Journal Search Engine
Search Advanced Search Adode Reader(link)
Download PDF Export Citaion korean bibliography PMC previewer
ISSN : 1598-7248 (Print)
ISSN : 2234-6473 (Online)
Industrial Engineering & Management Systems Vol.18 No.1 pp.89-103
DOI : https://doi.org/10.7232/iems.2019.18.1.089

An Importance-Performance Analysis of Sustainable Service Quality in Water and Sewerage Companies

Abrari Salleh*, Sha’ri Mohd Yusof, Norazli Othman
Razak Faculty of Technology and Informatics, Universiti Teknologi Malaysia, Malaysia
Corresponding Author, E-mail: irarba69@yahoo.com
December 12, 2017 October 16, 2018 December 10, 2018

ABSTRACT


Sustainable service quality (SUSSERV) is crucial to identify how far the company able to meet customers’ expectations will determine its sustainability. SUSSERV model with six independent variables namely tangibles, reliability, responsiveness, assurance, empathy, and sustainability has been developed by modifying the SERVQUAL instrument. This research employed a survey with quota sampling technique through 500 questionnaires comprised of 250 each to the customers of water (WC) and sewerage (SC) companies in the state of Selangor, Federal Territory (F.T.) Putrajaya and F.T. Kuala Lumpur. WC’s on the perceived service quality for all SUSSERV dimensions were much lower than SC. It was also found that there were no significant differences between the means of perceptions with the exception of eight out of 31 variables and three out of 31 variables in the category of Services (Water and Sewerage) and in Living Status (Type of Houses) respectively. The importance-performance analysis (IPA) indicates that the Assurance and Responsiveness dimensions were at high levels but Sustainability dimension was low for both performance (perceptions) and importance (expectations). The advantage of using the IPA is to assist companies to improve their services. The IPA indicates that both WC and SC services are rated as very reliable by the customers despite the dissatisfaction of majority of the customers with the services rendered by both companies investigated in the study. The main contribution of this study is the successful utilization of a modified SERVQUAL instrument that can measure sustainable service quality. Future research should focus on extending the use of this instrument in other states to further validate and test this instrument.



초록


    1. INTRODUCTION

    In Malaysia, the overall water services non-revenue water (NRW) is high at the average of 35.2% and a high number of inquiries or complaints about water supply and sewerage services in 2016. Both companies were also reported to be operating at net losses (The Malaysian Water Association, 2017). Currently, each state in Malaysia has only one main operator that provide water supply services. In this research, the water supply company (WC) belongs to state government whereas the sewerage company (SC) owned by the federal government. Therefore, the question of how sustainable of its service quality to the customers’ view is more critical than the loyalty. Sustainability factor is important to be included in determining service quality to ensure water industry companies will remain relevant to their customers especially it is now becoming more challenging in providing water and sewerage services (Moe and Rheingans, 2006).

    This research will explore and explain the impact of Malaysian water and sewerage companies’ service quality towards sustainability. This research should be able to empirically justify why the instrument for sustainable service quality or SUSSERV is needed to measure Malaysian water and sewerage services quality thus requiring some improvements or modifications the existing service quality model (SERVQUAL). This paper attempts to determine the perceived service quality and sustainability of the water supply and sewerage companies, and develop an instrument for sustainable service quality or SUSSERV to measure Malaysia’s water supply and sewerage services quality. This research is very important to many parties especially to ensure the quality of services deliverables (Office of the Tasmanian Economic Regulator, 2013) such as the government, regulator body, water companies, developer, manufacturer and other users.

    2. LITERATURE REVIEW

    2.1 Importance-Performance Analysis

    The Importance-Performance Analysis (IPA) model developed by Martilla and James (1977) was used as an alternative method to determine the importance of each variable of SUSSERV. The IPA model included four quadrant A, B, C, D as below:

    • i Quadrant A / Concentrate Here - The performance of a variable is lower than desires of the customer so that the company should increase its performance so that optimal.

    • ii Quadrant B / Keep Up The Good Work - Performance and desires of the customer are at high levels and appropriate, so the company sufficient to maintain performance variables.

    • iii Quadrant C / Low Priority - Performance and desires of the customer in a variable are at a lowlevel, so companies have not needed to make improvements.

    • iv Quadrant D / Possible Overkill - Performance of company is in a high level of performance but the desire of the customer will be the only variable is low, so companies need to reduce the results achieved to minimize the resources of the company.

    The IPA (including modified IPA) was used by other researchers to determine the importance of variables in service quality dimensions such as Tileng et al. (2013) (government/public services), Tzeng and Chang (2011) (food services), Al Jahwari et al. (2016) (tourism services), Izadi et al. (2017) and Lee et al. (2015) (health services), Dabestani et al. (2016), Choi et al. (2014), Blešić et al. (2014) (hotel services), Pak (2016), Galeeva (2016) (higher education), Kim et al. (2016) (sports). The IPA used a Cartesian model where means for performance/ satisfaction (perceptions) and importance (expectations) are plotted for each variable for water and sewerage services (see Table 1). The IPA also gives a better explanation of graphical view on which dimension the company should give more attention and priority (Galeeva, 2016).

