Frontline service employees and customer engagement: some further insights

Abstract

Purpose

This study aims to investigate whether frontline retail banking employees’ proactive personality helps in ameliorating customer engagement. This study further aims to investigate the mediational role of work engagement and service recovery performance in the employee proactivity – customer engagement relationship.

Design/methodology/approach

This study used a triadic approach for the collection of primary data. Each triad consisted of a customer, a frontline employee and an immediate colleague of the frontline employee. Structured questionnaires were used to solicit data from the respondents. Specifically, customers were asked to report their level of engagement with the bank and the recovery performance of the employee who redressed their grievances. Frontline employees responded to their level of work engagement while their colleagues reported about the proactive disposition of frontline employees at the workplace.

Findings

Empirical findings revealed under service scenario, Indian retail banking employees’ proactive disposition nurtures customer engagement. It was further observed that this relationship is sequentially mediated by work engagement and service recovery performance.

Originality/value

The role of frontline employees in enriching customer engagement has to date remained under-researched among marketing scholars. To the best of the authors’ knowledge, this study is the maiden attempt to relate frontline employee proactivity with customer engagement. Also, this study is one of the early research to investigate customer engagement under a service recovery context, thereby, opening pathways for further exploration.

Citation

Zahoor, A. and Khan, D. (2022), "Frontline service employees and customer engagement: some further insights", IIM Ranchi Journal of Management Studies, Vol. 1 No. 2, pp. 175-190. https://doi.org/10.1108/IRJMS-06-2021-0016

Introduction

In their study of mobile phone users, Cambra-Fierro et al. (2014) highlighted, probably for the first time, the role of frontline service personnel in shoring up customer engagement. They reported that the attitude of frontline employees significantly improves customer satisfaction and engagement. Their results also suggested that the impact of employee attitude on customer satisfaction and engagement is stronger when customers have registered a complaint v/s when no complaint has been registered. Two years later, Cambra-Fierro et al. (2016) found that effective complaint handling ameliorates customer engagement in a sample of customers from the financial services industry. More recently, similar findings were also reported by Cambra-Fierro and Melero-Polo (2017) who found that effective complaint-handling enhances customer engagement.

Despite the recently acknowledged indisputable significance of customer engagement for service organizations and the role of frontline staff in determining customer service perception (Berry, 1981), it is surprising that these few studies appear to be the only reference works that relate frontline service personnel attitudes with customer engagement. One possible reason explaining this scenario is that customer engagement is a relatively new entrant into the customer management and relational marketing literature (Verhoef et al., 2010) and is still passaging through early developmental stages.

Though preliminary in nature, the above-stated findings do suggest that frontline employees are significant in determining customer engagement, especially during a service recovery scenario. However, it is not yet understood what type of employees are more suitable to deliver effective service recovery and, thereby, enhance customer engagement. In essence, Cambra-Fierro et al. (2014, 2016) and Cambra-Fierro and Melero-Polo (2017) identified those employee actions that significantly determine the level of customer engagement. We expand their research to identify what type of employees are more likely to perform those actions. Additionally, given that customer engagement happens to be among the top research priorities of Marketing Science Institute (MSI, 2016) and that engaged customers are highly valuable for a company as they enhance profitability and hold future value, it is worth our while to explore the factors that significantly contribute towards realizing a highly engaged customer base.

Service marketing literature highlights that frontline employees are critical for customer service perception (Berry, 1981). Since service interactions are characteristically inseparable and intangible, it is the employee herself who shapes the quality of service exchange (Berry, 1981). They act as the spokespersons of the company, a form of the communication channel between the company and the customer and, in fact, customers often trust the information given by the employees more than any other source of corporate communication (Van Laer and De Ruyter, 2010). As such, service organizations need to gauge their frontline employee's performance outcomes to effectively cater to customer requirements.

Against this backdrop, we propose that frontline employees (of the Indian retail banking sector, in this case) with a proactive disposition would be more likely to satisfy aggrieved customers which, in turn, increases the level of customer engagement. We also attempt to offer an explanation why frontline employees' proactive personality enhances customer perception of service recovery and, consequently, nurtures customer engagement. For that purpose, we would explore work engagement as a possible mediating link. In effect, our proposed theoretical model suggests that work engagement and perceived recovery performance would sequentially mediate the influence of employee proactive personality on customer engagement. Towards that end, we use the job demands-resources (JD-R) and the social exchange theory (SET) as the theoretical underpinning.

