International Journal of Bank Marketing
Online relationship marketing and customer loyalty: a signaling theory
perspective
Sheena Lovia Boateng,
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Sheena Lovia Boateng, (2019) "Online relationship marketing and customer loyalty: a signaling
theory perspective", International Journal of Bank Marketing, Vol. 37 Issue: 1, pp.226-240, https://
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IJBM
37,1 Online relationship marketing and
customer loyalty: a signaling
theory perspective
226 Sheena Lovia Boateng
Department of Marketing and Entrepreneurship,
Received 15 January 2018
Revised 16 February 2018 University of Ghana Business School, Accra, Ghana
Accepted 7 April 2018
Abstract
Purpose – Online technologies have, in recent times, revolutionized the process of relationship building
between firms and their customers. Nonetheless, there is a limited focus and theorization when it comes to
explaining the link between online relationship marketing (ORM) activities and their impact on firm
relationship marketing (RM) objectives. Thus, the purpose of this paper is to examine the signaling role of
ORM activities in generating online trust and customer loyalty, through the lens of the signaling theory.
Design/methodology/approach – Data for the study were gathered through a survey of 429 Ghanaian
retail bank customers. Results were analyzed using structural equation modeling techniques.
Findings – The paper highlights the signaling role of engagement and interactivity online in influencing
banks’ RM outcomes per the signaling theory. It concludes that bank’s online relationship activities, over and
above the online tools utilized, need to communicate appropriate and useful signals in order to positively
influence online trust and loyalty among customers.
Originality/value – The study, in its uniqueness, utilizes the signaling theory to explain the role and impact
of online RM activities in the banking industry.
Keywords Relationship marketing, Interactivity, Engagement, Signalling theory, Customer loyalty, Online trust
Paper type Research paper
1. Introduction
It has become increasingly important for firms to build relationships with their customers
and other key stakeholders in the industries within which they operate. This can be
attributed, in part, to the advent of new forms of technology, and their ability to alter the
nature of relationships and the way they are developed between firms and their
stakeholders (Verma et al., 2016; Guo, 2014). Relationship marketing (RM) as a strategy
contributes immensely to the firm’s value proposition within the marketplace. It fosters
partnerships, which ultimately lead to profitable exchanges that help firms to acquire, retain
and adequately satisfy their customers (Kanagal, 2009). But, technological advances are
affecting customer expectations (Armistead and Kiely, 2003). Their needs and wants are
constantly evolving, and as a result, marketers must frequently evaluate their methods to
keep up with emerging trends.
Although, businesses all over the globe are leveraging new and emerging technologies
like e-mails (Huang and Shyu, 2009), Web 2.0 tools (Mitic and Kapoulas, 2012) and mobiles
in enhancing existing relationships, as well as creating new channels of interaction with
their customers, there is the need for extensive research regarding this phenomenon.
Researchers such as Ghazi et al. (2013) and Huang and Shyu (2009) opine that there is limited
research on firms’ RM activities online and how it influences other RM outcomes.
For instance, some scholars (Fam et al., 2004) have postulated the relevance of studying the
incorporation of firms’ RM activities online with other organizational practices and
International Journal of Bank
Marketing outcomes, to the advancement of existing knowledge on the role and potential of internet
Vol. 37 No. 1, 2019
pp. 226-240 technology in RM. Furthermore, there is also a need for more theorization in understanding
© Emerald Publishing Limited the relationship between the online variables and the RM constructs. That conceptual link
0265-2323
DOI 10.1108/IJBM-01-2018-0009 tends to be silent in extant online relationship marketing (ORM) research.
Downloaded by University of Ghana At 03:33 03 June 2019 (PT)
Thus, the purpose of this study is to examine the influence of ORM activities, namely, ORM and
engagement and interactivity, on customer loyalty, as mediated by online trust within the customer
banking industry. The remainder of this paper is organized as follows. First, the literature loyalty
review and hypotheses are discussed, highlighting the underpinning theory for the study.
