A case study of Al-Ain municipality inspectors
M.Y. El-Bassiouni, Department of Statistics, Faculty of Business and Economics, United Arab Emirates University, Al-Ain, United Arab Emirates
M. Madi, Department of Statistics, Faculty of Business and Economics, United Arab Emirates University, Al-Ain, United Arab Emirates
T. Zoubeidi, Department of Statistics, Faculty of Business and Economics, United Arab Emirates University, Al-Ain, United Arab Emirates
M.Y. Hassan, Department of Statistics, Faculty of Business and Economics, United Arab Emirates University, Al-Ain, United Arab Emirates
Purpose – The purpose of this paper is to develop customer satisfaction indices for the services provided by inspectors in certain departments of Al-Ain Municipality, the United Arab Emirates.
Design/methodology/approach – The methodology is based on customer satisfaction models with SERVQUAL survey input to produce indices of satisfaction and the drivers and outcomes of satisfaction. The survey data were collected via a stratified random sample of the customers who visited Al-Ain Municipality Customer Service Center (AMCSC) in spring 2008. Structural equation models were fitted to the data and goodness-of-fit was assessed.
Findings – The customer satisfaction indices and scores of customers’ trust were in the mid-eighties, indicating high levels of satisfaction and client trust.
Research limitations/implications – The limitations of the current study include the small sample size and the use of one indicator of the latent variable trust. Further research may focus more on prioritizing future efforts, improving quality, and performing cross-institutional benchmarking.
Practical implications – Opportunities for quality improvements were identified and some recommendations were provided.
Originality/value – Although the results lead to the conclusion that high levels of satisfaction and client trust were attained, there is a room for improvement. The AMCSC has to continuously improve the quality of its services in order to realize its mission.
United Arab Emirates; Customer satisfaction; Customer loyalty; Trust; SERVQUAL; Customer satisfaction index; Goodness of fit; Structural equations modelling; Survey sampling.
Journal of Economic and Administrative Sciences
Emerald Group Publishing Limited
The measurement of service quality has been the subject of investigation for the last 40 years (Parasuraman et al., 1985). Service quality can be defined as the ability of an organization to determine customer expectations correctly and to deliver the service at a quality level that will at least equal customers’ expectations (Brink and Berndt, 2008).
One of the most popular measures of service quality is the SERVQUAL, which is not only widely used in marketing, but also in other sectors including government institutions. The retail sector was the first targeted by the early studies of service quality, where ten latent factors were suggested to determine the overall perception of service quality. These are reliability, responsiveness, competence, access, courtesy, communications, credibility, security, understanding customer, and tangibles. These factors are conceptualized on a 22-item gab-based survey instrument (Parasuraman et al., 1988). The ten dimensions were refined to five key latent variables (Parasuraman et al., 1988; El-Bassiouni et al., 2011): tangibles, reliability, responsiveness, assurance, and empathy. SERVQUAL is conceptualized as a “perception-minus-expectations” service quality measurement framework (Parasuraman et al., 1991).
Customer loyalty is one of the most important subjects in the service literature (Andreassen and Lindestad, 1998; Patterson and Smith, 2003; Eshghi et al., 2007; Heskett and Sasser, 2010). Customer loyalty in service organizations is frequently addressed by many researchers in an effort to understand the determinants of customer loyalty and their implications in service industries. Most of the studies on this matter have concluded that customer satisfaction is one of the major determinants of customer loyalty (Parasuraman et al., 1988; Anderson and Sullivan, 1993; Andreassen and Lindestad, 1998; Lin and Wang, 2006). It has been the most important success factor of competition for a product or service provider (Zeithaml et al., 1996).
