
Sciences and International Affairs hereby consents to the printing of the present
dissertation, without hereby expressing any opinion on the views herein expressed.
St. Gallen, October 29, 2012
The President:
Prof. Dr. Thomas Bieger
tobi tobi
To my dear parents / Meinen lieben Eltern
Ines & Herbert Schlager
Acknowledgements
Although I hope that everybody who supported me while working on this
thesis knows how important his/her assistance was for me, I would like drop some
lines to express my sincere gratitude to a number of people that have particularly
encouraged me. First of all, I would like to thank my family. Each of them
have continuously inspired me from my first school day on until the last day as
a student. Always believing in me and showing me that it is never, under no
circumstances, worth to give up, I feel indebted to my mother everyday. Equally,
my father has contributed to the success by showing me that being close to nature
and that sometimes reducing things to the very basics is necessary to keep a clear
head. I am still deeply impressed of his skiing skills, hoping to go riding together
with him soon. My brother, who is a anaylsis-driven “mathemagician”, one of my
best friends, has not only directly contributed to this thesis by discussing several
sections, but also always had an open ear for my problems. I do not want to leave
my grandparents unmentioned, who all cannot read this, but to whom I owe so
much and who I regularly think of. With all my heart, I would like to thank my
family.
Besides my dear colleagues, co-authors, and students, I would like to express
a special thanks to Christine Scheef, who I had the chance to discuss a lot of ideas
with. She has always put my thesis above her problems. Without the discussions,
some parts of the thesis would surely not have reached the present level. Thank
you very much, Tine.
Of course, I want to thank my supervisor, Prof. Dr. Peter Maas, for his support
and guidance and for providing extraordinary working conditions at the Institute
of Insurance Economics. Peter always encouraged me for my work and assisted
whenever possible. I am also very grateful to my co-supervisor, Prof. Dr. Andreas
Herrmann, for his interest in my dissertation and my skills and for providing me
with excellent feedaback and creative ideas. After all, writing this thesis was a lot
of fun and all my friends, especially those from Innsbruck, have contributed to
this.
St. Gallen, December, 2012
Tobias Schlager
Vorwort
Obwohl ich hoffe, dass jeder, der mir beim Verfassen dieser Dissertation
geholfen hat, weiß, wie wichtig seine Unterstützung für mich war, möchte ich
die Menschen noch einmal ausdrücklich erwähnen, die mich besonders motiviert
haben. Zu allererst würde ich gerne meiner Familie danken. Jedes Familienmitglied hat mich auf seine Weise und zu jeder Zeit inspiriert, von meinem ersten
Schultag an bis zu meinem letzten Tag als Student. Immer an mich glaubend
und mir verdeutlichend, dass man nie aufgeben darf, war der grosse Mosaikstein,
den meine Mutter gelegt hat. Gleichermassen hat mein Vater dazu beigetragen,
indem er mich Naturverbundenheit gelehrt hat und das Verständnis, Dinge auf
das Wesentliche reduzieren, um einen klaren Kopf zu bewahren. Ich bin immer
noch begeistert von seinen Skikünsten und hoffe, bald mit ihm fahren zu können.
Mein Bruder, ein stets analysierender “Mathemagician” und einer meiner besten
Freunde, hat nicht nur direkt Abschnitte dieser Dissertation mit mir besprochen,
er hatte auch immer ein offenes Ohr für alle meine Probleme. Meine Grosseltern,
die diese Zeilen leider nicht mehr lesen können, sind an dieser Stelle nicht zu
vergessen. Ich verdanke auch ihnen sehr viel und denke oft an sie. Von ganzem
Herzen ein aufrichtiges Dankeschön an meine gesamte Familie.
Neben meinen werten Kollegen, Co-Autoren und Studenten möchte ich
meinen besonderen Dank an Christine Scheef ausdrücken, mit der ich viele Ideen
diskutieren konnte. Sie hat meine Dissertation immer über ihre eigenen Probleme
gestellt. Ohne unsere intensiven Diskussionen wären Teile meiner Arbeit nicht
annähernd auf dem derzeitigen Level. Vielen Dank, Tine.
Natürlich möchte ich auch meinem Betreuer, Prof. Dr. Peter Maas, für die
hervorragende Unterstützung und für die tollen Arbeitsbedingungen am Institut
für Versicherungswirtschaft danken. Meinem Zweitbetreuer, Prof. Dr. Andreas
Herrmann, bin ich für das Interesse an meiner Dissertation, das Vertrauen in meine
Fähigkeiten und für das exzellente Feedback sehr dankbar. Abschliessend möchte
ich noch erwähnen, dass mir das Schreiben der Dissertation eine Menge Spass
gemacht hat und alle meine Freunde - insbesondere jene aus Innsbruck - dazu
beigetragen haben.
St. Gallen, Dezember, 2012
Tobias Schlager
iv
Contents
Contents
Contents
List of Figures
iv
viii
List of Tables
ix
Summary / Zusammenfassung
x
I
Introduction
1
1
Research perspective
1
2
Theoretical framing
2
3
Practical relevance
4
4
Objectives
5
5
Summary and publication progress
9
BIBLIOGRAPHY
11
II Reframing Customer Value from a Dominant Logics Perspective
13
1
Introduction
14
2
The dominant logics and value concepts
17
3
Customer value’s nature
3.1 Phenomenological and experiential facet . . . . . . . . . . . . .
3.2 Context-specific facet . . . . . . . . . . . . . . . . . . . . . . .
3.3 Experience-based facet . . . . . . . . . . . . . . . . . . . . . .
20
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4
Customer value creation
4.1 Interaction . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.2 Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
24
25
26
C ONTENT
v
4.3
4.4
27
28
Focus on relationships . . . . . . . . . . . . . . . . . . . . . .
Holistic marketing processes . . . . . . . . . . . . . . . . . . .
5
A framework for customer value
31
6
Discussion
32
7
Theoretical and managerial implications
34
8
Future research
36
BIBLIOGRAPHY
40
III The Influence of the Employer Brand on Employee Attitudes Relevant for Service Branding: An Empirical Investigation
52
1
Introduction
53
2
Conceptual development
54
3
Hypotheses development
3.1 Economic value . . .
3.2 Development value .
3.3 Social value . . . . .
3.4 Diversity value . . .
3.5 Reputation value . .
4
Empirical study
64
5
Research findings
5.1 Current employees . . . . . . . . . . . . . . . . . . . . . . . .
5.2 Potential employees . . . . . . . . . . . . . . . . . . . . . . . .
66
68
71
6
Limitations/future research/discussion
72
7
Theoretical and managerial implications
7.1 Retain and align current employees . . . . . . . . . . . . . . . .
7.2 Attract potential employees . . . . . . . . . . . . . . . . . . . .
73
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8
Contents
Conclusion
76
BIBLIOGRAPHY
77
IV Accessing the Biggest Piece of the Pie: International
Segmentation with Emerging Markets
89
1
Introduction
91
2
Prior literature on EMs and segmentation
2.1 Conceptual approaches . . . . . . . . . . . . . . . . . . . . . .
2.2 Empirical studies . . . . . . . . . . . . . . . . . . . . . . . . .
93
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3
Why should EMs be different for segmentation?
3.1 Contextual embeddedness . . . . . . . . . . . . . . . . . . . .
3.2 Within-country heterogeneity . . . . . . . . . . . . . . . . . . .
95
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4
The requirements of EMs segmentation
4.1 Micro-level analysis . . . . . . . . . .
4.2 Selection of relevant respondents . . .
4.3 Segmentation variables and covariates
4.4 Holistic approach . . . . . . . . . . .
4.5 Data considerations . . . . . . . . . .
5
Model formulation and segmentation basis
109
5.1 Consumer channel behavior as the segmentation basis . . . . . . 109
5.2 Basic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
6
Numerical application
112
6.1 Data collection and analysis . . . . . . . . . . . . . . . . . . . 112
6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
7
Discussion and implications
120
7.1 Practical implications . . . . . . . . . . . . . . . . . . . . . . . 124
7.2 Future research . . . . . . . . . . . . . . . . . . . . . . . . . . 125
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98
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. 103
. 104
Appendix A Descriptive statistics
127
Appendix B Geographical and class-specific classification schemes
128
C ONTENT
vii
BIBLIOGRAPHY
129
V Nobody said Leaving was Easy: Biased Consumer Behavior in Research Shopping
140
1
Introduction
141
2
Conceptual model development
143
2.1 Decision-making towards research shopping . . . . . . . . . . . 143
2.2 Decision-making from a status quo bias perspective . . . . . . . 144
3
Hypotheses development
145
3.1 Biased purchase behavior . . . . . . . . . . . . . . . . . . . . . 145
3.2 Biased perceptions of channels for purchasing . . . . . . . . . . 149
4
Analysis and findings of Study 1 and Study 2
4.1 Study 1: Method . . . . . . . . . . . . .
4.2 Study 1: Analysis . . . . . . . . . . . . .
4.3 Study 2: Method . . . . . . . . . . . . .
4.4 Study 2: Analysis . . . . . . . . . . . . .
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152
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5
Discussion
168
6
Managerial implications
170
7
Limitations and future research
172
Appendix A Confirmatory factor analysis
175
Appendix B Graphical abstract
176
BIBLIOGRAPHY
177
Curriculum Vitæ
186
viii
List of Figures
List of Figures
Introduction
1
2
The Dissertation’s Focus on the Customer. . . . . . . . . . . . .
Localization of this Dissertation. . . . . . . . . . . . . . . . . .
1
4
8
Reframing Customer Value from a Dominant Logics Perspective
13
3
4
5
The Structure of the Article. . . . . . . . . . . . . . . . . . . .
The Changing Nature of Customer Value. . . . . . . . . . . . .
A Framework for Customer Value from a Dominant Logics Perspective. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
20
32
The Influence of the Employer Brand on Employee Attitudes
Relevant for Service Branding: An Empirical Investigation 52
6
7
The Perceived Employer Brand and Service Branding. . . . . .
Relationships between Employer Attractiveness and Employee
Response. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
58
70
Accessing the Biggest Piece of the Pie: International Segmentation with Emerging Markets
89
8
9
The Influence of (a) Psychographic and (b) Sociodemographic
Variables on Consumer Segment Distribution. . . . . . . . . . . 121
The Influence of Higher-level Covariates on Higher-level Segment
Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
Nobody said Leaving was Easy: Biased Consumer Behavior
in Research Shopping
140
10
11
12
Conceptual Model to Research Shopping. . . . . . . . . . . . . 145
The Central Hypotheses concerning the Effects of the Status Quo
Bias Theory on Research Shopping. . . . . . . . . . . . . . . . 152
The Probabilities of Research Shopping for Multi- and Singlechannel Search Behavior, and their difference. . . . . . . . . . . 161
C ONTENT
ix
List of Tables
Introduction
1
Reframing Customer Value from a Dominant Logics Perspective
13
The Influence of the Employer Brand on Employee Attitudes
Relevant for Service Branding: An Empirical Investigation 52
1
2
Summary of Confirmatory Factor Analysis and Reliability Estimates of Measurement Scales. . . . . . . . . . . . . . . . . . .
Results of Hypotheses Testing. . . . . . . . . . . . . . . . . . .
67
69
Accessing the Biggest Piece of the Pie: International Segmentation with Emerging Markets
89
3
4
5
6
The Changing Requirements of an International Segmentation
that Includes EMs. . . . . . . . . . . . . . . . . . . . . . . . .
Results of Class-Specific Model: Higher-level segments. . . . .
Model Results for Consumer and Higher-Level Segments. . . .
Consumer Segment Distribution for Each Higher-Level Segment.
108
115
118
119
Nobody said Leaving was Easy: Biased Consumer Behavior
in Research Shopping
140
7
8
9
10
11
Variance Inflation Factors and Pearson Correlations. . . . . . . .
Results of the Binary Logistic Random-Intercepts Regression. .
Manipulation of Satisfaction with the Channel. . . . . . . . . .
Means of Satisfaction Depending on the Specific Manipulation. .
Results of Mediation Testing. . . . . . . . . . . . . . . . . . . .
156
160
162
164
166
x
Summary
Dynamic interactions are a central part of today’s understanding of marketing.
Therein, the idea that interactions themselves constitute a potential source of
value for the customer becomes increasingly accepted. This dissertation critically
reflects upon recent ideas on various types of interactions with the customer in the
spotlight of considerations. The objectives are two-pronged: First, the customer’s
role as potentially proactive initiator is underscored which dissociates from the
traditional, company-focused point of view. Second, the importance of customer
interactions as decisive value driver is underlined.
In order to achieve this, the dissertation is structured in five parts: Part I
introduces the topic areas of the dissertation and outlines its overall objectives.
Guiding the subsequent parts, Part II deepens the theoretical perspective by means
of a conceptual discussion about how recently emerged logics reshape the understanding of customer value. Part III conceptually addresses and empirically
validates the implications of Part II on service branding. The two final parts, Part
IV and V, implicitly pursue the dominant logics’ notions by relating to a research
area specifically focused on interactions: Multichannel research.
S UMMARY / Z USAMMENFASSUNG
xi
Zusammenfassung
Dynamische Interaktionen stellen einen zentralen Bestandteil für das heutige
Verständnis von Marketing dar. Die Idee, dass Interaktionen an sich bereits einen
Wert für den Kunden darstellen, ist zunehmend akzeptiert. In der vorliegenden
Dissertation werden jüngste Entwicklungen bezüglich verschiedener Arten von
Interaktionen mit einem Fokus auf den Kunden kritisch reflektiert. Folglich sind
die Ziele zweigeteilt: Zum einen wird der Kunde als potenziell proaktiver Initiator
von Interaktionen verstanden, wodurch ein distanzierter Standpunkt gegenüber
der klassischen, unternehmensorientierten Perspektive eingenommen wird. Zum
anderen wird die Wichtigkeit von Kundeninteraktionen als entscheidender Werttreiber hervorgehoben.
Um dies zu erreichen, ist die Dissertation wie folgt strukturiert. Teil I dient
der Einführung in die Themenbereiche der Dissertation und verdeutlicht deren
konkrete Ziele. Teil II vertieft die theoretische Perspektive anhand einer konzeptionellen Aufarbeitung der Bedeutung von dominanten Logiken für Customer
Value. In Teil III wird dies aufgegriffen und ein konzeptionelles Framework für
Service Branding entwickelt, welches empirisch getestet wird. In den beiden
letzten Teile, Teil IV und V, werden die theoretischen Grundgedanken der Logiken
implizit verfolgt, indem ein Forschungsbereich vertieft wird, der hauptsächlich auf
Interaktionen basiert: die Multichannel- oder auch Interaktionswege-Forschung.
asdf
1
I
Introduction
The following introduction outlines the research perspective that the author of
this dissertation takes in and picks up current discussions in marketing literature
related to interactions. Building on this, the theoretical underpinning, the practical
relevance, and the objectives of the presented dissertation are briefly delineated.
The main body of the dissertation unfolds in the Parts II to V, which pertain
to individual articles that commonly emphasize the dynamic interactions that
customers engage in.
1
Research perspective
It is worthwhile taking a short excursus to the predominantly implicit assumptions
that the presented dissertation makes since these, along with the theoretical sophistication of the studied phenomena, determine the selection of methodologies
(Edmondson and McManus, 2007; Punch, 2005). While some paradigms, such as
the postmodernistic research, are based on the assumption that the reality develops over time, the positivistic and the postpositivistic perspective assume that an
objective reality exists (Rynes and Gephart, 2004). Thereby, the postpositivistic
perspective presents an softer form of positivism. The latter paradigms seem to
better fit the approaches and phenomena studied in this dissertation, i.e., interactions among various actors, since it is suggested that "we can objectively study
market interactions while ourselves remaining distant from those interactions"
(Deshpandé, 1983, p. 106). Further, most quantitative marketing articles implicitly
select a (post-)positivistic perspective (Hanson and Grimmer, 2007; Hirschman,
1986) which directly relates to the approaches of this dissertation. This brief
2
I I NTRODUCTION
elaboration on the research perspective shall guide the readers in understanding
the author’s intentions and the presented ideas.
2
Theoretical framing
Interactions between various stakeholders have attracted the spotlight of marketing
scholars since the field’s nascent states. In this course, most efforts have been
dedicated to interactions between companies and customers, which have recently
regained attention by the emergence of the service dominant (S-D) logic. The
logic was introduced in 2004 by the award-winning article "Evolving to a new
dominant logic for marketing" of Stephen Vargo and Robert Lusch. Among
other ideas, it accentuates a shift away from goods to service provision and lays
a focus on the co-creation of value by the customer (Vargo and Lusch, 2006).
By emphasizing the customer’s role as a co-creator the interactional facet of the
S-D logic is explicated, arguing that customers and companies idiosyncratically
create value through interactions (Vargo and Lusch, 2008). In the aftermath of
this path-breaking article, marketing literature has continuously shifted, which
is reflected by more than 100 published articles on the service-dominant logic, a
multitude of them in marketing’s top-tiers.
In a first attempt to project this perspective on customer value, Woodruff and
Flint (2006) critically reflect the S-D logic’s notions. Doing so, they underline
that value emerges in interactions between customers and companies. The Nordic
School, represented by Bo Edvardsson and colleagues, has further advanced these
ideas by introducing the customer dominant (C-D) logic (e.g., Edvardsson et al.,
2011; Heinonen et al., 2010). Advocating that the S-D logic is company-focused,
the C-D logic expresses the need for a rather exclusive focus on customers, their
needs, desires, and previous experiences. Despite the scarcity of scientific work on
the C-D logic and its thoughts, its ideas enhance literature in a way that underlines
how this dissertation understands the division of roles between customers and
2 Theoretical framing
3
companies: Albeit customers and companies have balanced roles when interacting,
the central focus is placed on the customer’s point of view.
An area that is inherently connected with the ideas of interactions as source of
value and the customer having a proactive role is the multichannel literature. The
research stream deals with questions arising regarding the multitude of possibilities
available to customers for interacting with companies. Implicitly referring to
the roles of companies and customers described above, Neslin et al. (2006)
conceptualize channels as "a customer contact point, or a medium through which
the firm and the customer interact" (p. 96). The dissertation acknowledges this
novel perspective and considers channels as more than one-way communication
or distribution channels, but rather as an offer from companies to customers to
interact (interaction point). Following this perspective, the power fades away from
companies to consumers since it is rather the customer having the choice about
how, when, and where to interact with whom. In particular, consumers can choose
the interaction points that best reflect their specific needs, and therefore, interaction
points represent a pivotal value driver of customer value (Montoya-Weiss et al.,
2003). In line, customers are assigned a rather active role. Synthesizing both, the
understanding that prevails in multichannel literature and the theoretical ideas of
both service- and customer-dominant logics, the central theme of this dissertation
can be framed:
Interactions per se are drivers of the (idiosyncratically)
determined customer value. Gaining further understanding of
these is necessary for facilitating the creation of customer value
through interactions.
The above-mentioned focus on interactions forms the common basis for the
research questions of the four articles presented in this dissertation. The central
position of the customer is graphically illustrated in Figure 1. Around the customer,
an exemplary set of actors with whom the customer may interact is sketched,
illustrating the various interactions studied in this dissertation.
4
I I NTRODUCTION
Figure 1: The Dissertation’s Focus on the Customer.
3
Practical relevance
In fact, interactions have not solely been a key topic in the marketing literature in
the past decade but also their practical relevance is paramount. How to interact
with customers constitutes a central pillar of most companies’ strategy. Whereas
companies such as Dell explicitly consider a specific type of interactions (online
interactions) in their strategy, the question how the customers may best connect
with the company is at least among the major topics of the agenda of other
companies.
On top of the relevance given by the Internet as interaction facilitator, recent technological advancements have further multiplied the challenges and opportunities for interacting with customers. Nowadays, mobile applications and
smartphone-optimized websites are increasingly gaining importance when evaluating how to connect with customers. In Europe, for example, already 51 percent of
the consumers dispose of smartphones while the number is even higher in the USA
with 63 percent (TechCrunch, 2011). On a more general level, a study of Yahoo!
Inc. and OMD (2006) shows that nearly two thirds of the consumers interact
4 Objectives
5
with multiple sources just for gathering information before making a purchase.
These figures are backed by a cross-industry study of Google (2009) finding that
only 40 to 60 percent of customers (depending on the industry) consult the same
interaction point for searching and purchasing. What is more, radically different
ways of interactions that consumers can engage in are created by new media. For
instance, whereas traditionally word-of-mouth (WOM) represented an important
way to gather information about a product, service or company, it is nowadays the
e-word-of-mouth (eWOM). Coevally, the range of customers reached by positive
or negative eWOM significantly exceeds the ones of traditional WOM, far beyond
what was imaginable a decade ago.
It is needless to say that the trend towards using the multitude of possibilities
for interacting with companies is still continuing. Along with the technological
developments and the simultaneously changing customer behavior it is imperative
for marketing practitioners to rethink old-fashioned strategies and practices.
4
Objectives
Acknowledging that marketing literature shifts towards a customer focus as well
as the practical relevance of these topics, this dissertation aims to substantialize
the knowledge concerning the complex and highly interactive context a consumer
is embedded in. On a theoretical level, the role of the customer as proactive
initiator of interactions and the notion of interactions constituting a source of
value is emphasized. To grasp the far-reaching consequences of the customers’
new role, the present dissertation takes a multidisciplinary approach integrating
perspectives from human resources, psychology, and management.
Accordingly, Part II has the objective to shed light on the influence of dominant
logics on the customer value concept, which has so far largely remained underinvestigated. Revisiting the creation of value, the part includes management
literature. Related to this, the objective of the Parts III-V is to contribute to
6
I I NTRODUCTION
specific topics from a dominant logics’ perspective. Particularly, Part III reflects
the importance of the employer brand for service branding integrating human
resources and marketing perspectives. To date the literature has neglected the
importance of favorable employee perceptions and the creation of a service brand.
The objective is to develop a profound understanding of the network of interactions,
where especially the customer’s interactions with various actors (i.e., the company
and the customer contact employee) are decisive. The last two parts deal with
the consumers’ behavior when interacting with companies, or more precisely,
consumer multichannel behavior. The first objective of both is to enhance literature
regarding the consumers’ interactive behavior with companies in the sense of what
Neslin et al. (2006) understand when referring to channels: Ways of interaction
instead of solely one-way communication. Thus, both articles emphasize the
consumers’ proactive role in interactions. While Part IV follows this notion by
establishing an international consumer segmentation, which includes emerging
markets, that founds on the so far largest variety of interaction points (several of
which are consumer-initiated interactions), Part V does so by explicitly focusing
on the consumers’ point of view throughout multiple phases of the buying process.
Additionally, it is worthwhile to note that by adopting the status quo bias theory,
the dissertation’s multidisciplinary perspective is reflected.
Besides the strong theoretical focus, this dissertation aims at reflecting an
innovative perspective on methods. Since each article adds to specific pieces
of theory, the applied methodologies vary considerably; as a whole, they reflect
the broad methodological variety of this dissertation. For instance, Part II is
purely conceptual, whereas on the other end of the scale Part V uses a random
intercept logit model combined with a simulation to isolate and interpret the
theoretical contribution. It is noteworthy that Part IV makes a methodological
contribution. Besides replicating currently applied segmentations, a multilevel
finite mixture model with contextual (higher-level) covariates was established to
reflect the emerging markets’ contextual peculiarities (Burgess and Steenkamp,
4 Objectives
7
2006). Thus, besides the theoretical contributions, the methodology was adapted
to fit the purpose and the state of the theory (Edmondson and McManus, 2007).
In sum, the objective of this dissertation is to theoretically and methodologically enhance current marketing knowledge and practices with a focus on the
customer interactions. A graphical classification of the four parts’ theoretical
background, the methods applied and the literature streams the parts contribute to
is illustrated in Figure 2.
8
I I NTRODUCTION
Figure 2: Theoretical and Methodological Localization, and Contributed Literature Streams
5 Summary and publication progress
5
9
Summary and publication progress
Article 1: Reframing Customer Value from a Dominant Logics Perspective
Although value has been accepted as the first and foremost driver of exchange,
only initial ideas regarding the impact of the service- and the customer-dominant
logic have been published. To fill this gap in literature, this article discusses
the impact of both logics on the customer value concept. A conceptual model
focusing on interactions with customers and the integration of the customer into
the companies’ processes is derived. The article has been accepted for publication
in the "Der Markt: International Journal of Marketing".
Article 2: The influence of the Employer Brand on Employee Attitudes relevant
for Service Branding: An Empirical Investigation
The second article directly applies the ideas of the service-dominant logic to
the creation of a service brand. In particular, the spotlight is put on the perceived
employer brand and its role in the creation of a service brand by employeecustomer interactions. Doing so, the article investigates employee satisfaction and
identification with the company as antecedent factors to the creation of a service
brand. Most of the hypothesized relationships between the perceived employer
brand and favorable outcomes were confirmed. The article is already published in
the "Journal of Services Marketing".
Article 3: Accessing the Biggest Piece of the Pie: International Segmentation with
Emerging Markets
In the third article, the requirements of an international segmentation that
includes emerging markets are established. Several important issues distinct to
this situation are pointed out. The ideas are numerically illustrated by a multilevel
finite mixture model that simultaneously estimates country and consumer segments
based on the consumers’ choice of interaction points (multichannel behavior). The
article was accepted for the "American Marketing Association Summer Educators
10
I I NTRODUCTION
Conference 2012" and the "41st Annual Conference of the European Marketing
Association". At the latter, it was nominated for the best paper award based on a
doctoral dissertation. Further, the article has yet passed the first review round in
the "Journal of International Marketing".
Article 4: Nobody said Leaving was Easy: Biased Consumer Behavior in Research
Shopping
Focusing on the consumer’s point of view in the buying process, the fourth
article investigates research shopping, which is delineated as the tendency to
switch channels between search and purchase phase. Applying the status quo
bias theory, the following question is investigated: "Why are consumers reluctant
to research shopping"? The findings of two studies, a large-scale survey and
a laboratory experiment, suggest that consumers who use a single channel for
searching, and in particular consumers satisfied with the search channel, are
prone to a status quo bias and therefore, reluctant to research shopping. The
article is in preparation for submission to the "International Journal of Research
in Marketing".
B IBLIOGRAPHY
11
Bibliography
Burgess, S. M. and J.-B. E. M. Steenkamp, 2006. Marketing renaissance: How
research in emerging markets advances marketing science and practice. International Journal of Research in Marketing, 23(4):337-356.
Deshpandé, R., 1983. "Paradigms Lost": On theory and method in research in
marketing. Journal of Marketing, 47(4):101-110.
Edmondson, A. C. and S. E. McManus, 2007. Methodological fit in management
field research. Academy of Management Review, 32(4):1155-1179.
Edvardsson, B., B. Tronvoll, and T. Gruber, 2011. Expanding understanding
of service exchange and value co-creation: A social construction approach.
Journal of the Academy of Marketing Science, 39(2):327-339.
Google, 2009. Research Online, Purchase Offline. (Available at http://www.fullvalue-of-search.de/studies).
Hanson, D. and M. Grimmer, 2007. The mix of qualitative and quantitative
research in major marketing journals, 1993-2002. European Journal of Marketing, 41(1/2):58-70.
Heinonen, K., T. Strandvik, K.-J. Mickelsson, B. Edvardsson, E. Sundström, and
P. Andersson, 2010. A customer-dominant logic of service. Journal of Service
Management, 21(4):531-548.
Hirschman, E. C., 1986. Humanistic inquiry in marketing research: Philosophy,
method, and criteria. Journal of Marketing Research, 23(August):237-2349.
Montoya-Weiss, M. M., G. B. Voss, and D. Grewal, 2003. Determinants of online
channel use and overall satisfaction with a relational, multichannel service
provider. Journal of the Academy of Marketing Science, 31(4):448-458.
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Neslin, S. A., D. Grewal, R. Leghorn, V. Shankar, M. L. Teerling, J. S. Thomas,
and P. C. Verhoef, 2006. Challenges and opportunities in multichannel customer management. Journal of Service Research, 9(2):95-112.
Punch, K. F., 2005. Introduction to social research: Quantitative and qualitative
approaches, Sage Publications Ltd., London, UK.
Rynes, S. and R. Gephart Jr, From the editors. Academy of Management Journal,
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TechCrunch. 2011. It’s still a feature phone world: Global smartphone penetration
at 27%. (Available at www.techcrunch.com).
Vargo, S. L. and R. F. Lusch, 2004. Evolving to a new dominant logic for
marketing. Journal of Marketing, 68(1):1-17.
Vargo, S. L. and R. F. Lusch, 2006. Service-dominant logic: What it is, what it
is not, what it might be. In R. F. Lusch and S. L. Vargo, editors, The servicedominant logic of marketing: Dialog, debate, and directions, pages 43-56,
M.E. Sharpe, Armonk, NY.
Vargo, S. L. and R. F. Lusch, 2008. Service-dominant logic: continuing the
evolution. Journal of the Academy of Marketing Science, 36(1):1-10.
Vargo, S. L., P. P. Maglio, and M. A. Akaka, 2008. On value and value co-creation:
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Yahoo! Inc. and OMD, 2006. Long & winding road: The route to the cash register.
(Available at http://files.shareholder.com/).