    2.2 Sustainability

    Sustainability does have an impact on the implementation of services and indirectly attributes to the quality of services. Social and economic factors are among the many attributes that are correlated with service quality. Sustainability basically consists of three components (Elkington, 1998;Fernando, 2012;Tajbakhsh and Hassini, 2015;Afful- Dadzie et al., 2016) namely (1) Economy, (2) Environment and (3) Society. However, Lehtinen (2012) suggested four criteria with an additional Relationship factor (transparency, risk management, partnerships). There is an element of cost and benefit or profit and loss for the purpose of measuring sustainability such as cost-efficient model (Benedetti et al., 2013). Sustainability has a positive relationship towards profitability, cost reduction, economic performance (growth) and competitive advantage thus will definitely impact the economy (Amran et al., 2010). Sustainable development is a major challenge and proves to be a daunting task to understand the inter-related complex issues. To date, sustainable development is an important concern for business and society, and even for those who for years argued in favor of the importance of change towards sustainable development, this issue is now perceived as being more apparent and urgent. The current crisis resulting from rapid industrialization has caused significant social and environmental side effects (Amran et al., 2010). The policymaker, especially in water and sewerage industry, will always want its industry to be sustainable and relevant to the consumers’ needs. The change will definitely involve many parties and strong political will (Moe and Rheingans, 2006) and support should be present to achieve its objectives. Companies wishing to achieve business excellence are intense marketing products and have resulted in shorter life cycles of new products. Business excellence will be achieved by companies which can react quickly to new market conditions and customer needs. They will also constantly look for creative solutions and continuous improvements or sustainability in products and processes. Gaining product sustainability is important but a difficult practice in business organizations (Ali et al., 2013). Therefore, meeting functional requirements and sustainability is critical for product success in the current market. Products compete on the basis of not only price, functions and diversity, but also sustainability. Sustainable product or system is its ability to work continuously during its life cycle with less impact on the environment (Hosseinpour et al., 2015).

    2.3 Quality

    Quality can be seen from different perspectives (Salleh and Yusof, 2016a) namely, product quality (technical quality), process quality (functional quality) and service quality (perceived quality) as shown in Figure 1. The most important concept is managing the perceived service quality by managing the gap between perceived services and expected services. It has been thus concluded that technical quality is more important than the functional quality. As such, treated water produced by the water operators is a good example of a technical quality or a technical outcome of the process. However, the customers are also interested to know water treatment process itself; curious about technology, tools or equipment used and how technical quality is obtained. It is important to them and to their view of the service they have received and this is called functional quality. The differentiation between technical quality and functional quality can also be seen in the hospital and healthcare services (Abuosi and Atinga, 2013) and also in higher learning institution (Kong and Muthusamy, 2011). This is because their services involve high technology tools, equipment, and peripherals which are related to functional quality. For manufacturing with total quality management (TQM) practices, other than service quality, process and product quality are being considered as well as technical quality because there is a positive relationship between TQM practices and market orientation (Lam et al., 2012).

    Process and product quality are interrelated in manufacturing whereby process quality has a direct relationship toward product quality performance and business performance (Agus and Hajinoor, 2012).

    2.4 Service Quality

    From Service Quality Model (Gronroos, 1984), then a SERVQUAL model (Parasuraman et al., 1988) with five dimensions has been developed. Although SERVQUAL model is proven to be a reliable and valid tool to measure service quality, it has not stopped the researchers from enhancing or extending its capability through some modifications to suit their objectives. Some modified SERVQUAL models used by scholars in previous research such as SERVPERF model measures service quality and performance or comparison of performance perceptions with the expectation (Cronin and Taylor, 1992), Bank Service Quality (BSQ) Index model revealed that reliable communication and responsiveness have a direct bearing on perceptions of quality (Abdullah et al., 2011) and Sports service quality (SSQ) model is used to investigate the relationship between emotional experience (EE) and user satisfaction (US) for sports competitions or training venues (Voon et al., 2014). Other researchers used the existing SERVQUAL model by Parasuraman et al. (1988) and modified the instrument to suit their research in the areas of study such as hospital and healthcare (Abuosi and Atinga, 2013;Amat Taap et al., 2011); manufacturing with TQM practices (Lam et al., 2012). Researchers in previous studies have modified the original SERVQUAL model in order to accommodate their areas of research while this research emphasized on another area that has a major impact on service quality, sustainability factors (Economy, Environment, and Society). Therefore, a modified SERVQUAL model will be used in this research with ‘sustainability’ as an additional dimension.

    3. METHODOLOGY

    3.1 Instrument and Data Collection

    For the purpose of this research, the authors have developed a SUSSERV model with thirty-one items comprises twenty-two attributes from the original SERVQUAL model (Parasuraman et al., 1988;Zeithaml et al., 1990). In addition, three attributes each (totaling nine) from the sustainability dimension namely economy, environment and society. Data acquired for this research is from primary data using a questionnaire that consists of three sections: Section A - General Information; Section B - Expectation (E) and Perception (P) of service quality; Section C - The important features of service quality. All the questions came in the formats of Multiple Choice, Likert Scale and Fixed Sum Scale using rating scales or response strategy to generate interval and interval data (Cooper and Schindler, 2001) that are easy to compare, tabulate and analyze. The consistency in the response categories allows trends to be tracked over time if the same questions are used. The questionnaire used 7-point Likert-scale where the respondents were asked to select the most appropriate number that corresponds to the extent they agree with a statement where 1 represent “Strongly Disagree” to 7 represent “Strongly Agree.” The 31 items (see Table 2) used to measure service quality perceptions. The sample size of 500 respondents was randomly selected and the answered questionnaires were collected from the sample population representing customers of water and sewerage service providers in Selangor, Federal Territory (F.T.) Kuala Lumpur, and F.T. Putrajaya (Salleh et al., 2017). The quota sampling was used and based on geographical factors (districts or locations) and category of services (water or sewerage) in service quality research (Voon et al., 2014;Omar et al., 2013) to improve representativeness (Cooper and Schindler, 2001). The quota derived from the number and percentage of account holders or customers of each water and sewerage companies in a district as compared to the total population in Selangor, F.T. Putrajaya, and F.T. Kuala Lumpur.

