Benchmarking: An International JournalAn analysis for service quality enhancement in electricity utility sector of India by SEMSuchismita Satapathy

Article information:To cite this document:Suchismita Satapathy , (2014),"An analysis for service quality enhancement in electricity utility sector ofIndia by SEM", Benchmarking: An International Journal, Vol. 21 Iss 6 pp. 964 - 986Permanent link to this document:http://dx.doi.org/10.1108/BIJ-10-2012-0071

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An analysis for service qualityenhancement in electricity utility

sector of India by SEMSuchismita Satapathy

School of Mechanical Engineering, KIIT University, Bhubaneswar, India

Abstract

Purpose – The purpose of this paper is to develop a new model – namely service qualityenhancement – in the electricity utility sector of south India and to test this model’s fitness.Design/methodology/approach – Effective customer satisfaction investigation is a very importantprecondition for a power supply enterprise to win in the market competition. The problems that needto be solved for the power supply enterprise are how to use advanced and practiced methods toevaluate electricity customer satisfaction, and how to use results of the evaluation to improve theirservice. Planning and operating modern electric power systems involves several interlinked andcomplex tasks. In this paper, structural equation modeling (SEM) is applied to the electricity utilityservice model to verify service quality satisfaction. To determine the scope of the electricity industryand its relationship with overall service quality, a questionnaire-based survey was conducted and astandard questionnaire was designed for all customers in different groups of stakeholders(i.e. domestic, industrial, agricultural, public organization). To investigate the respondents’ perceptionsregarding the service quality of the electricity utility industry, 200 questionnaires were sent, and fromthese 169 responses were collected, consisting of 53 responses from the domestic sector, 43 industrial,30 agricultural, and the remaining 43 from public organization consumers. Then a model wasconstructed using SEM and tested by Amos 18 to verify the casual relationship between the measureddimensions and the electricity service quality.Findings – The results indicate that electricity service has a direct relationship with the dimensionsof reliability, tangibility, empathy responsiveness, assurance, security and stability.Originality/value – In this era electricity is the vital need, but due to regular interruptions in theelectricity service, customer dissatisfaction is the measured issue. At the current time, the Indianelectricity utility sector enjoys a monopolistic business. Therefore, there is hardly any effort to improvethe service quality in this sector due to a lack of administrative pressure and reformation measures.Indian electricity consumers face many quality-related problems as far as the distribution of electricityis concerned. In order to assess service quality in electricity distribution, a survey has been conductedin this work to extract quality constructs from a customer’s perspective. One of the contributions ofthis work is that on the basis of results obtained from factor analysis of the survey data, an instrumenthas been proposed for the first time in the Indian context, consisting of seven dimensions (reliability,tangibility, empathy, responsiveness, assurance, security and stability). This instrument would help tomeasure the service quality of the Indian electricity utility sectors, and it provides insights formanagers and administrators to set a service standard to fill the service gap. SEM is a tool is used forconfirmatory and exploratory factor analysis. It is used to test and validate standard instruments.Through confirmatory and exploratory factor analysis using SEM, the proposed service quality modelhas been tested and validated for practical acceptance in Indian electricity industry.

Keywords Service quality, Structural equation modelling, Indian consumers,Customer service quality, Electricity service quality

Paper type Research paper

The current issue and full text archive of this journal is available atwww.emeraldinsight.com/1463-5771.htm

Received 31 October 2012Revised 27 February 2013Accepted 18 March 2013

Benchmarking: An InternationalJournalVol. 21 No. 6, 2014pp. 964-986r Emerald Group Publishing Limited1463-5771DOI 10.1108/BIJ-10-2012-0071

The author extends her heart-felt gratitude to the Editor-in-Chief of Benchmarking: AnInternational Journal and anonymous reviewer(s) for their constructive suggestions that helpedto improve the literal and technical content of this paper.

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1. IntroductionService is positioned at the center of economic activities in any society. As a matter offact, no economy can function without the infrastructure that service organizationsprovide in the form of transportation, education, health care and utilities (Fitzsimmonsand Fitzsimmons, 2000). In recent years, we have witnessed a major evolution amongindustrial nations, as they have evolved from being primarily manufacturing-based toprimarily service based. Therefore, knowing how to manage a service organizationeffectively has become a priority. Due to the increasing needs for better direction andmanagement of service organizations, operations management researchers andpractitioners have started to apply operations management concepts and techniquesthat have been developed in manufacturing sectors to service industries (Heineke,1995; Buler et al., 1996; Fitzsimmons and Fitzsimmons, 2000). Understanding theimpact of service-encounter constructs such as physical good quality, service quality,and the services cape on behavioral intentions has preoccupied services researchersfor more than two decades (Lovelock, 1983; Shostack, 1977). In addition to the vastamount of support found in service quality literature (e.g. Boulding et al., 1999;Cronin and Taylor, 1992; Zeithaml et al., 1996), the idea that customers prefer greaterservice quality is intuitive, particularly if price and other cost elements are heldconstant. Numerous models have been developed to measure customer perceptionsof service quality. Most of these models utilized face-to-face interaction betweencustomers and the employees of service providers to conceptualize a service qualitymeasurement model.