13
II
Reframing Customer Value from a Dominant Logics Perspective
Abstract
Customer Value (CV) is one of the most crucial concepts in the field of marketing.
Literature states that the identification and creation of CV is decisive for the
strategic success of any organization. Moreover, CV was coined a hot research
topic in the field of marketing for the years of 2010-2012 by the Marketing Science
Institute (2010). However, there is still no clear opinion about the concept per
se, as there is currently no accepted CV definition. What is more, since a broad
variety of fundamental developments, such as the rise of the service-dominant and
customer-dominant logic, touched the CV concept in the past years, there is a need
to reflect upon them. Therefore, the article addresses this challenge and analyzes
recent developments to provide a common basis for future research. Moreover,
the resulting implications for CV creation are provided. Focal questions that arise
when considering CV include: What is the nature of CV and how can value be
created considering the implications of both logics?1
Keywords: Customer value, S-D logic, C-D logic, Nature of value, Value creation
1 T. Schlager and P. Maas. This paper has been accepted for publication in the "Der Markt:
International Journal of Marketing".
14
1
II D OMINANT L OGICS
Introduction
Within the past few decades, there has been a broad shift from searching for
sources of competitive advantage within a company, to investigating external
sources of competitive advantage (Cleland and Bruno, 1996; Khalifa, 2004;
Woodruff, 1997). One of marketing’s most fundamental concepts, customer value
(CV), recognizes this by placing the focus on the customer. The relevance of
CV is not under discussion, as the creation of CV is considered the basis for a
company’s business success (Huber et al., 2001; McDougall and Levesque, 2000;
Payne and Holt, 2001; Porter, 1996; Woodruff, 1997), central to competitive
advantage (Khalifa, 2004; Kothandaraman and Wilson, 2001; Parasuraman, 1997),
and directly related to shareholder value (Cleland and Bruno, 1996). In 2007, the
American Marketing Association (2007) adapted its definition of marketing to
reflect the focus on value. More recently, Vargo and Lusch (2008a) defined value
as "idiosyncratic, experiential, contextual and meaning laden" (p. 7). However,
due to its complexity, there is still much discussion about the meaning of CV,
as several definitions have been attached to the term (Smith and Colgate, 2007).
The semantic heterogeneity is also highlighted by the multitude of different terms
that have been used in the context of CV (Voima et al., 2010). Recently, the
term customer has even come under discussion (Henneberg and Mouzas, 2008;
Vargo and Lusch, 2004; Woodruff and Flint, 2006). Consequently, the marketing
literature is still deficient in its understanding of CV (e.g., Blocker, 2011; Smith
and Colgate, 2007; Woodruff and Flint, 2006) and calls for further investigation
into CV are loudening (Voima et al., 2010).
On top of this deficient understanding, various developments emphasizing the
importance of the customer are increasingly addressed in the literature (Heinonen
et al., 2010; Shah et al., 2006; Vargo and Lusch, 2004). Two of these developments
substantially change the notion of CV, namely the rise of the service-dominant
(S-D) and customer-dominant (C-D) logic. There are two primary reasons for the
relevance of relating both logics to CV. On the one hand, both logics, especially
1 Introduction
15
the C-D logic, acknowledge the importance of the customer, and thus, inherently
strengthen the focus on the customer and, as a result, the CV concept. On the
other hand, the logics comprehensively summarize issues changing and advancing
CV (Voima et al., 2010; Woodruff and Flint, 2006). We regard the impact on CV
as twofold. Clearly, the scope in which CV is considered has risen (Edvardsson
et al., 2011; Gummesson, 2008; Woodruff and Flint, 2006), which makes CV
a concept that is even more vague than before. However, one might also argue
that the mentioned logics have a large potential to inform, and thus, to further
develop the CV concept (e.g., Voima et al., 2010; Woodruff and Flint, 2006). As
we believe more strongly in the second aspect, we acknowledge the importance of
registering the impacts for further enhancing CV.
An initial attempt to outline the implications from the SD logic on CV comes
from Woodruff and Flint (2006). The authors mention several important issues,
such as the changing role of the customer. Nevertheless, they do not fully take into
account recent trends, such as the importance of customer integration, the focus on
interactions, and other issues with an impact on CV that were recently addressed
by the S-D logic. For instance, a multitude of authors has discussed value and
its creation (e.g., Grönroos, 2011; Grönroos and Ravald, 2011; Grönroos, 2008;
Heinonen et al., 2010; Lusch and Vargo, 2006b; Payne et al., 2008; Vargo et al.,
2008; Voima et al., 2010). By doing so, they implicitly advance CV without being
directly related to CV. What is more, until now, no article has explicitly addressed
the implications of the most recent discussions for CV, neither of the S-D, nor of
the C-D logic. Intuitively, the following question arises: What impact do both
logics have on the CV concept?
The primary objective of our paper is to fill this gap in the literature and to
provide a basis for further research by evaluating the influences of the S-D and
C-D logic (Heinonen et al., 2010; Vargo and Lusch, 2008a; Vargo and Lusch,
2004). In particular, our article addresses the following research questions: How
did both logics change the understanding of CV? As implications regarding the
creation of value can primarily be recognized by taking into account the S-D and
16
II D OMINANT L OGICS
C-D logic’s perspectives, we also discuss what this means for value creation. In
doing so, we make particularly three contributions to the literature on CV:
• The nature of CV
• The ways of, and capabilities needed for, creating CV
• The scope in which the nature and creation of CV is considered
The scope of this paper is limited to the nature and the creation of CV, as our
primary objective is to provide a starting point for other scholars. Neither do
we enter in the current discussion, like the one regarding the roles within value
creation (e.g., Grönroos, 2011), nor do we aim to provide a completely new
conceptualization regarding CV, as we only focus on the changes implied by the
S-D and C-D logic.
The remainder of the article is organized as follows (see Figure 3). First, the
S-D and C-D logic are outlined with a particular focus on the resulting changes in
current value paradigms (i.e., from value-in-exchange to value-in-use). Next, we
discuss the nature of CV and the range of CV considerations as major changes
and enhancements are proposed by both logics. As a primary focus of the S-D
and C-D logic is laid on value creation, we outline ways of creating CV from
this novel perspective in a third step. Finally, a model of CV, from the logics
perspective, is established and briefly delineated by summarizing the key points.
While each section of the article emphasizes the current state of the literature, we
broadly outline avenues for future research, as well as theoretical and practical
implications, as we strive to provide a common parting ground.
2 The dominant logics and value concepts
17
Figure 3: The Structure of the Article.
2
The dominant logics and value concepts
Two recently developed logics that substantially changed the understanding of
marketing are the S-D and the C-D logic.2 As value considerations that are more
general in nature and that are dominantly discussed in the marketing literature,
namely value-in-use, value-in-context, and value-in-life, are closely linked to both
logics; we discuss them after presenting the logics’ primary thoughts.
The S-D logic was initiated in 2004 by the award-winning article Evolving to a
new dominant logic for marketing (Vargo and Lusch, 2004). It accentuates a shift
away from goods to service provision, lays a focus on intangible resources, the cocreation of value by the customer, and the notion of interaction and relationships,
and proposes a phenomenological facet of value (e.g., Vargo and Lusch, 2006).
Woodruff and Flint (2006) back the importance of the S-D logic for CV by
2 When using the logics, we refer to both upcoming logics as in a multitude of aspects, as they
appear to have similar consequences. In case they differ, we only refer to the applying logic.
18
II D OMINANT L OGICS
outlining several issues relevant for CV. As a result, an enormous impact on CV
regarding its nature and value creation, in general, can be acknowledged (Vargo
and Lusch, 2004, 2006, 2008a; Woodruff and Flint, 2006).
Recently, the C-D logic appeared in the literature picking up thoughts of the
S-D logic, as well as passing criticism on the S-D logic: According to the C-D
logic, the S-D logic is still a supplier-dominant logic. Arguing so, the C-D logic
even goes further in positioning the customer in the center by shifting the focus
from the company’s processes to the customer’s reality and history (Heinonen
et al., 2010). The primary distinction between both logics is the point of view.
While the S-D logic is supplier focused, the C-D logic refers to the customer as the
starting point of most relevant considerations. For instance, under the C-D logic,
service providers need to enhance their understanding of the customers’ history,
objectives and goals, followed by using services (Voima et al., 2010; Wikner,
2010). From a CV perspective, we regard this inherent focus on the customer as
useful, even though the C-D logic has primarily been applied to a service context.
Thus, although only a handful of articles have appeared regarding this logic, we
apply its notions on CV.
Both previously-mentioned logics, especially the rise of the S-D logic, have
a fundamental impact on the development of CV (Voima et al., 2010; Woodruff
and Flint, 2006). What they have in common is that they both emphasize the
importance of the CV concept and impose foundational challenges for the CV
concept (Heinonen et al., 2010; Vargo and Lusch, 2008a). More specifically, they
enhance discussions about value considerations that are closely tied to the nature
of CV and thus, appear as highly relevant for the presented article.
Broadly discussed and commonly accepted is the shift from value-in-exchange
to value-in-use (e.g., Ballantyne and Varey, 2006; Holbrook, 1994; Normann
and Ramirez, 1993; Ravald and Grönroos, 1996). Similarly, this development
is strongly underlined by both logics, as both even further develop the notion of
value-in-use. Newer considerations, substantially fostered by the S-D and C-D
2 The dominant logics and value concepts
19
logic, are presented by the value-in-context and value-in-life (Edvardsson et al.,
2011; Vargo et al., 2008; Voima et al., 2010). Thus, the three are briefly outlined
in order to grasp their ideas.
Value-in-use expresses the idea that value is created by using a product or
service, rather than by producing the product or service (e.g., Lusch et al., 2007;
Vargo and Lusch, 2004), which constitutes one key assumption of the S-D logic
(e.g., Edvardsson et al., 2011). Although similar notions have already appeared in
past studies (Alderson, 1957; Levitt, 1986; Vandermerwe, 1996), the idea has regained relevance (e.g., Heinonen et al., 2010). Therefore, its implications on CV,
especially regarding the understanding of CV and the methods of value creation,
need to be registered. This idea is advanced by value-in-context, which lays an
emphasis on the role of "other market-facing, public, and private resources" (Merz
et al., 2009, p. 330). In line with this, a notable article emphasizes the customers’
embeddedness into a social context by applying social construction theories
(Edvardsson et al., 2011), while others mention complexity theory as a theoretical
underpinning for a similar notion (Gummesson, 2008). Nevertheless, value-incontext has already received criticism as some scholars consider the idea as static
(Grönroos and Ravald, 2011). The C-D logic recognizes this by emphasizing the
highly dynamic and multi-contextual reality and life of the customer, implying
a significant emphasis on the customer’s experiences and history, especially in
service settings. As a result, the notion of value-in-life is proposed (Voima et
al., 2010). Whether value-in-context or value-in-life is more appropriate is not
yet commonly agreed upon. However, as this brief outline illustrates, substantial
influences on value considerations are presented by the emergence of the S-D and
C-D logic that, in turn, changes the understanding of CV.
20
3
II D OMINANT L OGICS
Customer value’s nature
As both logics influence the nature of CV, which has already been vague before,
there is a need to address them. Up to now, several facets of CV have repeatedly appeared in the literature regarding CV. Among those, CV is described as
being a trade-off between benefits and sacrifices (Ulaga, 2003; Zeithaml, 1988;
Gale, 1994), subjective (Kortge and Okonkwo, 1993; Nasution and Mavondo,
2008; Ulaga, 2003; Vargo and Lusch, 2004), multi-dimensional (Holbrook, 2005;
Matzler, 2000; Sweeney and Soutar, 2001; Ulaga, 2003) and relativistic (Gale,
1994; Holbrook, 1994, 2006). Moreover, it has been delineated as context-specific
and experiential (Holbrook, 2005). Although the ideas of several facets have
been picked up by both logics (i.e., CV’s context-specific and experiential nature),
substantial changes can be recognized which are summarized in the following
section. Figure 4 presents the development from the classical understanding of
CV to an understanding from a S-D and C-D logic’s perspective; each facet is
assigned the logic that mostly emphasizes its notion.
Figure 4: The Changing Nature of Customer Value.
3.1
Phenomenological and experiential facet
According to the S-D logic, a shift towards emphasizing the experiential and
phenomenological facet of CV can be acknowledged by the shift towards emphasizing the experiential and phenomenological facet of CV (Vargo and Lusch,
3.1
Phenomenological and experiential facet
21
2004; Woodruff and Flint, 2006). Both terms, experiential and phenomenological,
emphasize the co-creation role of the customer (Vargo and Lusch, 2008a). From
this understanding, experience does not refer to the customer’s past experiences;
it rather describes the perishability inherent to CV (Vargo and Lusch, 2008b).
This underscores that value cannot be inventoried and is not created solely by the
company that is, however, not new to the literature.
Prahalad and Ramaswamy (2004b, p. 137) use the term in a similar manner and emphasize the shift toward experiences as they state that "value is now
centered in the experiences of consumers". Similarly, even earlier contributions
suggest an experiential perspective that includes symbolic, hedonic, and aesthetic
aspects in the consumption process (Babin et al., 1994; Hirschmann and Holbrook, 1982). Although both terms are still discussed, phenomenological has
recently been preferred, as experiential implies several other meanings, such as
a focus on the past. In contrast, phenomenological emphasizes the idiosyncratic
determination of value (Vargo and Lusch, 2008a) without implying a focus on
the past. This accentuates that companies and customers have equal roles in
value determination, as the process between both parties is emphasized, while
the old-fashioned notion of value being provided by the company is no longer
supported. The idea is explicitly expressed by highlighting interactions when
considering the creation of CV as the S-D logic does. Woodruff and Flint (2006)
explain the phenomenological nature of CV as context-specific, interactive, and
attached with meanings.3 The C-D logic accepts that interactions facilitate the
creation of value, however, contrary to the S-D logic’s supplier-oriented approach,
it emphasizes that it is the customer, who ultimately determines the value created.
In doing so, the C-D logic stresses that other processes not directly related to
interactions also need to be considered (Heinonen et al., 2010).
The interactive aspect implied in the term phenomenological is closely linked
3 Although Woodruff and Flint’s (2006) interpretation of the term phenomenological mentions
context specificity, we treat this as a separate issue, as it has not been clearly delineated by the authors.
Moreover, a multitude of other articles has further developed the meaning of context-specific without
referring to it as the phenomenological aspect. Hence, we discuss this issue separately.
22
II D OMINANT L OGICS
to another important characteristic. Seeing the customer in a constant and interactive process with other actors, such as companies and other customers, bolds the
increasing focus on relational aspects, which is therefore underpinned by the S-D
(Vargo and Lusch, 2004) and C-D logic (Heinonen et al., 2009, 2010). Hence, an
increasing focus on relationships, rather than on transactions, is suggested (Vargo
and Lusch, 2008b). According to both logics, the customer is engaged in multiple
relationships, also to actors other than the company. The old-fashioned view on
relational aspects as being dyadic does not seem to be supportable anymore (e.g.,
Voima et al., 2010). Intuitively, the customer now appears as being embedded
within a context of other value determining resources and actors.
3.2
Context-specific facet
Following the notion of multiple relationships, the literature acknowledges the
important role of the customer’s external and social context in the assessment of
CV (Edvardsson et al., 2011; Voima et al., 2010). In doing so, the idea of the
customer, just like the company, being embedded in a system of other actors is
expressed (e.g., Vargo and Lusch, 2004). In this complex system, the customer
acts as a resource integrator, simultaneously combining various resources for
value creation. This implies that the customer uses his/her own resources (i.e.,
knowledge and skills) and the resources of other actors. Hence, CV is dependent
on a network of competences and resources.
While the previous literature has accepted CV’s dependence on the context,
the S-D and C-D logic have strongly emphasized and further developed this
idea. One of the first guiding conceptual articles was put forth by Edvardsson
et al. (2011). Social construction theories are applied to stress the importance
of the social context for value creation under the S-D logic. On this basis, four
propositions are delineated, two of which strongly underline the importance of the
social dimension of value: "Value has a collective and intersubjective dimension
and should be understood as value-in-social-context" (p. 333) and "the way in
3.3
Experience-based facet
23
which resources are assessed depends on the social context" (p. 334). It can be
concluded that research efforts begin to adapt a more differentiated point of view,
which includes the social context as an important variable for the determination of
value.
The C-D logic argues in a similar manner, stating that the dyadic approach that
was historically used is not enough to display the complex construct of CV (e.g.,
Voima et al., 2010). Hence, this logic also highlights that the customer is socially
embedded, interacting with other groups, such as other customers. Compared to
the S-D logic, the C-D logic more strongly emphasizes the customer’s point of
view. An even stronger focus is laid on the customer, and thus, on his/ her specific
context. In sum, while the social context is emphasized by both logics, the C-D
logic fully takes in the customer’s perspective (Voima et al., 2010).
3.3
Experience-based facet
Contrary to the experiential and phenomenological nature, the facet of CV that we
term experience-based refers to the past experiences of customers. As Voima et al.
(2010, p. 4) states, "the starting point is the customer’s reality and life". Value is
therefore regarded as part of the dynamically constructed and multi-framed reality
of each customer. Although earlier contributions also recognized similar aspects
(i.e., by terming it subjective, Woodruff, 1997), the focus on the customer’s history,
and thus, the experience-based nature of CV was especially emphasized by the CD
logic. In this light, value is termed value-in-life, which better explains the holistic
view of the customer’s life than value-in-context (Voima et al., 2010). With this
focus on the internal context, it is emphasized that value formation is extended
beyond the interactive processes and the visibility of companies and includes the
customer’s mental processes (Heinonen et al., 2010), resulting in an increase in
complexity as now the customer’s history is also considered.
A facet of the experience-based nature of value is its dynamic aspect, which
expresses the constantly changing and adapting evaluations of what customers
24
II D OMINANT L OGICS
value. It focuses on changes in customer-desired value (Blocker and Flint, 2007a,
b). Still, there is disagreement among researchers and practitioners regarding how
(Beverland and Lockshin, 2003, 2004) and why it occurs (Flint and Woodruff,
2001), which underpins its underdeveloped state (Blocker and Flint, 2007b). This
issue is more relevant than ever, as the customer’s value perceptions tend to change
at a revolutionary speed within today’s quickly changing environment (Flint et
al., 2002). Consequently, failing to respect the dynamic may directly harm a
company’s financial performance (Blocker and Flint, 2007b; Grieve and Ortiz,
2003). Additionally, Blocker and Flint (2007b) re-emphasize the importance
of knowledge about what customers will value in the future for building and
maintaining a sustainable advantage. As this research stream is closely connected
to the experience-based facet, it is especially the C-D logic that draws attention to
the dynamic and ever-changing nature of CV (Voima et al., 2010), partly supported
by the S-D logic (Madhavaram and Hunt, 2008).
4
Customer value creation
The S-D and C-D logic emphasize a fundamentally changed perspective on value
and value creation. Intuitively resulting from discussions regarding CV’s nature is
the question: How is CV creation considering this new standpoint? We address
this question by recognizing that former frameworks (i.e., the one put forth by
Smith and Colgate, 2007) have yet become outdated in the light of S-D and C-D
logic. What is worthwhile to note is that discussions about the role of the company,
as well as the customer in value creation, have appeared (Edvardsson et al., 2011;
Grönroos and Ravald, 2011; Gummesson, 2008). We do not directly enter these,
but instead reflect implications from the changing nature on a general level for
two reasons. First, by drawing implications on value creation on a higher level,
we aim to provide a basis for enhancing this discussion, while entering these
would go beyond the scope of this paper. Second, we regard discussions as highly
dependent on the point of view and the context (i.e., product vs. service context,
4.1
Interaction
25
Gummesson, 2008), which we do not discuss either. Thus, we take this avenue
and address questions, which are more general in nature. More specifically, we
identified interactions, a focus on relationships, and holistic marketing processes
as responsible for value creation.
4.1
Interaction
The recent literature concerning the S-D logic and value-in-use places a special
emphasis on company-customer interactions as source of CV. Although interactions have already been acknowledged as source of value by other frameworks
(e.g., Smith and Colgate, 2007), the notion is further enhanced. Emphasizing
interactions as a crucial concept from a S-D logic perspective, Grönroos and
Ravald (2011, p. 12) define interactions as "a mutual or reciprocal action where
two or more parties have an effect upon each other". On this basis, it is argued that
through interactive processes, companies can get actively involved in creating experiential value. Other contributions confirm this by seeing interactions as having
the ability to promote experiential and phenomenological value (Ballantyne and
Varey, 2006). Similarly, scholars assess that interactions provide the basis for forming the customers’ preferences (Mathwick et al., 2002). The importance of the
company-customer interaction is underlined by the ability to facilitate value and
to influence CV perceptions. As a result, it is broadly accepted that the customers’
creation of value is catalyzed through interactions (Grönroos, 2009; Grönroos
and Ravald, 2009, 2011; Tynan et al., 2010). Although the difference between
services and goods environments is still discussed, it appears that interactions
being especially important in a service-context is accepted. From the companies’
perspective, this is due to the multitude of opportunities for co-creating value with
the customer when acting within the customer’s sphere (Grönroos, 2008).
One other issue that comes into play is the superior ability to sense the customers’ needs within interactions (Tynan et al., 2010). Although interactions per
se are especially considered by the S-D logic, they may also provide a source of in-
26
II D OMINANT L OGICS
depth knowledge about the customer and his/her life, which is rather emphasized
by the C-D logic. More information can be generated, and as a result, companies
can develop better insights about their customers. This issue’s importance is
underscored by the C-D logic; therefore, we also argue that from a C-D logic
point of view those company-customer interactions are highly relevant.
4.2
Integration
Closely connected to, but still distinct from interactions as source of CV, is the
integration of resources (e.g., Mele et al., 2010). This includes the integration of
the customers’ resources which refers to providing customers with the opportunity
to participate within the companies processes (e.g., Möller, 2006). Integrating the
resources of customers and companies is treated as prerequisite for interactions.
As Grönroos and Ravald (2011, p. 11) state "an inherent aspect of interaction is
connectivity, i.e., the parties involved are in some contact with each other". With
the term connectivity, the authors emphasize the integration of the companies’ and
the customers’ resources. The importance of the integration of the companies’
and customers’ resources in the value creation process is strongly emphasized by
the S-D logic (e.g., Vargo and Lusch, 2004). In a similar manner, Edvardsson et
al. (2010) refer to getting close to customers by customer integration.
We argue that this proximity to the customer partly takes in an avenue that
is emphasized by the C-D logic. The C-D logic accepts the importance of interactions, but even goes one step further by also seeing "other activities and
experiences" (Heinonen et al., 2010, p. 535) that are not directly connected to
service-related interactions. From this position, customer processes, which are
not controlled by a company, are considered as a highly relevant part within
value creation. Hence, the challenge that needs to be addressed is discovering
the underlying issues that cannot be easily recognized by a company. When the
customer’s life is brought into the focus of value creation, an in-depth knowledge
of the customer becomes necessary. As Heinonen et al. (2010, p. 538) note, "we
4.3
Focus on relationships
27
first need to understand the customers’ lives" which renders the importance of a
fine-grained understanding of the customer. One way to gain this depth of understanding of the customer is integrating customers into the company’s processes.
The aim of integration may therefore not only be the creation of interactions,
but also the achievement of profound customer insights that go beyond insights
gained by traditional marketing research methods. Hence, integration can also be
considered a way for getting deep customer insights regarding what the C-D logic
describes as value-in-life.
We conclude by stating that the benefits of customer integration are twofold.
On the one hand, interactions are generated, which in turn, are considered a source
of CV. On the other hand, customer integration has the ability to generate in-depth
knowledge of the customer, which may provide insights regarding value-in-life
considerations and, as a result, for aligning future company actions.
4.3
Focus on relationships
When facing notions like interaction and integration, it becomes obvious that
relationships play an important role within value creation, which is a concept
that is extensively highlighted by both logics (Vargo and Lusch, 2004; Voima
et al., 2010; Woodruff and Flint, 2006). Both interaction and integration can
be considered a means to the end, which is relational value. This is due to
the important role of interactions in facilitating relationships (Payne et al., 2008;
Prahalad and Ramaswamy, 2004a; Sandström et al., 2008; Vargo and Lusch, 2004).
Generally, it is proposed that relationships themselves are a source of value (e.g.,
Howden and Pressey, 2008; Lindgreen and Wynstra, 2005); relational benefits
and sacrifices need to be considered in the value formation process (Grönroos,
1997; Payne and Holt, 2001; Ravald and Grönroos, 1996; Ulaga and Eggert, 2005).
In this light, trust and risk avoidance are especially considered focal constructs
(e.g., Lindgreen and Wynstra, 2005). Most commonly, the literature assesses the
value that emerges in relationships between companies and customers (Ulaga and
28
II D OMINANT L OGICS
Eggert, 2005, 2006b), but also the relationship between customers and customers
(Edvardsson et al., 2011; Voima et al., 2010) and between customers and brands
(Payne et al., 2009).
Similarly to the explanation regarding the integration of the customer into the
company’s processes, we argue about the focus on relationships. Relationships are
a source of value, but they also provide companies with a good understanding of
the customer and his/her needs. A multitude of studies has connected relationships
with the customers’ willingness to provide information (e.g., Lapierre, 2000;
Wuyts and Geyskens, 2005). Even intimate information regarding the customer’s
life can be sensed within a close relationship (Grayson, 2007; Wong et al., 2005).
Hence, information that is more accurate can be gained concerning what customers
value considering their life and experiences. Therefore, we propose that the
relevance of relationships can also be considered as an important part in fitting the
companies’ actions and offers to the customer’s life, stressed by the C-D logic.
In summary, although the relational facet is not new to the literature, the focus
on interaction and integration augments the importance of relationships from
both logics’ points of view. Thus, focusing on relationships can be proposed as a
fruitful way of creating value in a S-D and C-D logic context.
4.4
Holistic marketing processes
The customer’s relationships to other actors, and thus, the notion of customers
being embedded within a highly complex context, is emphasized by the two
discussed logics. Again, one catalyst is the rise of the S-D logic that emphasizes
the customer as resource integrator also using other resources for value creation
(e.g., Vargo and Lusch, 2004). Consistent with this, developments, such as shift to
the empowered and active customers (Edvardsson et al., 2011; Payne et al., 2009;
Vargo and Lusch, 2004), customer-customer and social interactions, have rapidly
gained importance. A similar argument holds true from a C-D logic point of
view, which emphasizes processes in value creation that are not directly related to
4.4
Holistic marketing processes
29
services (Heinonen et al., 2009). Therefore, we briefly review processes that have
been discussed for creating value in the face of the complexity delineated by the
S-D and C-D logic perspectives. In this light, Edvardsson et al. (2011) emphasized
the customers’ context as a source of value for the customer. That is, appearing
more valuable to the social context enhances the individuals’ value perception.
Hence, the relevant question to ask becomes: How can value creation through the
social context be enhanced? We argue that an initial answer to this question can
be found in the already discovered constructs and propose that branding provides
a fruitful way to enhance value creation within a complex social context, as we
will outline below.
Brodie et al. (2009) summarized previous articles about the influence of brandrelated constructs on perceived customer quality and perceived value. Similarly,
other articles have discussed the relationships between CV and branding (Brodie
et al., 2009; Nasution and Mavondo, 2008; Payne et al., 2009; Tynan et al., 2010).
A positive brand image indirectly enhances value-in-use (Strandvik and Rindell,
2010), a development which can be considered as one of the assumptions of the
S-D and C-D logic. Similarly, the social context is emphasized by both logics;
consumers form relationships with brands that reflect their social relationships
(Payne et al., 2009), and thus, reflect the customer’s accumulated experiences and
knowledge. Brands therefore act as comprehensive facilitator of CV. Although
earlier contributions have argued that resources, like brands, may constitute a
source of value (e.g., Dawar and Parker, 1994; Erdem and Swait, 1998; Möller,
2001; Srivastava et al., 2001), the idea of relating branding and company images
to CV is a more recent phenomenon and has become increasingly popular. For
instance, the importance of experiential value is emphasized by both logics (Vargo
and Lusch, 2004; Voima et al., 2010). Cova and Pace (2006) closely relate the
research on communities to branding by mentioning brand communities and
brand tribes. Accordingly, brand communities and tribes may leverage a brand’s
symbolic value, and therefore, provide a source of CV. Consistent with this Cretu
and Brodie (2007) confirm through their research that official and unofficial brand
30
II D OMINANT L OGICS
communities may be a source of CV, an idea recently supported by the literature
(Tynan et al., 2010). Thus, we state that brands and brand communities facilitate
the creation of value regarding the customers’ social context.
However, recently appeared literature advances to a more general level, considering brands that reflect the customer’s social context as a source of value creation.
Because of the network perspective on customer and company side respectively,
many-to-many marketing, rather than a dyadic view with a customer focus, is
proposed (Gummesson, 2008; Gummesson et al., 2010). With many-to-many
marketing, the variety of resources that are integrated for creating value, on the
customer as well as on the company side, are outlined. Accordingly, all of them
need to be aligned in order to foster value creation (Henneberg and Mouzas,
2008). Tying up with many-to-many marketing, the S-D logic acknowledges the
company’s function as resource integrator (e.g., Edvardsson et al., 2011; Vargo
and Lusch, 2004). As a result, the important role of networks on the company
side is underlined (Mele et al., 2010) and the idea of value networks, implying a
multi-directional focus on relationships and interactions, is becoming increasingly
accepted (Edvardsson et al., 2011; Lusch et al., 2010; Ulaga and Eggert, 2006a).
