A Decision-Making Method for Improving Logistics Services Quality by Integrating Fuzzy Kano Model with Importance-Performance Analysis

Qingliang Meng^1,2, Xuan Jiang², Lingling Bian²

¹Business School, Hunan University, Changsha, China.
²School of Management and Economics, Jiangsu University of Science and Technology, Zhenjiang, China.

DOI: 10.4236/jssm.2015.83034                

Abstract

In resources limited circumstances, seeking relationship between customer satisfaction and logistics service performance is meaningful for the development of logistics companies. Therefore, it is crucial for logistics companies to understand that logistics service quality attributes can increase satisfaction and their improvement priorities can help make better decisions. Thus, the identification of logistics service quality attributes importance and their contributions on improving customer satisfaction have become more necessary to logistics companies success. Considering traditional Kano model classification is subjective, the contribution of this study is, therefore, to integrate fuzzy Kano model with importance-performance analysis to address the shortcomings with using these two methods separately. What’s more, constructing a decision-making method can help logistics companies determine the priority of logistics service quality attributes. Finally, an empirical study on customer satisfaction was undertaken. The feasibility and effectiveness of this method had been verified.

Keywords

Fuzzy Kano Model, Importance-Performance Analysis, Logistics Service Quality, Decision-Making Method

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1]	Meidute-Kavaliauskiene, I., Aranskis, A. and Litvinenko, M. (2014) Consumer Satisfaction with the Quality of Logistics Services. Procedia-Social and Behavioral Sciences, 110, 330-340. http://dx.doi.org/10.1016/j.sbspro.2013.12.877
[2]	Subramanian, N., Abdulrahman, M.D. and Zhou, X.L. (2015) Reprint of “Integration of Logistics and Cloud Computing Service Providers: Cost and Green Benefits in the Chinese Context”. Transportation Research Part E: Logistics and Transportation Review, 74, 81-93. http://dx.doi.org/10.1016/j.tre.2014.12.006
[3]	Lee, Y.-C. and Huang, S.-Y. (2009) A New Fuzzy Concept Approach for Kano’s Model. Expert Systems with Applications, 36, 4479-4484. http://dx.doi.org/10.1016/j.eswa.2008.05.034
[4]	Brandt, D.R. (1988) How Service Marketers Can Identify Value-Enhancing Service Element. Journal of Services Marketing, 2, 35-41. http://dx.doi.org/10.1108/eb024732
[5]	Yang, C.-C. (2005) The Refined Kano’s Model and Its Application. Total Quality Management & Business Excellence, 16, 1127-1137. http://dx.doi.org/10.1080/14783360500235850
[6]	Zadeh, L.A. (1965) Fuzzy Sets. Information and Control, 8, 338-353. http://dx.doi.org/10.1016/S0019-9958(65)90241-X
[7]	Matzler, K. and Hinterhuber, H.H. (1998) How to Make Product Development Projects More Successful by Integrating Kano’s Model of Customer Satisfaction into Quality Function Deployment. Technovation, 18, 25-38. http://dx.doi.org/10.1016/S0166-4972(97)00072-2
[8]	Martilla, J.A. and James, J.C. (1977) Importance-Performance Analysis. Journal of Marketing, 41, 77-79. http://dx.doi.org/10.2307/1250495
[9]	Sampson, S.E. and Showalter, M.J. (1999) The Performance-Importance Response Function: Observations and Implications. The Service Industries Journal, 19, 1-25. http://dx.doi.org/10.1080/02642069900000027
[10]	Kano, N., Seraku, N., Takahashi, F. and Tsuji, S. (1984) Attractive Quality and Must-Be Quality. The Journal of the Japanese Society for Quality Control, 14, 39-48.