Service industries have become very competitive and customers are more concernedabout quality today. Nowadays, customers are very choosy about how they spendmoney. Quality products or services are their first and foremost preferences. For aservice provider, the challenge is to enhance their service and emphasize its desirabilityso that customers’ expectations are met. Analyzing basic utilities offers anotherchallenge to researchers because their inherent characteristics distinguish them fromother regular services. Little research has been undertaken on the service quality ofelectricity utilities, but it remains an important matter for research.

Anwar et al. (2010) have focussed on the application of quality function deploymentat a utility service company to improve its quality of service via the design of a houseof quality matrix. Zhang et al. (2009) have established a service quality evaluatingsystem to identify problems, and have presented some measures for solving problemsfor power supply enterprises in China. Wenyu et al. (2007) have established the indexsystem of supply enterprise’s service recovery quality and the weight of each index ismade clear using an improved analytic hierarchy process method. Ming et al. (2009)have established an index system to evaluate the service quality of power-supplyingenterprises. Bao-hua et al. (2007) have designed an electricity customer satisfactionevaluation index system based on the service blueprint theory, which covered everyprocess of the electricity supplier’s customer service. These researches have beenconducted in several different places but, the power quality (PQ) problems ofdistribution systems have not yet been studied extensively by utilities or research anddevelopment organizations, particularly in India. PQ standards are not well-developedor enforced on India’s power distribution networks. No recent PQ survey conducted inIndia has been reported in the extant literature. India’s State Electricity Boards (SEBs),which have enjoyed monopolistic power to generate and distribute electricity intheir respective states, are caught in a vicious cycle of resource shortages and pooroperational and financial performance. The financial weaknesses of most of the SEBs

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are mainly due to their irrational tariff structures and their high transmission anddistribution losses.

Poor service quality and poor PQ have ultimately degraded the Indian electricityservice. Due to these poor quality factors, the Indian electricity industry suffers fromfinancial losses and consumer dissatisfaction. So, an effective investigation concerningcustomer satisfaction would be a very important precondition for a power supplyenterprise to win in the Indian marketplace. How to use advanced and practicedmethods to evaluate electricity customers’ satisfaction, and how to use the evaluationresults to improve the service are problems that need to be solved by power supplyenterprises in India. Planning and operating modern electric power systemsinvolves several interlinked and complex tasks. In this paper structural equationmodeling (SEM) is applied to the electricity utility service model to verify servicequality satisfaction.

2. Empirical studies of service quality in electricity utility industryService quality can be judged by comparing expectations with performance. Servicequality can also be defined according to both what and how a product or service isdelivered. The SERVQUAL framework was developed in 1988. It is a method whichcan be used by service industries to evaluate their service quality. The five measures ofservice quality that SERVQUAL identifies are tangibility, reliability, responsiveness,assurance and empathy. Using a five-dimension scale composed of 21 serviceattributes, the SERVQUAL survey measures the gaps between customers’ perceptionsand their expectations. Improving service quality is a top priority for firms that aim todifferentiate their services in today’s highly competitive business environment. In orderto ensure quality of service, it is important that both primary, as well as supportingdelivery processes, are effective and efficient. The question of governance of a powersector is a challenge for researchers, policy makers and administrators to address. It isalso an important factor for the socioeconomic development of a nation. Power sectorsinvolve complex activities like generation, transmission, distribution and final deliveryof services to end-users, i.e. consumers. Initially, when electricity was first used duringthe pre-independence period, private enterprise was the main player in the country’spower sector. In recent years, the Indian power sector, an important segment of thenational infrastructure, has been the subject of a comprehensive reform program.Two types of governance processes have functioned in two different timeperiods – pre-reform and post-reform. During the pre-reform period, the state wasthe dominant actor in the governance of the power sector; since the introduction ofreforms, the market has dominated. Though reforms in the sector are under way, thequestions of why there should be reform, whether or not to reform, and how to reformare still being debated. The policies that are to be framed and the institutions that areto be created to execute the set policies are still contested subjects. Ultimately though,however, the sector is governed, the most important factor for India’s electricity servicesuppliers to address is poor service delivery. Poor service quality and poor PQultimately degrade the value of the Indian electricity service. Poor service qualitycauses the industry to suffer losses and creates consumer dissatisfaction.

