An Empirical Investigation of Customer Loyalty as a Mediator between the Relationship of Customer Relationship Management and Word of Mouth

Abstract

Customer relationship management (CRM) is an emerging concept in the consumer behavior literature. The current study examines the role of customer loyalty (CL) as a mediating variable between the relationship of Customer Relationship Management (CRM) and word of mouth (WOM) in the banking industry of Pakistan. This research investigates CRM from a customer perspective. A survey-based research design was employed in order to collect 250 responses of customers belong to the public and private banks of Pakistan. The researcher applied structural equation modeling technique for data analysis by using SmartPLS. Findings are evident that CRM has a direct and positive impact on customer loyalty and WOM. Moreover, CL mediated the relationship between CRM and WOM. Our endeavor successfully established the importance of CRM in order to cultivate CL and WOM.

Headnote

Customer relationship management (CRM) is an emerging concept in the consumer behavior literature. The current study examines the role of customer loyalty (CL) as a mediating variable between the relationship of Customer Relationship Management (CRM) and word of mouth (WOM) in the banking industry of Pakistan. This research investigates CRM from a customer perspective. A survey-based research design was employed in order to collect 250 responses of customers belong to the public and private banks of Pakistan. The researcher applied structural equation modeling technique for data analysis by using SmartPLS. Findings are evident that CRM has a direct and positive impact on customer loyalty and WOM. Moreover, CL mediated the relationship between CRM and WOM. Our endeavor successfully established the importance of CRM in order to cultivate CL and WOM.

Keywords: Customer Relationship Management (CRM), Customer Loyalty, Word of Mouth

INTRODUCTION

Kotler and Armstrong (2012) defined word of mouth (WOM) as post-purchase and post-consumption behavior. Satisfied and delighted customers spread positive WOM while unsatisfied customers cause negative WOM. WOM is more effective than any advertising, and this diffusion is at no cost. Lee Thomas, Mullen, and Fraedrich (2011) described WOM as personal communication among people about satisfaction or dissatisfaction of a brand. Al-alak and Alnawas (2010) regarded WOM as a useful mode of communication in the banking industry. Macintosh (2007) stated that in services organizations WOM positively affects the development of new prospects. Consumers considered 90 percent of advertising to be non-believable, but 90 percent of WOM believable (Lee Thomas et al., 2011). A satisfied consumer generally communicates his/her satisfaction to 3-5 people (Pruden and Vavra, 2004). The Internet introduced new manners of WOM advertising that can enhance its impact and scope as well (Mohr and Chiagouris, 2005). Ndubisi, Khoo-Lattimore, Yang, and Capel (2011) stated that 78 percent of consumers are affected and accepted WOM communication and of which 61 percent were affected by viral marketing

A plethora of research in consumer behavior has explored the motivations behind WOM. Customer loyalty, satisfaction, relationship marketing, and perceived value are those concepts which have been repeatedly studied as variables affecting WOM. Sivaraks, Krairit, and Tang (2011) empirically studied the relationship between customer relationship management (CRM) and WOM in online settings. Ejaz, Ahmed, and Ahmad (2013) demonstrated that how CRM practices affect customer satisfaction, loyalty, and WOM. Customer loyalty (CL) has been found one of the key predictors of WOM in the literature. CL has also been found as a significant contributor to firm profitability (E. W. Anderson, Fornell, & Lehmann, 1994). As WOM depends on CL, similarly CL is also dependent on a host of variables (Saleem, Zahra, Ahmad, & Ismail, 2016). Relationship marketing (RM) and CRM are however significant predictors of CL specifically in services and banking industry. Even though the direct relationships of CRM, CL, and WOM can be found in the consumer behavior literature, however, very little is known about the mediating role of CL between CRM and WOM. The present study is an endeavor to study the mediating role of CL between CRM and WOM in the banking sector of Pakistan. Furthermore, it was discovered that organizational perspective to measure the success of CRM is no more effective rather, customer focus is a more appropriate way to judge the success of CRM implementation, whether customers aware of CRM activities or not.

