Linking Consumer Trust, Repatronization and Advocacy with Intervention of Perceived Service Recovery and Zone-of-Tolerance

 

Dr. Arup Kumar Baksi

Associate Professor,  Dept. of Management and Business Administration, Aliah University, Kolkata, West Bengal

 

ABSTRACT:

The purpose of this study was to investigate the moderating effects of perceived service recovery process and zone-of-tolerance on consumer trust and its subsequent impact on consumer advocacy and initiation of repatronization. The study was carried out in the banking sector in India with State Bank of India (SBI); the largest nationalized bank was taken as a case. Results indicated that moderate to high perceived service recovery, initiated by SBI in response to perceived first-time service failures, is associated with elevated level of consumer satisfaction at the post service-failure phase, positive customer advocacy and intent to repatronize. Zone-of-tolerance was also found to have mediating effects on the relationship between perceived service recovery, customer trust and repatronization.

 

 

 

INTRODUCTION:

Service failures are inevitable and are considered to be damaging for service providers as it may trigger customer defection (Maxham III, 2001). Failure in service delivery may also increase the cost of acquiring new customers (Hart et al., 1990) which may subsequently influence in drooping profits (Smith et al, 1998). Empirical evidence was found by Zemke, 1999 that dissatisfied customers encountering service failures may engage in negative word-of-mouth, thereby, inhibiting prospects from patronization. Researchers found empirical support to the fact that satisfactory service recovery may reinforce customer satisfaction in the post-recovery phase, which, in certain cases, may surpass the degree of satisfaction in the pre-service failure phase – a phenomenon coined as ‘recovery-paradox’ (McCollough et al., 2000).

 

Considering the failure-prone nature in service delivery, it is absolutely imperative for a service firm to ensure quick response in the face of adversity namely customer dissatisfaction as dissatisfaction leads to negative advocacy. Research evidences are insufficient to significantly correlate customer advocacy with the marketing initiatives taken up by firms. However, zone-of-tolerance (ZOT), serving as a cognitive boundary of acceptable service standards, may, along with perceived service recovery efforts, affect the overall behavioral patterns of customers. The objectives of this paper are two-fold. It desires to understand the relationship that customer trust, repatronization and advocacy shares in a service-failure-service recovery ecosystem. In addition it also aims to assess the intervening effects of perceived service recovery and zone-of-tolerance on the aforesaid relationship. Service delivery has been considered to be error-prone (Grönroos 2006). Service failures would result in both tangible (physical stress) and intangible (emotional and cognitive stress) losses for the customers (Grönroos, 2006) which can lead to severe customer defection (Maxham III, 2001). To arrest this attrition rate the service firms must ensure service recovery encompassing the physical and cognitive dimensions of   the customers, thereby, helping them recover from physical stresses and regaining trust and confidence (Schweikhart et al 1993; Kenney, 1995; Miller et al 2000). Customers often fall back to register complain owing to a perceived service failure. The sustainability of customer-firm relationship is determined by the promptness and effectiveness of the complaint-redressal system launched by the service provider Morgan and Hunt (1994). According to DeWitt (2008), on one hand, complaints provide the service firms with an opportunity to reinforce the relationship with the aggrieved customers by assuring effective recovery mechanisms and on the other allow the service provider to refabricate the service offer to avoid further failures. Empirical evidence was found to understand the nature of ‘service-recovery’ as a pool of dynamic and robust marketing initiatives to regain the trust of customers who have encountered a service failure, or, in other words, have failed to accept the service offer as per their acceptable cognitive limits i.e. zone-of-tolerance (ZOT) (Baksi and Parida, 2013) . ZOT may be considered to be an effective signaling mechanism to understand the time to initiate a service-recovery. Researchers also found ZOT to be effective in identifying changes in the relationship between perceived service quality and its consequences in terms of behavioral manifestations by the customers (Teas and DeCarlo, 2004; Zeithaml, 1996; Liljander and Strandvik, 1993). However, literature did not reveal substantially the changes in the relationship between perceived service quality and behavioral intentions of the customers across the layers and limits of ZOT i.e below, over and within ZOT (Zeithaml et al, 1996, Teas and DeCarlo, 2004). But researchers did recognise the ability of the ZOT concept in understanding the variable degree of perceived service quality and its vis-a-vis consequences (Teas and DeCarlo, 2004; Walker and Baker, 2000; Zeithaml, 2000; Voss et al, 1998). To ensure that customer trust is regained the service provider may decide upon a viable service-recovery mix to gain cognitive-control over the dissatisfied customers. The major recovery strategies may range from addressing core-service failures to terminal failures and can focus on compensation, physical replacements, future assurance, maintaining communication etc. (Johnston and Michel, 2008; Luo and Homberg, 2007, 2008;  Rust and Chung, 2006, Yousafzai et al 2005; Hess et al 2003; Maxham and Netemeyer, 2002 Davidow, 2000).  Since service transactions incorporate a critical and complex mix of social, psychological, and consumer behaviors, it is expected that the cultural alignment of the customer will have an impact on the perception of service failures and its corresponding evaluations of the service recovery attempted. (Baksi and Parida, 2012). Studies have correlated satisfactory service recoveries with regaining and reinforcement of customer trust; repurchase intention and long-term loyalty intentions (Maxham and Netemeyer, 2000, 2003; Smith et al, 1999; Blodgett et al 1997). Maxham (2001) observed that effective service recoveries can rejuvenate customer satisfaction, repurchase intention and engaging in positive word-of-mouth. Service recovery being a relatively new management focus, there is dearth of adequate research addressing the pros and cons of the recovery concept (Baksi and Parida, 2013). Literatures remained absolutely inconclusive with regard to ZOT as a moderating tool to relate customer trust-advocacy-repatronization link. Further to this, no research work has been carried out to conceptualize ZOT for service recovery too where alike service quality, there can be an existence of adequate and desired level of service quality which may share relationship with customer trust, repatronization and advocacy.