    4. RESULT AND ANALYSIS

    4.1 Reliability and Validity Test

    Validity test will provide some assurance and confidence in our findings (Davis and Cosenza, 2000). There are three important characteristics used to value the measurement instrument in research namely validity, reliability and practicality (Cooper and Schindler, 2001). The reliability of data from N = 500 questionnaires was tested using SPSS version 22 to measure questionnaire consistency. The overall results of reliability test indicated that the questionnaire used in this research is reliable with a Cronbach Alpha value of > 0.70 (Hair et al., 2010) as shown in Table 2. The content validity was carried out by asking a feedback and opinions from the expert due to their direct involvement with water and sewerage services and understood the meaning of the sustainable concept (Joseph, 2013) on the variables and format used in the instrument, whilst construct validity of the instrument was identified using factor analysis (Cooper and Schindler, 2001). The structural equation modeling was performed to assess the validity and reliability of a latent construct used IBM SPSS-AMOS v.22 (Voon et al., 2014;Omar et al., 2013). For this study, univariate and multivariate outliers were tested using SPSS v.22 software. Amos SPSS v.22 also provides a test of univariate normality for each observed variable as well as a test of multivariate normality and attempts to detect outliers (Arbuckle, 2013). The test of normality for SUSSERV variables using Kolmogorov- Smirnov and Shapiro-Wilk found that all 31 expectations and perceptions variables were normally distributed and significant at 0.0001. Skewness and Kurtosis were used to judge the normality of data and the values of skewness and kurtosis fall within the acceptable range of -2 to +2, indicating that the data is fairly normal and the basic assumption of parametric testing is fulfilled (George and Mallery, 2010;Muzaffar, 2016).

    4.2 Descriptive Analysis

    The demographic characteristics of the respondents from the water and sewerage companies are from the aspect of the type of house, location, and race. The detailed analysis of descriptive statistics revealed that the type of respondents’ houses comprised of Bungalow (10%); Semi-Detached (6%); Terrace (43%); Condominium (9%); Apartment (19%) and Low-Medium Cost houses (13%) respectively from the total 500 respondents as shown in Table 1.

    The demographic characteristics of locations or areas which services were delivered to respondents are represented by WP KL-Putrajaya (22%); Gombak (12%); Petaling (3%); Klang (17%); Hulu Langat (13%); Sabak Bernam (1%); Ulu Selangor (5%); Kuala Selangor (2%); Kuala Langat (22%) and Sepang (3%) based on Quota Sampling Techniques used in this research (see Table 3) as shown in Table 2. The findings of this research were highly influenced by respondents who live in terrace houses who are considered as middle-class society.

    The demographic characteristics of the race of respondents comprised of Malay (54%); Chinese (26%); Indian (16%); and Others (4%) as shown in Table 3. This is close to the distribution of races in Malaysia population that comprised of Bumiputra (69%); Chinese (23%); Indian (7%); and Others (1%) whilst the sampling distribution of races in Selangor/F.T. Kuala Lumpur/F.T. Putrajaya’s population represented Bumiputra (57%); Chinese (30%); Indian (12%); and Others (1%). The small difference between population of Malay and Bumiputra was due to the “Others” race (4%) in the research sampling was included the Bumiputra race (Department of Statistics, Malaysia, 2017).

    The gaps between Perception (P) and Expectation (E) as showed in Table 4 indicates that the mean scores of customers’ perceptions of water services ranged from 4.50 to 4.91 whereby the mean scores for customers’ expectation of water services ranged from 5.62 to 6.05 thus all the gap scores ranged from -0.92 to -1.23. Subsequently, the mean scores of customers’ perceptions of sewerage services ranged from 4.64 to 4.98 whereby the mean scores of customers’ expectation on sewerage services ranged from 5.52 to 5.80 thus all the gap scores ranged from -0.67 to -0.96. The lowest perceived quality item (water services) with highest negative gap score of -1.23 was “give customers individual service,” and lowest perceived quality item (sewerage services) with highest negative gap score of -0.96 was “have their customersbest interest at heart”, which indicates that both the water company and sewerage company has less empathy or lost their personal touch on giving customers personal service.

    The overall mean score for service quality perception items for water and sewerage services were 4.73 and 4.81 respectively as compared to the overall mean score for service quality expectation items for water and sewerage services which were 5.79 and 5.66 respectively. These scores indicate a high customer expectation on water and sewerage companies regarding service quality because the standard of living is rising and Malaysian customers expecting more quality of services, especially in water and sewerage.

    4.3 Factor Analysis

    This study uses Exploratory Factor Analysis (EFA) to uncover the underlying structure of a relatively large set of variables. Factor Analysis has been used for providing insight and information into what constitutes for each dimension especially sustainability for the purpose of data summarization and data reduction (Hair et al., 2010). Subsequently, EFA was conducted using Principal Component Analysis (PCA) as the factor extraction method and used Varimax with Kaiser Normalization as the rotation method. PCA required some correlations to be greater than 0.30 and all variables were correlated by more than 0.30 using Kaiser-Meyer-Olkin (KMO) and Measure of Sampling Adequacy (MSA) which value must exceed 0.50 for the overall and individual variable. Variables less than 0.50 should be removed from the factor analysis one at a time, started with the smallest ones (Hair et al., 2010). The exploratory factor analysis extracted six factors which the ratio of cases to variables should be more than 5:1. The EFA results are presented in Table 5.

