Key-Driver Analysis with Extended Back-Propagation Neural Network Based Importance-Performance Analysis (BPNN-IPA)


Kahraman C. , Yanık Özbay S.

INTELLIGENT DECISION MAKING IN QUALITY MANAGEMENT: THEORY AND APPLICATIONS, cilt.97, ss.447-461, 2016 (Diğer Kurumların Hakemli Dergileri) identifier

  • Cilt numarası: 97
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1007/978-3-319-24499-0_15
  • Dergi Adı: INTELLIGENT DECISION MAKING IN QUALITY MANAGEMENT: THEORY AND APPLICATIONS
  • Sayfa Sayıları: ss.447-461

Özet

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.