UNDERSTANDING HAPPINESS ECONOMICS IN THE WORKING LIFE OF UNIVERSITY ACADEMICIANS: AN APPLICATION OF MACHINE-BASED RESAMPLING APPROACH TO REDUCE DATA CONSTRAINTS
Keywords:
happiness economics, academicians in university, machine-based resampling approachAbstract
This paper aims to investigate the happiness economics in the working lives of university academicians by applying machine-based resampling approaches to address data constraints. The findings could benefit organizations or universities by providing accurate oversight of academicians' well-being and serving as a guideline for researchers facing similar data constraints. The sample consists of 30 full-time university lecturers in Bangkok. To collect data, questionnaires were used through a simple sampling approach. The data concerning the happiness economics were analyzed using generalized linear models (GLMs). Additionally, bias analysis was applied to measure the models’ efficiency. The empirical results indicated that happiness was mainly driven by self-development and work progression, followed by the working environment, work burden, and evaluation dimensions. These factors had a statistically significant effect on overall working happiness, contributing more than 60 percent. Furthermore, the results obtained from the machine-based resampling could reduce the models’ biases when the resampling was between 100 and 1,000 rounds.
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