Moderating effects of zone of tolerance in automated service quality-behavioural intention relationship: A study using structural equation

modeling and CRM indexing.

Dr. Arup Kumar Baksi, Asst. Prof., DMS, BITM

Prof. (Dr.) Bivraj Bhusan Parida, Professor, Dept. of Tourism Mgmt., The University of Burdwan

Review of literatures revealed that service quality has emerged as a critical factor, as service industries continue to dominate world economy, for the service providers, particularly keeping in mind the intangibility and heterogeneity aspects of services which are potential inhibitors in perceptualizing services and their quality aspects.Over the years academic researchers explored the dimensionality of service quality. Zeithaml (1985,1988, 1991), Cronnin & Taylor (1992, 1994), Gronroos (1982,1984), Leonard and Sasser (1982), Rust & Zahorick (1993), Avkiran (1994) initiated the process of identifying the dimensions of service quality and the research is still on as technology has been integrated with the service design and delivery mechanism. Integration of Technology has changed the entire perspective of service design and delivery mechanism and the subsequent perception of service quality. Researchers explored the dimensions of ASQ

Research gap identified:1.dearth of research input in linking automated service quality (ASQ) and behavioural intention (BI).2.lack of elaborate research to assess the effect of ZOT on ASQ-BI relationship.3.dearth of research in indexing CRM performance and analysing its effect on customer satisfaction (CS) and perceived value (PV)

Objectives of the study:1.to understand the dimensionality of ASQ2.to examine the impact of perceived ASQ on BI3.to understand the impact of dimensions of ASQ on CS & PV4.to identify the possible relationship between CRMI and CS & PV5.to examine of effect of ZOT towards ASQ-BI link

Formulation of hypothesesH1: Automated service quality comprises of two dimensions namely core and recovery.H2: Automated service quality has an impact on behavioural intention of customers.H3:Core automated service quality shares relationship with customer satisfaction and perceived value.H4:Recovery automated service quality shares relationship with customer satisfaction and perceived value.H5: Customer satisfaction and perceived value were dependent on CRM index.H6: The relationship between core automated service quality and : a) behavioural intentions; (b) satisfaction; (c) perceived value and (d) CRM-index, is significantly high above and below the ZOT level compared to within the range of ZOT.H7: The relationship between recovery automated service quality and : a) behavioural intentions; (b) satisfaction; (c) perceived value and (d) CRM-index, is significantly high above and below the ZOT level compared to within the range of ZOT.

ASQ CS PV BI

CRMI

Proposed Research Model:

Methodology:• Industry chosen : Banking• Firm chosen : State Bank of India• Area of survey : Asansol, Durgapur & Bolpur• Research philosophy : Interpretivism• Research approach : Deductive• Research strategy : Survey• Time horizon : Cross-sectional• Data collection method : Sampling, Questionnaire• Sampling technique : Systematic Simple random• Scale used : 7 point Likert scale• Other scales used : Modified E-SERVQUAL (Zeithaml et al, 2005), Behavioural Intention Battery (BIB) (Zeithaml, 1996)

Modified 12-item satisfaction scale (Oliver, 1980),

Single-item ‘Perceived Value’ scale (Yap & Sweeney, 2007)• Sample size : 1560

Construct development for CRM-index

Peffers and Dos Santos (1996) developed a process for measuring the impact of information technology on overall performance of a bank using an S-shaped logistic model:

where y is the benefit of the technology application at time t, m is the upper bound on the benefits of the application, and a and b are constants that determine the shape of the curve.

 

Using similar logic to deduce CRM index (CRMI) with an assumption that CRMI will improve with improvement in performance of CRM components over time ‘t’, can be represented as: --1

Solving for eqn.1 for CRMI, --2

Equation-2 represents a S-shaped logistic model where 1 is the upper-bound on the CRMI from the CRMCP performance. It is assumed that the constant ‘a’ is zero since each service provider is supposed to initiate CRM induced services with a negligible CRMI and hence: --3

)1( 1 tCRMICRMCPdt

dCRMI

tCRMCPaeCRMI

1

1

tCRMCPeCRMI

1

1

Construct development for CRM-index (contd.)

