A Bayesian Analysis Approach for Bridging the Gap between Employee Expectations and Employee Satisfaction

Authors

  • R. Muruganandham Associate Professor, School of Management, Presidency University, Bengaluru.
  • J. Dinesh Assistant Professor, Faculty of Management, SRM Institute of Science and Technology, Kattankulathur Campus, Kattankulathur, Chengalpattu-
  • A. Muhammad Raheel Basha Consultant, Tata Consultancy Studies, Chennai, India
  • S. Keerthi Vasan Thiagarajar College of Engineering, Madurai.

Keywords:

Ergonomics, Motivation, Employées Expectations, Employées Satisfaction, Bayes Theorem, Employee engagement

Abstract

Satisfaction of the employees working in an organization is one of the major challenging tasks for any organization. In this research as reported in the title, the Bayesian theorem is applied to find out the combination of possible cases of highly influencing factors that were confirmed using correlation, and the various combinations which will lead to best-case scenarios and worst-case scenarios are found using Bayesian Theorem.  The novelty in the article is applying Baye’s theorem for the study which was undertaken. Baye’s theorem is a mathematical principle based on probability theory where the conditional probability approach is considered to study the likelihood of the outcome of the occurring event based on the previous occurrence.

Downloads

Download data is not yet available.

References

Agarwal, R. and Rodhain, F. (2002). Mine or Ours: Email privacy expectations, Employee attitudes and perceived work environment characteristics, 35th annual Hawaii International conference on system sciences, HICSS-35.

Bakker, A. B., & Schaufeli, W. B. (2008). Positive organizational behavior: engaged employees in flourishing organizations. Journal of Organizational Behavior, 29(2), 147–154. doi:10.1002/job.515

Bell, S. J., & Menguc, B. (2002). The employee-organization relationship, organizational citizenship behaviors, and superior service quality. Journal of Retailing, 78(2), 131–146. doi:10.1016/s0022-4359(02)00069-6

Bhanot, N., Rao, P. V., & Deshmukh, S. G. (2015). Enablers and Barriers of Sustainable Manufacturing: Results from a Survey of Researchers and Industry Professionals. Procedia CIRP, 29, 562–567. doi:10.1016/j.procir.2015.01.036015.

Blanco, R., Inza, I., Merino, M., Quiroga, J., & Larrañaga, P. (2005). Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. Journal of Biomedical Informatics, 38(5), 376–388. doi:10.1016/j.jbi.2005.05.004

Bruck, C. S., Allen, T. D., & Spector, P. E. (2002). The Relation between Work–Family Conflict and Job Satisfaction: A Finer-Grained Analysis. Journal of Vocational Behavior, 60(3), 336–353. doi:10.1006/jvbe.2001.1836

Calisir, F., & Calisir, F. (2004). The relation of interface usability characteristics, perceived usefulness, and perceived ease of use to end-user satisfaction with enterprise resource planning (ERP) systems. Computers in Human Behavior, 20(4), 505–515. doi:10.1016/j.chb.2003.10.004 .

Chatzidakis, S., & Staras, A. (2013). A Bayesian approach to unanticipated events frequency estimation in the decision making context of a nuclear research reactor facility. Annals of Nuclear Energy, 59, 169–175. doi:10.1016/j.anucene.2013.04.006

Chin, K.-S., Tang, D.-W., Yang, J.-B., Wong, S. Y., & Wang, H. (2009). Assessing new product development project risk by Bayesian network with a systematic probability generation methodology. Expert Systems with Applications, 36(6), 9879–9890. doi:10.1016/j.eswa.2009.02.019

Currivan, D. B. (1999). The Causal Order of Job Satisfaction and Organizational Commitment in Models of Employee Turnover. Human Resource Management Review, 9(4), 495–524. doi:10.1016/s1053-4822(99)00031-5

Daunizeau, J., Friston, K. J., & Kiebel, S. J. (2009). Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models. Physica D: Nonlinear Phenomena, 238(21), 2089–2118. doi:10.1016/j.physd.2009.08.002

Dejoy, D. M., Schaffer, B. S., Wilson, M. G., Vandenberg, R. J., & Butts, M. M. (2004). Creating safer workplaces: assessing the determinants and role of safety climate. Journal of Safety Research, 35(1), 81–90. doi:10.1016/j.jsr.2003.09.018

Diamond, G. A., & Kaul, S. (2004). Prior convictions. Journal of the American College of Cardiology, 43(11), 1929–1939. doi:10.1016/j.jacc.2004.01.035

Flood, P. C., Turner, T., Ramamoorthy, N., & Pearson, J. (2001). Causes and consequences of psychological contracts among knowledge workers in the high technology and financial services industries. The International Journal of Human Resource Management, 12(7), 1152–1165. doi:10.1080/09585190110068368 .