Theoretical framework

Job demands-resources model

The JD-R model, initially proposed by Demerouti et al. (2001), proposes that job characteristics can be classified into two broad categories – job demands and job resources. Job demands are those job aspects that require effort and generally result in fatigue, stress, etc. (Demerouti et al., 2001). Work overload, interpersonal conflicts, etc. are typical examples of job demands. On the other hand, job resources reflect those physical and psychological aspects of a job that facilitate achievement of work goals, buffer the impact of job demands on well-being and even cultivate personal growth (Demerouti et al., 2001). Examples of job resources include job autonomy, social support, etc.

Besides job demands, certain researchers (e.g. Brenninkmeijer et al., 2010) have deployed the JD-R framework to explore the role of personal resources in mitigating the negative consequences of high job demands. Personal resources are the idiosyncratic aspects of an individual that relate to her resilience and sense of ability to successfully control the environment (Hobfoll et al., 2003), e.g. self-efficacy, hope, optimism, resilience, etc. Past research evidence indicates that personal resources (self-efficacy, organization-based self-esteem and optimism) positively predict work engagement (Xanthopoulou et al., 2009a). Trailing the line of argument in the JD-R model, we expect that proactive personality (a personal resource) would invigorate employee work engagement and service recovery performance.

Social exchange theory

SET postulates that social interactions between people or groups of people are based on the exchange of resources whereby one party feels obliged to reciprocate the good gestures of the other (Gergen, 1969). The social exchange process commences when an actor extends a positive gesture towards a target, and the target then chooses to reciprocate. As such, interactions that spawn obligations lie at the heart of SET (Emerson, 1976). Across several decades, SET has been used as a theoretical lens to explain social interactions in a wide spectrum of organizational behaviours like commitment (Bishop et al., 2000), organizational support (Ladd and Henry, 2000), leadership (Liden et al., 1997), social power (Molm et al., 1999), justice (Tepper and Taylor, 2003), organizational citizenship behaviour (Organ, 1990), etc.

In the context of this study, when service employees (actor) undertake a proactive initiative to rectify a service failure and restore customer satisfaction, the customer (target), in response, feels obligated to recompense the recovery effort. She/he (target) reciprocates by showing loyalty towards the organization (actor) and even engaging with it (actor) at a level that surpasses mere loyalty, thereby, completing the mutually beneficial cycle of resource exchange. In accordance with SET principles, the relative benefits for both the parties exceed their respective costs and the interaction culminates in shared satisfaction.

Customer engagement

During the late 1990s and early 2000s, marketing philosophy shifted the paradigm from transactional to relational approach and marketing practitioners concentrated on fostering long-term association with customers that transcend beyond the purchase. Consequently, several customer-centric measures like customer satisfaction, customer retention, customer trust and commitment found their passage into the marketing literature, dislodging the traditional indicators such as share-of-wallet, frequency and monetary value. Even more recently, marketing scholars advocated that for competitive advantage to follow; contemporary service providers need to engage customers at a level that surpasses mere satisfaction and involvement. Hence, “customer engagement”.

Van Doorn et al. (2010) conceptualize customer engagement as “customers' behavioural manifestations that have a brand or firm focus, beyond purchase, resulting from motivational drivers” with a special focus on non-transactional customer behaviour. These non-transactional behaviours transcend beyond the simple realm of satisfaction, loyalty and repurchase intent. They reflect commitment, positive word-of-mouth, customer-to-customer recommendations and service co-creation (Kumar et al., 2010) – customer behaviours that not only influence company's existing profitability but, more importantly, reinforce the company image, influence future purchases of existing customers and even attracts new customers, thereby, enhancing corporate value over the longer time frame. Therefore, customer engagement captures the customer-firm interactions above and beyond the existing relational concepts such as involvement (Brodie et al., 2011), satisfaction and loyalty.

Antecedents of customer engagement

Employee proactive personality

With the objective to converge variegated perspectives on personality structure, Swietlik (1968) conceived a rubric – “reacting or proactive personality”. The concept, though, could not attract significant traction back then. Few years down the line, Bateman and Crant (1993) introduced the concept of proactive personality to organizational behaviour literature. They defined proactive personality as “the relatively stable tendency to effect environmental change”.