This is followed by the methodology and the results of the data analysis, obtained from
testing the conceptual model using structural equation modeling (SEM). The study
concludes with a discussion of the importance of the findings to ORM research and their 227
implications for bank managers; in addition to some directions for future research.
2. Literature review and hypotheses
2.1 Online relationship marketing (ORM)
The argument for technology and RM began as far back as the 1990s with studies by
authors such as Geiger and Martin (1999), who researched into internet technology as an RM
tool. Information technology (IT) has since been recognized as an effective medium for
managing relationships, due to its ability to facilitate interaction and communication
(Colgate et al., 2005). Therefore, IT plays a critical role when it comes to advancing the
practice of RM (Shapiro et al., 2004). This is corroborated by several marketing scholars,
who have over the years argued for the role of technology, as a necessary condition for the
development of effective firm–customer relationships (Mishra and Li, 2008; Howcroft et al.,
2007; Zineldin, 2000).
ORM captures the use of manifold interactive web features and internet enabled tools to
establish and develop mutually beneficial long-term relationships with their customers
(Gan et al., 2007; Harker, 1999). Firms host several applications over the internet, including
websites and social media pages through which they coordinate several relationship
development activities and processes (Shapiro et al., 2004). The attributes of these online
applications are often harnessed through activities like engagement, interactivity, advocacy,
personalization and collaboration with the intention of building successful relationships
with their customers (Ahn et al., 2014; Ching and Ellis, 2006).
2.2 The signaling role of ORM activities
Signaling theory will be utilized to explain the role of ORM activities in firm RM. The theory
emerged in response to information asymmetry – when parties in an exchange have access
to different types of information – between transacting parties in business relations
(Taj, 2016; Kirmani and Rao, 2000). This information asymmetry often occurs with regard to
the characteristics of the service provider and their ability to effectively satisfy the needs
of their customers, as well as distinguish themselves from other service providers
(Connelly et al., 2011). Signaling theory has been widely used in different fields to explain
customer choice phenomenon. It espouses three primary elements, namely, the signaler
(the service provider), the receiver (the customer) and the signal. The service provider in an
effort to influence the opinions of their customers, often communicate information
concerning their brand attributes and the quality of their service delivery. They desire
to assure customers of their credibility and integrity; and this information is converted into
signals, which are aptly transmitted to their customers using various mediums.
In recent times, firms are increasingly relying on the features of IT to send signals to
customers, with the intent of influencing their perceptions and behavioral intentions
(Benlian and Hess, 2011). More specifically, in relationship building they often relay signals
to customers through the deployment of various internet tools and applications like Secure
Socket Layer encryption and adept content and usability functions like help tabs and FAQ
(Frequently asked questions) to communicate transparency, security and privacy
(Mavlanova et al., 2012; Benlian and Hess, 2011). Additionally, other signals are also sent
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IJBM through the performance of specific activities over the internet including engagement,
37,1 interaction, personalization and collaboration to foster long lasting relationships with
customers (Guo, 2014; Farquhar and Rowley, 2006). Thus, for the purposes of this study,
engagement and interactivity are viewed as signals that are utilized by firms to elicit online
trust and loyalty among their customers.
228 2.3 Customer loyalty
Customer loyalty is one of the main factors that facilitate the attainment of firm competitive
advantage (Prentice and Loureiro, 2017). It is an important asset that helps firms to secure
future sales from their customers, as well as enhance their profitability (Kamran-Disfani
et al., 2017; Hallowell, 1996). Customer loyalty is defined as the positive attitude exhibited by
customers toward a given product or service provider, resulting in repeat purchase behavior
(Anderson and Srinivasan, 2003; Srinivasan et al., 2002). Initial conceptualizations of the
concept tended focus on the behavioral components (Kuehn, 1962), which made it difficult to
distinguish between true loyalty and spurious loyalty (customers who show behavioral
loyalty without attitudinal loyalty) (Kumar and Shah, 2004). But, some researchers aver that
customer loyalty is best measured using both attitudinal components and behavioral
components, in order to distinguish between the true loyalty and spurious loyalty that may
result (Kumar and Shah, 2004; Anderson and Srinivasan, 2003). Several factors influence
customer loyalty, including customer satisfaction (Hallowell, 1996) and customer trust,
which according to Pan et al. (2012) has a relatively high level of predictive power when it
comes to achieving customer loyalty. Even so, Srinivasan et al., 2002 have also highlighted
the significance of interactivity to customer loyalty.
