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A meta‐analysis of customer engagement behaviour

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Customer engagement behaviour has emerged as an influential concept in marketing and refers to customers' behavioural manifestation towards a firm originating from motivational drivers. To provide a comprehensive and generalisable picture of this concept, this study provides a meta‐analysis integrating data of 196 effect sizes of 184 publications with a sample of 146,380. The findings reveal engagement through two pathways: organic pathway as relationship‐oriented (perceived quality, perceived value and relationship quality) and promoted pathway as firm‐initiated (functional and experiential initiatives). Moderator analysis indicates that the influence of the two pathways on engagement depends on engagement context (online vs. offline), industry type (service vs. manufacturing) and product type (hedonic vs. utilitarian) and cultural context. Findings support attitudinal engagement–loyalty and behavioural engagement–firm performance linkage. Study results provide new insight into various engagement approaches and their relationship to each other. The authors offer recommendations to help marketers manage their customer engagement process more effectively.
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A meta-analysis of customer engagement behaviour
Mojtaba Barari* | Mitchell Ross | Sara Thaichon | Jiraporn Surachartkumtonkun
Department of Marketing, Griffith Business School, Griffith University, Southport, QLD, Australia
*email: mojtaba.barari@griffithuni.edu.au
Abstract
Customer engagement behaviour has emerged as an influential concept in marketing and refers to
customers’ behavioural manifestation toward a firm originating from motivational drivers. To
provide a comprehensive and generalizable picture of this concept, this study provides a meta-
analysis integrating data of 196 effect sizes of 184 publications with a sample of 146,380. The findings
reveal engagement through two pathways: organic pathway as relationship-oriented (perceived
quality, perceived value, and relationship quality) and promoted pathway as firm-initiated
(functional and experiential initiatives). Moderator analysis indicates that the influence of the two
pathways on engagement depends on engagement context (online vs. offline), industry type
(service vs. manufacturing) and product type (hedonic vs. utilitarian), and cultural context.
Findings support attitudinal engagementloyalty and behavioural engagementfirm performance
linkage. Study results provide new insight into various engagement approaches and their
relationship to each other. The authors offer recommendations to help marketers manage their
customer engagement process more effectively.
Keywords customer engagement behaviour, meta-analysis, organic engagement, promoted
engagement, engagement marketing, relationship marketing
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1. Introduction
Both academic research and business practice consider customer engagement behaviour to
be a key success factor in the long term (Kumar et al., 2010; Kumar & Pansari, 2016), as the
engaged customer is much more profitable for a business than other customers (Pansari &
Kumar, 2016). However, firms have conventionally focused on developing a relationship
with their current customers to influence purchase and re-purchase and firm performance
(Kumar et al., 2010; Palmatier, Dant, Grewal, & Evans, 2006), thereby regarding customers
with more transactional behaviour as better customers for firm profitability (Kumar &
Pansari, 2016). However, from a customer engagement perspective, customer contribution
to the firm is not limited to purchase-related metrics, as engagement behaviour results in,
for example, new product ideas or referral of new customers (van Doorn et al., 2010).
Therefore, customer engagement extends customer valuation from being merely
transactional and includes both transactional and non-transactional metrics (Kumar et al.,
2010).
A review of customer engagement literature reveals two main approaches to
engagement behaviour formation: relationship-oriented (named as organic pathway) and
firm-initiated (named as promoted pathway). The first pathway reflects the relationship
marketing view, in which customerfirm relationships organically, and over time, result in
customer engagement behaviour (Bowden, 2009; Palmatier, Kumar, & Harmeling, 2017),
therefore it is named as the organic pathway. The engagement literature extends
relationship marketing to include both tangible and intangible results (Vivek, Beatty, &
Morgan, 2012) and encompasses interaction with firm touchpoints to develop customer
firm relationship quality (Kumar, Rajan, Gupta, & Pozza, 2019; Pansari & Kumar, 2016) and
form customer engagement towards a firm or brand (Hollebeek, 2011).
In contrast to organic engagement, the second pathway relies on firm various
initiatives to directly influence customer engagement behaviour (Beckers, van Doorn, &
Verhoef, 2018), thus it is named as the promoted pathway. Using initiatives to leverage
engagement behaviour is not new to marketing practice, as financial incentives for referral
3
or customer participation in new product development were employed before the
emergence of the customer engagement concept (Ryu & Feick, 2007). However, these
techniques are limited to specific behaviours and are functional and task-oriented in nature
(Harmeling, Moffett, Arnold, & Carlson, 2017). The recent development in firm-initiated
engagement literature extends promoted engagement to include both functional and
experiential initiatives (Harmeling et al., 2017).
Although customer engagement as an independent concept has a relatively short
history (Lemon & Verhoef, 2016; Neulinger et al., 2020), it is actually quite a well-developed
concept in marketing literature. In the organic engagement literature, researchers have
virtual consensus as to the variables that affect engagement behaviour formation, but
diverge as to the consequence and importance of the variables (Bowden, 2009; McNeill &
Venter, 2019; Palmatier et al., 2017). Similarly, in promoted engagement, previous research
lacks consistency with regard to the effectiveness of direct engagement initiatives
(Harmeling et al., 2017). Moreover, previous work has ignored the relationship between
promoted and organic pathways and how firm-initiated activities could have both short-
and long-term effects on engagement behaviour (Harmeling et al., 2017). In addition, the
emergence of new technologiessuch as augmented and artificial reality and online social
mediaprovide more diversity to the customerfirm relationship (Steinhoff, Arli, Weaven,
& Kozlenkova, 2019) and customer engagement models (Wirtz, Orsingher, & Cho, 2019).
To address these issues, this research takes a meta-analytic approach to conduct a
systematic literature review of previous empirical research in customer engagement. The
result is an integrated and comprehensive model of customer engagement’s antecedents
and outcomes. In this model, the promoted and organic perspectives constitute two
distinct approaches to engagement, and the antecedents in the model are categorized on
the basis of these pathways. This framework allows us to study previous research in these
two approaches separately and provide theoretical and empirical insight into each
approach. Moreover, we define how the organic and promoted pathways are related,
resulting in a better picture of customer engagement antecedents.
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In this research, there is a direct relationship between engagement behaviour and
attitudinal engagement, both of which are impacted by organic and promoted pathways.
We include several moderators in the conceptual model to study the relationship between
engagement’s direct antecedents and attitudinal and behavioural engagement. The
moderator analysis provides great insight into the effectiveness of engagement formation
in a different context. Previous research supports the role of engagement in firm outcomes
(Kumar & Pansari, 2016). However, as engagement is more than purchase attitude and
behaviour, we carefully audit the relationship between engagement and its outcome to
provide a more realistic picture of engagement’s role in the enhancement of a firm's
performance.
Our meta analytics research has the following structure. First, we summarize the
previous definitions of customer engagement behaviour and distinguish this concept from
related concepts. Second, we review the history of customer engagement behaviour to
demonstrate its evolution. Third, we provide our meta-analysis framework and related
hypothesis to explain and justify the relationship between variables in this model. Fourth,
we describe our meta-analysis method to explain data collection and coding procedures,
effect size calculation, structural equation modelling, and analysis of moderators. Fifth, we
present results of our data analysis. Finally, we discuss our results in terms of theoretical
and managerial implications, limitations of our research, and further research directions.
2. Understanding customer engagement behaviour
2.1. Conceptualization of customer engagement behaviour
Customer engagement behaviour is defined as a behavioural manifestation toward the focal
firm, beyond purchase, resulting from motivational drivers (van Doorn et al., 2010). This
definition highlights the main characters of this concept. Initially, behavioural
manifestation reflects a customer’s voluntary resource contributions (mostly operant
resources, such as knowledge, experience, energy or time) with both the focal firm and
other actors, such as current or prospect customers (Kumar et al., 2010). Although this
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research focuses on the positive side of engagement behaviour, this behaviour is not always
beneficial for the focal firm (Azer & Alexander, 2020b; Naumann, Bowden, & Gabbott,
2020). Thus positively and negatively valenced engagement co-exist in the customer
relationship with the focal brand (Azer & Alexander, 2018; Bowden, Conduit, Hollebeek,
Luoma-Aho, & Solem, 2017) in which a customer may positively and/or negatively engage
with different aspect of the focal firm (Azer & Alexander, 2020a; Naumann, Bowden, &
Gabbott, 2017). Further, “toward the focal form” suggests that engagement behaviours
contribute to a firm’s marketing activities (Harmeling et al., 2017) and exclude customer
behaviours such as product consumption or disposal. Moreover, “beyond purchase” reflects
the non-transactional nature of engagement which is in contrast to transactional behaviour
such as product purchase (van Doorn et al., 2010), while “resulting from motivational
drivers indicates that the engagement behaviour originates from an attitudinal
engagement as an antecedent (Lemon & Verhoef, 2016; van Doorn et al., 2010). Although
customer engagement behaviour is a distinct concept in marketing, it exhibits similarities
with related concepts. To uncover this concept’s relationships and position regarding other
related concepts, we summarize the related concepts’ definitions, comparison, and
relationship with engagement behaviour in Appendix A.
Customer engagement behaviour includes a wide range of behaviours which we
classify into three categories. First, resource sharing with a firm in which the customer
shares operant resources (knowledge, energy, time) with a firm in the form of suggestions,
feedback, complaints, etc, to improve firm marketing functions (Kumar et al., 2010).
Second, resource sharing with other actors in which customers share operant resources
(knowledge, experience, energy, time) with other actors (such as other customers) in the
form of word of mouth, writing a comment, etc, to assist them (Jaakkola & Alexander, 2014;
van Doorn et al., 2010). Third, direct influencing in which customers affect other actors’
attitudes or behaviours toward the firm in the form of referring or changing their
perception toward the firm (Jaakkola & Alexander, 2014; V. Kumar et al., 2010).
6
2.2. Evolution of customer engagement behaviour
The current understanding of customer engagement behaviour is a result of the evolution
of this concept from a functional to relational approach and subsequently to the
transformational approach. The emergence of customer engagement as a unified concept
occurred in the early 2010s (Lemon & Verhoef, 2016) and is based on relationship marketing
theory (Palmatier et al., 2017). However, the root of this concept lies in initial research on
the firm's effort to promote engagement behaviour as a functional approach (Kumar et al.,
2010). Recent emergence of new technologies has led to formation of brand communities
on social media, revolutionizing customer-company relationship formation (Steinhoff et
al., 2019) and firm-initiated engagement (Beckers et al., 2018; Harmeling et al., 2017) and
creating a transformational approach. This evolution is described in Table 1.
Table 1 Development of customer engagement behaviour concept in marketing
Mid-1990s to mid-2000s
Mid-2000s to mid-2010s
Mid-2010s to today
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Functional
Relational
Transformational
Approach to
engagement
behaviour
The firm initiated /
Short-lived effect
Customer initiated /
Long-lived effect
Customer and firm
initiated / Dual effect
Theories
Exchange theory, equity
theory
Social exchange theory,
S-D logic
Social network theory,
service ecosystem
Key trends and
disruptions
Customers consider
valuable assets and firms
try to enjoy this resource
for competitive
advantage; however, it
has a transactional and
short-term approach to
engagement.
Customers consider
value co-creator and
relationships facilitate
engagement formation;
however, it requires a
long-term investment in
the relationship with
customers.
New technologies i.e.
social media, mobile
apps, augmented and
virtual reality transform
interactions between
customer - firm -other
actors in a network of
interaction.
Key insights
Monetary incentives
encourage the customer
to contribute to firm
marketing activities such
as referring to a new
customer.
Customer-firm dyadic
relationships over time
encourage the customer
to engage with the firm.
Technology empowers
the customer to engage
with the firm and other
actors and enables firms
through firm-initiated
engagement activities
and directly influence an
actor’s engagement
behaviour
Illustrative
resources
Buttle (1998);
Biyalogorsky, Gerstner,
and Libai (2001); Ryu and
Feick (2007).
Bowden (2009); Kumar
et al. (2010); Vivek et al.
(2012); Brodie et al.
(2011).
Harmeling et al. (2017);
Brodie, Ilic, Juric, and
Hollebeek (2013); Brodie,
Fehrer, Jaakkola, and
Conduit (2019).
2.2.1. Functional approach
Marketing’s long-ago shift from a product to a customer orientation acknowledged
the customer as a valuable resource for competitive advantage (Verhoef et al., 2010). As a
result, firms use monetary incentives such as discounts, vouchers, and gifts to encourage
customer participation in engagement behaviours such as new product development,
customer referrals, and word of mouth (Biyalogorsky et al., 2001; Ryu & Feick, 2007). This
approach is task-based, with customers completing structured tasks such as referring a new
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customer for which the firm provides rewards (Harmeling et al., 2017). This view of
engagement is based on social exchange (Ryu & Feick, 2007): as long as customers and firms
have some sort of benefits for each other, this economic exchange will continue (Guo,
Gruen, & Tang, 2017).
