Design of online survey system with an advanced IPA discrimination index for customer satisfaction assessment

Abstract

In recent years, Internet technology has become an increasingly popular method for collecting user feedback and assessing customer satisfaction via web-based survey systems. Based on the web-based online system, data or information can be collected immediately. To analyze customer satisfaction, Importance-Performance Analysis (IPA) and Parasuraman, Zeithaml & Berry (PZB) are just some of the enhanced analysis tools that measure user satisfaction. In this paper, we are introducing a novel web-based user feedback survey system with a proposed new IPA index. To identify customer satisfaction, the new IPA model can reveal a more accurate quantity comparison and find potentially helpful information for customer satisfaction. The PZB model of service quality is a tool that companies can use to identify expectations and perceptions about business or commerce behavior and communication. However, there is no in depth research that discusses the performance of inter-IPA results or PZB dimensions. The performance results have demonstrated that our proposed model with an advanced IPA discrimination index can judge and identify inter-survey results more accurately for customer satisfaction assessment.

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References

  1. 1.

    Aberer, K., Datta, A., Despotovic, Z., & Wombacher, A. (2003). Separating business process from user interaction in web-based information commerce. Electronic Commerce Research, 3(1–2), 83–111.

    Article  Google Scholar 

  2. 2.

    Agrawal, V., Lopomo, G., & Seshadri, S. (2002). Web based capacity allocation strategies for customers with heterogeneous preferences. Electronic Commerce Research, 2(4), 359–384.

    Article  Google Scholar 

  3. 3.

    Ainin, S., & Hisham, N. H. (2008). Applying importance-performance analysis to information systems: An exploratory case study. Journal of Information, Information Technology, and Organizations, 3, 96–103.

    Google Scholar 

  4. 4.

    Ali, Z. M., Ismail, M., Suradi, N. R. M., & Ismail, A. S. (2009). Importance-performance analysis and customer satisfaction index for express bus services. In Proceedings of the world congress on nature & biologically inspired computing (NaBIC 2009) (pp. 590–595).

  5. 5.

    Barbieri, C. (2010). An importance-performance analysis of the motivations behind agritourism and other farm enterprise developments in Canada. Journal of Rural and Community Development, 5(1), 1–20.

    Google Scholar 

  6. 6.

    Bhusan, P. B., & Kumar, B. A. (2011). Application of PZB service quality model in identifying service quality gap—a study on state bank of India. A Journal of Decision Making, 11(1), 1–10.

    Google Scholar 

  7. 7.

    Buter, B., Dijkshoorn, N., Modolo, D., Nguyen, Q., Noort, S. V., Poel, B. V. D., et al. (2011). Explorative visualization and analysis of a social network for arts: The case of deviantART. Journal of Convergence, 2(1), 87–94.

    Google Scholar 

  8. 8.

    Butt, H. S., & Murtaza, M. (2011). Measuring customer satisfaction w.r.t restaurant industry in Bahawalpur. European Journal of Business and Management, 3(5), 54–64.

    Google Scholar 

  9. 9.

    Caber, M., Albayrak, T., & Matzler, K. (2012). Classification of the destination attributes in the content of competitiveness (by revised importance-performance analysis). Journal of Vacation Marketing, 18(1), 43–56.

    Article  Google Scholar 

  10. 10.

    Chen, S. M., Lee, S. J., Mai, Y. T., Jheng, Y. D., & Liu, H. C. (2012). A novel multiple-option web-based questionnaire system with IPA and IPA discrimination index. International Journal of Digital Content Technology and Its Applications, 6(3), 295–303.

  11. 11.

    Cheng, J. H., Lai, C. Y., Chen, H. P., & Ou, C. L. (2010). The service quality analysis of public transportation system using PZB model—Dynamic bus information system. In Proceedings of the 2010 40th international conference on computers and industrial engineering (CIE) (pp. 1–5).

  12. 12.

    Chiang, Y. C. (2002). Using the technique of importance-performance analysis to evaluate interpreters and interpretive media in National Science and Technology Museum. Unpublished Master’s Dissertation, National Taichung Teachers College.

  13. 13.

    Chiu, C. M., & Wang, E. T. G. (2008). understanding web-based learning continuance intention: The role of subjective task value. Information & Management, 45(3), 194–201.

    Article  Google Scholar 

  14. 14.

    Chiu, S. I., Chiu, S. L., & Chiou, J. L. (2011). To promote educational quality of Taiwan vocational senior high school by six sigma. In Proceedings of society for information technology & teacher education international conference (pp. 4163–4169).

  15. 15.