2.4 Engagement
Vivek et al. (2012) defined engagement as “the intensity of an individual’s participation and
connection with the organization’s offerings and activities initiated by either the customer or the
firm” (p. 1). It is a concept that is viewed as a tool, which expedites the predictive power
of customer behaviors, such as their loyalty and referrals (Thakur, 2016). The engagement of
customers by firms online, often through social media, creates new opportunities for them to
provide better service to their customers. The evidence of which is exhibited through specific
customer behaviors including “liking” and commenting on content posted on the firm’s social
media page (Kabadayi and Price, 2014). It is a means of providing opportunities for value
co-creation, where customers can contribute to firm performance by providing feedback, as well
as playing the role of advocates for the firm’s products and services (Brodie et al., 2013;
Sashi, 2012). Thereby, creating actively engaged co-creators of value who assume
co-ownership of the firm’s brand, as ambassadors (Kandampully et al., 2015). Shin et al.
(2016) found a significant relationship between engagement and interactivity. Whereas, Brodie
et al. (2013) found that the customer engagement process often leads to customer trust.
Nonetheless, Kandampully et al. (2015) asserted that creating customer engagement will enable
firms to create an emotionally loyal customer base. Thus, it is hypothesized that:
H1. Engagement positively affects interactivity.
H2. Engagement positively affects online trust.
H3. Engagement positively affects customer loyalty.
2.5 Interactivity
Interactivity can be explained as the degree to which a dialogue can be established between
a firm and its customers online, through information sharing (Fang, 2012). There are four
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main aspects of interactivity, namely: reciprocity, responsiveness, non-verbal information ORM and
and speed of response (Yoo et al., 2015). Although, Fang (2012) noted that due to the spatial customer
and temporal separation between firms and their customers online, all these aspects of loyalty
interactivity are relatively complicated to achieve. Nevertheless, Mollen and Wilson (2010)
averred the pivotal role of reciprocal or two-way communication in the process. This view is
corroborated by Chen and Yen (2004) who suggest that reciprocity between firms and their
customers online is the most prominent element of interactivity. Thus, firms use online tools 229
such as e-mails to promote interactivity through frequent communication between
themselves and their customers, in the form of responses, questions, comments, feedback
and criticisms (Fang, 2012; Cobos et al., 2009). Per Jeon et al. (2016), perceived website
interactivity plays a critical role in inciting positive behavioral intentions among customers
online. Likewise, the findings of Etemad-Sajadi (2016) show that online interactivity in real-
time has a positive significant influence on online trust and customers’ intentions to
patronize a given brand. Therefore, this study hypothesizes that:
H4. Interactivity positively affects online trust.
H5. Interactivity positively affects customer loyalty.
2.6 Online trust
Online trust is a major element that contributes to the overall success of firm’s marketing
activities in the online context (Bleier and Eisenbeiss, 2015). Trust online is formed
between firms and customers when the latter develop positive views about their service
provider’s ability to satisfy their needs, their magnanimity, as well as their honesty in
their dealings online (Brun, Durif and Ricard, 2014; Brun, Rajaobelina and Ricard, 2014;
Urban et al., 2009). Brun, Durif and Ricard (2014) and Brun, Rajaobelina and Ricard (2014)
averred that simpler and easier customer experiences online coupled with the accessibility
and availability of a firm’s web-based relationship strategy are important in creating trust
online. Likewise, Pengnate and Sarathy (2017) found that both visual appeal and ease of
use of a firm’s online platforms are contributing factors in the formation of online trust.
