Customer Engagement and Loyalty: A Comparative Study Between Service Contexts

Abstract

The article discusses the effect of context on customer engagement and presents propensity to engage as an attitudinal antecedent of loyalty behaviors. We argue that customers may hold different propensity to engage depending on the specific service context, which in turn will influence more or less favorable behaviors. Data were collected through a convenience sample of 516 consumers in two settings, high and low contact services. Results revealed that propensity to engage and loyalty behaviors vary significantly between the contexts studied. We also conclude that the majority of loyalty behaviors are correlated, in both contexts, with customers’ propensity to engage.

Full text

SERVICES MARKETING QUARTERLY
, VOL. , NO. , –
http://dx.doi.org/./..
Customer Engagement and Loyalty: A Comparative Study
Between Service Contexts
Teresa Fernandes and Fabia Esteves
Faculty of Economics, University of Porto, Porto, Portugal
KEYWORDS
Customer engagement;
customer loyalty; propensity
to engage; services
ABSTRACT
The article discusses the eect of context on customer engage-
ment and presents propensity to engage as an attitudinal
antecedent of loyalty behaviors. We argue that customers may
hold dierent propensity to engage depending on the specic
service context, which in turn will inuence more or less favorable
behaviors. Data were collected through a convenience sample
of 516 consumers in two settings, high and low contact services.
Results revealed that propensity to engage and loyalty behaviors
vary signicantly between the contexts studied. We also conclude
that the majority of loyalty behaviors are correlated, in both con-
texts, with customers’ propensity to engage.
Introduction
Increasingly, long-term, sustainable competitive advantages depend on the rm’s
ability to retain, sustain, and nurture its customer base (Anderson, Fornell, & Maz-
vancheryl, 2004; Gruca & Rego, 2005; Rego et al., 2009; Van Doorn et al., 2010).
Customer relationships became one of the main issues in marketing, with several
authors emphasizing its importance in business.
Customer engagement (CE) refers to a broader “transcending” relational perspec-
tive (Vargo, 2009) and is described as a signicant tool for building and improving
relationships with customers, namely service relationships (Brodie, Hollebeek, Juric,
& Ilic, 2013). Engagement implies a deeper relationally based level and, thus, has an
important place in contributing to the understanding of customer outcomes, namely
loyalty-related outcomes (Bowden, 2009). Correspondingly, Verhoef et al. (2010)
revealed the increasing trend in companies trying to encourage their customers to
involve in this kind of nontransactional behaviors that go beyond purchase inten-
tions.
However, though the concept of CE is emerging in the marketing literature,
research is still in its infancy (Brodie, Hollebeek, Juric, & Ilic, 2011; Gambetti &
Gragna, 2010; Hollebeeck, 2011a). Most studies are descriptive or conceptual in
CONTACT TeresaFernandes tfernandes@fep.up.pt Faculty of Economics, University of Porto, R Dr Roberto Frias,
-, Porto, Portugal.
©  Taylor & FrancisGroup, LLC
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126 T. FERNANDES AND F. ESTEVES
nature (e.g., Brodie et al., 2011; Sashi, 2012; Van Doorn et al., 2010; Vivek et al., 2012)
and much of what has been written about engagement has its basis in management
practice rather than in academic research (Bowden, 2009). Moreover, while CE is
considered a multidimensional construct with cognitive, emotional and behavioral
components, few empirical research focus the attitudinal antecedents of engage-
ment Also, though CE is a context-dependent concept, most studies refer to spe-
cic settings (Vivek, 2009), namely online brand communities, instead of the “phys-
ical world,” and thus these are elds that require further attention (Brodie et al.,
2013). According to Brodie et al. (2011), engagement research across a wide range
of service contexts is expected to contribute more eectively to furthering scholarly
understanding of engagement processes.
