The Value of Self-Service: Long-Term Effects of Technology-Based Self-Service Usage on Customer Retention

Abstract

Advancements in information technology have changed the way customers experience a service encounter and their relationship with service providers. Especially technology-based self-service channels have found their way into the 21st century service economy. While research embraces these channels for their cost-efficiency, it has not examined whether a shift from personal to self-service affects customer-firm relationships. Drawing from the service-dominant logic and its central concept of value-in-context, we discuss customers’ value creation in self-service and personal service channels and examine the long-term impact of these channels on customer retention. Using longitudinal customer data, we investigate how the ratio of self-service vs. personal service use influences customer defection over time. Our findings suggest that the ratio of self-service vs. personal service used affects customer defection in an U-shaped manner, with intermediate levels of both, self-service and personal service use, being associated with the lowest likelihood of defection. We also find that this effect mitigates over time. We conclude that firms should not shift customers towards self-service channels completely, especially not at the beginning of a relationship. Our study underlines the importance to understand when and how self-service technologies can create valuable customer experiences and stresses the notion to actively manage customers’ co-creation of value.

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DOself-servicesreallypayoff?
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Do Self-Services Really Pay Off?
Anne Scherer
Nancy V. Wünderlich
Florian v. Wangenheim
Last Updated: December 2014
Basis of:
Scherer, A., Wünderlich, N.V., v. Wangenheim, F. (2015). The Value of Self-Service: The
Long-Term Effects of Technology-Based Self-Service Usage on Customer Retention. MIS
Quarterly, forthcoming.
Abstract: Advancements in information technology have changed the way customers
experience a service encounter and their relationship with service providers. Especially
technology-based self-service channels have found their way into the 21st century service
economy. While research embraces these channels for their cost-efficiency, it has not
examined whether a shift from personal to self-service affects customer-firm relationships.
Drawing from the service-dominant logic and its central concept of value-in-context, we
discuss customers’ value creation in self-service and personal service channels and examine
the long-term impact of these channels on customer retention. Using longitudinal customer
data, we investigate how the ratio of self-service vs. personal service use influences customer
defection over time. Our findings suggest that the ratio of self-service vs. personal service
used affects customer defection in an U-shaped manner, with intermediate levels of both, self-
service and personal service use, being associated with the lowest likelihood of defection. We
also find that this effect mitigates over time. We conclude that firms should not shift
customers towards self-service channels completely, especially not at the beginning of a
relationship. Our study underlines the importance to understand when and how self-service
technologies can create valuable customer experiences and stresses the notion to actively
manage customers’ co-creation of value.
Keywords: Self-service, e-service, value-in-context, customer retention, customer defection,
longitudinal

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Do Self-Services Really Pay Off?
INTRODUCTION
In the last decades, information technology has continuously changed the way customers
experience a service encounter and their relationship with a service provider. Today, 58% of
US bank customers prefer to conduct their financial businesses online, via ATM, or mobile
phone (American Bankers Association 2013), 59% of US customers prefer to shop their retail
or groceries on the Internet (Nielsen 2012), and 68% of airline customers worldwide check-in
for their flight online, via mobile phone, or self check-in kiosk at the airport (SITA 2012).
Through the introduction of such technology-based self-service channels, customers have
become “active participants” rather than a “passive audience” in service delivery (Prahalad
and Ramaswamy 2000).
Business press praises self-service channels for their great potential to increase firm
productivity while reducing the costs of service delivery at the same time. The costs for a
banking transaction, for instance, can be reduced from 1.15 US dollars to only 2 cents by
switching from an onsite to an online transaction (Moon and Frei 2000); the number of
passengers processed for a flight can be increased by up to 50 percent via self check-in
options (IATA in SITA 2009); or 2.5 employees can be replaced by one self-checkout kiosk
at the grocery store (The Economist 2009). Forecasts expect this trend in business practice to
continue, especially in the hospitality and health-care sector (The Economist 2009) and
through the rise of mobile self-service applications (Leggett 2013).
The appeal of self-service technologies has not only been recognized by practitioners,
but also by many scholars. Ever since the first introduction of self-service offers and
technology-based self-service channels, research has underlined the value of technology (e.g.,
Bitner et al. 2000; Dabholkar 1996) and the benefits of customers as “partial employees” from
a cost cutting and efficiency perspective (e.g., Fitzsimmons 1985; Lovelock and Young 1979;

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Mills et al. 1983). Today, this notion is well established in a number of research disciplines,
ranging from IS (e.g., Ba et al. 2010), management (e.g., Campbell and Frei 2010), to
marketing literature (e.g., Meuter et al. 2005). Next to the advantages for service providers,
research has also highlighted numerous advantages of self-service channels for customers,
such as an increased convenience (i.e., through greater accessibility and availability) and
improved control during the service process (e.g., Collier and Kimes 2013; Schumann et al.
2012; Zhu et al. 2007). Given its apparent benefits for both customer and provider, extensive
research has been conducted to understand customers’ motivation to adopt and continuously
use technology-based self-service channels and has identified important customer
characteristics (e.g., Hitt and Frei 2002; Xue et al. 2007), technology (or service channel)
characteristics (e.g., Collier and Kimes 2013; Meuter et al. 2005), as well as situational
components (e.g., Simon and Usunier 2007) crucial for customers’ self-service trial.
While current research generally highlights the benefits of self-service channels, it
mostly disregards prior findings on the merit of personal service channels for both customer
and firm, e.g. in terms of customization, trust, or close customer – firm relationships (e.g.,
Barnes 1997; Ennew and Binks 1999; Mittal and Lassar 1996). Instead of considering the
advantages of both service channels, more and more service providers actively “push” their
customers towards self-service channels (Langer et al. 2012; White et al. 2012). This is
alarming, as recent research indicates that the value customers can derive form self-service
channels differs from personal service channels in a way that does not allow a mere
substitution of these channels (Kumar and Telang 2012). Even more so, indications are that
self-service customers are not necessarily satisfied with a provider’s self-service channel, but
simply stuck with it (Buell et al. 2010), and self-service channels can harm customer loyalty
when used as a full substitute for personal service channels (Selnes and Hansen 2001). Given
these findings, a number of researchers have questioned the enthusiasm for self-service

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channels and call for an in depth investigation of their long-term effects on customer
relationships (Dabholkar and Bagozzi 2002; Meuter et al. 2005; Selnes and Hansen 2001).
Following this call, the present study investigates the differential effects of self-service and
personal service use on customer retention over time.
Hereby, the present study makes a number of significant contributions to extant
literature. First, we offer a new way of looking at customer retention in settings that offer
multiple service channels to customers. Drawing from the concept of value-in-context, we
discuss the differential effects of self-service vs. personal service channels on customer
relationships over time. As a central pillar of the service-dominant logic (S-D logic; Vargo
and Lush 2004, 2008), the concept of value-in-context provides a general framework for the
integration of established theories and research findings. Second, and in a related point, our
research demonstrates how S-D logic provides a unifying framework for theory application
and hypothesis development for empirical research. In particular, we show the benefits of
examining customer relationships from a S-D logic vantage point by fully explicating when
and how technology-based self-service offerings can create valuable customer experiences.
This view allows us to not only acknowledge the distinct features and capabilities of the
technology offered to the customer, but also the unique context in which the technology is
applied. Third, we extend previous media choice and media effectiveness research to
customer-firm interactions. While previous media research has focused on team collaboration
within organizations, we demonstrate that theories on media choice and media effectiveness
are helpful in characterizing various service channels and in discussing their impact over time.
Through the integration of media richness (Daft and Lengel 1986) and channel expansion
theory (Carlson and Zmud 1999), the present study departs from a mere static view of
technology and underlines the context-specifity of self-service technologies and their impact
on customer-firm relationships. Finally, we empirically test the hypothesized impact of self-

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service and personal service use on customer retention by applying survival analysis to a
unique longitudinal dataset that allows us to investigate the effects of both channels over time.
Our study is thus the first to fully account for the interactions between personal and self-
service channels and time.
From a managerial standpoint, the current study demonstrates that technology-based
self-service channels may not always lead to the desired results; instead, firms must consider
the unique value customers can derive from both self-service and personal service channels
over time. More specifically, insights from our research underline that firms need to consider
the capabilities of their service channels as well as the customers’ unique circumstances, such
as their duration with the provider, to fully leverage the potential of technology-based self-
service channels.
The remainder of this article proceeds as follows. We begin by presenting the
theoretical foundations of our research. Based on an S-D logic perspective, we first contrast
the provider’s value proposition in self-service and personal service channels and then
examine the value customers can derive from these differential propositions. We then discuss
the impact of both service channels and their interplay on customer defection by drawing
from theories on media richness and channel expansion. Based on this theoretical framework,
we derive our hypotheses regarding the consequences of self-service usage. We test these
hypotheses using longitudinal customer usage data (n=5,467) of a roadside assistance service
provider in the automotive industry. The study concludes with theoretical contributions and
managerial recommendations on how customer experiences and relationships can be
improved in multichannel self-service settings.
VALUE IN THE CONTEXT OF TECHNOLOGY-BASED SELF-SERVICE
Prior research provides ample evidence that customers are more likely to remain with their

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provider, if they consider this behavior beneficial (e.g., Oliver 1999). As Kim and Son (2009,
p. 53) explain, “loyalty – which indicates a favorable attitude toward maintaining a long-term
relationship with the provider – results from cognitive perceptions about the current value of
using the service”. Hence, to fully understand how technology-based self-service channels
affect customers’ retention to a service provider, we consider the value that customers can
derive from both personal and self-service channels over the duration of their relationship
with the provider.
The importance of a customer-derived value is evident in previous research. Especially
the concepts of value-in-use and value-in-context have gained momentum in service science
(e.g., Lusch and Vargo 2014; Chandler and Vargo 2011; Vargo and Akaka 2012). According
to these concepts, customers will only be willing to pay high prices or continue using service
offers of a firm when they can create value from their use. The concepts of value-in-use and
value-in-context are fundamental pillars of the service-dominant logic (S-D logic) of
marketing (Vargo and Lusch 2004, 2008). According to S-D logic, value creation is not
confined to the firm or separated from the customer. Instead, Vargo and Lusch (2008) propose
that value is always co-created. In other words, value is created with the customer through a
unique combination of the customer’s and the provider’s resources (e.g., through a customer’s
skills to use a self-service technology and the provider’s knowledge embedded in the self-
service technology that ensures an easy-to-use design). In S-D logic terms, customer and
provider are essentially resource integrators. Firms do not deliver or distribute value, they
make value propositions. That is, according to S-D logic, firms create and deliver resources
that enable customers to derive value, while customers are the ones who determine value by
incorporating the firm’s offering into their own lives. Given the dependence on the unique
resources and circumstances of a customer’s value creation, S-D logic also posits that value is
uniquely and contextually derived. Accordingly, every customer experiences and, in