 

2.1 Formulation of hypotheses and research model framework:

The review of literature allowed the researchers to frame the following hypotheses keeping in mind the focal objectives of the study:

H₁: Customer trust, repatronization and customer advocacy are dependent on perceived service recovery.

H₂: Customer trust, repatronization and customer advocacy are influenced by zone-of-tolerance.

H₃: Customer trust, repatronization and customer advocacy vary across the layers of zone-of-tolerance.

H₄: Higher perceived service recovery shall have a stronger effect of zone-of-tolerance on the relationship between customer trust, repatronization and customer advocacy.

H₅: Broader band of ZOT shall have stronger effect of perceived service recovery on the relationship between customer trust, repatronization and customer advocacy.

 

The researchers proposed the following conceptual (default) model for empirical testing (Fig.1):

 

Fig.1: Proposed research model

 

MATERIAL AND METHODS:

State Bank of India (SBI) was chosen as the premise of the study. Twenty five branches of SBI were selected that spread across ten prominent locations in southern part of West Bengal namely Asansol, Durgapur, Burdwan, Bankura, Bolpur, Siuri, Ranigunj, Hooghly, Chandannagar and Memari. The structured questionnaire was obtained after refinement by a pilot study. The researchers used systematic random sampling based on the database of customers accessed with permission from the bank branches. Service recovery was measured with a scale consisting of 29 items used by Kau and Loh (2006). The study adopted a 3-item scale as a measurement of customer trust (Baksi and Parida, 2013; DeWitt et al 2008). The measurement of repatronization was done by using a 4 item scale (Baksi and Parida, 2013; Maxham-III, 2001) while customer advocacy was measured also by a 4 item scale (Maxham-III, 2001). ZOT was measured by modifying Zeithaml et al’s (1996) usage of generating perception of services. The questionnaire used a 7 point Likert scale (Alkibisi and Lind, 2011) to generate response. 2000 questionnaires were used for the study.  A total number of 1589 usable responses were generated with a response rate of 79.45% (approximately). The internal reliability and validity of the measurement constructs were assessed by deploying exploratory factor analysis (EFA) by using the principal axis factoring process. It embarked on orthogonal rotation using VARIMAX. The discriminant validity, dimensionality and convergence of each factor construct were examined by applying confirmatory factor analysis (CFA). Structural Equation Modeling (SEM) with Maximum Likelihood Estimation (MLE) was applied to estimate the CFA models. IBM-SPSS 21 and LISREL 8.8 were used for the analytical part.