Several studies have examined the functional approach to customer engagement
behaviour, such as emphasizing referral programs to harness the power of word of mouth
Buttle (1998) and employing various methods from lowering the price to offering rewards
as tools to encourage customer engagement behaviour (Biyalogorsky et al., 2001). The main
advantage of the functional approach is its direct and immediate impact on customer
engagement behaviours (Harmeling et al., 2017). However, as engagement behaviour
depends on the presence of economic incentives, the functional approach has a short-lived
impact and it is not effective in all situations (Harmeling et al., 2017).
2.2.2. Relational approach
In contrast to the functional approach, the relational approach has long-term, dynamic,
process-oriented views on customer engagement behaviour (Bowden, 2009; Pansari &
Kumar, 2016). In this approach, the customer has moved from being an asset to being a
value co-creator with an active role in relationships between the customer and firm
(Jaakkola & Alexander, 2014). This approach relies mainly on social exchange theory, in
which the focus is on socio-emotional aspects of the customerfirm relationship (Guo et
al., 2017). Customers are predicted to reciprocate with positive thoughts, feelings, and
behaviours toward the firm after a satisfactory experience with the firm (Pervan, Bove, &
Johnson, 2009). Through the commitmenttrust process, satisfactory experience enhances
relationship quality and customer engagement behaviour (Bowden, 2009; Pansari &
Kumar, 2016). In addition, service-dominant logic (S-D) has strongly influenced the
development of the relational approach to engagement by considering customers
voluntary resource contribution (e.g., knowledge, experience, and time) in their
relationship with a firm (Jaakkola & Alexander, 2014).
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A relational approach is based primarily on the customer’s tendency to develop a
relationship and engage with the firm over time. The emergence of the internet and new
technology have changed both customer and firm roles in the customer engagement
process and have moved engagement study to the transformational level.
2.2.3. Transformational approach
The emergence of new technologies such as mobile apps and customized web pages created
a platform to develop a personalized relationship with customers (Steinhoff et al., 2019).
Moreover, augmented and virtual reality enable firms to add humanized features of
interaction, such as appearance or mental processes, to the customerfirm interaction
(Steinhoff et al., 2019). Additionally, social media and brand communities foster customer
firm interaction (Blazevic et al., 2013; Brodie et al., 2013; Gummerus, Coulter, Liljander,
Weman, & Pihlström, 2012) and include other actors such as other customers in this
interaction (Brodie et al., 2019). For the firm, these developments create an opportunity to
employ various methods to influence and enhance customer engagement behaviour
(Harmeling et al., 2017; Palmatier et al., 2017). For the customer, these developments not
only extend the value in their relationship with the brand (Gummerus, Liljander, Weman,
& Minna, 2012) but also allow them to participate in engagement behaviour with others in
their social network (Yannopoulou et al., 2019; Dolan, Conduit, Frethey-Bentham, Fahy, &
Goodman, 2019).
From a theoretical perspective, social network theory and service ecosystem extend
the study of engagement to a network level (Lin, Miao, Wei, & Moon, 2019) in which
engagement is not limited to interaction between a customer and focal firm but rather
embraces other actors such as other customers (Brodie et al., 2019). Numerous researchers
in and outside of the engagement literature have helped the emergence and development
of transformational engagement (Brodie et al., 2013; Gummerus, Coulter, et al., 2012;
Steinhoff et al., 2019; Wirtz et al., 2013), and recent research in customer engagement
extends this concept to include all actors who have a role in the engagement formation
10
process (Breidbach & Brodie, 2017; Brodie et al., 2019; Storbacka, Brodie, Böhmann, Maglio,
& Nenonen, 2016).
3. Conceptual framework
Our conceptual framework is rooted in two main perspectives on customer engagement
behaviour: organic pathway as relationship-oriented engagement and promoted pathway
as firm-initiated engagement. In our conceptual model (Fig. 1), the organic pathway is
developed based on the relationship marketing approach to engagement (Bowden, 2009)
while the promoted pathway reflects the firm-initiated approach to engagement
(Harmeling et al., 2017; Palmatier et al., 2017). The organic pathway to engagement is a
result of the relationship between customer and firm over time (Palmatier et al., 2017). In
this pathway satisfaction, trust, and commitment are the main factors by which new and
current customers become engaged customers (Bowden, 2009). To draw the relationship
between variables in this pathway, we draw on Aurier and N’Goala (2010) because it
provides a very clear picture of sequences of customerfirm relationship formation. As
indicated in Fig. 1, in this pathway, perceived quality’s impact on perceived value through
relationship quality (satisfactiontrustcommitment) influences customer attitudinal and
behavioural engagement.
In the promoted pathway, the firm employs various techniques and methods to
directly influence customer engagement behaviour (Harmeling et al., 2017). In this regard,
a firm’s efforts to influence engagement behaviour fall into two main areasfunctional and
experiential initiatives (Harmeling et al., 2017). In functional approach, a monetary
incentive is used to compensate customers for marketing tasks such as referral of a new
customer (Wirtz, Orsingher & Cho, 2019). In the experiential approach, initiatives are
mostly based on hedonic and social value and are aimed at creating an emotional bond
with the customer (Harmeling et al., 2017). This is considered to be an engagement
behaviour motivational driver (van Doorn et al., 2010). Fig. 1 indicates the development of
the proposed pathway, in which functional and experiential incentives both directly
influence customer engagement (Beckers et al., 2018; Harmeling et al., 2017).
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In our framework, we create a connection between organic and promoted pathways
through perceived value, in which both customers and firms’ efforts influence the
engagement formation process. From a firm-initiated engagement perspective, the firm
offers different values to directly influence engagement behaviour (Beckers et al., 2018).
Through perceived value, these initiatives influence customers’ long-term relationships
with the firm and create a long-lived effect for company engagement efforts. In addition,
in both the organic and promoted pathways, customer engagement leads to customer
loyalty and firm performance. These two variables are considered to be the main outcome
of customer engagement (Harmeling et al., 2017; V. Kumar & Pansari, 2016; van Doorn et
al., 2010).
Moderator selection was based on the importance of explaining the inconsistency in
previous engagement models, the emergence in empirical research coding and the number
of effect size (i.e. at least 10 effects size) (Gremler, Van Vaerenbergh, Brüggen, & Gwinner,
2019; Palmatier et al., 2006). In this regard, engagement formation seems to differ in the
online and offline context for both organic and promoted engagement (Harmeling et al.,
2017; Wirtz et al., 2019). Variables such as industry type (service vs. manufacturing) and
product type (hedonic vs. utilitarian) constitute important moderators in engagement
formation (Kumar & Pansari, 2016; Kumar et al., 2019). Although the type of market (B2B
versus B2C) is an important moderator for customer engagement (Pansari & Kumar, 2016),
as empirical studies in B2B are few, we do not include this variable in the model.
Furthermore, research in customer engagement indicates that culture has an important
role in customer engagement formation (Gupta, Pansari, & Kumar, 2018). Finally, similar
to previous meta-analysis variables such as sample composition (student vs. non-student),
publication status (published vs. not published) and quality of publication outlet are
controlled for this research (Blut & Wang, 2019; Gremler et al., 2019).
12
Fig. 1 Customer engagement framework
Promoted pathway
Perceived
Perceived
quality
Satisfaction
Trust
Commitment
Attitudinal
engagement
Behavioural
engagement
Relationship
quality
Customer
engagement
Loyalty
Firm
performance
Outcome
Moderators
Engagement context: Online vs. Offline
Industry type: Service vs. Manufacturing
Product type: Hedonic vs. Utilitarian
Cultural context: Power distance/
Individualism/ Masculinity / Uncertainty
avoidance.
Control variables
Experiential
initiative
Functional
initiative
Organic pathway
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3.1. Organic engagement pathway
3.1.1. Perceived quality and perceived value
Quality is defined as a customer’s judgment of a firm’s overall excellence or superiority
(Zeithaml, 1988). In our model, quality is not limited to service quality, and it includes both
products and services in both online and offline contexts (Carlson, Rahman, Voola, & De
Vries, 2018). In other words, perceived quality indicates the quality of customer experience
of firm offerings (Kumar et al., 2019; Pansari & Kumar, 2016). In the customer engagement
literature, perceived quality is considered to be one of the engagement antecedents (Wirtz
et al., 2013). In addition, value is defined as customer judgment of utility of the firm
offerings based on a trade-off between what customers give as cost and what they receive
as benefits (Zeithaml, 1988). The firm's ability to deliver superior value as compared to
competitors is considered a firm's main competitive advantage (Ravald & Grönroos, 1996).
Research in relationship marketing indicates perceived value to be a firm’s opportunity to
create and develop a relationship with customers (Palmatier et al., 2006; Ravald &
Grönroos, 1996). We propose a direct relationship between perceived quality and perceived
value, in which an increase in customer perceived quality of a company offering will
improve customer value perception. This relationship between perceived value and
perceived quality is supported by previous research (Aurier & N’Goala, 2010; Hu,
Kandampully, & Juwaheer, 2009). Thus, we expect:
H1: Perceived quality is positively related to perceived value.
3.1.2. Perceived quality and satisfaction
Satisfaction refers to the customer's overall evaluation of the purchase and consumption
experience (Anderson, Fornell, & Lehmann, 1994). The fundamental model of satisfaction
is the expectancydisconfirmation model, in which satisfaction is the result of a customer’s
comparison between expectation of the firm’s offering and the experienced performance
(Oliver, 1980). Satisfaction is an important predictor of customer behavioural response and
firm performance (Anderson, Fornell, & Mazvancheryl, 2004; Gupta & Zeithaml, 2006). The
14
relationship between quality and satisfaction has been widely studied (Anderson et al.,
1994), especially in service marketing (Olorunniwo, Hsu, & Udo, 2006). Moreover, the
customer engagement literature research supports the direct impact of perceived quality
on satisfaction (Verleye, Gemmel, & Rangarajan, 2013). Thus, we expect:
H2: Perceived quality is positively related to satisfaction.
3.1.3. Perceived value and satisfaction
Customers’ motivation to engage with a firm depends on the benefits they expect to receive
from their relationship with the firm (Vivek et al., 2012). Value is considered to be the main
predictor of customer satisfaction and the emergence of long-term relationships (Barari,
Ross, & Surachartkumtonkun, 2020; Ravald & Grönroos, 1996), and the linkage between
perceived value and customer satisfaction is supported in the relationship marketing
literature (Anderson et al., 1994). In the customer engagement literature, customers’
perceived value is considered the customer-based antecedent of the engagement (van
Doorn et al., 2010) in which an increase in perceived value enhances customer engagement
through customer satisfaction (Hapsari, Clemes, & Dean, 2017). Thus, we expect:
H3: Perceived value is positively related to satisfaction.
3.1.4. Satisfaction, trust, and commitment
Satisfaction, trust, and commitment constitute relationship quality as an overarching
construct (Lemon & Verhoef, 2016). Relationship quality is defined as a customer's overall
evaluation of the strength of the relationship with a firm (Palmatier et al., 2006). As noted
earlier, satisfaction is related to customers’ overall evaluation of their experience of firm
offerings (Anderson et al., 1994). Trust is defined as customer willingness to rely on an
exchange partner with whom a certain level of confidence has been built” (Moorman et al.,
1993). Trust is a necessary condition of customer commitment to the firm and, along with
commitment, is placed at the centre of the relationship marketing framework (Palmatier
et al., 2017). Trust in the relationship arises from partner reciprocity and non-opportunistic
15
behaviour (Vivek et al., 2012). Commitment is defined as a customer’s enduring desire to
maintain a valued relationship” (Moorman et al., 1993) and indicates the nature of the
relationship (Palmatier, Dant, & Grewal, 2007). An increase in customer commitment will
affect customer willingness to maintain valued relationships with the firm (Watson et al.,
2015). Satisfaction in relationship marketing is considered to be a fundamental
precondition of the customerfirm long-term relationship and a precondition of trust and
commitment (Aurier & N’Goala, 2010; Hennig‐Thurau & Klee, 1997). Similarly, in customer
engagement models, customer satisfaction is considered to be a direct predictor of trust
and commitment in the relationship between customer and firm (Bowden, 2009). Thus, we
expect:
H4: Satisfaction is positively related to (a) trust and (b) commitment.
Trust and commitment are at the heart of relationship marketing and are necessary for
developing and maintaining a successful relationship between customer and firm (Morgan
& Hunt, 1994; Palmatier et al., 2006). In this regard, research in relationship marketing
empirically supports the influence of trust on commitment in developing relationships
(Aurier & N’Goala, 2010; Hennig‐Thurau & Klee, 1997). Similarly, in customer engagement
models trust and commitment play an essential role in engagement development
(Hollebeek, 2011), in which trust influences customer commitment in the engagement
formation process (Bowden, 2009). Thus, we expect:
H5: Trust is positively related to commitment.
3.1.5. Commitment and customer engagement
Research in relationship marketing indicates that customer trust and commitment to a
relationship have a direct impact on customers’ transactional behaviour such as repurchase
(Aurier & N’Goala, 2010; Palmatier et al., 2006). For instance, trust and commitment
influence customer tendency to stay with the firm and repurchase from it (Palmatier et al.,
2006). Customer engagement comprises attitudes and behaviours that go beyond purchase
16
(Lemon & Verhoef, 2016) and reflect our two-dimensional view of customer engagement.