Proactive individuals exhibit a predisposition towards intentionally influencing their environment (Buss, 1987) to induce a change (Crant, 1995). Proactivity is a dispositional attribute, and proactive people do not acquiesce to environmental forces (Buss, 1987). Instead, they accumulate resources and motivate themselves to demonstrate favourable behaviour (Demerouti et al., 2015). Proactive people are relatively unrestrained by contextual factors (Bateman and Crant, 1993), they recognise opportunities, take personal initiative and persist to effect a meaningful transformation (Crant, 1995).

Literature on interactionist psychology argues that human behaviour is the outcome of individual–environment interaction (Snyder and Ickes, 1985). Individuals are as much influenced by the environment as contrariwise (Schneider, 1983). In an organizational setting, proactive employees, therefore, are inclined towards effecting consequential changes in their work environment that synchronizes their abilities and temperament with the requirements of the job and, thereby, enhances the prospects of superior performance.

Past research indicates that employee proactivity ameliorates job performance (Bakker et al., 2012) – both in-role (Bakker et al., 2012) and extra-role (Greguras and Diefendorff, 2010) performances. More recently, Zahoor (2020) reported that employee proactivity even enhances employees' service recovery performance. We take these previous findings a step further to explore whether employee proactivity would transcend beyond work performance to influence the intensity of customers' participation and connection with the service provider/offering. We believe that, in a service context, proactive employees better comprehend customer service requirements and respond by taking personal initiative that results in a service performance that is in congruity with customer expectations. Given that customer satisfaction is deemed critical for engagement (Van Doorn et al., 2010), employee proactivity appears to be a determining factor for improved customer engagement. Consequently, the following hypothesis has been proposed:

H1.

Frontline employees' proactive personality positively influences customer engagement.

Perceived recovery performance

Certain researchers (e.g. Verhoef and Lemon, 2013) argue that customer lifetime value (CLV) is underestimated when non-transactional behaviours (e.g. word-of-mouth) are not accounted for. However, managing customers is a pretty difficult task and organizations often make mistakes that impair the customer–company connection. This is particularly true for service organizations because of the high level of human involvement (Boshoff and Allen, 2000), resulting in service failures (a service performance that does not conform to the customer expectations). Mack et al. (2000) even went on to state that owing to the multidimensional character of services, failure in service delivery often takes the shape of a norm and is not an exception. This results in several adverse consequences for the service providers like negative word-of-mouth, defection (Zhou et al., 2014), etc.

Past research reveals a paradoxical scenario with regard to service recovery management. Academic researchers, on the one hand, are continually offering deeper insights into the dynamics of service recovery handling (Vaerenbergh and Orsingher, 2016) and practitioners, on the other, appear to be struggling with the design of effective service recovery strategies (Michel et al., 2009). That is precisely why research into the management of service recovery (i.e. actions initiated by the service provider to remedy the dissatisfaction caused by service failure) is so critical.

Effective service recovery contributes significantly to building a lasting relationship with customers. It rectifies the cognitive dissonance of initial failure and restores customer satisfaction. Lorenzoni and Lewis (2004) posit that satisfactory recovery sometimes even leads to greater satisfaction than what the customer would have experienced if the failure would not have occurred in the first place. Coupling Lorenzoni and Lewis’s (2004) findings with Van Doorn et al. (2010) that satisfaction is a pre-requisite for engagement, it is plausible to argue that effective service recovery would enhance customer engagement.

Moreover, SET and the principle of reciprocity would offer a theoretically grounded rationale for understanding the linkage between perceived service recovery and customer engagement. A satisfactory recovery is always delightful for the aggrieved customer because it restores her perception of justice and fair treatment. A delightful customer would oblige to reciprocate, and the gratitude may be expressed through enhanced engagement; besides perceived justice, in itself, also contributes to positively driving customer engagement (Cambra-Fierro et al., 2016). Against this backdrop, the following hypothesis has been proposed.

H2.

Frontline employee's service recovery performance positively influences customer engagement.