    For validation, all variables have the commonalities values of more than 0.50 except eight four variables (P5, P6, P9, P15, P16, P19, P32 and P28, are less than 0.50) were removed except P16 with value 0.487 is required for Assurance dimension left with minimum of two variables. Respecification of a measurement model can be challenging because many possible changes to a given model (Kline, 2011). The model suggests that there were five factor components satisfying Kaiser’s criteria based on eigenvalues greater than 1.0 altogether have been accounted (yields) for 53.76 percent of the total variance. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy (KMO index) yielding 0.939 more than 0.50 suggests that the correlation matrix is not an identity matrix and sampling is suitable to run the factor analysis. Based on these findings, it can be concluded that the SUSSERV with all six dimensions of is a valid and reliable instrument. In addition, the Bartlett’s Test of Sphericity (p = .000 < 0.05) confirms that there is a high correlation among the items. For convergent validity, Box’s Test of Equality of Covariance Matrices Test Results was significant (p = .000 < 0.05) with big different values of log determinants for water services (-13.535) and sewerage services (-7.741), however for discriminant validity, the squared correlation value (r2) yields a very weak results with value of 0.12 (< 0.50) together with significant Wilks’ Lambda value of 0.001.

    Therefore, as expected CFA required modification for model fitting. There is a need to remove undesired measures at an early stage of research. By performing a factor analysis before purification, there is a tendency to produce many more dimensions than can be conceptually identified (Churchill, 1979;Lai et al., 2007). The second tier of factor analysis was carried out using Confirmatory Factor Analysis (CFA) by excluding all low loading items (P5, P6, P9, P14, 15, P19, P23, and P28) based on EFA results. During the first run of CFA, the value is acceptable with Goodness Fit Indices (GFI) = 0.893 and Root of Mean Square Residual (RMR) = 0.051. The second model fits indices generated acceptable results with the value of GFI = 0.936 and RMR = 0.052 (Hair et al., 2010;Hu and Bentler, 1999) with 23 items after the exclusion of item P5, P6, P9, P14, 15, P19, P23, and P28 from the proposed SUSSERV’s instrument with 31 items. The parsimonious fit or Chi- Square Goodness of Fit Test (χ2/df) was also able to support model fit with value 2.08 below 3.0 thus disagreed with those who discredited the chi-square (χ2) test and has limitation on large sample size (Hooper et al., 2008;Barret, 2006;Gefen et al., 2000;Bentler and Bonnet, 1980). The incremental fit or Adjusted Fit Index (AGFI) with value of 0.90 is acceptable. The fitness indexes have been used and recommended the use of at least one fitness index from each category of model fit namely absolute fit, incremental fit and parsimonious fit (Ahmad, 2017;Hair et al., 2010;Hu and Bentler, 1999). The summary of three categories of model fit indexes and indices as shown in Table 6.

    4.4 SUSSERV Dimension Importance Scores

    SERVQUAL instrument identified the perception of importance dimension by dividing 100 points to all dimensions based on feedback from respondents (Zeithaml et al., 1990). Among the scholars used weighted SERVQUAL dimension importance scores are Kiran (2013) (customers services center); James et al. (2012) (agronomic services); Prithivirajh (2013) (furniture retailers). However, in this study, the importance of SUSSERV dimensions was identified based on respondents’ evaluation by ranking the dimension from “1” to “3” only as above mentioned on research instrument. The computation of SUSSERV dimensions scores or weighted scores established by multiplying the dimension weight or importance of dimensions and the dimension gap scores. The weighted average of importance scores for all six dimensions was used as an indicator of the companies’ overall service quality gap or performance. Table 7 below shows the allocation of the points among the dimensions and the average unweighted gap scores, weighted importance and weighted scores of SUSSERV dimensions for Water and Sewerage Services.

    All six dimensions were equally important to the respondents since their weighted importance score for each dimension ranged from 16% to 17% only. The result for weighted SUSSERV scores explained the level of satisfaction as compared to the gap scores and furthermore, they are close to the unweighted SUSSERV scores. The overall weighted SUSSERV scores are -0.96 (combined water and sewerage services), -1.06 (water services) and - 0.85 (sewerage services). This indicates that customers who received services from WC and SC were dissatisfied. Therefore, WC and SC need to improve their level of service quality and focus on most dissatisfaction dimension (see Table 7 and Figure 2).

    The overall SUSSERV importance weights for combined water and sewerage services (N = 500) showed the least importance was Tangible of 16.55%, however, the most important dimension was Responsiveness of 16.82%. Although the Sustainability a newly added dimension but ranked second from all six dimensions. However, the importance weight of Tangible dimension for both water and sewerage services (N = 500) was also low as compared to other dimensions due to customers’ observed there were no much high technology equipment or tools used at the counter or being occupied on sites.

    The average weighted importance SUSSERV scores for combined water and sewerage services (N = 500) was -15.95%, however for water services (N = 250) and sewerage services (N = 250) was -17.66% and -14.20% only. These findings indicated the least important and dissatisfied dimension for combined water and sewerage services (N = 500), for water services (N = 250), and for sewerage services (N = 250) is Empathy with highest negative values of -17.12%, -18.75 and -15.32 respectively thus remained unchanged and consistent as compared to the unweighted scores (-1.03, -1.13 and -0.92 respectively). With regard to the smallest important weight assigned to Empathy, the fact that all issues related to how WC and SC gave special attention and fulfilled their customers’ needs.

