Modelling of Quality in Service with Used Structural Equation Model (PLS): A Case Study of Urban Parks in Shiraz

Int. j. econ. manag. soc. sci., Vol(3), No (6), June, 2014. pp.334-342 TI Journals International Journal of Economy, Management and Social Sciences www.tijournals.com ISSN: 2306-7276 Copyright © 2014. All rights reserved for TI Journals. Modelling of Quality in Service with Used Structural Equation Model (PLS): A Case Study of Urban Parks in Shiraz Ali Mohammadi Associate Professor of management, Shiraz University, Iran. Mahin Sabet Sarvestani* MA in industrial management, Shiraz University, Iran. Mohammad Nazari Ph.D. student in management, Shiraz University, Iran. *Corresponding author: [email protected] Keywords Service Quality Urban Parks Structural Equation Model PLS 1. Abstract The complex and multidimensional nature of citizen satisfaction and service quality has led to the compilation of different definitions and models in literature review. In addition, the concern of service quality may differ from one country to another and from one industry to another. Therefore, it is crucial to develop measures of service quality that are pertinent to the country and culture where the service is offered. Hence, this research was carried out for the purpose of explaining the factors affecting the citizen satisfaction with service quality of urban parks in Iran and then analyzing those factors by using Partial Least Squares (PLS). Indicators in this model extracted from previous research on citizen satisfaction with service quality of urban parks and service quality models like SERVQUAL model, and Johnston et al. (1990).This survey resulted in six dimension: Aesthetic and visual aspect, Park amenities, Park facilities and services, Physical health and safety, Social and Psychological Safety, Dynamic management. This model has been tested in six urban parks in Shiraz through 360 questionnaires. The findings showed that dimension of service quality in this model account for almost 0.79 of the variability in the original variables. Also, the service quality measurement scale developed in this study can also be used to monitor and improve the quality of service delivered to citizens and provide valuable information about the dimensions that reflect citizens 'perceptions of service quality. Introduction Parks as one of the urban green space are positive elements of the urban environment and landscape [1]. Urban greens paces like nature parks contribute to quality of life by providing physical and psychological benefits, as well as ecosystem services [2]. Some of social benefits of urban green space includes recreational opportunities, aesthetic enjoyments, adjusting psychological well-being and physical health, enhancing social ties, and providing educational opportunities [3]. In other word, with the growing pace of urban lifestyles, public parks are increasingly becoming one of the primary venues for leisure pursuits [4]. Parks facilities and services offer various opportunities to fulfill individual, social, economic, and environmental benefits [5]. Hence environmental planners and researchers also recognize that the planning and establishment of parks and green spaces is a way to improve the quality of people’s lives. There is a challenge for park managers to manage such lands in ways that provide maximum benefits to city residents by seeking shortcuts to increase productivity in the era of tight budget. The first step to solving this challenge is determination dimensions of service quality from the perspective citizens. Although both public and private sectors have given much attention to the concept of customer satisfaction and service quality in the past couple of decades, there are still little empirical evidence on the usefulness of service quality models or techniques for understanding customer satisfaction in the context of public parks [4]. Hence the approach taken in this study is to determine the factors affecting the citizen satisfaction with service quality of urban parks in Iran and then analyzing those factors by using Partial Least Squares (PLS). 2. Theoretical framework and literature review The subject of service quality is very rich in context of definitions, models and measurement issue. Several researchers explored the subjects with varying perspectives and using different methodologies [6]. For example, Parasuraman et al was first one propose service quality is therefore a function of the magnitude and direction of the Expected Service-Perceived Service Gap [7]. Among researchers that propose perception and expectation are effective in evaluating of service quality including: Haywood-Farmer (1988), synthesized model of service quality [8-9]. Mattsson, (1992) added value approach to service quality [10]. Unlike, researcher noted researchers as Cronin and Taylor (1992) and Teas (1993) claim that perceptions only are better predictor of service quality [11-12]. Also, Some of researchers proposed integrative models of service quality, that integrate the concept of service quality in a network of related concepts, such as value, like, Sweeney et