Employee proactivity and work engagement: linkage

A positive and fulfilling work-related state of mind exhibited through vigour, dedication and absorption is characterised as work engagement (Schaufeli and Bakker, 2004). Engaged employees exhibit higher resilience and energy at work (vigour); are passionate about their work and associate challenge and sense of importance to what they do (dedication) besides remaining engrossed at work in a way that time appears fleeing (absorption) (Schaufeli and Bakker, 2004). An engaged worker manifests attentiveness, involvement and focus by cognitively integrating herself with the work role (Kahn, 1992).

Past research suggests job resources (like performance feedback, autonomy and social support) and personal resources (like organization-based self-esteem and self-efficacy) as significant determinants of work engagement (Bakker and Demerouti, 2008). More recently, few studies (e.g. Bakker et al., 2012; Zahoor, 2020; Zahoor, 2018) have related proactivity with work engagement and found encouraging results.

Bakker et al. (2012), based on a multi-industry sample from the Netherlands, reported a positive association between proactive personality and work engagement through job crafting. Zahoor (2018) observed similar findings in an Indian higher education setting where teacher proactivity was found to enhance work engagement and, concomitantly, student satisfaction and loyalty. In the Indian retail banking sector, Zahoor (2020) examined a direct association between proactivity and engagement. The author reported that frontline employees' proactive personality enhances their level of work engagement. Based on the above evidence, the following hypothesis has been proposed:

H3.

Frontline employees' proactive personality positively influences work engagement.

Work engagement and perceived service recovery: linkage

Past research suggests several important correlates of work engagement. For instance, engagement positively relates with in-role performance (Bakker et al., 2012), financial returns (Xanthopoulou et al., 2009b) and customer satisfaction (Salanova et al., 2005). Engagement even improves health and happiness (Bakker and Demerouti, 2008; Schaufeli and van Rhenen, 2006) while restraining some adverse psychosomatic problems that hamper performance, for example, headaches (Schaufeli et al., 2008). Given, happy employees are optimistic and confident (Cropanzano and Wright, 2001), the tenability of the argument that engaged employees will outperform the relatively less engaged ones by virtue of happiness and health can seldom be overstated.

Job performance is among the commonly examined outcomes of work engagement (Bakker et al., 2004, 2012). Several studies evidence that work engagement nourishes in-role performance (e.g. Bakker et al., 2012). However, the specific study of work engagement in a service recovery context is scant in the existing literature. Although Zahoor (2020) has provided an initial insight into the relevance of work engagement for recovery performance, this relationship is not yet fully explored in the marketing literature.

Further, the fact that work engagement enhances in-role performance does not suggest that engagement would nurture recovery performance also because the latter reflects a fundamentally different situation than the usual service performance. As a matter of fact, service recovery situation is a sort of aberration from the routine service delivery which requires an employee to empathetically comprehend the reason behind the service failure, gauge the customer retort to the failure and calculate her response, besides handling the customer incivility that might accompany the grievance. In contrast with the usual service performance, these aspects are largely an exclusive feature of recovery situations and, therefore, require a specific set of abilities on part of the employee. Under such conditions, engaged employees, being service and client-oriented, are likely to effectively address the customer grievances by responding calmly, quickly and patiently (Engelbrecht, 2006).

Besides, the Broaden-and-Build theory of positive emotions (Fredrickson, 2001) argues that positive emotions like joy and contentment improve an individual's cognitive faculty by building physical, intellectual and psychological resources. Consequently, the thought–action repertoire of the individual is broadened which enhances her ability to respond effectively to environmental stimuli. In the service recovery context, employee response to customer grievance is crucial for customer satisfaction with the recovery effort. Therefore, engaged employees, by experiencing positive emotions more often than less/non-engaged colleagues (Schaufeli and van Rhenen, 2006), would recover from service failures better than their less-engaged counterparts. In the light of these arguments, it appears plausible to propose.

H4.

Frontline employees' work engagement positively influences perceived recovery performance.

Further, our research is intended to delineate, theoretically, the path through which employee proactivity influences customer engagement. Towards that purpose, we incorporated work engagement and perceived recovery performance as the mediating links in our structural model (Figure 1). In the backdrop of the evidence offered from the existing literature for deriving H1–H4, representing the theoretically grounded direct linkages between the constructs under investigation, the mediational hypothesis to be tested for empirical support is formulated as H5.

H5.

Frontline employees' work engagement and perceived recovery performance sequentially mediate the relationship between proactive personality and customer engagement.

