How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets
This work introduces a data-driven approach based on k-means clustering with datasets elicited under a Picture fuzzy set (PFS) environment. With the vision, mission, and goals statement as a proxy for organizational identity, an actual case study is reported to demonstrate the application of the pro...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-04-01
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| Series: | International Journal of Information Management Data Insights |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667096823000046 |
| _version_ | 1797847735261462528 |
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| author | Adrian Ybañez Rosein Ancheta, Jr. Samantha Shane Evangelista Joerabell Lourdes Aro Fatima Maturan Nadine May Atibing Egberto Selerio, Jr. Kafferine Yamagishi Lanndon Ocampo |
| author_facet | Adrian Ybañez Rosein Ancheta, Jr. Samantha Shane Evangelista Joerabell Lourdes Aro Fatima Maturan Nadine May Atibing Egberto Selerio, Jr. Kafferine Yamagishi Lanndon Ocampo |
| author_sort | Adrian Ybañez |
| collection | DOAJ |
| description | This work introduces a data-driven approach based on k-means clustering with datasets elicited under a Picture fuzzy set (PFS) environment. With the vision, mission, and goals statement as a proxy for organizational identity, an actual case study is reported to demonstrate the application of the proposed approach. Four attributes were introduced to describe organizational identification: knowledge, perception, linking, and future attributes. Results revealed that out of 1,911 members, 56.67% of them are considered “proactive citizens”, 32.50% are “ambivalent citizens”, and 10.83% are “disengaged citizens”. Sensitivity analysis shows that the proposed approach is robust to changes in the model parameters. Characteristics of the types of citizens were discussed, and some managerial insights were outlined. Succinctly, this work contributes to the literature by exploring the integration of PFS to k-means clustering to characterize the extent of organizational identification of organizational stakeholders when presented with a new organizational identity – a relatively novel application in organization science. |
| first_indexed | 2024-04-09T18:16:12Z |
| format | Article |
| id | doaj.art-eca54168c94d4a69a4b3cf4342f086a6 |
| institution | Directory Open Access Journal |
| issn | 2667-0968 |
| language | English |
| last_indexed | 2024-04-09T18:16:12Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Information Management Data Insights |
| spelling | doaj.art-eca54168c94d4a69a4b3cf4342f086a62023-04-13T04:27:23ZengElsevierInternational Journal of Information Management Data Insights2667-09682023-04-0131100157How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasetsAdrian Ybañez0Rosein Ancheta, Jr.1Samantha Shane Evangelista2Joerabell Lourdes Aro3Fatima Maturan4Nadine May Atibing5Egberto Selerio, Jr.6Kafferine Yamagishi7Lanndon Ocampo8Institute of Molecular Genetics, Parasitology, and Vector-Borne Diseases, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesGraduate School, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesCenter for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, Philippines; Department of Tourism Management, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, PhilippinesGraduate School, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, Philippines; Center for Applied Mathematics and Operations Research, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, Philippines; Corresponding author at: Graduate School, Cebu Technological University, Corner M. J. Cuenco Ave. & R. Palma St., Cebu City 6000, Philippines.This work introduces a data-driven approach based on k-means clustering with datasets elicited under a Picture fuzzy set (PFS) environment. With the vision, mission, and goals statement as a proxy for organizational identity, an actual case study is reported to demonstrate the application of the proposed approach. Four attributes were introduced to describe organizational identification: knowledge, perception, linking, and future attributes. Results revealed that out of 1,911 members, 56.67% of them are considered “proactive citizens”, 32.50% are “ambivalent citizens”, and 10.83% are “disengaged citizens”. Sensitivity analysis shows that the proposed approach is robust to changes in the model parameters. Characteristics of the types of citizens were discussed, and some managerial insights were outlined. Succinctly, this work contributes to the literature by exploring the integration of PFS to k-means clustering to characterize the extent of organizational identification of organizational stakeholders when presented with a new organizational identity – a relatively novel application in organization science.http://www.sciencedirect.com/science/article/pii/S2667096823000046Big data analyticsk-means clusteringPicture fuzzy setsOrganizational identityOrganizational identification |
| spellingShingle | Adrian Ybañez Rosein Ancheta, Jr. Samantha Shane Evangelista Joerabell Lourdes Aro Fatima Maturan Nadine May Atibing Egberto Selerio, Jr. Kafferine Yamagishi Lanndon Ocampo How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets International Journal of Information Management Data Insights Big data analytics k-means clustering Picture fuzzy sets Organizational identity Organizational identification |
| title | How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets |
| title_full | How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets |
| title_fullStr | How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets |
| title_full_unstemmed | How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets |
| title_short | How can we use machine learning for characterizing organizational identification - a study using clustering with Picture fuzzy datasets |
| title_sort | how can we use machine learning for characterizing organizational identification a study using clustering with picture fuzzy datasets |
| topic | Big data analytics k-means clustering Picture fuzzy sets Organizational identity Organizational identification |
| url | http://www.sciencedirect.com/science/article/pii/S2667096823000046 |
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