It can, therefore, be argued that online trust is established mainly through customers’
self-perceptions based on their direct experiences and interaction with the firm online
(Bock et al., 2012). Online trust captures customer perceptions of how a firm’s
online platforms will deliver on their expectations, how believable they are, as well as the
level of confidence they inspire in the customer (Urban et al., 2009), all of which have been
found to have a positive effect on customer loyalty (Fullerton, 2014; Kim et al., 2011).
Hence, this study hypothesizes that:
H6. Online trust positively affects customer loyalty.
2.7 Mediating role of interactivity and online trust
Researchers, such as Xu and Sundar (2014), have demonstrated the relationship between
interactivity and engagement. Interactivity tends to enhance the appeal of interfaces to
customers online, through the enhancement of their stickiness. This is usually achieved by
means of the interactive features of various online applications utilized by firms in their
relationship building efforts. Researchers, such as Shin et al. (2016), opine the mediating role
of interactivity in predicting customer behavioral intentions during firm–customer
interactions. For this reason, it is hypothesized that:
H7a. Interactivity mediates the relationship between engagement and online trust.
H7b. Interactivity mediates the relationship between engagement and customer loyalty.
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IJBM Furthermore, Morgan and Hunt (1994) proffered that commitment and trust are the two key
37,1 variables that mediate the success of any long-term relational exchange; especially in the
online context (Casalo et al., 2007). For instance, Jeon et al. (2016) found that online trust fully
mediates the relationship between perceived website interactivity and repurchase intention
among customers of a travel website. This is verified by Wang et al. (2015) who statistically
identified the mediating role of online trust between hotel website quality and customers’
230 intentions to place a booking online. Therefore, the study hypothesizes that:
H8a. Online trust mediates the relationship between Engagement and customer loyalty.
H8b. Interactivity mediates the relationship between interactivity and customer loyalty.
Figure 1 presents the conceptual model for the study, and the hypothesized relationships
elaborated above.
3. Method
3.1 Sample and data collection
The study adopts a quantitative approach, using the survey technique to test the research
hypotheses presented above (Creswell, 2014; Ponterotto, 2005). Using the intercept approach
(Sharma, 2015), a sample of 429 bank customers were sampled from three of the major cities
in the southern part of Ghana referred to as “the golden triangle”, namely, Accra, Kumasi
and Takoradi to participate in the study (Modern Ghana, 2017). The banking industry was
selected due to its high level of advancement when it comes to the adoption and use of
emerging technologies in their service delivery activities (Price Waterhouse Coopers (PwC)
Ghana , 2015). The Ghanaian banking industry includes 32 local and international banks;
each having an official presence online either through a website or at least one of the many
social media sites, such as Facebook. This is a prerequisite in order to investigate issues
regarding their ORM activities and customer trust online. Thus, for the purposes of this
research, the respondents were selected based on their prior experience and interaction with
their banks’ relationship building efforts using these online platforms (Huck, 2012).
3.2 Measures
The constructs presented in Figure 1 were captured using multiple items measured on a
five-point Likert scale ranging from 1 “strongly disagree” to 5 “strongly agree.” Some of
measurement items for the constructs were adapted from previous research in RM both
Engagement
H3 (+)
H2 (+)
H8a–H8b
H1 (+)
Online Trust H6 (+) Customer Loyalty
H4 (+)
H5 (+)
Interactivity
Figure 1.
Conceptual model
H7a–H7b
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offline and online, while other measures were newly developed based on a review of existing ORM and
literature on ORM. Engagement was measured using four items adapted from Guo (2014) customer
and Cobos et al. (2009). Interactivity was measured with five items based on the works of loyalty
Cobos et al. (2009) and Farquhar and Rowley (2006). Customer trust online was also
measured using five items adapted from Wang et al. (2015) and Bilgihan and Bujisic (2015).
Whereas, customer loyalty was measured using five items which were developed going by
Labrecque (2014) and Huang and Shyu (2009). Each construct and their respective 231
measurement items are presented in Table I.