The term CRMCP is a function of the relative weight of the eigenvalue (RWE) of each CRM component multiplied by the average factor value (AVF) of the corresponding CRM component.

where, CRMCP1 = People dimension, CRMCP2 = Process dimension andCRMCP3 = Technology dimension

332211 CRMCPCRMCPCRMCPCRMCPCRMCPCRMCP AVFRWEAVFRWEAVFRWECRMCP

Data analysis and interpretation:

Exploratory factor analysis (EFA) was employed using principal axis factoring procedure with orthogonal rotation through VARIMAX process to understand the dimensionality of automated service quality. The factor loadings were restricted in two components and were nomenclated as:1.Core dimension of ASQ: (efficiency, web-system, commitment and security)2. Recovery dimension of ASQ: (responsiveness & contact)Results supported H1 emphasizing dual dimensionality of

ASQPairwise correlations for constructs in the study were obtained to understand the relationship between Core and Recovery dimensions of ASQ and BIB dimensions and between Customer satisfaction & Perceived value. Significant correlation was found at both 0.01 and 0.05 level (2-tailed)

Mean (sd) 1 2 3 4 5 6 7 8

1. Core 6.77 (1.19)

2. Recovery 5.98 (1.29) 0.56**

3. Loyalty 6.26 (1.43) 0.46** 0.32**

4. Switch 4.12 (1.69) -0.52** -0.31** -0.41**

5. Pay more 5.99 (1.21) 0.45** 0.21** 0.17* 0.23**

6. Int. complain 5.41 (1.33) 0.38** 0.29** 0.21** 0.13* 0.28**

7. Ext. complain 4.81 (1.44) -0.07 -0.19* -0.17* -0.11 -0.09 -0.15*

8. Satisfaction 6.75 (1.12) 0.51** 0.41** 0.36** 0.28** 0.21* 0.49** 0.44**

9. Perceived value 6.18 (1.34) 0.29** 0.33** 0.18* 0.51** 0.19* 0.29** 0.37** 0.33**

**Correlation significant at 0.01 level (2-tailed), *Correlation significant at 0.05 level (2-tailed)

Table:1: Pairwise correlations for constructs

To test the hypothesis H2- H7, composite variables were generated by obtaining the mean rating for all constructs across all the items for the desired and adequate level of service response. Zeithaml et al’s (1996) approach was adopted which was successfully implenmented by Yap and Sweeney (2007) in developing dummy variables to indicate the individual respondent’s perceptions of core and recovery automated service quality location both within and outside the ZOT. The relationship between automated service quality and outcomes across the ZOT for both core and recovery dimensions can be defined as:X = β0 + β 1 (Core) + β 2 (d1Core) + β 3 (d2Core) +εX = β 0 + β 1 (Recovery) + β 2 (d3Recovery) + β 3 (d4Recovery) +εWhere,X = composite scores for behavioural intentions, satisfaction and perceived valueCore=composite score for respondents’ perception of core dimension of automated service qualityRecovery = composite score for respondents’ perception of recovery dimension of automated service qualityd1/ d3=1, when perception of core/recovery quality is below acceptable level, 0, otherwise.d2/ d4=1, when perception of core/recovery quality is above acceptable level, 0, otherwise.β 1, β 2 and β 3 = unstandardized regression coefficientsβ 0 = constant in the equationε = error term

In the above stated equations, the slope inside the ZOT is β 1, for below the ZOT level it is β 1+β 2 and for above the level of ZOT β 1+β 3.

CRM is considered to be an effective integration of people process and technology which synchronizes well with the service market trinity with technology playing the role of a driver integrator. 17 CRM variables across these 3 dimensions were considered for the study.Factor analysis validated the measures used for Customer Relationship Management Index (CRMI) namely its three components people, process and technology. Exploratory factor analysis was deployed using orthogonal rotation. The reliability index was obtained as >0.70. The convergent validity was found to be >0.60 for all the items. Factor loading <.50 were discarded.

The relative weight of eigenvalue (RWE) and average factor value (AFV) were obtained for calculating the CRMI.

The CRMI was calculated as: 0.34

Multiple regression analysis provided support for H2 , H3 ,H4 ,H6 & H7

Dependent variables

Independent variables

Slope within the range of

ZOT

Changes in slope below

ZOT

Changes in slope above

ZOT

Core Recovery Core Recovery Core Recovery

Loyalty 0.59** 0.42** -0.07 -0.08 0.19* 0.08

Switch -0.02 -0.10 -0.23** -0.11* -0.09 -0.08

Will-to-pay-more 0.32** 0.17* 0.03 -0.04 0.07 0.10

Internal complain 0.19* 0.21* 0.01 -0.03 0.09 0.06

External complain -0.09 -0.04 -0.13* -0.14* 0.02 0.07

Satisfaction 0.47** 0.31** -0.11 0.04 0.10 0.12*

CRMI 0.67** 0.42** -0.06 -0.05 0.11 0.09

Perceived value 0.37** 0.17* -0.07 -0.09 0.06 0.08

# Standardised coefficients betas were considered, ** p<0.01, *p<0.05

To test Hypothesis-5, bivariate correlation was deployed to assess the relationship between customer satisfaction (CS-dependent variable) and CRM index (CRMI-independent variable). The Pearson coefficient (r) (r=.421**, p<.001) revealed a strong and positive correlation between customer satisfaction (CS) and CRM-index (CRMI) suggesting that higher the CRMI, higher will be the customer satisfaction.