LITERATURE REVIEW

Customer Relationship Management and Customer Loyalty

Customer relationship management (CRM) is a prevalent marketing strategy that is used to develop, nurture and strengthen customer relationships. The challenge of rising trend of customer defection rates has made CRM more significant than ever before in business history. Companies use CRM to increase market share and share of wallet through customization and personalization. CRM can help organizations to adopt measures like recency, frequency and monetary (RFM) to ensure customer retention (Baran, Galka, & Strunk, 2008).

Kincaid (2003) defined CRM based on factors like information, processes, technology, and people. Further, he explains that these factors are used strategically to manage customer's relationship with the company throughout the customer life cycle that is the total time of customer attachment with the company. Companies use CRM to cover four functions labeled: marketing, sales, product support and customer services. Information includes all types of data regarding customers, products or competitors. Processes are outcomes of CRM and include touch points or media like broadcast, mail, email, phone and personal that help customers to interact, initiate and complete process. Technology helps to develop CRM infrastructure by using the software, hardware, networking technology, databases and all security features that protect the whole system. People are as much important for CRM as a power supply for any system. This component is developed by training, education and rewards.

Sivaraks et al. (2011) grouped eleven different definitions that elaborate different facets of CRM like customer interactions, communication with customers, acquisition and retention of customers, customization, integration of marketing activities and five-pillar of CRM (segmentation, information networking, information technology systems, sales, and client support) approach. They studied CRM from three different aspects: technology point of view, business point of view, and customer point of view. To study CRM from customer perspective it can be defined along four main features: (1) delivery of right product at right time (2) multiple customer touch points to add value (3) system to collect customer feedback in order to use in customization and personalization of offerings (4) to establish customer trust in CRM systems.

CRM has proved significant to develop long term relationships and CL in the banking industry (Bhat & Darzi, 2016; Narang, Narang, & Nigam, 2011). CRM plays a pivotal role in customer acquisition, retention and loyalty are the primary goals marketing (Swift, 2001). Successful management of customer relations brings customer satisfaction as well as loyalty. Most of the studies published on CRM investigated CL as an outcome variable (Mithas, Krishnan, & Fornell, 2005; Sota, Chaudhry, Chamaría, & Chauhan, 2018). CRM has been repeatedly studied as a predictor of customer loyalty and retention (R. E. Anderson & Srinivasan, 2003). Hence the following hypothesis can be proposed:

H1: There is a positive relationship between CRM and customer loyalty

Customer Loyalty and Word of Mouth

CL is a sincere commitment to repurchase or re-utilize a favored product or service in the future even in the presence of strong influence of marketing activities of competitors that may cause defection (Yim, Tse, & Chan, 2008). All marketing processes and activities are aimed at building customer loyalty and providing customer value to nurture and maintain long-run relationships (Peng & Wang, 2006). A loyal customer may accept some awkward situation in the hope of a better future with a firm(Alrubaiee & Al-Nazer, 2010). Loyal customers show more rebuy and recommendation intentions than others (Kim et al., 2007). According to Bowen and Shoemaker (2003) customer loyalty means the probability of repurchase, partnering relationships and recommendations to purchase to others. It is the measure of the likelihood that the customer will come again and is prepared relationship and positive WOM.

Loyal customers are committed, have positive feelings about brands and submit this positive state of mind to others. Attitudinal loyal customers are much less vulnerable to negative information. Attitudinal loyalty leads customers towards positive intentions, re buying and recommendations to other members of the circle, attitudinal loyalty directs toward purchase intention and ultimately towards buying behavior and rebuying behavior that is behavioral loyalty and influenced by attitudinal loyalty (Donio', Massari, & Passiante, 2006). Liu, Guo, and Lee (2011) divided the factors that can affect customer retention and loyalty into pull in and push back forces; RQ was regarded as pull in force while switching cost as push back force. They divided two-factor theory of motivation into "hygiene" factors and satisfiers. Hygiene factors are necessary or least amount of services while satisfiers provide some extra benefits to customers. Furthermore, they studied long run customer loyalty and viewed it very useful for service providing firms. Sanchez-Franco, Ramos, and Velicia (2009) coined the term of "genuine customer loyalty."