In line with this notion, the final consumer’s role is described as network customer
(Henneberg and Mouzas, 2008). Mirroring this discussion on the customer-side, a
term that gets increasingly popular is network value, referring to all benefits and
sacrifices that network partners create through actions and relationships (Mele et
al., 2010). Accordingly, CV is the desirable outcome of value networks, which
provides a holistic view on CV not only considering the value-added (Srivastava
and Singh, 2010). From a S-D logic perspective, company networks may be
the source for providing an environment that fosters the co-creation of value by
the customer (Sandström et al., 2008). Although the idea of networks in terms
of value creation is not new to the literature, the S-D and C-D logic certainly
raise its importance in terms of creating value. By doing so, an organization that
extensively engages in network considerations with the customer being centered
in the middle of the objectives can enhance the facilitation of CV.
5 A framework for customer value
31
We conclude by stating that several opportunities for enhancing a favorable
customer context exist. On the highest level, a well-defined many-to-many marketing strategy, which constitutes a complex process, is proposed. Each part of
the process needs to be integrated and aligned. A more specific way of positively
influencing the customer’s context is to enhance the formation of customer communities and to engage in successful branding as the context is influenced. We
term the holistic efforts that provide the customer with a value-enhancing context
as holistic marketing processes.
5
A framework for customer value
Following the aim of our paper, which is to register influences of dominant logics
on CV, on the one hand, we addressed the nature of CV. On the other hand, we
mirrored the nature in terms of implications for CV creation. The model shown in
Figure 5 underpins the paper’s idea, which is to draw recommendations regarding
value creation based on the nature of CV. Hence we connect both sides, the
nature of value with the respective source of value creation. As we have already
comprehensively explained both issues, we outline the model on a high level.
By means of the presented model, we strongly emphasize the customers’ and
the company’s intertwined roles. This is done by displaying, for instance, the
focus on the interactions with, and integration of, the customer (Grönroos, 2011;
Payne et al., 2008). Furthermore, the embeddedness of both parties in a broader
context underpins the complexity that is proposed by recent research on value
(Edvardsson et al., 2011; Gummesson, 2008), which is closely connected to the
variety of relationships that exist. Adding to this, we include the customers’
history into the considerations, proposed by the C-D logic (Voima et al., 2010).
The network focus on the company side, with the underlying idea of many-tomany marketing, is regarded as necessary within an interconnected setting to
facilitate value creation. At the bottom of Figure 5, the underlying discussions
32
II D OMINANT L OGICS
about value are shown, which form the basis for explicating CV and relating it to
value creation. The increasing complexity of CV considerations is shown first as
value-in-use, then value-in-context, and finally value-in-life. Value-in-life and the
experience-based facet underpin the importance of in-depth knowledge, which is
displayed by insights resulting from interactions, customer integration and close
relationships. Regarding the value-in-life, practical approaches still need to be
found, as currently no research is dedicated to enhancing this type of value.
Certainly, the model remains abstract and does not display all of the complex
processes within the area of CV and the ways of creating CV, however, we argue
that the most relevant implications of our manuscript, and thus, of both logics, are
condensed to a model.
Figure 5: A Framework for Customer Value from a Dominant Logics
Perspective.
6
Discussion
Until today, no single opinion has been established regarding CV (Gallarza and Gil
Saura, 2006; Graf and Maas, 2008; Smith and Colgate, 2007). Within this vague
6 Discussion
33
context of CV, we recognize the ambiguous influence of both logics that changes
marketing literature. On the one hand, the S-D and C-D logic add complexity
to CV, making it an even more unseizable concept, while on the other hand, the
logics enrich the understanding of the concept as CV is inherently compatible
with the ideas of both logics (Voima et al., 2010; Woodruff and Flint, 2006). As
we believe in the second issue, we reframed the considerations about CV from
the perspective of currently discussed logics. We argue that by doing so, we
provide more clarity for the concept of CV. For instance, our article addresses
the embeddedness of both, the customer and the company, in the light of value
creation. Moreover, the novelty and relatedness of both logics to the value concept
strongly emphasizes the relevance of our paper: There is a clear need to reflect
their impacts on the CV literature.
One might argue about including the C-D logic as only few contributions have
been made so far. However, we see two important reasons for doing so. Firstly,
its thoughts are not completely new to the literature, as the considerations of the
C-D logic build upon well-established concepts (i.e., the inherent customer focus,
Drucker, 1974 cf. Heinonen et al., 2010). Secondly, as previously outlined, we
comply with articles concerning the C-D logic by arguing that its ideas further
refine the S-D logic with a focus on the customer (Heinonen et al., 2010; Voima
et al., 2010) and thus, have the potential to further inform the CV literature. In
this regard, the opinion of researchers arguing in favor of a C-D logic state that
the S-D logic is still deficient in several points (Heinonen et al., 2009); these are
included in our manuscript.
An issue that is still under discussion in the value literature is the question of
who is the value creator at all (e.g., Grönroos, 2008; Gummesson, 2008; Lusch
and Vargo, 2006a). On purpose, we did not enter this discussion because the
objective of our paper was to inform the CV literature with what is well-known
to date and would have exceeded the scope of the presented article. Hence, we
state that who creates value still needs to be reviewed by a multitude of articles.
34
II D OMINANT L OGICS
Consequently, we acknowledge that, in terms of other issues, research still has to
be conducted to enhance the understanding of CV.
Finally, as the nature of CV changes, such as the focus on the customer’s
history, context, and multiple relationships, the scope in which CV is considered
has substantially increased (Heinonen et al., 2010; Voima et al., 2010). Considering this, an important step for the development of CV is pointed out: Not only
companies, but increasingly, customers and their interactions with other actors are
considered relevant for CV (Edvardsson et al., 2011). The literature still needs
to determine whether one can translate these conceptual suggestions in practical
advice, which we believe is accentuated by our practical implications.
7
Theoretical and managerial implications
Our conceptual article entails several theoretical and managerial implications.
The most important implication for scholarly research is the common starting
point that we aimed at providing. Based on our article, the multi-facetted nature
of CV, which has been described by a broad variety of terms (e.g., subjective,
interactive), is condensed to mainly three facets that appear to be very important
from the perspective of the two dominant logics. Consequently, more clarity is
provided which may foster further research and, in turn, makes the concept more
attractive. Moreover, we argue that CV is further enhanced by our manuscript and
the conceptual framework we developed.
Although ongoing discussions (e.g., Grönroos, 2008; Möller, 2006; Voima
et al., 2010) coin the current literature on CV formation, and thus, affect CV
literature as such, we argue that several commonalities can be determined in both
logics’ implications on CV. For instance, both emphasize the value-in-context;
however, the C-D logic accentuates the importance of the customers’ lives, tagged
as value-in-life. Thus, we also contribute to the literature regarding both logics. In
line with arguing for some commonalities, we need to emphasize that implications
7 Theoretical and managerial implications
35
from S-D and C-D logic might lead to different conclusions in some areas. The
primary distinction is the C-D logic’s focus on the customer’s point of view,
while the S-D logic argues from a supplier perspective (e.g., Voima et al., 2010).
Thus, articles regarding CV might come to a different conclusion based on the
perspective applied. Researchers shall thoroughly evaluate whether to argue from
a S-D logic or a C-D logic point of view. For instance, when focusing on the
customer’s past in determining CV, one may be better advised to apply a C-D
logic, rather than an S-D logic approach. Whether the literature will reach a
consensus is still to be determined by future articles, however, we believe that this
article provides a solid basis to start from.
Finally, a primary implication of the presented article is the explicit emphasis
of the customer’s as well as the company’s embeddedness in a complex context.
This notion has the potential to guide researchers in the discussion about value
formation and to what degree the value can be determined by companies (Grönroos,
2008; Vargo and Lusch, 2008b).
Besides the suggested theoretical implications, we propose that the presented
article also has the potential to further inform practitioners. In the following,
we illustrate the practical value by means of two areas. First, we suggest that
the focus on interactions, as mainly emphasized by the S-D logic, highlights the
importance of outstanding performance when interacting with customers. This,
for instance, entails a thoughtful employee selection and training (e.g., Brodie,
2009), as it is mostly the customer-contact personnel that is interacting with
the customers. The aforementioned suggestion is similarly valid for generating
insights about customers and their (often not articulated) needs. In terms of
generating customer insights, implications derived from the S-D logic might differ
from the ones generated by the C-D logic as, for instance, mainly the latter logic
emphasizes the importance of value-in-life, while the former one, to date, does
not get explicit about this point (e.g., Voima et al., 2010). Here, well-trained
personnel, in combination with routines that make the knowledge about customers
explicit within a company (i.e., systems with detailed customer records), enables
36
II D OMINANT L OGICS
the generation of insights, which in turn, can guide companies in supporting CV
creation. Again, we want to stress that this recommendation illustrates that the
S-D and C-D logic can differ in their implications.
The second managerial implication to be stressed concerns value-in-context,
which is supported by both logics. Although companies are challenged by the
resulting complexity, they can use the interconnected world to more effectively
communicate with customers. For instance, Web 2.0 strategies can easily be
adapted, which makes reaching targeted customer groups easier. On the other
hand, the customers’ social environment partly determines value creation (e.g.,
Edvardsson et al., 2011), strongly emphasizing that companies need to engage in
consistent messages via all potential ways for reaching a customer.
8
Future research
Clearly, many articles have discussed the nature and creation of CV within the
last decade. Nevertheless, further research is needed to enhance the understanding
of the concept. For instance, the phenomenological nature of CV still calls
for further research, as discussions around the S-D logic are still ongoing (e.g.,
Mele et al., 2010). For example, the influence of companies is questioned when
value is phenomenological in nature (Heinonen et al., 2009). As these valid
issues arise, it becomes obvious that it still remains unclear what "uniquely and
phenomenologically determined by the customer" (Vargo and Lusch, 2008a, p.
7) means. Thus, creating value from a phenomenological perspective needs to
be investigated more extensively. However, the importance of use-situations
has broadly been overlooked by research on CV. Therefore, we suggest that
considering co-creation opportunities may enrich the current literature of CV
in order to better express the notion of CV. In line with this argument is that
the literature on the creation of CV has not profoundly evaluated the role of
customer integration and interaction, most commonly, only specific issues have
8 Future research
37
been assessed (e.g., Mele et al., 2010). Initial research may evaluate the influence
that providing opportunities for interaction have on the evaluations of CV. From
this starting point, scholars might enhance research on this issue and approach
more detailed research questions.
Similarly, the appearance of value-in-context and value-in-life implies that
a broad variety of unconsidered factors, such as the customer’s life or external
factors, provides the basis for the value assessment (Edvardsson et al., 2011;
Heinonen et al., 2010). It is stated that the customer is embedded within a social
network, which has so far only been proposed conceptually (Edvardsson et al.,
2011; Voima et al., 2010). Thus, we propose to further enhance recently appeared
approaches, such as the one of Edvardsson et al. (2011). Only few research
has been conducted within this area. Edvardsson et al. (2011) also suggest
a combination of research approaches. We underpin this, as this might help
triangulating the first constructs for research the customers’ social embeddedness.
Empirical approaches to the customer’s evaluation of value in the light of a socialcontext can determine its importance and inform other scholars. This is certainly
valid when it comes to value creation by means of the social context. In this
point, there is still a gap within the literature regarding the indirect creation of
CV as, for example, only few empirical studies exist up to now (Edvardsson et
al., 2011). On the company side, one question to be asked might be: How can
companies effectively influence the social context the customer is embedded in?
In our manuscript, we provided some hints to doing so; nevertheless, research
on this topic is still at the beginning. We also recommend researchers to begin
evaluating the fit of concepts used in other fields, such as sociology (Heinonen et
al., 2010).
In a similar manner, the literature has suggested that CV is formed by the
customer’s past experiences (Voima et al., 2010). A multitude of questions
arises regarding this first approach to value-in-life. A first step could be to
explore the relevant constructs, thus, we propose exploratory research. Similarly,
creating value becomes a challenge when parts of the value formation process
38
II D OMINANT L OGICS
are invisible. Clearly, deep customer insights need to be accumulated for doing
so. We propose further integrating the customer into the company’s processes,
and hence, determining his/her desires in case they change. Similarly, how to
deepen the relationships to customers might be a fruitful way for future research.
Nevertheless, no research has appeared regarding this issue due to its novelty.
Thus, we suggest that tackling this issue is a fruitful and informing approach.
Closely aligned to the importance of the accumulated experiences of customers
is the dynamic nature of CV. As researchers have started to acknowledge the
increasingly fast changing needs of customers, the research stream regarding the
dynamic nature of CV calls for further investigation. Hence, the whole area of CV
dynamics constitutes a broad avenue for future research (Blocker and Flint, 2007b).
This is underpinned, as the inherent dynamism of CV is emphasized by both
logics (Heinonen et al., 2010). Scholars may consider longitudinal approaches
for evaluating how changes in what customers value affect a company’s strategic
orientation and competitive advantage (Zhou et al., 2008).
As pointed out, a multitude of network actors need to be considered on the supplier and customer side (Cova and Salle, 2008a, b). This leverages the challenges
for current considerations. In the future, practitioners and researchers alike need
to take this into account, as ignoring this shift would mislead companies in the
decision-making process and would result in imperfect CV constructs. Thus, the
social context enters questions like: Who is the value creator? What are the specific roles within value creation? We also suggest other disciplines than marketing
to tackle this issue and strongly emphasize the value of interdisciplinary research.
For example, integrating the literature on organizational or inter-organizational
capabilities might enhance the CV literature in this point. Therefore, we suggest
closely connecting notions from fields like strategy (DeSarbo et al., 2010; Priem,
2007) and sociology (for instance regarding social network considerations on CV,
e.g., Edvardsson et al., 2011) to the CV literature. This may also result in a clearer
picture of CV and its creation. Thus, we re-emphasize conducting interdisciplinary
8 Future research
39
research to determine valuable approaches for enhancing knowledge regarding
the creation of CV.
In summary, we recommend that future studies place an emphasis on the
external environment and the past customer experiences to assess CV. As empirical
work is still broadly missing, scholars might engage in this regarding the proposed
topics. In addition, inter-disciplinary research from fields such as strategy and
sociology might further advance the CV concept.
40
II D OMINANT L OGICS
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III
III S ERVICE B RANDING
The Influence of the Employer Brand on
Employee Attitudes Relevant for Service
Branding: An Empirical Investigation
Abstract
There is scientific consensus that employees’ attitudes have a fundamental impact
on customers’ experiences. This article focuses on how to provoke favourable
employee attitudes that are relevant for the creation of the service brand. In this
context, the aim is to develop a framework that combines the concept of the
perceived employer brand with employee outcomes that are relevant for service
branding. Data were collected from a employees of a worldwide operating insurance company (N = 2,189) and analyzed using structural equation modelling.
First, the findings underpin the idea of a relationship between the perceived employer brand and service branding. Second, the influence of particular drivers for
employee attitudes is determined. Furthermore, long-term effects are considered
by investigating the influence of the perceived employer brand on potential employees’ identification. In sum, this paper highlights the influence of the perceived
employer brand on employees’ attitudes, which is especially important in service
settings.4
Keywords: Service branding, Employer brand, Service profit chain, Employee
attitudes, Customer satisfaction, Customer service management
4 T. Schlager, M. Bodderas, P. Maas, and J. L. Cachelin. This paper has been presented at the
11th International Research Conference in Service Management and the 30th annual meeting of the
Strategic Management Society". It is published in the "Journal of Services Marketing".
1 Introduction
1
53
Introduction
Recently, marketing literature has shifted from a goods-centred logic to a servicecentred logic (e.g., Vargo and Lusch, 2004), focusing mainly on the role of knowledge as a fundamental source of competitive advantage. In a further step, a broader
perspective rather than a dyadic approach between company and customers is
suggested (Brodie et al., 2009; Vargo and Lusch, 2008).
In the context of branding, literature discusses the importance of customer
experiences created through various forms of interactions, such as customercustomer, employee-customer, and company-customer interactions (Ballantyne
and Aitken, 2007; Brodie et al., 2009; Merz et al., 2009; Payne et al., 2009).
Considering this broad spectrum of interactions, the companies’ control seems to
diminish and it becomes obvious that not all relevant interactions, for example
customer-customer interactions, can be influenced directly by the company. Based
on these considerations, the following question arises: "How can a company still
efficiently influence the creation of customer experiences within the complex
network of interactions"?
Past studies have recognised the importance of employees (Brodie et al.,
2009; de Chernatony and Dall’Olmo Riley, 1997; de Chernatony et al., 2006),
pointing out that customers sense the employees’ behaviour and attitudes during
interactions (Vella et al., 2009). Based on these findings, our study investigates the
relationship between the perceived employer brand (EB) and positive employee
attitudes. In doing so, we focus on employee identification with the company and
employee satisfaction, seeing as research regarding customer-contact employees
(Pugh, 2001) or the service profit chain (Homburg et al., 2009) has proven these
factors to positively affect customers’ attitudes in employee-customer interactions.
Thus, although we do not investigate customer outcomes directly, we conclude by
proposing a relationship between the EB and service branding (SB).
To the best of our knowledge, the concept of the EB has not yet been proven
54
III S ERVICE B RANDING
as relevant for SB. We believe that our idea has two major advantages. On the one
hand, employee-customer interactions can be managed more efficiently than other
interactions that involve the customer such as customer-customer interactions,
which can rarely be directly influenced. On the other hand, the concept of the EB
is especially relevant in knowledge intensive contexts (Berthon et al., 2005). This
knowledge intensity is further emphasised by a service-centred context (Vargo and
Lusch, 2008). Recognising this potential, the central objective of our approach
is to make progress in relating the concept of the EB to SB. For this purpose,
we empirically examine the influence of EB dimensions that positively affect
employee attitudes, which in turn have been conceptually and empirically proven
to have a positive impact on customer experiences.
The rest of the article is structured as follows: First, we review branding within
a service-centred context and outline the importance of employees. Next, we
discuss the concept of the EB and why we propose it as an approach relevant to
SB, thus accounting for our research framework. Third, we present the hypotheses
that link the EB to SB-relevant employee outcomes. Finally, we empirically test
these hypotheses and conclude with a discussion.
2
Conceptual development
For the development of our conceptual model (Figure 6), we review the literature
on branding, SB, the service profit chain and employer attractiveness. Based on
this, we concretise the constructs of our framework and establish the hypotheses.
Hsieh et al. (2004) define a brand as the feelings, impressions, perceptions,
beliefs, and attitudes toward a company. Branding primarily focuses on external
stakeholders (Aurand et al., 2005) such as customers. Positive brand attitudes
affect customer behaviour, e.g., purchasing behaviour, and are thus crucial for a
company’s long-term success (Hoek et al., 2000). Therefore, research on this matter is fundamental for understanding how a customer’s attitudes can be influenced.
2 Conceptual development
55
The meaning of the brand is a consequence of customers’ experiences (Brodie
et al., 2009; Grace and O’Cass, 2004), which evolve between the interactions of
stakeholders (Ballantyne and Aitken, 2007; Merz et al., 2009) such as employees
and customers. Contrary to other approaches in the field of SB that consider
coordinated brand communities or brand tribes for example, our approach focuses
on the EB and consequently on indirectly creating the SB for customers through
employees. We believe that this has two crucial advantages: First, employees constitute an important link in establishing the service brand, as proposed in different
contexts by other scholars (e.g. Brodie et al., 2009) and second, a company can
manage employees more easily than customers due to the fact that they are closer
to the company.
Research in service marketing reflects the importance of employees and
employee-interactions (e.g., Brodie et al., 2009; Burmann and Zeplin, 2005;
Chung and Schneider, 2002; Palmer, 2010; Vella et al., 2009). Particularly in a
service-centred context, employees are able to build brand equity (de Chernatony
and Segal- Horn, 2003; King and Grace, 2005). Consequently, researchers agree
that, in a service-centred approach, customers’ experiences with the company are
primarily gathered through interactions with employees (Iacobucci and Ostrom,
1995). Therefore, one of the company’s tasks is to manage employee attitudes and
behaviours appropriately, because they become visible in the employee-customer
interaction (Pugh, 2001). As our discussion demonstrates, there is an imperative
need to focus on employees when considering branding from a service-centred
perspective. This brief review clarifies that, although research on SB recognises
the importance of employees, there is still a gap concerning the influence of
employees on SB.
The concept of the EB describes the degree of a company’s attractiveness
to current (Berthon et al., 2005) and potential employees (Collins and Stevens,
2002; Slaughter et al., 2004). Hence, a strong EB generates favourable attitudes
in potential employees. Berthon et al. (2005) state that there is a high similarity
between the employer brand and other concepts such as the internal marketing
56
III S ERVICE B RANDING
concept and employer branding. The first definition of the EB was given by
Ambler and Barrow (1996, p. 187), who defined the concept as "the package
of functional, economic and psychological benefits provided by employment
and identified with the employing company". In this approach to the EB, the
primary objective is to "provide a coherent framework for management to simplify
and focus priorities, increase productivity and improve recruitment, retention
and commitment". Thus, the main objective of the EB concept is to positively
influence current and prospective employees in order to attract high-potential
employees (Berthon et al., 2005).
Companies with a strong EB can reduce costs for acquiring employees by
enhancing favourable attitudes among current employees and, in doing so, also
increase employee retention. Similarly, companies can decrease compensation for
equally qualified and skilled employees compared to companies with a weaker EB
(Ritson, 2002). The EB becomes especially relevant in today’s business setting due
to the fact that high-potential and at the same time loyal employees are difficult
to find (Berthon et al., 2005; Chambers et al., 1998; Collins and Stevens, 2002;
Michaels et al., 2001). Companies that embrace EB tactics are well placed to gain
a strong position in the competitive labour market.
While EB studies do encompass theoretical research, they are primarily based
on practical research (Berthon et al., 2005). Areas of research include employer
branding, internal branding, internal advertising (Berthon et al., 2005), management (Gatewood et al., 1993), vocational behaviour (Soutar and Clarke, 1983),
psychology (Collins and Stevens, 2002), and marketing (Ambler and Barrow,
1996; Ambler, 2000; Ewing et al., 2002). Nevertheless, the EB, or employer
attractiveness, has not yet been applied to SB.
In order to set up a framework that comprises both the EB and SB, we build
upon the research of Brodie et al. (2009). They further developed a framework
on SB that was initially set up by Calonius (1986) and refined by other scholars
(Bitner, 1995; Grönroos, 1996, 2006, 2007). Their framework on SB discusses
2 Conceptual development
57
internal, external, and interactive marketing within the service-marketing triangle
of company, employees and customers. In our empirical investigation, we focus
exclusively on employee outcomes, seeing as a direct evaluation of customer outcomes is beyond the scope of our research. Nevertheless, we do discuss customer
outcomes by applying the framework of Brodie et al. (2009) for discussing SB
and the EB (Figure 6).
The framework of Brodie et al. (2009) argues that a company should first
create the service brand through internal marketing between the company and the
employees. A strong EB contributes to triggering favourable employee attitudes
(Berthon et al., 2005). This, in turn, leads to the second process of SB: The interactive marketing perspective. Here, employees are the main driver for customer
experiences and thus, for SB (Brodie et al., 2009; Hatch and Schultz, 2001). Consequently, it can be assumed that the EB fosters the attitudes of customer-contact
employees, which directly influence customer attitudes toward the brand (Mosley,
2007; Papasolomou and Vrontis, 2006). Moreover, the potential employees’ perceptions of the brand are reinforced as they sense the current employees’ positive
attitudes (Gatewood et al., 1993). Finally, SB includes the external marketing
perspective, which includes all processes of direct communication with customers.
Considering the external marketing perspective, we only empirically investigate
the direct positive effect on potential employees, and not the effect on customers.
Nevertheless, potential employees may be an important future link to consistent
SB, a point which emphasises the relevance of investigating this factor. The results of the discussion of the three relationships support that forming a strong EB
contributes to establishing a consistent service brand image. Figure 6 summarises
the proposed framework that links SB and the EB.
58
III S ERVICE B RANDING
Figure 6: The Perceived Employer Brand and Service Branding.
3
Hypotheses development
As the aim of our study is to research employee attitudes that provoke favourable
customer attitudes towards a brand, we must first determine relevant customer
attitudes. The first customer outcome that may predict future behaviour towards a
brand (Mittal and Kamakura, 2001) and provokes positive brand attitudes (Spreng
et al., 1995) is customer satisfaction. Furthermore, it has a significant effect
on attitudes as well as on loyalty toward a brand (Spreng et al., 1996), which
underpins its relationship to the creation of the service brand. Second, the ultimate
goal of the brand-building process is to create a brand relationship (Aaker, 1991;
Keller, 1993), which is reflected by the level of a customer’s identification with the
brand (Kimpakorn and Tocquer, 2010). Furthermore, a customer’s identification
with a company distinguishes the brand from others, which is one of the brand’s
most important aims (Kim et al., 2001).
In literature, these two customer outcomes - customer satisfaction and cus-
3.1
Economic value
59
tomer identification - are connected to employee attitudes. In a services context,
the service profit chain takes the view that customer satisfaction is triggered by
employee satisfaction (Heskett et al., 1997; Homburg et al., 2009) and customer
identification by employee identification (Homburg et al., 2009). These relationships have also been assessed in other studies (e.g., Brown and Lam, 2008;
Morrison, 1996; Punjaisri et al., 2009a, b; Rucci et al., 1998). Thus, in developing
the hypotheses, we focused on employee satisfaction and their identification with
the company.
On a high level, scholars divide the concept of the EB into two parts. On the
one hand, researchers take into account general attitudes towards the company. On
the other hand, perceived job attributes are included when constructing measurements for the EB (Berthon et al., 2005; Collins and Stevens, 2002). In developing
a measurement instrument for the EB, we partly applied the items put forth by
Berthon et al. (2005) that measure the perceived EB for potential employees. We
included three of their five dimensions: Economic value, development value, and
social value, as they seemed best suited to predict favourable employee attitudes
in the study by Berthon et al. (2005). However, we added two other dimensions:
Diversity value and reputation value, which are identified as important for employee attitudes as will be outlined in the following. Furthermore, we slightly
revised the items of the dimensions of Berthon et al. (2005) in order to better
reflect the investigation of current employees.
3.1
Economic value
The first dimension adapted from the conceptualisation of the perceived EB of
Berthon et al. (2005) is economic value. It comprises items such as a good
salary, a fair number of holidays, and reasonable retirement benefits. It is important to understand that the dimension includes both monetary and non-monetary
benefits. Numerous studies have investigated the influences of economic values
on employee attitudes. Benefits might constitute the most obvious factors in a
60
III S ERVICE B RANDING
person’s choice of workplace and have been mentioned as being an important
determinant of employer attractiveness (Weathington, 2008). Ash and Bendapudi (1996) indicate, that organizations provide benefits mainly for attracting
and retaining employees. It is stated that increasing payment (or higher salary in
general) is directly related to job satisfaction (e.g., Beutell and Wittig-Berman,
1999; Malka and Chatman, 2003; Sanchez and Brock, 1996) and identification
with the organization (Lee, 1971). As economic value provides a hard measure
for potential employees and, as a result, can easily be recognised by them as well,
we argue that the positive relationship between economic value and identification
also holds true for potential employees. Thus, we state the following hypotheses:
H1a. Economic value positively influences current employee satisfaction.
H1b. Economic value positively influences current employee identification.
H1c. Economic value positively influences potential employee identification.
3.2
Development value
Second, we adapted items of developmental value. This category refers to variables
such as good training opportunities, an environment, and a good mentoring
culture. Clearly, numerous studies have investigated the development potential
within a company in connection with employee satisfaction (Judge et al., 2000).
Furthermore, perceiving a company as having a high development value is closely
related to career management practices which focus on the development of the
employees’ skills (Greenhaus et al., 2000; Schnake et al., 2007). The perceived
opportunity for development and promotion has been assessed as being positively
related to employee satisfaction (Saari and Judge, 2004; Schnake et al., 2007).
Furthermore, scholars have argued that organizational employee development
programs are crucial in employee satisfaction (Jackson and Vitberg, 1987; Tansky
and Cohen, 2001).
3.3
Social value
61
Similarly, literature outlines the positive influence of development value on
organizational commitment, which describes the strength of an individual’s identification with and involvement in an organization (Lee, 1971; Mowday et al.,
1982). Lee (1971) determined the employees’ perceived rate of current and future
progress, as well as performance-reward relationships, to be strongly correlated
with organizational identification. Moreover, Tansky and Cohen (2001) examined
perceived organizational support, which is closely related to development value,
in connection with organizational commitment and found a close relationship.
Although commitment and identification are not identical constructs, both are
closely related (Bartels et al., 2007). Development value is one of the main reasons
for employees to apply for a certain job. We assume that potential employees
consider this and, as a result, are positively influenced. Thus, we propose the
following hypotheses:
H2a. Development value positively influences current employee satisfaction.
