Understanding the Self-Efficacy of Data Scientists

Alamir Costa Louro, Marcelo Moll Brandão and Larissa Alves Sincorá
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Alamir Costa Louro: Federal University of Espírito Santo, Vitória, Brazil
Marcelo Moll Brandão: Federal University of Espírito Santo, Vitória, Brazil
Larissa Alves Sincorá: Federal University of Espírito Santo, Vitória, Brazil

International Journal of Human Capital and Information Technology Professionals (IJHCITP), 2020, vol. 11, issue 2, 50-63

Abstract: The self-efficacy of Brazilian data scientists' professional profiles was analyzed to launch new views on this profession, marked by fast technological changes and with a body of knowledge and an incommensurable scope of skills, as understood by these professionals. A grounded theory was built using a qualitative approach. It found the coping theory to explain the phenomenon after the emergence of self-preservation, as an adaptation strategy, and self-efficacy, as a striking feature of the profession. A practical implication is that self-efficacy has trade-offs both to threats and opportunities in the process of becoming a data scientist. The present article describes the value of the coping theory makes possible an in-depth view of the analytical expertise influence on threats and opportunities, and on technology adaptation choices.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jhcitp:v:11:y:2020:i:2:p:50-63

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