Employee Satisfaction of Academics in Sri Lanka: A Logistic Regression Approach

Employee Satisfaction of Academics in Sri Lanka: A Logistic Regression Approach

Nilushi Dias

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T. M. J. A. Cooray

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Wasantha Rajapakse

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α Sri Lanka Institute of Information Technology

Employee Satisfaction of Academics in Sri Lanka: A Logistic Regression Approach

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Abstract

This study has mainly focused on the use of multinomial logistic regression in predicting employee satisfaction of the academics in Sri Lankan universities. A questionnaire was used to gather data from acdemics and it is prepared to collect demographic data and eight main factors. Demographic factors were analyzed with multinomial logistic regression, and it resulted in three elements namely, sector, salary, and gender. Before examining the main factors in the questionnaire, a reliability analysis was done. Factors were analyzed with multinomial logistic regression and resulted in different models and the best model out of all is presented in this paper. By comparing the models with R-squared values, goodness-of-fit statistics and residuals, the best model was obtained. This study revealed thatfitting of the abilities and knowledge with the job, ability to use the full potential in work, superior behavior and freedom are significant factors in predicting employee satisfaction of academics in Sri Lankan universities.

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

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Not applicable for this article.

How to Cite This Article

Nilushi Dias. 2018. \u201cEmployee Satisfaction of Academics in Sri Lanka: A Logistic Regression Approach\u201d. Global Journal of Computer Science and Technology - D: Neural & AI GJCST-D Volume 18 (GJCST Volume 18 Issue D3): .

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GJCST Volume 18 Issue D3
Pg. 5- 12

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Journal Specifications

Crossref Journal DOI 10.17406/gjcst

Print ISSN 0975-4350

e-ISSN 0975-4172

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GJCST-D Classification: J.1

Submission ReceivedDecember 10, 2017
Peer Review Double Blind
Handling Editor
Accepted January 1, 2018
Published January 15, 2018

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December 13, 2018

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Article Matrices

Total Score: 103

Country: Sri Lanka

Subject: Global Journal of Computer Science and Technology - D: Neural & AI

Authors: Nilushi Dias, T. M. J. A. Cooray, Wasantha Rajapakse (PhD/Dr. count: 0)

View Count (all-time): 260

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Publish Date: 2018 12, Thu

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Total Views: 5488

Total Downloads: 1499

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