The aim of this study is to discuss the eect of context as a factor that can facilitate
and/or inhibit CE. We argue that customers may hold dierent propensity to engage
in a relationship with their provider depending on the specic service context. Dif-
ferent propensities will, in turn, inuence more or less favorable behavioral out-
comes. We begin by presenting the literature relevant to this study, namely CE and
customers’ propensity to engage in relationships with their service providers. Next,
we present the research methodology and discuss main results. Through a conve-
nience sample of 516 consumers, we conduct a cross-sectional survey to examine
dierences in customers’ propensity to engage and loyalty behaviors in two service
settings, health care and retailing. Finally, we conclude the article by presenting nal
conclusions, contributions, and suggestions for future research.
The concept of customer engagement
Engagement was rst conceptualized by Kahn (1990), who studied its psychologi-
cal preconditions. Recently, organizations have been launching programs to engage
customers and measuring levels of CE as a response to the growing resistance of con-
sumers to traditional marketing programs (Bagozzi & Dholakia, 2006). Since 2005,
the term “engagement” has been increasingly used in the broader academic market-
ing literature (Brodie et al., 2011). However, in spite of the use of this term in recent
practitioner and academic literature, systematic conceptualizations of engagement
in marketing are scarce (Vivek et al., 2012) and a general consensus has not yet been
reached (Hollebeeck, 2011a; Javornik & Mandelli, 2013).
The denition presented by Brodie (2011) can be considered to be the most com-
prehensive CE denition in the literature. According to Brodie et al. (2011,2013),
CE represents a highly context-dependent psychological state, characterized by a
specic intensity level that plays a center role in the process of relational exchange.
Moreover, other relational concepts can act as antecedents and/or consequences in
CE processes. As such, CE is dened as “a multidimensional concept comprising
cognitive, emotional, and/or behavioral dimensions” (Brodie et al, 2011, p. 260).
Also Patterson et al. (2006) dened CE as the level of a customer’s physical, cog-
nitive, and emotional presence in their relationship with a service organization.
Engagement is acknowledged as a potentially highly context-specic variable that
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SERVICES MARKETING QUARTERLY 127
may impact consumer choice in relation to brands, products, or organizations (Pat-
terson et al., 2006). Bowden (2009) described CE as “a psychological process” driv-
ing customer loyalty, and primarily concerned with examining the formation and
development of customer relationships. Engagement is further characterized by dif-
fering levels, which are individual and/or context specic (Bowden, 2009; Sprott et
al., 2009).
Though considered as a multidimensional construct, the behavioral dimension
of CE appears as dominant in the literature (Brodie et al., 2011) and has been largely
adopted (Javornik & Mandelli, 2013). To authors such as Jakkola and Alexander
(2014), Van Doorn et al. (2010), and Pham and Avnet (2009), engagement is dened
primarily with reference to specic customer activity types or patterns. For these
authors, the concept of CE aggregates the multiple ways customer behaviors beyond
transactions may inuence the rm (Jakkola & Alexander, 2014). Customers engage
in a number of behaviors that strengthen their relationship with the product, com-
pany, or brand that go beyond mere purchasing behavior (Gummerus et al., 2012),
including also word-of-mouth (WOM), recommendations, cross-buying, and active
voice/complaints (Van Doorn et al., 2010). Furthermore, engaged customers are
expected to show a stronger preference for premium products and lower price sensi-
tivity (Ramkumar et al., 2013), proving to be more protable than their nonengaged
counterparts (Voyles, 2007). These loyalty-related outcomes beyond purchase may
be better predicted by CE than by other conventional marketing constructs such as
quality or satisfaction, which fail to capture the depth of relationships consumers
form with what they consume (Bowden, 2009; Hollebeek, 2011a,2011b; Patterson
et al., 2006). Namely, CE is anticipated to contribute to the core relationship market-
ing tenets of customer repeat patronage, retention, and loyalty (Verhoef et al., 2010).