Methodology

The methodology adopted to operationalize this study is spread across three broad stages. First, we approached several customers in the branch premises of six organizations from the Indian retail banking sector. We enquired from the customers if they remember a recent service interaction with the bank that they were not satisfied with because it did not match up to their expectations. Those customers who have had such an experience revealed that they either complained about it with the bank or chose to ignore it. In the context of this study, our concern was those customers who experienced a service failure and chose to register a complaint with the service provider.

Second, we asked the aggrieved customers to identify the employee (which, as anticipated, in all the cases was a frontline employee) whom they approached for redressal of their grievance. The customer was then requested to rate the recovery performance of the employee using a structured questionnaire. In addition, the customer was also solicited to report her level of engagement with the service provider. Subsequently, we approached the corresponding employee to seek her response regarding her work engagement. The frontline employee's perception of work engagement was also recorded through a structured questionnaire.

During the initial stage of data collection, we observed that in the majority of the cases, several customers from a particular branch identified a single employee whom they had approached for service recovery. To address this concern, we substantially increased the number of branches for the collection of data and restricted the number of customers and, consequently, employees approached per branch. However, this did not eliminate our problem entirely and we were still left with a quite a few instances where multiple customers reflected on the recovery performance of a single employee. Consequently, in order to avoid a mismatch between the employee and customer responses, we considered not more than three customer responses for every frontline employee. The customer perceptions, in such cases, were parcelled into a single indicator as recommended by Hall et al. (1999).

Finally, we identified the immediate colleague of the frontline employee whose work engagement we already measured. In line with Bakker et al. (2012), the colleague was asked to rate the frontline employee's proactive tendency at the workplace. We ensured that the colleagues to be paired with the frontline employees have adequate shared work experience (minimum 2 years in this case) to be able to accurately reflect upon the proactivity in the personality of the frontline employee.

In effect, we created triads of respondents to acquire data for the study. Each triad consisted of one customer (in cases where more than one customer reported on a single frontline employee, the responses were parcelled as detailed above), one frontline employee and one colleague of the frontline employee. Further, as suggested by Hennig-Thurau (2004), we used a common identification number to match the responses of customers with those of frontline employees and their immediate colleagues.

Respondent profile

In total, we approached 1987 customers from several branches of six Indian retail banks. The respondent banks include the private sector, public sector as well as foreign banks. The customers were sampled on the basis of a stratified sampling procedure with Delhi, Srinagar, Chandigarh, Jammu, Anantnag and Udhampur as various strata. We adopted geographic stratification to ensure that urban, semi-urban as well as rural customers are sampled for the study.

From 1987 customers approached, 968 were identified with a prior experience of service failure and who had registered a complaint with the bank. Of the 968 customers, 883 (91.22%) were handed over the questionnaires designed to capture their perception of customer engagement and recovery performance of the employee who redressed their grievance. The remaining did not wish to participate in the study.

Consequently, we distributed 794 questionnaires each to the frontline employees and their immediate colleagues. The frontline employees returned 678 (85.40%) filled questionnaires, while 674 (84.89%) questionnaires were received from the colleagues of frontline employees. During the preliminary data screening procedure, certain questionnaires were discarded because of inconsistency or incompleteness. The questionnaires for which the matching equivalents were missing were also discarded. As a result, the final sample consists of 837 customers, 649 frontline employees and an equal number of their colleagues.

In the final sample of customers, 456 (54.49%) were males and 381 (45.52%) were females. Among the frontline employees, males accounted for 52.54% (341) of the total sample and the remaining 308 (47.46%) were females. With regard to the colleagues of frontline employees, 347 (53.47%) were males and 302 (46.53%) were females. The average age of frontline employees in the sample is 33.16 years with average work experience equal to 6.47 years. The average age of the colleagues of frontline employees is 34.06 years and their average work experience is 7.34 years. Last, the average shared work experience between the frontline employees and their colleagues equals 3.17 years.