    The overall SUSSERV importance weights for water services indicated that WC should pay more attention to improve their level of service quality and focus on the least important perceived dimension Tangible of 16.45% but not significantly different from the most important perceived dimension Responsiveness of 16.82% (see Table 7). However, the overall SUSSERV importance weights for sewerage services indicated that the least important perceived dimension is Sustainability of 16.58%, thus WC should pay more attention to increase their level of service quality than the most important perceived dimension Responsiveness of 16.78% (see Table 7). The above finding indicated both water services (N = 250) and sewerage services (N = 250) gained the highest value of importance weights on Responsiveness dimension thus showed that it is highly perceived amongst the customers of WC and SC especially on the aspect of prompt services and always assist on customers’ requests. Although the importance weight scores of SUSSERV dimension was different to the average unweighted scores of SUSSERV dimensions, the average weighted scores showed similar results with the average unweighted scores with highest and lowest dissatisfied dimension are Empathy and Reliability respectively. The result attributed to all importance weight of SUSSERV dimensions are similar to each other or least different.

    4.5 Importance-Performance Analysis

    The IPA using a Cartesian model where mean for performance/ satisfaction (perceptions) and importance (expectations) is plotted for each variable for water and sewerage services (see Table 7). The importance-performance analysis for both water supply and sewerage services (see Figures 3 and 5) indicated the Assurance and Responsiveness dimensions were at high levels for both performance (perceptions) and importance (expectations) of the customers, so the company needs to maintain its performance on this dimensions, thus consistent with findings by Bahadori et al. (2015). However, sustainability dimension a new item created for sustainable service quality or SUSSERV and the customers were still unfamiliar thus resulted to lower perceptions (performance) and expectations (importance) for both water supply and sewerage services.

    The overall percentage of SUSSERV importance dimensions for water supply services (see Figure 3) and the detailed 31 variables or statements (see Figure 4) suggested that the performance of the water supply company for Reliability dimension was in a high level of performance but low expectations of the customers. Therefore, the company should minimize the resources of the company and spending for other weak dimensions where the performance or perceptions are lower than desires of the customer so that the company should increase its performance and urgently required such Empathy and Sustainability. Three dimensions namely Tangible, Assurance and Responsiveness were at high levels for both performance (perceptions) and importance (expectations) of the customers, so the company needs to maintain its performance on these dimensions.

    The overall percentage of SUSSERV importance dimensions for sewerage services (see Figure 5) and the detailed 31 variables or statements (see Figure 6) suggested that the performance of the sewerage company for Reliability dimension was in a high level of performance but low expectations of the customers. Therefore, the company should minimize the resources of the company and spending for other weak dimensions where the performance or perceptions are lower than desires of the customer so that the company should increase its performance and urgently required such Empathy and Tangible however for Sustainability, no improvement needed due to low expectations of customers. However, for Sustainability, improvement is required due to low performance and low expectations of customers to shift to quadrant A. Two dimensions, Assurance and Responsiveness were at high level for both performance (perceptions) and importance (expectations) of the customers, so the company needs to maintain its performance on these dimensions.

    The above importance-performance analysis for both water supply and sewerage services indicated the Assurance and Responsiveness dimensions were at high levels for both performance (perceptions) and importance (expectations) of the customers, so the company needs to maintain its performance on these dimensions thus consistent with findings by Bahadori et al. (2015). On contrary, sustainability dimension a new item created for sustainable service quality (SUSSERV) and the customers were still unfamiliar thus resulted to lower perceptions (performance) and expectations (importance) for both water supply (WC) and sewerage services (SC) so not required improvement. The IPA also found that there were significant differences between WC and SC for Tangible dimensions. WC has high importance-performance compared to SC has high importance but low performance thus required immediate improvement of its service.

    5. DISCUSSION

    The EFA results proposed that sustainability factor have been satisfactorily explained and the data for all factors (KMO) are suitable for factor analysis especially the reliability of the SUSSERV instrument. The CFA has shown the construct validity for all 25 items comprises of 18 items from the existing SERVQUAL and 7 items from additional Sustainability dimension thus suggested that SUSSERV model is proven valid. Based on the above results and analysis indicate that all dimensions were negatively perceived meaning dissatisfaction among the respondents towards services rendered by both water and sewerage companies. In Selangor, the water services NRW is still high at 32% and also a high number of inquiries and complaints about sewerage services in 2016 (The Malaysian Water Association, 2017). The NRW is one of the operational risk need to be faced and overcome by water operator due to water leaks and theft (Hope and Rouse, 2013). The finding consistent with previous studies by Abdul Talib et al. (2014) and Chau and Kao (2009) suggested that low service quality have a positive relationship or low profitability, economic performance (growth), cost reducing and competitive advantage.

    The findings suggested that both companies negatively perceived by customers, however, the sewerage company’s perceived quality for all SUSSERV dimensions much lower than the sewerage company. The sewerage company under Ministry of Finance and considered a public-owned or government-owned company apply lower tariffs, invest more and are more efficient in the use of labor than the water services company of which prior to mid-year 2016 was mixed-owned firm between the state government and individual thus consistent with a previous study (Guerrini et al., 2011). It is also suggested that the water services company should take some necessary actions or positives initiatives for improvement such as increasing the water reserve margin from 0% to more than 5% to avoid frequent water disruption and to gain customers’ or public confidence thus in line with prior research (Akinboade et al., 2012).