In contrast to the relationship marketing literature, customer engagement research
indicates that among the relationship quality components, only commitment is a direct
predictor of customer engagement (Bowden, 2009). In contrast to transactional behaviour,
although in non-transactional behaviour satisfaction and trust are necessary to develop a
relationship, they are not sufficient to directly influence customer attitudinal and
behavioural engagement. Thus, we expect:
H6: Commitment is positively related to (a) attitudinal and (b) behavioural
engagement
3.2. Promoted engagement pathway
3.2.1. Functional and behavioural initiatives
Firms employ various forms of economic incentives as a reward to promote customer
engagement behaviour (Beckers et al., 2018; van Doorn et al., 2010). These incentives are
mainly extrinsic and utilitarian rewards (Vivek et al., 2012), in the form of money, discounts,
and information to compensate customers who contribute to the firm’s marketing function
(Harmeling et al., 2017). These incentives encourage customers to refer a new customer,
support other customers or provide positive comments about the firm (Kumar et al., 2010;
Kumar & Pansari, 2016). Research indicates a significant direct impact of these incentives
on engagement behaviours (Ryu & Feick, 2007; Wirtz et al., 2019). Thus, we expect:
H7: Functional initiative is positively related to behavioural engagement.
3.2.2. Experiential initiative and attitudinal engagement
Firm activities that are mostly based on multisensory, hedonic, and social benefits create
customer attitudinal engagement with the firm (van Doorn et al., 2010). Here, the firm
employs various programs, events, or activities to directly influence customer engagement
(Vivek et al., 2012) and, cover the deficiencies of functional initiatives by providing a
17
pleasant experience for customers (Harmeling et al., 2017) and influence customers’
attitudinal engagement with firms. Thus, we expect:
H8: Experiential initiative is positively related to attitudinal engagement.
3.3. Organic and promoted pathways association
3.3.1. Functional and experiential initiative and perceived value
Functional and experiential initiatives also have an indirect effect on engagement. We
define the indirect effect for these two variables as they facilitate instant short-term
customer engagement and also create long-term customer engagement (Harmeling et al.,
2017). Although research in firm-initiated engagement is limited to the direct influence of
these initiatives on customer engagement (Ryu & Feick, 2007; Wirtz et al., 2019), we predict
that through perceived value, functional and experiential initiatives will indirectly
influence customer engagement over time. Functional and experiential initiatives can be
utilitarian and hedonic, increasing customers’ received value of their relationship and
influencing customers’ perceived value. Thus, we expect:
H9: (a) Functional and (b) experiential initiative are positively related to perceived
value.
3.4. Customer engagement and outcomes
3.4.1. Attitudinal and behavioural engagement
As noted earlier, customer engagement comprises attitudes and behaviours that go beyond
purchase (Lemon & Verhoef, 2016). While customer engagement behaviour definition
indicates attitudinal engagement as a driver of behavioural engagement (van Doorn et al.,
2010), the relationship between them has not been sufficiently studied. In a related area,
employee engagement studies show that attitudinal engagement occurs before behavioural
engagement and is the prerequisite of the behavioural component (Saks, 2006). It seems
18
unwise for an organization to start developing behavioural engagement when attitudinal
engagement is not present (Shuck & Wollard, 2010). Thus, we expect:
H10: Attitudinal engagement is positively related to behavioural engagement.
3.4.2. Attitudinal engagement and loyalty
Loyalty is defined as a customer's positive attitude and behaviour toward a brand, and it is
manifested in a customer’s tendency to repurchase a preferred brand (Oliver, 1999). The
loyalty chain holds that customer loyalty starts from cognitive loyalty as an information
component, then turns to affective loyalty and finally leads to behavioural loyalty (Oliver,
1999). In this chain, only attitudinal engagement, through cognitive and affective loyalty,
could influence loyalty. In other word, attitudinal engagement enhances favourable
attitudes toward a firm or accelerates the transition from attitudinal to behavioural loyalty
(Harrigan et al., 2018; So, King, Sparks, & Wang, 2016). In this regard, research has shown
how higher brand engagement in self-concept as an attitudinal component of customer
engagement leads to higher customer loyalty (Sprott, Czellar, & Spangenberg, 2009). Thus,
we expect:
H11: Attitudinal engagement is positively related to loyalty.
3.4.3. Behavioural engagement and firm performance
Firm performance comprises a firm’s actual and objective performance enhancement such
as sales, profit, and share of wallet (Palmatier et al., 2006) and originates from customer
firm long-term relationships (Palmatier et al., 2007) and firm engagement activities
(Beckers et al., 2018; Harmeling et al., 2017). Business performance indicates firm financial
improvement, while engagement behaviour is related to non-financial and non-
transactional attitude and behaviour (van Doorn et al., 2010). In contrast to the
engagementloyalty relationship, behavioural engagement has the potential to directly
influence firm performance (Verhoef et al., 2010). In this sense, engagement behaviours
19
such as referral of a new customer or providing positive reviews in social media enhance
firm performance (Kumar & Pansari, 2016). Thus, we expect:
H12: Behavioral engagement is positively related to performance.
3.5. Potential moderators
3.5.1. Engagement context: online versus offline
The emergence of internet-based channels extends customerfirm interaction from face to
face and in a physical store to an online context (Steinhoff et al., 2019; Verma, Sharma, &
Sheth, 2016). In both organic and promoted pathways, new technologies have empowered
customers and facilitate customer participation in engagement behaviours (Wirtz et al.,
2013). However, compared to the face to face context, the online context is not effective for
creating an emotional bond between customers and the company (Steinhoff et al., 2019).
In contrast, direct and face to face interaction with customers is a wide-ranging
opportunity for firms to develop long-term relationships with customers and enhance their
attitudinal engagement (Palmatier et al., 2006). Thus, we expect:
H13: The positive effects of (a) commitment and (b) experimental initiative on
attitudinal engagement are stronger in an offline than online context.
H14: The positive effects of (a) commitment and (b) functional initiative on
behavioural engagement are stronger in an online than offline context.
3.5.2. Industry type: service versus manufacturing
This variable moderates the direct antecedent of engagement and attitudinal and
behavioural engagement differently in organic and promoted pathways. For the organic
pathway, a customer in service contexts has an important role in the service production
and delivery process (Kumar et al., 2019). In contrast, recent development in e-commerce
have diminished the interaction between customers and firms in goods-based industries
20
and customers are able to buy products with one click (Steinhoff et al., 2019). Hence, in an
organic engagement pathway customers are more involved with service-based firms than
with manufacturing (Kumar & Pansari, 2016; Kumar et al., 2019), and this direct interaction
with a customer provides numerous opportunities for firms to enhance their relationship
with the customer and enhance customer engagement (Pansari & Kumar, 2016). However,
in contrast to goods, services are intangible and inconsistent (Bowen, 1990). Therefore, it
could be harder for firms to convince and encourage customer attitudinal and behavioural
engagement directly and in a short time. The tangible and persistent nature of goods
facilitates customer attitudinal and behavioural engagement formation. Thus, we expect:
H15: The positive effects of commitment on (a) attitudinal and (b) behavioural
engagement are stronger in the service than in the manufacturing industries.
H16: The positive effects of (a) experiential initiative on attitudinal engagement and (b)
functional initiative on behavioural engagement are stronger in the manufacturing
than in the service industries.
3.5.3. Product type: hedonic versus utilitarian
Although all products have both hedonic and utilitarian characteristics, products can be
categorized as primarily hedonic or utilitarian (Dhar & Wertenbroch, 2000). Utilitarian
products such as banking products and services are mostly functional and instrumental,
while hedonic products such as theme parks have experiential and sensorial value (Babin,
Darden, & Griffin, 1994). Customers’ tendency to develop and maintain their relationship
is higher for hedonic than utilitarian products (Bowden, Gabbott, & Naumann, 2014).
Therefore, in the organic pathway, customer engagement formation is easier for hedonic
than for utilitarian products (Hollebeek, 2013). For promoted pathways, past studies show
the customer has a higher tendency toward hedonic than utilitarian products and it has
higher protentional to influence customer attitudinal and behavioural response (Barari et
al., 2020). Therefore, customer attitudinal and behavioural engagement in the promoted
21
pathway would be stronger for the hedonic than for the utilitarian product. Thus, we
expect:
H17: The positive effects of commitment on (a) attitudinal and (b) behavioural
engagement are stronger for hedonic than utilitarian products.
H18: The positive effects of (a) experiential initiative on attitudinal engagement and (b)
functional initiative on behavioural engagement are stronger for hedonic than
utilitarian products.
3.5.4. Cultural context
Power distance: It is the amount of individual acceptance of inequality in power,
authority, or status in society (Hofstede, Hofstede, & Minkov, 2005). In countries with high
power distance, hierarchy and distribution of power among people are important and
accepted (Hofstede, 2001). In contrast, people in low power distance cultures do not accept
and support hierarchy and power distribution in society (Hofstede, 2001). Hence, in high
power distance countries, people like to find a way to send a signal to other people to show
their status or power. Therefore, in both the organic and promoted pathways, customers
with high power distance are expected to participate in attitudinal and behavioural
engagement more than customers in low power distance countries (Gupta et al., 2018),
because customers in high power distance cultures consider engagement as a way to send
the signal of reputation and status to other customers (Samaha, Beck, & Palmatier, 2014).
Thus, we expect:
H19: The positive effects of commitment on (a) attitudinal and (b) behavioural
engagement are stronger in cultures with higher power distance.
22
H20: The positive effects of (a) experiential initiative on attitudinal engagement and
(b) functional initiative on behavioural engagement are stronger in cultures with
higher power distance.
Individualism: It reflects the extent to which individuals’ priority is their personal goal,
motivation, or desire, whereas collectivism gives priority to the group (Hofstede et al.,
2005). People in individualistic cultures act on the basis of their personal needs whereas in
collectivistic cultures behaviour is shaped by group goals rather than personal goals
(Hofstede, 2001). In this regard, research in relationship marketing shows developing a
customerfirm relationship is easier and more profitable in a collectivist culture than in an
individualistic culture (Samaha et al., 2014). Although individualism could present
difficulties in developing a long-term relationship, the relationship between the direct
antecedent of engagement and customer engagement is stronger in an individualistic than
in a collectivist culture in both organic and promoted pathways. In an individualist culture,
customers base action on their personal goals, and when the relationship meets their needs,
they like to do something to compensate for the direct and indirect benefits of their
relationship with a firm. In this regard, previous research shows that customer engagement
is more common in an individualistic culture than in a collectivistic culture (Gupta et al.,
2018; Pick & Eisend, 2013). Thus, we expect:
H21: The positive effects of commitment on (a) attitudinal and (b) behavioural
engagement are stronger in cultures with higher individualism.
H22: The positive effects of experiential and functional initiative on (a) attitudinal and
(b) behavioural engagement respectively are stronger in cultures with higher
individualism.
Masculinity: It refers to the extent to which tough (masculine) values rather than
tender (feminine) values direct people's behaviour in society (Hofstede et al., 2005).
23
While the dominant value in a masculine culture is rationalism, relational values are
prevalent in the feminine culture (Hofstede, 2001). The focus in the masculine culture is on
competitiveness and achievement, whereas in the feminine culture reciprocity, mutuality,
and benevolence guide people's behaviour (Hofstede et al., 2005). From this view, customer
commitment to the relationship is higher in feminine cultures than in masculine cultures
(Pick & Eisend, 2013) and feminine values encourage customers to reciprocate the direct or
indirect benefits they received from their relationship with the firm (Samaha et al., 2014).
The relationship between direct antecedents of engagement and attitudinal and
behavioural engagement in both the promoted and organic pathways is expected to be
higher in a feminine culture than in a masculine culture. Thus, we expect:
H23: The positive effects of commitment on (a) attitudinal and (b) behavioural
engagement are stronger in cultures with lower masculinity.
H24: The positive effects of (a) experiential initiative on attitudinal engagement and (b)
functional initiative on behavioural engagement are stronger in cultures with lower
masculinity.
Uncertainty avoidance: It refers to the extent to which people in a culture tolerate an
unknown or uncertain future (Hofstede et al., 2005). Individuals with high uncertainty
avoidance avoid unpredictability and ambiguity because they feel this situation will
threaten them (Hofstede, 2001). In contrast, people from low uncertainty countries have a
more relaxed attitude toward unknown situations (Hofstede, 2001). It is expected that high
uncertainty increases customers’ tendency to develop a long-term and stable relationship
with the firm to decrease the future unpredictability (Samaha et al., 2014). In contrast,
engagement attitude and behaviour encourage the customer to engage in some behaviour
that may increase future uncertainty. For instance, referring a new customer or writing a
comment about firm performance may increase future unpredictability. The relationship
between direct antecedents of engagement and attitudinal and behavioural engagement in
24
both the promoted and organic pathways is expected to be higher in a lower uncertainty
avoidance culture than in a higher uncertainty avoidance culture. Thus, we expect:
H25: The positive effects of commitment on (a) attitudinal and (b) behavioural
engagement are stronger in cultures with lower uncertainty avoidance.