However, the potential contribution of engagement to customer loyalty is just now
starting to transpire in the literature (Bowden, 2009), and corroboration of these
contentions is yet to be undertaken through empirical research (Javornik & Man-
delli, 2012; Brodie et al., 2011; Roberts & Alpert, 2010). Moreover, although the uni-
dimensional approaches possess the merit of simplicity, they fall short in reecting
the rich conceptual scope of engagement (Brodie et al., 2011).
Towards a multidimensional concept of customer
engagement: Propensity to engage
The prominent perspective of literature considers CE as a multidimensional con-
struct with cognitive, emotional, and behavioral components. Nevertheless, over
40% of the denitions in the literature express engagement as a unidimensional
concept, with the behavioral dimension appearing as dominant (Brodie et al.,
2011). However, the most important factors aecting engagement are attitudinal
antecedents (Van Doorn et al., 2010). Engaging in a relationship is not signaled
only by behavior, but more by the attitudes and reasons why the behavior occurs
(Venetis & Ghauri, 2004). According to attitude research (Fishbein & Ajzen, 1975),
a person’s behavior is determined by its intention to perform it. Intention has been
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128 T. FERNANDES AND F. ESTEVES
viewed as the willingness to continue a course of action or activity, such as engaging
in a relationship (Wetzels, et al., 1998), and would be closer to overt behavior than
the cognitions and aective components on which they are based.
Several denitions emphasize the role of attitudes in the creation of a state
of engagement. In the organizational behavior literature, employee engagement is
argued to be positively related to an individual’s attitudes, intentions, and behaviors
(Saks, 2006). Bowden (2009) illustrated engagement as an iterative process, com-
mencing with customer satisfaction and including attitudinal antecedents such as
rational and emotional bonds, trust, and involvement, culminating in an end state
of engaged and loyal customers. Also Vivek et al. (2012) stated that an engaged indi-
vidual may develop more favorable attitudes toward a product, company, or brand,
which strengthens the psychological process and increases the likelihood of a posi-
tive behavioral response.
A considerable body of consumer behavior literature has dealt with the attitude–
behavior relationship. Perhaps the most popular is the framework developed by Dick
and Basu (1994), in which a distinction is made between attitudinal and behav-
ioral loyalty. The authors stated that loyalty has two dimensions: relative attitude
and repeat patronage behavior. They identied four loyalty categories: loyalty (posi-
tive relative attitude, high repeat patronage), latent loyalty (positive relative attitude,
but low repeat patronage), spurious loyalty (high repeat patronage, low relative atti-
tude), and no loyalty (low on both dimensions). The high patronage of spurious
loyal customers may be explained by factors such as habitual buying, nancial incen-
tives, convenience, and lack of alternatives. As such, the behavioral approach may
not yield a comprehensive insight into the underlying reasons. Instead it is a con-
sumer’s disposition in terms of intentions that plays an important role (Fernandes
& Proença, 2013).
In this study, we dene propensity to engage as representing a consumer’s ten-
dency or proneness to engage in relationships with providers of a particular prod-
uct/service category, leading to dierent CE levels. The individual’s propensity to
engage has been referred to in the literature in reference to behaviors such as WOM,
complaint behavior, or co-creation activities (Bijmolt et al., 2010; Bowden, 2009;
Dellarocas & Narayan, 2006; Fuchs, Prandelli, & Schreier, 2010; Hoyer, Chandy,
Dorotic, Krat, & Singh, 2010; Javornik & Mandelli, 2012; Wirtz et al., 2013).