 

Data Analysis and Interpretation:

The results of the EFA were displayed in Table-1. Cronbach's alpha was found to be significant i.e. >.7, (Nunnally and Bernstein, 1994) for all constructs allowing the researchers to conclude that the internal consistency of the measuring instruments used were adequate.  The construct reliability (CR) measured consistently well over .6 (Hair et al., 1998). The average variance extracted (AVE) surpassed minimum requirement of .5 (Haier et al., 1998). The KMO measured 0.907 confirming that sample size is adequate to perform EFA Hutcheson and Sofroniou, 1999).  Barlett’s sphericity test (Chi-square=1532.209, df=289, p<0.001) indicated absence of homoscedasticity and fit for data reduction (Cooper and Schindler, 1998). Perceived service recovery scale was reduced to 12 items. EFA revealed significant factor loading for customer trust (3 items), repatronization (2 items) and customer advocacy (3 items).

 

Table-1: Measurement of reliability and validity of the variables

 

The researchers opted for bivariate correlation analysis to understand the relationship between the variables under study. The composite means were obtained across the constructs for each variable. The results of bivariate correlation analysis (Table-2) partially supported H₁, and H₂.

 

Table: 2 Results of bivariate correlation analysis between the major variables

Variables	Perceived service recovery(PSR)	Zone-of-tolerance (ZOT)	Customer trust (CT)	Repatronization (REP)	Customer advocacy (CA)
Perceived service recovery (PSR)	1
Zone-of-tolerance (ZOT)	0.201**	1
Customer trust (CT)	0.297**	0.109*	1
Repatronization (REP)	0.331**	0.061	0.185**	1
Customer advocacy (CA)	0.154**	0.098*	0.205**	0.266**	1

**Correlation significant at 0.01 level (2 tailed),

*Correlation significant at 0.05 level (2-tailed)

 

Multiple regression analysis with dummy variables was deployed. The usage of dummy variable was justified as a mix of scales, both interval and attitudinal, were used to understand the probable impact of perceived service recovery on customer trust, repatronization and advocacy across the three stages of ZOT. The following regression equation was obtained:

 

X₁/ X₂/ X₃ = β₀ + β₁ (PSR) + β₂(d₁*PSR) + β₃(d₂*PSR) + ε₁

where,    X₁ = CT, X₂ = REP, X₃ = CA, PSR = PSR, d₁  = 1, when PSR<adequate level, 0 otherwise, d₂  = 1, when PSR>desired level, 0 otherwise, β₁, β₂, β₃ = unstandardized regression coefficients., β₀ = constant in the equation, ε  = error term

 

The slope inside, below and above the ZOT are represented by β₁, β₁+ β₂ and β₁+ β₃ respectively. The results (Table-3) displayed a significant rise in CT, REP and CA above the ZOT level while the customers remain relatively insensitive within the ZOT width.

 

Table:3 Regression results across ZOT levels

** indicates p<0.01, * indicates p<0.05

 

The results of the regression analysis (Table-3) supported H_3.

To have an understanding about the intervening effects of PSR and ZOT on customer trust, repatronization and customer advocacy, hierarchical regression analysis was used. Three regression models were obtained and the major focus was given on the binary effects:

 

(a) CT = β₀ + β₁*PSR + β₂*ZOT + β₃*PSR*ZOT + ε_i

(b) REP = β₀ + β₁*CT + β₂*PSR + β₃*ZOT + β₄*CT*PSR + β₅*CT*ZOT + β₆*PSR*ZOT β₇*CT*PSR*ZOT + ε_i

(c) CA = β₀ + β₁*CT +β₂*REP + β₃*PSR + β₄*ZOT + β₅*CT*PSR + β₆*CT*ZOT + β₇*REP*PSR + β₈*REP*ZOT + β₉*CT*PSR*ZOT + β₉*CT*REP*PSR*ZOT + ε_i

 

