Abstract
A set of answers to questions to employees of various companies in Lithuania may refer to various positive and negative aspects of the attitudes of employees. These are called Organizational Citizenship Behavior (positive) and Counterproductive Work Behavior (negative). The components in the answers may be grouped by expert knowledge, and by statistical analysis and, according to these approaches, based on expert domain knowledge by management specialists, fuzzy signature structures describing the mutual effects of single elements in the questionnaire may be created. There are some slight differences between the two results, that indicate that expert knowledge is sometimes not objective. An additional step applying hybrid Generalised Reduced Gradient algorithm and Genetic Evolutionary Algorithm for heuristic optimization of the aggregation parameters in the Fuzzy Signatures reveals a final model according to the responses. These latter results raise some new questions, including the idea of the use of undeterministic graphs, thus resulting in Fuzzy Fuzzy Signatures. The method could be applied to other similar multicomponent vague data pools.
| Original language | English |
|---|---|
| Title of host publication | Studies in Computational Intelligence |
| Publisher | Springer Verlag |
| Pages | 157-165 |
| Number of pages | 9 |
| DOIs | |
| Publication status | Published - jan. 1 2020 |
Publication series
| Name | Studies in Computational Intelligence |
|---|---|
| Volume | 819 |
| ISSN (Print) | 1860-949X |
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ASJC Scopus subject areas
- Artificial Intelligence
Cite this
A combined fuzzy and least squares method approach for the evaluation of management questionnaires. / Kóczy, L.; Purvinis, Ojaras; Susnienė, Dalia.
Studies in Computational Intelligence. Springer Verlag, 2020. p. 157-165 (Studies in Computational Intelligence; Vol. 819).Research output: Chapter
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TY - CHAP
T1 - A combined fuzzy and least squares method approach for the evaluation of management questionnaires
AU - Kóczy, L.
AU - Purvinis, Ojaras
AU - Susnienė, Dalia
PY - 2020/1/1
Y1 - 2020/1/1
N2 - A set of answers to questions to employees of various companies in Lithuania may refer to various positive and negative aspects of the attitudes of employees. These are called Organizational Citizenship Behavior (positive) and Counterproductive Work Behavior (negative). The components in the answers may be grouped by expert knowledge, and by statistical analysis and, according to these approaches, based on expert domain knowledge by management specialists, fuzzy signature structures describing the mutual effects of single elements in the questionnaire may be created. There are some slight differences between the two results, that indicate that expert knowledge is sometimes not objective. An additional step applying hybrid Generalised Reduced Gradient algorithm and Genetic Evolutionary Algorithm for heuristic optimization of the aggregation parameters in the Fuzzy Signatures reveals a final model according to the responses. These latter results raise some new questions, including the idea of the use of undeterministic graphs, thus resulting in Fuzzy Fuzzy Signatures. The method could be applied to other similar multicomponent vague data pools.
AB - A set of answers to questions to employees of various companies in Lithuania may refer to various positive and negative aspects of the attitudes of employees. These are called Organizational Citizenship Behavior (positive) and Counterproductive Work Behavior (negative). The components in the answers may be grouped by expert knowledge, and by statistical analysis and, according to these approaches, based on expert domain knowledge by management specialists, fuzzy signature structures describing the mutual effects of single elements in the questionnaire may be created. There are some slight differences between the two results, that indicate that expert knowledge is sometimes not objective. An additional step applying hybrid Generalised Reduced Gradient algorithm and Genetic Evolutionary Algorithm for heuristic optimization of the aggregation parameters in the Fuzzy Signatures reveals a final model according to the responses. These latter results raise some new questions, including the idea of the use of undeterministic graphs, thus resulting in Fuzzy Fuzzy Signatures. The method could be applied to other similar multicomponent vague data pools.
KW - Fuzzy signature
KW - Least squares method
KW - OCB
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UR - http://www.scopus.com/inward/citedby.url?scp=85066128694&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-16024-1_20
DO - 10.1007/978-3-030-16024-1_20
M3 - Chapter
AN - SCOPUS:85066128694
T3 - Studies in Computational Intelligence
SP - 157
EP - 165
BT - Studies in Computational Intelligence
PB - Springer Verlag
ER -