Name
Abstract | ||
|---|---|---|
Managerial studies often rely on gathering abstract, numerical data from human resources. Data is gathered about employees' attitudes and perceptions regarding organizations and their practices. Models tested using this data account for probabilistic uncertainty in the data, but not for vague uncertainty. In a previous study, Adrian (1998) explored the utility of allowing respondents to draw fuzzy membership functions over the set of questionnaire answers instead of just picking one response. Her results showed good qualitative value can be obtained from this format. In this paper, we look at a quantitative analysis of these linguistic responses. In particular, we develop classification algorithms for vectors of fuzzy sets and apply these algorithms to such “linguistic vectors” derived from a set of eleven subjects answering questions about job satisfaction and organizational commitment |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1109/FUZZY.2000.838691 | Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference |
Keywords | DocType | Volume |
classification,fuzzy set theory,management science,probability,fuzzy membership functions,human resources,linguistic classifiers,management questionnaires,organizations,probabilistic uncertainty,quantitative analysis,classification algorithms,organizational commitment,feedback,prototypes,application software,job satisfaction,uncertainty,data engineering,fuzzy set,computer science,fuzzy sets,human resource,testing | Conference | 1 |
ISSN | ISBN | Citations |
1098-7584 | 0-7803-5877-5 | 5 |
PageRank | References | Authors |
0.95 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James M. Keller | 1 | 3201 | 436.69 |
| S. Auephanwiriyakul | 2 | 246 | 39.45 |
| Adrian, A. | 3 | 5 | 0.95 |