    Coghlan, A. (2012). Facilitating reef tourism management through an innovative importance-performance analysis method. Tourism Management, 33(4), 767–775.

    Article  Google Scholar 

  16. 16.

    Duan, Y., Edwards, J. S., & Xu, M. X. (2005). Web-based expert systems: Benefits and challenges. Information & Management, 42(3), 799–811.

    Article  Google Scholar 

  17. 17.

    Gonzalez, J. L., & Marcelnez, R. (2011). Phoenix: Fault-tolerant distributed web storage based on URLs. Journal of Convergence, 2(1), 79–86.

    Google Scholar 

  18. 18.

    Graf, S., Liu, T. C., Kinshuk, Chen, N. S., & Yang, S. J. H. (2009). Learning styles and cognitive traits—Their relationship and its benefits in web-based educational systems. Computers in Human Behavior, 25(6), 1280–1289.

    Article  Google Scholar 

  19. 19.

    Han, J. Y., Wang, X. Y., & Xu, S. H. (2012). Evaluation on service quality of kindergarten based on SERVQUAL. In Proceedings of the 2012 international conference on management science and engineering (ICMSE) (pp. 1973–1978).

  20. 20.

    Hsiao, K. F., & Rashvand, H. F. (2011). Integrating body language movements in augmented reality learning environment. Human-centric Computing and Information Sciences, 1(1), 1–10.

    Article  Google Scholar 

  21. 21.

    Huang, G. Q., Yee, W. Y., & Mak, K. L. (2001). Development of a web-based system for engineering change management. Robotics and Computer-Integrated Manufacturing, 17(3), 255–267.

    Article  Google Scholar 

  22. 22.

    Kim, H. J., & Caytiles, R. D. (2012). A self-directed dynamic web-based learning environment: Personalized learning framework. Information-an international interdisciplinary journal, 15(8), 3265–3276.

    Google Scholar 

  23. 23.

    Lai, L. S. L., & To, W. M. (2010). Importance-performance analysis for public management decision making: An empirical study of China’s Macao special administrative region. Management Decision, 48(2), 277–295.

    Article  Google Scholar 

  24. 24.

    Levenburg, N. M., & Magal, S. R. (2005). Applying importance-performance analysis to evaluate e-business strategies among small firms. e-Service Journal, 3, 29–48.

    Article  Google Scholar 

  25. 25.

    Liu, H. C., Mai, Y. T., Jheng, Y. D., Liang, W. L., Chen, S. M., & Lee, S. J. (2011). A novel discrimination index of importance-performance analysis model. In Proceedings of the IEEE international conference of machine learning and cybernetics (ICMLC 2011) (Vol. 3, pp. 938–942).

  26. 26.

    Lu, J., Wang, L. Z., Yu, C. S., & Wu, J. Y. (2009). E-auction web assessment model in China. Electronic Commerce Research, 9(3), 149–172.

    Article  Google Scholar 

  27. 27.

    Magal, R. A. (2001). Web-based reputation management systems: Problems and suggested solutions. Electronic Commerce Research, 1(4), 403–417.

    Article  Google Scholar 

  28. 28.

    Magal, S. R., & Levenburg, N. M. (2005). Using importance-performance analysis to evaluate e-business strategies among small businesses. In Proceedings of the 38th Hawaii international conference on system science (pp. 1–10).

  29. 29.

    Martilla, J. A., & James, J. C. (1977). Importance-performance analysis. Journal of Marketing, 41(1), 77–79.

    Article  Google Scholar 

  30. 30.

    Meredith, A., Hussain, Z., & Griffiths, M. D. (2009). Online gaming: A scoping study of massively multi-player online role playing games. Electronic Commerce Research, 9(1–2), 3–26.

    Article  Google Scholar 

  31. 31.

    Mikulić, J., Paunović, Z., & Prebežac, D. (2012). An extended neural network-based importance-performance analysis for enhancing wine fair experience. Journal of Travel & Tourism Marketing, 29(8), 744–759.

    Article  Google Scholar 

  32. 32.

    Murdy, S., & Pike, S. (2012). Perceptions of visitor relationship marketing opportunities by destination marketers an importance-performance analysis. Tourism Management, 33(5), 1281–1285.

    Article  Google Scholar 

  33. 33.

    O’ Sullivan, E. L. (1991). Marketing for Parks, recreation, and leisure. State College, PA: Venture.

  34. 34.

    Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). Communication and control process in the delivery of service quality. Journal of Marketing, 52(2), 35–48.

    Article  Google Scholar 

  35. 35.

    Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12–40.

    Google Scholar 

  36. 36.

