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http://dx.doi.org/10.5392/JKCA.2021.21.05.047

Self-Leadership as Antecedent of Organizational Commitment and Intention to Leave among Data Scientists  

Jung, Chang Mo (현대모비스 데이터사이언스팀)
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Abstract
Data scientists are new knowledge workers representing the knowledge economy era. Knowledge workers perform unstandardized works that solve ambiguity-intensive problems. Therefore, self-leadership, which emphasizes self-motivated, autonomous judgment and execution, significantly influences their work-related outcomes. Even knowledge workers have high occupational commitment, they usually show low organizational commitment. Knowledge workers' intention to leave is also relatively high due to this reason. This study focused on data scientists' self-leadership, predicted that self-leadership would increase an organization's commitment and intention to leave. Based on the trait activation theory(TAT), the author also confirmed how perceived job autonomy enhances self-leadership influences. Results showed that data scientists' self-leadership significantly lowered intention to leave through organizational commitment and this mediating effect was moderated by perceived job autonomy. This study broadened the theoretical understanding the effects of knowledge workers' self-leadership and presented practical implications for managing data scientists.
Keywords
Data Scientist; Knowledge Worker; Self-Leadership; Perceived Job Autonomy; Intention to Leave;
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