4. Results
4.1 Sample characteristics
Respondents were largely male (63.4 percent), with females accounting for only
36.6 percent. The dominant age group was 31–40 years (42.4 percent), with 34 percent
having a Diploma/HND followed by those with a secondary level education (31.9 percent),
tertiary – undergraduate (24 percent), tertiary – postgraduate (8.9 percent) and primary
education (1.2 percent). Overall, the entire sample indicated that they communicated with
their banks online, mainly through e-mails (100 percent), and at other times through the
banks website (79 percent), as well as their Facebook and Twitter pages using social
media (38.5 percent).
t-value
Construct and measurement items β (significance) CR AVE α
Engagement 0.90 0.70 0.90
I “like” content posted on my bank’s Facebook page 0.92 Fixed
I retweet comments posted on my bank’s Twitter handle 0.71 18.12***
My bank’s website has hotlinks to their Twitter/Facebook pages 0.87 26.45***
Other customers provide helpful information on my bank’s
Facebook page 0.82 23.51***
Interactivity 0.95 0.80 0.95
My bank has site navigation tools on their website 0.89 Fixed
My bank’s website has a search tool that enables me to locate items 0.93 30.50***
I get the desired answers to my online enquiries 0.90 28.36***
24-hour live chat/help is available on my bank’s website 0.85 24.54***
My bank’s online platforms provide mechanisms that help me to
evaluate and select appropriate products and services 0.90 28.36***
Online trust 0.84 0.52 0.84
I can count on my bank to ensure that transactions carried out on its
website are without error 0.81 Fixed
I think that the information presented onmy bank’s website is reliable 0.78 17.28***
My bank keeps customers’ best interests in mind 0.57 11.95***
I think that my bank would not do anything intentional on their
website that would be unfair to customers 0.82 18.32***
I feel like my privacy is protected while transacting with my
bank online 0.60 12.59***
Customer loyalty 0.86 0.55 0.85
I would be willing to pay a higher price for my bank’s services over
other banks 0.64 Fixed
I prefer my bank to its competitors 0.74 12.57***
My bank is the best bank for me 0.75 12.74***
I would be willing to defend my bank in the face of any controversy 0.82 13.56***
I would consider my bank as my first choice for patronizing Table I.
banking services 0.77 12.99*** Measurement
Notes: n¼ 429. ***p ⩽ 0.001 model results
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IJBM 4.2 Psychometric properties of the scales
37,1 Confirmatory factor analysis was performed to assess the reliability and validity of the
constructs and scales used. To do this, a measurement model was estimated, consisting of
four latent factors, as presented in Figure 1. The outcome of which is presented in Table I.
Model fit was evaluated using χ2/df¼ 2.77 ( po0.001), incremental fit index (IFI)¼ 0.96,
Tucker–Lewis index (TLI)¼ 0.95, comparative fit index (CFI)¼ 0.96 and root mean
232 square error of approximation (RMSEA) ¼ 0.06. The model estimated shows a good fit with
the data, as all the fit indices outlined above fall within the acceptable cut-off limits
(Schreiber, 2008). The factor loadings of all the measurement items for each construct
are also detailed in Table I. Each factor loading in the table is greater than 0.5, satisfying the
requirements for acceptability (Marticotte and Arcand, 2017; Hair et al., 2006).
To determine the reliability of the four constructs their composite reliabilities (CR) were
extracted. These ranged from 0.84 to 0.95, exceeding the prescribed criterion of 0.6 and
above (Bagozzi and Yi, 2012). To test the inter-item reliability, Cronbach’s α values were
considered all of which exceeded the suggested criteria of 0.70, as presented in Table I.
To evaluate the construct validity, the convergent and discriminant validities were each
examined (Fornell and Larcker, 1981). Regarding convergent validity, the average variance
extracted (AVE) for each construct was examined. These were all found to be above 0.5
(see Table I), indicating convergent validity (Khan et al., 2016). While for discriminant
validity, the AVE values for each construct are compared with the squared individual
inter-construct correlations, as presented in Table II (Fornell and Larcker, 1981). It emerged
that all the AVE values were greater than the square of each inter-construct correlation;
thereby satisfying the criteria for discriminant validity.