H2b. Development value positively influences current employee identification.
H2c. Development value positively influences potential employee identification.
3.3
Social value
The third dimension, social value, encompasses a strong team spirit, competent coworkers, a friendly relationship amongst individual co-workers, and a respectful
environment. The items that constitute social value have frequently been assessed
by scholars as they appear to be important drivers for positive employee attitudes
(e.g., Saari and Judge, 2004). The role of co-workers and supervisors (competent
or friendly) in enhancing favourable employee attitudes like satisfaction has
been well-established in literature (e.g., Saari and Judge, 2004). Satisfaction
has a positive impact on employee commitment, a construct strongly related to
employee identification with the company (Avery et al., 2007; Harrison et al.,
2006). In investigating the role of employee satisfaction as a moderator between
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III S ERVICE B RANDING
social value and employee identification, there is evidence that there is indeed
a direct relationship between social value and employee identification. Social
value has been investigated in numerous fields such as literature concerning
organizational justice, social exchange, or organizational citizenship behaviour.
The idea common to all three fields is that employees’ behaviour and attitudes can
arise toward different groups within an organization, such as whole departments or
individual co-workers (Lavelle et al., 2007). Organizational justice, for example,
creates commitment and identification with the organization.
In conclusion, enhancing employee identification lies within the responsibility
of different levels of a company. Potential employees may also get an insight
into the degree of social value at the company itself, for example during the
recruiting process (Backhaus and Tikoo, 2004) or by being in contact with current
employees who are in charge of identifying potential employees. As a result, the
outlined argumentation holds true for future employees. Therefore, we propose
the following three hypotheses regarding social value:
H3a. Social value positively influences current employee satisfaction.
H3b. Social value positively influences current employee identification.
H3c. Social value positively influences potential employee identification.
3.4
Diversity value
The fourth dimension, diversity value, refers to interesting job characteristics and
is similar to interest value conceptualised by Berthon et al. (2005). However, we
added several items that have been mentioned in the context of the EB such as
challenging tasks (Backhaus and Tikoo, 2004; Saari and Judge, 2004; Towers
Perrin, 2005) and a broad variety of tasks (Backhaus and Tikoo, 2004). Studies
have shown that interesting job characteristics, and thus diversity value, are
one of the most important determinants of job satisfaction (Judge and Church,
3.5
Reputation value
63
2000; Jurgensen, 1978), and others assessed it as the best predictor of overall job
satisfaction (Saari and Judge, 2004). The amount of interesting work is found
to influence the level of organizational identification (Lee, 1971). It is crucial
to realise that potential employees may easily perceive these characteristics of a
company as functional aspects, challenges, or unique opportunities of employment
during the recruiting process and job descriptions provided by companies. This
leads to the following hypotheses:
H4a. Diversity value positively influences current employee satisfaction.
H4b. Diversity value positively influences current employee identification.
H4c. Diversity value positively influences potential employee identification.
3.5
Reputation value
Finally, we added reputation value to the dimensions of Berthon et al. (2005).
Cable and Turban (2001) describe the employer’s reputation as a job seeker’s
beliefs about how other people evaluate an employer. Thus, we included items
such as well-known products, good reputation of the company among friends, and
good brand to have on one’s résumé. Several researchers have pointed out that
current employees find value in working for a company with a good reputation.
As an outcome of a company’s reputation, scholars have found a higher degree
of identification with the company (Lievens et al., 2007; Smidts et al., 2001;
Wilden et al., 2010). Similarly, a higher level of employee identification with a
company is created by an attractive external image and an attractive organizational
identity (Bhattacharya et al., 1995; Dukerich et al., 2002; Towers Perrin, 2005).
Reputational value is closely related to perceived external prestige, which creates
employee identification with the company (Bartels et al., 2007; Bergami and
Bagozzi, 2000; Carmeli, 2005; Mael and Ashforth, 1992). Literature suggests
that this holds true especially for potential employees (Edwards, 2010; Burmann
et al., 2008). Several empirical studies confirm the importance of a company’s
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III S ERVICE B RANDING
reputation for attracting new high-potential employees (Cable and Graham, 2000;
Preston and O’Bannon, 1997).
To our knowledge, the relationship between a company’s reputation and employee satisfaction has not yet been investigated. However, there has been research
on the relationship between a company’s value and customer satisfaction. Several studies indicate that reputational assets (e.g., company brand and reputation)
positively affect customer satisfaction. This relationship has been proven in a
multitude of contexts (Andreassen and Lindestad, 1998; Helm, 2006; Wiertz et
al., 2004). We take this finding as also being valid for employee outcomes and
hence, we assume a similar relationship in the employee context. As a result, we
propose the following hypotheses:
H5a. Reputation value positively influences current employee satisfaction.
H5b. Reputation value positively influences current employee identification.
H5c. Reputation value positively influences potential employee identification.
In summary, we connected the EB with employee outcomes, which are considered to positively influence the customer outcomes relevant for SB. In the
following, we will empirically investigate the hypotheses on a dimensional level
to point out whether, and how, the EB can support favourable employee responses.
This aims to provide managerial implications at a dimensional level on how a
company can influence employee attitudes by refining dimensions of the EB.
4
Empirical study
An insurance provider was chosen as the object of the analysis, seeing, as the
insurance business is a good example of a service intensive sector. The relationship
between the EB, employee response, and SB is particularly relevant for companies’
differentiation from competition, as most of the customers cannot distinguish
4 Empirical study
65
between products (Schlesinger and Schulenburg, 1993). Insurance policies are
intangible, complex, and trust-based, which represents a great challenge for the
customer-contact employees. Thus, for SB, in particular for insurance providers,
it is crucial to attract the right employees and to develop favourable employee
attitudes.
Data were collected through a self-administered online survey. In 2008, employees of an internationally operating Swiss insurance provider were contacted
and 2,189 employees from 12 countries responded. The questionnaire was distributed in English only as this is the company’s business language. The age
of these employees ranged from 20 to 60 years. Only few were younger than
27 years (12 percent) and 19 percent were between 28 and 35 years old. The
majority of the employees were older: 45 percent were between 36 and 49 years
old and 24 percent were older than 50. Overall, there were slightly more male (55
percent) than female respondents. Participants rated a broad range of items aimed
at determining an employee’s evaluation of the respective dimensions of the EB:
Development value, social value, reputation value, economic value, and diversity
value. Scale items were partly adapted from Berthon et al. (2005).
Respondents were first asked to evaluate the EB dimensions regarding their
current employer ("company today"), and rate them according to importance.
Afterwards, respondents were asked to imagine an ideal employer who they
would choose to work for ("company of choice") and correspondingly evaluate
the EB dimensions based on their expectations. A five-point Likert-type scale
was used in this study, 5 indicating maximum agreement and 1 indicating no
agreement. Employee satisfaction and employee identification with the company
were both measured on a single item scale. Although different approaches were
considered, we chose to apply a global scale, measuring the overall evaluation of
job satisfaction (Bettencourt and Brown, 1997).
66
5
III S ERVICE B RANDING
Research findings
Measurement quality was assessed using confirmatory factor analysis [CFA].
The fit indices suggested that the proposed constructs fit the data well across the
"company today" model, chi-square [c 2 ] is 1,839.57 with degrees of freedom [df]
are 237; the comparative fit index [CFI] is 0.930; and root means square error
of approximation [RMSEA] is 0.056. Similar values pertain to the company of
choice, c 2 is 1,608.56; df are 237; RMSEA is 0.060; CFI is 0.937 (Hu and Bentler,
1995). Comparing both CFA models based on CFI values, it seems that data better
fits the model for the "company of choice", since the CFI and the RMSEA values
are higher.
All loadings except one (i.e., high job security) exceed 0.50 across "company
today" and "company of choice", and every indicator z-value was found to be
significant (see Table 1). Cronbach’s a scores (Bagozzi and Yi, 1988) and average
variance extracted for all measurement scales indicate sufficient reliability. In
terms of convergent validity, the proportions of variance in the items explained by
the other factors are below the 0.50 criterion given by Fornell and Larcker (1981),
except social value and diversity value for "company today", both of which are at
0.524. In addition, the co-variance between the factors is lower than the variance in
the items explained by each of these, satisfying the discriminate validity criterion
(Fornell and Larcker, 1981). However, the variance of development value shared
with the factor social value for "company today" (0.438) is only slightly lower
than the variance in the items explained by the factor development value (0.446).
5 Research findings
67
Measurement scales and respective indicators
Company today
Company of choice
Economic Value (Cronbach’s a / Variance)
a = 0.708 / .426
a = .828 / .425
Good salary
.61
.75 (28.55)
Good retirement benefits
.59 (20.94)
.80 (24.93)
.66 (28.60)
Fair amount of vacation
.52 (19.13)
Good health benefits
.51 (18.52)
.80 (17.76)
High job security
.53 (19.30)
.48 (28.55)
a = .821 / .446
a = .823 / .280
Development Value (Cronbach’s a / Variance)
Good internal training opportunities
.63
Good mentoring culture
.73 (27.92)
.71 (25.05)
Room for creativity
.66 (25.82)
.71 (25.79)
Empowering environment
.71 (27.33)
.74 (26.22)
Good recognition for individual work
.73 (27.80)
.75 (25.03)
a = .841 / .524
a = .860 / .248
Social Value (Cronbach’s a / Variance)
.66 (25.03)
Respectful environment
.69
.63
Friendly relationship amongst individual co-workers
.51(21.87)
.57 (19.66)
.75 (24.55)
Strong team spirit
.63 (26.70)
Competent co-workers
.57 (24.19)
.76 (24.76)
Good managers
.80 (33.08)
.76 (24.75)
People first attitude
Diversity Value (Cronbach’s a / Variance)
.77 (32.08)
.63 (19.66)
a = .851 / .524
a = .870 / .311
Good variety of work activities
.73
.73
Challenging tasks
.82 (35.99)
.88 (34.12)
Interesting tasks
Reputation Value (Cronbach’s a / Variance)
.89 (37.72)
.90 (34.90)
a = .816 / .391
a = .867 / .352
Good quality of products
.68
Well known products
.64 (24.82)
.81 (30.33)
.77 (31.16)
Innovative products
.60 (23.20)
.78 (26.16)
Good reputation of the company amongst friends
.70 (26.29)
.66 (26.98)
Good brand to have on the resume
.70 (26.22)
.67 (30.33)
Fit indices
c2
1839.57
1608.56
df
237
237
RMSEA
.056
.06
TLI
.918
.926
CFI
.930
.937
Notes:
z-Values shown in parentheses.
Table 1: Summary of Confirmatory Factor Analysis and Reliability Estimates of
Measurement Scales.
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III S ERVICE B RANDING
As discussed before, we assume that employees’ evaluation of the value of
working with an employer influences his or her attitude towards the employer. To
empirically verify the supposed relationships, as formulated in the hypotheses,
two models were evaluated that include links between employees’ perceptions
of distinct EB dimensions and employees’ attitudes toward their employer. We
applied structural equation modelling (SEM) using the maximum likelihood
estimation method.
Table 2 shows the final results from the structural models. In the "company
today" model, c 2 is 2,082.905 with df = 276 and c 2 /d f ratio of 7.547, a CFI
of 0.930, and a RMSEA of 0.055 - which indicates that the model fits the data
well. The second structural model was assessed for "company of choice", which
indicates a better model fit concerning the Chi-square and comparative fit index:
c 2 = 1,703.45, df = 330; c 2 /d f = 6.654; CFI = 0.936; and RMSEA = 0.06. Not
all paths were supported in the model on "company today" since the z-values’
associations with the structural path were not statistically significant (p < 0.10) or
the signs were incorrect. Similarly, two hypothesised paths had to be excluded
from the model of "company of choice", as the structural relationships were found
not to be significant.
5.1
Current employees
The empirical results confirm that the perceived EB affects employees’ satisfaction
and identification with the company, as shown in Table 2. Testing the path from
economic value to employees’ job satisfaction at "company today" supports H1a
(b = 0.15, p < 0.01); however, the effect of economic value on identification with
the company, as hypothesised by H1b, cannot be supported, as the structural path
is not significant. Employees’ development value had a significantly positive
effect (b = 0.33, p < 0.01) on employees’ job satisfaction working with the
"company today" (H2a), whereas there was no significantly positive effect on
the identification with the company (H2b). In terms of perceived social value,
5.1
Hyp.
Current employees
Description
69
Exp. sign
b p
Result
Fit indices
Company today
Economic Value –> Satisfaction
+
.15 ***
Yes
c2
2082.905
H2a Development Value –> Satisfaction
+
.33 ***
Yes
df
276
H3a
Social Value –> Satisfaction
+
.20 ***
Yes
RMSEA .055
H4a
Diversity Value –> Satisfaction
+
.14 ***
Yes
TLI
.917
H5a
Reputation Value –> Satisfaction
+
-.03
No
CFI
.930
Economic Value –> Identification
+
.02
No
H2b Development Value –> Identification
+
.07
No
H3b
Social Value –> Identification
+
.19 **
Yes
H4b
Diversity Value –> Identification
+
.03
No
H5b
Reputation Value –> Identification
+
.49 ***
Yes
H1a
H1b
Fit indices
Company of choice
Economic Value –> Identification
+
-.04
No
c2
1703.446
H2c Development Value –> Identification
+
.15
No
df
256
H3c
Social Value –> Identification
+
.42 ***
Yes
RMSEA .060
H4c
Diversity Value –> Identification
+
.16 ***
Yes
TLI
.925
H5c
Reputation Value –> Identification
+
.37 ***
Yes
CFI
.936
H1c
Notes:
* p < 0.10; ** p < 0.05; *** p < 0.01.
Table 2: Results of Hypotheses Testing.
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III S ERVICE B RANDING
Figure 7: Relationships between Employer Attractiveness and Employee
Response.
5.2
Potential employees
71
H3a and H3b can be clearly supported. Social value is found to significantly
impact both employee’s job satisfaction (b = 0.20, p < 0.01) and identification
with "company today" (b = 0.19, p < 0.01). The empirical results support the
hypothesised significantly positive effect of "diversity value" on job satisfaction
H4a (b = 0.14, p < 0.01). On the other hand, the estimated effect on identification
with the company, as hypothesised by H4b, is not significant. Finally, in regard
to the effect of reputation value on employees’ job satisfaction and identification
with "company today", Table 2 indicates that the assumed effect on job satisfaction
cannot be supported. Thus, H5a is rejected. However, the path from reputation
value to identification with company today is significantly positive (b = 0.49, p <
0.01), supporting H5b.
It is obvious that, to support the creation of the service brand among current
employees, a mixture of measures that aim at current employees’ satisfaction as
well as measures that aim at current employees’ identification needs to be considered. For example, social value was proven to support both favourable employee
outcomes. Therefore, it is reasonable for companies to enhance this dimension.
We also suggest that the dimensions that have the strongest influence on identification with the company and satisfaction should be taken into account when creating
the service brand. Concerning satisfaction, the dimension development value is
found to have the highest influence. In order to promote identification with the
company, reputation value in particular needs to be delivered to current employees.
Clearly, it is not an easy task to find the right proportion, as some dimensions
clearly support one outcome but not the other (i.e., development value).
5.2
Potential employees
Identification with the company of choice was tested as a dependent variable in
order to analyse the potential expectations of employees. Employing the structural
relationships in the company of choice model indicates what potential employees
expect as value. However, other than what is proposed in H1c and H2c, what
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III S ERVICE B RANDING
employees expect as development value and economic value is not a positive driver
for employees’ identification with an employer of their choice. The expected
social value has the strongest positive effect on identification (b = 0.42, p < 0.01).
Therefore, H3c is supported. As proposed by H4c, what employees expect as
diversity value influences their identification with an employer (b = 0.16, p <
0.01). Thus, when considering potential employees that are important for the
long-term success of the service brand, the provision of development value and
reputation value in particular needs to be leveraged. Similarly, reputation value is
a strong positive driver for employees’ identification with "company of choice" (b
= 0.37, p < 0.01), as assumed by H5c. As these dimensions have also been proven
to provoke favourable employee outcomes among current employees, a company
should focus on them when creating the service brand through the EB.
6
Limitations/future research/discussion
Studies in service marketing are confronted with the challenge of understanding
complex interactions of different constructs, perspectives, and concepts. To name
but a few, a service brand is linked to research on service quality (Vella et al.,
2009), customer value (Brodie et al., 2009) and customer experience (Palmer,
2010). Due to the wide range of research streams concerning SB, we propose to
focus on the employee’s role. To do so, we explore the relationship between the
perceived EB (Berthon et al., 2005; Davies et al., 2004) and employee outcomes
relevant to SB.
The survey encompasses the employee perspective of companies’ ability to
create value and influence employee satisfaction and identification. The link
between employees’ attitudes, customers’ experiences, and finally the creation
of the service brand are theoretically examined (Brodie et al., 2009; Burmann
and Zeplin, 2005; Palmer, 2010; Vella et al., 2009). However, the impact of EB
on customer outcomes has not been empirically investigated by our study. To
7 Theoretical and managerial implications
73
confirm our approach, future research needs to investigate employees’ attitudes
and customers’ experiences simultaneously. Furthermore, the study was conducted
in one company from one service industry only (the insurance industry). Hence,
further research is called for to investigate the characteristics of different industries.
Valid and reliable comparisons of companies across different industries need to
be investigated in order to determine differences and similarities in value drivers.
This would provide evidence as to whether development value, social value
and reputation value are universal or industry-specific drivers on employees’
favourable attitude towards the employer and thus industry-specific determinants
of SB. Nevertheless, we are confident that our suggested relationships hold true
also in other contexts as the hypotheses are based on literature not investigating a
insurance context.
7
Theoretical and managerial implications
Despite these limitations, our study provides valuable insights to enhance the
understanding of SB. A service brand is created in the triangle between company,
employee, and customer. We state that a strong EB is an efficient instrument for
fostering employee outcomes related to SB. It enhances employee outcomes in
such a way that they, in turn, influence customer experience positively and thereby
indirectly influence SB as follows:
• a strong EB results in employee satisfaction and identification with the
company;
• satisfied and identified employees influence customers’ experiences positively and are therefore conducive to the creation of the service brand;
and
• the long-term creation of a consistent service brand is assured by also
considering potential employees.
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III S ERVICE B RANDING
Next, our investigation related the EB to the service profit chain. By measuring
employee satisfaction and employee identification with a company, both the
conventional and the complementary service profit chain (Homburg et al., 2009)
can be influenced by the EB. Therefore, our paper suggests that EB enhances
the well-researched service profit chain and indirectly pays off by leveraging a
company’s profits (Anderson and Mittal, 2000; Heskett et al., 2003; Heskett et al.,
1994). As a result, companies may gain a competitive advantage by strengthening
the EB, which enables them to manage their employees’ skills and favourable
attitudes. This is strategically relevant to employee-customer interaction (Brodie
et al., 2009; Homburg et al., 2009; Vargo and Lusch, 2004; Vella et al., 2009).
Overall, the empirical results support that a strong EB enables companies to
influence current and prospective employees. The relation between the EB and
favourable employee outcomes is described empirically. Theoretically, we argue
that these employee outcomes, in turn, provoke favourable customer outcomes
that lead to the creation of the service brand. Thus, we propose the EB as a support
for SB activities.
7.1
Retain and align current employees
As shown, the EB supports favourable employee attitudes that enhance the customers’ experiences during interaction (Bernhardt et al., 2000; Bettencourt and
Brown, 1997; Hartline and Ferrell, 1996; Homburg and Stock, 2004; Homburg
et al., 2009; Pugh, 2001) and therefore hold a key role in the service setting.
Customers’ experiences during the service delivery process shape their image of
the company’s brand (Berry et al., 2002; Brodie et al., 2009; Vargo and Lusch,
2004).
Hence, companies need to actively manage the EB because it assists in the
creation of a company’s service brand. In doing this, it is important to deliver
value to employees that enhances the level of employee satisfaction and results
in their identification with the employer, which may in turn positively influence
7.2
Attract potential employees
75
customers’ experiences in the employee-customer interaction. The results support
the idea that delivering value to employees encourages employees’ favourable
attitudes (i.e., satisfaction and identification). Companies should focus on those
value dimensions that are most likely to satisfy and identify employees - namely
development value, social value, and diversity value. In order to actively influence
employee satisfaction, human resource management ought to provide training
opportunities, room for creativity and mentoring while creating an environment
that empowers employees. Moreover, companies should build a social culture
that focuses on friendly relationships among co-workers, adopt a "people-first"
attitude, and provide interesting and challenging tasks for employees.
7.2
Attract potential employees
The research findings support the assumption that a company’s ability to create
and to deliver an attractive image of the employer to the potential labour market increases the likelihood of attracting high-potential applicants (Collins and
Stevens, 2002; Slaughter et al., 2004), which is of great significance to the company’s future. Therefore, it must be imperative for companies to foster those value
dimensions that attract potential employees: Social value, which encompasses the
social image of a company, including a respectful environment; friendly relationships among co-workers; and a "people-first" attitude, which attracts employees
even more than the good reputation of products or company. Hence, companies
that support a strong culture of corporate social responsibility have an advantage
regarding competition for talented and identified employees. Apart from social
value, reputational aspects also (Cable and Graham, 2000; Edwards, 2010) influence potential employees’ identification with a company. This means that a
company needs to invest in a good reputation or well-known products. Hence,
service providers are challenged to build a good reputation in order to attract
identified high-potentials that may create the future service brand.
76
8
III S ERVICE B RANDING
Conclusion
Our objective was to further enhance the understanding of service branding by
providing insight into the complex relationship between the employer brand and
employee attitudes. A company can effectively influence the creation of favourable
employee attitudes that are closely related to customer experiences with a strong
employer brand. Our article investigates the whole concept of the employer
brand by considering current as well as potential employees, who are relevant for
the future creation of the service brand. Nevertheless, we have not investigated
customer outcomes, and consequently, we have only built a theoretical bridge to
service branding, an area lacking in-depth research (Brodie et al., 2009; Merz et
al., 2009).
B IBLIOGRAPHY
77
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IV
Accessing the Biggest Piece of the Pie: International Segmentation with Emerging Markets
Abstract
The increasing attractiveness of emerging markets (EMs), along with the progressing internationalization raises the question whether setting up a unified marketing
strategy for EMs and high income countries (HICs) is appropriate. In this paper,
the requirements of an international segmentation including EMs are elaborated.
The established requirements are numerically tested by applying multilevel finite
mixture modeling on global consumer multichannel (search and purchase) behavior, thereby providing an initial large-scale study that compares consumers from
EMs and HICs. In this course, existing finite mixture models are enhanced to
reflect institutional effects, which are deemed tremendous in EMs, by combining
information on consumer channel usage patterns with consumer and contextual
covariates. Findings broadly support the suggested requirements, contributing to
international segmentation and EMs literature. Practitioners can directly use the
insights of this study for establishing an international marketing strategy, whereas
the key finding proposes that a case-by-case analysis for EMs is necessary and
that solely considering EMs as such derives wrong conclusions about international
segmentation.5
5 T. Schlager and P. Maas. Previous versions of this paper were accepted for presentation and
the American Marketing Association Summer Marketing Educators’ Conference 2012, and the 41st
Annual Conference of the European Marketing Academy 2012. At the latter conference, it has been
nominated for the award "Best paper based on a doctoral dissertation". Further, it has been handed in
at the "Journal of International Marketing" where it has yet advanced to the second review round.
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Keywords: International segmentation, Emerging markets, Contextual embeddedness, Within-country heterogeneity, Finite mixture model
1 Introduction
1
91
Introduction
“The fast rise of emerging economies has driven a shift whereby the
centers of economic growth are distributed across developed
and developing economies - it’s a truly multipolar world”
- Justin Yifu Lin, the World Bank’s chief economist and senior
vice president for development economics (2011)
As this quote illustrates, the proliferation of so-called EMs constitutes a
tremendous opportunity, and thus, "success in EMs is crucial to the future of many
of our companies" (Burgess and Steenkamp, 2006, p. 338). However, entering and
operating in EMs appears to be a mixed blessing for multinational corporations
(MNCs). Stories of success, for instance Nokia’s, show the possibilities given.
In China, the company facilitated relationships to local specialty retailers in the
late 2010s. Hence, Nokia was able to exploit the rise of mobile phone penetration
and to sell to the growth market located in tertiary regions (Chang and Horng,
2010). Contrary to this, companies such as Danone have provided us with excellent examples of how to fail due to EMs-specific circumstances. Although not
having fully grasped the underlying logic of Brazil’s dairy products distribution,
the French company focused on direct distribution despite the largest consumer
segment buying with smaller retailers; Danone’s profit margins eluded (Sehgal et
al., 2010).
In line with both examples, scholars see reasonable evidence to state that
consumers from EMs substantially differ from those in well-investigated HICs
(Burgess and Steenkamp, 2006; Sheth, 2011). Accordingly, taking in the MNCs’
perspective, Sheth (2011, p. 176) explicitly questions, "should a company extend
or adjust its marketing mix to suit the local [emerging] markets"? To answer this,
MNCs must have a thorough understanding of EMs consumers’ preferences and
especially, of the differences to HICs consumers since ignoring their peculiarities
can end up in long-enduring difficulties up to complete failures.
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The scholarly field dedicated to revealing similarities and dissimilarities among
consumers from different countries is international segmentation. Studies confirm
heterogeneity between consumers from different countries (Deshpandé and Farley,
2004; Johnson and Tellis, 2008; Zhou et al., 2002), however, there is an emerging
body of literature that argues for homogeneity among specific consumer groups
across countries. Thus, simply considering a "country-as-segment"-strategy no
longer appears to be acceptable (e.g., De Mooij and de Mooij, 2010; Hassan and
Katsanis, 1994; Lemmens et al., 2007; Stremersch and Tellis, 2004; Ter Hofstede
et al., 2002). This discussion is extended based on EMs since they have been
assigned a special role, for example, in terms of the high level of heterogeneity
among consumers (e.g., Douglas and Craig, 2011) and the rapid changes (Burgess
and Steenkamp, 2006; Sheth, 2011), which raises crucial questions. First, to what
degree do consumers of EMs differ from those of HICs? Second, how should
segmentation approaches be arranged to unmask heterogeneity and therefore, the
truly underlying segments?
Regarding both questions, we acknowledge a huge dearth in literature. Subsequently, calls for further research on EMs and international segmentation from
scholars (e.g., Burgess and Steenkamp, 2006; Cleveland et al., 2011; Cui and
Liu, 2000; Dibb and Simkin, 2009; Papadopoulos and Martín, 2011; Sheth,
2011; Steenkamp and Ter Hofstede, 2002), as well as from editors (Bolton, 2003;
Burgess and Steenkamp, 2011; Dekimpe, 2009; Griffith, 2012; Papadopoulos and
Martín, 2011), are loudening. Research opportunities regarding EMs are even
tagged as "paramount" (Burgess and Steenkamp, 2006, p. 338).
The purpose of this article is to fill these gaps by shedding light on how
EMs’ and HICs’ consumers differ; specifically, an approach to international
segmentation that includes EMs is established. Primarily, we argue for splitting
countries in smaller pieces according to geographical and class-specific patterns,
accompanied by using distinct variables to detect institutional influences. The tone
of this article is neither to "Easternize", which is to solely focus on EMs, nor to
"Westernize", but rather to provide a balanced view on international segmentation.
2 Prior literature on EMs and segmentation
93
Thereby, the presented research makes substantial contributions - theoretically,
methodologically, and practically. First, we add to international segmentation
literature by specifically elaborating how to segment when EMs are included in the
country sample. Next, our methodological objective is to enhance finite mixture
modeling’s usefulness for international segmentation by including contextual
covariates. Third, we add to empirical studies in the domain of EMs as "first, what
we need is comparative empirical research on the actual behavior of customers
[in EMs]" (Sheth, 2011, p. 179). In particular, we unmask similarities and
dissimilarities of consumers across countries regarding multichannel behavior,
which directly informs marketers when formulating an international marketing
strategy or when entering into EMs. Thus, we also provide implications for current
multichannel literature.
For realizing these goals, we structure the article as follows. First of all, we
briefly delineate conceptual and empirical approaches to international segmentation and EMs. Next, we discuss the peculiarities of EMs and elaborate the specific
requirements and foci of an international segmentation that includes EMs. Thirdly,
we extend finite mixture modeling for our purpose and present a numerical example on consumer channel behavior. Finally, we reflect on our study by discussing
findings, theoretical and managerial implications, as well as avenues for future
research.
2
2.1
Prior literature on EMs and segmentation
Conceptual approaches
In their seminal article on EMs, Burgess and Steenkamp (2006) exhaustively
discuss the peculiarities of EMs. Further, they give advice to researchers regarding
the challenges of conducting research in EMs. Adding to this, Sheth (2011)
highlights five points that differentiate EMs from HICs (market heterogeneity,
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sociopolitical governance, unbranded competition, inadequate infrastructure, and
chronic shortages of resources), thereby questioning the ideas of marketing as
researched and practiced in HICs at their pillars regarding its usefulness in EMs.