Research instrument

We measured employee proactive personality using the 10-item version of Bateman and Crant's (1993) Proactive Personality Scale (PPS). Sample item: Mr./Ms. _____ (Name of the frontline employee) is always looking for better ways to do things. Work engagement was gauged through the shortened nine-items of the Utrecht Work Engagement Scale (Schaufeli et al., 2006). The scale uses three items each for vigour, dedication and absorption. Sample items: At my work, I feel bursting with energy (Vigor); I am proud of the work that I do (Dedication); I am immersed in my work (Absorption). Customer-perceived service recovery performance was measured using three items from Wen and Chi (2013) study of airline passengers. The scale was linguistically adjusted to suit the requirements of this study. Sample item: the employee provided service recovery that met my needs. Finally, we used three items based on Sprott et al. (2009) to capture customer’s level of engagement with the service provider. Sample item: I like sharing my experience as a customer of this company with other customers.

Scale purification

The scale purification procedure involved the assessment of the psychometric properties of the research instrument. Specifically, we examined the validity (convergent, discriminant and nomological validity) and reliability of the scales used for data collection. Convergent validity of the instrument was ascertained using two measures (factor loadings and average variance extracted [AVE]); discriminant validity through one measure (maximum shared squared variance); and one measure was adopted to examine the reliability of the scales (Cronbach's alpha).

We constructed a measurement model in AMOS 23 with the observed indicators imposed on their respective latent constructs. The model depicted a satisfactory fit with the data (χ² = 139.21, p > 0.05, goodness-of-fit index = 0.91, adjusted goodness-of-fit index = 0.85, comparative fit index = 0.93, root mean square error of approximation = 0.05). The values of model fit indices suggest that the proposed model offers a reasonable explanation of the observed covariance matrix of the constructs investigated in this study.

The results of confirmatory factor analysis (CFA) indicated that the factor loadings of all the observed indicators are satisfactorily above the threshold level of 0.40 (Nunnally and Bernstein, 1994) and are statistically significant. Moreover, in line with Fornell and Larcker (1981), we also computed AVE to ascertain how much variance in each construct is explained by the items imposed on it. The results revealed that the AVE of all constructs is above 0.50. Taken together, the CFA and AVE results offer adequate support for the convergent validity of the research instrument.

In order to assess the discriminant validity of the research scale, we calculated maximum shared squared variance (MSV) and then compared it with AVE values. For discriminant validity, the MSV must be lower than AVE (Fornell and Larcker, 1981). As exhibited in Table 1, the AVE values for all constructs are greater than MSV. Hence, the research instrument exhibits no serious concern regarding discriminant validity. Further, Cronbach's alpha values were used to ascertain the reliability of the research instrument. The alpha values of all sub-scales were above the cut-off mark of 0.70 (Cronbach, 1951), thus indicating satisfactory internal consistency of the research instrument (Table 1). Finally, the inter-construct correlations (Table 1) aptly reflect the conceptual framework of theorized relationships, thereby supporting the nomological validity of the scale.

Furthermore, we used Harman's single-factor test (Podsakoff et al., 2003) to evaluate common method bias (CMB) for the self-perceived responses. The results indicated that the single underlying factor explained 32.68% of the variance in the items, thereby eliminating the possibility of CMB. Also, Pavlou et al. (2007) suggest that CMB is likely if the inter-construct correlations are unreasonably high (above 0.90) which is not the case for the present study (Table 1).

Data analysis and results

We tested the hypothesized relationships using structural equation modelling in AMOS 23. Our strategy for empirical analysis covers the estimation of the direct impact of (1) employee proactive personality on work engagement, (2) work engagement on service recovery performance, (3) service recovery performance on customer engagement and (4) Employee proactivity on customer engagement. Additionally, the mediating role of work engagement and service recovery performance in the employee proactivity – customer engagement relationship is tested.

The structural model of hypothesized relationships indicated a reasonable fit with the observed data as revealed through the model-fit indices (χ² = 94.32, p > 0.05, GFI = 0.93, AGFI = 0.87, CFI = 0.92, RMR = 0.031, RMSEA = 0.051). Further, the empirical results suggest that employee proactive personality positively influences work engagement (β = 0.42, p < 0.05) which in turn exerts a significant driver influence on perceived service recovery performance (β = 0.47, p < 0.05). Moreover, service recovery performance (β = 0.45, p < 0.05) and employee proactive personality (β = 0.37, p < 0.05) are found to be significant determinants of customer engagement (Table 2). Collectively, the above findings offer adequate support to accept hypotheses H1–H4.