4.3 SEM analysis and hypotheses testing
SEM was used to investigate the causal relationships between the predictor and outcome
variables that are hypothesized in the research framework. This was done through the
estimation of separate structural models in Amos 22 to test the direct and mediating
relationships. The estimates for the test of direct relationships are presented in Table III and
graphically depicted in Figure 2. These capture the findings of the tests for H1 through to
H6. As can be observed from Table III, all the hypotheses were supported (H1, H3, H4, H5,
H6), except for one (H2). Engagement was found to have a significant positive effect on
interactivity ( β¼ 0.71, po0.001) and customer loyalty ( β¼ 0.17, p¼ 0.03). While
interactivity was found to have a significant positive influence on online trust ( β¼ 0.75,
po0.001) and a significant negative effect on customer loyalty ( β¼ 0.66, p¼ 0.001).
The negative effect of interactivity on customer loyalty came as a bit of a surprise; given
that interactivity has been found and is expected to contribute positively to enhancing
customer loyalty (Etemad-Sajadi, 2016; Cyr et al., 2009). Nevertheless, the relationship
Construct 1 2 3 4
1. Engagement 0.70
2. Interactivity 0.50 0.80
3. Online trust 0.34 0.62 0.52
4. Customer loyalty 0.10 0.09 0.24 0.55
Mean 2.5 3.5 4.1 4.0
Table II. SD 1.2 1.2 0.7 0.7
Convergent and Notes: Average variances extracted (AVE) are on the diagonal (in italic); squared correlations are off-diagonal.
discriminant validities The AVEs for each construct are far greater than the corresponding inter-construct square correlations, thereby
of the measures supporting discriminant validity
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between engagement and online trust was found to be insignificant ( β¼ 0.66, p¼ 0.001), as ORM and
seen in Table III. This outcome substantiates the work of scholars such as Kandampully customer
et al. (2015) and Brodie et al. (2013), who have demonstrated the significant influence of loyalty
engagement and interactivity in eliciting online trust and creating a loyal customer base.
Overall, the model shows good fit, with χ2/df¼ 2.77 ( po0.001), IFI¼ 0.96, TLI¼ 0.95,
CFI¼ 0.96 and RMSEA¼ 0.06 (Schreiber, 2008).
233
4.4 Mediation analysis
Amediation analysis was subsequently conducted using a bootstrap sample of 2,000. Table IV
highlights the outcome of this analysis, indicating the presence of both partial and full
mediation relationships. It can be observed from the table that engagement affects online trust
through interactivity but has no significant direct effect on it; thereby demonstrating that
interactivity fully mediates the relationship between engagement and online trust. Also,
interactivity was found to partially mediate the relationship between engagement and
customer loyalty, since the direct path from engagement to customer loyalty, as well as the
indirect path through interactivity were both found to be significant. Thus, providing support
for H7a and H7b. Additionally, the direct path from interactivity to customer loyalty, and the
indirect path through online trust are both significant. This signifies that online trust partially
mediates the relationship between interactivity and customer loyalty, as predicted in H8b.
These findings are consistent with previous researchers such as Kamran-Disfani et al. (2017)
who emphasize the pertinent role of trust as a mediator in firm–customer interactions. They also
Hypothesis Path description β t-value Results
H1 Engagement → interactivity 0.71 15.98*** Supported
H2 Engagement → online trust 0.06 0.97 Not supported
H3 Engagement → customer loyalty 0.17 2.24* Supported
H4 Interactivity → online trust 0.75 11.74*** Supported
H5 Interactivity → customer loyalty −0.34 −3.18*** Supported
H6 Online trust → customer loyalty 0.66 6.29*** Supported Table III.
Notes: *p⩽ 0.05; ***p⩽ 0.001 Direct path analysis
Engagement
0.17*
0.06
R2=0.62 R2=0.27
0.71*** Online Trust 0.66*** Customer Loyalty
0.75***
–0.34***
Interactivity
Figure 2.