Since consumers often vary highly in their willingness to engage, rms will be
confronted with engaged-prone customers and “other customers” (Bijmolt et al.,
2010). According to Brodie et al. (2011), CE levels may vary in a continuum, includ-
ing customers that are “nonengaged,” “marginally engaged,” “engaged,” and “highly
engaged” (i.e., exhibiting dierent levels of cognitive, emotional, and/or behav-
ioral engagement). Hollebeek (2011a) proposed an engagement-based segmenta-
tion framework, resulting in dierentially engaged customer segments and dier-
ent propensities to develop certain loyalty-related behaviors. Also, Bryson and Hand
(2007) ranged customers from “actively disengaged” to “fully engaged.” Identifying
segments of consumers who are particularly willing to engage across the customer
base may help companies to ne-tune their strategies. According to Van Doorn et
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SERVICES MARKETING QUARTERLY 129
al. (2010), customers can be classied and segmented according to “their propensity
to engage and the types of engagement behaviors they display” (p. 263).
The eect of context on engagement and propensity to engage
Several factors may aect customers’ propensity to engage and resulting levels
of engagement. Van Doorn et al. (2010) suggested several factors that can facili-
tate and/or inhibit engagement, including context-based factors. Hollebeek (2011b)
considered that the particular level of interactivity pertaining to specic engage-
ment levels depends on factors such as particular contextual conditions. Moreover,
according to Patterson et al. (2006), engagement levels may vary by factors includ-
ing industry and product/service attributes. Vibert and Shields (2003) addressed the
importance of considering the contextual nature of engagement. Brodie et al. (2011)
stated that CE is subject to a context-and/or a stakeholder-specic expression.
In terms of context-based factors, engagement has been studied primarily in
online settings, namely virtual brand communities (e.g., Brodie et al., 2013; Gum-
merus et al., 2012; Lee, Kim, & Kim, 2011; Wirtz et al., 2013), given its conducive,
interactive, and relationship centric nature (Tsai & Men, 2013), while other settings
remain largely unexplored in academic research. Nowadays, with communication
technologies and information systems, it is possible to interact with and among con-
sumers (Brodie et al., 2013). Online brand or user communities allow strengthening
consumer relationships and engaging with brands (Bolton et al., 2013). Brand com-
munity members sharing aninterest may create a bond (De Valck et al., 2009), turn-
ing the community into a powerful engagement platform (Shawhney et al., 2005).
Since engagement is a “context-dependent state of mind” (Hollebeek, 2011a,
p. 790), contextual factors can aect customers’ propensity to engage in service
relationships and types of engagement behaviors displayed. For instance, engag-
ing consumers is generally easier in high involvement, interaction-based contexts as
opposed to low-involvement ones (Bolton & Saxena-Iyer, 2009). With reduced ser-
vices, commoditized products, increased availability, and reduced switching costs
consumers may not attach much value to engage in a relationship with their provider
(Pressey & Mathews, 2000). Conversely, highly complex professional services may
be situations for which customers may desire to engage in a relationship (Burnham,
Frels, & Mahajan, 2003; Harrisson-Walker, 2001; Venetis & Ghauri, 2004).
However, the context-dependent perspective remains to be explored further on.
Javornik and Mandelli (2012) studied the FMCG (fast-moving consumer goods)
industry, a setting that can encounter challenges when trying to intensely engage
and establish meaningful interactions with customers (Leahy, 2011). The authors
studied levels of engagement of consumers in the industry through indicators such
as willingness to repurchase, to recommend, and to complain. They concluded that
loyalty is a signicant characteristic of those who are willing to engage intensely
and that engagement with FMCG brands is challenging and specic. In this sense,
Javornik and Mandelli (2012) suggested that it would be important to investigate
how CE diers across dierent industries, and discover how high (or low) is the
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130 T. FERNANDES AND F. ESTEVES
willingness for CE within dierent product categories. In a study on online brand
communities, Wirtz et al. (2013) considered that high involvement purchases and
product complexity will moderate online brand communities’ impact on engage-
ment. The authors called for the need to further investigate how consumers can
be segmented according to their propensity for engagement and what drives this
engagement, namely comparing “contexts that dier in their focus” (p. 239). Also,
according to Brodie et al. (2011,2013) and Hollebeek (2011a), since engagement is
a context-dependent concept, there is a need for comparative research across a wide
range of service contexts, focusing not only online but also oine settings. Our study
aims to address these literature gaps.
