Abstract
Importance-performance analysis (IPA) is a popular prioritization tool used to formulate effective and efficient quality improvement strategies for products and services. Since its introduction in 1977, IPA has undergone numerous enhancements and extensions, mostly with regard to the operationalization of attribute-importance. Recently, studies have promoted neural network -based IPA approaches to determine attribute-importance more reliably compared to traditional approaches. This chapter describes the application of back-propagation neural networks (BPNN) in an extended IPA framework with the goal of discovering key areas of quality improvements. The value of the extended BPNN-based IPA is demonstrated using an empirical case example of airport service quality .
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References
Broyden, C.G., Dennis, J.E., More, J.J.: On the local and superlinear convergence of quasi-newton methods. IMA J. Appl. Math. 12, 223–246 (1973)
Budescu, D.V.: Dominance analysis: a new approach to the problem of relative importance of predictors in multiple regression. Psychol. Bull. 114, 542–551 (1993)
Deng, W.J., Chen, W.C., Pei, W.: Back-propagation neural network based importance-performance analysis for determining critical service attributes. Expert Syst. Appl. 34, 1115–1125 (2008)
DeTienne, K.B., DeTienne, D.H., Joshi, S.A.: Neural networks as statistical tools for business researchers. Organ. Res. Methods 6, 236–265 (2003)
Garson, G.D.: Interpreting neural-network connection weights. AI Expert 6, 47–51 (1991)
Genizi, A.: Decomposition of R2 in multiple regression with correlated regressors. Stat. Sinica 3, 407–420 (1993)
Grønholdt, L., Martensen, A.: Analysing customer satisfaction data: a comparison of regression and artificial neural networks. Int. J. Market Res. 47, 121–130 (2005)
Haykin, S.: Neural networks: a comprehensive foundation. Prentice-Hall, Upper Saddle River (1999)
Hu, H.Y., Lee, Y.C., Yen, T.M., Tsai, C.H.: Using BPNN and DEMATEL to modify importance-performance analysis model: a study of the computer industry. Expert Syst. Appl. 36, 9969–9979 (2009)
Huo, L., Jiang, B., Ning, T., Yin, B.: A BP neural network predictor model for stock price. In Intelligent Computing Methodologies, pp. 362–368. Springer International Publishing, New York (2014)
Johnson, J.W.: A heuristic method for estimating the relative weight of predictor variables in multiple regression. Multivar. Behav. Res. 35, 1–19 (2000)
Kruskal, W.H.: Relative importance by averaging over orderings. Am. Stat. 41, 6–10 (1987)
Kuo, R.J., Tseng, Y.S., Chen, Z.Y.: Integration of fuzzy neural network and artificial immune system-based back-propagation neural network for sales forecasting using qualitative and quantitative data. J. Intell. Manuf. 1–17 (2014)
Martilla, J.A., James, J.C.: Importance-performance analysis. J. Mark. 41, 77–79 (1977)
Mikulić, J., Prebežac, D.: Rethinking the importance grid as a research tool for quality managers. Total Qual. Manag. 22, 993–1006 (2011)
Mikulić, J., Prebežac, D.: Accounting for dynamics in attribute-importance and for competitor performance to enhance reliability of BPNN-based importance-performance analysis. Expert Syst. Appl. 39, 5144–5153 (2012)
Mikulić, J., Paunović, Z., Prebežac, D.: An extended neural network-based importance-performance analysis for enhancing wine fair experience. J. Travel Tour. Mark. 29, 744–759 (2012)
Moller, M.F.: A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw. 6, 525–533 (1993)
Myers, J.H., Alpert, M.I.: Semantic confusion in attitude research: salience vs Importance vs. Determinance. Adv. Consum. Res. 4, 106–110 (1977)
Olden, J.D., Jackson, D.A.: Illuminating the ‘‘Black Box’’: a randomization approach for understanding variable contributions in artificial neural networks. Ecol. Model. 154, 135–150 (2002)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representation by error propagation. Parallel Distrib. Proc. 1, 318–362 (1986)
Subbaiah, R.M., Dey, P., Nijhawan, R.: Artificial neural network in breast lesions from fine-needle aspiration cytology smear. Diagn. Cytopathol. 42, 218–224 (2014)
Sung, A.H.: Ranking importance of input parameters of neural networks. Expert Syst. Appl. 15, 405–411 (1998)
Van Ittersum, K., Pennings, J.M.E., Wansink, B., van Trijp, H.C.M.: The validity of attribute-importance measurement: a review. J. Bus. Res. 60, 1177–1190 (2007)
Weiner, J.L., Tang, J.: Multicollinearity in Customer Satisfaction Research. White paper, Ipsos Loyalty (2005)
Zong, R., Zhi, Y., Yao, B., Gao, J., Stec, A.A.: Classification and identification of soot source with principal component analysis and back-propagation neural network. Aust. J. Forensic Sci. 46, 224–233 (2014)
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Mikulić, J., Krešić, D., Miličević, K. (2016). Key-Driver Analysis with Extended Back-Propagation Neural Network Based Importance-Performance Analysis (BPNN-IPA). In: Kahraman, C., Yanik, S. (eds) Intelligent Decision Making in Quality Management. Intelligent Systems Reference Library, vol 97. Springer, Cham. https://doi.org/10.1007/978-3-319-24499-0_15
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DOI: https://doi.org/10.1007/978-3-319-24499-0_15
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