H26: The positive effects of (a) experiential initiative on attitudinal engagement and (b)
functional initiative on behavioural engagement are stronger in cultures with lower
uncertainty avoidance.
3.6. Control variables
We define several control variables to ensure that the heterogeneity of the result is not
because of these variables (Gremler et al., 2019). For sample characteristics, we control
student versus non-student samples. Student samples are more homogenous than non-
student samples, creating a bigger effect size and lower error variance (Blut & Wang, 2019).
We also control the quality of the journal in which the article is published, because a high-
quality journal has a rigorous process to ensure the quality of the publications (Hunter &
Schmidt, 2004). Finally, we assess the publication status to control the difference between
published and unpublished research. Researchers prefer to publish significant effects
rather than insignificant effects (Hunter & Schmidt, 2004) and this preference may affect
the meta-analysis results.
4. Methods
Systematic review papers can be of several types, namely structured review focusing on
widely used methods, theories and constructs (Canabal & White III, 2008; Kahiya, 2018;
Paul & Singh, 2017; Rosado-Serrano, Paul, & Dikova, 2018, Paul, 2019), framework-based
(Paul & Benito, 2018), hybrid-narrative with a framework for setting future research agenda
(Dabić et al., 2020; Kumar, Paul, & Unnithan, 2019; Paul, Parthasarathy, & Gupta, 2017),
theory-based review (Gilal, Zhang, Paul, & Gilal, 2019; Paul & Rosado-Serrano, 2019), meta-
25
analysis (Rana & Paul, 2020; Knoll & Matthes, 2017), bibliometric review (Randhawa,
Wilden, & Hohberger, 2016), and review aiming for model/framework development (Paul
& Mas, 2019). This research adopted a meta-analysis approach to review customer
engagement behaviours literature which is considered an effective way to expand the
boundaries of a research domain (Grewal, Puccinelli, & Monroe, 2018). In contrast to
narrative literature review methods, meta-analysis allows us to statically integrate and
synthesise previous researches in customer engagement behaviour to resolve inconsistency
and create accumulate knowledge in this area (Palmatier, Houston, & Hulland, 2018)
4.1. Data collection and coding
To collect the required data, we followed several approaches. First, to identify relevant
studies, we selected several electronics databases: ABI/INFORM Global, Business Source
Complete, ProQuest Digital Dissertations, Scopus, SSRN, and Google Scholar. For the
search terms we used the keywords “customer engagement,” “brand engagement,”
“consumer engagement,” and “user engagement to include all research related to customer
engagement literature. These key terms were combined with related keywords such as
brand community,” “fan page,” “social media,” Facebook,” mobile, virtual,”
gamification,” “artificial intelligence,” “augmented reality,” “event marketing,” “referral
marketing,” and “engagement marketing” to cover related research in this area. Moreover,
we included in our search process all types of empirical publications, such as peer review
research, book chapters, dissertations, conference papers, and working papers. We also did
manual searches using the title and abstract of articles published in top journals in
marketing and management. Finally, we checked the reference list of articles related to
customer engagement in top journals and articles that have a high citation rate in Google
Scholar to find articles related to our research (Frigerio et al., 2020).
After completing the search process, we applied some inclusion and exclusion
criteria to restrict our research. First, we considered only research that studied antecedents
and consequences of individual (i.e., customer, consumer, or user) engagement with a
26
firm/brand or related touchpoints (i.e., brand community, mobile application) that
resulted in customer engagement with a firm. Thus, we excluded individual engagement
with objects that were not related to customer engagement (student engagement,
employee engagement) or customer engagement with specific firm (i.e., place engagement,
city engagement). Second, as the term engagement is very common in various research
areas, we included only research that provides solid conceptual and operational definitions
of the customer engagement concept to assess their relevancy to our research. Third, we
included only empirical research report correlation matrices or other statistical
information (e.g., standardized regression coefficients, t-values) that we could use to
calculate a correlation coefficient. Therefore, we excluded theoretical papers, qualitative
investigations, and quantitative studies that did not report findings of antecedents or
outcomes of customer engagement. Applying research criteria led to 184 records with 196
effect sizes that meet all our criteria (Appendix B).
To code these studies, we developed a coding protocol that included a detailed coding
manual with descriptions of each variable (Table 2). An independent coder who was an
expert in the engagement area and not involved in this research was hired to check the
quality of the coding. Overall inter-coder agreement is higher than 95%, confirming coding
quality. Differences in coding were resolved through discussion. When a single study used
the same sample to provide more than one effect size for the same relationship, we used an
average to calculate the effect size. When a study reported multiple effect sizes for the same
relationship, but these were independent, we included them as separate effect sizes.
27
Table 2 Constructs definition and aliases
Constructs
Definitions
Common aliases
Representative papers
Perceived
quality
Customer’s judgment of a firm
offering’s overall excellence or
superiority.
Product and service
perception, product quality,
service quality, experience
quality.
Zeithaml, Berry, and
Parasuraman (1996)
Perceived
value
Customer’s perceived positive
benefits from the firm offering.
Hedonic value, utilitarian
value, social value, economic
value, experiential value,
perceived value.
Zeithaml et al. (1996)
Functional
initiative
The firm-initiated activities in
which various economic incentives
are employed to promote customer
engagement behaviour.
Referral program, referral
campaign, word of mouth
program.
Beckers et al. (2018);
Harmeling et al. (2017)
Experiential
initiative
The firm-initiated activities in
which various hedonic and social
benefits are employed to enhance
attitudinal engagement.
Experiential marketing,
event marketing,
gamification
Harmeling et al. (2017)
Satisfaction
The positive affective or emotional
state resulting from the appraisal of
the firm offering.
Satisfaction with the
relationship, product or
service.
Geyskens and
Steenkamp (2000)
Trust
Confidence in the reliability and
integrity of a service provider.
Trustworthiness, credibility,
benevolence, honesty.
Morgan and Hunt
(1994)
Commitme
nt
Desire and willingness to maintain a
valued relationship with the firm in
different touchpoints.
Affective and behavioural
commitment, obligation,
normative commitment.
Morgan and Hunt
(1994)
Attitudinal
A customer's level of brand-related
thought processing and/or brand-
related effect in a particular
consumer/brand interaction
elaboration.
Cognitive and emotional
engagement, emotional
bonding and emotional
attachment.
Hollebeek, Glynn, and
Brodie (2014)
Behavioural
A customer's level of energy, effort
and time spent on a brand in a
particular consumer/brand
interaction.
Engagement behaviours,
customer referral, word of
mouth, user-generated
content.
Hollebeek et al. (2014)
Loyalty
A collection of attitudes aligned
with a series of purchase behaviours
that systematically favour one entity
over competing entities.
Brand loyalty, attitudinal
loyalty, purchase intention,
re-purchase intention
Oliver (1999)
Firm
performance
Firm measurable performance
enhancements.
Sales, market share, the
share of wallet, profitability.
Watson et al. (2015)
28
4.2. Effect size calculation
As most research in customer engagement reports the correlation matrix, the correlation
coefficient is used to calculate the effect size. If the publication did not report this
coefficient, the regression coefficient and Peterson and Brown (2005) formula are used to
transform the beta coefficients to the correlation coefficient. The Hunter and Schmidt
(2004) approach and random-effects model are employed to calculate the effect size. In the
first step, effect sizes were corrected for measurement error. Each correlation was divided
by the square root of the product of the reliabilities of the independent and dependent
variables, and after adjustment of the correlation for measurement error, the sample size
was used to weight correlations (Iyer, Blut, Xiao, & Grewal, 2019) (Appendix C). The
reliability-adjusted and sample size-weighted correlation is used to pool the correlation
matrix for the structural equation model and moderator analysis. Q-statistic test and I2
statistics are used to test the homogeny of effect sizes. The significance of Q-statistic and
the percentage higher than 75% of I2 statistics indicate the variance in effect size
distribution (Rana & Paul, 2020). Finally, to address the file-drawer problem, Rosenthal
(1979) formula is used to calculate fail-safe Ns (FSNs) (Appendix D).
4.3. Structural equation modelling
We used the meta-analytic structural equation modelling procedure to assess the research
framework and test the hypotheses, except the moderator analysis. As the sample size in
the individual research is not large enough, the statistical power of rejecting incorrect
models in structural equation modelling is not high. Thus, reported models in the literature
may not be the correct models or the best models (Grewal et al., 2018). Meta-analytic
structural equation modelling helps researchers to test different models and demonstrate
the superiority of one type of process or mechanism over another (Grewal et al., 2018). All
effect sizes (k = 196) considered in this analysis and reliability-adjusted and sample size
weighted correlations (N = 146,380) are used to create the pooled correlation matrix as
input for structural equation model. Similar to previous meta-analysis procedures (Blut &
Wang, 2019), missing correlations, especially the correlations between functional and
29
experiential initiatives and service quality and relationship quality components, were filled
by correlations of related researches.
4.4. Moderator analysis
Following previous research (Iyer et al., 2019; Samaha et al., 2014), we employed a random-
effects regression model to study the role of moderators in our model. Hence, reliability-
adjusted and sample size-weighted correlations are considered as dependent variables and
moderator variables as independent variables to explain the variability in the effect sizes.
On the basis of our conceptual model, we conduct two separate regression models for
attitudinal and behavioural engagement. In both models, engagement context (1 = face to
face, 0 = online), industry type (1 = service vs. 0 = manufacturing), and product type (1 =
hedonic, 0 = utilitarian) were defined as dummy variables. The cultural context is
considered a continuous variable. Hofstede et al. (2005) score of cultural dimensions
(power distance, individualism, masculinity, uncertainty avoidance) is used to measure
cultural context as a moderator (ranging from 1 to 100).
5. Results
5.1. Descriptive statistics
Table 3 provides the descriptive analysis. Attitudinal and behavioural engagement both
have a positive correlation with antecedents, meditators, and outcome variables. Most
correlations are stronger for attitudinal engagement than for behavioral engagement. The
result of the Q-test for all pair correlations is significant, which indicates heterogeneity
among effect sizes. Similarly, the I2 test for all pair correlations is higher than 75%, which
shows heterogeneity between studies. The analysis shows that all FSNs for all pairs
correlations are higher than Rosenthal (1979) suggested threshold (5k + 10) and assures the
robustness of the findings against publication bias. In addition, the symmetric funnel plot
analysis indicates that publication bias is unlikely. Finally, the exclusion of sample size and
effect size outliers does not affect the study results.
Table 3 Descriptive statistics for customer engagement framework
30
Relationship
k
N
rcw
LCI
UCI
Q
I2
FSN
Attitudinal engagement (AE)
Perceived quality → AE
16
6,436
.46
.43
.57
208*
93%
7794
Perceived value → AE
49
32,619
.60
.47
.59
2220*
98%
7034
Satisfaction → AE
23
10,317
.52
.44
.59
625*
96%
516
Trust → AE
20
7,325
.48
.42
.59
454*
96%
892
Commitment → AE
22
7,534
.60
.54
.69
565*
96%
2068
Experiential → AE
11
1,331
.51
.42
.65
47*
89%
722
Loyalty → AE
63
25,492
.52
.51
.59
1652*
96%
9267
Behavioural engagement (BE)
Perceived quality → BE
30
59,64
.27
.37
.49
828*
96%
4892
Perceived value → BE
72
41,164
.50
.43
.52
2229*
97%
7671
Satisfaction → BE
42
66,08
9
.57
.40
.50
1388*
97%
914
Trust → BE
25
8,791
.44
.39
.54
513*
95%
3630
Commitment → BE
26
9,082
.53
.53
.56
815*
97%
1868
Functional → BE
16
5,329
.36
.36
.40
298*
95%
3017
Performance → BE
11
3,116
.48
.40
.79
760*
99%
4556
AE → BE
56
23,855
.56
.52
.60
1365*
96%
3009
K: number of effect sizes; N: cumulative sample size; rcw: reliability adjusted and sample size weighted
correlation; LCI: 95%- lower confidence interval; UCI: 95%-upper confidence interval; Q=Q statistic;
I2=I2-statistic; FSN=fail-safe N, Note: *p < .01 (rcw: two-tailed)
5.2. Results of a structural equation modelling
In accordance with prior work (Pick & Eisend, 2013), we employed a chi-square difference
test (Δχ2/df) to reach an optimal model. To do so, we developed alternative models based
on our framework in Fig. 1 and compared the results with the original model. First, we
created a direct relationship between perceived quality and perceived value with attitudinal
and behavioural engagement to test their direct effect on customer engagement. The result
indicates a decrease in chi-square in comparison to the original model (Δχ2/df = -1519.62).
Moreover, we put all relationship quality dimensions (satisfaction, trust, and commitment)
as a direct mediator between antecedents and both attitudinal and behavioural
engagement. Again, the result indicates a decrease in chi-square compared to our original
model (Δχ2/df = -361.367). Finally, the result of our customer engagement behaviour
framework testing is presented in Fig 2.