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consequence, values a service offer and a technology differently. The term “value-in-context”
reflects this phenomenological perspective of value and entails that value is always co-
created, contextually specific, and contingent on the integration of market, public and private
resources (Chandler and Vargo 2011; Lusch and Vargo 2014).
The importance of a contextual and phenomenological perspective is also highlighted
in the current IS notion on the social construction of technology (e.g., Orlikowski and Barley
2001). According to this notion, both physical and social aspects need to be considered when
examining technological artifacts. Technology is thus no longer considered a mere bundle of
physical features and capabilities; instead, it is acknowledged that technology is always
embedded in some time and place (e.g., Orlikowski and Iacono 2001; Al-Natour and Benbasat
2009). As a result, researchers posit that instead of only considering the distinct features and
capabilities of a technology, it is important to account for the unique social context in which
the technology is applied. Just as in S-D logic, technology is thus seen as a product of human
action as well as a medium for human action (Orlikowski 1992; Vargo and Akaka 2012).
Taken together, our previous discussion suggests that the value-in-context customers
can derive from service is not only highly dependent on firm-provided resources such as the
unique capabilities of the utilized channel (e.g., self-service vs. personal service channel), but
also dependent on privately accessed resources such as the consumers’ unique knowledge,
skills and abilities to use this channel effectively in a particular situation (e.g., for a complex
task). In order to understand customer retention to a service firm in multichannel service
settings, we thus examine 1) the offering a firm is making (i.e., the value proposition) and 2)
the customer’s unique resources and circumstances that determine the value that is co-created
at last (i.e., the value-in-context). We discuss the different capabilities and characteristics of
personal and self-service channels from a media richness point of view and integrate channel
expansion theory to understand when and how customers can create unique value from these

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different service offerings. Figure 1 provides an overview of the theoretical foundation and
underpinnings of this study. We will discuss these aspects in detail in the following.
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Figure 1. Conceptual Framework of this Research
Value Proposition – Characteristics and Capabilities of Self-Service and Personal
Service Channels
According to prior research, two focal aspects characterize a technology-based self-service
channel. First and foremost, technology-based self-service channels entail a mere interaction
between customer and technology (e.g., Kumar and Telang 2012). The service provider
representative is no longer directly involved in the provision of the service. Hence,
technology-based self-service channels do not support directed and dyadic communication
between customer and service provider representatives (Schultze 2003). Second, self-service
channels require customers to become increasingly involved in the service process (Campbell
et al. 2011) and deliver the service through the mere interaction with the firm’s automatic
system (i.e., the information technology). In S-D logic terms then, customers are not only co-
creators of value in self-service channels, but also active co-producers of the core offering
itself (Vargo and Lusch 2008). Common examples of such a technology-based self-service
Value-in-Context
(integration of market, public
and private resources)
Service
Channel
(value proposition)
Task Characteristics
(e.g., complexity)
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Media Richness Theory!
Service-Dominant
Logic of Marketing
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Channel Expansion Theory!
Customer Characteristics
(e.g., experience with the provider)
Customer
Retention
Focus of empirical
investigation
Theoretical
underpinnings
Note:

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channel include Web-based self-service portals or interactive voice-response units (Kumar
and Telang 2012).
In contrast to technology-based self-service channels, personal service channels
always involve the presence of a service provider representative and entail a direct interaction
and communication between customer and service employee. They are often also referred to
as “assisted channels” as firm representatives actively assist customers during service delivery
(Kumar and Telang 2012). Given the advancements in information technology, however, both
parties do not need to be physically co-located. Their interaction can be mediated through a
technology such as the telephone. The importance of a mere awareness of a human
communication partner in technology-mediated service delivery is evident in previous
research (e.g., Wünderlich et al. 2013). Once humans are aware of a human communication
partner in a technology-mediated encounter, they have been shown to act more sociable, show
more mirth, and spend more time on a task (Morkes et al. 1999). We thus do not only
consider face-to-face encounters as personal service channels, but also regard technology-
mediated service channels such as the telephone as personal service channels – as long as they
entail a customer’s awareness of the presence of a human counterpart and a direct interaction
between the two.
In both, self-service and personal service channels, service providers offer distinctive
resources to their customers. To examine these channel capabilities in detail and contrast a
firm’s value propositions in self-service and personal service channels, we draw from media
richness theory (MRT; Daft and Lengel 1986; Daft et al. 1987). We integrate channel
expansion theory (Carlson and Zmud 1999) in a later step, to acknowledge the context-
specifity of technology and a customer’s value creation. According to MRT, media can be
characterized by their ability to convey communicative cues, give immediate feedback,
support language variety, and allow personalization. Clearly, these media characteristics are

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also closely related to the intimacy of an exchange (Rice 1993) and the idea of social
presence, which posits that media differ in their ability to convey the presence of
communicating participants (Short et al. 1976).
Following this differentiation, self-service channels can be characterized and
contrasted to personal service channels by their lower personalization, the reduced number of
cues transmitted simultaneously, and the lower symbol set offered. The fact that self-service
channels always entail a customer’s sole interaction with information technology underlines
that these service channels are more standardized and allow less customization and
personalization than interactive personal service channels (Ba et al. 2010; Cyr et al. 2007;
Davis et al. 2011). Online accessible “frequently asked questions” (FAQ), for instance, allow
customers to get an answer to common problems encountered by customers. As these FAQs
are very standardized, they do not allow customers to interpret any other cues (e.g.
trustworthy behavior, comforting voice, etc.) than the ones provided by the firm’s Web site.
Moreover, this Web-based self-service does not allow personalized attention to the individual
question at hand and does not (necessarily) offer immediate feedback to the specific problem.
As the example illustrates, self-service channels are rather lean, highly standardized, and do
not include personalized attention to customer needs. Nonetheless, these channels often make
use of technology features that offer customers easy accessibility (e.g., nearby ATM or
ubiquity of mobile online application vs. a bank’s branch), great availability (e.g., 24/7 vs. a
bank’s office hours), and thus increased flexibility and high efficiency of information
acquisition (e.g., Choudhury and Karahanna 2008).
In contrast, personal service channels are highly interactive (Venkatesan et al. 2007),
which greatly supports personalized service to customer needs (Ba et al. 2010), individualized
feedback, and language variety. Consider the FAQ example again: Customers, who use a
personal service channel and call-in to discuss their problem or even visit a service branch of

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their provider to talk to a service representative, can explain their problem in detail. Through
an immediate feedback and an interactive give-and-take between customer and employee,
both parties can reach a mutual understanding of the problem and how it can best be solved.
The personal service channel thus allows tailoring the service to the customer’s specific needs
and wishes (i.e., offers high personalization) and enables service employees to anticipate
customer needs more easily through the support of language variety and the greater number of
communicative cues transmitted. As the interpersonal nature of the exchange allows
individual attention and feedback to occur (Barnes et al. 2000), personal service channels also
offer social benefits for customers in terms of “familiarity, personal recognition, friendship,
and social support” (Gwinner et al. 1998, p. 102). Simply put, it is the responsiveness that
only a human being can offer that differentiates the capabilities of a personal service channel
from a technology-based service channel (Ba et al. 2010).
Value-in-Context – When and how Self-Service and Personal Service Channels can
Create Valuable Customer Experiences
The value customers can derive from a service channel does not only depend on the
capabilities and characteristics of the particular channel, but also on the unique circumstances
and the person using it (Dennis et al. 2008). Thus, according to the current notion of a
context-specifity of technology (e.g., Orlikowski and Barley 2001; Al-Natour and Benbasat
2009) and S-D logic’s concept of a value-in-context, it is important to not only consider the
physical features and capabilities of the firm-provided technology, but also the unique context
(e.g., time, place, and people) in which it is applied.
In its original form, MRT (Daft and Lengel 1986) introduced a number of
characteristics that defined a medium. This richness scale was considered static and hence
(pre-)defined the effectiveness of a medium to accomplish a given task. In particular, Daft and
Lengel (1986) proposed that equivocal tasks, which require the exchange of complex

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knowledge and are ambiguous in their interpretation, are best solved through rich media (i.e.,
media which allow transmitting a greater number of communicative cues, language variety,
immediate feedback, and personalization). Users relying on lean media for complex and
ambiguous tasks should encounter a lower outcome-quality. This prediction has been
supported in a number of prior studies. For instance, research on team collaboration has
demonstrated that while teams can perform complex tasks through lean media, it takes them
longer to reach a shared understanding and solve a task (Walther 1992). Similarly, research on
complex industrial (B2B) service settings has shown that an appropriate match of media
richness and service type results in improved customer loyalty, as an appropriate use of rich
media can create personal linkages through the rich interaction and socialization of customer
and provider (Vickery et al. 2004). In further support of this notion, Sheer and Chen (2004)
have demonstrated that rich media may not only be used to accomplish a complex task more
efficiently, but also to satisfy relational goals of communicating partners.
More recently, IS researchers have concluded that “some services may be too
complex, rather rare, or may need to be complemented by human interaction” (Ba et al. 2010,
p. 424). Indeed, in their study of a Web-based self-service, Kumar and Telang (2012) find that
once information is unambiguously provided on a Web portal, customers substitute their use
of personal-assisted call-center calls with the self-service Web portal. However, once the
information on the Web portal is ambiguous, the introduction of the Web-based self-service
increases customers’ usage of personal-assisted service channels. The authors’ conclusion
parallels central MRT propositions, stressing the notion that a self-service channel should be
most appropriate for simple, unambiguous tasks, as too complex and ambiguous tasks confuse
self-service customers and consequently also increase the additional use of the call-center.
The above reasoning suggests that customers may not always derive the same value
from a self-service and a personal service channel. Instead, customers should be able to derive