Research framework and methodology
Though the concept of CE is emerging in the marketing literature, there is still
a lack of consensus regarding the nature of specic concepts as CE antecedents,
concurrent factors, and/or consequences (Brodie et al., 2011; Hollebeeck, 2011a).
The aim of this study is to investigate dierences in customers’ propensity to
engage in a service relationship (attitudinal antecedent) and related loyalty out-
comes (behavioral consequence), based on contextual factors (concurrent factor).
We argue that customers may hold dierent propensity to engage in a service rela-
tionship given the context considered, and that these dierences, in turn, aect
the more or less favorable customer behaviors in the relationship. Though CE
is anticipated to contribute to customer repeat patronage, retention, and loyalty
(Verhoef et al., 2010), the potential contribution of engagement to customer loy-
alty is just now starting to transpire in the literature and is still underresearched
(Bowden, 2009).
The empirical research was conducted in service contexts given its inherently
relational nature (Grönroos, 2004) and the particular applicability of engagement
in service settings, since its level of interactivity and reciprocal nature (Bolton &
Saxena-Iyer, 2009) is widely acknowledged (Bowden, 2009; Patterson et al., 2006).
Two distinct service contexts were considered, dened according to the level of cus-
tomer involvement with the service provider (Lovelock & Wright, 2004). The rst
is health care services, where the consumer has a high degree of involvement with
the service provider (physician), the service is personalized, and it is centered on
people. High involvement services require greater investment in relationship build-
ing and oer higher levels of familiarity and trust, that would ultimately lead to the
customization of the service (Ganesan-Lim, Russell-Bennett, & Dagger, 2008) and
higher levels of engagement (Bowden, 2009). Especially in medical service encoun-
ters, which are frequently characterized by a large degree of anxiety, patients desire
to be acknowledged as people—they want to be listened to and treated with patience
(Bloemer, deRuyter, & Wetzels, 1999). Thus, in service industries such as health care,
the benets of engagement may go beyond mere consumption (Van Doorn et al.,
2010). In our study, we have considered physicians/dentists, chosen by customers.
The second context is retailing, where the degree of involvement with the customer
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SERVICES MARKETING QUARTERLY 131
is lower (Lovelock & Wright, 2004). Low involvement is typical of discrete, routine,
and mundane services, where the main emphasis is the standardization of processes
(Ganesan-Lim et al., 2008). Services with a low degree of involvement with the cus-
tomer tend to maximize the importance of highly structured actions to stimulate
new business and are not based on personal and customized relations between the
client and the provider (Folkes & Patrick 2003). Specically, services that oer stan-
dardized service solutions with moderate contact prioritize functional benets (Paul
et al., 2009). In our study, we have specically considered the case of supermarkets—
a grocery and convenience retailer where the majority of transactions are “discrete,
short-term, one-o acts” (Pressey & Mathews, 2000, p. 273) and a transactional,
rather than a relational approach may be the most appropriate (Gilbert & Sumner,
2004).
Taking into account the contexts chosen, it becomes relevant to try to understand
how CE diers according to the context analyzed. Dierent engagement propensi-
ties are predicted to generate distinct behavioral outcomes (Hollebeek, 2011a). It
is thus signicant (a) rstly, to assess the dierences in customers’ propensity to
engage between the contexts presented in order to understand if this proneness
changes from context to context; and (b) secondly, to make the same analysis for
loyalty behaviors. The second part of the study analyzes the correlation, in both
contexts, between customers’ propensity to engage and key suitable dimensions of
customer behaviors referred to in the literature. Namely, loyalty behaviors like repeat
purchase, WOM, willingness-to-pay a price premium, and active voice/complaints,
were all considered suitable for oine contexts and found consistent across dierent
types of service industries (Bloemer et al., 1999). Attention is focused on testing the
following hypotheses:
H1: There are signicant dierences between customers’ loyalty behaviors in the health
care context when compared with the retailing context.