Loyal customers are committed, have positive feelings about brands and submit this positive state of mind to others. Attitudinal loyal customers are much less vulnerable to negative information. CL leads customers towards positive intentions, re buying and recommendations to other members of the circle, attitudinal loyalty directs toward purchase intention and ultimately towards buying behavior and rebuying behavior that is behavioral loyalty and influenced by attitudinal loyalty(Donio' et al., 2006). Customer loyalty plays a vital role in nurturing customer retention and WOM (Kassim & Asiah Abdullah, 2010; Söderlund, 2006). Based on this discussion it can be proposed as:

H2: There is a positive relationship between customer loyalty word of mouth

Mediating Relationships

The marketing literature provides evidence in support of direct relationships of CRM and CL as well as CRM and WOM (Bhat & Darzi, 2016; Narang et al., 2011; Sivaraks et al., 2011). Hence based on available literature, we can propose:

H3: CL mediates the relationship of CRM and WOM

METHODOLOGY

Data Collection Instrument

The current study constructed data collection instruments by using measures from existing literature. Scale to measure CRM was adapted from Wu and Li (2011) and slightly modified to the banking sector. Four items of CL and WOM were again adopted form Wu and Li (2011). The study employed fivepoint Likert scale ranging from "1=strongly disagree" to "5=strongly agree". I was already used in the banking sector and proved effective (Ndubisi et al., 2011).

Population, Sample and Data Collection

Ndubisi et al. (2011) recommended bank intercept method to collect data from walk-in customers. This method was applied to get responses from the customers of major commercial banks of Islamabad, being the representative city of Pakistan. The researchers used purposive sampling to identify whether the walk-in customer is the account holder or not.

Sample size determination is one of the critical decisions in scientific research. Sekaran and Bougie (2016) suggested that a sample size larger than 30 and less than 500 is appropriate in social sciences. Hair, Anderson, Tatham, and Black (1998) proposed that the sample size can also be determined based on the ratio of the number of parameters or indicators in the structural model to the number of respondents. This ratio may be 1:10, 1:15 and 1:20 (Hair et al., 1998; Jackson, 2003; Kline, 2015). Hence the researchers decided to collect 260 responses based on ratio of 1:20. For this purpose 400 questionnaires were distributed, subsequently we found 249 responses complete and correct to include in the analysis.

RESULTS AND FINDINGS

Researchers analyzed data with the help of structural equation modeling (SEM) by using Smart PLS. it was done in two steps. Measurement and structural model were analyzed for the goodness of data and hypothesis testing respectively.

Measurement Model

Measurement model calculates the reliability and validity of the model based on individual constructs. Cronbatch's Alfa(a) and composite reliability (CR) values determine the reliability of constructs. If these values are found above 0.7 the constructs are considered relaiable (Hair, Ringle, & Sarstedt, 2011). Table 2 shows that our results fulfill these criteria for the current study.

Outer loadings and average variance extracted (AVE) are indicators of convergent validity. All outer loading values are above 0.708 (Table 1) and all AVE values are above 0.5 (table 2). Hence the results confirm convergent validity (Hair, Hult, Ringle, & Sarstedt, 2016).

Moreover, the discriminant validity is tested by using Fornell and Larcker (1981) method and cross loadings. Fornell and Larcker (1981) method says that square root of AVE of each construct should be greater than its highest correlation with other constructs. Table 3 reflects the values to validate the said criteria. If the factor loadings of all indicators of a construct are greater than any of its cross loading it will confirm (Hair et al., 2016). Results shown in table 1 confirm the discriminant validity.

Structural Model

The current study employed structural model for hypothesis testing. Standardized path coefficients (ß), the coefficient of determination (R2) and level of significance (t-value) were used for hypothesis testing. Hair et al. (2016) described that the values of ß are close to 1 reflects stronger relationships and vice versa. The threshold of t-value is 1.96 at 10% level of significance for two tailed tests. The R2 values calculate the change produced in the dependent variable by the independent variable (Hair et al., 2011). The SEM results shown in table 4 supports all hypotheses.