Cui et al. (2006) have proposed an applicable method to assess service quality whichhas four grades based on fuzzy set theory. They have used the SERVQUAL scale(i.e. tangibility, reliability, responsiveness, empathy and assurance), basing it on aconcept of “perceived” quality. Zhang et al., (2009) have established a service qualityevaluating system to find problems and, using this, they have presented some

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measures to solve problems for power supply enterprises in China. After dividingquality into “PQ” and “service quality,” they expanded the scale to take into accountseven dimensions (i.e. tangibility, reliability, responsiveness, empathy, assurance,security and stability).

The identified dimensions are explained as follows:

. Empathy: providing care via individualized attention. Quick and helpfulresponses offered to the needs of customers.

. Tangibility: the physical aspects of the service facilities, equipment and theappearance of the business site.

. Reliability: performing the service dependably, accurately and consistently.

. Assurance: knowledge, ability, humility, demonstrated attitude, reliability andself-confidence of service personnel.

. Responsiveness: the willingness to provide prompt service.

. Security: guide the customer to use power with security.

. Stability: quality and reliability of power supply with least or no interruptions.

Basic utilities offer another challenge to researchers because their inherentcharacteristics distinguish them from other regular services. Shaikh et al. (2012)focussed on the right initiation at the right time to ease control actions in order toenhance the stability, reliability and security of the power system. This focus provideda model for creating preventive plans to minimize the chances of failure in a powersystem. Rengarajan and Loganathan (2012) explained fuzzy-logic-based powertheft prevention and PQ improvement by comparing the total load supplied by thedistribution transformer with the total load used by the consumer. The observed errorsignal was used to identify the power theft.

Wyk et al. (1992) have briefly explored the advent of quality management systems,the rapid growth in their popularity and their benefits for service industries suchas electricity utilities. Lamedica et al. (2001) have explained that the problem of thePQ cost of industrial customers is most important in the present circumstances of acompetitive electricity market. Frazier (1999) has described that the purpose of a powersystem is to provide economical and reliable electricity services to its end-users.Hamoud and El-Nahas (2003) have described a probabilistic method for evaluatingthe level of supply reliability to a customer entering into a performance-based contractwith a transmission provider. W.W. Carter (1989) has discussed the most commonpower problems that plague industries in this era. In his paper, C.K.S. Chan (1993) hasdescribed the increasing demand for good services from the utility industries’customers. Mamlook et al. (2009) have described a fuzzy inference model for short-termload forecasting in power system operation. Rekettye and Pinter (2010) haveestablished the relationship between satisfaction and price acceptance in the context ofthe Hungarian electricity utility sector. In business markets, technical support andproduct customization have been shown to be additional drivers of customers’satisfaction levels.

Even though few researches have been undertaken on the service quality ofelectricity utility industries, it is still an important matter for research since, byimproving service quality, the development of the utility industries will be possible.Delgado et al. (2007) described all of the steps of the work they have done and presented

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the customer satisfaction index scores measured before their study was conductedin 2001 and then at the end of 2006, after their application of quality functiondeployment. Chau (2009) reviewed the evolution and development of customer serviceperformance measures in the UK electricity sector since its privatization in 1989,and then examined the impact of a specific recent energy regulatory requirement(known as the information and incentives project (IIP)) on the organizationalmanagement of an exemplar electricity distribution company. Wattana and Sharma(2011) have examined the veracity of this argument by analyzing both the technicaland environmental performance of the Thai electricity industry. Parida et al. (2011)have explored the methods already proposed in various literatures to overcome theissues associated with VAR management in a competitive environment. Managingreactive power support services in a competitive electricity market environment hasbecome an important constituent of ancillary services. Zerriffi (2008) has examinedsuccess and failure in the use of small-scale technologies for rural electrification.Srivastava et al. (2012) have factorized the environmental and social pillars ofsustainable development when defining access to modern energy forms whichsignificantly inform the level of effort involved in meeting the goal of energy accessto all. Azadeh and Movaghar (2010) have suggested an integrated approach based ondata envelopment analysis and principal component analysis for efficiency assessmentand optimization of transmission systems. Immonen et al. (2011) have analyzed thetransformation of procurement management by Finnish electricity distributionnetwork operators regarding the maintenance and construction of distributionnetworks. Catal~ao et al. (2007) have proposed a neural network approach forforecasting short-term electricity prices. Michael and Mariappan (2012) have modeledthe Electrical Service Centre of Goa Electricity Department using the Markov modeland the Electrical Service Centre has been modeled in a simulation system. Gyamfiand Krumdieck (2012) have described a bottom-up diversified demand model thatcan be used to estimate the load profile of residential customers in a given region.Simpson et al. (2012) have provided a methodology for tracking electricity marketliberalization for policy makers and investors. Hibbard and Todd Schatzki (2012) havereviewed factors at the intersection of electricity and natural gas markets andoperations, and present ways to address the risks. Kowsari and Zerriffi (2011) haveexplored various dimensions of household energy use in order to design strategies toprovide secure access to modern energy services.