al. (1997) indicated service quality is important in value and willingness to buy [13]. Oh (1999) focused on service quality is an antecedent of customer satisfaction and perceived customer value [14] and Dabholkar et al. (2000) in antecedents and mediator model examined role of antecedents (reliability, personal attention, comfort and features), consequences (customer satisfaction and behavioral intentions), and mediators (service quality) in service quality [15]. It is clear, although, there is many models of service quality but only some of them operationalize concept of quality. Some researchers have tried to operationalize the concept of quality service are: Gronroos (1984) identified 335 Modelling of Quality in Service with Used Structural Equation Model (PLS): A Case Study of Urban Parks in Shiraz International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. three components of service quality, namely: technical quality; functional quality; and image [6]. Parasuraman and et. al. (1985) used five dimensions so tangibles, reliability, responsiveness, assurance and empathy so as dimension of quality in SERVQUAl tool [16]. Johnston et al. (1990) suggested 17-component structure (access, appearance/aesthetics, availability, cleanliness/tidiness, comfort, communication, competence, courtesy, friendliness, reliability, responsiveness, security, attentiveness/helpfulness, care, commitment, functionality, integrity) as service quality determinants [17]. Freeman and Dart (1993) conclude that service quality is a seven-dimensional construct. Robinson and Pidd (1998) propose 19 dimensions of service quality in the context of management science projects [18]. Grönroos (1990) postulated six criteria of perceived good service quality: professionalism and skills; attitudes and behavior; accessibility and flexibility; reliability and trustworthiness; recovery; reputation and credibility [19]. Albrecht and Zemke (1985) suggested care and concern, spontaneity, problem solving and recovery [17]. Also, The Citizen-Centred Service Network (CCSN) and the Canadian Centre for Management Development (CCMD) developed the Common Measurements Tool (CMT) as a standardized survey instrument for public service organizations. The instrument allows users to identify customer expectations and priorities with the intention of improving services. Dimensions this tool are: responsiveness, reliability, access and facilities, communication and cost [20]. The above arguments show many studies on service quality are carried out in various fields but such studies in the field of urban parks and green spaces, has been done less, some of the most important ones include. Hamilton et al. (1991) was concerned with whether or not five dimensions of SERVQUAL described service quality in the context of parks. They noted that service quality in urban parks has four dimensions: tangibles, reliability, responsiveness and assurance [21]. Hayati and et al. (2011) used 5 dimensions of SERVQUL model for measuring citizens' satisfaction with the service quality provided in Tabriz Parks [22]. Demir and et al (2010) surveyed Transportability management factor, Management of user control factor, Informing management factor, Visual management factor, Recreational opportunity Management factor to determine the user satisfaction related to management practices on the recreational areas in Mogan park, Ankara and coastal area of Mugada, Bartın [23]. SadeghiNaeni (2010) surveyed safety variable as one of dimension of service quality in neighborhoods and district Tehran parks [24]. Noe and Uysal (1997) described overall satisfaction as a function of instrumental (as fencing, restrooms, lighting, shade or facility conditions) and expressive factors (fishing, walk around island, guided fort tour, guardroom exhibits and sales items), expectations, and past-use. Their study revealed that expressive and instrumental factors may be stronger predictors of overall satisfaction than are the expectation factors or past-use [25]. Eng and Niininen (2005) examined quality of the services and facilities provided by public parks with SERVQUAL model. They revealed that the attributes corresponding to performance of service delivery involve the interaction between non-human aspects of physical environment and emotional experience of users which differ from common human aspects of service quality [4]. Muderrisoglu and et al (2010) surveyed satisfaction level of users by using gap analyses method in significant green spaces of Ankara [26]. Wang and Zhang (2014) proposed six factors influencing the service quality of urban parks, namely, place environment, landscape environment, culture environment, eco-environment, traffic and facilities environment [27]. 3. Dimension of service quality of urban parks According to all literature surveys, there is no general agreement on the indices or the dimensions of service quality in urban parks. Hence, the various steps that have been followed in development research model are: 1. Review of research related to service quality and standards and the criteria referred to citizen satisfaction with urban parks. 