CRM produced 38.6 % variation in customer loyalty and 58.4 % variation in WOM. These values who higher level of variation in social sciences (Hair et al., 2016; Hair et al., 2011). The effect size (f2) was used to check the relative impact of exogenous variables. The values of f2 shown in table 5 reflect a moderately high relative impact (Hair et al., 2016). Q2 values show the models out of sample predictive relevance.

Mediation Analysis

The researchers adopted the bootstrapping method to run mediation test at 95% confidence level. It is the most recommended method for mediation analysis (Hayes, 2009; Zhao, Lynch Jr, & Chen, 2010). Our results supported the role of customer loyalty as mediators between the relationship of CRM and WOM. Moreover, as all relationships showed positive signs hence, it confirms complementary mediation (Hair et al., 2016).

DISCUSSION

Summary

The current study aimed at empirical investigation of direct and indirect effects of CRM on CL and WOM. Furthermore, authors studied CRM from the customer perspective. One of the objectives was to test the mediating role of CL between the relationship of CRM and WOM in the banking sector of Pakistan. Two hypotheses were designed to test direct effects and one to test the mediation effect. Findings were not only consistent with previous studies but also contributed to existing literature. Results regarding the impact of CRM on CL (H1) confirmed the findings of previous studies (Bhat & Darzi, 2016; Narang et al., 2011; Sivaraks et al., 2011). Similarly, the result of impact of CL on WOM (H2) was also found consistent to the previous literature (Bowen & Shoemaker, 2003; Kim et al., 2007). The mediating role of CL was a contribution towards literature. However it was strongly supported by consumer behavior literature (Bhat & Darzi, 2016; Bowen & Shoemaker, 2003; Kim et al., 2007; Narang et al., 2011; Sivaraks et al., 2011).

Theoretical Implications

The current study has implications at three levels. First, it provides empirical evidence on how the customer-focused CRM influences the CL. Second, it gives understanding about the influence of CL on WOM. Third, the present research endeavor provides the underlying mediating mechanism to carry the impact of CRM to WOM via CL. This study successfully establishes the decisive role of the customer-focused CRM to cultivate CL. Customer focused CRM believes in making customers informed about CRM related activities, initiatives, and benefits. The moment CRM related initiatives are realized in the form of service quality and other consumer benefits, the consumer shows a high level of commitment towards the firm. This commitment becomes first attitudinal and then behavioral loyalty. Such kind of emotional as well as behavioral attachment reinforced motivates consumers to involve in WOM behavior. Hence it can be inferred that the CL is such a compelling force that intriguers the consumer to spread positive word of mouth.

Managerial Implications

Our study once again has implications for managers at three counts. First, this study successfully proves the importance of customer-focused CRM. Banks should adopt CRM and successfully realized to consumers the presence of CRM related initiatives. Second, this kind of actions can cultivate loyalty among customers. CL is more important than customer development as new customer development incurred more cost than to serve the existing one in the service sector. Third, the CRM initiatives and CL will create positive WOM in the market. If firms can make sure the realization of customer-centric CRM initiatives it can successfully generate CL and positive WOM among customers. Furthermore, WOM is also vital for firms because it is more believable than any advertising.

Limitations and future research

This study has a few limitations like any empirical investigations. These limitations can be discussed on two counts. First theoretical or conceptual limitations others are methodological limitations. Conceptually we were limited to only one dependent one independent and one mediating variable. Methodologically we used nonprobability sampling design and applied SEM-PLS for hypothesis testing. Future researchers can explore further avenues by making this study aa s starting point. The conceptual framework can be extended by adding more dependent, independent or mediating variables. Probability sampling can also be applied in the future to increase the generalization of findings. Furthermore, experimental design can also be used by future researchers to replicate the current study. Moreover, the same study can be replicated in other cultures or industries. Future researchers can also study e-CRM and eWOM in the banking industry. Comparative studies can also be conducted by including control or moderating variables.

Sidebar

Cite this paper: Rehman, Z., Raza, A., Ilyas, S., Faisal, M. M., & Zia, M. H. (2020). an empirical investigation of customer loyalty as a mediator between the relationship of customer relationship management and word of mouth. Paradigms, SI(1), 27-31._

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