3. Hypotheses developmentMediation refers to a process or mechanism through which one variable (i.e.exogenous) causes variation in another variable (i.e. endogenous). Studies designed totest for moderation may provide stronger tests of mediation than the partial and wholecovariance approaches typically used. It is useful to distinguish between moderationand mediation. Moderation carries with it no connotation of causality, unlikemediation which implies a causal order. Based on the arguments discussed above, theresearcher formulated the following hypothesis.

The main purpose of this study is to develop and test a hypothesized modelrepresenting the elements that contribute to the progress of electricity utility serviceat an aggregate level. The variables comprising the constructs in this study playan important role in detecting the overall relationships accounted for by the model. Thehypothesis is developed as per the previous literature review by Zhang et al. (2009).The seven factors of the perceived attribute of electricity service (reliability, tangibility,

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responsiveness, empathy, assurance, security and stability) lead to the schematicdiagram of the model (Figure 1) based on the following hypothesis and the conversionof the diagram into an AMOS graphic (Figure 2).

This study has put forward seven hypotheses to be examined empirically:

H1. Reliability has a positive and direct influence on electricity service qualityadoption.

H2. Tangibility has a positive and direct influence on electricity service qualityadoption.

H3. Empathy has a positive and direct influence on electricity service qualityadoption.

H4. Responsiveness has a positive and direct influence on electricity service qualityadoption.

H5. Security has a positive and direct influence on electricity service qualityadoption.

H6. Stability has a positive and direct influence on electricity service qualityadoption.

H7. Assurance has a positive and direct influence on electricity service qualityadoption.

4. Construct measures and data collectionTo determine customer satisfaction with the service quality of the electricity utilityindustry and their relationships with the overall service quality, a standardquestionnaire was designed for all the customers of different categories, i.e. domestic,agricultural, industrial, commercial and others. The questionnaire consisted of 26items which investigated the respondents’ perceptions of the service quality of theelectricity utility industry. In all, 200 questionnaires were circulated to different

Reliability

Tangibility

Empathy

Responsiveness

Assurance

Security

Stability

Electricity UtilityService

Figure 1.Schematic diagram of the

model based on the seven-factor hypothesis

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consumers in South India by internet, phone and personal contact. From them, 150responses are obtained. This response rate is 75 percent, which is good for this type ofsurvey. The respondents were advised to respond to each item of the questionnaireusing a seven-point Likert-type scale (1¼ totally disagree, 2¼ partially disagree,3¼ somewhat disagree, 4¼ no opinion, 5¼ somewhat agree, 6¼ partially agree and7¼ totally agree). The collected data or responses regarding the electricity utilityindustry were subjected to various statistical analyses such as factor analysis usingprincipal component method, followed by the use of varimax rotation via SPSS 17.0Social Sciences (SPSS). After confirmatory factor analysis (CFA) the dimensions suchas stability, security, responsiveness, reliability, empathy, tangibility and assurancewere named according to the character of the responses.

5. Procedure for data analysisExploratory factor analysis on useful responses has been conducted usingprincipal component method followed by the use of varimax rotation via SPSS 17.0Social Sciences (SPSS), a technique that includes descriptive statistics, correlationanalysis and AMOS package for SEM, and Bayesian estimation and testing. In all, 22items were loaded more than 0.6. These 22 items were categorized under sevendimensions constituting various variables for a proposed instrument for measuring theservice quality of the electricity industry. The items that failed to get loaded more than0.6 were not considered for further analysis. They refer to items 6, 8, 15 and 17 in theAppendix. The percentage of total variance explained was found to be 79 percent,which is an acceptable value for the principal component varimax rotated factorloading procedure. After determining seven constructs (stability, security,responsiveness, reliability, empathy, tangibility and assurance) SEM was carried outby using the AMOS package.

VAR00003

VAR00001

VAR00002

VAR00006

VAR00007

VAR00004

VAR00005

VAR00014

VAR00012

VAR00013

VAR00011

VAR00008

VAR00009

VAR00015

VAR00010

Reliability

Tangibility

Empathy

Responsiveness

Assurance

Security

Stability

Electricity Service Quality

VAR00016

e3

e11

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e13

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Notes: Electricity Service �2=207.4, df=98, RMSEA= 0.102

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Figure 2.Electricity service modelby AMOS graphics

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5.1 SEMSEM, as a method for measuring relationships among latent variables, has beenaround since the early twentieth century and originated in Sewall Wright’s work(Bollen, 1989). When applying the SEM technique for analytical procedures, manyissues are involved. These issues may concern various overall fit indices and theselection of the appropriate approach (Lei and Wu, 2007). Although there is littleconsensus on the recommended sample size for SEM, Sivo et al. (2006) and Hoelter(1983) proposed a “critical sample size” of 200. In other words, as a rule of thumb, anynumber above 200 is understood to provide sufficient statistical power for dataanalysis. There are several indicators of goodness-of-fit and most SEM scholarsrecommend evaluating the models by observing more than one of these indicators(Bentler and Wu, 2002; Hair et al., 1998).