2. Interviews with faculty members, consultants and employees of the parks and green space of Shiraz municipality 3. Listing certain criteria related to citizen satisfaction from service quality of urban parks in Iran. 4. Interviews with some of citizens for more important measures. 5. Determining main dimensions service quality in urban parks in Iran and designing the final model Finally, the results of this procedure are shown in table1. Table 1: SQ Dimensions and indicators codes Dimensions and indicators Z Aesthetic and visual aspects park Z4 Green space and plotting park Z5 Fountain, water flow and lake Z6 Visual symbols Ali Mohammadi, Mahin Sabet Sarvestani *, Mohammad Nazari International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. codes Dimensions and indicators Z7 Furniture layout park (color, variety and balance with the environment) Z8 Information boards (where exposure and visibility) R Park amenities R10 WC (number and location) R11 Drinker (number and location) R12 Food and drink R13 Benches, awnings, alcove, trash and,... R14 Car parking T Park facilities and services T16 Sports and leisure facilities fitness with different age groups in the park T17 Variety of recreational and sporting services T18 cost T19 Cultural and social programs diversity EB Physical health and safety of park EB21 Safety playground, equipment, landscaping, parks, and waterfronts... EB22 Cleanliness and hygiene park EB23 Cleanness and hygiene WC EB24 Light of all the park EB25 Warning signs in the park (where exposure and visibility) EB26 Insects and vermin control EA Social and psychological safety of park EA28 The number of police and park security guard EA29 Responsiveness EA30 Responsibility EA31 Sense of social safety and security in the park area M Dynamic management of the park M33 Informing about programs and park facilities M34 Maintenance of green spaces of park in all seasons M35 Maintenance of park facilities M36 Authorities to engage with citizens 336 337 Modelling of Quality in Service with Used Structural Equation Model (PLS): A Case Study of Urban Parks in Shiraz International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. Z R T EB SERVICE QUALITY EA M Figure 1: Inner model in PLS 4. Data Analysis 4.1 Partial Least Squares Partial Least Squares is a family of regression based methods designed for the analysis of high dimensional data in a low-structure environment. Its origin lies in the sixties, seventies and eighties of the previous century, when Herman O.A. Wold vigorously pursued the creation and construction of models and methods for the social sciences, where “soft models and soft data” were the rule rather than the exception, and where approaches strongly oriented at prediction would be of great value [28]. PLS‑SEM is, as the name implies, a more “regression-based” approach that minimizes the residual variances of the endogenous constructs [29]. PLS path models are formally defined by two sets of linear equations: the inner model and the outer model. The inner model specifies the relationships between unobserved or latent variables, whereas the outer model specifies the relationships between a latent variable and its observed or manifest variables [30]. 4.2 Assessing the measurement model For the assessment of validity two validity subtypes are usually examined: the convergent validity and the discriminant validity. Convergent validity measures the extent to which a construct converges in its indicators by explaining the items’ variance [31]. Fornell and Larcker (1981) suggest using the average variance extracted (AVE) as a criterion of convergent validity. An AVE value of at least 0.5 indicates sufficient convergent validity, meaning that a latent variable is able to explain more than half of the variance of its indicators on average [32]. Discriminant validity is a rather complementary concept: Two conceptually different concepts should exhibit sufficient difference (i.e. the joint set of indicators is expected not to be unidimensional). In PLS path modeling, two measures of discriminant validity have been put forward: The Fornell–Larcker criterion and the cross-loadings. The Fornell–Larcker criterion (1981) postulates that a latent variable shares more variance with its assigned indicators than with any other latent variable. In statistical terms, the AVE of each latent variable should be greater than the latent variable’s highest squared correlation with any other latent variable. The second criterion of discriminant validity is usually a bit more liberal: The loading of each indicator is expected to be greater than all of its cross-loadings (Chin, 1998; Götz et.al., 2009). Although the Fornell–Larcker criterion assesses discriminant validity on the construct level, the cross-loadings allow this kind of evaluation on the indicator level [30]. 