Although both articles underline the specific characteristics of EMs, they rather
discuss general issues instead of focusing on segmentation. Yet, no research has
investigated the requirements of market segmentation, be it solely for EMs, or
for an international segmentation that includes EMs among other countries. In
this international context, segmentation typically refers to grouping consumers
across countries (Bijmolt et al., 2004; Hassan and Katsanis, 1994; Steenkamp
and Ter Hofstede, 2002; Ter Hofstede et al., 1999). An initial approach to
organizing international segmentation comes from Wind and Douglas (1972).
They developed a hierarchical framework that first determines country segments
and then suggests finding consumer segments within those, thus omitting the
existence of cross-country segments. Additionally, although EMs are used to
exemplify differences in countries, no specific conclusions are derived regarding
the requirements of including them in the sample. Since that article, a multitude
of other articles has approached international segmentation, the most advanced
presented by Steenkamp and Ter Hofstede (2002). They summarized seven
areas needing special attention in international marketing, namely the level of
aggregation, the segmentation basis, construct equivalence, measure equivalence,
sampling equivalence, the segmentation method and the sample size. Though
referring to a "truly international segmentation" (p. 187), EMs are not devoted
special attention. For example, institutional influences that are described as highly
relevant in EMs (Burgess and Steenkamp, 2006) are not discussed.
2.2
Empirical studies
Similarly, empirical studies that have taken the challenge of segmenting on an
international level add to the knowledge regarding their implementation. Among
those, some have focused on grouping consumers or countries separately, while
others have highlighted forming consumer and country segments simultaneously.
3 Why should EMs be different for segmentation?
95
For instance, Peterson and Malhotra (2000) cluster countries by measuring consumers’ quality of life perceptions. They indicate their usefulness for international
segmentation and provide the largest segmentation study with 165 countries worldwide so far. An important implication is that rather basic constructs tend to be
applicable for segmenting HICs and EMs. In line with this, Agarwal, Malhotra,
and Bolton (2010) found cross-culture-segments in their comparison of consumers’
service quality perceptions in India and the US regarding their perceptions of
service quality. These insights confirm the usefulness of culture as segmentation
variable for EMs. Gaston-Breton and Martín (2011) provide an auspicious approach that first segments countries based on economic indicators, such as the
market size and potential. In a second step, consumers are clustered based on
Inglehart’s values, accentuating the notion of basic values to be useful in international segmentation. Cleveland, Papadopoulos, and Laroche (2011) find ethnic
identity and cosmopolitanism to be stable across cultures and countries. Moreover,
they provide one of the few studies that include EMs (India and Mexico), although
EM-specific implications are not drawn. All of the delineated studies include
EMs, nevertheless, none has directed the focus of attention on the requirements of
an approach that includes EMs.
3
3.1
Why should EMs be different for segmentation?
Contextual embeddedness
Although a general tendency toward the convergence of consumption patterns
across global markets can be acknowledged (Townsend et al., 2009), scholars
emphasize characteristics distinct to EMs (Burgess and Steenkamp, 2006; Sheth,
2011). As Burgess and Steenkamp (2006, p. 345) outline, "the heterogeneity of
EM populations increases the importance of considering possibly hidden institutional context effects". Traditionally, definitions of EMs refer to a country’s
purchasing power parity (PPP) or gross domestic product (GDP) (Burgess and
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Steenkamp, 2006). Undoubtedly, this attaches homogeneity to EMs and heterogeneity between EMs and HICs as economic factors have substantial influence on
consumer behavior (Clark, 1990; Jain, 1989; Johnson and Tellis, 2008). Equally, it
is noteworthy that EMs are typically coined by high growth rates. China yielded a
double-digit GDP growth rate over the past decade, which introduces a constantly
and rapidly changing environment resulting in similarly fast adapting consumer
preferences. Besides these, literature underscores the influence of culture on
consumers’ attitudes and behaviors (e.g., Johnson and Tellis, 2008). Contrary to
the prevailing egalitarism and autonomy in HICs, EMs are rather characterized
by hierarchy and embeddedness (Schwartz, 2004). For instance, Steenkamp and
Geyskens (2006) investigated the determinants of the perceived value that consumers derive from using a company’s website. They found that individualistic
cultures attach more importance to pleasure, privacy protection, and customization
than collectivistic cultures. Consistently, Bolton et al. (2010) found differences
between consumers from China and the US regarding the role of a referent on the
determination of price fairness, vis-à-vis positioning collectivistic and individualistic cultures. In a similar way, the regulative context of EMs is less consistent
than in HICs (Burgess and Steenkamp, 2006). For instance, online communities, such as Facebook and YouTube are only limited in access in China due to
the country’s strict regulations (Brown et al., 2007). Additionally, competition
in EMs is less fierce than in HICs since they are typically dominated by large
government-operated companies or nongovernmental organizations. Below these,
the competitive landscape is embossed by submonopolies, limiting consumer
choice (Sheth, 2011). In sum, EMs’ consumers are embedded in a very specific,
highly dynamic context that considerably influences consumers’ preferences and
behavior.
3.2
Within-country heterogeneity
The fragmented landscape, in which EMs’ consumers are embedded, constitutes
a considerable challenge for any segmentation as it results in high heterogene-
3.2
Within-country heterogeneity
97
ity; in general, the context differs across regions (Sheth, 2011). The prevailing
dissimilarity among consumers poses a severe threat to a central requirement of
segmentation, namely "segment homogeneity" (Wind, 1978). On consumer side,
geographical mobility is decisive in determining homogeneous segments. In an
early approach, Andreasen (1966) claims that "other consumptions patterns [. . . ]
are appropriate" in different geographical regions (p. 346). As economic and
diffusion literature states, mobility is needed for developing similar behaviors and
consumption habits, whereas dissimilarities arise as a result of immobility (e.g.,
Agarwal, 2003). Diverging consumer preferences and behaviors are more likely
to develop across the regions of EMs since the lower class, also referred to as
"bottom-of-the-pyramid", is limited in terms of their mobility (Cui and Liu, 2001)
and their possibilities for exchange. For instance, the lower class has little access
to transportation, electricity, or internet (Sheth, 2011). While they are forced to
buy in local stores, members of the upper class may be free to make a choice.
Missing exchange among consumers and immobility in particular unfold their
influence in large EMs, such as China and India (e.g., Enderwick, 2009), due
to their sheer size. Taking both as examples, more than 1 billion people live in
each country, thereby outstripping Europe with only about 700 million people
by far and paired with the limited ability to change location, geography-specific
consumption patterns tend to develop. Further, there is a large gap between the
upper class, which is assumed to converge to western consumer behavior, while
the lower class is particularly limited in their behavior, which underscores the
within-country heterogeneity (Alden et al., 2006; Burgess and Steenkamp, 2006).
Finally, an issue closely related to geography is language. For instance, China
has seven official languages; however, more than 80 languages are used on a
general basis. The number of different accents in conversational Chinese is even
higher, which introduces a further source of heterogeneity (e.g., Craig and Douglas, 2006). Accordingly, EMs deviate from HICs in their level of within-country
heterogeneity.
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4.1
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The requirements of EMs segmentation
Micro-level analysis
In their article on international segmentation, Steenkamp and Ter Hofstede (2002)
differentiate between three levels of geographic aggregation: No aggregation,
country-level aggregation, and regional aggregation. Especially pursuing a "onecountry-as-segment"-strategy receives only little support nowadays (e.g., Ter
Hofstede et al., 2002). Following the notion of within-country heterogeneity, a
finer-grained segmentation for EMs appears even more relevant. An good example for the high within-country dispersion is Hong Kong, which has historically
been affiliated to the British Empire and is now related to China. Reasonably,
Hong Kong differs from China in terms of its development, which is reflected in
diverging preferences and behaviors. While registering all states into the same
higher-level segment would violate the assumption of within-country heterogeneity, for example, the question may arise whether upper class consumers from
poorer regions are misclassified when solely considering regions. Hence, besides
a more precise geographic segmentation, researchers may also come to using a
social-class-based segmentation. Considering the Western lifestyle of the upper
class in EMs (Burgess and Steenkamp, 2006), global segment membership might
be similarly determined based on the class a consumer belongs to. Commonly
attached to class-membership is the living area, that is, members of the lower class
tend to live within rural areas whereas the upper class rather inhabits metropolitan
areas (Craig and Douglas, 2006). In line with the various sources of within-country
heterogeneity and the notion that specific consumer groups of EMs rather resemble
HICs’ consumers, disregarding the diversity of EMs in segmentation would fail to
identify consumer groups with similar patterns. We suggest that microanalysis
is necessary in EMs to reflect the high level of within-country heterogeneity, for
instance, based on geographical aspects or the consumers’ class-membership.
4.2
Selection of relevant respondents
4.2
Selection of relevant respondents
99
A main concern in any segmentation is the determination of the unit of analysis (Wind, 1978), which constitutes a main issue in EMs research (Burgess and
Steenkamp, 2006). Particularly, the selection of the appropriate consumers is a twopronged challenge. First, as emphasized, EMs tend to develop rapidly (Burgess
and Steenkamp, 2006; Sheth, 2011), leading to similarly quickly changing consumer preferences; consumers in EMs become "moving targets". Consequently,
consumer segments in EMs change quickly, endangering "segment stability", a
strongly advocated prerequisite of segmentations (e.g., Wedel and Kamakura,
1999; Wind, 1978). Mitigating this problem is difficult; at least partly a trade-off
between more accurately segmenting current segments and determining future
segments is necessary. On the one hand, when selecting a representative sample,
good approximations about the current segments can be made; however, estimates
about the future segment distributions might not be feasible. On the other hand,
when selecting consumers that represent a future-oriented group, such as early
adopters, inferences about future consumer segment distribution can be drawn;
nevertheless short-term decisions may be imprecise. Hence, depending on the
segmentation’s objective, prudential estimates about the growth of the segments
may be necessary.
Second, while segmentation studies in HICs typically rely on individual
respondents, the collectivistic nature in EMs and the high cultural embeddedness
may require taking into account different decision makers (Brown et al., 2005;
Burgess and Steenkamp, 2006) since the value of individual choice theory for
collectivistic cultures is questioned (Steenkamp, 2001). On a consumer level, Lee
and Green (1991) determined the role of relevant others in the purchasing process.
In comparing Korea and the US, they found that in the EM, the opinion of relevant
others has a substantial impact on the decision, whereas in the individualistic
country, a person’s own attitude is decisive. It is worthwhile to note, however,
that not each EM is rather collectivistic. Family members or peer-group leaders
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IV E MERGING M ARKETS
exhibit a substantial influence (Talukdar et al., 2002). Hence, when consumers
interact with others in decision-making, solely relying on individual opinions
may be biased, whereas, more meaningful segmentations can be derived when
identifying or controlling for relevant others.
4.3
Segmentation variables and covariates
Similar to the micro-level analysis, which opens avenues for finding global consumer segments, variables and covariates for an international segmentation should
be thoroughly chosen in order to detect patterns underlying the segments. In
particular, we advocate construct equivalence, the focus on stable variables, and
the inclusion of contextual covariates.
Construct Equivalence
Myers et al. (2000) as well as Steenkamp and Ter Hofstede (2002) emphasize
the importance of construct equivalence for international studies, which refers
to whether the variables used for segmentation differ in their meanings across
countries (Steenkamp and Ter Hofstede, 2002). The rationale behind a focus on
construct equivalence in international segmentation is that "segmentation criteria
for one market may not work in another" (Cunningham and Crissy, 1972, p. 100).
While this is crucial for international studies, an even higher focus needs to be laid
on construct equivalence when EMs are included in the sample since there is reasonable evidence that EMs significantly differ in their decision-making processes
(e.g., Burgess and Steenkamp, 2006). To date, only few constructs have been
confirmed to be useful for cross-border research (Deshpandé, 1999). In reflecting
this idea, Sheth (2011) points out that prior to deriving constructs, scholars first
need to investigate actual consumer behavior, which is then suggested to be a solid
segmentation basis. Sociodemographic variables, income and the social class
(Burgess and Steenkamp, 2006; Cunningham and Crissy, 1972; Kamakura and
Wedel, 1995; Sheth, 2011) cannot only be used for defining micro-level analysis,
but may also serve as covariates, depending on the segmentation’s objectives. In
4.3
Segmentation variables and covariates
101
Brazil, for instance, Cunningham et al. (1974) found distinct shopping behaviors,
such as the product selection, the relevant services, the information sources, and
the transportation system used for arriving at the store, depending on the class a
consumer belongs to. Consistently, Steenkamp and De Jong (2010) recognized
that demographic variables are important indicators of Western behavior in EMs.
However, as shown by recent research (Strizhakova et al., 2012; Strizhakova et
al., 2011), patterns might need to be adapted. In studies on Russia and Brazil,
Strizhakova et al. (2012) found four different segments within the young adult
cohort; two of them, the "glocally-engaged" and the "globally-engaged", are
particularly prone to global firms and brands. Thinking in cohorts rather than in
age might therefore be more germane in EMs concerning the affinity to MNCs’
products. Reviewing previous segmentation studies, several further types of variables have been developed for cross-border segmentation, among them attitudes
and perceptions (e.g., Agarwal et al., 2010; Lemmens et al., 2007; Peterson and
Malhotra, 2000; Ter Hofstede et al., 1999; Yavas et al., 1992), and values (e.g.,
Gaston-Breton and Martín, 2011), which have thus been confirmed regarding
construct equivalence.
Focus on Stable Variables
The rapidly changing context in EMs is not solely relevant in the selection of
respondents; a considerable challenge is presented by finding a stable segmentation basis, or by controlling for dynamics. To a certain degree a trade-off between
considering rapid changes and selecting solid segmentation variables is inevitable
when including EMs. Yet, research has identified variables that are fairly stable
and thus, can be drawn on for an international segmentation that includes EMs.
An individual’s value system not only shows high construct equivalence, it also
tends to remain rather stable over time (Bilsky and Schwartz, 1994) as it reflects
an individual’s fundamental beliefs. Moreover, values contain considerable explanatory power for consumption and purchase behavior (Burgess, 1992). The
most prominent system to measure values stems from Schwartz (1992), which
has been extensively applied and tested by international research and for EMs
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IV E MERGING M ARKETS
(Steenkamp and Burgess, 2002). Closely related are psychographic measures,
like the personality traits, which also have been confirmed to be useful for EMs
research by conceptual (Burgess and Steenkamp, 2006) and empirical articles
(e.g., Gaston-Breton and Martín, 2011; McCrae and Terracciano, 2005). Both
can be applied, however, in general, we advocate for awareness of the variables’
stability when deriving a segmentation that includes EMs.
Inclusion of Contextual Variables
Referring to the specific context in which EMs’ consumers are embedded, it
becomes obvious that researchers need to take circumstances that potentially limit
or alter consumers’ behavior or preferences into account. In line with Burgess and
Steenkamp (2006) who propose the systematic inclusion of institutional factors in
EMs research from a contingency-theoretic point of view, we suggest contextual
covariates to be a central requirement for EMs segmentations. With respect to the
classifications of EMs the main criterion of differentiation refers to a country’s
economic state; however, within large EMs there is a need for a finer-grained
distinction of contextual variables as regions may substantially differ. Again
taking the example of China, the Eastern part with the metropolitan areas averages
a PPP per capita of nearly US Dollar 15,000 while the Southern and the Central
part average a PPP below US dollar 7,000 (National Bureau of Statistics of China,
2011). When living in an area with a considerably low PPP, supply of specific
goods may be limited. Typically, the technological context, for instance, the
penetration of new media, strongly correlates with a country’s economic state
(Hsieh et al., 2004). Thus, new media in EMs is not that widespread today as
compared to HICs; on a more general level, Sheth (2011, p. 169) described
these factors as the "inadequate infrastructure [of EMs]", which considerably
restricts consumers in their behavior. Closely related is the level of urbanization
(Inman., 2004); usually, the more rural the area in EMs is, the less access to
internet is provided. For instance, even upper class consumers in rural areas of
China might lack a broadband Internet connection, which in turn may lead to a
missing information source and a considerably limited choice of products. Finally,
4.4
Holistic approach
103
a characteristic that coins the context and has been identified as influential by a
variety of empirical and conceptual papers is culture (e.g., Douglas and Craig,
1997, 2006; Hsieh et al., 2004). Several frameworks for classifying countries
according to culture are suggested (Hofstede, 1983, 1991; Schwartz, 1992), which
can be drawn on for EMs. To wrap up, the context needs to be reflected when
determining international segments.
4.4
Holistic approach
Segmentation studies have the inherent aim to aggregate consumers and to simplify
the reality in order to find feasible managerial approaches. However, in line
with Sheth (2011) who questions the possibility of segmentation approaches,
we remind researchers not to oversimplify the real situation as the approaches
for EMs may not arrive at a similar simple level as in HICs. Referring to the
fragmented supply side, the highly heterogeneous consumer market, and the
contextual embeddedness, an abstraction of the reality may fail to provide a
reliable picture of a market’s landscape. Consistently, Burgess and Steenkamp
(2006) name the presence of confounding institutional effects in EMs, which may
result in wrong inferences about the segment membership. Complex interactions
could disguise the real reason for consumer behavior; therefore, unmasking these
relationships is particularly relevant for the success of a segmentation that includes
EMs. On an individual level, Burgess and Steenkamp (2006) exemplify this by
means of shopper profiles: Data collected by supermarket scanners tends to be
highly heterogeneous in EMs, even from the same location. In case the consumers’
profiles are connected to systemic differences, one may accumulate shopper and
purchase data to control for patterns that influence the purchase. On a contextual
level, the slow technology adoption in EMs serves as example. Whereas one may
attribute the slow technology adoption to the cultural or technological context,
the reason might be of regulative nature (Calantone et al., 2006). Attaching
the development to cultural aspects only, would fail to reveal the underlying
mechanism. Accordingly, MNCs need to determine the sometimes-unobvious
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IV E MERGING M ARKETS
source of heterogeneity when segmenting consumers. In this regard, we stress
that it is not necessarily sufficient to focus on few variables only. A combination
of micro-data with contextual variables might be necessary, when segmenting in
EMs. Additionally, due to the outlined interactions among variables that influence
the segmentation basis, considering an interplay between variables could be taken
into account. Depending on the segmentation’s aim, and the magnitude of the
confounding influence, researchers might adapt segmentation in a way that they
are not biased as illustrated by the example of the shopper profiles. Eventually,
compromises between providing a realistic segmentation and a simplification may
be necessary.
4.5
Data considerations
In terms of methodological considerations, our article predominantly marks out
potential issues with primary and secondary data in EMs; however, we do not
join discussions concerning what analysis approach best fits an international
segmentation that includes EMs.
Deriving Primary Data
An important issue when collecting data in EMs is to check for measurement equivalence, which refers to whether the operationalized measures of the
constructs are comparable across groups or across countries (Myers et al., 2000;
Steenkamp and Baumgartner, 1998). In fact, this issue is particularly relevant
as the tremendous differences between EMs and HICs result in a serious threat
to measurement equivalence, which potentially hampers any international segmentation. In attempting measurement equivalence, primarily Steenkamp and
colleagues have advanced our knowledge. Steenkamp and Baumgartner (1998) describe different levels of measure invariance in cross-national consumer research,
and a framework for treating this issue. Further, Steenkamp and Ter Hofstede
(2002) partition measurement equivalence into three parts: Calibration, score, and
translation equivalence issues.
4.5
Data considerations
105
Calibration equivalence refers to the comparability or equivalence of units.
Among other constructs, these may be monetary units or measures of weight
(Steenkamp and Ter Hofstede, 2002). As they emphasize, aforementioned can
easily be corrected for missing calibration equivalence, nevertheless, sociodemographic variables can be more challenging. For instance, EMs and HICs differ
in their definition of class membership; given the diverging PPP in HICs and
EMs, belonging to the upper class is marked by different income levels. Hence,
researchers may need to adapt the specification of classes to a broader level to
make inferences about the respective class membership.
Score equivalence determines the equivalence of the observed scores on equal
measures (Myers et al., 2000). Because of missing score equivalence, the observed scores across countries lack of comparability, which directly relates to
invalid results in international segmentation (Steenkamp and Ter Hofstede, 2002).
Missing score equivalence may stem from cultural or cross-national differences,
or response styles with regard to the selected measures (De Jong et al., 2008;
Steenkamp and Baumgartner, 1998); as such, including EMs increases the likelihood of biased data. Especially constructs that are likely to be affected by a
specific response style (i.e., extreme response style) need attention. Although possibilities to detect, control and correct response styles and missing measurement
equivalence exist (e.g., Brown et al., 2005), we recommend mitigating problems
upfront. A pretest in the specific EMs might reveal potential deficits in the conceptual approach of the segmentation. When particularly little is known, focus
groups or in-depth interviews with EMs’ consumers may indicate systemic threats
to score equivalence that might have remained unrevealed otherwise.
Additionally, translation issues need to be resolved. As outlined, several
languages are used on a common basis in EMs, especially in large EMs such
as China or India. Mainly, due to the size of EMs, different meanings may be
attached to similar words within the same EM. Discussions with insiders of the
specific EM or with natives may be necessary. In a similar vein, translation
equivalence as termed by Steenkamp and Ter Hofstede (2002) is certainly an issue
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IV E MERGING M ARKETS
when using primary data from EMs. To enhance translation equivalence, common
approaches are presented by backtranslation or parallel translation (Douglas and
Craig, 2006).
However, as for research in EMs in general (Burgess and Steenkamp, 2006),
respecting those three issues above might not mitigate deriving invalid data.
The commonly lower level of education, expressed by lower literacy rates and
numerical skills, underlines the importance of easily-comprehensive questions
whereas longer scales should only be used if inevitable (Lonner and Berry, 1986).
When especially little is known about the segmentation basis in the respective EM,
qualitative techniques and pretests can serve for testing the measures (Burgess
and Steenkamp, 2006). Thus, when conducting segmentation studies in EMs, one
needs to thoroughly consider the research settings and the operationalization of
questions to derive meaningful data in terms of measurement equivalence.
Obtaining Meaningful Secondary Data
The contextual embeddedness entails the imperative to focus on variables that
describe the institutional context. Though variables like the living area might
as well be derived by directly asking respondents, contextual data, such as the
internet penetration, needs to be obtained from secondary data sources. First, data
should stem from a reliable source, which is, contrary to HICs, rather difficult
for EMs (Burgess and Steenkamp, 2006). Although several international market
research agencies have opened offices in multiple EMs, some EMs have yet
received little attention; finding data from a single source may not be possible.
Taking the PPP of China as example, varying numbers are provided by distinct
institutions. Thus, data and its sources need to be thoroughly inspected as numbers
may potentially even be estimated. Specifically, Burgess and Steenkamp (2006)
advise researchers to consult technical reports of the data sources to mitigate
using unreliable sources. However, standardized data for EMs suffers from being
solely available on a country-level, whereas local information sources, such as
Chinastatistics or Indiastats.com, provides more accurate data on a regional basis
4.5
Data considerations
107
which is increasingly relevant, as within-country heterogeneity entails the need
for micro-level analysis. Using countries as proxies for influences, such as culture,
needs to be critically reflected; the advantages of this procedure (to reflect the
contextual embeddedness), need to be set aside its disadvantages (Schaffer and
Riordan, 2003). Still, when micro-level data is available from reliable sources,
there is a need for bringing the data on a comparable level for the segmentation
requiring to check for calibration equivalence, and partly to adapt data. The
Internet penetration in various countries exemplifies this. While HICs tend to
measure Internet penetration based on the number of subscribers, data for EMs
may be provided based on actual users (i.e., Instituto Brasiliero de Geografia e
Estatistica, 2010). Finally, secondary data needs to be checked for the relevance
concerning the targeted population (Burgess and Steenkamp, 2006). Table 3
summarizes the established requirements of an international market segmentation
that includes EMs.
Traditional characteristics
Novel requirements
108
Research phase
Conceptual level
Level of analysis
Macro-level analysis
–>
Micro-level analysis
a) Regional / statewise analysis
b) Area-level analysis (rural vs. metropolitan)
c) Class-level analysis (upper class vs. bottom-of-the-pyramid)
Selection of relevant respondents
Individuals
–>
Relevant respondents (to unmask decision-making processes)
a) Cautious reliance on individuals
b) Consideration of peer-group leaders (word-of-mouth)
c) Focus on group-effects (consumer interactions)
Segmentation variables and covariates
Construct equivalence
–>
Careful variable and covariate selection
a) Emphasis on construct equivalence (lack of variables / covariates)
b) Evaluation of variables’ / covariates’ temporal stability
c) Inclusion of contextual covariates
Extent of the approach
Focused approach
–>
Holistic approach
a) Adoption of individual-level covariates
b) Adoption of contextual covariates
c) Consideration of interactions among variables / covariates
Methodological level
Primary data
Measurement equivalence
–>
Valid and reliable primary data
a) Emphasis on measurement equivalence
• Calibration equivalence
• Score equivalence
b) Usage of simplified research settings / constructs
Context level data
Standardized global data
–>
Comparable Secondary Micro-level Data
a) Inclusion of comparable micro-level data
b) Evaluation of reliability of micro-level data
c) Data adjustment (calibration equivalence)
Table 3: The Changing Requirements of an International Segmentation that Includes EMs.
IV E MERGING M ARKETS
• Translation equivalence
5 Model formulation and segmentation basis
5
109
Model formulation and segmentation basis
For numerically illustrating our approach, we apply multilevel finite mixture
modeling, which is popular for international segmentation (e.g., Bijmolt et al.,
2004; Ter Hofstede et al., 1999; Wedel et al., 1998). The methodology assumes
that each consumer belongs to a specific segment, while uncertainty about class
membership is implied (Vermunt and Magidson, 2002).
5.1
Consumer channel behavior as the segmentation basis
In our case consumers’ decisions about what combination of channels to employ
for searching and purchasing financial services provides the basis for segmentation (Neslin et al., 2006; Verhoef et al., 2007). We applied this setting for three
reasons: First, using constructs that have not yet been confirmed for EMs research is avoided, whereas behavioral patterns constitute a rather reliable measure.
Second, we suggest that consumer multichannel behavior provides an excellent
example for grasping contextual influences. For instance, EMs are characterized
by collectivistic culture patterns which are expected to more heavily rely on peers
for information search. Third, Sheth (2011) explicitly emphasizes, "first, what
we need is comparative empirical research on the actual behavior of customers"
(p. 179). Besides these advantages, it is worth noting that consumer channel
behavior is a highly relevant consumer behavioral pattern (Konus et al., 2008).
Especially as consumers can adopt multiple channels, companies strive for a deep
understanding about how to organize channels (e.g., Neslin and Shankar, 2009)
and decrease complexity by means of segmentation.
5.2
Basic model
The objective is to determine segments according to the channel selection, presented by binary data. Nominal latent class analysis is used for determining
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IV E MERGING M ARKETS
segment membership with respect to the binary data structure at hands. For deriving the basic model, we borrowed from Bijmolt et al. (2004). In accordance,
let
i = 1, . . . , I denote the international sample of consumers,
j = 1, . . . , J denote the higher-level groups (e.g., geographical regions
or classes), and
k = 1, . . . , K denote whether a specific channel is used for searching, and
whether it was used for buying.
Our data regarding searching is therefore structured as J x I x K table. In case
a consumer i from a higher-level group j decides to use a channel k for searching
or buying, we denote the vector Yi jk = 1, otherwise Yi jk = 0. As consumers could
choose up to three channels, vector weights were adjusted to 0 for inexistent
combinations. The buying channel is added as categorical variable. As we assume
a limited number of consumer segments in one country, and a limited number of
higher-level segments, let
s = 1, . . . , S denote consumer segments, and
t = 1, . . . , T denote country segments.
