Publication: Discovery of happiness and employee engagement relationship using KDD method
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Issued Date
2012-12-01
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2-s2.0-84881145627
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Mahidol University
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SCOPUS
Bibliographic Citation
Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012. (2012), 860-865
Suggested Citation
Songpol Ongwattanakul, Chalermpol Chamchan, Nopraenue S. Dhirathiti Discovery of happiness and employee engagement relationship using KDD method. Proceedings - 2012 7th International Conference on Computing and Convergence Technology (ICCIT, ICEI and ICACT), ICCCT 2012. (2012), 860-865. Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/14004
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Title
Discovery of happiness and employee engagement relationship using KDD method
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Abstract
Humans are widely regarded as the most valuable resources in modern organizations. However, larger number of humans employed may not always contribute to company's success. There are evidences that smaller companies could outperform the larger ones. In fact, one of the keys to the success is the employee engagement. The higher engagement will lead to the better organization performance, but the side effects could result in more unhappy employees. In fact, employee engagement and happiness are two independent factors that are typically perceived, in general, as two opposite parameters. To promote sustainable engagement, the Happy Workplace concept has been introduced in order to keep talented employees engaged as long as possible. With this initiative, a happiness assessment system called Happinometer has been developed and deployed in public by the Institute for Population and Social Research (IPSR) at Mahidol University. Several government agencies and private enterprises have already participated in the Happinometer program. In this research, the employee engagement factors are extracted from the Happinometer database using a Knowledge Discovery in Database (KDD) technique called decision tree. The extracted rules are filtered and ranked according to its support and accuracy. This initial knowledge composes of 17 rules that are further separated into 4 generations for analysis. Finally, the discovered knowledge is validated by human experts. Additional feedbacks from experts state that the KDD method is more efficient in the analysis and interpretation of the discovered factors and capable of unveiling the hidden knowledge. © 2012 AICIT.