Customer satisfaction had been recognized as a central concept of quality evaluation of service (Anderson et al., 1994). Contrary to the customer's satisfaction positive effects, low perceived value causes customers to switch into other competing businesses to increase their perceived value, which consequently contributes to a decline in loyalty (Anderson and Srinivasan, 2003). Some studies have found that perceived expectation directly and positively influences customer satisfaction and customer loyalty (Chiou, 2004). These were supported by investigations conducted by Lin and Wang (2006), whose study concluded that wholehearted beliefs could lead to customer satisfaction, which, in turn, influences customer loyalty. Corporate image and brand image also positively affects customer loyalty and customer satisfaction (Andreassen and Lindestad, 1998; Javalgi and Moberg, 1997). Other studies have found that service quality is a strong determinant of customer satisfaction and customer loyalty (Buzzell and Gale, 1987; Zeithaml et al., 1996; Kumar et al., 2008). Since customer loyalty has become a target for organizations, a major concern is to find out the determinants of customer loyalty. The marketing and service literature abound with studies indicated that customer satisfaction is one of the prime determinants of customer loyalty (Parasuraman et al., 1988; Anderson and Sullivan, 1993; Andreassen and Lindestad, 1998; Lin and Wang, 2006; Heskett and Sasser, 2010).
Although service quality can be defined as the ability of an organization to determine customer expectations correctly and to deliver the service at a quality level that will at least equal customers’ expectations (Brink and Berndt, 2008), people's perceptions of quality in public and commercial sector service delivery are different (Groonhaug and Arndt, 1979). We focus here on the government public service, which has to meet the expectations of the citizen at large. Therefore, in the government public sector, the idea of service quality frequently includes notions of accessibility, equity, and respect for the individual (Klaus, 1985). Importance of the public service is established both economically and socially (Lusch et al., 2010). The most important factors of influence on public (and commercial) service quality could be defined as the access to services, places, timetables, waiting times, or being accessible through communication systems (Sancho, 1999). Administration has to use suitable language that customers understand easily, and provide information about work process. The activities of government are more heavily influenced by external forces, such as media and special interest groups (Crompton and Lamb, 1986). Governments have spent much effort in enhancing process and service to citizens. Many governments strive in improving public service that meet or exceed the expectations of the recipients (Jakka, 2004), and the competitiveness of governments will be evaluated on the basis of service quality rather than the quantity of service provided to citizens (Giannoccaro et al., 2008).
The customer satisfaction index (CSI) has been a universal indicator of customer evaluations of the quality of goods and services available to consumers/clients (Fornell, 1992; Fornell et al., 1996; Martensen et al., 2000; Edvardsson et al., 2000; Bruhn and Grund, 2000; Cassel and Eklöf, 2001). It is the only uniform, cross-industry/government measure of customer satisfaction. It produces indices of satisfaction, its causes and effects, for various economic sectors, industries, private sector companies, and government agencies.
This study is aimed at determining customer satisfaction indices for the services provided by the Al-Ain Municipality Customer Service Center (AMCSC), United Arab Emirates. It focusses on the services provided by inspectors in the Departments of Building Permits and Road and Traffic.
The rest of the paper is organized as follows. In Section 2, we describe the methodology used to produce the satisfaction indices, and the drivers and outcomes of satisfaction. The study results are presented in Section 3. They include the structural equation models, their goodness-of-fit measures, and the resulting satisfaction scores. A brief discussion of the results and some recommendations are included in Sections 4 and 5, respectively.
To measure customer satisfaction with the services provided by Al-Ain municipality inspectors and develop the CSI, the AMCSC was asked to identify major customer segments, central to its mission, for which to measure satisfaction and the causes and effects of that satisfaction. The AMCSC chose two customer segments comprising the clients who have dealt regularly with AMCSC in the past two years for the purpose of conducting transactions with the Departments of Building Permits and Roads and Traffic. Thus, a stratified random sample was used, where the two strata represent the two designated departments. Further, the random sampling was conducted systematically by interviewing customers at 15-minute intervals. About 250 interviews were completed for the customers of the department building permits, in contrast to around 150 interviews for the less frequent customers of the department of roads and traffic. The demographic profiles of the respondents are described in Sections 3.2 and 3.3. These profiles were reviewed with the AMCSC and are believed to represent the population of AMCSC customers.
The questionnaires used were designed by the research team to be segment specific in terms of activities and outcomes, but followed a format common to segments; a format that allows cause and effect modeling using the CSI model. Customer interviews were conducted by professional interviewers selected by AMCSC and trained by the research team.
The AMCSC defined customer's trust as the most important outcome for the customer segments measured. This outcome was measured by probing the respondent's willingness to speak positively about the services provided by the target department. This willingness is denoted “positive attitude” and was the only indicator of the trust latent variable. Since it is unreasonable to assume that the ensuing error variance is 0, it is argued that an arbitrary reliability value of 0.85 is a better assumption than an equally arbitrary value of 1 (Joreskog and Sorbom, 1993). In this regard, it is recommended to use more than one indicator of trust in future research.
