برآورد شاخص رضایت مشتری فروشگاه‌های عرضه مواد غذایی، مطالعه موردی: بازارهای ارزاق عمومی شهرداری مشهد

نوع مقاله : مقالات پژوهشی

نویسندگان

• زهرا گلریز ضیائی ¹
• رضا مقدسی ¹
• سعید یزدانی ²

¹ دانشگاه آزاد اسلامی، واحدعلوم و تحقیقات تهران

² دانشگاه آزاد اسلامی، واحدعلوم و تحققات تهران

https://doi.org/10.22067/jead2.v0i0.40763

چکیده

مطالعه حاضر به تعریف و برآورد شاخص رضایت مشتری بازارهای ارزاق عمومی شهرداری مشهد می‌پردازد. بدین منظور پس از تعریف مدل به روش مدل‌سازی معادلات ساختاری از روش‌های حداقل مربعات جزئی و حداکثر آنتروپی تعمیم یافته برای برآورد مدل تعریف شده استفاده می‌کند. پس از برآورد، نتایج دو روش برای انتخاب روش برتر با استفاده از معیار میانگین مربع خطاها با یکدیگر مقایسه می‌شوند. داده‌های مورد استفاده در این تحقیق به صورت پیمایشی و بر مبنای روش نمونه‌گیری روی هدف از 367 نفر از مشتریان 16 بازار ارزاق عمومی موجود در شهر مشهد جمع‌آوری گردید. نتایج تحقیق نشان دهنده برتری روش حداقل مربعات جزئی بر روش حداکثر آنتروپی می‌باشد و ارزش ادراکی و تصویر ادراکی را به عنوان عوامل موثر بر رضایت مشتری و تعریف و تمجید از بازار را به عنوان مهم‌ترین نتیجه وفاداری مشتری به بازار معرفی نموده و بر این اساس نظارت بیشتر بر سطح قیمت‌های این بازارها و توجه به فاکتورهایی هم‌چون نظافت، دکوراسیون و ایجاد جو مطلوب و لذت بخش در فروشگاه‌ها را برای بهبود وضعیت این دو متغیر پیشنهاد می‌کند.

کلیدواژه‌ها

• حداکثر آنتروپی تعمیم یافته
• حداقل مربعات جزئی
• شاخص رضایت مشتری

20.1001.1.20084722.1394.29.2.8.8

عنوان مقاله [English]

Estimation of Customer Satisfaction Index of Food Markets, Case Study: Mashhad Municipality’s Hypermarkets

نویسندگان [English]

• Z. Golriz Ziaie ¹
• R. Moghaddasi ¹
• S. Yazdani ²

¹ Tehran Science and Research Branch, Islamic Azad University

² Tehran Science and Research Branch, Islamic Azad University

چکیده [English]

Introduction: In today’s competitive environment delivering high quality service is the key for a sustainable competitive advantage. Customer satisfaction has a positive effect on an organization’s profitability. Satisfied customers form the foundation of any successful business because customer satisfaction leads to repeated purchases, brand loyalty, and positive expression of satisfaction to other people about the business. Here, we introduce a new index formeasuring customer satisfaction for hypermarkets in Iran. The new assumption of thisresearch is that other factors affect customer satisfaction via image. They contribute in making image of hypermarket in customer mind and, through it, have most effect on customer satisfaction.
Materials and Methods: The current paper defines and estimates customer satisfaction index for Mashhad municipality hypermarkets. For this purpose, after definition of the model with structural equation modeling, it uses partial least square and generalized maximum entropy for estimating the defined model. After estimation, results of two manners will be compared with mean squared error for choosing the best way. The data used in this study are collected as a survey and are based on purposeful sampling method from 367 customers of 16 municipality hypermarkets available in Mashhad. Assumptions of this study are as follows:
Assumption 1: Increasing perceived quality of supplied products and services in a store will increase customer satisfaction.
Assumption 2: Increasing perceived value of services and supplied products in a store will increase customer satisfaction.
Assumption 3: Increasing a customer’s perceived quality of supplied products in a store will increase his perceived value.
Assumption 4: Increasing a customer’s perceived quality of services in a store willincrease his perceived value.
Assumption 5: Positive perceived image of a store will increase customer satisfaction.
Assumption 6: Perceived quality of supplied products and services will have a positive effect onperceived image.
Assumption 7: Perceived value of supplied products and services will have a positive effect on perceived image.
Assumption 8: Increasing customer satisfaction will increase customer loyalty.
Assumption 9: Customer loyalty will lead to his praise, price insensitivity and complaint behavior.
Results and Discussion: Results of the study represents PLS as best manner and introduces perceived value and image as factors affecting customer satisfaction and praise as the result of customer loyalty and so suggests more surveillance on price level of these hypermarkets and creates a good atmosphere in them for improvement of these two indicators. All tests on PLS results indicate good and adaptable estimates. Average of composite reliability index is 0.85 and it is higher than 0.7 for all indices. So, the model has high reliability and all blocks arehomogenous.The average of communality index is 0.58. It means that on the average0.58 percent of manifested variables variability is explained by latent variables. With regardto the output of the model, the power of explanation is confirmed. In this model,the average of redundancy index is 0.2 and larger than 0.125, so it is an adaptable number. Also the GOF index is estimated to be 0.48 and implies on good estimating of the model. The square rootsof the AVE values proved to be greater than the rest of the values for all the measuredvariables used in this study, confirming the existence of discriminant validity.
The CSI index was 3.77, which shows customers are relatively satisfied with services of these stores. Between all variables, score of loyalty was lower than others and shows that these storesmust try harder for attaining customer loyalty.
Conclusion: In recent years, customer satisfaction index has been evaluated and measured in different countries across organizations, industries and national level, but in Iran this index is not studied enough. In this paper we devise a suitable model for measuring customer satisfaction index for hypermarkets in Iran. We consider new relationships between factors affecting satisfaction, as we assume that perceived quality and value have most of their effects on customer satisfaction via perceived image. We used PLS and ME Methods for estimating the model and our results confirmed our assumption. Also,the results of this study represent PLS as the best manner. Given the importance of customer satisfaction index, the model presented in this study can be used as a basic model adoptable by governmental and private institutions and organizations in this industry, especially municipality of Mashhad, after due research works.