The main study used SEM because it has two advantages:

(1) its estimation of multiple and interrelated dependence relationships; and

(2) the ability to represent unobserved concepts in these relationships and accountfor measurement error in the estimation process (Hair et al., 1998, p. 584).

Lam et al. (2012) have examined the relationship between total quality management,market orientation and service quality in the Malaysian service industry. Hui et al.(2010) have identified and analyzed crucial variables of customer satisfaction in respectof residential facility management (FM) services to enable FM companies to deliverhigh quality services by using SEM. Sandy et al. (2011) used SEM to investigate theeffects of relationship benefits on relationship quality and aspects of service quality,namely technical and functional quality, and their subsequent influence on word-of-mouth behavior. Qin et al. (2010) have modified the SERVPERF scale by incorporatingthe additional dimension of recoverability, and empirically tested and refined themodified SERVPERF instrument using survey data from China. They have assessedthe potential antecedents of customer satisfaction in the fast food industry in China viaSEM. In other words, a series of split but independent multiple regressions weresimultaneously estimated by SEM. Therefore, the direct and indirect effect has beenidentified (Tate, 1998). Some reasons for the widespread use of these models are theirparsimony (they belong to the family of linear models), their ability to model complexsystems (where simultaneous and reciprocal relationships may be present, such as therelationship between quality and satisfaction), and their ability to model relationshipsamong non-observable variables, while taking measurement errors into account (whichare usually sizeable in questionnaire data and can result in biased estimates if ignored).In this research the perceived attributes of service quality of the electricity utilityservice itself consisted of seven factors: reliability, tangibility, responsiveness,empathy, assurance, security and stability.

6. Evaluation of model fitAccording to the usual procedures, goodness-of-fit is assessed by checking thestatistical and substantive validity of estimates, the convergence of the estimationprocedure, the empirical identification of the model, the statistical significance of theparameters, and the goodness-of-fit to the covariance matrix. Since complex models areinevitably misspecified to a certain extent, the standard test of the hypothesis of perfectfit to the population covariance matrix is given less importance than measures of thedegree of approximation between the model and the population covariance matrix.

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The root mean squared error of approximation (RMSEA) is selected as such a measure.Values equal to 0.05 or lower are generally considered to be acceptable. The samplingdistribution for the RMSEA can be derived, which makes it possible to computeconfidence intervals. These intervals allow researchers to test for close fit and not onlyfor exact fit, as the w2 does. If both extremes of the confidence interval are below 0.05,then the hypothesis of close fit is rejected in favor of the hypothesis of better-than-closefit. If both extremes of the confidence interval are above 0.05, then the hypothesisof close fit is rejected in favor of the hypothesis of bad fit. Several well-knowngoodness-of-fit indices (GFI) were used to evaluate model fit: the w2, the comparative fitindex (CFI), the unadjusted GFI, the normed fit index (NFI), the Tucker-Lewis index(TLI), the RMSEA and the standardized root mean square error residual. The CFIand GFI (Hair et al., 1998), NFI and RMSEA (Steiger, 1990) have been used to judge themodel fit. CFI is a recommended index of overall fit. GFI measures the fitness of amodel compared to another model (Hair et al., 1998). NFI measures the proportion bywhich a model is improved in terms of fit compared to the base model (Hair et al., 1998).RMSEA provides information in terms of discrepancy per degree of freedom for amodel (Steiger, 1990). As suggested in the literature (Bollen, 1989), model fit should beassessed by employing several indices. The accepted thresholds for these indices areC2/df ratio should be less than three; the values of GFI, RFI, NFI, and CFI shouldbe 40.90; and RMSEA is recommended to be up to 0.05, and is acceptable up to 0.08for better quality (Hair et al., 1998).

7. Model analysisElectricity service quality refers to both PQ and service delivery. Customer satisfactionis the only measure of electricity service quality. So, item 16 in the Appendix (i.e.consumer satisfied with the electricity service provided) is the only variable taken formeasuring.

After CFA for electricity utility service, the seven dimensions are found astangibility, reliability, empathy, assurance, responsiveness, security and stability.In Table II, these seven dimensions are shown.

Then SEM analysis using AMOS 18.0 was conducted to test the fitness of the model.The model is based on the developed hypothesis for an electricity utility service.

The analysis is then applied via SEM using a covariance-based approach, focussedon two steps: validating the measurement model and fitting the structural model(Anderson and Gerbing, 1988). The former is accomplished primarily through CFA,while the latter is accomplished primarily through path analysis with latent variables.All analyses are performed on a covariance matrix, using maximum likelihoodestimation, and on the entire set simultaneously (Anderson et al., 1987).