The consumer segment membership represents the latent trait, Xi j for each consumer of a higher-level group using a specific combination of channels. Equally,
Z j describes the higher-level segment membership. Equation (1) denotes the
conditional probability of using a specific combination of channels for searching
and buying Yi j for a consumer, depending on the membership of higher-level
group to a higher-level segment (Vermunt, 2001). N j represents the sample size of
a higher-level group. The respective equation of the multilevel mixture model is
specified as follows:
P(Y j ) =
T
Â
t=1
"
Ni
P(Z j = t) ’
"
S
K
 P(Xi j = s|Z j = t) ⇥ ’ P(Yi jk |Xi j = s)
i=1 s=1
k=1
##
(1)
5.2
Basic model
111
Denoted above is firstly, the probability of a higher-level group belonging to a
specific higher-level segment, secondly, the probability of a consumer belonging
to a specific consumer segment, given a specific higher-level segment membership,
and the probability of a consumer using a specific combination of channels. As
Bijmolt et al. (2004) describe, the probability of using a specific combination of
channels is therefore a weighted probability (with the weights represent the probability of a consumer to belong to a specific consumer segment and higher-level
group belonging to a higher-level segment respectively). We specify consumer
covariates as concomitant variables (Gupta and Chintagunta, 1994). Let V be
the respective covariate on consumer level (with 1  l  L). We denote the logit
equation including consumer covariates Vli j as covariate for consumer i within
country j:
◆
✓
L
exp g0s0 t + Â gls0 Vli j
l=1
◆
✓
P(Xi j = s0 |Z j = t) = S
L
 exp g0st +  glsVli j
s=1
(2)
l=1
This delineation is consistent with the basic multilevel finite mixture model
as proposed for international segmentation by Bijmolt et al. (2004). For a more
detailed model delineation please refer to their article. To reflect the scholarly
emphasis on the impact of the context (e.g., Burgess and Steenkamp, 2006;
Douglas and Craig, 2011), we extended the model in a way that allows for
including higher-level covariates. Again, the covariates were assumed to directly
influence the higher-level segments. In this respect, let W m j be the higher-level
covariate (with 1  m  M for the higher-level covariates). We delineate equation
(3):
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P(Xi j = s0 |Vli j , Z j = t 0 |Wm j ) =
✓
6.1
◆
exp g0s0 t 0 Wm j + Â gls0 Vli j
l=1
◆
✓
S
L
exp
g
W
+
g
V
Â
 ls li j
0st m j
s=1
6
L
(3)
l=1
Numerical application
Data collection and analysis
The study was conducted at the end of 2010. For collecting data, we employed
online surveys, which were broadly discussed and double-checked with bilingual
country representatives to avoid ambiguous descriptions among countries and
languages in line with our suggestions. For reaching consumers, we worked
together with local research contacts to guarantee reliable information (Hoskisson
et al., 2000). After initial questions, the channels were described in detail to
ensure a common understanding among respondents (Burgess and Steenkamp,
2006; Steenkamp and Ter Hofstede, 2002). At the end of the questionnaire,
respondents were asked questions regarding demographic patterns (age, gender,
living area), which were used as covariates. As psychographic measure we used
the three of the NEO-PIR personality traits, namely conscientiousness, openness,
and agreeableness, validated by Rammstedt and John (2007) which constitutes a
temporarily stable measure. We chose those three as they are assumed to relate to
consumer multichannel behavior. For instance, openness might be an indicator
for using personal channels since consumers are likely to find more pleasure
in interacting with others. Similarly, conscientious consumers are proposed
to gather information through multiple channels rather than relying on solely
one channel. Additionally, we prechecked the distribution of other covariates
regarding comparability. This provided us with 18,239 useful questionnaires from
five continents (Asia, Australia, Europe, North-America and South-America),
including six EMs: Brazil, China, India, Mexico, Poland, and the Czech Republic,
6.1
Data collection and analysis
113
which represent EMs from all continents but Africa. Though no EM from Africa
was included, we argue that the sample is appropriate as it includes multiple EMs
that have a considerably different context.
The primary information was complemented with secondary data on contextual characteristics. Most information was readily available at the database of the
Worldbank (2011); however, very specific information had to be compiled from
local sources to reflect the consumers’ actual context (Bureau of Economic Analysis, 2011; Census India, 2011; Statista, 2011; Census Bureau United States, 2011;
Indiastat, 2010; Instituto Brasiliero de Geografia e Estatistica, 2011; National
Bureau of Statistics of China, 2011; Statistics Bureau, 2011; Telecommunication
Carriers Association, 2011; Telecom Regulatory Authority India, 2011; VMW
Analytic Service, 2011). Specifically, we included the PPP, the living density
(inhabitants per square kilometer), and internet penetration. This partially required
native speakers to translate the information and to validate its comparability. Hofstede’s value dimensions served as proxy for culture. We used two dimensions,
individualism and uncertainty avoidance, which were specified according to the
recently updated levels (Hofstede, 2001). Again, those two were selected as we
assumed a relationship to multichannel behavior. For example, when being averse
to uncertainty, it is suggested that more channels are used for gathering information as well as personal channels are being preferred to online channels. Some
information was not available whether for the specific year or for the specific
geographic region. In this case, covariates were extrapolated from country-wise
information.
Before analyzing segments, data was weighted within (to correct for deviances
of the respective country’s demography by case weights) and between countries
(to obtain inferences about real segment sizes) (Schaffer and Riordan, 2003;
Steenkamp and Ter Hofstede, 2002) according to age, gender, income and level
of urbanization. The descriptive results of the consumer’s choice regarding the
search and purchase channel are displayed in Appendix A.
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6.2
IV E MERGING M ARKETS
Results
Following the notion of within-country heterogeneity, we divided countries based
on two criteria: The first model uses a geographic split, whereas the second model
apportions consumer’s based on their class membership (see Appendix B). In order
to avoid arriving at a local maximum and to check the robustness of the estimation,
the geographical and the class-specific split were estimated ten times using the
Expectation Maximization algorithm (Vermunt, 2004). As of the large sample
size in our study, we used the Consistent Aikake’s Information Criteria (CAIC;
Bozdogan, 1987) for selecting the most parsimonious and superior model (Bijmolt
et al., 2004). The lowest CAIC value and, therefore, the optimal combination of
consumer and higher-level segments is obtained at 14 consumer and 8 higher-level
segments with a CAIC value of 168,116.526, using the class-specific separation.
We continued the analysis with the class-specific model as the geographic split
displayed a worse model fit (CAIC: 168,841.822). This also indicates that a
micro-level analysis specified according to class membership provides a superior
split than a regional split. The final model was validated by n-fold splits. More
precisely, randomly selected parts (i.e., for the 2-fold validation one half) of the
sample were dropped. The model values are reported in Table 4.
6.2
Results
115
Results and Model Fit Statistics
Segment
1
UK
2
Singapore, Malaysia, India low
3
Spain, Korea, France, Brazil (high and low),
Mexico (high, medium, low)
4
Japan (high, medium, low), Germany (high, medium)
5
China (high , medium, low)
6
US (high), Poland (high, medium, low),
7
Austria, Belgium, India (high, medium), US (middle, low)
8
Australia, Sweden, NL
CAIC
168116.526
2-fold
3-fold
4-fold
5-fold
Consumer
29.43 %
30.19 %
30.10 %
32.09 %
31.56 %
Higher-level
0%
0.07 %
0.03 %
0.09 %
0.08 %
Consumer
.657
.645
.651
.629
.633
Higher-level
1.000
.988
.993
.983
.985
Consumer
.540
.533
.553
.506
.512
Higher-level
1.000
.985
.992
.980
.982
CZ (high, medium, low), Germany (low)
Classification errors
Entropy R2 :
Standard R2 :
Table 4: Results of Class-Specific Model: Higher-level segments.
At first glance, the large number of consumer segments (i.e., 14) does not
appear practicable. However, we argue differently for three reasons. First, in
our study consumers were able to specify behavior from eight search and four
buying channels. For example, if only the combination of one search channel
was chosen, 32 possible combinations arose. To realistically describe segments,
consumers were able to check up to three channels for the search phase (i.e.,
searching via a provider’s website and through a personal contact, while buying
via an independent web provider) which theoretically leaves room for thousands
of unique combinations. Second, within several higher-level segments, specific
consumer segments nearly do not exist, which fairly reduces the number of
segments to be consider for a specific market (Bijmolt et al., 2004). Finally,
five consumer segments (existing in nearly all regional segments) account for 62
percent of the consumers. We conclude that the number of consumer segments
does not affect the practicability of our results.
We used three values to assess how well our model predicts class membership.
First, as suggested by previous studies, we applied an entropy measure which
116
IV E MERGING M ARKETS
is a measure similar to the R2 value. Here, higher values (up to 1) confirmed a
good fit, equivalent to a high reduction of classification errors. The consumer
entropy measure was fairly high with .657 in the light of 14 identified consumer
segments. Country entropy measures reached a deterministic level with a value of
1.0; hence, countries can be classified into one segment without any uncertainty.
Next, we displayed the percentage of misclassifications of consumers and higherlevel segments, which was considerably low with 29.4 percent of the consumers
and 0 percent of the countries misclassified. The standard R2 that has explanatory
power in a nominal model yields a value of .54 for consumer segments and 1.0 for
country segments.
Posterior classifications describe the probabilities for various results. Table
5 shows the probabilistic distributions of the usage of a specific channel into
consumer as well as country segments. Overall the probability to use the offline
provider for searching is highest (58 %), followed by the website of a provider
(49 %) and the peers (46 %). The least attractive channel for searching is via
journals (11 %). Note that the accumulated percentage will be 300 in maximum,
as consumers were asked to specify up to three search channels. Similarly,
the probability to buy via an offline provider is highest (51 %), followed by
the independent offline channel (20 %). Briefly, we will introduce those three
consumer segments, which show the highest percentages in a country segment
(consumer segments 4, 7, and 12). Segment 4 focuses on online channels, most
likely to search via a provider’s website (100 %) or an independent website (40 %).
Further, the services are predominantly bought through the providers website (72
%), thus representing an online affine group. Segment 7 is fairly provider focused,
mainly using the offline channel (98 %) or website (64 %). For buying, the offline
channel of the provider is highly preferred (91 %). Segment 12 typically prefers
independent offline channels (i.e., intermediaries that are not directly related to a
specific company) for searching (98 %) and for buying (91 %). Hence, commonly
expected combinations have been preferred by the respondents. Segments 6, 10,
11, and 14 are negligible as they, in sum only represent less than 5 % of the
6.2
Results
117
consumers. Table 6 reports the probability for any consumer segment to appear in
any higher-level segment.
118
Channel usage probabilities
x
Channel usage probabilities
Search
of consumer segments
Channels
1
2
3
4
5
Size
.06
.09
.08
.13
.12
Onl. provider
.94
.35
.11
1
.10
Onl. independent
.12
.99
.09
.40
.06
Forum
.23
.07
.07
.15
Offl. provider
.61
.36
.98
.33
.81
7
8
9
.14
.08
.11
.64
.76
.40
1
.11
.32
.34
.61
.27
.86
.17
.87
.56
.39
.41
.67
.14
.08
.13
.09
.08
.86
.41
.11
.98
.12
.65
1
.18
.45
.69
.32
.91
.98
.43
.44
.64
.49
.29
.41
.32
.40
.37
.17
.12
.08
.19
.16
.10
.10
.13
.69
.71
.51
.64
.55
.57
.52
.58
.41
.49
.26
.38
.50
.47
.39
.46
.47
.11
.06
.31
.14
.14
.14
.14
.09
.11
.13
.16
.13
.22
.15
.16
.12
.10
.10
.11
.05
.12
.11
.12
.11
.13
.30
.07
.15
.20
.23
.15
.15
.29
.18
.06
.15
.29
.04
.05
.19
.06
.11
.07
.12
.11
.21
.59
.27
.65
.63
.45
.52
.51
.50
.46
.51
.71
.13
.14
.24
.17
.16
.19
.24
.28
.14
.20
.10
.06
.16
.10
.10
.10
.09
.72
.05
.41
.10
.24
.06
.53
.10
.04
.74
.07
1
1
.99
.98
Notes:
The largest probability and relative size is underlined (for searching and buying).
The second largest value is boldface (for searching and buying).
Probabilities below .03 not shown.
Onl. = online; Offl. = offline; Ads = advertisements; independent = not directly related to the company bought from.
Table 5: Model Results for Consumer and Higher-Level Segments.
IV E MERGING M ARKETS
.39
.17
.03
.61
.51
.49
.09
.33
.51
.31
.12
.12
.52
.24
.43
.14
.25
.31
.21
.36
.04
.25
8
.08
.45
.07
Offl. independent
7
.16
.47
Ads
.91
6
.22
.57
.41
1
5
.08
.27
.25
.64
4
.13
.36
.19
.44
.19
3
.21
.95
.08
.20
.87
2
.08
.45
.95
.41
.41
Overall
1
.03
.11
.98
.29
.50
14
.47
.10
.89
Offl. provider
13
.05
.94
.21
.45
.23
12
.13
.58
.41
.46
Onl. provider
11
1
.55
Peers
Onl. independent
10
.11
Offl. independent
Journals
Purchase
6
of higher-level segments
6.2
Results
119
Consumer segments
1
2
.21
1
4
5
6
7
8
9
.04
.40
.04
.06
.14
.16
10
11
12
.04
.16
.08
.07
.06
.07
13
.04
.04
.16
.05
.11
.16
.25
.19
.07
.09
.09
.12
.06
.05
.14
.10
.10
6
.17
.13
.10
.09
.09
.05
.07
.17
.10
7
.05
.06
.14
.18
.13
.03
.11
.25
.04
8
.06
.29
.07
.21
.11
.14
.10
3
.12
4
5
.32
.22
14
.08
.14
.25
2
Country segment
3
.12
.07
.06
.06
.04
Notes:
Across the country segments, the largest probability and relative size is underlined, the second
largest value is bold face.
Consumer segments below .03 in a higher-level segment not shown.
Table 6: Consumer Segment Distribution for Each Higher-Level Segment.
Effects of Covariates
As described, we included several values on consumer level in order to improve segmentation. In evaluating the effects of covariates, we followed the way
suggested by Bijmolt et al. (2004). After the solution that minimized CAIC values
was determined, we used this combination of higher-level and consumer segments
and dropped each covariate once. In case the model statistics did not improve
notably, we concluded that the respective covariate improves the estimation of
segments significantly. All consumer covariates improved the model by considerably lowering the CAIC. To validate their influence we used Log-Likelihood
difference tests, which were all significant at a .95 confidence level.
The least effect was displayed by agreeableness (CAIC: 169,327.7) as the
values approximate the full model (CAIC 168,116.5). The highest influence can
be recognized for age (CAIC: 170,004.1) and gender (CAIC: 169,561.7). In sum,
the models confirmed the usefulness of including socio-demographic, decisionmaking, and psychographic covariates for international segmentation. The largest
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IV E MERGING M ARKETS
consumer segments are displayed by three psychographic and socio-demographic
covariates in Figure 8.
We have proposed that the context critically determines consumer behavior,
thus, we included contextual covariates to determine their usefulness on the formation of channel segments. In particular, we specified the countries’ PPP per capita,
Internet penetration and living density as economic and geographic covariates
and added cultural dimensions, namely individualism and uncertainty avoidance.
Again, the covariates significantly improved the segmentation at a .95 confidence
level when using Log-Likelihood difference tests. Especially leaving out cultural
covariates displayed worse values as compared to the full model, namely uncertainty avoidance (CAIC: 168,765.3) and individualism (CAIC: 168,720.3). In sum,
our proposed segmentation approach, in terms of the contextual embeddedness
and the holistic model, considerably advances the accuracy and reliability of the
international segmentation. The effect of uncertainty avoidance, individualism,
and PPP is illustrated in Figure 9.
7
Discussion and implications
Market segmentation presents a considerable challenge, particularly in an international context (Douglas and Craig, 2006). While HICs have yet attracted
substantial research efforts, insights on EMs are so far sparse. Moreover, an
international segmentation approach with HICs and EMs has not yet been developed. Our article conceptually derives and numerically tests a model for doing so.
The results broadly confirm that the proposed requirements are necessary for a
successful segmentation approach. We formed higher-level segments based on regional and income characteristics to reflect within-country heterogeneity (Burgess
and Steenkamp, 2006; Sheth, 2011), which is highly actionable in terms of the
very specific segments derived (e.g., Ter Hofstede et al., 2002). Although the
class-specific analysis is superior to splitting on a regional basis, the proposition
7 Discussion and implications
(a)
(b)
Notes:
Only the segments with more than 6 % of the actual consumers shown.
Figure 8: The Influence of (a) Psychographic and (b) Sociodemographic
Variables on Consumer Segment Distribution.
121
122
IV E MERGING M ARKETS
Figure 9: The Influence of Higher-level Covariates on Higher-level Segment
Distribution.
that high-income classes of EMs are consistently more similar to HICs’ consumers
cannot be fully supported: The upper class of India, for instance, appears in one
segment with several HICs. Similarly, the lower class of Germany is classified
in one segment with several EMs; however, also the upper class of the US is
registered into this higher level segment. Although this is counterintuitive, it
may be explained when considering the development of usage patterns and of
the industry (Burgess and Steenkamp, 2006). The lower class consumers in EMs
might use the personal channel for buying financial services as they have not
accommodated to using Internet for such purchases yet. This, however, is similar
to the upper class in HICs, where personal contact is often used due to the better
service. Thus, as consumer preferences regarding how to search and buy financial
services are still developing in EMs, which suggests including the development of
the specific industry as another variable. Although we specified the micro-level
analysis based on classes, we solely asked consumers that already have Internet
access, which attaches higher homogeneity to consumers worldwide. While this
7 Discussion and implications
123
constitutes a limitation in terms of omitting a specific restriction of consumer
behavior, it also provides higher stability in terms of the extensive Internet growth
rates in EMs. Depending on the time horizon and the distribution strategy of
MNCs, which predominantly focus on online strategies, online consumers are
likely to approximate the relevant respondents. After all, the segmentation is
future-oriented by using an online sample.
Additionally, EMs did not consistently show up in one segment which indicates
that there is considerable heterogeneity between different EMs. This strongly
advocates against considering EMs as homogeneously different from HICs, but
rather indicates that scholars need to examine them on a case-by-case level. The
notion of embeddedness and the holistic model was reflected by considering
contextual factors and the high number of covariates. Their usefulness was clearly
supported since all contextual variables consistently improved the segmentation.
Although at first sight not all segments relate to each other, considering covariates
explains the higher segments as illustrated by Figure 9.
It is noteworthy that the numerical example provides a first step into EMs
research, which has been strongly advocated by multiple scholars (e.g., Burgess
and Steenkamp, 2006). In line with Sheth (2011), we relied on consumer behavior
as to date other constructs miss a validation for EMs. Thereby, we enhance
the understanding about international channel segments and their channel usage
patterns. In recognizing that different segments exist in different countries, and
that country segments are not as intuitive as expected, marketing practitioners may
rethink current segmentation approaches. Equally, the applied covariates have yet
been specified as valid for research on an international level. For instance, we
used psychographic elements that have been suggested to be globally relevant by
including the five personality traits into the segmentation (Burgess and Steenkamp,
2006). As a result, we were able to better fit the segmentation.
Besides the theoretical input, also methodological contributions can be recognized. In the presented article multilevel, finite mixture modeling is applied
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IV E MERGING M ARKETS
(e.g., Bijmolt et al., 2004; Ter Hofstede et al., 1999), which we further extended
by considering contextual information. In our example, the inclusion of covariates that are allowed to directly affect higher-level segments, ends in deriving a
significantly better model which underlines their usefulness.
In sum, our approach contributes to several fields of research. Primarily, by
establishing the requirements of an international segmentation including EMs, we
enhanced international segmentation practices to fit the distinct characteristics of
EMs (Burgess and Steenkamp, 2006; Sheth, 2011). Methodologically, we see
the presented results as first evidence for the relevance of country covariates for
segmentation, in particular for EMs. Moreover, multichannel scholars find, to our
knowledge, a first international classification that is based on consumer channel
behavior.
7.1
Practical implications
MNCs have yet acknowledged the potential of EMs. Since decades, they strive to
expand their businesses beyond the boundaries of HICs to elude fierce competition in HICs and to exploit the extensive growth rates in EMs (Prahalad, 2004).
Specifically, MNCs should determine global segments based on class-specific
as well as geographical patterns, whereas using a "country-as-a-segment" strategy is inferior. However, we need to emphasize that treating all EMs equally
should be reconsidered. To acknowledge that EMs are highly diverse is essential
to establish a successful international marketing or internationalization strategy.
One additional key issue becomes obvious when considering the results of our
segmentation: Grouping countries according to continents is not effective. This
has direct implications on the MNCs’ organization structure, which should reflect
the markets’ heterogeneity. Predominantly, internal structures are grouped based
on geographic proximity (i.e., PUMA’s organization is split into North America,
Americas, Central Europe, Eastern Europe Middle East Africa, and Asia- Pacific),
which is, according to our results, inferior. Further, a key question for MNCs that
7.2
Future research
125
strive to expand in direction of EMs is "How can interactions with consumers be
arranged"? To the extent, our approach is highly valuable as our segmentation
basis is considered as an essential pillar of every MNCs marketing strategy: The
distribution of goods and services. MNCs can therefore gain crucial insights when
expanding beyond the borders of HICs.
Segmentation can be valued according to being identifiable, substantial, accessible, and actionable (Wedel and Kamakura, 1999). We postulated that the same
criteria are necessary to value the covariates that assist in identifying segments.
Viewed in this light, all of the aforementioned criteria can be considered satisfied
by the factors and covariates employed in the presented study. Consumer channel
behavior is a highly business-relevant behavior (e.g., Konus et al., 2008). In terms
of stability, it is noteworthy that we collected data via an online survey. Despite
this procedure’s drawbacks (i.e., reflecting the segment size of consumers with
internet access only), considerable advantages are at hand. We propose that rather
stable and future-oriented segmentation results are derived in the light of the twoor three-digit internet access growth rates in EMs.
Although our data is constrained to the boundaries of the financial services
sector, we claim to provide valuable insights also beyond the industry’s borders.
Previous multichannel studies have shown notable parallels between the consumers’ behavior facing different products and services (e.g., Konus et al., 2008;
Verhoef et al., 2007). That is, when being used to search online and purchase
offline within one product category, similar behavior is likely for other products
or services. Thus, our research entails information for international marketing
practitioners, outreaching the financial services sector.
7.2
Future research
The findings and ideas of this article can guide future studies. In the beginning,
we split countries into three regions to detect within-country heterogeneity (e.g.,
Burgess and Steenkamp, 2006; Cui and Liu, 2000), which leaves space for finer-
126
IV E MERGING M ARKETS
grained research. For instance, one might conduct research on a statewise level,
which could give further insights about the geographic segment distribution. Then,
we segmented based on consumers’ class membership, which displayed superior
results. Future research might combine both classifications to derive even more
specific results for unmasking the source of heterogeneity in EMs. Additionally,
our sample misses EMs from Africa. Previous research has shown interesting
findings for this continent (e.g., Steenkamp and Burgess, 2002), advocating the
inclusion of African countries. Our methodology revised current multilevel finite
mixture models by including contextual covariates. Particularly, we specified
them equivalently to consumer covariates (Gupta and Chintagunta, 1994). Our
approach showed to be useful in terms of model fit, but specific research dedicated
at further testing concurrent approaches is necessary. For example, an interesting
question is whether a two-step approach that first estimates on a consumer level,
and subsequently forms higher-level segments with covariates, is superior to our
approach.
Finally, in terms of future research we recommend scholars to look beyond
current segmentation practices. In our approach, we used a rather complex
procedure with consumer data enriched by contextual information. However, in
the light of the tremendous institutional influence and the contextual limitations
(Sheth, 2011) that are prevalent in EMs, future research might also consider
approaching segmentation in EMs from a completely different perspective. For
instance, questions to be asked are: How close can international segmentation be
that solely focuses on contextual data predict consumer behavior? Accordingly, is
consumer data necessary for finding consumer segments in EMs?
We conclude by stating that, nonetheless the limitations, our approach shows
especially one key finding: International marketing scholars, as well as MNCs,
need to strive for a highly distinguished view on EMs. Just classifying EMs as
such, while leaving out country- and within-country-specific circumstances, fails
to provide a complete picture.
A
Descriptive statistics
Search channels
Country
Australia
Austria
Belgium
Brazil
China
Czech Republic
France
Germany
India
Japan
Korea
Malaysia
Mexico
Netherlands
Poland
Singapore
Spain
Sweden
UK
USA
Purchase channels
Online
Offline
Additional
Online
Offline
N Provider Indepen- Forum Provider Indepen
Peers
Ads Journals Provider Indepen Provider Independent
-dent
-dent
dent
654
612
634
1,406
1,372
757
697
1,313
1,316
1,378
707
713
672
597
664
679
675
685
1,281
1,427
.55
.40
.51
.51
.62
.42
.52
.48
.27
.50
.47
.38
.60
.69
.32
.32
.58
.72
.60
.41
.40
.33
.21
.18
.29
.40
.37
.52
.22
.51
.23
.15
.12
.25
.42
.22
.26
.49
.70
.38
.06
.08
.08
.15
.23
.15
.09
.12
.14
.05
.15
.21
.06
.09
.25
.16
.15
.12
.09
.08
.53
.63
.61
.70
.64
.58
.72
.51
.68
.52
.66
.73
.80
.44
.63
.66
.64
.54
.28
.51
.29
.58
.51
.37
.40
.41
.21
.41
.46
.28
.32
.38
.41
.35
.35
.53
.44
.20
.28
.48
.41
.44
.47
.44
.40
.58
.36
.38
.52
.46
.53
.64
.46
.44
.53
.62
.46
.38
.34
.47
.26
.06
.09
.17
.07
.06
.11
.06
.29
.18
.20
.16
.13
.10
.10
.11
.11
.07
.20
.14
.07
.11
.11
.12
.03
.15
.12
.17
.14
.09
.06
.08
.12
.11
.13
.04
.07
.13
.07
.08
.26
.05
.11
.16
.20
.16
.14
.15
.08
.20
.19
.07
.12
.26
.14
.06
.19
.29
.33
.19
.08
.07
.04
.04
.06
.08
.05
.17
.06
.24
.07
.04
.02
.11
.06
.04
.07
.15
.27
.10
.52
.54
.54
.64
.55
.50
.73
.45
.56
.43
.59
.69
.65
.40
.58
.59
.53
.51
.26
.43
.16
.35
.31
.16
.19
.26
.09
.22
.29
.13
.15
.20
.20
.22
.22
.30
.20
.05
.14
.27
Total 18,239
.49
.35
.13
.59
.38
.46
.14
.10
.17
.10
.52
.20
A Descriptive statistics
Appendices
Notes:
EMs in bold, independent = not directly related to the company bought from.
Ads = advertisements.
127
128
B
IV E MERGING M ARKETS
Geographical and class-specific classification schemes
Geographical classification
based on:
• China: We used the article of Cui and Lui (2000) that split China in seven regions. However,
we further simplified the classification and only accounted for three regions: North (formerly
North-West, North and North-East), East, and South (formerly South-West, South and Central)
China.
• India: Similarly, India was split into three parts based on the classification of Husain, Dutta,
and Ghosh (2011). Hence, we classified India in South, North (formerly North, Central and
East), and West India. We acknowledge splitting both countries into three geographic regions
as reasonable, as the biggest difference in China and India prevail between northern and
southern parts (Douglas and Craig, 2011).
• Brazil: Brazil was split in three parts, North (reaching to Golás in the south), south (reaching
up to Minas Gerais in the north) and the metropolitan area (Sao Paolo and Rio de Janeiro).
Note: To also investigate potential heterogeneity of HICs, we also split Germany, Japan, and USA into
regional segments.
• Japan: Japan was split into the Northern, Southern part. Further, the district Kanto (the region
around Tokyo) was separated.
• USA: The USA was split into West, Central, and East. Central was formed by Alabama,
Arkansas, Illinois, Indiana, Iowa, Kansas, Kentucky, Louisiana, Michigan, Minnesota, Mississippi, Missouri, Nebraska, Ohio, Oklahoma, North and South Dakota, Tennessee, Texas and
Wisconsin. All states Western to this were classified as West, all Eastern to this as East.
• Germany: Germany was divided into the Western part (former Federal Republic of Germany)
and the Eastern part (former German Democratic Republic).
Income classification
were established as follows:
• Each country used for the geographical classification was split into three parts, the upper class,
the medium and the lower class.
• To mitigate calibration equivalence, we based the development of the classes on income. Up
front distributing the questionnaires, we changed the levels according to the countries’ income
distribution. In the analysis, we rechecked this by classifying the 20 percent of the consumers
with the highest income as the upper class. Similarly, the 20 percent of the consumers with the
lowest income as the lower class.
B IBLIOGRAPHY
129
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V
V R ESEARCH S HOPPING
Nobody said Leaving was Easy: Biased Consumer Behavior in Research Shopping
Abstract
Based on two studies, this article reveals the underlying mechanisms that determine research shopping, described as switching channels between searching and
purchasing a product or service. Findings indicate that the status quo bias theory
is valuable to determine consumers’ biased perceptions as well as biased behavior
in terms of the reluctance to research shopping. These two types are investigated
via the channels employed for searching and overall evaluations of the search
phase. In doing so, this article contributes to the multichannel literature by substantiating knowledge about research shopping and adding to the knowledge about
the difference among multichannel and single-channel consumers. Additionally,
it strengthens the status quo bias theory as theoretical underpinning in marketing
literature.6
Keywords: Research shopping, Status quo bias theory, Biased channel perceptions,
Biased channel choice, Multichannel search behavior
6 T. Schlager. This paper is in preparation for submission to the "International Journal of Research
in Marketing".
1 Introduction
1
141
Introduction
More and more consumers are taking advantage of the broad variety of available
channels. Accordingly, an increasing number of consumers uses different channels
to search and purchase products or services. For instance, a recent study of Google
(2009) illustrates that 40 percent to 60 percent of German consumers, depending
on the product, switch channels between the search and the purchase. In academic
research, this phenomenon, described as "the propensity of consumers to research
the product in one channel [. . . ], and then purchase it through another channel",
has been dubbed research shopping (Verhoef et al., 2007, p. 129).
In fact, research shopping has significant implications for companies. Consumers tend to be less loyal to firms when switching channels within the purchase
process (Chiu et al., 2011; Nunes and Cespedes, 2003), however, multichannel
consumers also tend to spend more money (e.g., Kumar and Venkatesan, 2005;
Kuswaha and Shankar, 2005; Myers et al., 2004; Neslin et al., 2006; Venkatesan
et al., 2007) which directly relates to a company’s revenues. Therefore, for the
sake of business success, companies are highly challenged to manage the channels
offered throughout the consumers’ buying process (Geyskens et al., 2002; Konus
et al., 2008).