کلیدواژه‌ها [English]

• Customer satisfaction index
• Generalized Maximum Entropy
• Partial least square

مراجع

1- Amani J., Khezri Azar H., and Mahmoodi H. 2012. Introduction of structural equation modeling in partial least square method and application of it in behavioral research, Online journal Psychological Science, 1:41-55(in Persian)

2- Abul Kashem M., and Shahidul Islam M. 2012. Customer satisfaction of super stores in Bangladesh- An explorative study. Journal of Economics and Sustainable Development, 3(7): 24-30.

3- Akpinar M.G., Gul M., and Gulvan S. 2011. An assessment of consumer satisfaction for food retail chains: A case study from Turkey, International European Forum, Innsbruck-Igls, Austria, number 121995. Available at: http://www.fooddynamics.org

4- Al-Nasser A.D. 2003. Customer satisfaction measurement models: Generalized maximum entropy approach. Pak Statist, 19(2): 213-226.

5- Angelova B., and Zekiri J. 2011. Measuring customer satisfaction with service quality using American Customer Satisfaction Model (ACSI Model). International journal of academic research in business and social science, 1(3): 232-258.

6- Bayol M.P., De La Foye A., Teliierm C., and Tenenhaus M. 2000.Use of PLS path modeling to estimate the European customer satisfaction index(ECSI) model. Statistica Applicata,12(3):361-375.

7- Bloemer J., and De.Ruyter K. 1998. On the relationship between store image, store satisfaction and store loyalty. European Journal of Marketing, 32( 5/6): 499-513.

8- Bloemer J., and Odekerken- Schroder G. 2002. Store satisfaction and store loyalty explained by customer – and store related factors. Journal of Customer satisfaction, Dissatisfaction and Complaining behavior, 15: 70-80.

9- Ciavolino E., and Al-Naser A. 2009. Comparing generalized maximum entropy and partial least squares methods for structural equation models. Journal of Nonparametric Statistics,21(8):1017-1036.

10- Hackl P., Scharitzer D., and Zuba R. 2000. Customer satisfaction in the Austrian food retail market. Total Quality Management,11(7) :999-1006.

11- Jafari Samimi A., and Mohammadi R. 2011. Measuring customer satisfaction index (CSI) in Iranian tile industry using PlS path modeling technique. Middle-East Journal of Scientific Research, 8 (1):141-149.

12- Latan H., and Ghozali I. 2013. Partial least squares: Concept and application path modelling using program XLSTAT-PLS. Badan Penerbit Universitas Diponegoro, Semarang.

13- M. Marx N.J., Erasmu A.C. 2006. Customer Satisfaction with Customer Service and Service Quality in Supermarkets in a Third World Context. Asia Pacific Advances in Customer research,7 :77-85.

14- Nikookar G.H., and Ghaffari A. 2012. Modeling customer satisfaction in the food industry of Iran. Journal of Basic Application of Science Research,2(4): 4067-4072.

15- O’Loughlin C., and Coenders G. 2002. Application of the European Customer Satisfaction Index to Postal Services. Structural Equation Models versus Partial Least Squares. Working Paper, Department of Economics, University of Girona, Available at: http://ideas.repec.org.

16- Park Y.J., Heo P.S., and Rim M.H. 2008. Measurement of a customer satisfaction index for improvement of mobile RFID services in Korea. ETRI Journal,30 (5):634-643.

17- Rosa Bastos J.A., and Gallego P.M. 2008. Pharmacies customer satisfaction and loyalty- A framework analysis. Document de Trabajo 01/08 .Available at: http://campus.usal.es/~empresa/09_master/pdf/01_08.pdf

18- Sathyapriya P., Nagabhusana R., and Nanda S. 2012. Customer satisfaction of retail services offered in Palamudhir Nizhayam. International Journal of Research in Finance & Marketing,2( 2): 379-396.

19- Vinzi V.E., Trinchera L., and Amato S. 2010. PLS path modeling: from foundations to recent developments an open issues for model assessment and improvement. p.47-82. Handbook of partial least square. Springer Handbooks of Computational statistics.