    Park, K. O., Song, Y. T., Kim, J. S., & Jeong, G. B. (2011). Web-based Korean National Health and Nutrition Examination Survey System in the cloud computing environment. In Proceedings of 2011 First ACIS/JNU international conference on computers, networks, systems and industrial engineering (CNSI) (pp. 484–488).

  37. 37.

    Park, Y. J., Heo, P. S., Rim, M. H., & Park, D. S. (2008). Customer satisfaction index measurement and importance-performance analysis for improvement of the Mobile RFID Services in Korea. In Proceedings of the international conference on management of engineering & technology (PICMET 2008), Portland (pp. 2657–2665).

  38. 38.

    Pike, S., & Larkin, I. (2005). Benchmarking student evaluations of a postgraduate unit using importance-performance analysis. In Proceedings of Australia New Zealand Marketing Academy Conference (ANZMAC 2005) (pp. 173–179).

  39. 39.

    Ramanathan, R. (2010). E-commerce success criteria: Determining which criteria count most. Electronic Commerce Research, 10(2), 191–208.

    Article  Google Scholar 

  40. 40.

    Sampson, S. E., & Showalter, M. J. (1999). The performance-importance response function: Observations and implications. The Service Industries Journal, 19(3), 1–25.

    Article  Google Scholar 

  41. 41.

    Shakur, M., Doherty, N., & Ellis-Chadwick, F. (2012). Importance-performance analysis of retail website service quality. In Proceedings of the 2012 conference was entitled Marketing: catching the technology wave (AM2012) (pp. 1–10).

  42. 42.

    Shtykh, R. Y., & Jin, Q. (2011). A human-centric integrated approach to web information search and sharing. Human-centric Computing and Information Sciences, 1(2), 1–37.

    Google Scholar 

  43. 43.

    Silva, F., & Fernandes, P. (2010). Using importance-performance analysis in evaluating institutions of higher education: A case study. In Proceedings of the 2010 international conference on education and management technology (ICEMT) (pp. 121–123).

  44. 44.

    Switzer, J. A., Hall, C., Gross, H., Waller, J., Nichols, F. T., Wang, S., Adams, R. J., & Hess, D. C. (2009). A web-based telestroke system facilitates rapid treatment of acute ischemic stroke patients in rural emergency departments. The Journal of Emergency Medicine, 36(1), 12–18.

  45. 45.

    Tseng, M. L. (2011). Importance-performance analysis of municipal solid waste management in uncertainty. Environmental Monitoring and Assessment, 172, 171–187.

    Article  Google Scholar 

  46. 46.

    Valvi, A. C., & Fragkos, K. C. (2012). Critical review of the E-loyalty literature: A purchase-centred framework. Electronic Commerce Research, 12(3), 331–378.

    Article  Google Scholar 

  47. 47.

    Wu, C. H., Chang, Y. C., Lee, Y. C., & Lin, S. B. (2011). Measuring service quality of car maintenance. In Proceedings of the 2011 international conference on management and service science (MASS) (pp. 1–4).

  48. 48.

    Yoon, S., & Suh, H. (2004). Ensuring IT consulting SERVQUAL and user satisfaction: A modified measurement tool. Information Systems Frontiers, 6(4), 341–351.

    Article  Google Scholar 

  49. 49.

    Zhang, R., Li, X., & Zhang, Y. (2010). Service quality, customer satisfaction and customer loyalty of mobile communication industry in China. Journal of Global Academy of Marketing Science, 20(3), 269–277.

    Article  Google Scholar 

  50. 50.

    Zhao, X. (2012). A review on service quality and student satisfaction of higher education. Soft Computing in Information Communication Technology, 158, 115–122.

    Article  Google Scholar 

  51. 51.

    Zhu, Z., Zhu, T., Zhu, Y., & Sun, M. (2012). Study on effective assessment of practical teaching in university based on IPA. Lecture Notes in Electrical Engineering, 111, 391–398.

    Article  Google Scholar 

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Acknowledgments

This work was supported in part by the National Science Council, Taiwan, under grant NSC 99-2221-E-164-006 and NSC 100-2511-S-468-001.

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Correspondence to Yi-Ting Mai.

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Hsiang-Chuan Liu, Yi-Ting Mai, and Yu-Du Jheng have contributed equally to this work.

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Liu, HC., Jeng, BC., Mai, YT. et al. Design of online survey system with an advanced IPA discrimination index for customer satisfaction assessment. Electron Commer Res 14, 223–243 (2014). https://doi.org/10.1007/s10660-014-9141-8

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Keywords

  • Web survey
  • User satisfaction
  • IPA
  • PZB
  • IPA-index