R2=0.50 Results of the
structural model
Notes: *p0.05; ***p0.001
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IJBM
37,1 Direct without Direct with Indirect MediationRelationship Hypothesis mediator ( p-value) mediator ( p-value) effect type
Engagement → interactivity H7a 0.06 (0.31) 0.06 (0.33) *** Full
→ online trust mediation
Engagement → interactivity H7b 0.19 (*) 0.17 (*) *** Partial
→ customer loyalty mediation
234 Engagement → online trust H8a 0.19 (*) 0.17 (*) 0.17 No
→ customer loyalty mediation
Table IV. Interactivity → online trust H8b 0.18 (*) −3.18 (***) *** Partial
Results of → customer loyalty mediation
mediation analysis Notes: *p⩽ 0.05; ***p⩽ 0.001
corroborate the outcome of studies conducted by scholars such as Shin et al. (2016), which
identify interactivity as a significant mediator between engagement and certain perceived
customer attitudes. Nonetheless, when the effect of engagement on customer loyalty was
mediated by trust, the indirect effect was insignificant; thereby failing to provide support forH8a.
5. Discussion and conclusion
This research sought to examine the influence of firms’ engagement and interactivity
activities online on customer loyalty; and how these relationships are mediated by trust
online within the banking industry. The outcome of the data analysis largely supports the
hypothesized relationships specified in the conceptual model (see Figure 1), as depicted in
Tables III and IV. Specifically, by applying the signaling theory to explain these
relationships within the online context, this study suggests that irrespective of the
technological platform being used by a signaling party, the right signals must always be
sent to receivers for them to assess.
From the perspective of signaling theory, Mollen and Wilson (2010) conceptualized
engagement as “a discrete experiential property, independent of interactivity” (p. 920),
which involves the customer utilizing their reasoning, problem solving, decision making,
as well as their evaluation skills to assess a signal and respond to that signal. In reference
to this, it was observed that some Ghanaian banks provide hotlinks on their websites to
their respective social media pages, where customers are able to “like” content
and “repost” and “retweet” content posted by both the bank and other users online.
The provision of these hotlinks is in line with the literature, since customer engagement
has been proffered to come about mainly on social media (Wirtz et al., 2013). Thus, the
significant influence of engagement on interactivity and customer loyalty was expected,
given that previous research asserts the positive effects of customer engagement on
interactivity and customer loyalty (Khan et al., 2016; Kandampully et al., 2015). However,
the lack of influence of engagement on online trust contests the assertion of researchers
such as Brodie et al. (2013) who purport that customers who are engaged by their service
providers online can build trust. This is demonstrated by Wu and Chang (2005) who aver
that it is only when online trust exists in an online community that the customer can
meaningfully engage. Additionally, the relationship between interactivity and online
trust, as well as customer loyalty, were both proven to be significant. Sensory experience
is of great importance to customers within the Ghanaian context when purchasing
products and services (Boateng et al., 2011). However, this experience disappears to a large
extent within the online medium. Interactivity serves to mitigate the negative effects of
this lack of physical interaction by providing semblances of a human presence in
customer dealings with their banks online.
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Consequently, it communicates an air of reliability and dependability to the customer, as ORM and
put forth by the signaling theory. This is in line with the findings of Beuckels and Hudders customer
(2016), which asserts that interactivity generally has a positive effect on customers’ attitudes loyalty
toward service providers online. Nonetheless, the findings reveal a negative relationship
between interactivity and customer loyalty. It could be inferred that this negative
relationship between interactivity and customer loyalty is associated with the relative
complexity involved in firms achieving the various aspects of interactivity. As earlier 235
explained from signaling theory, for the aims of the signaling party to be achieved, the
signals sent need to be received and properly understood by the receiver for it to have an
effect (Mavlanova et al., 2016). Much of the efforts for ORM activities of Ghanaian banks are
through their websites and e-mails, which facilitate specific components of interactivity like
reciprocity and synchronicity, to the neglect of other dimensions, such as controllability and
demonstrability (Abdullah et al., 2016). Thus, the banks could benefit from the creation of a
higher contact environment with their customers online, which incorporates an array of the
different dimensions of interactivity, to enhance the positive effects of signals sent to
influence customer loyalty. Furthermore, online trust was found to significantly affect
customer loyalty. This indicates that to be able to endear their customers to remain loyal,
banks need to enhance their trust inducing measures online.