H2: There are signicant dierences between customers’ propensity to engage in a rela-
tionship in the health care context when compared with the retailing context.
H3: Customers’ propensity to engage is correlated with customers’ loyalty behaviors
(WOM, repeat purchase, willingness-to-pay a price premium, and active voice/complaints)
in both contexts.
Data were collected through a self-administered, online, cross-sectional survey. A
convenience sample of 516 consumers was used to perform signicance and corre-
lation tests. In order to guarantee some level of engagement, and following Bowden’s
(2009) perspective on repeat customers, respondents were directed to think about
specic providers (in health care and retailing) they patronized. Respondents were
then asked to respond to the remaining questions focusing on that particular
provider. The questionnaire was divided into three parts: the rst part aimed to char-
acterize the sample, the second part evaluated propensity to engage and customers’
loyalty behaviors in the health care setting, and the third part evaluated propen-
sity to engage and customers’ loyalty behaviors in retailing. All constructs were
measured based on multi-item scales established in previous research (Bloemer,
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132 T. FERNANDES AND F. ESTEVES
Odekerken-Schröder, & Kestens, 2003; Odekerken-Schröder et al., 2003; Zeithaml,
Berry, & Parasuraman, 1996) and assessed in a 7-point Likert scale (1 =completely
disagree to 7 =completely agree).
Research ndings
The majority of the respondents (59.3%) were female, between 25 and 34 years old
(49.2%), and with a bachelor’s degree (40.9%).
To verify H1 and H2, respectively, we performed independence ttests in order to
analyze dierences between customers’ behaviors (Table 1) and customers’ propen-
sity to engage (Table 2) in both contexts, item by item. For all items and dimensions,
the average value assigned to the health care context is dierent from the average
value assigned to the retailing context and the dierences observed are statistically
signicant (p=.000 <.05). The only exception is the second item of the repeat
purchase dimension.
Findings suggest that there are statistically signicant dierences in behavioral
manifestations between the contexts analyzed (H1), with the exception of one item
on the repeat purchase dimension. Thus, context seems to have an impact on behav-
iors that go beyond purchase. Health care customers tend to exhibit more favor-
able behaviors than retailing customers, since context conditions are more con-
ducive. Namely, in the health care context, customers are more likely to spread
WOM and less likely to be price sensitive, while in the retailing context cus-
tomers are more likely to complain than in health care. Though customer feed-
back, particularly complaints, is considered as a manifestation of CE (e.g., Javornik
& Mandelli, 2012; Bijmolt et al., 2010), in the high involvement context cus-
tomers seem to exhibit a noncomplaining behavior. One possible reason is that
a sense of dependence and lack of control over the situation, like it may happen
in a complex service like health care, can lead customers to avoid complaining in
the event of a negative encounter (Tronvoll, 2011; Van Doorn et al., 2010). Con-
versely, in retailing services, where processes are more standardized and interac-
tion is less personal, customers may nd it less dicult to complain (Nimako &
Mensah, 2012).
As expected, it was also possible to verify that there are signicant dierences in
customers’ propensity to engage between the contexts analyzed (H2), in favor of the
health care context, where customers have direct contact with the service provider
and the degree of customization and interaction is higher.
To verify H3, we rst performed an exploratory factorial analysis (Table 3). Four
factors were extracted, referring to each one of the four behavioral manifestations
considered (repeat purchase, WOM, price sensitivity, and complaints). The same
was done for customers’ propensity to engage, reaching accepted values for all con-
structs.
In order to verify the existence of a positive relationship between consumer rela-
tionship proneness and their behavioral intentions in both contexts (H3), a correla-
tion analysis was performed. The results are presented in Table 4.
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SERVICES MARKETING QUARTERLY 133
Table . Hypothesis  testing results: student ttest per formedto customer behaviors in both contexts.