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most value from self-service channels when these lean and highly standardized channels are
used for easy and repetitive tasks. More precisely, MRT’s predictions on media effectiveness
imply that customers’ beliefs about duration appropriateness are unlikely to be met when self-
service channels are used to accomplish rather complex tasks. The same is reasonable for
personal service channels and simple tasks. Simple tasks merely require media that are high in
transmission rather than processing capabilities (Zigurs and Buckland 1998). Hence, using
personal service channels that allow an individual give-and-take to process information would
overcomplicate service processes (Vickery et al. 2004). Consider a banking transaction. If a
customer conducted a simple transaction through an assisted teller in a bank’s branch, this
would increase the customer’s effort in initiating and accomplishing the task (i.e., getting
back and forth to the branch, reaching an available teller, explaining the task, etc.) and in
consequence unnecessarily increase customers’ transaction costs. Clearly, using personal
service channels for such an easy task, would not only decrease customers’ contact beliefs
(i.e., inappropriate duration, too much information, unnecessary intimacy), but also deprive
them of the benefits self-service channels offer (e.g., easy accessibility, increased
availability). Taken together, this implies that customers should derive most value from rich,
personal service channels when tasks are complex and ambiguous and from lean, standardized
self-service channels when tasks are easy and repetitive.!
More recent extensions of MRT underline that even the perceived richness of a
medium is context-specific (e.g., Carlson and Zmud 1999; Dennis et al. 2008). That is, the
richness of a medium is no longer considered static or predefined. Instead, research suggests
that even very lean media can be perceived as rich over time, once customers learn how to use
them correctly and more efficiently (Walther 1992). Following this line of thought, channel
expansion theory (Carlson and Zmud 1999) posits that a user’s perceived richness of a
medium does not only depend on its characteristics, but also on the user’s unique experience

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with it. More precisely, the theory predicts that users perceive the same medium or
communication channel quite differently once they have acquired knowledge-building
experiences, i.e. experience with a particular channel, communication topic, context, or
interaction partner. These knowledge-building experiences increase users’ skills and abilities
to communicate effectively in various contexts and, in consequence, the users’ perceived
richness of a medium. While the original theory has focused on e-mail communication, it has
been validated in a number of further channels such as instant messaging (D’Urso and Rains
2008). For the context of multiple service channels, this line of research suggests that next to
task characteristics, unique customer characteristics and circumstances affect the value-in-
context a customer can derive from a certain channel.
According to the key tenets of channel expansion theory, customers should hence also
be able to derive value from self-service channels even when used for complex tasks. As
Campbell and colleagues (2011) note, customers’ unique skills and capabilities are especially
important for value-creation in self-service settings. Once customers are confident in their
own skills, they can easily deliver more complex service offers by themselves. Indeed,
Beuningen and colleagues (2009) have shown that novice customers’ self-efficacy, i.e. their
perception of their own ability to accomplish a task successfully, increases their perceptions
of service performance and the overall value they derive from a technology-based self-service
channel. This suggests that even when tasks are more complex, customers can derive value
from self-service channels when confident in their own skills and abilities. Similarly,
customers can derive value from a personal service even when used for a rather simple and
repetitive task. Some customers, for instance, derive a high relational value from personal
service encounters through the enjoyment of building up a relationship, while others derive a
higher economic value from self-service encounters through increased customization and
more control. The extent of such value creation, however, again strongly depends on the

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customers’ unique characteristics. Chan et al. (2010), for instance, have demonstrated that
customers from a highly collectivist cultural background can derive greater relational value
through their participation in service production than their individualist counterparts. Self-
service research also acknowledges that customers differ in their need for interaction with
service personnel (e.g., Meuter et al. 2005). While some customers are known to simply enjoy
“doing it by themselves” (i.e., using self-service channels), as it enables them to derive
experiential benefits (Campbell et al. 2011; Lusch et al. 2007), others enjoy human interaction
as it enables them to create close social bonds to the provider. As suggested in media
effectiveness research, Chan et al. (2010) also propose, but have not tested, that time or
experience with a provider might also affect the co-creation of both economic and relational
value.
Table 1 summarizes our discussion of the value-in-context customers can derive from
a provider’s value proposition in self-service and personal service channels. Taken together,
the above reasoning highlights the notion that the value customers can co-create in a
particular service channel (i.e., the value-in-context) differs markedly, when considering the
differences between customers’ resources (i.e., ability, motivation, knowledge) and unique
service circumstances (e.g., complexity of the service task). The importance of these
contextual aspects is evident in previous research. For instance, in an early study on the
impact of customer contact on service satisfaction, Bearden and colleagues (1998) most
generally propose that satisfaction should be enhanced when the level of customer-firm
contact matches a customer’s schema of anticipated contact. Similarly, Lusch and colleagues
(2007) propose that co-production opportunities, as offered in self-service channels, should
always match a customer’s desired level of involvement. More recently, Collier and Kimes
(2013) have even suggested that customers’ allocated resources in a self-service context, such
as the cognitive load surrounding the technology, should match the required resources of a

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task. When discussing how both self-service and personal service channels can impact a
customer’s relationship to a service provider, we will thus keep the individual characteristics
and resources of a customer, the resource requirements of a task, and the unique capabilities
of a service channel in mind.!
Table 1. Value in Self-Service and Personal Service Channels
Self-Service Channel
Personal Service Channel
The Value-
Proposition
what the firm
offers
reduced number of cues leads to efficiency of
information exchange (e.g., Choudhury and
Karahanna 2008)
rich in relational information, high in social
context cues (e.g., Cyr et al. 2007)
automated responses lead to accessibility
and flexibility (e.g., Wallace et al. 2004)
human feedback; immediate and
individualized attention (e.g., Venkatesan
et al. 2007)
few personal touches or social cues (e.g.,
Cyr et al. 2007; Davis et al. 2011)
highly personalized interactions (e.g.,
Barnes et al. 2000)
The Value-in-
Context
when the
customer can
benefit
tasks are unambiguous and repetitive;
service is not complex or new (e.g., Campbell
et al. 2011; Kumar and Telang 2012; Selnes
and Hansen 2001)
tasks are equivocal and ambiguous;
service is complex, critical or new (e.g.,
Selnes and Hansen 2001; Vickery et al.
2004)!
customers have expertise, self-efficiency,
and motivation to use self-service channels
(e.g., Beuningen et al. 2009)
customers do not have the skills,
motivation, and abilities to deliver service
or solve a task alone / via technology (e.g.,
Meuter et al. 2005)
customers enjoy "doing it themselves" and
wish to be in control (e.g., Campbell et al.
2011; Davis et al. 2011; Lusch et al. 2007)
customers enjoy human interaction, need
to gain trust, overcome anxiety (e.g., Chan
et al. 2010; Dabholkar 1996)
CUSTOMER RETENTION IN SELF-SERVICE SERVICE CONTEXTS
The Impact of Self-Service and Personal Service Channels on Customer Retention
Scholars have observed that customers need to solve a variety of tasks in service settings,
ranging from rather simple, repetitive tasks to more complex and demanding tasks (Selnes
and Hansen 2001). Following our reasoning above, it becomes clear that these different tasks
pose different requirements to the capabilities of the service channel and the customers’ skills

!
17!
and abilities to derive a value-in-context. Most generally, self-service channels are
characterized by their low personalization and the reduced number of cues transmitted,
whereas personal service channels are high in personalization, language variety, and
immediacy of personal feedback. In order to avoid overcomplification, key tenets of MRT
thus imply that self-service technologies lend themselves for rather easy and repetitive tasks,
while personal service channels should offer the best performance for rather complex tasks
(see Table 1). This suggests that customers, who choose between different channels of service
delivery to experience the most appropriate service channel for their different tasks and their
own capabilities, should be able to derive most value from these channels and hence their
relationship to the firm.
Prior research supports the idea that “two is better than one”. Accordingly, Schultze
(2003) recommends that firms should complement their technology-based self-service
channels with (personal) service relationships to offset the possibly detrimental effects of
arm’s-length relationships typically found in self-service channels. This makes intuitive sense
when considering that firms use personal communication channels (e.g., the telephone)
mainly to foster interpersonal communication within embedded relationships, whereas self-
service technologies are primarily used to support impersonal communication (Schultze and
Orlikowski 2004). More generally, researchers agree that offering multiple channels for
workplace communications can enhance employee’s job performance (Zhang and Venkathesh
2011), just as multiple, complementary service channels to customers can have positive
effects on customers’ post-adoption behaviors (Parthasarathy and Bhattacherjee 1998),
customer retention (e.g., Campbell and Frei 2010; Hitt and Frei 2002; Wallace et al. 2004),
and even firm profit (Ba et al. 2010). More specifically, however, a few studies have shown
that once usage of online channels increases, customer loyalty decreases (Neslin et al. 2006).
Service science thus posits that self-service channels are not always suitable. In their study on