A seminal article on research shopping comes from Verhoef et al. (2007),
investigating the phenomenon by means of three mechanisms: Attribute-based
decision making, channel lock-in, and channel synergies. However, the framework
calls for further investigations concerning at least two shortcomings. First, the
possibility of searching in multiple channels is precluded although this is highly
relevant since most of the consumers yet apply more than one channel for searching
(Kopalle et al., 2009; Pauwels et al., 2011; Shankar et al., 2011; Yahoo! Inc. and
OMD, 2006). Second, the truly underlying decision making processes, moderators
and mediators, have not been clarified which presents a tremendous opportunity
(Grewal and Levy, 2007). Consistently, calls for further research on consumer
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V R ESEARCH S HOPPING
shopping behavior and research shopping in particular are loudening (Chiu et al.,
2011; Kopalle et al., 2009; Pauwels et al., 2011; Pentina et al., 2009).
To deepen our understanding of research shopping, the article applies the
status quo bias theory (Kahneman et al., 1991; Samuelson and Zeckhauser, 1988).
This theory would propose that consumers tend to stick with the search channel
rather than switching to an alternative even when the alternative is perceived as
superior. Allowing for multiple search channels, research shopping is considered
as switching away from the most important search channel for the final purchase,
thereby exploiting this channel for the information search only. Hence, the
relevant question is reformulated as: Will a consumer stick to or leave the search
channel that provides the most important information for the final purchase?
More specifically, two ways how the status quo bias affects research shopping are
suggested. First, a bias exists when consumers tend to be reluctant to research
shopping although higher utility for purchasing is perceived for an alternative
channel (directly biased purchase behavior). Second, consumers are biased when
they systematically perceive less utility of the alternative channel for purchasing
solely due to not representing the status quo channel (Falk et al., 2007; Yen and
Chuang, 2008). Accordingly, since these perceptions are assumed to determine
behavior, research shopping is mitigated (biased perceptions, indirectly biased
behavior). The article makes following contributions.
1. It adds to the multichannel literature by untangling the process underlying
research shopping. Thereby, so far neglected mechanisms are revealed as
for instance the role of overall evaluations of the search phase.
2. The article contributes to the discussion concerning the special role of
multichannel customers since the difference between multichannel and
single-channel search consumer behavior is spotlighted.
3. The article adds knowledge regarding the rarely applied status quo bias
theory by projecting its thoughts to a marketing setting.
2 Conceptual model development
143
For realizing these contributions, this article is structured as follows: A conceptual
model is developed outlining potential biases in research shopping. Second,
hypotheses regarding nonlinear functions and the influence of channels used in the
search phase are established. Adding to this, the influence of consumers’ overall
evaluations of the search phase on research shopping is delineated. Next, the
hypotheses are empirically tested by means of a global consumer study and a
laboratory experiment. Finally, managerial and theoretical implications as well as
further avenues for research are outlined.
2
2.1
Conceptual model development
Decision-making towards research shopping
Consumers have multiple choices regarding what channel to use in each phase of
the buying process. In case the consumer switches channels between the search
and purchase phase research shopping is present. Now assume that, contrary to
Verhoef et al. (2007), multiple channels can be used for searching. For instance,
one channel is central to information acquisition; however, a second channel is
used to complement the information at hand. Thus, considering this extension
to the article of Verhoef et al., two search situations are given, one for a single
channel search and the extension for a multichannel search. For clarity, let the
most important search channel be the primary search channel and the concurrent
channel be the alternative search channel.
Following economic theory, when confronted with multiple alternatives, consumers pick the option that promises the highest utility (Kahnemann and Tversky,
1979). In the described situation, channel literature suggests that the utility of
using one channel depends on the perception of the alternative channel (Chiu et al.,
2011). Positive perceptions of an alternative channel decrease the intention to use
the primary search channel (Balasubramanian et al., 2005; Montoya-Weiss et al.,
144
V R ESEARCH S HOPPING
2003). Thus, it is rather the relative evaluation of the utilities of the primary and
alternative search channel for making the purchase that is decisive for research
shopping.
2.2
Decision-making from a status quo bias perspective
A significant part of the literature has shown instances where behavior deviates
from what traditional economic theory predicts (e.g., Kahneman et al., 1991).
More specifically, the assumption that the expected utility is the sole determinant
of consumer behavior does not necessarily hold true (Hershey et al., 1982). A
notable exception is presented by the status quo bias theory (e.g., Samuelson and
Zeckhauser, 1988), which is described as the "propensity of a decision-maker to
choose an option because it is the status quo, the default option" (Dean, 2008, p.
4). From this perspective, consumers display a higher tendency to remain with the
primary search channel throughout the purchase process. They will only switch
channels to make a purchase when the alternative search channel is perceived
better by far as in this case consumers have sufficient motive for research shopping
(Falk et al., 2007; Gal, 2006). This outlines the first facet suggested for the status
quo bias: Consumers are reluctant to research shopping although higher utility is
derived from purchasing in the alternative search channel (biased behavior).
Besides the directly biased behavior, consumers may also be biased in their
perceptions. For instance, Falk et al. (2007) describe the status quo bias as
outcome of systematically biased perceptions of a new self-service channel since
these drive the probability to keep the status quo channel. Thus, it is suggested
that consumers perceive higher utility of the primary search channel for making
the purchase solely due to representing the status quo, while perceiving less utility
of alternative search channel. This describes the second type of the status quo bias:
Consumers indirectly avoid research shopping since the perceptions of the primary
and alternative search channel are systematically altered (biased perception).
In sum, this paper suggests two mechanisms of how the status quo bias theory
3 Hypotheses development
145
applies to research shopping. First, consumers have the propensity to remain with
the search channel for purchasing, even if the alternative channel is evaluated as
superior. Second, the perceptions of the channels are systematically influenced
by the status quo option; the primary search channel is evaluated more favorably
for purchasing, whereas the alternative channel is evaluated worse resulting in a
lower probability of research shopping (see Figure 10).
Figure 10: Conceptual Model: The two Potential Status Quo Biases in Research
Shopping.
3
3.1
Hypotheses development
Biased purchase behavior
In the conceptual model, it is suggested that the explanatory power of economic
theory for research shopping is limited when a clearly defined status quo exists. In
the following, it will be relied on the difference between consumers who use one
channel compared to those who use multiple channels for searching (e.g., Neslin
et al., 2006). Specifically, it is assumed that consumers who solely use a single
channel for searching are prone to the status quo bias since a clear status quo
option exists (the primary and in this situation the only search channel), whereas
multichannel consumers are not since they cannot stick with both channels for
146
V R ESEARCH S HOPPING
making the purchase. More precisely, their status quo is the use of both channels,
however, they need to make a choice for making the purchase. Accordingly, two
situations with different utilities derived from research shopping are identified
and by comparing both consumer types, the status quo bias is isolated and its
magnitude determined.
Single-channel search behavior
In the first situation, a consumer relies on one channel for searching which
is suggested to be equivalent to the status quo. As Verhoef et al. (2007) and
Konus et al. (2008) suggest, despite several attributes determining the utility
across several phases of the purchasing process, some are phase-specific. For
instance, attributes, such as the ease of comparing different options are highly
valued when searching, while others, such as the effort for buying are rather related
to purchasing. Although the consumer cannot fully foresee the utility of buying
with the primary search channel, even less can be said about the alternative search
channel. Correspondingly, when searching in one channel the consumer knows
less about the specific utility of the alternative search channel for purchasing as the
consumer did not experience the alternative search channel in the search phase.
This introduces uncertainty toward purchasing with the alternative search
channel, which is acknowledged as trigger of the status quo bias (e.g., Fernandez
and Rodrik, 1991; Hartman et al., 1991; Ortoleva, 2008). For instance, Sääksjärvi and Samiee (2007) examined the adoption of cyber brands and argued that
consumers tend to buy brands that were more familiar due to the status quo bias.
Similarly, Ciccione (2004) found that uncertainty concerning an outcome negatively influences a government’s willingness to implement a reform, representing
a status quo bias. What is more, literature has yet acknowledged a reflective facet
of the status quo bias. That is, using only one search channel results in a lower
perceived uncertainty of the primary search channel. For example, Roca et al.
(2006) tested whether or not the status quo is preserved in ambiguous situations.
Using an experimental procedure participants were introduced to a decision with
3.1
Biased purchase behavior
147
a vague outcome as the status quo and a clearly defined outcome as the alternative.
As most participants retained their current status, it was concluded that the bias
decreases the perceived uncertainty towards the status quo.
The above argumentation implies that the difference in the perceived uncertainty between the primary and the alternative search channel will be large for
consumers who only use one search channel. Accordingly, the propensity for using the primary search channel for purchasing increases (Bewley, 2002; Ortoleva,
2008; Samuelson and Zeckhauser, 1988). Even when the perceived utility of the
alternative search channel is higher for purchasing than the utility of the primary
search channel, the primary channel is given a higher preference. Consequently, it
is proposed that consumers tend to avoid research shopping when they have only
used one channel for searching favoring the status quo bias.
Multichannel search behavior
Situation two specifies a multichannel behavioral pattern during the search
phase. Although a primary search channel is specified, it is supposed that no status
quo bias is present since a second, alternative search channel is used for collecting
information about the product. Based on partially overlapping features of channels
throughout the search and purchase phase (e.g., Konus et al., 2008; Verhoef et al.,
2007), consumers will have an initial understanding of the potential experience of
making the purchase in the alternative channel. Accordingly, uncertainty about
the alternative channel is considerably lower than in situation 1; consumers can
evaluate both channels and pick the superior option that determines the utility of
research shopping. As such, it is suggested that the low uncertainty towards the
alternative channel eliminates the status quo bias and that the search channel that
imposes the higher utility for purchasing is chosen. For instance, when consumers
predominantly search online, while partly using the offline channel to complement
information, they will feel less uncertain to buy with the offline channel. Based
on the absence of a status quo bias for multichannel search customers, the first
hypotheses are framed:
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V R ESEARCH S HOPPING
H 1a. The difference in perceived attributes of the alternative and the primary
search channel for making the purchase determines the probability of research
shopping.
H 1b. Using multiple channels in the search process increases the probability of
research shopping.
After reflecting the peculiarities of both situations, the status quo bias calls
for further investigation especially concerning how the bias fully unfolds its
magnitude. Specifically, literature has argued that consumers who search in one
channel need to overcome the barrier induced by a status quo bias (Falk et al., 2007;
Gal, 2006), whereas consumers that search in multiple channels are supposed
to switch the primary search channel for making the purchase when perceiving
the alternative channel superior to the primary search channel. Registering both
situations, it is suggested that the difference in the utility of research shopping
between using multiple and single channels during the search phase is largest
when consumers expect a slightly higher utility from the alternative search channel
than from the primary search channel; multichannel search consumers would
immediately switch when expecting the alternative channel to be superior for
buying. Accordingly, the difference between using multiple channels or a single
channel in the search phase will be smaller at the extreme of the relative evaluation.
Further, it is suggested that the curve will peak beyond the point where the
alternative search channel is perceived as equal to the primary search channel,
since both remain with the primary search channel when perceiving it better for
making the purchase. As a result, H 1c-d are suggested:
H 1c. The curve reflecting the difference in the probabilities of research shopping
between multichannel and single-channel consumers is inversely U-shaped.
H 1d. The difference in probability curve will peak beyond the point where
equally perceiving both channels for making the purchase.
3.2
Biased perceptions of channels for purchasing
3.2
Biased perceptions of channels for purchasing
149
In the conceptual model altered perceptions of the primary and the alternative
search channel were suggested to present the second type of status quo bias. To
shed light on this, the single channel search situation, which is suggested to be
necessary for a status quo bias, is further investigated. In this course, literature
suggests that once a status quo is defined, it is taken as a reference (Anderson,
2003; Falk et al., 2007). That is, every upcoming alternative is evaluated in the
light of the status quo. When the current state is perceived as particularly favorable,
the absolute magnitude of losses is larger since the well-perceived state is at stake
(e.g., Anderson, 2003; Falk et al., 2007; Yen and Chuang, 2008). For instance,
Yen and Chuang (2008) argued that "happy individuals who feel confident in
following a ’business as usual’ procedure should be more likely to select the status
quo option" (p. 524). In a series of experiments, they found support for this,
while negative emotions mitigated keeping the status quo. Martin et al. (1993)
supports this notion showing that consumers who report negative evaluations
rather rely on systematic processing, while positive evaluations enhance heuristic
information processing. Hence, when negatively evaluating the primary search
channel, consumers more thoroughly re-evaluate both channels for making the
purchase, whereas a positive evaluation may even exclude the alternative search
channel from being considered for purchasing. Correspondingly, it is suggested
that favorable evaluations of the search phase directly enhance the propensity to
maintain the status quo, while unfavorable evaluations increase the probability of
research shopping.
In channel literature, a main indicator of positive (and negative) evaluations is
the consumers’ level of satisfaction (e.g., Geyskens et al., 1999; Montoya-Weiss et
al., 2003; Shankar et al., 2003; Van Birgelen et al., 2006). Moreover, satisfaction
has yet been recognized as trigger of the status quo bias (Falk et al., 2007). Thus,
consumer satisfaction with the search phase will systematically alter the reference
150
V R ESEARCH S HOPPING
point that provokes the status quo bias. Hence, the following hypothesis can be
established:
H 2a. Satisfaction with the search channel decreases the probability of research
shopping.
Besides these initial suggestions, the status quo bias theory explicitly suggests
irrational elements (e.g., Kahneman et al., 1991). In line, unrelatedly altered perceptions are one central indication for the presence of a status quo bias (Baron and
Ritov, 1994). These are present when the consumers’ perceptions of an alternative
channel’s attributes are influenced by the overall evaluation with the primary channel for searching. A trigger of the status quo that alters consumers’ perceptions is
loss aversion. Loss aversion delineates that consumers overemphasize potential
losses compared to gains (e.g., Bostrom and Ord, 2006; Kahneman et al., 1991;
Rubaltelli et al., 2005; Yen and Chuang, 2008). As a result of loss aversion,
subjective certainty with the status quo increases (Yen and Chuang, 2008). In the
context of research shopping, this implies that loss aversion concerning the current state decreases, resulting in better-perceived attributes of the primary search
channel. For the alternative search channel, however, loss aversion leads to even
worse perceived attributes for the alternative channel. In line, Falk et al. (2007)
suggested that the intention to adapt an unknown channel is primarily influenced
by the evaluation of currently used channels. Studying offline-banking customers
they discovered a negative influence of satisfaction with the offline channel on the
perceptions of the newly introduced self-service channel. Perceptions were found
to be biased which in turn lowered the usage intentions for the self-service channel.
In particular, it is suggested that consumers tend to systematically perceive the
alternative search channel’s attributes for purchasing as worse, while they more
positively evaluate the primary search channel’s attributes.
To sum up, using a well-perceived current state has been noticed as a decisive
facet in provoking a status quo bias since larger potential losses are perceived (e.g.,
Falk et al., 2007; Yen and Chuang, 2008). Correspondingly, it is proposed that
3.2
Biased perceptions of channels for purchasing
151
both channels’ attributes are misperceived in favor of the primary search channel
when a clearly defined status quo exists (e.g., Kim and Kankanhalli, 2009). As
such, these evaluations lower the tendency to switch the channel for making the
purchase. It is noteworthy that the suggested mechanism is substantially different
from the lock-in effect, which specifies the superiority of a specific combination
of channels (e.g., Verhoef et al., 2007) or switching costs (e.g., Shankar et al.,
2003) since both mechanisms would not imply misperceptions of the alternative
channel (Falk et al., 2007). Following three hypotheses are established:
H 2b. The perceived attributes of the search channels for purchasing partially
mediate the influence of satisfaction with the search channel on the probability
of research shopping.
H 2c. Satisfaction with the search channel negatively influences the perceived
attributes of the alternative channel for purchasing.
H 2d. Satisfaction with the search channel positively influences the perceived
attributes of the primary channel for purchasing.
To sum up, two types of status quo bias are suggested. First, biased behavior is
investigated by comparing the combination of channels used for searching. In case
two channels are used, it is suggested that consumers display unbiased behavior.
Contrary, consumers are suggested to be reluctant to research shopping when only
one channel was applied for searching, which is in accordance with the status quo
bias theory (H1a-c). Second, consumers’ perceptions of the search channels used
for purchasing are biased when being satisfied with the search phase (H2a-d). The
central hypotheses are summarized in Figure 11.
152
V R ESEARCH S HOPPING
Notes:
Attalternative are the perceived attributes of the alternative search channel
for making the purchase.
Att primary are the perceived attributes of the primary search channel
for making the purchase.
Figure 11: The Central Hypotheses concerning the Effects of the Status Quo
Bias Theory on Research Shopping.
4
Analysis and findings of Study 1 and Study 2
Both studies consider a simplified 2 x 2 matrix of research shopping. That
is, research shopping from an offline to an online channel and vice versa is
investigated. In particular, Study 1 examines how the search channels influence
research shopping (H1a-d) via a large-scale survey to provide evidence for the
proposed non-linear functions. Study 2 extends these findings by capturing the
influence of positive evaluations of the search phase on research shopping (H2a-d).
To isolate the effect and to provide initial causal inference, a laboratory experiment
was designed.
4.1
Study 1: Method
4.1
Study 1: Method
153
Study overview and design
For the large-scale study, a survey was developed asking participants about
their buying processes regarding financial services. Draft versions were thoroughly discussed with industry executives in the participating countries to gain
insights regarding country-specific issues. Additionally, a pretest with 589 U.S.
respondents was conducted. Following the participants’ comments, scales and
items were adapted. After the survey was sent out for translation into thirteen
languages, it was discussed at least twice with contact persons of each country to
determine whether fine-grained culture or language-specific differences posed a
problem (Burgess and Steenkamp, 2006). To distribute the questionnaire, panels
representative for each participating country were used. The questionnaire was
accompanied with a cover page that explained the purpose of the study. After
initial questions, the channels were described to facilitate respondents in correctly
classifying search and purchase channels. The procedure provided 16,276 useful
questionnaires from five continents (Asia, Australia, Europe, North America, and
South America). Sample characteristics are distributed as follows: Mean age
was 39.4 (S.D. = 11.66); 48.56 percent were female and 51.54 percent male;
most respondents graduated from a university (58.6 %), a third (34.4 %) had a
high-school degree, and the rest (7.0 %) had not completed any degree. In sum,
9,760 persons used the offline channel for searching as primary search channel,
while 6,516 chose the online option. However, 11,611 participants took the offline
choice for buying. Only 4,665 made their purchase via an online channel.
Measurement
Consumers were directly asked to specify the channels they used for searching,
as well as their primary search channel. To validate that the primary channel was
dominantly used, consumers were required to disclose the amount of time spent
in each channel. If a consumer spent twice as much time in the primary search
154
V R ESEARCH S HOPPING
channel as compared to the alternative, it was concluded that an actual primary
channel was indicated. Moreover, the time spent was used as a covariate. To derive
the binary outcome variable, research shopping, participants were asked to specify
the purchase channel. To evaluate the channels for buying ten attributes developed
in previous literature were used; to fit the context minor changes were made.
Factor analyses condensed the items to three factors, namely buying perceived
usefulness, perceived risk, and buying perceived ease of use; the respective sources
of the items are displayed in Appendix A. All channel attributes were measured
using semantic differentials without predetermined scales (e.g., for the item How
much effort is searching for insurance via channel x? scale items reach from a
lot of effort to no effort at all). Advantages of this method include the high face
validity (Hawkins et al., 1974; Nunnally, 1978) and its simplicity (Osgood et al.,
1957). For the analysis, the scale was partitioned into six equal intervals. To
isolate the effect several control variables were used. Experience was assessed as
a binary variable checking whether consumers recently bought a financial service.
Additionally, a binary variable describing the type of product was used to measure
its complexity, the relative price advantage of a channel as a relative value for
money on a six-point scale, and a binary variable was used for whether an online
or offline search channel was used. Finally, gender and age were controlled.
4.2
Study 1: Analysis
Study 1: Test of validities and reliability
Confirmatory factor analysis (CFA), based on offline and online channels, was
employed to condense the items to factors and to assess their unidimensionality,
as well as their validity. The results were verified by the commonly used fitindices except the c 2 which would be inflated due to the extensive sample size
(Jöreskog, 1969). They indicated that the data fit the model well: The standardized
loadings were high and significant (see Appendix A), ranging from .54 to .85 (a
< .001 level), and in conjunction with a battery of fit-indices the model fit was
4.2
Study 1: Analysis
155
deemed to be acceptable. The comparative fit index [CFI] is .984; Tucker-Lewis
index [TLI] is .976; incremental fit index [IFI] is .984; root mean square error of
approximation [RMSEA] is .039. This exceeds general recommendations (e.g., Hu
and Bentler 1999); in line with the average variance extracted, convergent validity
was achieved. Discriminant validity was examined by the analysis suggested by
Anderson and Gerbing (1988), assessing whether the measurement error-corrected
correlation parameters between the constructs are significantly different from 1.0.
This analysis supported that discriminant validity was given. Cronbach’s a scores
(Bagozzi and Yi, 1988) for all measurement scales indicate sufficient reliability.
Even though the discriminant validity is indicated by the results, and hence
multicollinearity is an unlikely problem (Grewal et al., 2004), variance inflation
factors (VIF) were calculated (see Table 7). The highest value was reported for
age (7.36), whereas the average VIF was 2.81 confirming that multicollinearity
does not pose a problem. For further analyses the factors’ items were averaged.
156
VIF Mean
SE
Age 7.36 39.41 11.66
Age
Gender
Expe-
Com-
rience
plexity
Price
.51
.5
.04 ***
Experience 1.88
.42
.49
.19 *** -.02
Product complexity 1.68
.38
.49
Price 1.84
-.86
0
Search channel (off-, online) 2.45
.4
.49
-.04 *** .09 *** .04 *** .09 *** .25 ***
Search channels used 2.01
.53
.5
-.02
-.02
*
* .01
-.07 *** .02
**
0
Time spent for search (%) 5.92 51.43 19.17 .03 *** -.01
-.01
1.81 -.09 *** .04 *** -.01
2.26
Buying Ease 1.16
2.1
-.32
PU
Buying Buying
Risk
EOU
1
-.24 ***
0
Buying Risk 2.19 1.13
Time spent Buying
1
* .01
-.76
# Search
1
Gender 2.01
Buying Usefulness 2.37
Search
channel channel(s) used searching
.09 *** -.04 ***
-.06 *** -.01
1
0
-.02
1
1
* .08 *** -.03
.03 *** -.12 *** -.02
**
1
** -.05
.04 *** .56 *** .47 *** .05
***
1
*** -.13
***
*** -.69 ***
0
-.04 *** -.51 *** -.51 *** -.05
***
0
-.04 *** .26 *** -.04 *** .08
*** -.09
Table 7: Variance Inflation Factors and Pearson Correlations.
1
1
*** .29 *** -.19 ***
1
V R ESEARCH S HOPPING
Notes:
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
Correlations of orthogonalized factors shown (as used in regression).
.1
4.2
Study 1: Analysis
157
Study 1: Estimation procedure / results
The probability of the binary outcome (i.e., research shopping) was estimated
using a random effect logit model. Let rsic be a binary variable, specifying if
research shopping is present, rsic = 1, or not, rsic = 0, for a consumer i using a set
of search channels c (c = 1 when using a single search channel, otherwise c = 2).
The relative utility related to research shopping is specified as latent variable, rs⇤ic .
This is denoted as follows:
8
< 1 if
rsic
: 0 if
rs⇤ic
0
rs⇤ic
< 0
, i = 1 , ..., n
(4)
Four groups of variables reflect and isolate the patterns described by the conceptual model: Consumer, product as well as search phase-specific characteristics,
and the relative evaluation of the perceived attributes of the search channels for
making the purchase. We can denote the relationship between these four groups
and the latent variable as
rs⇤ic =
d 1,...,B bic + b 1 PRODic + b 2 T IMEic + b 3CHANNELic
K
V
+b 4VALUEic + Â Â b5kv (aick
k=1 v=1
where
(2)
pick )v + eic⇤ + hc
(5)
158
V R ESEARCH S HOPPING
d 1,...,B = {d 1 , ... , d B }, or coefficients that capture the effect
of consumer covariates,
bic = {1  b  B}, or consumer covariates (age, gender,
experience),
PRODic = the product type purchased (complex vs. incomplex),
T IME ic = the time spent in the primary search channel
during the search phase,
CHANNELic = the search channel (online / offline),
VALUE ic = the relative value for money expected in a channel,
k = {1  k  K}, are various channel dimensions for
buying (i.e., usefulness),
v = {1  v  V}, or the degree of polynomial used
(i.e., up to the third),
pic = the primary search channel,
aic = the alternative search channel,
eic⇤ = the error (assumed to be logistically distributed), and
(2)
hc
= the random intercept for the number of search
channels used.
As coupling function, the presented model is specified as a random effects
logit function of the latent underlying variable derived from a channel for buying.
Results broadly confirm the model specified to explain research shopping. The
random intercept model for the search situation was significantly superior when
including all polynomials (Dc 2 = 43.31; p < .001). Additionally, a multilevel
random intercept model for consumers was tested which, however, did not significantly improve the model (Dc 2 = .02; p = .987). For researching the functional
relationship between the factors determining the channels attractiveness and for
mitigating multicollinearity issues, orthogonalized polynomials were employed
(Kleinbaum et al., 1998, p. 293). The results are displayed in model 3 to model 5
(with subsequently testing the nonlinear effects against model 2, see Table 8); all
displayed significant improvement. It was concluded that allowing for nonlinear
effects significantly improved the model, also shown in the full model (with all
4.2
Study 1: Analysis
159
polynomials included). All factors show high significance for explaining research
shopping. Specifically, buying perceived usefulness yields the highest impact
among the factors (b = .453, p < .001). Similarly, perceived risk of buying significantly decreases the probability of switching channels between searching and
buying (b = -.212, p < .001). The factor with the least influence is the perceived
ease of buying of a channel (b = .127, p < .001). In sum, hypothesis H1a was
broadly supported.
To interpret the effect of using multiple against a single channel for searching
(H1b-H1d) simulation was used (Zelner, 2009). In particular, the posterior means
of the predicted probabilities of research shopping were simulated for both situations varying the three explanatory variables (perceived usefulness, perceived risk,
and perceived ease of use), all of the other variables held at their observed values
(Van Everdingen et al., 2011). In a similar vein, the difference in the probability
of research shopping between both situations was checked. The results confirm
that using multiple channels within the search phase positively influences the
probability of research shopping, offering support for H1b. For buying perceived
usefulness, perceived risk, and perceived ease of use (differentiated by offline
and online search) they are displayed in Figure 12, which is equivalent to the
visualization of Van Nierop et al. (2008). When searching offline, no inverse
U-shaped curve was found for perceived usefulness and perceived risk since the
curve did not decrease at the extremes of the scale. However, for perceived ease
of use the curve yielded an inverse U-shaped form. For searching online the inversely U-shaped was supported for all three explanatory variables. The effect of
using two channels, instead of one, has the highest influence when the alternative
search channel is evaluated as slightly better than the primary search channel.
When the attributes of the alternative channel for buying are perceived better by
far, consumers switch channels for purchasing, regardless of the applied search
channels. Thus, H1c and H1d were partially confirmed.