4.3 Assessing the Structural Model Because PLS makes no distributional assumption, other than predictor specification, in its procedure for estimating parameters, traditional parametric-based techniques for significance testing/ evaluation would not be appropriate. Instead, Wold (1980, 1982b) argued for tests consistent with the distribution-free/predictive approach of PLS. In other words, rather than based on covariance fit, evaluation of PLS models should apply prediction-oriented measures that are also nonparametric. To that extent, the R-square for depend LVs, the Stone-Geisser (Geisser, 1975; Stone 1974) test for predictive relevance, and Fornell and Larcker,s (1981) average variance extracted measure are used to assess predictiveness, whereas resampling procedures such as jacknifing and bootstrapping are used to examine the stability of estimates [33]. 5. Methodology Population of this study is significant, green spaces of Shiraz, namely Azadi, Koohpayeh, Jannat, Mellat, Rasavi and Hashemi. Due to the lack of accurate statistics of visitors to the park, number of the population is considered infinite that according to the table of Morgan the sample size 384 was considered. The survey was conducted in July and August 2012 through the distribution of questionnaires to 384 citizens in Shiraz that 360 questionnaire were returned. Cronbach's alpha coefficient was used to assess the reliability of the research questionnaire which is equal to 91%. Ali Mohammadi, Mahin Sabet Sarvestani *, Mohammad Nazari 338 International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. 6. Results In order to statistically analyze the measurement and structural models, this study used Smart PLS software for Partial Least Squares (PLS). In measurement models the internal consistency was assessed by using Cronbach’s alpha and composite reliability. The findings of this survey are shown in Table 2. Table 2: Cronbachs Alpha, Composite Reliability and AVE in outer models construct Cronbachs Alpha Composite Reliability AVE EA 0.843 0.894 0.680 EB 0.776 0.842 0.473 M 0.802 0.871 0.628 R 0.721 0.816 0.473 T 0.731 0.834 0.561 TS 0.860 0.886 0.547 ZZ 0.786 0.895 0.537 As it is shown in table 3, all of dimension of service quality in urban parks have reliability values that are larger than the preferred level of 0.7, which indicate good internal consistency. To check convergent validity, each latent variable’s Average Variance Extracted (AVE) is evaluated. Again from table 4, it is found that all of the AVE values are greater than the acceptable threshold of 0.5, so convergent validity is confirmed. In order to ensure discriminant validity, Fornell and Larcker (1981) suggest the AVE of each latent variable should be higher than the squared correlations with all other latent variables. Table 3 are indicated Fornell-Larcker Criterion Analysis. Table 3: Fornell-Larcker Criterion Analysis for Checking Discriminant Validity EA EB M R T EA 0.825 EB 0.484 0.687 M 0.521 0.681 0.793 R 0.342 0.637 0.534 0.688 T 0.360 0.464 0.515 0.547 0.749 ZZ 0.277 0.541 0.480 0.544 0.447 ZZ 0.733 As it is illustrated in tables 3 discriminant validity is well established. In addition, for evaluating discriminate validity on the indicator level, Chin (1998) proposed cross-loading method. We calculated cross-loading of all reflective measures in the model that its results are reported in table 4. Table 4: Loadings and Cross-Loadings for Reflective Measures EB EA M R T ZZ TS AB21 0.689 0.279 0.516 0.507 0.471 0.433 0.544 AB22 0.765 0.354 0.495 0.469 0.264 0.341 0.590 AB23 0.732 0.332 0.494 0.524 0.243 0.362 0.525 AB24 0.609 0.399 0.417 0.330 0.269 0.285 0.383 339 Modelling of Quality in Service with Used Structural Equation Model (PLS): A Case Study of Urban Parks in Shiraz International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. AB25 0.670 0.350 0.479 0.436 0.395 0.464 0.502 AB26 0.648 0.312 0.403 0.340 0.274 0.338 0.522 Aa28 0.347 0.739 0.331 0.241 0.185 0.197 0.410 Aa29 0.394 0.853 0.443 0.274 0.321 0.220 0.449 Aa30 0.402 0.886 0.491 0.341 0.369 0.264 0.544 Aa31 0.443 0.813 0.437 0.267 0.292 0.226 0.574 M33 0.456 0.356 0.729 0.390 0.457 0.376 0.508 M34 0.562 0.422 0.801 0.422 0.371 0.375 0.615 M35 0.575 0.420 0.836 0.479 0.384 0.413 0.620 M36 0.556 0.450 0.801 0.401 0.436 0.359 0.601 R10 0.497 0.272 0.390 0.723 0.299 0.397 0.484 R11 0.422 0.269 0.394 0.756 0.370 0.368 0.496 R12 0.349 0.206 0.319 0.646 0.386 0.240 0.381 R13 0.552 0.261 0.459 0.731 0.496 0.529 0.571 R14 0.324 0.148 0.230 0.567 0.314 0.274 0.359 T16 0.317 0.198 0.389 0.434 0.797 0.378 0.482 T17 0.410 0.223 0.434 0.470 0.843 0.434 0.519 T18 0.344 0.334 0.319 0.413 0.729 0.284 0.466 T19 0.311 0.342 0.401 0.307 0.603 0.222 0.411 z4 0.411 0.200 0.413 0.367 0.273 0.698 0.511 z5 0.338 0.195 0.271 0.335 0.303 0.696 0.380 z6 0.330 0.143 0.324 0.395 0.376 0.764 0.429 z7 0.450 0.249 0.361 0.441 0.351 0.760 0.527 z8 0.429 0.216 0.366 0.443 0.337 0.745 0.463 Z9 0.457 0.274 0.437 0.506 0.447 0.664 0.694 AB27 0.741 0.440 0.597 0.517 0.355 0.410 0.767 T20 0.473 0.334 0.496 0.498 0.691 0.468 0.718 R15 0.542 0.326 0.481 0.682 0.591 0.543 0.750 M37 0.590 0.478 0.720 0.464 0.419 0.469 0.782 Aa32 0.476 0.631 0.485 0.337 0.302 0.270 0.643 G38 0.588 0.539 0.601 0.498 0.456 0.483 0.808 As table 4 is displayed each indicator has load highest on the construct it is intended to measure. In other hand, all latent variables were well correlated with their own manifest. So, the