!
18!
when particular service designs can create value, Campbell and colleagues (2011) assert that
self-service channels are particularly suited for relatively simple interactions that are highly
repetitive, while personal service channels might be more important when it is not just about
gathering information. In further support for this notion, media choice and effectiveness
research finds that rich and personal channels are often used and particularly lend themselves
to accomplish relational goals rather than the mere exchange of information (Sheer and Chen
2004; Vickery et al. 2004). Following this vantage point, researchers have put forward the
idea that customer defection should be lowest for customers that use both, personal as well as
self-service channels (Selnes and Hansen 2001). That is, customers should consider a personal
service channel more meaningful and beneficial, when they use self-service channels for
simple tasks and personal service channels for more complex tasks. Indeed, Bendapudi and
Berry (1997) demonstrate that the expertise of a service worker creates both trust and
dependency on the provider. However, as this expertise is more likely to be revealed when
accomplishing a demanding task suggests that customers should value personal service
encounters more when used for complex rather than for easy tasks.
Taken together, previous research indicates that customers who experience the
appropriate service channel for the demands imposed by their portfolio of tasks and for their
unique preferences, skills, and abilities, should achieve the best service outcome and derive
the most value from their relationship with the provider. Customers, who use only one
particular service channel, however, should run the risk of an unsatisfactory outcome and
most likely derive a lower value from their relationship with a service provider overall. If we
consider that customers will only remain with a provider when they can derive a value from
this relationship, this discussion suggests that customers who experience the best of both
worlds should be most likely to remain with their provider. Customers who continuously use
one service channel for all their service demands, on the contrary, should be deprived of some

!
19!
of the benefits the different channels can offer. These customers should hence be least likely
to remain with their provider – or put differently, they should be most likely to exit the service
relationship.
Consequently, we propose that
H1: The ratio of self-service vs. personal service use influences a customer’s
likelihood of defection in a U-shaped manner, with high levels of self-service or
personal service usage being associated with the highest chance of defection and
intermediate levels of self-service and personal service usage being associated with the
lowest chance of defection.
The Moderating Effect of Time
As noted above, the context and circumstances of a service encounter affect the customer’s
perception of richness and hence the appropriateness of a medium (Walther 1992). In
practice, customers’ perception of a medium may change over time as their own individual
characteristics, capabilities, and experiences change (D’Urso and Rains 2008). Channel
expansion theory (Carlson and Zmud 1999) suggests that customers, who continuously use a
lean medium to conduct the same task, will become accustomed to the peculiarities of this
medium and expand their perception of its capacity as well as their own ability to accomplish
this task. Users who continuously communicate via e-mail, for instance, may learn how to
display varying levels of formality and also learn how to interpret an increasing number of
cues (e.g., through the exchange of similes and the like). Similarly, Walther (1992) proposes
that – although it might take longer – users can establish close relationships even through
rather lean media. Consequently, we argue that over time the value a particular service
channel provides for the customer, changes in the eyes of the customer, i.e., its value depends
on the context. As customers become experienced in performing a particular task through a

!
20!
certain channel, they continue to improve their task performance and efficiency. This again
will increase the value-in-context the customer derives from this particular service channel.
A recent study by Wang and colleagues (2012) supports this notion. In their
exploratory study on customers’ choice of technology-based self-service channels, the authors
find that positive past experience with a self-service channel boosted customers’ confidence
and self-efficacy to successfully deliver a service offer by themselves. The authors conclude
that past experience is a strong determinant of customers’ attitude towards and actual use of a
particular service channel. Taken one step further, previous research also suggests that
customer familiarity and experience might be central in understanding customer retention in
service settings (e.g., Buell et al. 2010). Langer et al. (2012), for instance, demonstrate that
customers’ future intention to purchase a product from a firm increases once they are familiar
and experienced with the firm’s channel. This suggests that mere experience with the firm
creates trust and close bonds to an organization above and beyond what personal interactions
accomplish through social bonding (Bendapudi and Berry 1997). Consequently, customers
who have continuously used a particular service of the provider may not only consider this
service as an effective way to solve their task, but also establish a close and trusting
relationship with their provider. The mean of interaction thus becomes less crucial for their
usage decision as other bonding mechanisms are activated.
Analogous to our reasoning above, it is likely that the way a service offer is delivered
(self-service vs. personal service) is more important for customers who are not experienced
with their provider and his service offers than for those who know how to effectively use
various service channels and already experience a close relationship. Consequently, we expect
the impact of the self-service ratio on customer defection to be most important at the
beginning of a customer relationship and subsequently decrease in impact over time:

!
21!
H2: The longer a customer has been with a particular provider, the less strong the
effect of the self-service ratio on that customer’s chance of defection.
RESEARCH DESIGN AND METHODS
Research Setting
The setting of our study is a roadside assistance service in the automotive industry. The
service can be contracted for a flat fee, which allows customers to obtain information either
through a web search within their navigation system or call a service employee, who provides
the desired information and sends it directly into the navigation system (e.g., address of a
nearby automatic teller machine or restaurant). Industry examples for such a roadside
assistance service are BMW’s “Connected Drive”, Chrysler’s “OnStar”, or Volvo’s
“OnCall”.
Although roadside assistance service offers have not been focus of any previous self-
service study, they offer two major advantages. First, as Selnes and Hansen (2001) point out,
examining customer loyalty in a service context, where personal service channels have
predominated the offering in the past and self-service channels are just now being introduced,
poses the threat that customers are already bonded to the service provider. Effects on
customer loyalty thus cannot be clearly distinguished between the impact of self-service usage
and past bonding to the firm. In fact, Falk et al. (2007) demonstrate that customers display a
status-quo bias when evaluating a new self-service channel that was added to a traditional
personal service channel. As the roadside assistance service of car manufacturers is a
relatively new service offering that introduced self-service and personal service channels at
the same time, we are able to examine the effects and trade-offs of personal service and self-
service channels on customer relationships more closely. Second, our provider of interest (car
manufacturer) charges a flat fee for his roadside assistance service that includes both self-

!
22!
service and personal service channels. There are no incentives for customers to migrate to a
possibly cheaper self-service channel that might bias customers’ retention decision.
Oftentimes, service providers give price discounts for customers who actively use their self-
service instead of their personal service channel (Ba et al. 2010; Campbell et al. 2011). For
example, many banks offer a 1-cent bonus for every transaction the customer completes
online or airlines offer their customers cheaper electronic tickets for their flight. These
incentives bias the effect of self-service usage on customer defection. The absence of
incentives allows us to examine the effects and trade-offs of personal service and self-service
channels on customer relationships in a more controlled environment.
Description of the Data
We test our model on a customer database of a major European car manufacturer and roadside
assistance service provider, including monthly time-discrete usage data from September 2007
to September 2009. A random sample of 30,000 customers was drawn. In order to avoid left
truncation1, however, we limit our data in this study to six cohorts, with the earliest cohort
starting September 2007 when observations start. Additionally, we only include active
customers in our analysis, as many customers purchase the service offer as a bundle with their
navigation system without the intention to use it. To avoid including customers in our analysis
that use the service offer initially when the car dealer introduces it to them at the point of sale,
we define active customers as customers who make use of the service offer at least once every
six months during the entire observation window. The resulting sample consists of 5,467
customers and 105,715 observations respectively. The structure of our data is illustrated in
Figure 2.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
1 As Bolton (1998) points out, left truncation leads to biased results in standard Cox models, as loyal customers
are over-represented.

!
23!
!
!
Figure 2. Structure of the Data
The roadside assistance service includes both interpersonal calls as well as various
online offers. As all offerings are included in a flat fee, price does not affect the service
channel used by a customer. The database includes individual usage patterns such as monthly
usage of the online channel and monthly calls to the provider’s call center, as well as detailed
information on the start of the service contract or the age and model of car the customer owns.
To examine the long-term effects of self-service usage, we rely on measures of online service
usage (i.e. the monthly number of logins) for self-service usage, calls to the call-center (i.e.
the monthly number of calls) for personal service usage, and information on whether or not
the customer has withdrawn from the service contract as of September 30, 2009. Of all 5,467
customers in our final database, 2,274 cancel their contract within the observation period.
Variable Operationalization
We measure our central predictor Self-Service Ratio as a ratio of customers’ self-service
usage in relation to their overall use of personal- and self-service offers. To make our analyses
less dependent on extreme levels of self-service usage (number of monthly logins to the
online channel: mean: 5.69; standard deviation: 13.19; maximum: 310), we use the natural
Time%
Start
Cohort Sep. 2007: n = 488, 6991 observations
Cohort Oct. 2007: n = 940, 17349 observations
Cohort Nov. 2007: n = 1045, 20166 observations
Cohort Dec. 2007: n = 1127, 23179 observations
Cohort Jan. 2008: n = 1049, 21280 observations
Cohort Feb. 2008: n = 818, 16750 observations
Observa,on%Period%

!
24!
logarithm of both self-service and personal service usage. We also use a time-varying
measure for our Self-Service Ratio that is updated each month. To estimate whether or not this
ratio needs to be balanced on the long- rather than the short-run, we estimate two models.
Model 1 uses a Self-Service Ratio that uses monthly usage data, whereas Model 2 uses a Self-
Service Ratio that is based on a 90-day moving average of customers’ self-service and
personal service usage. Accordingly, we divide the 90-day moving average of self-service
used by the 90-day moving average of all offerings used for Model 2. Just as with our Self-
Service Ratio for Model 1, the Self-Service Ratio for Model 2 is updated each month. To
examine the U-shaped effect of the Self-Service Ratio, we also include Self-Service Ratio2 in
our model, which is simply the square of our initial Self-Service Ratio measure (on a one- or
three month basis for Model 1 and Model 2 respectively).
Following prior longitudinal studies on customer defection and retention (e.g., Nitzan
and Libai 2011), we also include the variable Delta-Use in our analysis. The variable reflects
changes in customers’ usage behaviors, which might be indicative for customers’ satisfaction
and future-intentions (Nitzan and Libai 2011). As Bolton and Lemon (1999) demonstrate,
customers dynamically change their evaluation of a service offer by putting the benefits they
derive from prior use in relation to the associated economic costs (i.e., by evaluating the
payment equity). The authors show that customers are more satisfied with the provider if they
perceive an exchange with a provider as highly equitable. As the economic costs remain
constant in our setting, customers should perceive a lower payment equity if they decrease
their usage levels. Consequently, decreases in customers’ Delta-Use should lower their
satisfaction and loyalty to a provider. We measure Delta-Use as the proportion of a month’s
overall (i.e., self-service and personal service) usage level to a 90-day moving average of the
customer’s overall usage in the three preceding months.