Concerning the mediating roles of interactivity and online trust, the findings provide
evidence for the full mediation effect of interactivity on the relationship between
engagement and online trust, whereas interactivity only partially mediates the impact of
engagement on customer loyalty. Therefore, an enhancement of bank–customer interactions
online will most likely influence customers’ decision of whether or not to trust their banks
online, or switch based on engagement levels. But, online trust only partially mediates the
influence of interactivity on bank customers’ decisions to remain loyal. These outcomes
confirm the results of previous studies, which provide similar results in testing these
relationships. For instance, Shin et al. (2016) found that the significant effects of the range of
thumb movements allowed on a smartphone, on customer engagement and behavioral
intention, were mediated by perceived interactivity. Similarly, Bart et al. (2005) found that
online trust partially mediates the relationships between website characteristics and
customer behavioral intent. This suggests that it is imperative for banks to utilize the tools
of engagement and online interactivity in building online trust and increasing switching
barriers among their customer base to keep them loyal. It is easier to retain existing
customers than to repeatedly go out in search of new ones; therefore, banks need to
continually upgrade the reliability and functionality of their online platforms to encourage
customers to keep utilizing the online medium in conducting business with them into the
long term. As Herington and Weaven (2007) explained, quality service provision online is
usually expected by bank customers. Hence, higher levels of online service quality will play
a key role in maintaining bank–customer relationships.
The findings of this study have yielded several conclusions concerning ORM in the
banking industry. First, banks across the globe are engaged in intense competition through
the introduction of new forms on IT, often over the internet to sustain and enhance their
competitive advantage. Though laudable, it is equally important for these online
technologies used in ORM to emit the right and useful signals to engender online trust and
loyalty among customers. Banks also need to develop the necessary policies and strategies
to leverage these online technologies to build successful long-term relationships with
customers. Moreover, the level of reliability of information provided via banks’ online
platforms, in addition to the usability, and functionality of these online channels also have a
noteworthy role in influencing customer perceptions and intentions.
Even so, there are several opportunities for future research to expand this study.
To begin, this study was limited to a specific industry, the banking industry. Thus, to
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IJBM further explore the applicability of the underpinning theory, and compare results obtained,
37,1 future research could be directed at other sectors of the financial service sector like
insurance services, investments and pensions. Second, the study focused on an emerging
economy – Ghana, and studied a limited number of ORM activities. Ghana is a context
where comparably internet usage (19.6 percent penetration in 2015) is just stepping out of its
nascent stages (Internet World Stats, 2016). Thus, some new and emerging technologies like
236 social media are yet to catch up with consumers and even formally with banks. As a result,
social media was the least represented internet application used by banks surveyed in this
study. This limited use or adoption of such internet applications tends to influence the
nature of ORM activities conducted by banks or experienced by customers. It is, therefore,
likely that a future study in a country which exhibits a higher internet penetration or use of
social media may be a good test for the ORM activities and relationships espoused in the
conceptual framework.
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About the author
Dr Sheena Lovia Boateng has a keen interest in marketing and also in working in academia. She holds
Doctoral and Undergraduate degrees in Marketing from the University of Ghana. She is the first female
to graduate with a PhD in Marketing from the University of Ghana. Her objective is to use her expertise
in the field of marketing, digital technologies and business analysis to research and offer related
services to small, medium and large firms in Ghana and other emerging and developed economies.
She has published five journal publications, four published conference papers, three book chapters, and
one working paper. Since beginning her doctoral career, she has collaborated with other faculty to
obtain not less than $200,000 in research and project funds. These research and project funds have
been obtained from organizations including Danish International Development Agency (DANIDA),
World Bank and United States International Development Agency (USAID). Dr Sheena Lovia Boateng
can be contacted at: lovia.okai@gmail.com
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