Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Evaluating weapon systems using fuzzy arithmetic operations
Fuzzy Sets and Systems
Neural networks and logistic regression: Part I
Computational Statistics & Data Analysis
Neural networks and logistic regression: Part II
Computational Statistics & Data Analysis
Using fuzzy numbers to evaluate perceived service quality
Fuzzy Sets and Systems - Special issue on fuzzy numbers and uncertainty
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neural Networks for Statistical Modeling
Neural Networks for Statistical Modeling
Handbook of Neural Computing Applications
Handbook of Neural Computing Applications
Expert Systems with Applications: An International Journal
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
Fuzzy Delphi and back-propagation model for sales forecasting in PCB industry
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Analyzing supply chain operation models with the PC-algorithm and the neural network
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Modeling customer satisfaction for new product development using a PSO-based ANFIS approach
Applied Soft Computing
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems
Expert Systems with Applications: An International Journal
Predicting the impact of hospital health information technology adoption on patient satisfaction
Artificial Intelligence in Medicine
International Journal of Decision Support System Technology
A multidimensional data model using the fuzzy model based on the semantic translation
Information Systems Frontiers
Fuzzy Assessment of Health Information System Users' Security Awareness
Journal of Medical Systems
| Hi-index | 12.06 |
Importance-performance analysis (IPA) is a simple but effective means of assisting practitioners in prioritizing service attributes when attempting to enhance service quality and customer satisfaction. As numerous studies have demonstrated, attribute performance and overall satisfaction have a non-linear relationship, attribute importance and attribute performance have a causal relationship and the customer's self-stated importance is not the actual importance of service attribute. These findings raise questions regarding the applicability of conventional IPA. Furthermore, Human perceptions and attitudes are subjective and vague. Traditional assessments of service quality or customer satisfaction that used Likert scale to represent customer perceptions based on linguistic assessments are impractical. Moreover, some revised IPA that used statistical methods to acquire the implicitly derived importance of attributes always had some unreality assumptions. Therefore, this study presents a Fuzzy Neural based IPA (FN-IPA) which integrates fuzzy set theory, back-propagation neural network and three-factor theory to effectively and adequately assist practitioners in identifying critical service attributes.