!
25!
In addition to a customer’s change in usage levels, we also include a customer’s
absolute level of usage in our analysis. According to previous research the frequency of use –
also referred to as the contact frequency (Dagger et al. 2009) or frequency of interaction
(Chen and Hitt 2002; Schultze and Orlikowski 2004) – reflects the depth of a customer-firm
relationship (Bolton et al. 2004). Frequent interactions have been found to increase
customers’ perceived relationship strength as they increase customers’ relational bonds to the
provider (Dagger et al. 2009). Additionally, frequent usage can create significant switching
costs for customers as it familiarizes them with the peculiarity of their provider and
consequently increases their effort to reach the same level of familiarity and comfort with new
providers (e.g., Campbell and Frei 2010; Chen and Hitt 2002). We therefore include the
variable Frequency in the analysis, which we measure as a 90-day moving average of all
service offerings used over a three-month period. Again, our variable Frequency is a time-
varying measure, just as our variables Self-Service Ratio and Delta-Use. We include all time-
varying measures with a one-period time lag in our analysis.
Next to these time-varying measures, we include the constant variables Duration, Age
of Car, and Car Group as control measures in our analysis. With our variable Duration we
simply capture the number of days between the date of our observation begin and the date of
the official start of a customer’s contract. As mentioned earlier, we pool six cohorts in our
analysis. The variable Duration basically controls for the cohort a customer belongs to.
Although specific to our research setting, we also need to control for the construction
year of the car a customer owns, as this is strongly intertwined with the customer’s service
contract. Customers, who wish to sell their car and buy an automobile of a competitor, also
need to cancel their contract with the roadside assistance service provider of interest. As the
chance of selling a car and thus quitting the service contract increases with its age, we include

!
26!
the variable Age of Car in the analysis. We measure this variable as the number of years since
the construction of the automobile.
Finally, we include the model of car a customer owns in our analysis. An additional
survey of customers randomly drawn from our database indicates that customers’
characteristics differ significantly across car models. For example, customers owning big
luxury car models tend to be older and less technology enthusiastic. Customers owning less
expensive models or sporty convertibles tend to be younger and more technology enthusiastic.
While previous literature suggests that especially young and technology-affine customers are
prone to try and use a technology-based self-service (e.g., Hitt and Frei 2002; Meuter et al.
2005; Xue et al. 2007), we assert that these individual characteristics also affect an
individual’s hazard of defection. That is, we assume that customers’ characteristics do not
only affect their first-time use of self-service offers, but also their likelihood to try service
alternatives by competitors. For young and technology-enthusiastic customers, for instance,
the perceived effort to learn how to use an alternative service by a competitor, i.e. switching
costs, will be lower than for older, less technology-enthusiastic customers. Although we do
not have access to these individual level customer characteristics in our final database, this
additional information suggests that we do have high within-group correlation on a car-model
level, which impacts an individual’s hazard of defection. To account for this unobserved
heterogeneity we include the variable Car Group as a shared risk factor – or frailty – in our
final model specification.
Table 2 describes the measurement of our predictor and control variables.

!
27!
Table 2. Variables for Model of Defection in Service Settings
Independent
Variables
Measured as
Impact on
Defection
SSRatioi,t
proportion of self-service usaget-1 in Δt to sum of self-service and
personal usaget-1 in Δt
-
SSRatioi,t
2
the square of our initial Self-Service Ratio measure
+
Delta Usei,t
proportion of month's overall usage level to average usage of the
three preceding months
(-)
Frequencyi,t
number of all service offers used each month, lagged moving
average over three-month period
(-)
Durationi
number of days between observation start and start of contract
(-)
Car Agei
number of years since construction
(+)
Shared
Frailty
included on car group level
Note: Subscript "i" = Variable does not change over time; subscript "i,t" time-varying variable that is updated each month.
We estimate two models with the SSRatioi,t based on ∆t = 30 days and 90 days
ANALYSIS
We use survival analysis to model customer defection in self-service settings. In particular,
we use Cox’s (1972) proportional hazard model and an extended Cox model with shared
frailty (Kleinbaum and Klein 2005). Hazard models are especially suited for duration data as
they take right censoring into account (Helsen and Schmittlein 1993) and allow time-varying
measures of variables (Nitzan and Libai 2011). In comparison to a binary-choice model that
only takes a (0,1) outcome into account, the hazard model considers additional information
such as detailed survival times and censoring. Hazard models thus offer greater stability and
predictive accuracy for duration data (Bolton 1998; Helsen and Schmittlein 1993). Given
these advantages, a number of empirical studies have relied on hazard models to analyze
defection and profitable lifetime duration in customer relationships (e.g., Bolton 1998; Nitzan
and Libai 2011; Reinartz and Kumar 2003).
One of the most commonly used hazard models is the proportional hazard (PH) model.
In contrast to parametric hazard models, the PH model leaves the underlying survivor

!
28!
function unspecified. This allows us to avoid a misspecification of the model while still
reaching reasonable results as the model approximates the correct parametric form
(Kleinbaum and Klein 2005). Additionally, the PH model enables us to incorporate dynamic
effects of variables on survival time through the inclusion of time-dependent covariates in an
extended Cox model (Bowman 2004). At its basis, the PH model describes the hazard rate
hi(t) of an individual i as:
(1)
where h0(t) describes the baseline hazard function of time that remains unspecified and βi xi
describes the impact of the explanatory X variables. Estimates of βi are obtained through
partial likelihood estimation. ‘Partial’ means that the Cox model does not consider
probabilities for all subjects, but restrains the likelihood estimation to only those subjects who
fail. As the PH model does not consider the times at which failures occur, but rather the
ordering of failures, we need to handle tied failures (i.e., failures occurring at the same time)
in our dataset. We do this using the Efron approximation (Efron 1977). This approach is more
accurate than the commonly used Breslow approximation (Cleves et al. 2008).
As can be seen in Equation (1), one of the main assumptions of the PH model is that
the X’s are time-independent. That is, the PH model assumes the hazard ratio (HR) – defined
as a comparison of any two specifications of the X’s (i.e., predictors) – remains constant over
time. We check this PH assumption with two widely recommended tests (Box-Steffensmeier
and Zorn 2001): First, we rely on a goodness-of-fit testing approach using scaled Schoenfeld
residuals (Grambsch and Therneau 1994). The underlying idea of this test is to check if
Schoenfeld residuals of our explanatory variables are unrelated to survival time. We
implement this test using STATA’s estat phtest command. Second, we also incorporate time-
dependent covariates to assess the PH assumption. For this approach, we extend the PH
€
hit,X
( )=h0t
( ) e
β
ixi
∑

!
29!
model to include interactions of our covariates with a function of time (t, ln(t) and t2). Hereby,
we fit one model per covariate and function of time to test each covariate separately as well as
one model including all covariates to test covariates jointly. Again it is assumed, for the PH
assumption to hold, that covariates are unrelated to survival time and thus interactions of
covariates with a function of time to be insignificant.
Results of both tests imply that some of our variables violate the PH assumption. As a
consequence, we extended the standard Cox model to include interaction effects between
offending covariates and time (Xi x t) in our final analysis to avoid a misspecification and
increase the accuracy of our estimates. To guide our decision on which covariate to add as a
time-varying effect, we place most emphasis on the results of the scaled Schoenfeld residuals
and use the results of our time-dependent covariate tests only in cases when in doubt.2 Table
3 provides an overview of the results of our evaluation of the PH assumption.
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
2 Using interactions with time to test the PH assumption as well as to correct for violations of it has received
criticism (e.g., Box-Steffensheimer and Zorn 2001; Grambsch and Therneau 1994). Basing the decision of
whether a covariate violates the PH assumption on TVC tests alone, is often misguided as correlations of
covariates and their interactions with functions of time may bias conclusions (Box-Steffenshimer and Zorn 2001,
p. 978).
Xi x t Xi x t2Xi x ln(t)
Model 1
Overall
(t)
Model 2
Overall
(t)
CarAge .019*** .0007*** .18** .0317*** .0288*** .18 (24.00)*** .18 (37.36)***
Duration .001*** .00003*** .02*** .0012*** .0013*** .23 (65.15)*** .22 (92.96)***
Frequency .002*** .00006*** .03*** .0003 .0007** .03 (2.08) .05 (8.81)***
DeltaUse - .001**
-
.00004** -.02** .00008 .0012 -.005 (0.06) .02 (0.98)
1mo SSRatio - .004
-
.0004 .07 0.1370 .04 (1.73)
1mo SSRatio2-.012
-
.0006 -.04
-
0.1376 -.05 (2.18)
3mos SSRatio .015 .0003 .25 .2005** .06 (5.95)**
3mos SSRatio2.003
-
.00002 .10
-
.1882**
-
.07 (6.30)**
*** Significant at p < .001. ** Significant at p < .01. * Significant at p < .05. + p < .15
Tests based on
Schoenfeld Residuals
Table 3. Evaluation of the Proportional Hazards Assumption
Tests based on reestimation with
Time-Varying Covariates
Model 2
roh(χ2)
Model 1
roh(χ2)
Note: Main effects were included in the reestimation with time-varying covariates (TVC). However, for testing the PH assumption,
only coefficients and p-values of time-interactions are displayed.
!!!!+!
!!!!+!
!!!!+!
!!!!+!