7.1 Analysis of results after factor loadingsFirst of all factorial analysis was done and fifteen items loaded more than 0.7 wereselected for further analysis under seven constructs: reliability, tangibility,responsiveness, assurance, empathy, security and stability. The percentage of totalvariance explained was found to be 80 percent, which is an acceptable value for theprincipal component varimax rotated factor loading procedure. The value of a for alldimensions was 0.813, which is well above the acceptable value of 0.70 fordemonstrating the internal consistency of the established scale (Nunnally, 1978). Thevalue of Kaiser-Meyer-Olkin (KMO), which is a measure of sampling adequacy, wasfound to be 0.81, indicating that the factor analysis test proceeded correctly and the

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sample used was adequate as the minimum acceptable value of KMO is 0.5 (Othmanand Owen, 2001). Therefore, it can be concluded that the matrix did not suffer frommulticollinearity or singularity. The result of Bartlett’s test of sphericity shows that it ishighly significant (sig.¼ 0.000), which indicates that the factor analysis processes werecorrect and suitable for testing multidimensionality (Othman and Owen, 2001). So,these constructs are useful for testing the model for service quality in the electricityutility sector SEM (Table I).

7.2 Analysis of results by SEMA CFA of the measurement model specifying the posited relationships of the observedindicators to the latent constructs – with all constructs allowed to be inter-correlatedfreely was tested using AMOS 18.0.

The model electricity service (Figure 2) was plotted by using AMOS graphics, butthis model was not suitably fit as the RMSEA value was 0.102 more than the standardvalue of 0.085, so this model did not pass the values of GFI, TLI, CFI40.9. So as per themodification indices, the model was modified and Figure 3 was formed to offer arevised electricity service model.

In this revised electricity service model, discriminant validity was assessed byexamining the correlation between the factors which should not be 40.85. Thecorrelation between two exogenous constructs higher than 0.85 shows a lack ofdiscriminant validity. For this study, all constructs have discriminant validity withlow-strength correlations, ranging from 0.04 to 0.70. The test of the structural modelwas performed by using SEM. The test of the structural model included estimating thepath coefficients, which indicate the strengths of the relationships between theexogenous constructs and the endogenous construct, and the R2-value, whichrepresents the amount of variance explained by the exogenous constructs (predictors).The path coefficients in the SEM model represent the unstandardized regressioncoefficients. The structural model reflecting the assumed linear, causal relationshipsamong the constructs was tested with the data collected from the validated measures.Modification indices have been implemented by Rahman (2012) to measure howservice quality influenced telecom customers’ perceptions in Bangladesh.

ItemFactor

1Factor

2Factor

3Factor

4Factor

5Factor

6Factor

7Cronbach’s a

0.813

Reliability Item 11 0.806 0.869Item 12 0.756 0.799Item 23 0.769 0.747

Tangibility Item 18 0.736 0.724Item 19 0.751 0.839

Assurance Item 5 0.788 0.882Item 7 0.710 0.840

Empathy Item 9 0.736 0.746Item 20 0.734 0.839

Responsiveness Item 24 0.745 0.692Item 25 0.808 0.888

Stability Item 1 0.766 0.763Item 4 0.750 0.813

Security Item 8 0.771 0.806Item 17 0.788 0.811

Table I.Factor matrix

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In Figure 3 covariance is plotted between e3 and e11, e3 and e4, e4 and e15, e13 and e15,e7 and e10, e7 and e15, and e11 and e12 to fit this model suitably. It is found that themodel shows a best fit when variable 12 (in cases where there is any problem witha transformer, it is immediately replaced) is the common variable between empathyand security. Likewise, stability and security both have variable 2 (load shedding iseffected according to the statutory guidelines as per the OERC norms and governmentpolicy) and variable 13 (in necessary conditions, load enhancement is done) in common.

The construct responsiveness when added with variable 1 (advance information isreceived about power shutdowns and notices and regulations are provided). andvariable 3 (the quality of power supplied is provided regularly without interruption),demonstrates that the model is fit for analysis. Empathy when added with variable 5(the correct energy bill is served regularly and in complete shape) and responsivenessadded with variable 4 (for the ease of customers, the mode of payment of electric billsare through cash collecting counter, e-payment mobile vans and through customer carecentre) illustrate that the revised model is a best fit model for the electricity service.