31
Fig. 2 Path model results (Note: ***p < .01)
5.2.1. Organic pathway
As can be seen in table 4, in the organic pathway perceived quality has a direct and
significant impact on both perceived value (H1; β = .29) and satisfaction (H2; β = .22).
However, this effect is higher for the perceived qualityvalue linkage than the perceived
qualitysatisfaction linkage quality-value = .29 > βquality-satisfaction = .22). In addition, perceived
value has a significant effect on customer satisfaction (H3; β = .44), and compared to
perceived quality, it is a better predictor of customer satisfaction value-satisfaction = .44 >
βquality-satisfaction = .22). For relationship quality components, satisfaction is a significant
predictor of both trust (H4a; β = .57) and commitment (H4b; β = .17), and this effect is
higher for satisfactiontrust than for satisfaction–commitment Satisfaction-trust = .57 >
βSatisfaction-commitment = .17). Like satisfaction, trust has a significant effect on commitment (H5;
β = .50). However, trust is a much better predictor of commitment than satisfaction Trust-
commitment = .50 > βSatisfaction-commitment = .17). Among the relationship quality components, only
commitment has a direct and significant effect on both attitudinal (H6a; β = .51) and
Perceived
value
(R2 =.48)
Perceived
quality
Trust
(R2 = .33)
Commitment
(R2 = .37)
Attitudinal
engagement
(R2 = .50)
engagement
Loyalty
(R2 = .26)
Firm
performance
(R2 = .23)
Experiential
Functional
initiatives
.22***
.44***
.
29***
.42***
.28***
.40***
.01***
.57***
.17***
.50***
.51***
.30***
.52***
.48***
.37***
Organic pathway
Promoted pathway
32
behavioral engagement (H6b; β = .30), and this effect is stronger for the commitment
attitudinal engagement linkage than for the commitmentbehavioral engagement linkage
commitment-attitudinal = .51 > β commitment-behavioral = .30).
Table 4 Hypothesis testing results
Hypothesis
Structural paths
β-value
p-value
Findings
H1
Perceived quality Perceived value
.29
***
Supported
H2
Perceived quality Satisfaction
.22
***
Supported
H3
Perceived value Satisfaction
.44
***
Supported
H4a
Satisfaction Trust
.57
***
Supported
H4b
Satisfaction Commitment
.17
***
Supported
H5
Trust Commitment
.50
***
Supported
H6a
Commitment Attitudinal engagement
.51
***
Supported
H6b
Commitment Behavioural engagement
.30
***
Supported
H7
Functional initiative Behavioural engagement
.01
***
Supported
H8
Experiential initiative Attitudinal engagement
.40
***
Supported
H9a
Functional initiative Perceived value
.42
***
Supported
H9b
Experiential initiative Perceived value
.28
***
Supported
H10
Attitudinal engagement Behavioural
engagement
.37
***
Supported
H11
Attitudinal engagement Loyalty
.52
***
Supported
H12
Behavioural engagement Performance
.48
***
Supported
Note: ***p < .01
5.2.2. Promoted pathway
From table 4, in the promoted pathway, functional and experimental initiatives have dual
functions in our model. The functional initiative has a direct and significant effect on
behavioural engagement (H7; β = .01) and the experiential initiative has a direct and
significant impact on attitudinal engagement (H8; β = .24). However, the relationship
between experimental initiative and attitudinal engagement is much stronger than that for
functional initiative and behavioural engagement Experiential-attitudinal = .40 > βFunctional -
behavioural = .01). For the indirect effect in the promoted pathway, the effect of both functional
initiative (H9a; β = .42) and experimental initiative (H9b; β = .28) on perceived value is
significant. In contrast to the direct effect, in the indirect effect the experimental initiative
33
attitudinal engagement linkage is stronger than the functional initiativebehavioral
engagement linkage Functional-value = .42 < βExperiential-value = .28).
5.2.3. Engagement and its consequences
As predicted in the research framework, table 4 indicates attitudinal engagement has a
significant effect on behavioural engagement (H10; β = .37). and this effect is higher than
the impact of commitment on behavioral engagement attitudinal - behavioral = .37 > β commitment-
behavioral = .30). Moreover, attitudinal engagement is a significant driver of customer loyalty
(H11; β = .52) and behavioural engagement has a significant effect on firm performance
(H12; β = .48).
5.3. Results of moderator analysis
Table 5 indicates the influence of moderator variables on linkages between attitudinal and
behavioural engagement and its direct drivers.
Table 5 Results of moderator analysis
Organic pathway
Promoted pathway
Moderators
Commitment
AE
Commitment
BE
Experiential
initiative AE
Functional
initiative BE
Engagement context
Online (vs. offline)
-.026*
.397**
-.132*
.205*
Industry type
Services (vs.
manufacturing)
.049
.102
-.0212**
-.351**
Product type
Hedonic (vs.
utilitarian)
.311**
.325**
.0564*
.0303*
Cultural context
Power distance
-.002
.074**
-.001
.010*
Individualism
.0118**
.088**
.132**
0.080*
Masculinity
-.055**
-.185**
-.0876**
-.022***
Uncertainty
avoidance
-.0206**
-.042**
-.0342**
-.010**
R-Square
46%
49%
47%
43%
AE (Attitudinal engagement) and BE (Behavioural engagement)
Note: *p < .1, **p < .05, ***p < .01
34
5.3.1. Engagement context
As expected, for engagement context, the relationships between commitment and
attitudinal engagement (H13a; β = -.026) and experiential and attitudinal engagement
(H14a; β = -.132) are significantly stronger in the offline than the online context. In contrast,
the commitment behavioural engagement (H13b; β = .397) and functional behavioural
engagement (H14b; β = .205) relationships are significantly higher in the online than the
offline context. Therefore, the online context is more effective in behavioural engagement
formation and the offline context is more effective for attitudinal engagement
development.
5.3.2. Industry type
In the organic pathway, the linkages between commitment and attitudinal engagement
(H15a; β = .049) and commitment and behavioural engagement (H15b; β = .102) are higher
for the service industry than for manufacturing, although they are not significant. In the
promoted pathway, both experiential and attitudinal engagement (H16a; β = -.0212) and
functional and behavioural engagement (H16b; β = -.351) linkages are significantly higher
in manufacturing than in the service industry. The result indicates that industry type as a
moderator is only significant in promoted engagement and its effectiveness is higher in the
manufacturing than in the service industries.
5.3.3. Product type
The relationships between commitment and attitudinal engagement (H17a; β = .311) and
commitment and behavioural engagement (H17b; β = .325) in the organic pathway are
positive and significant. In the promoted pathway, the linkages between experiential and
attitudinal engagement (H18a; β = .0564) and functional and behavioural engagement
(H18b; β = .0303) are higher among hedonic than utilitarian products. Patterns are
consistent in the product type moderation effect in both the organic and promoted
pathways, indicating that customer engagement is greater for hedonic products than
35
utilitarian products. Also, these relationships are much stronger in the organic than the
promoted pathway.
5.3.4. Cultural context
In the organic pathway, power distance significantly and positively moderates the
relationship between commitment and behavioural engagement (H19b; β = .074), but this
relationship is not significant for commitment and attitudinal engagement (H19a; β = -
.002). Similarly, in the promoted pathway, the relationship between functional initiatives
and behavioural engagement (H20b; β = .010) is significantly moderated by power distance,
but moderation is not significant for the experiential and attitudinal engagement linkage
(H20a; β = -.001). Therefore, power distance as a moderator is the only effect for the
relationships between direct antecedent of engagement and behavioural engagement. In
the organic pathway, individualism significantly and positively moderates the relationships
between commitment and attitudinal engagement (H21a; β = .0118) and commitment and
behavioural engagement (H21b; β = .088). Similarly, in the promoted pathway,
experimental and attitudinal engagement relationships (H22a; β = .132) and functional and
behavioural engagement relationships (H22b; β = 0.080) are significantly and positively
moderated by individualism. The result indicates that in both pathways, customer
engagement formation is more effective in individualistic countries than in collectivistic
countries. In the organic pathway, an increase in masculinity’s negative moderation of the
relationships between commitment and attitudinal engagement (H23a; β = -.055) and
behavioural engagement (H23b; β = -.185). Similarly, in the promoted pathway,
individualism significantly and negatively moderates the relationship between experiential
initiatives and attitudinal engagement (H24a; β = -.0876) and functional initiatives and
behavioural engagement (H24b; β = -.022). These results confirm that in both promoted
and organic pathways, attitudinal and behavioural engagement formation is more effective
in a feminine than in a masculine culture. Finally, in both the organic and promoted
pathways, uncertainty avoidance significantly and negatively moderated relationships
between commitment and altitudinal engagement (H25a; β = -.0206), commitment and
36
behavioural engagement (H25b; β = -.042), experimental initiatives and attitudinal
engagement (H26a; β = -.0342), and functional initiatives and behavioural engagement
(H26b; β = -.010). This result indicates that customer engagement formation in both the
organic and promoted pathways is more effective in countries with a low uncertainty
avoidance context than in countries with a high uncertainty context.
5.3.5. Control variables
The analysis of the control variables indicates there is no significant difference between
student and non-student samples. Furthermore, publication outlet quality does not
moderate the relationship between the direct antecedent of engagement and both
attitudinal and behavioural engagement. Finally, the result of the publication status as the
third control variable shows that this variable does not have a significant moderation effect.
In conclusion, we did not find any particular pattern for the defined control variables.
6. Discussion
Our research has several theoretical implications and contributes notably to the customer
engagement literature. Additionally, our findings have implications for marketing
managers. We summarize our main research findings and theoretical and managerial
implications in Table 6.
Table 6: Summary of research findings and implications
Key findings
Research and managerial implications
Organic pathway
Perceived quality and value indirectly and through
relationship quality influence customer
engagement.
Satisfaction and trust as two relationship quality
components through commitment influence
attitudinal and behavioural engagement.
Customer engagement formation in organic
pathway requires a long-term investment
in which only customer commitment in
this process is a direct predictor of
attitudinal and behavioural engagement
Promoted pathway
Functional and experiential initiatives have a
direct and indirect effect on customer
engagement. Experiential initiatives have mostly
direct while functional initiatives have a mostly
Direct firm-initiated engagement is more
effective in attitudinal than behavioural
engagement formation. Evaluating
functional and experiential initiatives
37
indirect (through perceived value) influence on
customer engagement.
effectiveness require to consider their dual
effect on customer engagement.
Attitudinal and behavioural engagement
Customer engagement includes attitudinal and
behavioural engagement in which attitudinal
engagement is a direct predictor of behavioural
engagement.
Customer engagement is a multidimensional
concept and engagement formation
requires a focus on both components
especially attitudinal engagement.
Outcome
Altitudinal engagement influence loyalty and
behavioural engagement impact on firm
performance.
The relationship between engagement and
its outcomes is limit to attitude-loyalty and
behaviour-performance linkage.
Moderator
In both organic and promoted pathway,
behavioural engagement formation is more
effective in online but attitudinal in the face to
face context.
Customer engagement formation in online
and offline context completes each other to
optimizing engagement formation.
Although industry type is not an effective
moderator for organic pathway, surprisingly
promoted engagement is more effective in
manufacturing than service context.
In promoted strategy, compare to
manufacture, direct engagement strategies
less effective for service providers
Customer engagement formation in both
promoted and organic pathways is higher in
hedonic than utilitarian products and services.
Focusing on the hedonic characteristics of
products and services is a great
opportunity for firms to develop customer
engagement.
In both promoted and organic pathways, customer
engagement will increase in cultures with higher
power distance, higher individualism, lower
masculinity, and lower uncertainty distance.
Cultural context considers as important
customer engagement moderator in which
people from different countries have
various tendencies to engage with the firm.
6.1. Theoretical implications
Testing our conceptual model revealed several important implications for customer
engagement behaviour literature, especially with respect to organic and promoted
engagement pathways.
In the organic pathway, engagement formation takes place over time and is the result
of a high-quality relationship between customer and firm. These findings align with results
of prior research (Bowden, 2009; Hollebeek, 2011) on the role of relationship quality
(satisfaction, trust, and commitment) as a mediator of customer engagement. Moreover,
our model supports previous models (Pansari & Kumar, 2016), in which customer
38
experience and satisfaction are important predictors of customer engagement. However,
we covered all relevant variables as antecedents and mediators in the model to provide a
more comprehensive view of this concept in organic pathway. In contrast to Vivek et al.
(2012), we include perceived value, trust, and commitment as antecedents of customer
engagement behaviour and not as the outcome of engagement. However, customer
engagement behaviour formation is an ongoing process (Bowden, 2009; Sashi, 2012), and
perceived quality, perceived value, and relationship quality could be considered to be an
antecedent of customer engagement. Moreover, the model has similarities to relationship
marketing models (Aurier & N’Goala, 2010; Palmatier et al., 2006), in which variables such
as satisfaction, trust, and commitment directly influence customer purchase-related
behaviour. However, in our model, only commitment as a component of relationship
quality has a direct impact on engagement as customer non-purchase attitude and
behaviour (Brodie et al., 2011; Hollebeek, 2011).