!
30!
To account for an unobserved heterogeneity shared by groups of customers, we also
incorporate a ‘shared frailty’ in our Cox model. In survival analysis the frailty α describes an
unobservable risk factor or random effect that enters multiplicatively on the hazard function.
A shared frailty model hereby assumes that the unexplained heterogeneity or frailty is shared
among individuals, i.e. it is common for a group of individuals. The shared frailty αj hence
accounts for within-group correlations in the hazard. Based on our findings through the
additional survey, we include a shared frailty on a Car Group level. This way, we allow
individuals within each car group to be correlated and share the same frailty, whereas
individuals across different car groups may differ in their frailty. The hazard function
conditional on the frailty can be expressed as
(2)
where αj is the shared frailty of an individual i belonging to group j and the shared frailty is
gamma distributed (with mean 1 and variance θ). As a shared frailty model requires a
sufficient amount of data (Cleves et al. 2008), we pool our six cohorts instead of estimating
our model for each cohort separately. However, to test the robustness of our findings, we will
also estimate our model for each cohort separately without the inclusion of a shared frailty.
The final specifications for Model 1 and Model 2 are given in Equation (3), with the
Self-Service Ratio measured on a monthly and on a 3-month level in Model 1 and Model 2
respectively. The hazard of defection for a customer i belonging to group j at time t is
(3)
where h0 describes the baseline hazard, αj the shared frailty on a car group level and the
subscript “ijt” indicates a time-varying measure of our variables Self-Service Ratio, Delta-Use
€
hi,jt
α
j,X
( )=h0(t)
α
je
β
ixi,j
∑
€
hi,jt
( )=h0t
( )
α
jexp
β
1Self Service Ratioi,j,t+
β
2Self Se rvice Ratioi,j,t
2+
β
3Delta Usei,j,t+
β
4Frequency i,j,t+
β
5Durationi,j+
β
6Car Agei,j
$
%
&
&
'
(
)
)

!
31!
and Frequency. As mentioned earlier, we also include interaction terms with time for those
predictors that violate the proportional hazard assumption. This is the case for the variables
Duration, CarAge, Frequency, Self-Service Ratio and Self-Service Ratio2 for both Model 1
and Model 2. We include these interaction terms with a linear function of time f(t).
RESULTS
We obtain results using STATA’s stcox command. The effective sample size for Model 1 is
5,311 subjects and for Model 2 is 5,414 subjects due to missing values. Table 4 summarizes
the resulting coefficients and p-values for Model 1 and Model 2. As theta is significantly
different from zero for both models (.032, p < .01 and .048, p = .00 for Model 1 and Model 2
respectively), we must conclude that some car groups are in fact more “frail” than others.
Note that all resulting estimates are thus conditional on the unobserved frailty.

!
32!
Table 4. Coefficients (Standard Errors) of the Cox Model with Frailty
Hypothesized
Impact
Model 1
Model 2
Main Effects
CarAge
-.0350 (.102)
.0377 (.073)
Duration
-.0214 (.002)***
-.0207 (.001)***
Frequency
-.0148 (.002)***
-.0194 (.002)***
DeltaUse
-.0701 (.005)***
-.0761 (.005)***
1mo SSRatio
-
- 2.141 (1.125)+
1mo SSRatio2
+
2.008 (1.051)+
3mos SSRatio
-
-3.724 (.861)***
3mos SSRatio2
+
3.263 (.824)***
Time-Varying Effects
CarAge x Time
.0307 (.007)***
.0276 (.005)***
Duration x Time
.0013 (.0002)***
.0013 (.0001)***
Frequency x Time
.0003 (.0001)*
.0004 (.0001)***
1mo SSRatio
+
.1417 (.085)+
1mo SSRatio2
-
-.1422 (.079)+
3mos SSRatio x Time
+
.2069 (.067)**
3mos SSRatio2 x Time
-
-.1938 (.063)**
Shared Frailty - theta
.0327 (.029)***
.0485 (.034)***
Model Fit
Log-Likelihood
-7452.5285
-11928.913
AIC
14927
14923
R2pv
.31
.31
R2pe
.42
.43
Note: Time-varying effects were only included, when the predictor violated the PH assumption.
*** Significant at p < .001. ** Significant at p < .01. * Significant at p < .05. + p < .07
As can be seen in Table 4, our final models display a satisfying level of model fit. It is
important to note, however, that there is no commonly agreed upon measure to illustrate a
model’s fit for hazard models. In this study, we focus on a measure that most closely
resembles a measure of explained variance commonly used in linear regression to ease
interpretation. We thus estimate our models’ fit by relying on the measure of explained
variation R2pv proposed by Royston (2006) and endorsed by Hosmer et al. (2008). According
to this work, the explained variation R2pv is defined as

!
33!
where
Hereby, R2pe describes a measure of explained randomness (O’Quigley et al. 2005) that is
based on the likelihood ratio statistic X2 for comparing the fully fitted model with the null
model divided by the number of events e. We provide the resulting R2pe and R2pv estimates for
our models in Table 4.
The Effect of Personal vs. Self-Service Usage
We hypothesized that the Self-Service Ratio has a U-shaped effect on a customer’s hazard of
defection, with intermediate levels of both self-service and personal service usage having the
lowest hazard of defection (H1). We tested this assumption by introducing the squared term
Self-Service Ratio2 to our analysis. As Table 4 illustrates, the signs of our coefficients
(negative for the linear term and positive for the squared term) are in the proposed direction
for both models, however, the effect is only significant for Model 2 (p < .001 for both the
linear and squared term). This result demonstrates that a self-service ratio that is balanced
over a three-month (rather than a one-month) period significantly lowers the hazard of
defection. More precisely we find that for a given frailty level, customers with a 3-month Self-
Service Ratio of .57 have the lowest hazard of defection.3 The higher or lower the Self-Service
Ratio from this point-estimate, the higher the hazard of defection. This result strongly
supports our Hypothesis 1.
The fact that our variable Self-Service Ratio does not have a significant effect on the
hazard of defection in Model 1 demonstrates that the time horizon on which the ratio is
measured is crucial for its impact on defection. In comparison to Model 2, we measured the
Self-Service Ratio in Model 1 on a lagged monthly level instead of the 3-month-level, all else
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
3 Following the notion that the minimum of a U-shaped effect can be estimated with min(x) = - b/2a, which in
our case equals –(-3.725)/(2 x 3.263) = .57
€
€
Rpv
2=Rpe
2
Rpe
2+
π
2
6
1−Rpe
2
( )
€
Rpe
2=1−exp −X2
e
#
$
%
&
'
(

!
34!
being equal. While the coefficients of both the linear and the squared effect of our variable
Self-Service Ratio are again in the proposed direction (-2.141 and 2.008), they are marginally
not significant p = .057 and p = .056 respectively). However, we believe that this does not
harm our H1, but merely demonstrates that a customer’s Self-Service Ratio needs to be
balanced on a three-month time horizon, rather than on a monthly level.
To further demonstrate the importance of the Self-Service Ratio, we conduct a
likelihood ratio test that compares a “traditional” usage model, including usage Frequency,
Delta-Use and control variables, to our full model, including the Self-Service Ratio measured
on a three-month time span. The test statistic illustrates a significant improvement of the
model through the inclusion of the variable Self-Service Ratio (χ2(4) = 37.78, p < .001) and
thus underlines the importance of a customer’s self-service ratio in understanding customer
defection.
Overall our analyses provide strong support for our main hypothesis H1. Accordingly,
customers who use self-service and personal service channels at an intermediate level within
three months are less likely to defect, whereas customers who rarely use self-service offerings
and customers who mostly use self-service offerings within the same time-span are more
likely to defect. This underlines our assumption that customers who experience and take the
best of both service channels, are more likely to remain with their provider.
The Moderating Effect of Time
In hypothesis H2 we proposed that a customer’s Self-Service Ratio should be most important
in the beginning of a customer-firm relationship and then continuously decrease in importance
over time, as customers gain more experience with their provider and his channel
peculiarities. As noted above, one of the main assumptions of a Cox model is that the hazards
are proportional. The PH assumption hence demands that predictors remain constant and are
unrelated to survival time. We tested this PH assumption and found that a number of

!
35!
predictors violate it. Accordingly we included interaction terms of our offending predictors
and time in our final models.
The results of our analyses are also in support of our hypothesis H2. As can be seen in
Table 4, both the direct impact of Self-Service Ratio and Self-Service Ratio2 as well as their
interaction with time are statistically significant. While the results indicate that the Self-
Service Ratio directly impacts a customer’s hazard of defection in the proposed U-shaped
manner, we also find that this effect is reduced over time. The signs of the interaction
coefficients thus aim in the opposite direction (.20 and -.19 for the linear and squared term in
Model 2 respectively) and are statistically significant (both p = .002). These results strongly
support our hypothesis H2.
The Effect of Observed and Unobserved Heterogeneity
Observed Heterogeneity: In line with prior research, we find that high usage (Frequency: -
.02, p < .01) as well as an increased usage (Delta-Use: -.08, p < .01) lowers the hazard of
defection. The inclusion of time-interactions, however, demonstrates that the impact of
Frequency significantly decreases over time (.0004, p < .01). Additionally, we find that
customers in later cohorts have a lower hazard of defection (-.02, p = .00). As we included the
variable Duration as a constant to control for the cohort the customer belongs to, it is not
surprising to find that this effect also decreases with time (.001, p = .00). Although specific to
our service setting, we further find that the Age of Car significantly increases a customer’s
hazard of defection over time (.03, p = .00). That is, the longer customers own a car, the more
likely they are to sell their car and consequently quit their service contract.
Unobserved Heterogeneity: We account for unobserved heterogeneity on a group level by
estimating a shared frailty model. Hereby, we assume that customers owning the same model
of car (Car Group) share a common risk or frailty. As a likelihood-ratio test of H0: θ = 0

!
36!
confirms that theta is significantly different from zero (θ = .05, χ2 (2) = 19.93, p < .001), we
must conclude that there is significant within-group correlation. All reported estimates above
are thus conditional on the frailty.4 As a subject’s frailty can deepen our understanding of our
effects, we obtain estimates for the frailty at an individual level and plot the survivor function
at the lowest, mean (baseline), and highest frailty level. Hereby, we re-center the Self-Service
Ratio to produce a baseline survivor function that resembles a customer with a three-month
Self-Service Ratio of .57 (the minimum of our U-shaped effect). Figure 3 illustrates the
resulting survivor functions for various frailty levels. The comparison of the survivor
functions at the three frailty levels shows that customers with a high frailty level have a far
inferior survival experience when duration with the provider exceeds 10 months than
customers with low frailty levels.
Figure 3. Survivor Functions across Frailty Levels
!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
4 i.e., theta is held fixed at its optimal level. Accordingly, a Cox shared frailty model first optimizes theta and
then fits a standard Cox model via panelized likelihood. For more information see Cleves et al. (2008) and
Therneau and Grambsch (2000).