For the services provided by the inspector, AMCSC identified four drivers of satisfaction: Responsiveness, measured by questions on ease of interaction and promptness of response; Reliability, measured by professionalism and knowledge of inspector and effectiveness of inspector; Empathy, measured by courteousness of inspector and personal attention; and Assurance, measured by confidence in inspector and sincerity. It should be noted that the latent variable representing Tangibles was dropped as it pertains to the Customer Service Center rather than the inspector.
The CSI is a weighted average of three questions that measure overall satisfaction; fallen short of or exceeded expectations; and comparison to an ideal. The model does the weighting to maximize the effect of satisfaction on the outcome at the bottom right of the models in Figures 1 and 2.
The AMCSC has also identified the principal drivers of satisfaction. The effects of these drivers on customer satisfaction/dissatisfaction are estimated by the CSI model. The model parameters were estimated using the structural equations modeling procedure in LISREL version 8.54 (Joreskog and Sorbom, 1993). The models for the inspector services in the building permits and roads and traffic departments are shown in Figures 1 and 2. These models should be viewed as a cause and effect models that move from left to right, with customer satisfaction (CSI) in the middle.
3.1 Factor reliabilities and goodness-of-fit measures
The composite reliabilities of the various factors are shown in Table I. All values in the table are >0.7 indicating very reliable factors.
Hartwick and Bakri (1994) noted that in large samples, the χ 2 statistic will almost always be significant, since χ 2 is a direct function of sample size. Consequently, they suggested other measures of overall model goodness of fit, namely, that χ 2/degrees of freedom3, the non-normed fit index (NNFI)0.90 and the comparative fit index (CFI)0.90, see also Segars and Groove (1993) and Chau (1997). Further, Bagozzi and Yi (1988) stated that a value of the adjusted goodness-of-fit index (AGFI)0.90 suggest meaningful models from a pragmatic point of view. On the other hand, Browne and Cudeck (1989) suggest that values of the root mean square error of approximation (RMSEA) up to 0.08 represent reasonable errors of approximation. Table II shows that the observed values of these measures satisfy the recommended conditions.
3.2 The building permits inspector model
A demographic profile of those who responded to the building permits survey shows that 99 percent were males and 1 percent females. The average age of respondents was 37, with 31 percent under the age of 30 and only 11 percent were 50 or older. About 63 percent have college education, 8 percent have high school diploma, whereas 27 percent have some primary/preparatory education. Around 13 percent were nationals, 69 percent were Arabs and 19 percent were Asians.
For this model which appears in Figure 1, only empathy was highly correlated with the other three aspects and was therefore dropped out of the analysis. The results for the six indicators of the three basic drivers of satisfaction were as follows on a 0-100 scale: for responsiveness, ease of interaction was lowest at 84, two points less than the rating of promptness of response, for reliability, both professionalism and knowledge and effectiveness of inspector scored 86, whereas for assurance, confidence in inspector scored 84, a significant six points less than sincerity.
In regard to the three identified drivers of satisfaction, assurance scored 87 followed by reliability at 86 and responsiveness at 85. These three estimates have resulted in a perceived quality score of 86.
The three indicators of satisfaction were scored at 83, 81, and 88 for the comparison to ideal, the agreement between experienced and anticipated quality and overall satisfaction, respectively. The CSI for the services of the inspectors of the building permits department was 84 on a 0-100 scale.
The index of client trust was 85 on a 0-100 scale. The clients have indicated confidence that the inspectors working with the Department of Building Permits will continue to provide high-quality services.
With regard to the effects, or “impact” of each component on subsequent components, all three drivers of satisfaction equally impact perceived quality at about 0.36. Further, perceived quality has an impact of 0.46 on CSI, which in turn has an impact of 0.87 on client trust. Thus, if the scores of all measures of satisfaction, except sincerity, are raised to 90, perceived quality would increase by 4.36 points [(90−85)×0.36+(90−86)×0.37+(90−87)×0.36] to 90.4. The CSI would in turn increase by 2.01 [4.36×0.46] to about 86. By the same token, client trust would also go up by 1.74 [2.01×0.87] to become 86.7. Responsiveness and reliability offer good opportunities for improvement. This can be achieved by raising the ratings of all their measures of satisfaction to 90.