    The findings were limited to the water supply company located in Selangor and F.T. was not necessarily generalizable to other states but the sewerage company also providing the same services in other states of Malaysia. Other restrictions of this study are the data for this study were collected using self-reported survey questionnaire, thus, respondents can decide to pick, or not to pick, to take part, or not take part, in the survey; and the water supply and sewerage service provider are two different companies and shareholders.

    6. CONCLUSION

    Based on the literature review and empirical evidence, the authors propose that the SUSSERV model has achieved the research objective as the instrument was tested to be valid and reliable where it can be used to measure sustainable service quality of Malaysian WC and SC. Previous efforts and focus made on water quality and water treatment or process quality based were more technical in nature, thus this paper is an attempt to fill the gap between services, product and process quality by including sustainability. The findings can be used as a reference and guidance for regulatory body and government to evaluate the service quality or analyze the performance of the water and sewerage companies.

    Major findings involved the IPA has proved that both water supply and sewerage services are reliable to the customers even though the overall services rendered are below the expectation of the customers. Furthermore, the novelty and contribution of this study is a valid SUSSERV instrument has been developed to measure the sustainable service quality of water and sewerage companies. The IPA findings indicated that SC should improve its service quality, particularly in Quadrant A dimension namely Empathy and Tangible. Both WC and SC should minimize resources in Reliability dimension due to the customers can rely on their services. Future research should focus on using this instrument in other states in Malaysia to further validate and test this instrument. The stakeholders in water services industry will be able to further investigate and overcome their weaknesses using this model and can be used in the near future especially in Malaysia. Finally, future research should also determine the employees’ perceptions of service quality towards customers’ expectations (Gap 1) and the management’s perceptions towards service quality specifications (Gap 2) based on SERVQUAL model.

    ACKNOWLEDGMENT

    We acknowledge with gratitude the Ministry of Higher Education (MOHE) Malaysia and Universiti Teknologi Malaysia (UTM) for their confidence in our expertise and financial support.

    Figure

    IEMS-18-1-89_F1.gif

    Type of Quality from Different Perspectives (Salleh and Yusof, 2016b).

    IEMS-18-1-89_F2.gif

    SUSSERV importance weights – combined water and sewerage services (N = 500).

    IEMS-18-1-89_F3.gif

    Importance performance analysis (IPA) of six dimensions of SUSSERV on water supply services.

    IEMS-18-1-89_F4.gif

    Importance-performance analysis (IPA) of 31 variables of SUSSERV on water services.

    IEMS-18-1-89_F5.gif

    Importance performance analysis (IPA) of six dimensions of SUSSERV on sewerage services.

    IEMS-18-1-89_F6.gif

    Importance performance analysis (IPA) of 31 variables of SUSSERV on sewerage services

    Table

    Demographic factor – type of house

    Demographic factor – location of premise

    Demographic factor – race of respondent

    The gaps between perception and expectation on water and sewerage services

    Exploratory factor analysis of six dimensions/rotated factors (Perceptions)

    (a) Removed after Exploratory Factor Analysis (EFA) during Confirmatory Factor Analysis (CFA).

    The summary of model fit indexes and indices value

    The average unweighted gap scores, importance weighted and average weighted scores of SUSSERV dimensions (Water and Sewerage services of the table)