While examining the mediating effects, it was observed that work engagement partially mediates the relationship between the proactive personality of employees and their service recovery performance (direct effect = 0.18 - p < 0.05, indirect effect = 0.20 - p < 0.01). Similarly, perceived recovery performance partially explains, by way of mediation, the positive impact of work engagement on customer engagement (direct effect = 0.17 - p < 0.05, indirect effect = 0.21 - p < 0.01). Finally, it was found that work engagement and perceived recovery performance operate as sequential mediators in the relationship between employee proactivity and customer engagement (direct effect = 0.271 - p < 0.05, indirect effect = 0.099 - p < 0.01). Consequently, the mediational hypothesis, H5, can be safely accepted.

Discussion

Notwithstanding the undeniable significance of customer engagement for the future of service organizations, the role of frontline staff in ameliorating customer engagement has, somehow, attracted limited research focus. Although some studies (e.g. Cambra-Fierro and Melero-Polo, 2017) do offer preliminary insight, this research domain is largely underexplored. Against this backdrop, the current research attempted to explore whether the proactive disposition of frontline service personnel in the Indian retail banking sector helps in nurturing customer engagement, specifically, under the service recovery context. If it does, what variables would explain such an effect.

Our primary hypotheses H1–H4 were confirmed, thereby, indicating that proactive frontline employees exhibit greater engagement in their work role which, in turn, enhances their performance while recovering from service failures. Once aggrieved customers feel that their grievances have been satisfactorily addressed, they tend to develop a stronger relationship with the service provider. The findings of this study fall broadly in line with Cambra-Fierro et al. (2014, 2016), and Cambra-Fierro and Melero-Polo (2017) – perhaps the only few empirical works relating frontline employee attributes with customer engagement.

Regarding the association between proactive personality, work engagement and service recovery, our results corroborate earlier findings of Zahoor (2020) and Bakker et al. (2012). Zahoor (2020) found that proactive personality positively influences work engagement and recovery performance. Bakker et al. (2012) also reported a significant driver influence of employee proactivity on work engagement. We extended these findings to emphasize the significance of the proactive personality of frontline staff for nurturing customer engagement. Our findings hold considerable value for marketing theory and practice because of the impact that customer engagement bears on the future of a business organization.

The principles of reciprocity theory and SET (Shore and Shore, 1995) offer a meaningful explanation of the transcending effect of frontline staffs’ proactivity on customer engagement. A delightful experience of service recovery generates a feeling of appreciation and inclines the customer to reciprocate the gratitude. And what better way to express the regard than developing a stronger relationship with the service provider and even act as an ambassador of the brand by sharing the pleasant experience with other customers.

In the context of SET, Hollebeek (2011) comments that a social exchange entails a favour (e.g. a satisfactory service recovery) by one party (e.g. a frontline service employee) to another (e.g. a customer) motivated by the expectation of a future return (e.g. engagement). Therefore, customers consciously decide to engage with the brand/service provider based on their perception of benefits and costs (Harrigan et al., 2017). Since, an effective service recovery, besides creating a positive feeling, remedies the costs/losses (economic, physical as well as psychological) of initial service failures (Zhou et al., 2014), it would certainly encourage the customer to decidedly engage with the service provider.

Managerial implications

The results of this study carry significant implications for marketing practitioners. First and foremost, banking companies must consider the meaningful role of their frontline staff in generating an engaged customer base who hold substantial value regarding the future of the company. As customer engagement improves corporate reputation and financial value of companies (Van Doorn et al., 2010) through non-transactional behaviours such as blogging and referrals, banks are convincingly advised to emphasize the type of employees manning their customer contact counters. Specifically, we recommend that employees with a proactive disposition must be preferred to occupy frontline desks in Indian retail banks. It would be particularly relevant because aggrieved customers first approach the frontline staff for rectification of service failures and the first-hand impression of a compassionate and efficient recovery effort might go a long way in nurturing a strongly engaged customer base.

Towards that purpose, Indian banks need to upgrade their recruitment and selection procedures to incorporate a mechanism for evaluation of the proactive tendency of the candidates. In addition, Robbins et al. (2009) suggest that, apart from heredity, human personality is also equally shaped by the environment. Based on the theory of interactionist psychology, Snyder and Ickes (1985) argue that an individual influence her environment as much as she is influenced by it. Taken together, these theories suggest that employee proactivity may be nurtured by suitable managerial intervention in the work environment of employees. Hence, not only should employee proactivity be emphasized during selection and recruitment procedures but Indian retail banking companies are recommended to design the work environment in a way that cultivates proactivity.