!
37!
Robustness Checks
We re-analyzed our proposed model with all main and time-varying effects for each cohort
separately to test the robustness of our results. Table 5 summarizes the resulting coefficients
and model fit statistics for each individual cohort. Respective sample sizes of each cohort are
also displayed. Note that the small sample sizes preclude the inclusion of a shared frailty on a
car group level in these analyses, as shared frailty models require a sufficient amount of data
to model within-group correlations (Cleves et al. 2008). To still account for a possible within-
group correlation, however, we adjusted the standard errors of our estimated parameters for
the clusters in our Car Group variable as proposed by Cleves et al. (2008, pp. 156).
The results of the robustness checks give us confidence in our proposed model and our
previous results. In all analyzed cohorts both the linear and the squared term of Self-Service
Ratio are in the proposed direction and the results have statistical significance in support for
H1 in four out of six cohorts. The interaction term of Self-Service Ratio and time also
supports our proposition that the variable’s main effect reduces over time. Again, these
interaction effects have statistical significance in support for hypothesis H2 in four cohorts.
Cohort 1
(n=479)
Cohort 2
(n=935)
Cohort 3
(n=1039)
Cohort 4
(n=1122)
Cohort 5
(n=1031)
Cohort 6
(n=808)
Main Effects
CarAge -.140 (.174) -.104 (.255) -.105 (.161) -.077 (.089) -.021 (.124) -.032 (.193)
Frequency -.013 (.003)*** -.017 (.007)* -.035 (.005)*** -.027 (.009)** -.022 (.003)*** -.018 (.005)***
DeltaUse -.041 (.011)*** -.066 (.017)*** -.142 (.011)*** -.063 (.032)* -.099 (.019)*** -.086 (.012)***
3mos SSRatio -.482 (1.59) - 5.569 (2.64)* - 2.441 (.059)*** - 4.194 (1.601)** -.717 (1.27) - 5.36 (1.85)**
3mos SSRatio2 .023 (1.51) 5.093 (2.19)* 1.573 (.633)** 2.958 (1.544)* .628 (1.16) 5.11 (1.26)***
Time-Varying Effects
CarAge x Time .031 (.012)** .036 (.010)*** .038 (.007)*** .030 (.010)** .041 (.007)*** .028 (.012)*
Frequency x Time .0004 (.0002)* .0006 (.0002)** .0002 (.0004) .001 (.0003)** .00007 (.0003) .0005 (.0004)
3mos SSRatio x Time -.105 (.118)
.348 (.192)+ .174 (.082)* .268 (.114)* -.038 (.082) .267 (.131)*
3mos SSRatio2 x Time .135 (.107) -.345 (.172)* -.134 (.069)* -.210 (.105)* .022 (.070) -.022 (.086)**
Model Fit
Log-Likelihood - 1168 - 1817 - 1679 - 1627 - 1732 - 1240
AIC 2350 3648 3372 3269 3479 2494
Table 5. Coefficients (Standard Errors) for the Cox Model based on Individual Cohorts
*** Significa nt at p < .001. ** Significant at p < .01. * Significant at p < .05. + p < .07

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38!
The fact that these effects are not significant across all cohorts might be an indication for a
lack of statistical power as the number of events rather than the number of subjects
determines the statistical power of the analysis in survival models (Hsieh and Lavori 2000).
Thus, despite these shortcomings, the results of the robustness checks strongly support our
model.
DISCUSSION
This research is the first to investigate customer retention in a technology-based self-service
setting using data from a longitudinal customer database and considering interactions between
service channels and time. Our results give food for thought about the prevailing enthusiasm
for technology-based self-service channels in current business practice and research. We show
that customers exit a service relationship most likely when merely using one channel for
service delivery, be it a technology-based self-service or personal service channel. Our
research thus underlines the importance of offering various channels of service delivery;
moreover, it underlines the importance of considering the value customers can derive from
different service channels over the duration of their relationship to a provider instead of
merely pushing customers towards potentially more cost-efficient self-service channels.
Theoretical Contributions
This study contributes to research and the advancement of our theoretical knowledge in
several ways. First and foremost, our study is among the first to focus on the interplay of
personal and self-service channels and time when trying to understand customer retention in
multichannel service settings. Based on S-D logic (Vargo and Lusch 2004, 2008) that places
“high priority on understanding customer experiences over time” (Lusch et al. 2007, p.11),
our study emphasizes and discusses customer’s value creation in technology-based self-
service and personal service channels. Our theoretical discussion on when and how customers

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39!
can derive valuable experiences from self-service and personal service channels offers a new
way of looking at customer retention in multichannel settings. Although it is clear that
ultimately, customers will remain with a provider as long as they consider a provider’s offer
valuable (e.g., Oliver 1999; Kim and Son 2009), previous research on the impact of
information technology on customer retention has not considered theoretical aspects of value
creation, and in particular, the different value propositions firms offer to customers in
different service channels and the unique value-in-context customers can subsequently derive
from these offers. Moreover, research on self-service technologies has not contrasted
customers’ value creation between self-service and personal service channels, although
researchers have emphasized the need for a theoretically sound and comprehensive customer
experience framework in self-service settings (Verhoef et al. 2009). Building on our
theoretical discussion, we find first empirical evidence that customers who experience the
best of both worlds (i.e., the service provider’s self-service and personal service channels) are
more likely to remain with their provider than those who restrict themselves to one particular
channel alone. In addition, our empirical investigation stresses the importance of time, i.e.,
customers’ experience and expertise with a particular channel, for value-creation and
retention decisions. Both findings underline the importance of considering both, the different
value propositions service providers offer in various channels and the unique value customers
can derive from these propositions over time. This rather holistic approach parallels the notion
in current IS literature on the social construction of technology as it considers both the unique
features and capabilities of technology as well as the unique context in which it is applied
(Orlikowski and Barley 2001). The present conceptual framework can hence guide future
empirical work on the impact of technology and may easily be applied in and extended to
other settings. Consider for example, remote (i.e., technology-mediated) vs. location-based
(i.e., onsite) service offerings (Wünderlich et al. 2013). While first attempts have been made
to understand customer reactions to service separation (Keh and Pang 2010), they disregard

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40!
that providers themselves should play a vital role in actively managing customers’
experiences and their co-creation of value. Drawing from S-D logic, our study stresses the
importance of an active customer experience management rather than a (reactive) relationship
management and provides a first framework that might advance our understanding of
customer experiences and customer retention in (multichannel) service settings.
Second, this research supports and extends previous findings from IS research.
Schultze and Orlikowski (2004) were among the first to discuss the drawback of a
technology-based self-service channel for a trusting B2B relationship. More recently, Kumar
and Telang (2012) also note, that self-service channels may have detrimental effects for firms
as customers increase their usage in both self-service and personal service channels, when the
information in self-service channels does not fully answer a customer’s query. While our
research underlines the notion that self-service channels may not always be beneficial, we
extend previous research by analyzing the impact of both channels on customer relationships
over time. Hence, our research offers greater clarity on how customers’ self-service vs.
personal service usage affects their decision to remain or exit a relationship. By taking a long-
term view, we can also demonstrate how important it is to consider the unique context of
technology. Thus, we can demonstrate that the negative impact of exclusively using a
technology-based self-service at the beginning of a relationship lessens the longer a customer
has been with a provider.
Third, this research contributes to media effectiveness research by extending its focus
to customer-firm interactions in service encounters. While most of prior research on media
effectiveness has focused on the context of team collaboration within organizations (e.g.,
Maruping and Agarwal 2004), we show that media richness and channel expansion theory can
help advance our understanding of customer-firm interactions as well. One important aspect
of service provision is that customers are an integral part of it and actively co-create the value

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41!
of their service experience or even actively co-produce parts of the service offer (Vargo and
Lusch 2008). Accordingly, customers, firms, employees, and partners collaborate in the
service process by exchanging knowledge, regardless of whether it is a technology-generated
service channel or personal-assisted channel (Lusch et al. 2007). Our study shows that
examining customer relationships in service settings from a media richness perspective helps
advance our understanding of the unique characteristics and capabilities of a service channel
and which service channel should be most valuable for customers to accomplish a given task.
Fourth, this research adds to discussions in multichannel literature on whether or not
customers should be encouraged to be multichannel (e.g., Neslin et al. 2006; Neslin and
Shankar 2009). Prior research suggests that customers should be encouraged to be
multichannel when it increases customer loyalty, while it should be discouraged when it
merely increases customers’ convenience without adding to the firm’s share of wallet (Neslin
and Shankar 2009). The present study shows that there is no clear-cut answer to this question.
Instead, this research demonstrates that customers should be encouraged to be multichannel at
the beginning of a customer relationship. This offers the advantage for customers to
experience the best of both worlds, while providers can make full use of the benefits both
self-service and personal service channels offer. Our study, however, also shows that this
multichannel behavior is less important the longer a customer has been with a particular
provider. That is, once customers are experienced and self-efficient enough to make full use
of their preferred channel (i.e., create a high value-in-context), a tendency to move towards
one particular channel should not have detrimental effects on their loyalty to a provider.
Instead of taking a black-or-white view on the efficiency of particular service channels, our
study advances knowledge in multichannel research by stressing the importance of customers’
unique resources and capabilities to derive value from a particular channel at a certain point in
time.