Figures 4 and 5 show the results of the analysis. Properties of the causal paths(unstandardized path coefficients (b), standard error of regression weight, probabilityvalues, hypotheses result) are shown in Tables IV and V, respectively. A CFA wasconducted to assess the measurement model, including seven constructs and it wasrefined according to the modification indices allowing some pairs of error terms tohave non-zero covariance. Since the generally used GFIs such as w2, GFI and adjustedgoodness-of-fit index are considerably influenced by variations in sample size and nonormality of the variables, current researchers recommend that a model reporting therelative w2/df and more robust measures such as CFI, TLI, and RMSEA will oftenprovide sufficient unique information to evaluate a model (Hair et al., 1998). Themeasurement model fit showed that the ratio w2/df¼ 0.643(45), CFI¼ 1.00040.9,TFI¼ 1.12940.9, IFI¼ 1.06640.9, and RMSEA¼ 0.000 (o0.07), met the generallyrecommended threshold levels. All fitness indices were close to one and the RMSEAvalue was 0.000. Therefore, the hypothesized structural model for this study wasvalidated and accepted. Table II shows the details of goodness of fit of the revisedservice quality model.

After the goodness-of-fit test of the model, the path analysis was carried out tocheck the supported and not supported paths of the revised electricity service model.Path testing was done by considering the seven hypotheses. As for the hypotheses,interpretation of the results was primarily focussed on individual paths. Analysis ofthese results was based on the p value computed for each path coefficient of interest.The probability of getting a critical ratio as large as.1.96 is significantly different from0 at the 0.001 level (two-tailed), so that suitably supports the path. The probability ofgetting a critical ratio as large as 3.195 in absolute value is 0.001. This means that theregression weight for responsiveness in the prediction of electric service quality issignificantly different from 0 at the 0.001 level (two-tailed). The probability of getting acritical ratio as large as 2.693 in absolute value is 0.007, which means the regressionweight for security in the prediction of electric service quality is significantly differentfrom 0 at the 0.01 level (two-tailed). The probability of getting a critical ratio as large as2.655 in absolute value is 0.008, so the regression weight for reliability in the predictionof electric service quality is significantly different from zero at the 0.01 level (two-tailed). So these three hypotheses (H1, H4, H5) are positive. The other remaininghypotheses are also supported. For H2, the probability of getting a critical ratio as largeas 2.05 in absolute value is 0.006. Tables III and IV show that among the seven

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Construct Path Construct

Unstandardizedpath

coefficients(b)

Standarderror of

regressionweight Probability

Hypothesisresult

Electricityservice

’ Reliability �0.547 �0.525 0.008 Fail to reject H0 ata¼ 0.01

Electricityservice

’ Tangibility �0.349 �0.295 0.010 Fail to reject H0 ata¼ 0.01

Electricityservice

’ Assurance 0.622 0.453 0.006 Fail to reject H0 ata¼ 0.01

Electricityservice

’ Responsiveness 1.051 0.602 0.001 Fail to reject H0 ata¼ 0.01

Electricityservice

’ Security �0.301 �0.397 0.007 Fail to reject H0 ata¼ 0.01

Electricityservice

’ Empathy �0.142 �0.111 0.010 Fail to reject H0 ata¼ 0.01

Electricityservice

’ Stability �0.025 �0.033 0.004 Fail to reject H0 ata¼ 0.01

Table III.The unstandardizedregression weightsand the correspondingprobability values

Hypothesis Hypothesis statement Result

H1 Reliability has a positive and direct influence on electricityservice quality adoption

Supported

H2 Tangibility has a positive and direct influence on electricityservice quality adoption

Supported

H3 Empathy has a positive and direct influence on electricityservice quality adoption

Supported

H4 Responsiveness has a positive and direct influence on electricityservice quality adoption

Supported

H5 Security has a positive and direct influence on electricity servicequality adoption

Supported

H6 Stability has a positive and direct influence on electricityservice quality adoption

Supported

H7 Assurance has a positive and direct influence on electricityservice quality adoption

SupportedTable IV.The result of hypothesistesting

Goodness-of-fit indicators Hypothesized structural model

GFI (goodness-of-fit index) 0.944NFI (normed fit index) 0.900RFI (relative fit index) 0.829IFI (incremental fit index) 1.066TLI (Tucker-Lewis index) 1.129CFI (comparative fit index) 1.000RMSEA (root mean square error of approximation) 0.000w2 45.0Degrees of freedom 70Probability level 0.991

Table II.Goodness-of-fit

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hypotheses of the revised model, the H1, H2, H3, H4, H5, H6, H7 (reliability,tangibility, empathy, responsiveness, assurance, security and stability) shows apositive and direct influence on electricity service and so the hypotheses are supported.

Table V indicates that the seven exogenous constructs jointly explained 0.631squared multiple correlations in service quality of electricity utility service adoption,which is fit for this revised model for electricity service.

In Figure 6 (the final revised model), it is shown that reliability, security andresponsiveness are the only significant predictors which influence electricity serviceadoption in this study. Therefore, electricity policy makers and stakeholders mustfocus on these three constructs for modifying electricity policies to achieve maximumcustomer satisfaction. As for the remaining constructs, they are not the significantpredictors in this study. This is due to the nature of the population itself. Sometimes theage, time, place and understanding of questions also impact the fitness of a hypothesisand model substantially. So, in future, another sample may be tested to check therevised electricity model.