    CS CRMI

CSPearson Correlation 1.000 .421**

Sig. (2-tailed)   .000

N 1560 1560

CRMIPearson Correlation .421** 1.000

Sig. (2-tailed) .000  N 1560 1560

To further asses the strength of associationship between the variables and to examine the predictive capacity of CRMI to predict customer satisfaction (CS) regression analysis was deployed.

Model Summary ANOVA Regression coefficients

R R2 adjusted R2 f sig β t sig.

.639 .408 .406 232.116 .000 .548 19.619 .000a. Dependent variable: Customer satisfaction, b. Predictor: CRM index (CRMI)

Structural Equation Modeling was used to test the nomological validity of the proposed model. CFA was used to understand the dimensionality, convergence and discriminant validity for each construct. SEM and MLE was applied to estimate the CFA models. The fit-statistics were found to be acceptable. The research model holds well as the fit-indices supported adequately the model-fit to the data.

COR1

COR1

COR1

COR1

REC1

REC2

ASQ CS

BI+

BI-

PV

CS1 CS2 CS3 PV1 PV2

CRMI

CRMI1CRMI2

λ1=1.00

λ2=0.81

λ3=0.67

λ4=0.73

λ5=0.89

λ6=0.82

Є1-1.16

Є2-1.19

Є3-1.09

Є4-1.24

Є5-1.10

Є6-1.27

λ7=1.00

λ8=0.67 λ9=0.71

λ10=1.00

λ11=0.84

λ12=1.00 λ13=.91

λ14=1.00 λ15=0.87λ16=1.00

λ17=0.71

Є7-1.01 Є8-1.17 Є9-1.12 Є10-1.09 Є11-1.01

Є12-1.31 Є13-1.29

Є14-1.22

Є15-1.19

β1=0.89

β2=0.83

β3=0.79

β4=0.81β5=0.69 β6=0.67 β7=0.71

β8=0.91

β9=0.93

Structural model showing the path analysis using SEM

Conclusion, Managerial implication and Future options:

• The study revealed that automated service quality primarily consisted of core and recovery dimensions as was identified by Zeithaml et al (2005). • The study further confirmed that both core and recovery automated service quality had a significant effect on loyalty, satisfaction and perceived value and expressed a significant and negative effect on propensity to switch and external complaints. • Core automated service elements were found to be more effective on loyalty dimension while recovery elements induced strong satisfaction. • CRM index was found instrumental in predicting the level of customer satisfaction and was found to be directly proportional. • The study confirmed the effect of ZOT on automated service quality-behavioural intentions link, but the effect were limited to the range of ZOT only. The link did not hold good above the level of ZOT. The negative connotations of behavioural intentions namely propensity to switch and external complaints were found to have negative and significant link with automated service quality below the ZOT range.

Conclusion, Managerial implication and Future options (contd.):

The study found that both the core and recovery automated service quality dimensions significantly enhanced customer satisfaction and perceived value leading to increase in positive behavioural intent like loyalty, will-to-pay-more and decrease the negative behavioural intentions namely propensity-to-switch and external complains within the range of ZOT and particularly when the services were perceived to be higher than the adequate range of acceptance. The bankers must note that if customers fail to perceive the automated service quality above the minimum level, the effectiveness any kind of service augmentation decreases. Further to this it was often found that the service providers are interested to push the service level high enough to stimulate the customers perceive beyond the desired level which the study did not confirm. Therefore the service providers must be absolutely sure about the cost involvement towards driving the service level beyond the desired level.

The study was limited to a single industry (bank) and it was cross-sectional with reference to specific demographic locations. Therefore generalized inference and applications may not be possible. Future research works may be taken up with larger sample size with adequate research extrapolations in the area of identifying Customer Life-time Value, Customer equity etc. in the context of the link between automated service quality-behavioural intentions both above and below the ZOT. Further to this comparative shift in ZOT level can be studied by taking multiple homogeneous service providers into consideration.

Conclusion, Managerial implication and Future options (contd.):

Thank You