In the promoted engagement pathway, functional and experiential initiatives
influence customer engagement directly and indirectly through perceived value. Previous
research in firm-initiated engagement has merely focused on the direct impact of firm-
initiated activities on engagement (Ryu & Feick, 2007; Tafesse, 2016; Wirtz et al., 2019).
However, the current research indicates that the effect of functional and experiential
initiatives is not limited to direct effects and that they are connected to the organic pathway
through perceived value. Moreover, the direct effect of experiential initiatives is much
stronger than functional initiatives. Functional initiatives are short-lived and sometimes
are not cost-effective for directly influencing engagement behaviours, leaving the
experimental initiative as more effective in engagement formation (Harmeling et al., 2017).
In contrast to the direct effects, the indirect effect of functional initiative is more effective
than experiential initiatives. Compared to the intangible nature of experiential initiatives,
the tangible and utilitarian nature of functional initiatives has a higher impact on customer
perceived value. These findings provide a better picture of the dual impact of firm-initiated
engagement activities on attitudinal and behavioural engagement.
39
This study also provides insight into attitudinal and behavioural components of
customer engagement and the relationship between them. Although the customer
engagement behaviour literature supports the motivational driver of engagement
behaviour (Lemon & Verhoef, 2016; van Doorn et al., 2010), the importance of behavioural
engagement for firm performance encourages researchers to focus primarily on this
component (Kumar & Pansari, 2016; Pansari & Kumar, 2016). While we confirm the
importance of behavioural engagement, attitudinal engagement seems critical to
engagement formation. In both organic and promoted engagement, the relationship
between commitment and experimental initiatives with attitudinal engagement is much
stronger than the commitment and functionalbehavioural engagement linkage.
Engagement formation seems to require more focus on attitudinal components than on
behavioural components. Moreover, research in customer engagement has not studied the
relationship between engagement components. Much as in employee engagement (Saks,
2006; Shuck & Wollard, 2010), attitudinal engagement is an important pre-condition of
behavioural engagement. Again, these findings highlight the influential role of attitudinal
engagement in customer engagement formation.
Our results confirm that the relationships between attitudinal and behavioural
engagement and its outcomes (e.g., customer loyalty and firm performance) are limited to
attitudinal engagementloyalty and behavioural engagementfirm performance. On the
basis of the cognitiveaffectivebehaviour hierarchy (Oliver, 1999), we confirmed only
attitudinal engagement as a logical predictor of customer loyalty. Similarly, for the
relationship between engagement and firm performance, our findings indicate that only
behavioural engagement has the potential to directly influence firm performance (Beckers
et al., 2018; Kumar & Pansari, 2016) and the impact of attitudinal engagement on
performance is indirectly through behavioural engagement. These findings provide a better
picture of the relationship between engagement and its outcomes.
Furthermore, the moderator analysis indicates that the majority of the defined
variables significantly moderated the relationship between the direct antecedent of
40
engagement and customer engagement in both the organic and promoted pathways. Our
research provides important insights into the effectiveness of engagement formation in an
online compared to offline context, indicating that in both the organic and promoted
pathways, a face to face context is more effective for developing attitudinal engagement
but the online context is more suitable for behavioural engagement. The online context is
less effective than face to face interaction to create an emotional bond with a customer
(Steinhoff et al., 2019; Verma et al., 2016). In contrast, new technologies, especially social
media, empower the customer to participate in engagement behaviours (Brodie et al., 2013).
For industry type as moderator, in contrast to research in customer engagement
(Kumar et al., 2019; Pansari & Kumar, 2016), we found no significant differences between
service and manufacturing industries in engagement formation. This finding indicates an
organic pathway, as service industries have no advantage over manufacturing in customer
engagement formation. In the promoted pathway firm-initiated engagement activities are
more effective in manufacturing than in the service industry. In contrast to goods, the
intangible and inconsistent nature of service (Bowen, 1990) seems to impede the influence
of functional and experimental initiatives on customer engagement formation. For product
type as moderator, in both organic and promoted pathways, product type significantly
moderates the relationship between direct antecedents of engagement and customer
engagement, in which engagement is much stronger in hedonic than utilitarian products.
These results confirm research findings of the role of hedonic products in enhancing
customerfirm relationships (Barari et al., 2020) and customer engagement (Hollebeek,
2013).
The result of examining cultural context as a moderator provides great insight into
customer engagement from a cross-cultural perspective. In both organic and promoted
pathways, an increase in power distance scores will strengthen the influence of direct
antecedents of engagement and customers’ tendency to participate in engagement
behaviours. This finding confirms the role of engagement behaviour as a signal of expertise
and status to others (Gupta et al., 2018; Samaha et al., 2014). Moreover, in both organic and
41
promoted pathways, engagement is higher in individualism than collectivism. These
relationships differ from relationship marketing findings, in which developing and
maintaining the relationship with the customer is easier in the collectivist than in the
individualistic culture (Samaha et al., 2014). Customers in individualistic cultures seem to
have a trade-off view of their interaction with the firm (Pick & Eisend, 2013). Therefore,
when they have a high-quality relationship with a firm (i.e., an organic pathway) or have
received benefits from the firm (i.e., the promoted pathway), their tendency to engage is
higher than in collectivist cultures. For masculinity, in both the organic and promoted
pathways, the relationships between attitudinal and behavioural engagement and their
antecedents are stronger in feminine than in masculine cultures. In a feminine culture,
people are more reciprocal and more relationship-oriented than in a masculine culture
(Pick & Eisend, 2013), boosting the role of relationship quality and firm initiatives in
engagement formation. Finally, engagement is higher among low uncertainty cultures in
both the organic and promoted pathway. Although the relationship marketing literature
indicates that relationship development reduced customer future uncertainty (Samaha et
al., 2014), engagement seems to increase customer uncertainty. Customer engagement
requires risky attitudes and behaviour, such as referring a new customer or writing a
comment on social media. Therefore, an increase in uncertainty avoidance will decrease
effectiveness of engagement formation.
6.2. Managerial implications
Our customer engagement behaviour model provides some key insights for marketing
managers to consider in developing and implementing their engagement strategy (Table
6). For practitioners, the findings reveal two main strategies to influence customer
engagement: organic and promoted strategies. The organic strategy considers the firms
long-term investment in its relationship with the customer to form attitudinal and
behaviour engagement. In this strategy, marketing managers should be aware of the role
of offering quality in customer perceived value and the effect of these two aspects of their
value proposition on customer satisfaction. Marketing managers could employ
42
technological advancement, especially in a social media brand community, to enhance
customer perceived quality and value of the firm’s value proposition. For instance, the
online brand community provides diverse unique benefits that improve customers’
experience of the firm’s product and services (Gummerus, et al., 2012; Wirtz et al., 2013).
However, perceived quality and value are not sufficient for engagement formation, which
requires enhancing relationship quality. In this regard, previous research in relationship
marketing (Aurier & N’Goala, 2010; Palmatier et al., 2006) and online relationship
marketing (Steinhoff et al., 2019; Verma et al., 2016) provides guidelines for developing and
maintaining customer relationships. Also, marketing managers should be aware that only
commitment has a direct impact on customer engagement (Brodie et al., 2011; Hollebeek,
2011). Therefore, marketing managers should have a detailed plan for choosing appropriate
customer segments for engagement formation over time.
In the promoted strategy, the marketing manager has the ability to influence
customer engagement directly by employing functional and experiential initiatives. While
experiential initiatives are quite effective in creating attitudinal engagement, functional
initiatives have a very weak influence on behavioral engagement. If marketing managers
evaluate their promoted strategy on the basis of short-term influence, they should invest
more in experiential initiatives in the form of a game or event, especially in social media,
to indirectly and through attitudinal engagement influence customer engagement
behaviour. Moreover, in organic pathways functional and experiential initiatives through
perceived value influence customer engagement. Therefore, marketing managers could
combine their organic and promoted engagement strategies in which they target their
current customers with a well-established relationship. This approach could create synergy
between these two engagement strategies and optimize customer engagement attitude and
behaviour.
From an empirical perspective, customer engagement is equal to behavioural
engagement. However, the marketing manager should know that complete and sustainable
customer engagement requires a focus on both components of engagement, but especially
43
attitudinal engagement, because in both organic and promoted pathways it is easier to form
attitudinal rather than behavioural engagement. Moreover, attitudinal engagement is a
very good predictor of behavioural engagement. Therefore, the marketing manager should
invest more in attitudinal engagement to influence customer engagement behaviours. In
addition, our findings indicate that behavioural engagement influences only business
performance, whereas achieving customer loyalty requires investment in altitudinal
engagement. All of these findings indicate to marketing managers that engagement is a
two-dimensional concept in which attitudinal engagement has a critical role in
engagement formation.
Moderator analysis indicates that several context variables play a critical role in
engagement formation. Marketing managers should understand that in both organic and
promoted engagement, the online and offline context complement each other, and both
are important to achieve optimal customer engagement. For instance, face to face events
and especially customeremployee interactions are important in creating an emotional
bond with customers, whereas in an online context features such as social media are
effective in fostering customer participation in engagement behaviours. Furthermore, a
promoted engagement strategy is more effective for tangible products than services.
Therefore, to optimize their engagement efforts, marketing managers in service industries
should focus on organic rather than promoted engagement. Additionally, this research
indicates that marketing managers should focus on the hedonic character of their offering
to facilitate customer attitudinal and behavioural engagement formation, and they should
consider the role of culture in engagement formation in both the organic and the promoted
strategy. For instance, engagement strategy is more effective in cultures with a higher
power distance, individualism, feminine character, and lower uncertainty avoidance, which
mostly reflects that engagement is more effective in western than eastern cultures. Hence,
especially in multinational firms, marketing managers should consider these differences
between countries in their engagement strategy development.
44
6.3. Limitations and further research
Although meta-analysis provides a comprehensive and generalizable view of previous
research in an area, it has some limitations. As our conceptual model is developed on the
basis of previous empirical research on engagement, the model is limited to variables
studied in previous research. Previous research has mostly studied customer engagement
as a positive concept while engagement behaviour includes both positive and negative
dimension. Besides, as previous research has focused mainly on firm-related antecedents
and outcomes of engagement, our model suffers from customer-related factors. For
instance, the psychological differences among customers will affect customer relationship
formation and customer response to promoted engagement initiatives. Similarly, our
outcome is only reflective of the benefits of customer engagement for the firm, as it lacks
customer-related outcomes. Moderator analysis is a very important part of meta-analysis
and allows the researcher to explain heterogeneity in effect sizes. As customer engagement
is an emerging research area, empirical research in this area is limited. Therefore, we could
not include B2B versus B2C as important moderators in our moderator analysis.
Our review and synthesis of the customer engagement literature allow us to
recommend several areas for further study. Digital engagement, especially in social media,
is a quite new and growing area. The nature of the online context provides added value for
the customer and additional channels for customers’ engagement behaviour. As our
generic engagement model does not consider all of these complexities, digital engagement
literature is a worthwhile venue in which to conduct an independent meta-analysis to show
the nature and mechanism of online engagement. Moreover, our review indicates the
relationship between engagement and its outcome needs more consideration, especially in
social media. As engagement comprises non-transactional attitude and behaviour, future
research requires investigation of how these factors influence customer value and firm
performance. Furthermore, customer engagement behaviour research is limited to the
dyadic relationship between customer and firm, whereas in the new business models, such
as the sharing economy, engagement emergence and manifestation take place in a complex
45
network of interaction between different actors. As previously mentioned in the
transformational approach to engagement, actor engagement is a promising area in which
customer engagement extends to consider the role of other actors. Thus, we call for further
research to extend the understanding of engagement behaviour by studying this concept
in new contexts such as the sharing economy.
References
Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer satisfaction, market share, and
profitability: Findings from Sweden. Journal of marketing, 58(3), 53-66.
Anderson, E. W., Fornell, C., & Mazvancheryl, S. K. (2004). Customer satisfaction and shareholder
value. Journal of marketing, 68(4), 172-185.
Aurier, P., & N’Goala, G. (2010). The differing and mediating roles of trust and relationship
commitment in service relationship maintenance and development. Journal of the Academy
of Marketing Science, 38(3), 303-325.
Azer, J., & Alexander, M. (2020a). Direct and indirect negatively valenced engagement behavior.
Journal of Services Marketing.
Azer, J., & Alexander, M. (2020b). Negative customer engagement behaviour: the interplay of
intensity and valence in online networks. Journal of Marketing Management, 36(3-4), 361-
383.
Azer, J., & Alexander, M. J. (2018). Conceptualizing negatively valenced influencing behavior: forms
and triggers. Journal of Service Management, 29(3), 468-490.
Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring hedonic and utilitarian
shopping value. Journal of consumer research, 20(4), 644-656.
Barari, M., Ross, M., & Surachartkumtonkun, J. (2020). Negative and positive customer shopping
experience in an online context. Journal of Retailing and Consumer Services, 53, 101985.