3.3 The roads and traffic inspector model
A demographic profile of those who responded to the roads and traffic survey shows that 99 percent were males and 1 percent females. The average age of respondents is 36, with 30 percent under the age of 30 and only 15 percent were 50 or older. About 66 percent have college education, 9 percent have high school diploma whereas 25 percent have some primary/preparatory education. Around 15 percent were nationals, 73 percent were Arabs and 12 percent were Asians.
For this model which appears in Figure 2, only empathy was highly correlated with the other three aspects and was therefore dropped out of the analysis. The results for the six indicators of the three basic drivers of satisfaction were as follows on a 0-100 scale: for responsiveness, ease of interaction and promptness of response were lowest at 80. For reliability, professionalism and knowledge scored 81, while effectiveness of inspector scored 84, whereas for assurance, confidence in inspector scored 91, three points higher than sincerity.
In regard to the three identified drivers of satisfaction, assurance scored 90 followed by reliability at 82 and responsiveness at 80. These three estimates have resulted in a perceived quality score of 84.
The three indicators of satisfaction were scored at 81, 82, and 89 for the comparison to ideal, the agreement between experienced and anticipated quality and overall satisfaction, respectively. The CSI for the services of the inspectors of the roads and traffic department was 84 on a 0-100.
The index of client trust was 83 on a 0-100 scale. The clients have indicated confidence that the inspectors working with the Department of Roads and Traffic will continue to provide high-quality services.
Consider the model in Figure 2 to examine the multivariate components in context, and to look at the effects, or “impact” of each component on subsequent components. All three drivers of satisfaction equally impact perceived quality at about 0.36. Further, perceived quality has an impact of 0.62 on CSI, which in turn has an impact of 0.79 on client trust. Thus, if the scores of all measures of satisfaction, except sincerity, are raised to 90, perceived quality would increase by 6.56 points [(90−80)×0.36+(90−82)×0.37] to 90.6. The CSI would in turn increase by 4.05 [6.56×0.62] to about 88. By the same token, client trust would also go up by 3.2 [4.05×0.79] to become 86.2.
This study aims to evaluate the satisfaction of the customers of the Departments of Building Permits and Roads and Traffic with the quality of services provided by the departments’ inspectors. The corresponding indices of customer satisfaction and trust were computed using a cause and effect CSI model based on customers’ interviews (see Table III).
The CSI and trust indices for the services provided by the inspectors of the two departments are very similar and relatively high, with CSI of 84 and trust indices of 83 and 85. The relatively high CSI and trust scores indicate high levels of satisfaction and client trust while the similarity of the scores suggests homogeneity in the quality of services across departments.
All three drivers of satisfaction with the building permits inspector services scored in the mid-80s. In the roads and traffic model, assurance scored highest with a score of 90 while the other two drivers of satisfaction, reliability and responsiveness, scored in the lower 80s. Assurance was measured by confidence in inspectors, and perception of their sincerity. Responsiveness reflects the easiness and smoothness of the service delivery process and the promptness in completing the service, while reliability is measured by the level of the inspectors’ knowledge and skills, and their effectiveness in delivering the services.
The substantial gap between the score of assurance and the scores of reliability and responsiveness in the roads and traffic department model suggests the latter two drivers as the best dimensions for future improvement of inspector services in this department. Raising these two drivers of satisfaction to 90 would increase the score of perceived quality to 90.6, CSI to 88 and the index of trust to 86.2 (see Tables III and IV).
In the case of the building permits department, all three drivers of satisfaction have scores between 85 and 87 and their corresponding measures, except for “sincerity of inspector” have scores between 84 and 86. Thus, efforts for the improvement of customers’ satisfaction with the inspectors’ services in this department should focus on the easiness and smoothness of the service delivery process, the promptness in completing the service, the level of the inspectors’ knowledge and skills, the inspectors’ effectiveness in delivering the services, and the customers’ confidence in the inspectors. Raising the scores of these five measures from the mid-80s to 90 would increase the scores of the three drivers of satisfaction to 90, the perceived quality to 90.4, CSI to 86, and the trust index to 86.7 (see Tables III and IV).