Behavioral
Intentions Items description
Average Healthcare
Context
Average Retailing
Context t Sig.
Word of WOM Say positive things , , . .∗
Mouth WOM Recommend XYZ to , , . .∗
(WOM) someone who seeks
your advice
WOM Encourage friends and
relatives to do business
with XYZ
, , . .∗
Repeat RP Consider XYZ your first , , . .∗
Purchase RP choice to buy services
(RP) Do more business with
XYZ in the next few
years
, ,
−.
.
RP Do less business with XYZ
in the next few years
, ,
−.
.∗
Price
Sensitivity
(PS)
PS Take some of your
business to a
competitor that offers
better prices
, ,
−.
.∗
PS Continue to do business
with XYZ if its prices
increase somewhat
, , . .∗
PS Pay a higher price than
competitors charge for
the benefits you
currently receive from
XYZ
, , . .∗
Complaints
(C)
C Switch to a competitor if
you experience a
problem with XYZ’s
service
, ,
−.
.∗
C Complain to other
customers if you
experience a problem
with XYZ’s service
, ,
−.
.∗
C Complain to external
agencies, such as
consumer
organizations, if you
experience a problem
with XYZ’s service
, ,
−.
.∗
C Complain to XYZ’s
employees if you
experience a problem
with XYZ’s service
, ,
−.
.∗
∗Statistically significant at p<..
Note. Adapted from Zeithaml, V., Berr y, L.& Parasuraman, A. (); Bloemer, J., Odekerken-Schrӧder, G. & Kestens, L.
()
In both contexts, the correlations between customers’ propensity of engage and
WOM, repeat purchase and price sensitivity are signicant (p<.01). Repeat pur-
chase and WOM have correlation coecients larger than 0.5, which means that
there is a strong correlation in both contexts. Price sensitivity presents a moder-
ate correlation with propensity to engage. In the case of complaints, both contexts
show low, nonsignicant correlation coecients (below 0.25). Following previous
ndings (H1), while some engaged customers may nd that complaints are a sec-
ond opportunity given to companies, others may feel uncomfortable complaining
due to their loyalty to the rm (Nimako & Mensah, 2012). Also, rms may need to
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134 T. FERNANDES AND F. ESTEVES
Table . Hypothesis  testing results: student ttest performed to customers’propensity to engage in
both contexts.
Items description
Average
Healthcare
Context
Average Retailing
Context t Sig.
Customer Propensity to
Engage (CPE)
CPE Generally, I am someone
who likes to be a
regular customer of a
service provider
, , . .∗
CPE Generally, I am someone
who wants to be a
steady customer of the
same service provider
, , . .∗
CPE Generally, I am someone
who is willing to ‘go
the extra mile’to
purchase at the same
service provider
, , . .∗
∗Statistically significant at p<..
Note. Adapted from Odekerken-Schrӧder, G., De Wulf, K. & Schumacher, P. ().
Table . Exploratory factorial analysis results for customers’ propensity to engage and customer
behaviors in both contexts.
Customer
behaviors Context Number of components KMO Variance extracted % Cronbach’s alpha
Word of mouth Health care  . . .
Retailing  . . .
Repeat purchase Health care  . . .
Retailing  . . .
Price sensitivity Health care  . . .
Retailing  . . .
Complaints Health care  . . .
Retailing  . . .
Customer
propensity to
engage
Health care  . . .
Retailing  . . .
Table . Hypothesis  testing results: correlation between customers’ propensity to engage and cus-
tomer behaviors in both contexts.
Customer propensity to engage
Health care context Retailing context
Customer behaviors Pearson correlation Sig. (two-tailed) Pearson correlation Sig. (two-tailed)
Word of mouth .  . 
Repeat purchase .  . 
Price sensitivity .  . 