Beckers, S. F. M., van Doorn, J., & Verhoef, P. C. (2018). Good, better, engaged? The effect of
company-initiated customer engagement behavior on shareholder value. Journal of the
Academy of Marketing Science, 46(3), 366-383.
Biyalogorsky, E., Gerstner, E., & Libai, B. (2001). Customer referral management: Optimal reward
programs. Marketing Science, 20(1), 82-95.
Blazevic, V., Hammedi, W., Garnefeld, I., Rust, R. T., Keiningham, T., Andreassen, T. W., . . . Carl,
W. (2013). Beyond traditional word-of-mouth: an expanded model of customer-driven
influence. Journal of Service Management, 24(3), 294-313.
Blut, M., & Wang, C. (2019). Technology readiness: a meta-analysis of conceptualizations of the
construct and its impact on technology usage. Journal of the Academy of Marketing Science,
1-21.
46
Bowden, J. L.-H. (2009). The process of customer engagement: A conceptual framework. Journal of
Marketing Theory and Practice, 17(1), 63-74.
Bowden, J. L.-H., Conduit, J., Hollebeek, L. D., Luoma-Aho, V., & Solem, B. A. (2017). Engagement
valence duality and spillover effects in online brand communities. Journal of Service Theory
and Practice, 27(4), 877-897.
Bowden, J. L. H., Gabbott, M., & Naumann, K. (2014). Service relationships and the customer
disengagement engagement conundrum. Journal of Marketing Management, 31(7-8), 774-
806.
Bowen, J. (1990). Development of a taxonomy of services to gain strategic marketing insights.
Journal of the Academy of Marketing Science, 18(1), 43-49.
Breidbach, C. F., & Brodie, R. J. (2017). Engagement platforms in the sharing economy. Journal of
Service Theory and Practice, 27(4), 761-777.
Brodie, R. J., Fehrer, J. A., Jaakkola, E., & Conduit, J. (2019). Actor Engagement in Networks: Defining
the Conceptual Domain. Journal of Service Research, 22(2), 173-188.
Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer Engagement: Conceptual Domain,
Fundamental Propositions, and Implications for Research. Journal of Service Research, 14(3),
252-271.
Brodie, R. J., Ilic, A., Juric, B., & Hollebeek, L. (2013). Consumer engagement in a virtual brand
community: An exploratory analysis. Journal of Business Research, 66(1), 105-114.
Buttle, F. A. (1998). Word of mouth: understanding and managing referral marketing. Journal of
strategic Marketing, 6(3), 241-254.
Canabal, A., & White III, G. O. (2008). Entry mode research: Past and future. International Business
Review, 17(3), 267-284.
Carlson, J., Rahman, M., Voola, R., & De Vries, N. (2018). Customer engagement behaviours in social
media: capturing innovation opportunities. Journal of Services Marketing, 32(1), 83-94.
Dabić, M., Vlačić, B., Paul, J., Dana, L.-P., Sahasranamam, S., & Glinka, B. (2020). Immigrant
entrepreneurship: A review and research agenda. Journal of Business Research, 113, 25-38.
Dhar, R., & Wertenbroch, K. (2000). Consumer choice between hedonic and utilitarian goods.
Journal of Marketing Research, 37(1), 60-71.
Dolan, R., Conduit, J., Frethey-Bentham, C., Fahy, J., & Goodman, S. (2019). Social media
engagement behavior. European Journal of Marketing, 53(10), 2213-2243.
Doney, P. M., & Cannon, J. P. (1997). An examination of the nature of trust in buyerseller
relationships. Journal of marketing, 61(2), 35-51.
Frigerio, M., Ottaviani, C., Vandone, D., 2020. A meta‐analytic investigation of consumer over‐
indebtedness: The role of impulsivity. International Journal of Consumer Studies, 44(4), 328-
342.
Geyskens, I., & Steenkamp, J.-B. E. (2000). Economic and social satisfaction: measurement and
relevance to marketing channel relationships. Journal of retailing, 76(1), 11-32.
47
Gilal, F. G., Zhang, J., Paul, J., & Gilal, N. G. (2019). The role of self-determination theory in
marketing science: An integrative review and agenda for research. European Management
Journal, 37(1), 29-44.
Gremler, D. D., Van Vaerenbergh, Y., Brüggen, E. C., & Gwinner, K. P. (2019). Understanding and
managing customer relational benefits in services: a meta-analysis. Journal of the Academy
of Marketing Science, 1-19.
Grewal, D., Puccinelli, N., & Monroe, K. B. (2018). Meta-analysis: integrating accumulated
knowledge. Journal of the Academy of Marketing Science, 46(1), 9-30.
Gummerus, J., Liljander, V., Weman, E., & Minna, P. m. (2012). Customer engagement in a Facebook
brand community. Management Research Review, 35(9), 857-877.
Guo, L., Gruen, T. W., & Tang, C. (2017). Seeing relationships through the lens of psychological
contracts: the structure of consumer service relationships. Journal of the Academy of
Marketing Science, 45(3), 357-376.
Gupta, S., Pansari, A., & Kumar, V. (2018). Global Customer Engagement. Journal of International
Marketing, 26(1), 4-29.
Gupta, S., & Zeithaml, V. (2006). Customer metrics and their impact on financial performance.
Marketing Science, 25(6), 718-739.
Hapsari, R., Clemes, M. D., & Dean, D. (2017). The impact of service quality, customer engagement
and selected marketing constructs on airline passenger loyalty. International Journal of
Quality and Service Sciences, 9(1), 21-40.
Harmeling, C. M., Moffett, J. W., Arnold, M. J., & Carlson, B. D. (2017). Toward a theory of customer
engagement marketing. Journal of the Academy of Marketing Science, 45(3), 312-335.
Harrigan, P., Evers, U., Miles, M. P., & Daly, T. (2018). Customer engagement and the relationship
between involvement, engagement, self-brand connection and brand usage intent. Journal
of Business Research, 88, 388-396.
Hennig‐Thurau, T., & Klee, A. (1997). The impact of customer satisfaction and relationship quality
on customer retention: A critical reassessment and model development. Psychology &
marketing, 14(8), 737-764.
Hofstede, G. (2001). Culture's consequences: Comparing values, behaviors, institutions and
organizations across nations: Sage publications.
Hofstede, G., Hofstede, G. J., & Minkov, M. (2005). Cultures and organizations: Software of the mind
(Vol. 2): Citeseer.
Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the loyalty nexus.
Journal of Marketing Management, 27(7-8), 785-807.
Hollebeek, L. D. (2013). The customer engagement/value interface: An exploratory investigation.
Australasian Marketing Journal, 21(1), 17-24.
Hollebeek, L. D., Glynn, M. S., & Brodie, R. J. (2014). Consumer brand engagement in social media:
Conceptualization, scale development and validation. Journal of interactive marketing,
28(2), 149-165.
48
Hu, H.-H., Kandampully, J., & Juwaheer, T. D. (2009). Relationships and impacts of service quality,
perceived value, customer satisfaction, and image: an empirical study. The service industries
journal, 29(2), 111-125.
Hunter, J. E., & Schmidt, F. L. (2004). Methods of meta-analysis: Correcting error and bias in research
findings: Sage.
Iyer, G. R., Blut, M., Xiao, S. H., & Grewal, D. (2019). Impulse buying: a meta-analytic review. Journal
of the Academy of Marketing Science, 1-21.
Jaakkola, E., & Alexander, M. (2014). The Role of Customer Engagement Behavior in Value Co-
Creation: A Service System Perspective. Journal of Service Research, 17(3), 247-261.
Kahiya, E. T. (2018). Five decades of research on export barriers: Review and future directions.
International Business Review, 27(6), 1172-1188.
Kevin Kam Fung, S., King, C., Sparks, B. A., & Wang, Y. (2016). Enhancing customer relationships
with retail service brands. Journal of Service Management, 27(2), 170-193.
Knoll, J., & Matthes, J. (2017). The effectiveness of celebrity endorsements: a meta-analysis. Journal
of the Academy of Marketing Science, 45(1), 55-75.
Kumar, A., Paul, J., & Unnithan, A. B. (2019). ‘Masstige’marketing: A review of research on outward
foreign. Journal of Business Research, 113, 384-398.
Kumar, V., Aksoy, L., Donkers, B., Venkatesan, R., Wiesel, T., & Tillmanns, S. (2010). Undervalued
or Overvalued Customers: Capturing Total Customer Engagement Value. Journal of Service
Research, 13(3), 297-310.
Kumar, V., & Pansari, A. (2016). Competitive Advantage Through Engagement. Journal of Marketing
Research, 53(4), 497-514.
Kumar, V., Rajan, B., Gupta, S., & Pozza, I. D. (2019). Customer engagement in service. Journal of
the Academy of Marketing Science, 47(1), 138-160.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the
Customer Journey. Journal of marketing, 80(6), 69-96.
Lin, M., Miao, L., Wei, W., & Moon, H. (2019). Peer Engagement Behaviors: Conceptualization and
Research Directions. Journal of Service Research, 22(4), 388-403.
McNeill, L., & Venter, B. (2019). Identity, self‐concept and young women’s engagement with
collaborative, sustainable fashion consumption models. International Journal of Consumer
Studies, 43(4), 368-378.
Moorman, C., Deshpande, R., & Zaltman, G. (1993). Factors affecting trust in market research
relationships. Journal of marketing, 57(1), 81-101.
Morgan, R. M., & Hunt, S. D. (1994). The commitment-trust theory of relationship marketing.
Journal of marketing, 58(3), 20-38.
Naumann, K., Bowden, J., & Gabbott, M. (2020). Expanding customer engagement: the role of
negative engagement, dual valences and contexts. European Journal of Marketing.
Naumann, K., Bowden, J. L.-H., & Gabbott, M. (2017). Exploring customer engagement valences in
the social services. Asia Pacific Journal of Marketing and Logistics, 29(4), 890-912.
49
Neulinger, A., Bársony, F., Gjorevska, N., Lazányi, O., Pataki, G., Takács, S., & Török, A. (2020).
Engagement and subjective well‐being in alternative food networks: The case of Hungary.
International Journal of Consumer Studies, 44(4), 306-315.
Ng, S., Plewa, C., & Sweeny, J. (2016). Customer engagement with a service offering: a framework
for complex services. In: Routledge.
Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction
decisions. Journal of Marketing Research, 17(4), 460-469.
Oliver, R. L. (1999). Whence consumer loyalty? Journal of marketing, 63(4_suppl1), 33-44.
Olorunniwo, F., Hsu, M. K., & Udo, G. J. (2006). Service quality, customer satisfaction, and
behavioral intentions in the service factory. Journal of Services Marketing, 20(1), 59-72.
Palmatier, R. W., Dant, R. P., & Grewal, D. (2007). A Comparative Longitudinal Analysis of
Theoretical Perspectives of Interorganizational Relationship Performance. Journal of
marketing, 71(4), 172-194.
Palmatier, R. W., Dant, R. P., Grewal, D., & Evans, K. R. (2006). Factors influencing the effectiveness
of relationship marketing: a meta-analysis. Journal of marketing, 70(4), 136-153.
Palmatier, R. W., Houston, M. B., & Hulland, J. (2018). Review articles: purpose, process, and
structure. Journal of the Academy of Marketing Science, 46(1), 1-5.
Palmatier, R. W., Kumar, V., & Harmeling, C. M. (2017). Customer engagement marketing: Springer.
Paul, J. (2019). Marketing in emerging markets: a review, theoretical synthesis and extension.
International Journal of Emerging Markets, 15(3), 446-468.
Pansari, A., & Kumar, V. (2016). Customer engagement: the construct, antecedents, and
consequences. Journal of the Academy of Marketing Science, 45(3), 294-311.
Paul, J., & Benito, G. R. (2018). A review of research on outward foreign direct investment from
emerging countries, including China: what do we know, how do we know and where should
we be heading? Asia Pacific Business Review, 24(1), 90-115.
Paul, J., & Mas, E. (2019). Toward a 7-P framework for international marketing. Journal of Strategic
Marketing, 1-21.
Paul, J., Parthasarathy, S., & Gupta, P. (2017). Exporting challenges of SMEs: A review and future
research agenda. Journal of world business, 52(3), 327-342.
Paul, J., & Rosado-Serrano, A. (2019). Gradual internationalization vs born-global/international new
venture models. International Marketing Review, 36(6), 830-858.
Paul, J., & Singh, G. (2017). The 45 years of foreign direct investment research: Approaches, advances
and analytical areas. The World Economy, 40(11), 2512-2527.
Pervan, S. J., Bove, L. L., & Johnson, L. W. (2009). Reciprocity as a key stabilizing norm of
interpersonal marketing relationships: Scale development and validation. Industrial
Marketing Management, 38(1), 60-70.
Peterson, R. A., & Brown, S. P. (2005). On the use of beta coefficients in meta-analysis. Journal of
Applied Psychology, 90(1), 175.
50
Pick, D., & Eisend, M. (2013). Buyers’ perceived switching costs and switching: a meta-analytic
assessment of their antecedents. Journal of the Academy of Marketing Science, 42(2), 186-
204.