G.R. Elkahlout. G.R. (2006).Bayes estimators for the extreme value reliability function,International Journal of Computers and mathematics with applications, Vol. 51, pp 673-679.

Gieder, K. D., Karpanty, S. M., Fraser, J. D., Catlin, D. H., Gutierrez, B. T., Plant, N. G., … Robert Thieler, E. (2014). A Bayesian network approach to predicting nest presence of the federally-threatened piping plover (Charadrius melodus) using barrier island features. Ecological Modelling, 276, 38–50. doi:10.1016/j.ecolmodel.2014.01.005

Goldstein, M., & Bedford, T. (2007). The Bayes linear approach to inference and decision-making for a reliability programme. Reliability Engineering & System Safety, 92(10), 1344–1352. doi:10.1016/j.ress.2006.09.010

Green, N. (2005). A Bayesian network coding scheme for annotating biomedical information presented to genetic counseling clients. Journal of Biomedical Informatics, 38(2), 130–144. doi:10.1016/j.jbi.2004.10.001

Higgins, H. M., Huxley, J. N., Wapenaar, W., & Green, M. J. (2014). Quantifying veterinarians’ beliefs on disease control and exploring the effect of new evidence: A Bayesian approach. Journal of Dairy Science, 97(6), 3394–3408. doi:10.3168/jds.2013-7087

Iqbal, M. (2000). On photon correlation measurements of colloidal size distributions using Bayesian strategies. Journal of computational and Applied mathematics, Vol. 126, pp 77-89.

Karabulut, A. T. (2015). Effects of Innovation Strategy on Firm Performance: A Study Conducted on Manufacturing Firms in Turkey. Procedia - Social and Behavioral Sciences, 195, 1338–1347. doi:10.1016/j.sbspro.2015.06.314 .

Lee, S.-M., & Abbott, P. A. (2003). Bayesian networks for knowledge discovery in large datasets: basics for nurse researchers. Journal of Biomedical Informatics, 36(4-5), 389–399. doi:10.1016/j.jbi.2003.09.022

Schaufeli, W. B., Bakker, A. B., & Van Rhenen, W. (2009). How changes in job demands and resources predict burnout, work engagement, and sickness absenteeism. Journal of Organizational Behavior, 30(7), 893–917. doi:10.1002/job.595

Stahl, Günter K. & Miller, Edwin L. & Tung, Rosalie L., (2002).Toward the boundaryless career: a closer look at the expatriate career concept and the perceived implications of an international assignment," Journal of World Business, Elsevier, vol. 37(3), pages 216-227

Suebnukarn, S., & Haddawy, P. (2006). A Bayesian approach to generating tutorial hints in a collaborative medical problem-based learning system. Artificial Intelligence in Medicine, 38(1), 5–24. doi:10.1016/j.artmed.2005.04.003

Tudor, A. T., Zaharie, M., & Osoian, C. (2014). Innovation Development Needs in Manufacturing Companies. Procedia Technology, 12, 505–510. doi:10.1016/j.protcy.2013.12.522

Xanthopoulou, D., Bakker, A. B., Demerouti, E., & Schaufeli, W. B. (2009). Work engagement and financial returns: A diary study on the role of job and personal resources. Journal of Occupational and Organizational Psychology, 82(1), 183–200. doi:10.1348/096317908x285633.

Yusof, H. M., Mustapha, R., Mohamad, S. A. M. S., & Bunian, M. S. (2012). Measurement Model of Employability Skills using Confirmatory Factor Analysis. Procedia - Social and Behavioral Sciences, 56, 348–356. doi:10.1016/j.sbspro.2012.09.663

Zhang, A. Y., Tsui, A. S., Song, L. J., Li, C., & Jia, L. (2008). How do I trust thee? The employee-organization relationship, supervisory support, and middle manager trust in the organization. Human Resource Management, 47(1), 111–132. doi:10.1002/hrm.20200

Zhu, Q., & Sarkis, J. (2004). Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. Journal of Operations Management, 22(3), 265–289. doi:10.1016/j.jom.2004.01.005.

Downloads

Published

27.12.2023

How to Cite

Muruganandham, R. ., Dinesh, J. ., Raheel Basha, A. M. ., & Vasan , S. K. . (2023). A Bayesian Analysis Approach for Bridging the Gap between Employee Expectations and Employee Satisfaction. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 405–419. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4334

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.