    REFERENCES

    1. Abdul Talib, H. H. , Mohd Ali, K. A. , and Idris, F.(2014), Critical success factors of quality management practices among SMEs in the food processing industry in Malaysia, Journal of Small Business and Enterprise Development, 21(1), 152-176.
    2. Abdullah, F. , Suhaimi, R. , Saban, G., and Hamali, J.(2011), Bank service quality (BSQ) Index: An indicator of service performance, International Journal of Quality and Reliability Management, 28(5), 542-555.
    3. Abuosi, A. A. and Atinga, R. A. (2013), Service quality in healthcare institutions: Establishing the gaps for policy action, International Journal of Health Care Quality Assurance, 26(5), 481-492.
    4. Afful-Dadzie, A. , Afful-Dadzie, E. , and Turkson, C.(2016), A TOPSIS extension framework for re-conceptualizing sustainability measurement, Kybernetes, 45(1), 70-86.
    5. Agus, A. and Hajinoor, M. S. (2012), Lean production supply chain management as driver towards enhancing products quality and business performance: Case research of manufacturing companies in Malaysia, International Journal of Quality and Reliability Management, 29(1), 92-121.
    6. Ahmad, M. F. (2017), Application of Structural Equation Modeling (SEM) in Quantitative Research, Penerbit UTHM, Johor.
    7. Akinboade, O. A. , Kinfack, E. C. , and Mokwena, M. P.(2012), An analysis of citizen satisfaction with public service delivery in the Sedibeng district municipality of South Africa, International Journal of Social Economics, 39(3), 182-199.
    8. Al Jahwari, D. S. , Sirakaya-Turk, E. , and Altintas, V.(2016), Evaluating communication competency of tour guides using a modified importance-performance analysis (MIPA), International Journal of Contemporary Hospitality Management, 28(1), 195-218.
    9. Ali, A. J. , Islam, M. A. , and Howe L. P.(2013), A study of sustainability of continuous improvement in the manufacturing industries in Malaysia: Organizational self‐assessment as a mediator, Management of Environmental Quality: An International Journal, 24(3), 408-426.
    10. Amat Taap, M. , Chong, S. C. , Kumar, M., and Fong, T. K.(2011), Measuring service quality of conventional and Islamic bank: A comparative analysis, International Journal of Quality and Reliability Management, 28(8), 822-840.
    11. Amran, A. , Abdul Khalid, S. N. , Abdul Razak, D., and Haron, H.(2010), Development of MBA with specialisation in sustainable development: The experience of Universiti Sains Malaysia, International Journal of Sustainability in Higher Education, 11(3), 260-273.
    12. Arbuckle, J. L. (2013). IBM® SPSS® Amos™ 22 User’s Guide, IBM.
    13. Bahadori, M. , Raadabadi, M. , Ravangard, R., and Baldacchino, D.(2015), Factors affecting dental service quality, International Journal of Health Care Quality Assurance, 28(7), 678-689.
    14. Barrett, P. (2006), Structural equation modelling: Adjudging model fit, Personality and Individual Differences, 42(5), 815-824.
    15. Benedetti, L. , Langeveld, J. , Nieuwenhuijzen, A. F. V., Jonge, J., Klein, J., Flameling, T., Nopens, I., Zanten, O., and Weijers, S.(2013), Cost-effective solutions for water quality improvement in the Dommel River supported by sewer-WWTP-river integrated modelling, Water Science and Technology, IWA Publishing, 68(5), 965-973.
    16. Bentler, P. M. and Bonnet, D. C. (1980), Significance tests and goodness of fit in the analysis of covariance structures, Psychological Bulletin, 88(3), 588-606
    17. Blešić, I. , Popov-Raljić, J. , Uravić, L., Stankov, U., Đeri, L., Pantelić, M., and Armenski, T.(2014), An importance- performance analysis of service quality in spa hotels, Economic Research-Ekonomska Istraživanja, 27(1), 483-495.
    18. Chau, V. S. and Kao, Y. Y. (2009), Bridge over troubled water or long and winding road?: Gap‐5 in airline service quality performance measures, Managing Service Quality: An International Journal, 19(1), 106-134.
    19. Choi, H. C. , Lee, W. , Sung, H. K., and Chiu, C. F.(2014), Evaluation of the service performance: Application of the zone of tolerance with importance-performance analysis of a convention facility. In Arch G. Woodside, Metin Kozak (ed.), Tourists’ Behaviors and Evaluations (Advances in Culture, Tourism and Hospitality Research, Volume 9), Emerald Group Publishing Limited, 9-19.
    20. Churchill, G. A. Jr.(1979), A paradigm for developing better measures of marketing constructs, Journal of Marketing Research, 16(1), 64-73.
    21. Cooper, D. M. and Schindler, P. S. (2001), Business Research Methods (7th ed.), McGraw-Hill Irwin, New York, NY.
    22. Cronin, J. J. and Taylor, S. A. (1992), Measuring service quality: A re-examination and extension, Journal of Marketing, 56(3), 55-68.
    23. Dabestani, R. , Shahin, A. , Saljoughian, M., and Shirouyehzad, H.(2016), Importance-performance analysis of service quality dimensions for the customer groups segmented by DEA: The case of four-star hotels, International Journal of Quality & Reliability Management, 33(2), 160-177.
    24. Davis, D. and Cosenza, R. M. (2000), Business Research for Decision Making (5th ed.), Duxbury Press, Department of Statistics, Malaysia.
    25. Department of Statistics, Malaysia(2017), Current Population Estimates 2017, ISSN 2462-2273.
    26. Elkington, J. (1998), Accounting for the triple bottom line, Measuring Business Excellence, 2(3), 18-22.
    27. Fernando, R. (2012), Sustainable globalization and implications for strategic corporate and national sustainability, Corporate Governance: The International Journal of Business in Society, 12(4), 579-589.
    28. Galeeva, R. B. (2016), SERVQUAL application, and adaptation for educational service quality assessments in Russian higher education, Quality Assurance in Education, 24(3), 329-348.
    29. Gefen, D. , Straub, D. W. , and Boudreau, M.(2000), Structural equation modeling techniques and regression: Guidelines for research practice, Communications of Associations for Information System, 4(Article 7), 1-77.
    30. George, D. and Mallery, P. (2010), SPSS for Windows Step by Step: A Simple Guide and Reference 17.0 Update (10th ed.), Pearson, Boston.
    31. Gronroos, C. (1984), A service quality model and its marketing implications, European Journal of Marketing, 18(4), 36-44.
    32. Guerrini, A. , Romano, G. , and Bettina, C.(2011), Factors affecting the performance of water utility companies, International Journal of Public Sector Management, 24(6), 543-566.
