Preferences of job choices and its attributes among millennial generations: Evidence from vavuniya campus in Sri lanka - Jinadasa, P.G.P.H.K.; Thayaparan, A. (University of Jaffna, 2020)
The objective of the study is to examine the factors influencing the preferences of job choices and its attributes among undergraduates who are related to millennial generation at their first job in Vavuniya campus in Sri Lanka. Total of 100 undergraduate students from third and fourth years who are following business management degree program in the campus were selected during the period of 2019/2020 as the sample of millennial generation in the study. Frequency of choices on degree programs as well as job choices reveal that, 43% of the students are following special degree in accounting and finance whereas, only 8% of them following special degree in marketing management. 18% of them following business economics specialization and 13% of them following human resource management as their specialization degree program in the campus. Frequency of job choices implies that, 40% of the students choose manufacturing sector as their first job while 19% of them preferred banking and academic sectors. Multinomial logit model was used to identify the impact of job attributes on four types of job choices such as, banking sector, apparel manufacturing, audit firm and academic sector which was taken as dependent variable with the five explanatory variables namely, basic salary, contract types, career path, job location and reputation in the study. Its results reveal that salary, types of contract, career path and reputation have significantly affected the job choices whereas job location is insignificant in the model. Findings of the study is important for academics to revise their curriculum towards job oriented as well as suggest the managers for designing jobs to attract and retain the best talented workers in labour market in future.
Keywords: Career path; Frequency of choices on degree programs; Job attributes and job choices; Millennial generation; Multinomial logit regression