manifest variables “describe” their latent appropriately and the blocks were therefore validated. Ali Mohammadi, Mahin Sabet Sarvestani *, Mohammad Nazari 340 International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. The quality of each outer model is measured through the communality index for each variable, which represent the proportion of variance in the measurement variables accounted for by the latent variable. Table 5 is indicated communality index for six dimensions of service quality with urban parks. The results showed that communalities in all of variables were above 0.5 and had sufficient explanation. Table 5: communality of variable Dimensions Communality EA 0.680 EB 0.473 M 0.628 R 0.473 T 0.561 TS 0.547 ZZ 0.537 Finally, because of the outer models evaluation provided evidence of reliability and validity, we examined inner model estimates. 6.1 Inner model evaluation After the construct measures have been confirmed as reliable and valid, the next step is to assess the structural model results. The primary evaluation criteria for the structural model are the R² measures, which represents the amount of explained variance of each endogenous latent variable and the level and significance of the path coefficients [29]. In our research, the R2 value of the dependent variable (service quality) is 0.79.in other word, 79% of the variation in citizen satisfaction is related to variables of research model and 29%of this variance is not includes. The path coefficients for six-dimension model are all significant at 95% level. The variable "Social and Psychological Safety" is the most important construct citizen satisfaction with service quality of urban parks (The path coefficient is 0.226) and the less important is the "Park amenities" (The path coefficient is 0.15). The path coefficients of other variables are shown in table 6. Table 6. The path coefficients of inner model Dimensions TS EA 0.226 EB 0.211 M 0.217 R 0.150 T 0.167 ZZ 0.202 Each path coefficient’s significance in PLS was assessed by means of a Bootstrapping procedure. In this procedure, a large number of subsamples (e.g., 5000) are taken from the original sample with replacement to give bootstrap standard errors, which in turn gives approximate T-values for significance testing of the structural path. The Bootstrap result approximates the normality of data [34]. The procedure produces samples consisting of the same number of units as in the original sample. The number of resamples has to be specified. The default is 100 but a higher number (such as 200) may lead to more reasonable standard error estimates [35]. 341 Modelling of Quality in Service with Used Structural Equation Model (PLS): A Case Study of Urban Parks in Shiraz International Journal of Economy, Management and Social Sciences Vol(3), No (6), June, 2014. Using a two-tailed t-test with a significance level of 5%, the path coefficient will be significant if the T-statistics is larger than 1.96. Tables 8 present the path coefficients by PLS estimation and mean path coefficients, STDEV and t-values by bootstrapping for structural model. All path coefficients in the inner model are statistically significant. Because all of the T-Statistics are larger than 1.96 so we can say that the inner model path coefficients are highly significant. Table 7. Path Coefficients (Mean, STDEV, T-Values) at the 0.05 significance level Original Sample Sample Mean Standard Deviation Standard Error T Statistics 800 T Statistics 1000 EA -> TS 0.226 0.224 0.039 0.039 5.867 5.826 EB -> TS 0.212 0.215 0.044 0.044 4.813 4.705 M -> TS 0.218 0.218 0.046 0.046 4.698 4.830 R -> TS 0.150 0.146 0.041 0.041 3.630 3.640 T -> TS 0.166 0.166 0.031 0.031 5.292 5.062 ZZ -> TS 0.201 0.202 0.032 0.032 6.205 5.872 According to the empirical analysis of the proposed model, we think that the results are acceptable and this model may be the proper model in measuring service quality within urban parks in Iran. 7. Conclusion The purpose of the current study was to gain a better understanding of the factors determining the citizen perception of service quality in the urban parks in Iran through the Partial Least Squares (PLS) approach. The results of PLS analysis appeared that the factors representing a judgment about facilities and services in urban parks of Iran are: Physical health and safety of park, Social and psychological safety of park, Aesthetic and visual aspects park, Park facilities and services, Park amenities and Dynamic management of the park. The second major finding was prediction of service quality was almost strong (R2=0.79). Although the proposed model has explained 79% of factor affecting citizen satisfaction with service quality of urban parks in Iran, it does not explain 29% of them that there is a need to identify and assess. It is important to recognize the vital role of the proposed model for municipal and organization of parks and urban green space. They can use this model as a diagnostic tool to identify strengths and weaknesses in their services, suggesting the guidance for performance improvement. 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