    33. Hair, J. F. , Black, W. C. , Babin, B. J., and Anderson, R. E.(2010), Multivariate Data Analysis A Global Perspective (7th ed.), Pearson Education.
    34. Hooper, D. , Coughlan, J. , and Mullen, M. R.(2008), Structural equation modelling: Guidelines for determining model fit, The Electronic Journal of Business Research Methods, 6(1), 53-59.
    35. Hope, R. and Rouse, M. (2013), Risks and responses to universal drinking water security, Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences, The Royal Society Publishing, 20120417.
    36. Hosseinpour, A. , Peng, Q. , and Gu, P.(2015), A benchmark- based method for sustainable product design, Benchmarking: An International Journal, 22(4), 643-664.
    37. Hu, L.T. and Bentler, P. M. (1999), Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives, Structural Equation Modeling, 6 (1), 1-55.
    38. Izadi, A. , Jahani, Y. , Rafiei, S., Masoud, A., and Vali, L.(2017), Evaluating health service quality: Using importance performance analysis, International Journal of Health Care Quality Assurance. 30(7), 656-663.
    39. James, O. M. , Emmanuel, O. D. , and Robert, A.(2012), Assessing farmers’ satisfaction of agronomic services received in ghana using the servqual model: A case study of Kumasi Metropolis, International Journal of Business and Social Science, 3(19), 51-60.
    40. Joseph, C. (2013), Understanding sustainable development concept in Malaysia, Social Responsibility Journal, 9(3), 441-453.
    41. Kiran, R. S. (2013), Analysis of service quality using the SERVQUAL method and importance analysis (IA) for the customer service (S) in a service center in India, International Journal of Commerce & Business Studies, 1(1), 09-21.
    42. Kim, S. K. , Yim, B. H. , Byon, K. K., Yu, J. G., Lee, S. M., and Park, J. A.(2016), Spectator perception of service quality attributes associated with Shanghai Formula one: Importance and performance analysis approach, International Journal of Sports Marketing and Sponsorship, 17(2), 153-171.
    43. Kline, R. B. (2011), Principles and Practice of Structural Equation Modeling (3rd ed.), The Guilford Press A Division of Guilford Publications, Inc. Spring Street, New York, NY.
    44. Kong, S. M. and Muthusamy, K. (2011), Using service gaps to classify quality attributes, The TQM Journal, 23(2), 145-163.
    45. Lai, F. , Hutchinson, J. , Li, D., and Bai, C.(2007), An empirical assessment and application of SERVQUAL in mainland China’s mobile communications industry, International Journal of Quality & Reliability Management, 24(3), 244 -262.
    46. Lam, S. Y. , Lee, V. H. , Ooi, K. B., and Phusavat, K.(2012), A structural equation model of TQM, market orientation and service quality: Evidence from developing country, Managing Service Quality: An International Journal, 22(3), 281-309.
    47. Lee, Y. C. , Wu, H. H. , Hsieh, W. L., Weng, S. J., Hsieh, L. P., and Huang, C. H.(2015), Applying importanceperformance analysis to patient safety culture, International Journal of Health Care Quality Assurance, 28(8), 826-840.
    48. Lehtinen, U. (2012), Sustainability and local food procurement: A case study of Finnish public catering, British Food Journal, 114(8), 1053-1071.
    49. Martilla, J. A. and James, J. A. (1977), Importanceperformance analysis, Journal of Marketing, 41(1), 77-79.
    50. Moe, C. L. and Rheingans, R. D. (2006), Global challenges in water, sanitation and health, Journal of Water and Health, 4(S1), 41-57.
    51. Muzaffar, M. B. (2016), The Development and validation of a scale to measure training culture: The TC scale, Journal of Culture, Society and Development, 23(1), 49-58.
    52. Office of the Tasmanian Economic Regulator(2013), Tasmanian Water and Sewerage Industry. Second Issued: 12 April 2013 (Version 2), Hobart, Tasmania.
    53. Omar, N. A. , Che Wel, C. A. , Aziz, N. A., and Alam, S. S.(2013), Investigating the structural relationship between loyalty programme service quality, satisfaction and loyalty for retail loyalty programmes: Evidence from Malaysia, Measuring Business Excellence, 17(1), 33-50.
    54. Pak, R. J. (2016), Combination of importance-performance analysis and response surface methodology for enhancingsatisfaction, International Journal of Quality & Reliability Management, 33(6), 792-802.
    55. Parasuraman, A. , Zeithaml, V. A. , and Berry, L. L.(1988), SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality, Journal of Retailing, 64(1), 12-40.
    56. Prithivirajh, S. (2013), An application of SERVQUAL to determine customer satisfaction of furniture retailers in Southern Africa: A cross - national study, Unpublished Ph.D Thesis, Vaal Campus of the North-West University, SA.
    57. Salleh, A. and Yusof, S. M. (2016a), Service quality of water and sewerage companies, Proceedings of the 17th Asia Pacific Industrial Engineering & Management Systems Conference 2016 (APIEMS 2016), Taipei, Taiwan.
    58. Salleh, A. and Yusof, S. M. (2016b), Sustainable service quality of water and sewerage companies, Journal of Business and Social Review in Emerging Economies, 2(1), 1-10.
    59. Salleh, A. , Yusof, S. M. , and Othman, N.(2017), Measuring sustainable service quality (SUSSERV) of Malaysian water and sewerage companies, Proceedings of the 18th Asia Pacific Industrial Engineering & Management Systems Conference 2017 (APIEMS 2017), Yogyakarta, Indonesia.
    60. Tajbakhsh, A. and Hassini, E. (2015), Performance measurement of sustainable supply chains: A review and research questions, International Journal of Productivity and Performance Management, 64(6), 744-783.
    61. The Malaysian Water Association(2017), Malaysia Water Industry Guide, Malaysia.
    62. Tileng, M. Y. , Utomo, W. H. , and Latuperissa, R.(2013), Analysis of service quality using servqual method and importance performance analysis (IPA) in population department, Tomohon City, International Journal of Computer Applications (0975-8887), 70(19), 24-30.
    63. Tzeng, G. H and Chang, H. F. (2011), Applying importance- performance analysis as a service quality measure in food service industry, Journal of Technology and Management & Innovation, 6(3), 106-115.
    64. Voon, B. H. , Lee, N. , and Murray, D.(2014), Sports service quality for event venues: Evidence from Malaysia, Sports, Business and Management: An International Journal, 4(2), 125-141.
    65. Zeithaml, Z. A. , Parasuraman, A. , and Berry, L. L.(1990), Delivering Quality Service: Balancing Customer Perceptions and Expectations, Collier Macmillan Publishers, London.