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Finally, our study contributes to the recent discussions on IT and service productivity.
Currently, research strongly supports the premise that productivity and costs can be improved
by standardizing and automating service processes and transferring work to the customer
through self-service (e.g., Kumar and Telang 2012; Rust and Huang 2012). Results of this
study demonstrate that this view may be too simplistic. Instead of pushing customers to
“cheap” service channels, this research highlights that productivity can be optimized by
balancing customers’ use of personal and self-service channels and, more broadly, by finding
the best fit of customers’ resources and the firm’s service offer. This implies that personal
service channels, too, can improve service productivity. In fact, Vickery et al. (2004)
demonstrate personal service channels can enhance service operations through the fast
accomplishment of rather complex tasks. Similarly, Kumar and Telang (2012) find that Web-
based self-service channels can lead to costly consequences as customers make additional use
of a call-center after failing to accomplish an ambiguous task via self-service. By building on
previous media effectiveness research and S-D logic’s central concept of value-in-context,
this research provides theoretical and empirical support for the notion that technology-based
self-service channels are best to be used in conjunction with a personal service channel,
especially at the beginning of a relationship. Overall, our study underlines the call by Rust
and Huang (2009) prompting practitioners to find the optimal balance between technology-
based self-service and personal service channels.
Limitations and Implications for Further Research
Although our research gives some first insight into the long-term effects of self-service usage
on customer relationships, we believe there are several aspects that could help to develop this
understanding even further. First, the focus of our empirical study is one particular service
provider with a Web-based self-service portal and a personal-assisted call-center. Clearly, the
focus on one particular firm limits the generalizability of our findings. However, the unique

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43!
dataset allowed us to examine the interplay of self-service and personal service channels and
time more closely. Future research could contribute to this research by analyzing the retention
decision of customers in different industries and for different service designs. Technology-
based self-service channels can take many forms. They range from Web-based self-service
portals, to automatic voice response units, to location-based self-service kiosks. Additionally,
self-service channels become increasingly personalized (e.g., Tam and Ho 2006; Zhang et al.
2011) and increasingly leverage information technology to provide multiple supporting
service offers to supply the missing human touch (Cenfetelli et al. 2008). Similarly, personal
service channels are no longer constrained to a physical location. They, too, can be delivered
in multiple ways, such as via telephone, instant messaging, or in person. Since the setting of
our study is rather restrictive with a comparison of a voice-to-voice and a simple screen-to-
screen service offer, future research should examine if and under what circumstances our
results extend to different service industries and various service designs.
Second, this research focuses on the proportion of self-service and personal service
used and its impact of customer defection. Our underlying assumption of the U-shaped
relationship between the two variables relies on the basic idea of varying degrees of a task’s
complexity and ambiguity. Drawing from theories on media richness and channel expansion,
we propose that some tasks are more suited for self-service channels than others. While
previous research (Ba et al. 2010; Kumar and Telang 2012; Selnes and Hansen 2001) and our
results emphasize this idea, we did not measure task complexity or ambiguity itself. Hence,
future research could contribute to current knowledge by examining the appropriateness of
various tasks for different means of service delivery and the impact of the task’s delivery
mode on customer relationships. In particular, it might be interesting to distinguish tasks by
looking at various degrees of task complexities, the perceived risk of the task for the customer

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(Wünderlich et al. 2013) or, more generally, the criticality of different tasks as suggested by
Keh and Pang (2010).
Third, despite the fact that our study has included a shared frailty to account for
customer heterogeneity, it might be helpful to segment customers based on demographic and
attitudinal data. This way, firms will gain a deeper understanding of the individual success
factors for customer retention across customer segments and how these different segments can
be addressed more effectively. Future research could, for instance, assess the implications of
our conceptual framework in an intercultural setting. Research has demonstrated that
individual cultural orientations of customers influence their expectations and motivations
within service settings. Customers with a rather individualistic value orientation, for example,
have been shown to be more concerned about economic value rather than the creation of
relationships (Chan et al. 2010). Consequently, these customers prefer their own rewards,
efficient communication and time savings, whereas more collectivistic oriented customers
might value personal interactions to achieve a common goal. It would be interesting to know
how these aspects transfer to our framework and also impact customer relationships and
defection decisions in self-service settings.
Managerial Implications
To date, both business practice and research highlight the benefits of technology-based self-
service channels, such as an increased operational performance and reduced costs (e.g., Ba et
al. 2010; Kumar and Telang 2012; Schultze and Orlikowski 2004). Given these apparent
advantages, more and more businesses actively push customers to self-service channels
(Langer et al. 2012; White et al. 2012). The present study demonstrates that this approach
may not always be beneficial for the firm. The analyses of this study indicate that technology-
based self-service channels may harm customer retention. In particular, results reveal that
customers, who use both, a technology-based self-service and a traditional personal service

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45!
channel at the beginning of a customer-firm relationship, are more likely to remain with the
provider than customers who merely rely on one particular channel. From a managerial point-
of-view, this suggests that migrating customers to technology-based self-service channels at
the beginning of a customer-firm relationship can lead to costly rather than cost-cutting
consequences. To avoid the possible dark side of technology-based self-service channels,
managers should hence allow customers to experience their relationship with a provider
through a variety of service channels - especially when they are new to a provider. This
approach allows the firm and the customer to experience the best of both worlds: On the one
hand, customers can benefit from the convenient accessibility and flexibility of self-service
channels, while enjoying the personalized attention in personal service channels. On the other
hand, firms can establish trust and social bonds to the customer through a personal interaction
in traditional encounters, while reducing their operational costs through efficient self-service
channels.!!
The theoretical discussion of our study also underlines the importance of the unique
context in which a particular service channel is utilized. More specifically, we propose that
managers should offer technology-based self-service channels for rather repetitive and
unambiguous tasks, whereas personal service channels should be available for complex and
ambiguous tasks. While we do not have the data to empirically support this claim, we find
support for it in previous research (Kumar and Telang 2012; Selnes and Hansen 2001).
Kumar and Telang (2012) note, however, that the applicability of technology-self-service
channels for a certain task does not only depend on the ambiguity of the task, but also on the
particular design of the self-service channel. Similarly, our research suggests that customers
who have been with a provider for a longer time and hence have gained experience with
various service channels of a provider may also be experienced enough to efficiently conduct
a complex task via self-service. However, given the lack of empirical data to clearly support

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46!
this assumption, more detailed research on the impact of the service task is needed to guide
managerial decisions on which tasks to automate and which customer segments to address
with self-service channels.
Next to the service task, varying customer characteristics add to the contextual
heterogeneity present in any service setting. Thus, another important aspect managers need to
account for in their service design is customer heterogeneity. As our study demonstrates,
customers who make frequent and increasing use of a provider’s service are at a lower risk of
defection. Furthermore, we find that some customer groups are inherently more frail than
others. Previous research suggests that young customers, who are still unaccustomed to one
specific service channel of a provider, might not only be more likely to try service innovations
such as a technology-based self-service channel, but might also display lower anxiety to try
competitors’ alternative offers (i.e., switching costs might be lower; Campbell and Frei 2010).
From a managerial point-of-view this has two important implications: First, managers should
investigate the frailty levels of their customer segments by making full use of information on
customers’ characteristics and usage history. Second, management should ensure that each
customer segment is addressed appropriately. That is, managers should pay close attention to
what type of tasks different customer segments are willing to perform by themselves (i.e., via
self-service, see Campbell et al. 2011). New customers, who merely display interest in self-
service channels and share a high risk to defect, for instance, could be encouraged to
experience personal service channels as well. Long-term customers, who merely show interest
in personal service channels, on the other hand, should be informed about the benefits of self-
service channels and also be familiarized with their use. However, given that customers’
unique capabilities play a central role in the value they can derive from a particular channel, it
is important to note that mangers also need to learn how to unlock these capabilities (Davis et
al. 2011) and actively foster customer learning for resource integration (Hibbert et al. 2012).

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Overall, our study emphasizes the importance for managers to understand how
customers experience their relationship with a provider through a variety of channels and over
time. Rather than optimizing individual service channels in terms of service quality or service
productivity, service providers should concentrate on a more holistic view of a customer’s
service experience in a multichannel setting and the unique value-in-context customers can
derive from each channel over the duration of their relationship to the firm.

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48!
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Author Biographies
Anne Scherer is a post-doctoral researcher at the Department of Management, Technology,
and Economics at the ETH Zürich, Switzerland. She completed her PhD in Marketing at the
Technische Universität München, Germany, in 2013. In her research, Anne is primarily
interested in the impact of technology on consumer behavior and customer-firm relationships.
The present article is based on her doctoral dissertation, mentored by the second author and
chaired by the third author.
Nancy V. Wünderlich is a professor and chair of service management at University of
Paderborn, Germany. She earned her PhD from Technische Universität München, Germany.
Her research focuses on issues related to technology in service delivery, including adoption of
new service types, branding of technology-intensive services, customer management, and
service profitability. Her work has appeared in journals including Journal of Service
Research, Journal of Retailing and Marketing Letters. She has received best article and best
dissertation awards from the American Marketing Association (SERVSIG), the Society of
Marketing Advances, the Academy of Marketing Science, and the German Ministry for
Education and Research, among others.
Florian v. Wangenheim is professor of technology marketing at the ETH Zürich,
Switzerland. His main research fields are technology-intensive service management and
value-based customer management. For his work, he received the best service paper award
from the American Marketing Association in 2007, and various research awards from
organizations such as the Academy of Management (AoM), the German Federal Ministry of
Higher Education (BMBF), the Academy of Marketing Science (AMS), the German
Marketing Association (DMV), and the German Association of Business Professors (VHB).
His research has appeared in the Journal of Marketing, Journal of the Academy of Marketing
Science, Journal of Retailing, Journal of International Business Studies, MSI Research Report
Series, Journal of Service Research, among others. He currently serves on the editorial boards
of the Journal of Marketing and the Journal of Service Research.

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Author Contact Information
Anne Scherer (corresponding author)
ETH Zürich
Department MTEC
Chair of Technology Marketing
Weinbergstrasse 56/58
8092 Zurich
Switzerland
Phone: +41 (0)44 632 67 37
Email: ascherer@ethz.ch
Nancy V. Wünderlich
University of Paderborn
Department of Business Administration and Economics
Chair of Service Management
Warburger Straße 100
33098 Paderborn
Germany
Phone: +49 (0)5251 603693
Email: Nancy.Wuenderlich@wiwi.uni-paderborn.de
Florian v. Wangenheim
ETH Zürich
Department MTEC
Chair of Technology Marketing
Weinbergstrasse 56/58
8092 Zurich
Switzerland
Phone: +41 (0)44 632 69 24
Email: fwangenheim@ethz.ch