Rana, J., & Paul, J. (2020). Health motive and the purchase of organic food: A meta‐analytic review.
International Journal of Consumer Studies, 44(2), 162-171.
Randhawa, K., Wilden, R., & Hohberger, J. (2016). A bibliometric review of open innovation: Setting
a research agenda. Journal of Product Innovation Management, 33(6), 750-772.
Ravald, A., & Grönroos, C. (1996). The value concept and relationship marketing. European Journal
of Marketing, 30(2), 19-30.
Rosado-Serrano, A., Paul, J., & Dikova, D. (2018). International franchising: A literature review and
research agenda. Journal of Business Research, 85, 238-257.
Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological bulletin,
86(3), 638.
Ryu, G., & Feick, L. (2007). A penny for your thoughts: Referral reward programs and referral
likelihood. Journal of marketing, 71(1), 84-94.
Saks, A. M. (2006). Antecedents and consequences of employee engagement. Journal of managerial
psychology, 21(7), 600-619.
Samaha, S. A., Beck, J. T., & Palmatier, R. W. (2014). The Role of Culture in International
Relationship Marketing. Journal of marketing, 78(5), 78-98.
Sashi, C. M. (2012). Customer engagement, buyer‐seller relationships, and social media.
Management Decision, 50(2), 253-272.
Shuck, B., & Wollard, K. (2010). Employee engagement and HRD: A seminal review of the
foundations. Human resource development review, 9(1), 89-110.
So, K. K. F., King, C., Sparks, B. A., & Wang, Y. (2016). Enhancing customer relationships with retail
service brands. Journal of Service Management, 27(2), 170-193.
So, K. K. F., King, C., Sparks, B. A., & Wang, Y. (2016). The Role of Customer Engagement in Building
Consumer Loyalty to Tourism Brands. Journal of Travel Research, 55(1), 64-78.
Sprott, D., Czellar, S., & Spangenberg, E. (2009). The importance of a general measure of brand
engagement on market behavior: Development and validation of a scale. Journal of
Marketing Research, 46(1), 92-104.
Steinhoff, L., Arli, D., Weaven, S., & Kozlenkova, I. V. (2019). Online relationship marketing. Journal
of the Academy of Marketing Science, 47(3), 369-393.
Storbacka, K., Brodie, R. J., Böhmann, T., Maglio, P. P., & Nenonen, S. (2016). Actor engagement as
a microfoundation for value co-creation. Journal of Business Research, 69(8), 3008-3017.
Tafesse, W. (2016). An experiential model of consumer engagement in social media. Journal of
Product & Brand Management, 25(5), 424-434.
van Doorn, J., Lemon, K. N., Mittal, V., Nass, S., Pick, D., Pirner, P., & Verhoef, P. C. (2010). Customer
Engagement Behavior: Theoretical Foundations and Research Directions. Journal of Service
Research, 13(3), 253-266.
51
Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer Engagement as a New Perspective in
Customer Management. Journal of Service Research, 13(3), 247-252.
Verleye, K., Gemmel, P., & Rangarajan, D. (2013). Managing Engagement Behaviors in a Network of
Customers and Stakeholders. Journal of Service Research, 17(1), 68-84.
Verma, V., Sharma, D., & Sheth, J. (2016). Does relationship marketing matter in online retailing?
A meta-analytic approach. Journal of the Academy of Marketing Science, 44(2), 206-217.
Vivek, S. D., Beatty, S. E., & Morgan, R. M. (2012). Customer Engagement: Exploring Customer
Relationships Beyond Purchase. Journal of Marketing Theory and Practice, 20(2), 122-146.
Watson, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015). Building, measuring, and
profiting from customer loyalty. Journal of the Academy of Marketing Science, 43(6), 790-
825.
Wirtz, J., Aksoy, L., den Ambtman, A., Bloemer, J., Horváth, C., Ramaseshan, B., . . . Kandampully,
J. (2013). Managing brands and customer engagement in online brand communities. Journal
of Service Management, 24(3), 223-244.
Wirtz, J., Orsingher, C., & Cho, H. (2019). Engaging customers through online and offline referral
reward programs. European Journal of Marketing, 53(9), 1962-1987.
Yannopoulou, N., Liu, M.J., Bian, X., Heath, T., 2019. Exploring social change through social media:
The case of the Facebook group Indignant Citizens. International Journal of Consumer
Studies, 43 (4), 348-357.
Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: a means-end model and
synthesis of evidence. Journal of marketing, 52(3), 2-22.
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service
quality. Journal of marketing, 60(2), 31-46.
52
Web Appendix A: Customer engagement behaviour and related concepts
Although customer engagement behaviour is a distinct concept in marketing, it exhibits
similarities with related concepts. To uncover this concept relationships and position with
respect to other related concepts, we summarize the related concepts’ definitions,
comparison, and relationship with engagement behaviour in Table 1.
Table 1 Concepts related to customer engagement behaviour
Related
concepts
Definition
Comparison to engagement
Relationship
to
engagement
Involvement
Customer’s perceived
importance of an object,
especially product, regard to
their needs, values, and interests
(Mittal, 1995).
Involvement determines
customer motivation to gather
information to control
purchase-related risk whereas
engagement is behaviour
beyond the purchase.
Potential
moderator
and
antecedent
Flow
State of the mind in which
people completely immerse and
absorb in an activity to the
extent people oblivious to time
and what happing around them
(Czikszentmihalyi, 1990).
Flow is an ephemeral
psychological state while
engagement behaviour is the
more enduring disposition.
Potential
antecedent
Customer
experience
Customer’s cognitive, affective,
emotional, social, and physical
responses to their interaction
with firm touchpoints (Verhoef
et al., 2009).
Customer experience is
customer passive response to
firm marketing actions
whereas engagement
behaviour is customer active
actions towards the firm.
Potential
antecedent
Satisfaction
A customer’s overall evaluation
of firm performance to fulfil
their expectations over time
Geyskens and Steenkamp
(2000).
Satisfaction is customer
overall shopping-related
judgment while engagement is
a result of a mature
relationship and it is beyond
purchase behaviour.
Potential
antecedent
Trust
Customer tendency to rely on an
exchange partner in whom one
has confidence (Moorman,
Deshpande, & Zaltman, 1993).
Trust indicates the breadth of
the customer-firm
relationship while
engagement is the result of a
mature relationship.
Potential
antecedent
53
Commitment
An enduring desire to maintain
a valued relationship (Moorman
et al., 1993).
Commitment is the depth of
customer-firm relationships
while engagement behaviour
is the result of a mature
relationship.
Potential
antecedent
Loyalty
Customer consistent purchase of
the brand over time resulting
from a favourable attitude
(Watson, Beck, Henderson, &
Palmatier, 2015).
Loyalty is a purchase-related
concept while engagement is
behaviour which is beyond the
purchase.
Potential
outcome
Customer involvement indicates the importance of an object for a customer (Mittal,
1995), and in the marketing literature it is used to categorize products and services, such as
low- and high-involvement products (Pansari & Kumar, 2016). Thus, an increase in
customer involvement raises customer effort to gather information before purchase to
manage purchase risk (Brodie, Hollebeek, Juric, & Ilic, 2011). In contrast, engagement is a
behaviour beyond purchase, which is directed to firm marketing activity and its formation
after purchase (Palmatier et al., 2017). Therefore, involvement is considered as both a
potential moderator (Pansari & Kumar, 2016) and the antecedent of engagement
(Hollebeek, 2011). Flow is considered to be a customer’s full immersion and absorption in
an activity (Czikszentmihalyi, 1990). However, flow is an ephemeral and short-lived
psychological state, whereas engagement behaviour forms over time and has a more
enduring nature (Hollebeek, 2011). Thus flow has been studied as potential antecedents of
customer engagement behaviour (Ng, Plewa, & Sweeny, 2016). Customer experience is
defined as customer’s passive reaction to their interaction with firm marketing activities,
whereas engaged customers actively participate in firm-related activities (Hollebeek, 2011).
Therefore, customer experience mostly affects customerfirm relationship formation and
is considered to be a potential antecedent of customer engagement behaviour (Kumar et
al., 2019). Satisfaction, trust and commitment constitute customerfirm relationship
quality and indicate the maturity of a relationship (Lemon & Verhoef, 2016). In contrast,
engagement behaviour includes a customer’s tendency to participate in the relationship
54
beyond purchase behaviour (Verhoef, Reinartz, & Krafft, 2010). Therefore, satisfaction,
trust, and commitment are critical antecedents of customer engagement (Aurier & N’Goala,
2010). Finally, loyalty indicates a customer’s positive attitude toward a firm or brand, which
results in the consistent repurchase of a product (Watson et al., 2015). In contrast,
engagement is a customer’s non-transactional behaviour (Pansari & Kumar, 2016).
References
Aurier, P., & N’Goala, G. (2010). The differing and mediating roles of trust and relationship
commitment in service relationship maintenance and development. Journal of the Academy
of Marketing Science, 38(3), 303-325.
Brodie, R. J., Hollebeek, L. D., Juric, B., & Ilic, A. (2011). Customer Engagement: Conceptual Domain,
Fundamental Propositions, and Implications for Research. Journal of Service Research, 14(3),
252-271.
Czikszentmihalyi, M. (1990). Flow: The psychology of optimal experience: New York: Harper & Row.
Geyskens, I., & Steenkamp, J.-B. E. (2000). Economic and social satisfaction: measurement and
relevance to marketing channel relationships. Journal of retailing, 76(1), 11-32.
Hollebeek, L. D. (2011). Demystifying customer brand engagement: Exploring the loyalty nexus.
Journal of Marketing Management, 27(7-8), 785-807.
Kumar, V., Rajan, B., Gupta, S., & Pozza, I. D. (2019). Customer engagement in service. Journal of
the Academy of Marketing Science, 47(1), 138-160.
Lemon, K. N., & Verhoef, P. C. (2016). Understanding Customer Experience Throughout the
Customer Journey. Journal of marketing, 80(6), 69-96.
Mittal, B. (1995). A comparative analysis of four scales of consumer involvement. Psychology &
marketing, 12(7), 663-682.
Moorman, C., Deshpande, R., & Zaltman, G. (1993). Factors affecting trust in market research
relationships. Journal of marketing, 57(1), 81-101.
Ng, S., Plewa, C., & Sweeny, J. (2016). Customer engagement with a service offering: a framework
for complex services. In: Routledge.
Palmatier, R. W., Kumar, V., & Harmeling, C. M. (2017). Customer engagement marketing: Springer.
Pansari, A., & Kumar, V. (2016). Customer engagement: the construct, antecedents, and
consequences. Journal of the Academy of Marketing Science, 45(3), 294-311.
Verhoef, P. C., Lemon, K. N., Parasuraman, A., Roggeveen, A., Tsiros, M., & Schlesinger, L. A. (2009).
Customer experience creation: Determinants, dynamics and management strategies.
Journal of retailing, 85(1), 31-41.
Verhoef, P. C., Reinartz, W. J., & Krafft, M. (2010). Customer Engagement as a New Perspective in
Customer Management. Journal of Service Research, 13(3), 247-252.
55
Watson, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015). Building, measuring, and
profiting from customer loyalty. Journal of the Academy of Marketing Science, 43(6), 790-
825.
Watson, G. F., Beck, J. T., Henderson, C. M., & Palmatier, R. W. (2015). Building, measuring, and
profiting from customer loyalty. Journal of the Academy of Marketing Science, 43(6), 790-
825.
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Recent marketing and service research highlights the detrimental impact of negative customer engagement behaviour (CEB) in online social networks. Nevertheless, the extant literature captures the impact of what customers say about service providers in their negative reviews and fails to provide any understanding of different intensity levels of negative engagement. This article marks the first attempt to provide a more nuanced view of negative CEB by investigating the impact of six forms of negatively valenced influencing behaviour (NVIB) using two online experiments. Our results provide new insights into intensity levels of NVIB and how they are moderated by positive reviews. Practically, this paper addresses one of the challenges for service providers in managing NVIBs, centred on understanding the heterogeneity of its forms. The results suggest that managers use semantic tools to detect the intensity levels of NVIB and to prioritise handling and/or mitigating the more intense NVIBs when they occur.
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Underpinned by the Bagozzi and Dholakia (1999) goal setting and striving framework this research firstly develops a negative online customer experience model after which regulatory focus theory is used to compare this model with a positive online customer experience model. Analysis of responses from 201 respondents in the first study shows service failure causes negative affective and cognitive experience and has an impact on dissatisfaction and negative word of mouth in the online retailing context. Moreover, results of a second study among 200 respondents indicates that while customer priority in a successful shopping context is affective experience, in a service failure the customer priority moves from an affective to a cognitive experience. Similarly, compared to cognitive experience, affective experience has a higher impact on customer satisfaction and positive word of mouth in a successful shopping context, while in an unsuccessful shopping context cognitive experience has higher impact on dissatisfaction and negative word of mouth. The findings of this study contribute to customer experience management in both successful and unsuccessful shopping situations.
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