Abstract
Many researchers have employed fuzzy linguistic scales to measure customer fuzzy perceptions and increase the reliability of their research. However, complex response methods may affect the precision of responses and the willingness of respondents. Thus, based on the concepts of distance and probability, we propose a simple response method for fuzzy linguistic scales to address these issues. In addition to a performance evaluation matrix to quickly identify items in need of improvement, we included order statistics and nonparametric statistics in a performance improvement verification model that solves the problems posed by sample size differences in pre- and post-improvement. This model is also applicable when the population does not follow a normal distribution. Finally, we used a case study with a CALL system to demonstrate the application of the proposed model, which fulfils the spirit of continuing improvement in total quality management.
| Original language | English |
|---|---|
| Pages (from-to) | 936-952 |
| Number of pages | 17 |
| Journal | Total Quality Management and Business Excellence |
| Volume | 30 |
| Issue number | 7-8 |
| DOIs | |
| Publication status | Published - 2019 May 19 |
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All Science Journal Classification (ASJC) codes
- Business, Management and Accounting(all)
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Development and application of performance improvement verification model : a case study of an e-learning system. / Chen, Kuen Suan; Chang, Huo-Tsan; Yu, Chun Min.
In: Total Quality Management and Business Excellence, Vol. 30, No. 7-8, 19.05.2019, p. 936-952.Research output: Contribution to journal › Article
TY - JOUR
T1 - Development and application of performance improvement verification model
T2 - a case study of an e-learning system
AU - Chen, Kuen Suan
AU - Chang, Huo-Tsan
AU - Yu, Chun Min
PY - 2019/5/19
Y1 - 2019/5/19
N2 - Many researchers have employed fuzzy linguistic scales to measure customer fuzzy perceptions and increase the reliability of their research. However, complex response methods may affect the precision of responses and the willingness of respondents. Thus, based on the concepts of distance and probability, we propose a simple response method for fuzzy linguistic scales to address these issues. In addition to a performance evaluation matrix to quickly identify items in need of improvement, we included order statistics and nonparametric statistics in a performance improvement verification model that solves the problems posed by sample size differences in pre- and post-improvement. This model is also applicable when the population does not follow a normal distribution. Finally, we used a case study with a CALL system to demonstrate the application of the proposed model, which fulfils the spirit of continuing improvement in total quality management.
AB - Many researchers have employed fuzzy linguistic scales to measure customer fuzzy perceptions and increase the reliability of their research. However, complex response methods may affect the precision of responses and the willingness of respondents. Thus, based on the concepts of distance and probability, we propose a simple response method for fuzzy linguistic scales to address these issues. In addition to a performance evaluation matrix to quickly identify items in need of improvement, we included order statistics and nonparametric statistics in a performance improvement verification model that solves the problems posed by sample size differences in pre- and post-improvement. This model is also applicable when the population does not follow a normal distribution. Finally, we used a case study with a CALL system to demonstrate the application of the proposed model, which fulfils the spirit of continuing improvement in total quality management.
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UR - http://www.scopus.com/inward/citedby.url?scp=85040996196&partnerID=8YFLogxK
U2 - 10.1080/14783363.2017.1416287
DO - 10.1080/14783363.2017.1416287
M3 - Article
VL - 30
SP - 936
EP - 952
JO - Total Quality Management and Business Excellence
JF - Total Quality Management and Business Excellence
SN - 1478-3363
IS - 7-8
ER -