Abstract
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.
Highlights
Lambert and Sharma [1] presented the performance evaluation matrix (PEM) for operating systems that collect users’ or customers’ perceptions
In the PEM, customer perception of the importance of an item is represented by the vertical axis, and customer satisfaction with the item itself is represented by the horizontal axis
We demonstrate the efficacy of the method through the presentation of a case study of a computer-assisted language learning (CALL) system
Summary
Lambert and Sharma [1] presented the performance evaluation matrix (PEM) for operating systems that collect users’ or customers’ perceptions. Compared with other assessment methods that need complicated data comparison, the PEM makes it easy to determine which service items most urgently require improvement, maintenance, or adjustment. Huang, and Chen [9] suggested that the conventional method of the PEM was subject to sampling error They proposed deriving the joint confidence interval of the importance and satisfaction indices based on the central limit theorem and replacing the point estimates of the PEM with these joint confidence intervals. This method overcomes sampling error, it is complicated and difficult to use [4,6,9].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
