Doctor of Philosophy in International Business Administration (Ph.D.-IB)
Although high unemployment rates exist in many countries of the world, companies continue to struggle to hire candidates whose personality traits and competencies match their needs. High unemployment rates and a global talent shortage can be seen as a fascinating contradiction that merits future research. This study investigates suitable personnel selection methods that assist recruiters in seeking the right person with the potential to contribute most to a business. It consolidates theories about personnel selection in a cross-national setting and link them with specific personality traits, job competencies, and person-organization fit (P-O fit) that could predict and enhance employee outcomes. The Most Valuable Employees and Average Contribution Employees can be classified by using Data Envelopment analysis (DEA) and Naïve Bayesian analysis based on a set of standards. Using analysis of variance (ANOVA), different aspects of personality traits, employee competencies, P-O fit, and biographic information, these characteristics are found to influence the key workplace outcomes in the United States and China. In both countries, the three dimensions in the HEXACO personality inventory (Honesty-Humility, Conscientiousness, and Openness to Experience) are found to significantly effect the workplace outcomes. In addition, job competencies, and P-O fit are also proven to be significantly associated with the key work outcomes in these two countries. There are several limitations in this study associated with a relative small number of primary data collected from them. However, this study employs a web-based dynamic survey system to save time, improve the response rate, and provide assessment accuracy. Furthermore, DEA and Naïve Bayesian classifiers help to separate the Most Valuable Employees from other employees, which may have practical significance for both researchers and practitioners.
Wang, Wei, "Cross-National Effectiveness of Cognitive/Affective, Demographic, and Skills Selection Criteria" (2018). Theses and Dissertations. 27.