The results of hierarchical regression analysis were displayed in Table-4. Model-1 depicted direct effects which confirmed acceptance of H₁ and H₂ as PSR and ZOT were found to have a significant and direct impact on CT, REP and CA. Model-2 and 3 represented the binary interaction effects and lend support to H₄. Results indicated that with the increase in customers’ PSR the impact of ZOT on CT (β = .138**, p<0.01), REP (β = .188**, p<0.01) and CA (β = .097**, p<0.01) increases. Model 4 revealed only one significant ternary interaction between variables. It showed that customers’ decision to repatronize service provider will be strengthened under influence of ZOT if PSR imparts a direct and positive impact on CT (β = .099**, p<0.01). It was also revealed that CA remained unaffected under the influence of ZOT and PSR with repurchase intention. Model 5 represented the only quaternary interaction suggesting that customer advocacy will significantly increase under the combined effects of PSR and ZOT with increased customer trust and intention to repatronize (β = .116**, p<0.01). The results of binary, ternary and quaternary interaction between the variables confirmed H₄ and H_5.

 

Table-4: Regression models testing the interaction effects (equation-1)

Indep. Variables	Dependent variable: Customer Trust
	Model-1 β (t value)	Model-2 β (t value)	Model-3 β (t value)	Model-4 β (t value)	Model-5 β (t value)	VIF
PSR	.198**					2.466
ZOT	.093*					1.699
Binary interaction effects
PSR*ZOT		.138**				2.444
Adjusted R2	.544	.489
F-value	89.46	106.83
Indep. Variables	Dependent variable: Repatronization
CT	.218**					2.344
PSR	.163**					1.945
ZOT	.017					1.805
Binary interaction effects
CT*PSR		.298**				2.721
CT*ZOT			.210**			2.812
PSR*ZOT			.188**			1.993
Ternary interaction effects
CT*PSR*ZOT				.099**		2.001
Adjusted R2	.492	.499	.516	.519.
F-value	98.32**	89.41**	76.31**	73.62**
Indep. Variables	Dependent variable: Customer advocacy
CT	.176**					1.889
REP	.101**					2.119
PSR	.081*					2.005
ZOT	.018					1.912
Binary interaction effects
REP*PSR		.100*				1.699
REP*ZOT			.027			1.599
PSR*ZOT			.097**			2.385
Ternary interaction effects
REP*PSR*ZOT				.020		1.603
Quaternary interaction effect
CT*REP*PSR*ZOT					.116**	2.431

a. Dependent variable: CT, REP, CA,   b. Independent variable: PSR, ZOT, CT (for 1^st eqn.)

 

Structural equation modeling was applied to estimate the CFA models. A number of goodness-of-fit statistics were obtained. The GFI (0.987) and AGFI (0.981) scores (Table-5) for all the constructs were found to be >.900 indicating a good fit has been achieved (Hair et al, 1998). The CFI (0.973) and RMSEA (0.059) confirmed adequate model fit as per Bentler, 1992. The Chi-square (χ²=865.16, df=199, p=0.000) is significant at p<0.001 . The structural model was shown in Fig.2.

 

Table-5: Goodness-of-fit indices

 

 

Fig.2: The final structural model showing the moderating effects

CONCLUSION:

The study portrayed a significant and direct impact of perceived service recovery on behavioral intentions of customers namely customer trust, repatronization and customer advocacy. In addition to recovery initiatives, zone-of-tolerance also proved to be a significant predictor of the variables representing behavioural intentions. Perceived service recovery was found to have intervening effects on the relationship between customer trust, repatronization and advocacy. Trust, repurchase intention and advocacy as behavioral expressions were found to be significantly increased under the mediating effects of perceived service recovery. The study further revealed that customers remain relatively insensitive across their ZOT, while their behavioral intentions significantly vary both above and below the levels of their ZOT. ZOT was also found to have mediating effects on the behavioral triangulation under study. The managerial implications of the study centred on the technology driven complex banking services and the increased rate of failures in service delivery. Personalized services have created assorted ZOTs for individual customers which are critical determinant of positive behavioral intentions. Therefore for a banker it has become imperative to track down the recovery action to ensure customer satisfaction.  The study can have future extrapolations by including other variables that may impact zone-of-tolerance level namely relationship inertia, switching cost, loyalty etc. and can be laterally expanded to include other service sectors for a generalized conclusion.

 

CONFLICT OF INTEREST:

The authors declare no conflict of interest.

 

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