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Data scientist is one of the most coveted professions of the moment (Davenport & Patil, 2012, Acito & Khatri, 2014), since professionals who dominate new technologies tend to position themselves differently in the face of market demands, which recently seek individuals with developed knowledge in statistics, mathematics, and computing (Braganza, Brooks, Nepelski, Ali, & Moro, 2017). The present work gives more in-depth insights into the characteristics of this relevant professional.
According to Grublješič and Jaklič (2015), data science professionals’ self-efficacy is considered the main human factor for the initial acceptance process of new technologies. In the IT industry, there are diverse factors to study such as human and organizational factors (HOF) (Misra, Colomo-Palacios, Pusatli, & Soto-Acosta, 2013; Fernández Sanz, Gómez Pérez, Díez-Folledo, & Misra, 2016), but the present work opted to emerge from the psychology literature, like in the work of Xu and Maitland (2019) that also relates data science and self-efficacy.
Self-efficacy, in its common conceptualization in the IT literature, deals with individuals' beliefs about their abilities to competently use computers (Compeau & Higgins, 1999). However, the IT approach is an adaptation of the seminal concept of self-efficacy, a foundation of human agency, meaning a belief in the ability to adapt to a specific situation (Bandura, 1999). The present work assumed the seminal, broader understanding from psychology to cover the ability of individuals to solve problems using science competently.
Thus, self-efficacy is understood here as the central point of this profession. It has demanded the researchers to hold a deeper understanding of the dichotomy of its self-perception and market requisites. Thus, aside from the self-efficacy literature related to IT, and usually from 90ths (Compeau & Higgins, 1995; Compeau & Higgins, 1999), the researchers understand that this construct requires a broader meaning for future studies about data science, assuming it as different from common IT careers. Researchers assumed that data science involves more science than data.
In Brazilian culture, there both incentives for new ideas and a permanent invitation to creativity (Fleith, 2011). In this way, with Brazilian data scientists as the research sample, it is pertinent to discuss the chosen professional profile, marked by characteristics primarily related to creativity, high expectations about original ideas, and abstraction potential (Provost & Fawcett, 2013). This new profession also needs to be understood from the point of view of a management discipline because data science allows academics to expand possibilities regarding further organizational questions and better responses to old ones (George, Osinga, Lavie, & Scott, 2016).
Data scientists are marked by quick and continuous technological changes and by demand for peculiar behaviors and expertise (Chen, Chiang, & Storey, 2012)—factors that the researchers conjecture in the course of the interviews as related to self-efficacy, which in turn influence the effort expectancy and technology anxiety.
In an effort to respond to perceived limitations, the present qualitative research was not confined to a description of findings, and it ultimately reached a different place than initially intended. The initial caveat of using the Adoption and Use of Technology Theory was in setting the starting point for the research instrument. The original context was the understanding of two constructs with different positions and degrees of importance in the global scenario of this literature—the “effort expectancy” and “technology anxiety” constructs. From the data, during the interview process, the theoretical focus of the research changed to Coping Theory with the emergence of self-efficacy as a striking feature found among data scientists.