Complaints . . . .
develop processes/platforms to support customer voice in order to inuence subse-
quent behaviors like active voice (Van Doorn et al., 2010) and this may be a more
relevant factor to facilitate and explain complaints than engagement. Finally, it is
possible that complaining is determined by other antecedents besides engagement,
such as the subjective probability that complaining will be successful, the attitude
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SERVICES MARKETING QUARTERLY 135
towards the act of complaining and the perceived cost of complaining (Bloemer
et al., 1999).
Conclusion
The aim of this study was to examine the eect of context on CE, presenting propen-
sity to engage as an attitudinal antecedent of customer loyalty behaviors. We argue
that customers may hold dierent propensity to engage in a relationship with their
provider depending on the specic service context. Dierent propensities will, in
turn, inuence more or less favorable customer behaviors. Results revealed that
propensity to engage and customer behaviors vary signicantly among the con-
texts studied. In the high involvement service (health care), customers exhibit higher
propensity to engage and show more favorable behaviors, when compared with the
low involvement service (retailing). It was also found that the majority of customer
behaviors are, in both contexts, correlated with customers’ propensity to engage.
Only complaint behavior did not prove to be related with propensity to engage.
Also, complaint behavior was found to be more likely in retailing when compared
to health care. This may be due to the inuence of other more important factors
than engagement, such as personal (e.g., customer dependence, more likely in health
care), situational (e.g., existence of standardized processes to complain, more likely
in retailing), or the attitude towards the act of complaining.
Our study makes several contributions. Engagement research across a wide range
of service contexts is required, since till now studies have been largely descriptive,
conceptual, or managerial in nature and focused on specic online settings. Also,
while the behavioral dimension of CE appears as dominant in the literature, few
empirical research studies have focused on the attitudinal antecedents of CE and
thus fall short in reecting the rich conceptual scope of engagement. By investigating
the eect of service context on CE, presenting propensity to engage as an attitudinal
antecedent of customer behaviors using a large sample survey, our study contributes
to bridge these literature gaps.
On a managerial level, the results of this study enhance insights on CE and
are expected to be valuable for practitioners seeking to improve customer rela-
tionships, retention, and loyalty, namely according to their specic service con-
text. For instance, in services like health care, high in credence qualities, WOM is
particularly valuable and may be even more important than repurchase behavior.
The more complex and variable the service, the more interested prospective cus-
tomers are likely to be in the opinion of customers who have experienced it (Berry
& Seltman, 2007). These companies may be interested in targeting customers with
a high propensity to WOM. According to Van Doorm et al. (2010), some compa-
nies reward customers for referrals, which may include social or expertise recogni-
tion. Also, rms may provide platforms to facilitate customer-to-customer (C2C)
engagement. Mayo Clinic is an example within the health care sector that trusts
WOM increasingly conveyed through social media and uses patients as advertis-
ers for the brand (Berry & Seltman, 2007). The results of this study also provide
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136 T. FERNANDES AND F. ESTEVES
guidance to managers for segmenting customers according to their propensity to
engage and the types of behaviors they display, instead of wasting resources with
nonprone or unengaged consumers. For instance, customers who do not purchase a
lot but exhibit many other loyalty outcomes may be a potential segment to deserve
increase investment and nurturing by the rm.
However, this study is not without limitations. First, the limitations of data col-
lection warrant caution in generalizing these results beyond the population sam-
pled. Replication of this study with random sampling procedures would clearly
add weight to the reported results. Second, further research using a longitudinal
design could better address engagement processes dynamics (Brodie et al., 2011).
This study could also be improved with access to database information on cus-
tomer actual behavior history since measurements were based on self-reports. Also,
while we used a traditional battery of customer loyalty behaviors, other “higher
order” behaviors could be studied such as willingness to cooperate or acquiescence
(Fernandes & Proença, 2013; Jones & Taylor, 2007). Additionally, a number of con-
current factors, such as rm or customer-based, not investigated here may prove
to be signicant to future studies. Future researchers may also consider replicating
this study across other service contexts or even consumer goods to examine how
our conclusions can apply in other purchase contexts.
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