160
Model (1)
Model (2)
Model (3)
Model (4)
Model (5)
Full model
Single search
Multi search
Covariates only No nonlinearities Polynomials usefulness Polynomials risk Polynomials ease All polynomials All polynomials All polynomials
b p
Age -.000
SE
(.002)
Gender -.053
b p
.002
b p
SE
b p
.002
(.002)
.002
(.045) -.042
SE
(.002)
b
.002
SE
(.002)
b p
.002
b p
SE
b p
SE
.001
(.003)
.003
(.003)
(.045) -.118
(.073)
.018
(.058)
-.036
(.047)
-.089 + (.047) -.093 * (.047) -.094 * (.048) -.149 * (.076) -.045
(.062)
Product complexity -.132 ** (.046) -.135 ** (.047) -.135 **
(.047)
-.134 ** (.047) -.133 ** (.047) -.130 ** (.048) -.235 ** (.076) -.047
(.062)
.075 ***
(.014)
Searchchannel (off-, online) 2.003 *** (.048) 1.556 *** (.054) 1.537 ***
(.055)
1.576 *** (.055) 1.551 *** (.054) 1.563 *** (.055) 1.290 *** (.092) 1.708 *** (.069)
Time spent in search (%) -.014 *** (.001) -.013 *** (.001) -.013 ***
(.001)
-.013 *** (.001) -.013 *** (.001) -.013 *** (.001) -.009 *** (.002) -.016 *** (.002)
DBuyingUse f ulness
.073 *** (.014)
.488 *** (.035)
DBuyingRisk
DBuyingEase
.461 ***
(.039)
-.231 *** (.033) -.231 ***
(.033)
.112 *** (.026)
DBuyinguse f ulness2
DBuyingUse f ulness3
.112 ***
(.026)
.038
(.030)
-.081 **
.074 *** (.014)
(.045) -.044
SE
(.002)
(.045)
.227 *** (.012)
(.045) -.046
p
Experience -.088 + (.046) -.089 + (.047) -.090 +
Price
(.044) -.039
SE
(.002)
.492 *** (.035)
.072 *** (.014)
.488 *** (.035)
.453 *** (.041)
.082 *** (.022)
.391 *** (.064)
.069 *** (.018)
.496 *** (.053)
-.223 *** (.037) -.232 *** (.033) -.212 *** (.038) -.282 *** (.064) -.174 *** (.048)
.114 *** (.026)
.121 *** (.027)
(.027)
DBuyingRisk2
DBuyingRisk3
.075 *** (.014)
.127 *** (.027)
.186 *** (.042)
.082 * (.036)
.061 + (.034)
.076
.046
(.054)
(.044)
-.095 *** (.028) -.083 + (.045) -.105 ** (.036)
.012
(.028)
.031
-.053 * (.025)
DBuyingEase2
DBuyingEase3
(.031) -.005
(.052)
.047
(.039)
-.075 ** (.026) -.084 * (.042) -.069 * (.034)
-.107 *** (.026) -.122 *** (.027) -.123 ** (.043) -.109 ** (.036)
-.033
Constant -1.545 *** (.346) -1.729 *** (.340) -1.723 ***
(.341)
Intercept -.761 *** (.502) -.785 *** (.503) -.783 ***
(.503)
(.026) -.028
(.026) -.047
(.041) -.023
(.035)
-1.733 *** (.340) -1.753 *** (.338) -1.744 *** (.338) -2.030 *** (.171) -1.355 *** (.141)
-.785 *** (.503) -.792 *** (.502) -.792 *** (.503)
16,276
16,276
16,276
16,276
16,276
16,276
a
7,622
a
8,654
AIC
c2
13199.198
12701.311
12696.498
12698.763
12684.310
12669.999
5148.946
7503.000
2474.031
2650.921
2684.898
2646.711
2655.532
2703.289
862.489
1861.979
LL
-6590.599
-6338.655
-6334.249
-6335.382
-6328.155
-6317.000
-2556.473
-3733.500
LL comparison
-6804.952
-6534.276
-6530.177
-6530.413
-6520.447
-6508.402
a
a
-
508.89***
8.81*
6.55*
21.00***
43.31***
a
a
LR test base model
Notes:
Unstandardized estimates shown.
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
a) No random intercepts used since the sample was split according to the specific search channels used.
Table 8: Results of the Binary Logistic Random-Intercepts Regression.
V R ESEARCH S HOPPING
N
4.2
Study 1: Analysis
161
Notes:
The blue line indicates the probability of research shopping when using multiple channels for
for searching.
The green line indicates the same values when using one channel for searching.
The red solid line indicates the predicted change in the probability of research shopping
The red dotted lines indicate the boundaries of the 95 % confidence intervals.
The explanatory variable is varied along the horizontal axis holding the nonfocal variables at
at their observed values.
Figure 12: The Probabilities of Research Shopping for Multi- and
Single-channel Search Behavior, and their difference (for full sample / offline
search / online search, each for the relative perceived usefulness, perceived risk
and perceived ease of use of the purchase channels).
162
4.3
V R ESEARCH S HOPPING
Study 2: Method
Study overview and design
Study 2 implemented a 2 (offline versus online) x 2 (high risk versus low risk)
x 2 (high usefulness versus low usefulness) between-subjects experimental design;
in sum eight conditions were developed. The treatments relate to the factors
of the survey and manipulate the level of satisfaction as in previous research
on satisfaction (e.g., Homburg et al., 2005). All manipulations were tested in
advance. To manipulate the offline search channel, video screenings with a tied
agent were recorded and shown to the participants in a separate room to guarantee
a homogeneous treatment. For the online treatment, four different websites were
designed. Table 9 offers a description of each manipulation.
Search Channel Attributes
Offline
Usefulness
Dimensions
Visual appearance, competence
Accuracy of information, ambiguous
Risk
Online
Usefulness
Risk
information
Visual appearance, competence
Favorable
Unfavorable
Quality award mentioned, screened No quality award mentioned, screened
person with suite
person without suite
Relevant, clear-cut information, no
Irrelevant, misleading information,
reference to conditions and terms,
reference to condition and terms,
easy language
difficult language
Quality seal, clearly arranged
No quality seal, confusingly arranged
information
information
Accuracy of information, ambiguous
Relevant, clear-cut information, no
Irrelevant, misleading information,
information
reference to conditions and terms
reference to conditions and terms
Table 9: Manipulation of Satisfaction with the Channel.
The experiment was designed around a hypothetical car insurance named
"Santander". The sample consisted of 309 business students of a Swiss business
school. Given the nature of the product, business school students are regarded
as an appropriate sample. The students were randomly assigned to an experimental condition. Before the experiment, participants were briefly instructed and
provided with a text explaining the task. After the experiment, a questionnaire
was handed out requesting participants to evaluate the search phase and their
4.4
Study 2: Analysis
163
expectations toward purchasing in the current and alternative channel. In total,
284 questionnaires were filled out completely.
Measurement
Satisfaction with the search channel was assessed using a three-item construct,
previously used by Voss et al. (2010). As the scale has not yet been applied in
the channel context, it was slightly adapted. Consumer satisfaction forms the
independent variable and directly influences the perceptions of the search channels’
attributes for purchasing and research shopping. To conceptualize the mediating
constructs, the same items as in Study 1 were used; however, a seven-point scale
was applied. Research shopping was measured by directly asking whether the
participant would also purchase in the search channel.
4.4
Study 2: Analysis
Study 2: Test of validities, reliability, and manipulation checks
The confirmatory factor analysis was specified equally to Study 1, however,
satisfaction was added. All of the commonly used fit values were satisfying. The
value for c 2 /d f is 2.243, the comparative fit index [CFI] is .947, the Tucker-Lewis
index [TLI] is .927, and the incremental fit index [IFI] is .947. The root mean
square error of approximation [RMSEA] slightly exceeded the recommended
value of .05, indicating .065. Additionally, all path coefficients between the factor
and the respective items were highly significant (a < .001 level). Convergent
validity was confirmed for all factors except perceived usefulness of the online
channel, showing an average variance extracted of .414, which is rather low. As the
results of the following analysis were highly significant, and to keep consistency
with Study 1, the factor was not excluded. The same tests for discriminant validity
as in Study 1 broadly confirmed the factors’ unidimensionality. In sum, the
confirmatory factor analysis deemed the model to fit the data (see Appendix A).
Equally to Study 1, the factors’ items were averaged for further analysis.
164
V R ESEARCH S HOPPING
The experimental conditions broadly displayed the intended effect as the
conditions resulted in different levels of satisfaction with the respective channel.
Under both experimental settings (offline and online), the most favorable condition yielded highest means of satisfaction. Consistently, the lowest values for
both constructs were displayed for the least favorable condition (see Table 10).
Further checks affirmed the treatments’ usefulness. For offline, the difference
in mean satisfaction was significant across the groups (Mhighsatis f action = 5.42;
Mmedsatis f action = 3.31; Mlowsatis f action = 2.03; F = 44.55; p < .001). For the online
manipulation, similar results were obtained (Mhighsatis f action = 4.29; Mmedsatis f action
= 3.05; Mlowsatis f action = 2.64; F = 9.56; p < .001). Also the manipulations of each
single dimension yielded similarly significant results. It was concluded that the
manipulations showed the aimed effect.
Factors
Outcome
Quality Risk Satisfaction Mean (SD)
Offline
Online
1
+
+
2
+
−
5.42 (1.50)
3
−
+
4
−
−
2.03 (1.12)
1
+
+
4.29 (1.80)
2
+
−
3
−
+
4
−
−
3.31 (1.82)
3.05 (1.45)
2.64 (1.40)
Notes:
+ = favorable manipulation.
− = unfavorable manipulation.
Table 10: Means of Satisfaction Depending on the Specific Manipulation.
Study 2: Results
To test the hypotheses 2a-d, linear (for the relationships between satisfaction
and the perceptions), as well as logistic regression (for the probabilistic determination of research shopping) analysis were applied. As suggested by H2a and H2b,
4.4
Study 2: Analysis
165
it was tested for the potential mediation effect of satisfaction with the search phase
by the perceived attributes of the channels for buying. Again, the relative evaluation of channel attributes was used. In terms of the dichotomous outcome variable
research shopping, the method proposed by MacKinnon and Dwyer (1993) and
further discussed by Kenny (2008) was applied. Specifically, each coefficient
was multiplied by the predictor’s standard deviation and subsequently divided by
the outcome variable’s standard deviation. This process aims at guaranteeing the
coefficients’ comparability. Next, direct and indirect effects were bootstrapped.
The results from the estimation largely support the established hypotheses.
The bias-corrected confidence interval for the indirect effect of satisfaction
shows significance on a 95 percent level, with a coefficient of -.548 (LB = -.642;
UB = -.414). Similarly, significance is indicated for the direct path between
satisfaction and research shopping with a coefficient of -.438 (LB = -.697; UB =
-.179). The proportion of the total effect of satisfaction on research shopping that
was mediated, calculated based on the method proposed by Alwin and Hauser
(1975), is 42.5 percent which is regarded as substantial. Moreover, the effect size
for satisfaction with the search channel considerably decreased (from a coefficient
of -.631 to a coefficient of -.438) when the channel perceptions for purchasing
were included in the model. The McFadden’s pseudo R2 increased from .183 to
.307. Not all of the direct effects of the perceptions showed significance, so the
calculation with the relative evaluation of perceived attributes was repeated as in
Study 1, showing significant values. Conclusively, the results supported the partial
mediation hypothesized by H2a and H2b. What is more, the results of Study 1
were underpinned by the significant coefficients of each relative perception of the
channels for buying. The overall results are summarized in Table 11.
Model (2)
Model (3)
Model (4)
b
p
SE
b
p
SE
b
Conclusion
Full direct model
p SE 95 % LB95 % UB b
p SE 95 % LB95 % UB
Age
–>
RS
-.031
.062
-.004
.064 -.129
.121
Partial mediation
Gender
–>
RS
.213
.3
.236
.343 -.435
.908
H3a, H3b
SSAT (total) –>
RS
-.631 *** .087
-.414 -.438 ** .132 -.697
-.179
-.548***.056 -.642
166
Model (1)
Covariates onlyDirect effects on attributes Bootstrapped indirect effects
Influence on
SSAT
–>
SSAT
SSAT
SSAT
SSAT
SSAT
BU prim.
BU prim.
.399 ***
.024
perceptions:
–> BRISK prim.
-.329 ***
.027
Supported for
–> BE prim.
–> BU altern.
–>BRISKaltern.
.087 **
.033
offline, partially
-.101 **
.032
supported for
.139 ***
.033
online search
-.114 ***
.032
–> BEaltern.
–>
RS
BRISK prim. –>
H3c, H3d
-.146
.242 -.620
.328
RS
.352 + .214 -.066
.771
–>
RS
-.047
BU altern. –>
RS
.876 *** .207
BRISKaltern. –>
BEaltern. –>
RS
-.102
BE prim.
Constant
R2
LL
–>
RS
RS
3.21 *
1.5
.169 -.377
.284
Partial support
.471
1.28
for Study 1
.206 -.505
.301
.367 * .166
.042
.693
2.02
-1.02
5.06
1.55
.183
.307
-151.9
-128.9
Table 11: Results of Mediation Testing.
V R ESEARCH S HOPPING
Notes:
Unstandardized coefficients shown.
+ p < 0.10, * p < 0.05, ** p < 0.01, *** p < 0.001.
BU = buying perceived usefulness.
BRISK = buying perceived risk.
BE = buying perceived ease.
SSAT = search channel satisfaction.
RS = Research shopping (1/0).
4.4
Study 2: Analysis
167
The central hypotheses regarding satisfaction with the search channel (H2c
and H2d) proposed that satisfaction with the search channel is positively related
to the perceptions of the primary search channel for purchasing, and negatively
related to the perceptions of the alternative search channel for purchasing. In
the overall model all hypotheses can be confirmed. Satisfaction significantly
influences perceived usefulness (b = .399; p < .001), perceived risk (b = -.329;
p < .001), and perceived ease of use (b = .087; p = .008) of the primary search
channel for purchasing. Similarly, satisfaction with the search channel unfavorably
influences the perceptions of the alternative search channel for purchasing. The
regression estimates were highly significant and meaningful; satisfaction with the
search channel unfavorably influences perceived usefulness (b = -.101; p = .002),
perceived risk (b = .139; p < .001), and perceived ease of use (b = -.114; p < .001)
of the alternative search channel. Finally, it was checked for differences between
searching offline and online as Study 1 indicated that they exist.
The results for searching offline clearly pointed out that satisfaction leads to
systematically worse perceptions of the alternative channel’s attributes (perceived
usefulness: b = -.072; p = .008, perceived risk: b = .178; p < .001, perceived ease
of use: b = -.130; p = .003), while perceiving the primary channel’s attributes more
favorable (perceived usefulness: b = .430; p < .001, perceived risk: b = -.347; p <
.001, perceived ease of use: b = .072; p = .087). Contrary to this, the hypotheses
for searching online were not fully confirmed. Satisfaction with the online search
favorably influences perceived usefulness (b = .340; p < .001), perceived risk (b
= -.266; p < .001), and perceived ease of use (b = .136; p = .012) of the primary
search channel for purchasing. However, only one of the hypothesized negative
links between satisfaction with the search channel and the perceptions of the
alternative channel was found, namely for the influence of satisfaction on the
perceived ease of use of the alternative channel (b = -.117; p = .027). All other
hypotheses were rejected (perceived usefulness: b = -.011; p = .810; perceived
risk: b = -.004; p = .925). To sum up, the influence of satisfaction is particularly
168
V R ESEARCH S HOPPING
salient when searching offline. However, the proposed effect remains rather silent
when searching online.
5
Discussion
The studies investigated the business- and research-relevant phenomenon of research shopping. Contrary to other approaches (e.g., Verhoef et al., 2007), the
focus was rather on the question "Why would consumers stick with a channel"?
For doing so, the status quo bias theory was suggested to serve as underlying
theoretical perspective (Kahneman et al., 1991; Samuelson and Zeckhauser, 1988).
Whereas previous studies forego taking into account multiple search channels,
the current study investigates consumer channel behavior more realistically since
most consumers nowadays do not rely on a single search channel (Pauwels et al.,
2011; Yahoo! Inc. and OMD, 2006). Thus, to assess the status quo bias theory’s
value for explaining research shopping, two mechanisms were investigated.
First, biased behavior towards research shopping was investigated, thereby
determining the boundaries of economics theory (Brown and Dant, 2008). Specifically, it was suggested and empirically validated that using a single channel for
searching would trigger a status quo bias decreasing the probability of research
shopping. First, this was indicated by the increased probability of research shopping when complementing information search through a second channel, although
from an economics theory perspective this should not be decisive (Kahneman
et al., 1991). Additionally, the functional form of the difference in probabilities
of research shopping was elaborated. Findings revealed that the hypothesized
inverted U-shape is particularly salient when searching online, however, is largely
negligible when searching offline. Arguing with the threshold introduced by Gal
(2006), this might as well indicate that the status quo bias is substantially more
accentuated for searching offline. From this point of view, even more incentives
must be provided for offline searchers to provoke research shopping. This is also
5 Discussion
169
in line with Verhoef et al.’s (2007) findings that online consumers are more prone
to research shopping. In sum, although the hypotheses were not fully supported, it
is suggested that consumers who search online were less susceptible to a status
quo bias.
In sum, the nonlinear relationships concerning the difference in probabilities
between using single versus multiple channels for searching strongly advocate the
applicability of the status quo bias theory to research shopping, whereas the bias is
particularly pronounced when searching offline (Falk et al., 2007). Moreover, the
findings confirm previous literature, which proposed that multichannel consumers
differ from other consumers in their decision-making (e.g., Neslin et al., 2006;
Rangaswamy and Van Bruggen, 2005; Konus et al., 2008).
Second, it was focused on biased perceptions regarding the purchase channels
as indirect source of the status quo bias (e.g., Yen and Chuang, 2008). In line with
Falk et al. (2007), satisfaction with the search phase was identified to provoke
biased perceptions of the purchase phase, which in turn, determine research
shopping. The findings of Study 1 were supported, as the results showed robust
and significant effects for switching from an offline to an online channel, while
the effect was not present vice versa. Literature suggesting channel choice as
depending on the respective phase of the buying process (e.g., Pookulangara and
Natesan, 2010) aids in interpreting these findings. Specifically, as consumers tend
to get a first impression by using an online channel they might switch channels,
regardless of whether they are satisfied or not. This type of research shopping
referred to as "web-to-store" shopping (Pauwels et al., 2011) is by far more
common, as confirmed by the descriptive statistics of both studies. Additionally,
consumers possess different motivations in their channel choice (Kukar-Kinney et
al., 2009). As such, using an offline channel is considered as larger commitment to
purchasing or as the last link before purchasing. Hence, when favorably perceiving
searching offline, consumers largely disregard alternative channels and buy right
away. In sum, the described facets of the status quo bias theory predominantly
unfold their influence when searching offline; however, both mechanisms need to
170
V R ESEARCH S HOPPING
be considered to provide a complete picture of how the status quo bias theory is
applicable to research shopping.
Finally, a goal of the current investigation was to contribute to literature on the
rarely used status quo bias theory (Samuelson and Zeckhauser, 1988). Although it
has received popularity in other disciplines (e.g., Johnson et al., 1993; Rubaltelli
et al., 2005), to date, this theory has rarely been applied in marketing settings. The
findings suggest that a status quo bias is largely applicable to research shopping,
and thus, a further instance that underlines the theory’s robustness is shown.
Moreover, while most research that contributes to the status quo bias theory has
applied experimental approaches, the current study contributes to the literature
via a large-scale study.
6
Managerial implications
Assuming that the biases found in this study cannot be directly used to advise
companies in refining their multichannel strategies would be misleading. Research
has provided substantial evidence that multichannel consumers are more profitable
than consumers who use a single channel (e.g., Kumar and Venkatesan, 2005;
Venkatesan et al., 2007). To persuade single channel consumers to become (more
profitable) multichannel consumers, the status quo bias must be overcome. The
necessity of a well-integrated and orchestrated system of channels is suggested
(Chatterjee, 2010; Neslin et al., 2006; Reynolds et al., 2006). For instance,
practitioners could provide online terminals in their brick-and-mortar stores to
reduce uncertainty toward the commonly more cost-efficient online channels
(Campbell and Frei, 2010; Hitt and Frei, 2002). Accordingly, direct links to
offline channels on company websites may reduce existing uncertainties when
buying offline. Both options enhance the utility that is derived from buying within
the same company’s channels by diminishing a potential bias (Gal, 2006) to
switch the channel. In line with recent research (Ansari et al., 2008; Thomas
6 Managerial implications
171
and Sullivan, 2005), it is recommended that communication efforts of companies
need to be aligned to further guide consumers through the company’s maze of
channels. For instance, by effectively communicating the advantages of using an
online channel to make purchases, the consumer’s expected utility using a specific
channel increases, while uncertainty toward the channel decreases. This would
magnify a company’s efforts to bring consumers to employ a specific channel.
Nevertheless, a pivotal factor to this strategy is creating a firm-lock-in (Verhoef
et al., 2007), or making research shoppers so-called "loyal research shoppers"
(Neslin and Shankar, 2009). If no burden to keeping consumers from switching
companies can be established, practitioners may be well-advised to use the status
quo bias to deter consumers from research shopping (Neslin and Shankar, 2009)
because the probability of switching companies decreases simultaneously (e.g.,
Chiu et al., 2011; Nunes and Cespedes, 2003).
A major finding of Study 1 is that consumers become research shoppers
when they search via multiple channels. Hence, to avoid research shopping, the
relevant question is how companies can prevent consumers from using multiple
channels for searching (as one-stop shoppers; Neslin and Shankar, 2009). Since
the effect is especially accentuated when searching online, companies should
provide comprehensive, clear-cut information on their website to make using
a second search channel obsolete. This, in turn, would raise the hurdle that is
imposed by the status quo bias and reduce the probability of research shopping.
Adding to this, Study 2 identifies satisfaction with the search phase as a key
variable in embracing the advantages of a status quo bias. This is considerably
more pronounced when searching offline. Thus, companies should define search
satisfaction as focal channel performance variable. In the conducted experiment,
satisfaction via the accuracy of information and appearance of customer contact
personnel was manipulated; many more relevant dimensions, such as a website’s
graphic style, exist (e.g., Montoya-Weiss et al., 2003) when striving to increase
the bias.
172
V R ESEARCH S HOPPING
Finally, in line with Valenzuela et al. (2008), it was suggested that the presence
of a status quo bias may also result in higher levels of satisfaction with the buying
process. Consumers might not feel being forced to make a trade-off between what
channel to employ because they are biased toward the search channel; therefore,
not being status quo-biased may be negatively related to the satisfaction of the
overall buying process. Thus, when striving to make consumers search and
purchase with the same channel, practitioners can directly use this bias to enhance
overall consumer satisfaction with the buying process.
7
Limitations and future research
As with all studies, the current investigation has limitations that can guide future
research. First, this article focuses on online and offline channels to provide
initial evidence on a potential status quo bias. Previous literature has investigated
a broader variety of channels (i.e. catalogs, Verhoef et al., 2007) using other
approaches and theoretical underpinnings. While the plurality of channels has
much increased, few resonances can be found in research. Thus, considering the
status quo bias for other types of channels constitutes an opportunity for further
research.
Next, it was controlled for experience by determining consumers’ last purchases of financial services as a means to generate valid findings. However, one
might also consider search and purchase experiences of other products and services as relevant (e.g., Neslin et al., 2006) to overcome the status quo bias. To
further isolate and extend the findings of the current study in direction of the
suggested research questions, one can conduct longitudinal research that spans
several industries.
Related to experience is the phenomenon of channel migration, referring to
using one channel for making the purchase and over time migrating to another
for future purchases (Ansari et al., 2008). Similarly as for research shopping, it
7 Limitations and future research
173
is suggested that the status quo bias theory holds important implications for this
phenomenon. In line, we expect interesting findings from using the status quo
bias theory for further channel-related phenomena.
It is acknowledged that also other theories might come to similar hypotheses
as the status quo bias theory. For instance, omission bias and inertia have been described as being closely related to status quo bias (e.g., Anderson, 2003; Chernev,
2004; Ritov and Baron, 1992). According to both, there is a preference for the
option that does not require action (Spranca et al., 1991); however, viewing the
search and purchase phase as partially separate (i.e., consumers do not necessarily
buy a product or service when having searched, in particular in the financial services sector where spontaneous purchases are rare) also implies that keeping the
status quo requires taking some action. Certainly, this constitutes a basic assumption of the presented article, however, to date studies motivate this assumption’s
validity since the purchasing process becomes increasingly fragmented (Schaefer,
2011). Hence, both mechanisms only play a minor role in research shopping
while the main effect is accredited to the status quo bias. Additionally, Study 2
focuses on evaluations of channels as trigger of foregoing research shopping, and
not on self-perceptions as inertia would propose (Falk et al., 2007). Thus, it is
suggested that inertia, as well as the omission bias play a minor role for explaining
the effects found in both studies. However, both omission bias and inertia present
excellent opportunities for further investigating research shopping.
In line, deciphering the effects of the status quo bias theory is an example for
applying alternative theories to a phenomenon that has primarily been considered
from similar theoretical perspectives, such as the theory of reasoned action or
planned behavior (e.g., Pookulangara and Natesan, 2010; Verhoef et al., 2007).
This allows for generating novel theoretical insights since both aforementioned
theories are in line with economics theory. It is worthwhile to note that not
all hypotheses were confirmed, thus, further theories may be helpful to explore
consumer channel behavior and provide additional insights into research shopping.
For instance, combining switching cost theoretical considerations (e.g., Burnham
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V R ESEARCH S HOPPING
et al., 2003; Shankar et al., 2011) with the status quo bias theory may provide
information on the boundaries of the selected theoretical perspective. Certainly,
also theories from other disciplines, such as consumer psychology, have the
potential to further complement the current findings.
A highly attractive avenue for future research is to examine the status quo
bias in the combination of research shopping and switching companies, which
is dubbed cross channel shopping (e.g., Brynjolfsson et al., 2009; Chiu et al.,
2011). This is certainly attractive as profound knowledge regarding the correlation
between using different channels and companies when searching and purchasing
a product or service is still missing (e.g., Chiu et al., 2011; Nunes and Cespedes,
2003).
Finally, compared to studies that use overall evaluations of risk and usefulness
for determining channel choice (e.g., Montoya-Weiss et al., 2003; Verhoef et al.,
2007), consumers were directly asked for their perceptions of channel attributes.
Certainly, one limitation of this procedure is its difficulty in terms of the confirmatory factor analysis. Although the factors employed were largely supported
by both analyses, items needed to be dropped because of multi-dimensionality.
Moreover, one factor out of twelve did not reach an average variance extracted
of .5, which did not alter the basic mechanism since the effects in the follow-up
analyses were deemed substantial.
In conclusion, this article emphasizes, notwithstanding the limitations, the
usefulness of the status quo bias in explaining research shopping. Doing so, the
theoretical lens applied in this article contributes to current knowledge in the area
of multichannel consumer research.
A Confirmatory factor analysis
175
Appendices
A
Confirmatory factor analysis
Measurement scales and respective indicators
SL
AVE
Cr’s a
CR
surveyexp.surveyexp.surveyexp.survey exp
Buying perceived usefulness
Offline
.617 .672 .858 .882 .865
Provides good selection of products (a), (d)
.81 .77
Way of buying fits my needs (a), (e)
.83 .86
Makes me feel unique (e)
.66 .75
Gives good advice (a), (b)
.83 .89
Provides good value for money (a) (y)
Buying perceived risk
.706 .757 .829 .861 .827 .862
Presents risk to get wrong product (a), (b)
.84 .85
Presents risk to get low quality (a)
.84 .89
Buying perceived ease
.633 .724 .769 .833 .775 .840
Is a lot of effort (a), (d)
.82 .89
Is time-consuming (a), (d)
.77 .81
Buying perceived usefulness
Online
.554 .414 .819 .676 .829 .738
Provides good selection of products (a), (d)
.76 .65
Way of buying fits my needs (a), (e)
.83 .69
Makes me feel unique (e)
.54 .60
Gives good advice (a), (b)
.81 .63
Provides good value for money (a) (y)
Buying perceived risk
.714 .564 .835 .718 .721 .833
Presents risk to get wrong product (a), (b)
.85 .79
Presents risk to get low quality (a)
.84 .71
Buying perceived ease
.633 .733 .769 .837 .775 .846
Is a lot of effort (a), (d)
.82 .90
Is time-consuming (a), (d)
.77 .81
Satisfaction (c) (x)
Satisfaction
-
Pleased with overall service
-
.84
Shopping is a delightful experience
-
.93
I am completely satisfied
-
.91
.800
-
.920
-
.923
Notes:
SL = Standardized loading; AVE = Average variance extracted; Cr’s a = Cronbach’s alpha; CR =
Composite reliability.
(x) Satisfaction is used for the experiment only.
(y) Deleted from factor analysis, but included as control variable.
Sources:
(a) Verhoef et al. (2007)
(b) Montoya-Weiss et al. (2003)
(c) Voss, Godfrey and Seiders (2010)
(d) Baker et al. (2002)
(e) Srinivasan, Anderson, and Ponnavolu (2002)
x
176
B
V R ESEARCH S HOPPING
Graphical abstract
B IBLIOGRAPHY
177
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Curriculum Vitæ
Personal Information
Name:
Date of Birth:
Place of Birth:
Nationality:
Tobias Franz Schlager
13th of August 1983
Freilassing, Germany
German
Education
02/2010 - present
07/2011 - 08/2011
07/2008 - 08/2008
10/2003 - 01/2009
09/2006 - 05/2007
09/1993 - 07/2002
University of St. Gallen (HSG), St. Gallen, Switzerland
Doctoral Studies in Management (Specialization: Strategy)
University of Michigan, Ann Arbor, United States
Summer Program in Quantitative Research Methods
Harvard University, Cambridge, United States
Summer Program (Major: International Marketing)
University of Innsbruck, Austria
Magister in International Business Administration
(Major: Marketing, Strategy)
Wilfrid Laurier University, Waterloo, Canada
Study Abroad Program (Major: Marketing, Strategy)
Rottmayr Gymnasium, Laufen, Germany
Abitur (A-Levels)
Work Experience
02/2010 - present
01/2011 - present
05/2009 - 01/2010
10/2008 - 05/2009
10/2007 - 06/2008
05/2007 - 10/2007
Institute of Insurance Economics
University of St. Gallen, Switzerland
Project Manager and Research Associate
Universal Information Broking (UIBk), Innsbruck, Austria
Founder and Member of the Supervisory Board
LICETUS GmbH & Co. KG, Munich, Germany
Business Consultant
Institute for Strategic Management, Tourism and
Marketing
University of Innsbruck, Austria
Teaching Assistant
Austria PUMA Dassler GmbH, Salzburg, Austria
Marketing Manager Trainee
PUMA AG, Herzogenaurach, Germany
Internship International Product Marketing