Since service recovery scenarios mostly entail customer-related stressors such as customers' ambiguous expectations, verbal aggression, etc. (Choi et al., 2014), service providers must focus on restraining the effects of such behaviours (Zahoor, 2020). As proactive individuals are highly engaged and experience lower exhaustion (Hakanen et al., 2006), they tend to deliver effective recovery and ensure a positive customer service experience which, ultimately, will be manifested through deeply engaged customers.

It is certainly arguable, but utopian at the same time, that service failures must not happen at the very first instance, which will eliminate the need for recovery altogether. However, a high level of human involvement in services renders mistakes inevitable and, consequently, a recovery indispensable. Particularly, owing to the Indian Government’s financial inclusion programme (Jan Dhan Yojna), retail banks are facing unabating customer contact which makes service failures unavoidable. Under such a condition, if service organizations in general and Indian retail banking companies in particular aim to garner the benefits of customer engagement, then service recovery management does not remain optional, instead, it must be deeply integrated into the overall service delivery framework.

Limitations and directions for future research

Like any other research work, this study is not entirely free from limitations. Firstly, we used cross-sectional data in this study and, therefore, causal inferences must be drawn with caution. Although, causality is not exclusively limited to longitudinal design and certain researchers (e.g. Wunsch et al., 2010) argue that cross-sectional studies also offer a reasonable basis to derive causal inferences. Essentially, causality is dependent on the modelling strategy being structural or not which, in turn, is based on the background knowledge, recursive decomposition and invariance (Wunsch et al., 2010). Even though our background literature is considerably supportive of these conditions, a relatively robust longitudinal research design would certainly generate further insight into the dynamics of employee proactivity – customer engagement linkage.

Secondly, our study evidence that employee proactivity nurtures customer engagement by improving work engagement and recovery performance. However, we did not delve into the factors that determine employee proactivity. As such, future research must focus on how the work environment in banks can be designed to cultivate proactivity.

Thirdly, previous research indicates that more than personality, individual performance is improved by the enactment of personality (Daniel, 2006). Based on this evidence, it would be interesting to investigate the influence of proactive behaviour (e.g. job crafting) on employee service recovery performance. It is expected that manifested proactive behaviours might explain variance in recovery performance above and beyond proactive personality.

Finally, primary data for this study was gathered from only the northern region of India and only from the banking sector. Though the banking sector is a significant driver of economic growth and prosperity of any nation is largely dependent on the efficiency of its banking operations (Ehimare, 2012), this study still needs to be replicated in other contexts to provide a more reasonable basis for generalization of results.

Figures

Figure 1

Proposed model of structural linkages

Table 1

Descriptive statistics, inter-construct correlations, reliability coefficients, AVE and MSV

Constructs	AVE	MSV	Proactive personality	Work engagement	Service recovery performance	Customer engagement
Proactive personality	0.61	0.22	0.80
Work engagement	0.53	0.18	0.43*	0.76
Service recovery performance	0.51	0.15	0.46*	0.39*	0.79
Customer engagement	0.52	0.22	0.47*	0.42*	0.39*	0.81
Cronbach's alpha			0.86	0.81	0.84	0.79
Mean			3.54	3.67	3.69	3.85
Standard deviation			0.614	0.706	0.693	0.677

Note(s): *p < 0.05, AVE = Average variance extracted, MSV = Maximum shared variance

Source(s): Data compilation by the authors

Table 2

Standardized estimates of direct and mediating effects

Paths	Direct effect	Indirect effect	Critical ratio	p-value
Proactive personality → Work engagement	0.42		13.429	<0.05
Work engagement → Service recovery performance	0.47		14.358	-do-
Service recovery performance → Customer engagement	0.45		14.157	-do-
Proactive personality → Customer engagement	0.37		12.018	-do-
Proactive personality → Work engagement → Service recovery performance		0.20	8.327	<0.01
Work engagement → service recovery performance → Customer engagement		0.21	8.973	-do-
Proactive personality → Work engagement → Service recovery performance → Customer engagement		0.099	6.236	-do-

Source(s): Data compilation by the authors