8. DiscussionElectricity utility industries face stiff competition in the marketplace especially sinceeconomic liberalization in India. It is vital to understand the requirements of customersso that policies can be formulated accordingly, in order to improve customersatisfaction and customer retention. The study has applied factor analysis to a surveyquestionnaire specifically designed to capture perceptions of customers in four Indiansectors, namely agriculture, industrial and commercial, public organizations anddomestic users. Repeated factor analysis was done using the principal componentmethod followed by varimax rotation using SPSS 17.0. Thereafter, fifteen items loadedmore than 0.7 were selected for further analysis under seven constructs: reliability,tangibility, responsiveness, assurance, empathy, security and stability. The percentageof total variance explained was found to be 80 percent, which is an acceptable value forthe principal component varimax rotated factor loading procedure. The internalconsistency of the actual survey data was tested by computing Cronbach’s a. The valueof a for all dimensions was 0.813 which is well above the acceptable value of 0.70 fordemonstrating the internal consistency of the established scale (Nunnally, 1978). Thevalue of KMO, which is a measure of sampling adequacy, was found to be 0.81indicating that the factor analysis test had proceeded correctly and that the sampleused was adequate as the minimum acceptable value of KMO is 0.5 (Othman andOwen, 2001). Therefore, it can be concluded that the matrix did not suffer frommulticollinearity or singularity. The result of Bartlett’s test of sphericity showed that inthis case it was highly significant (sig.¼ 0.000). This indicated that the factor analysisprocesses was correct and suitable for testing multidimensionality (Othman and Owen,2001). Therefore, the statistical test results demonstrated that the proposed items andall dimensions of instruments were sound enough for analysis. A model was formedand SEM was applied to test the model and hypothesis. The measurement model fitshowed that the ratio w2/df¼ 0.643(o5), CFI¼ 1.00040.9, TFI¼ 1.12940.9,

Endogenous variable Squared multiple correlation (SMC)¼R2

Electricity service quality 0.631

Table V.Squared multiple

correlation results

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Figure 6.Final revised model afterhypothesis testing

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IFI¼ 1.06640.9, and RMSEA¼ 0.000 (o0.07) met the generally recommendedthreshold levels. All fitness indices were close to one and the RMSEA value was 0.000.Previous researches have only explained the service quality of electricity modeltheoretically, but in this paper the empirical model was tested and the test resultshowed the satisfactoriness of the hypothesized model. Therefore, the hypothesizedstructural model for this study was validated and accepted. This study demonstrateshow to investigate electricity utility service beyond description and definition, with thevariables comprising the constructs being tested to see how well they define andmeasure the underlying constructs and how important the role they play is incapturing the overall causal relationships accounted for by the model. This studyillustrates the causal relationships between the constructs, and explains thecomposition of each construct in terms of the roles played by the various variablesin defining their underlying constructs. It can help policy makers understand how toallocate resources to affect the economy, society, available resources and customersatisfaction in the electricity industry aggregately.

9. Conclusion, limitations and extensionsSEMs are complex methods of data analysis. By using this form of analysis, thehypothesis of electricity utility tested the constructs for service satisfaction. In thispaper the model was formed for electricity service then it was tested and the validityand reliability of the model were also tested. This model shows that there is a direct andpositive relationship between the seven dimensions of the model constructed for thestudy and electricity utility service. Now, this electricity model can be practicallyapplied to form/modify new policies for electricity utility service providers in all partsof India.

In future, research must be done by varying sample sizes. This model can also betested in all parts of India and other countries to measure the model’s more generalsuitability. This technique can be applied to other service and product industries too.SEM is a causal modeling approach that combines cause-effect information withstatistical data to provide a quantitative assessment of the relationships among studiedvariables. If the relationships are significant, the theoretical construction is consideredvalid and can be used to provide guidelines for the application of the model in practice.Although SEM is good for empirical validation of theoretically based causalrelationships and also for prediction to some extent, it is not suitable for diagnoses ofsituations and thus has limitations in managerial decision support. Moreover, SEMmainly models linear relationships. If the relationships are non-linear, the potential ofindependent variables to explain the variance of dependent variables would not beaccurately known, resulting in poor prediction and diagnosis. These are the fewlimitations of SEM.

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Further reading

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Appendix

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About the author

Suchismita Satapathy currently works as an Assistant Professor in the School of MechanicalEngineering, KIIT University, Bhubaneswar, and has done so for the last two years. Her area ofresearch is service quality management. Her areas of interest include total quality management,statistical process control and service quality management. She has published more than fiveresearch papers in various international and national conferences and journals. SuchismitaSatapathy can be contacted at: suchismitasatapathy9@gmail.com

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints

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