A main limitation of the current study is the use of a single indicator of trust. Therefore, it is recommended in future research to use more (at least two) such indicators.
Further, it is worth pointing out that estimates of the model are subject to measurement errors due to sampling variability. With the survey sample size and modeling methodology used for CSI, a rise or drop of less than three points is not necessarily a change for better or for worse. The change may be real, but it can also be the result of sampling error. However, if AMCSC continues to measure its customers’ satisfaction over a multi-year period, they will be able to detect trends – hopefully, a rise in satisfaction as it become more responsive to the needs and interests of its customers. Thus, it is also recommended that the AMCSC use a larger sample size.
As well, it should be noted that lag time exists between inaugurating an improvement in a program and users becoming both aware of the improvement and evaluating it favorably. Certainly, favorable publicity about a change can impact customer perceptions. Thus, it is recommended that the AMCSC allocate public relations and advertising budgets to communicate the changes that they make. In contrast, negative events or publicity can cause customer satisfaction to drop, and typically have more downward effect than positive events have upward effect. Thus, the AMCSC should keep in mind the potential impact that widely communicated events – both negative and positive – may have on their customer satisfaction score.
The best use the AMCSC can make of this study, however, is for learning how customers evaluate the services provided, and then identifying which activities has the most impact on the perception of the quality of the services that the inspectors deliver. This research is a tool with which to prioritize future efforts to improve quality and, through quality, customer satisfaction, and the desired outcome – in this case, client trust in the AMCSC.
In order to improve customers’ satisfaction and trust in the services provided by the inspectors of the Departments of Building Permits and Roads and Traffic, it is recommended that Al-Ain municipality reconsider the following drivers of satisfaction:
- In the Department of Roads and Traffic, the best candidates for improving customer satisfaction with the services provided by inspectors are reliability and responsiveness. Improvement efforts should focus on the easiness and smoothness of the service delivery process, the promptness in processing the service, the level of the inspectors’ knowledge and skills, and their effectiveness in delivering the services. A plausible target for these improvement efforts is to raise the scores of satisfaction about reliability and responsiveness to 90.
- In the Department of Building Permits, all three drivers of satisfaction are at about the same level. Thus, efforts for improvement should encompass them all. Improvements should focus on the easiness and smoothness of the service delivery process, the promptness in processing the service, the level of the inspectors’ knowledge and skills, and their effectiveness in delivering the services, and the customers’ confidence in the inspectors.
The endeavor to improve the quality of services and customers’ satisfaction with the inspectors’ services includes the periodic assessment and surveys of customers’ expectations and opinions. Thus, Al-Ain municipality should repeat this study periodically.
Figure 1 Model for the Department of Building Permits – inspector
Figure 2 Model for the Department of Roads and Traffic – inspector
Table I Composite (factor) reliabilities
Table II Goodness-of-fit measures
Table III Customer satisfaction scores
Table IV Impact of drivers of satisfaction on customer satisfaction and trust
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About the authors
Mohamed Yahia El-Bassiouni holds a PhD (1977) in Statistics from Oregon State University and MSc (1972) in Statistics from Cairo University. He joined UAE University in 1991 where he is currently employed as Chairman and Professor of Statistics. His research encompasses linear models, sampling surveys and business and social statistics. Mohamed Yahia El-Bassiouni is the corresponding author and can be contacted at: firstname.lastname@example.org
Mohamed Madi holds a PhD (1989) in Statistics from University of Wisconsin-Madison and MSc (1983) in Statistics from Stanford University. He joined UAE University in 1990 where he is currently employed as Associate Dean, DBA Director and Professor of Statistics. His research encompasses decision theory, reliability and life testing.
Taoufik Zoubeidi holds a PhD (1988) and MA (1984) in Statistics from the University of Michigan. He joined UAE University in 1994 where he is currently employed as Assistant Vice-Provost for Undergraduate Education and Professor of Statistics. His research encompasses statistical applications in health and social sciences.
Mohamed Yusuf Hassan holds a PhD (1999) in Statistics from the University of California, and MA (1993) in Mathematics from San Francisco State University. He joined UAE University in 1999 where he is currently employed as an Associate Professor of Statistics. His research encompasses time series analysis, mixture models, and marked point processes.