Next Article in Journal
About Aczél Inequality and Some Bounds for Several Statistical Indicators
Next Article in Special Issue
Using FQFD and FGRA to Enhance the Advertising Effectiveness of Cross-Regional E-Commerce Platforms
Previous Article in Journal
Asymptotic Convergence of Soft-Constrained Neural Networks for Density Estimation
Previous Article in Special Issue
Possibility Measure of Accepting Statistical Hypothesis
Open AccessArticle

Constructing Fuzzy Hypothesis Methods to Determine Critical-To-Quality Service Items

1
Department of Industrial Education and Technology, National Changhua University of Education, Changhua 50074, Taiwan
2
Language Center, National Chin-Yi University of Technology, Taichung 41170, Taiwan
3
Department of Industrial Engineering and Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan
4
Institute of Innovation and Circular Economy, Taichung 41354, Taiwan
5
Department of Business Administration, Chaoyang University of Technology, Taichung 41349, Taiwan
*
Authors to whom correspondence should be addressed.
Mathematics 2020, 8(4), 573; https://doi.org/10.3390/math8040573
Received: 5 March 2020 / Revised: 30 March 2020 / Accepted: 7 April 2020 / Published: 12 April 2020
This paper constructs a performance evaluation matrix (PEM) with beta distribution. Beta is between zero and one, making it a suitable indicator to describe customer ratings of importance and satisfaction from 0% to 100%. According to the spirit of ceaseless improvement put forward by total quality management, the average ratings are set as the standard, and then the coordinates of each satisfaction and importance item is located in the performance areas. This makes it easy to identify critical-to-quality items that require improvement. However, the data collection method of questionnaires inevitably involves sampling error, and the opinions of customers are often ambiguous. To solve these problems, we constructed a fuzzy testing method based on confidence intervals. The use of confidence intervals decreases the chance of misjudgment caused by sampling errors, and more precisely gets closer to customers’ voices. This result is more reasonable than the traditional statistical testing principle. The proposed methods are applied to assessment of a computer-assisted language learning (CALL) system to display their competence. View Full-Text
Keywords: performance evaluation matrix; fuzzy hypothesis testing; fuzzy membership function; critical to quality; sampling error performance evaluation matrix; fuzzy hypothesis testing; fuzzy membership function; critical to quality; sampling error
Show Figures

Figure 1

MDPI and ACS Style

Yu, C.-H.; Liu, C.-C.; Chen, K.-S.; Yu, C.-M. Constructing Fuzzy Hypothesis Methods to Determine Critical-To-Quality Service Items. Mathematics 2020, 8, 573.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop