重要度−満足度マトリクスにおける境界の基準化と有意性の適用
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1 I Tetsuya Okamoto / Miok Im / 1990 CS Customer Satisfaction No.1 Heskett et al ISO International Organization for Standardization ISO ,848 ISO p summer / No.408
2 3 Importance-Performance Analysis IPA 4 IPA II 1 IPA Martilla & James 1977 Martilla & James 1977 IPA Importance Performance 高 低 Ⅱ 過剰遂行 Possible Overkill Ⅲ 低優先度 Low Priority 低 重要度 Ⅰ 重点維持 Keep Up The Good Work Ⅳ 重点改善 Concentrate Here Keep Up The Good Work Possible Overkill 高 1 Martilla & James 1977 p % 4 CS Customer Satisfaction 2007 pp CS 5 Martilla & James 1977 Importance-Performance Grid Importance-Performance Matrix 005
3 Low Priority Concentrate Here Martilla & James IPA Michael et al., 2000 pp Michael et al., 2000 p Hill et al Hema & Samuel 2011 IPA summer / No.408
4 7 3 Martilla & James 1977 IPA Dolinski Deng et al Feng et al Martilla & James Martilla & James Oh Matzler et al performance asymmetric IPA 007
5 Heskett et al III Reverse Quality Element summer / No.408
6 Deng et al Feng et al t0 t 5% t t t
7 IV CO NOX N SD Standard Deviation IPA 2 3 Michael et al summer / No.408
8 2 N=515 N=169 N=85 t SD T SD H SD T H H T t * * p<.10; *p<.05; **p<.01; ***p< N=515 N=206 N=95 SD SD SD N=515 N=169 N=85 t t t *** ** *** ** ** * *** ** *** ** ** ** ** ** ** R R F *** *** *** *p<.05; **p<.01; ***p< % 9 011
9 4 10 A 4 A B C B 2 A B A 3 A B5 C B C summer / No.408
10 A B C A B A B6 B C
11 5 A B C A B C IMP1 IMP2 r=0.056 = = % r= = = PER1 PER2 10% 5% 6 N 10 IMP1 IMP2 PER1 PER2 IMP1 IMP2 PER1 PER2 IMP1 Pearson IMP r PER PER IMP1 Kendall IMP PER PER IMP1 Spearman IMP PER PER p<.10; *p<.05; **p<.01; ***p< summer / No.408
12 r=0.566 =0.449 =0.535 r= =0.045= A B B C 2 3 B C C3 C 5% C C 4 V
13 t 2 1 3C Customer Company Competitor Martilla & James 1977 Company Customer Competitor 2 3C Performance 1994 Swan & Combs pp pp p summer / No.408
14 A 12 Deng, W., Kuo, Y. and Chen, W.(2008), Revise importance-performance analysis: three-factor and benchmarking, The Service Industries Journal, 28(1), pp Dolinsky, L.(1991), Considering the Competition in Strategy Development: An Extension of Importance- Performance Analysis, Journal of Health Care Marketing, 11(1), pp Feng, M., Mangan, J., Wong, C., Xu, M. and Lalwani, C.(2014), Investing the different approaches to importance-performance analysis, The Service Industries Journal, 34(12), pp Hema, N. M. and Samuel, S.(2011), Importance- Performance Analysis to determine Service Quality of a Restaurant Service An Empirical Study, Advances In Management, 4(2), pp Heskett, J. L., Jones, T. O., Loveman, G. W., Sasser, W. E., Jr. and Schlesinger, L. A. (1994), Putting the Service-Profit Chain to Work, Harvard Business Review, March-April, pp r= 0.687(0.041) =0.611(0.022) =0.817(0.017) r= 0.792(0.011) =0.592(0.028) =0.619 (0.75) 017
15 Hill, N., MacDougall, R. and Brierley, J. (1995), How to Measure Customer Satisfaction, Gower Pub Co Johnson, M. D. and Gustafsson, A.(2000), Improving Customer Satisfaction, Loyalty, and Profit, John Wiley & Sons, Inc (2) pp Martilla, J. A. and James, J. C Importance- Performance Analysis, Journal of Marketing, 41, pp (1) pp Matzler, K., Sauerwein, E. and Heischmidt, K. A.(2003), Importance-Performance Analysis Revisited: The Role of the Factor Structure of Customer Satisfaction, The Service Industries Journal, 23(2), pp Matzler, K., Bailom, F., Hinterhuber, H. H.(2004), The asymmetric relationship between attribute-level performance and overall satisfaction: a reconsideration of the importance-performance analysis, Industrial Marketing Management, 33, pp pp pp Oh, H.(2001), Revisiting importance-performance analysis, Tourism Management, 22, pp CS 1994 Swan, J. E. and Combs, L. J.(1976), Product Performance and Customer Satisfaction, Journal of Marketing, 40, pp summer / No.408
16 Boundary Standardization and Significance Application in the Importance-Performance Matrix Tetsuya Okamoto Miok Im This study looks at three problems through an examination of previous research on the importance-performance matrix: the matrix involves both self-stated importance and statistical inferred importance; the matrix lacks a competitive perspective; and the matrix fails to define standards for boundaries between quadrants. To solve these problems, the study suggests applying β obtained by multiple linear regression analysis on the horizontal axis of the matrix and relative performance to a competing company on the vertical axis, setting quadrant boundaries to the origin, and employing statistical significance tests. The following three results were obtained through an analysis of data gathered from car owners in Japan. First, on the importance-performance matrix, product attributes plotted between each quadrant fluctuated considerably, either by applying self-stated importance or β on the horizontal axis or by applying performance or relative performance on the vertical axis. Second, fluctuations in the first analysis result were caused by two factors, namely, low correlations between self-stated importance and β, and between performance and relative performance and an absence of proper standards for establishing quadrant boundaries. Lastly, to avoid instability of the first result, using t-test s statistical significance to identify particular product attributes was confirmed as contributing to objective interpretations of the analysis results. Boundary Standardization and Significance Application in the Importance-Performance Matrix Tetsuya Okamoto Miok Im 019
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