Antecedents of big data analytics adoption: an analysis with future managers in a developing country

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Artigo
Data de publicação
2022
Periódico
Bottom Line
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18
Autores
Moraes G.H.S.M.
Pelegrini G.C.
de Marchi L.P.
Pinheiro G.T.
Cappellozza A.
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© 2022, Emerald Publishing Limited.Purpose: This paper aims to identify the antecedents’ factors that positively and negatively influence the intention to use big data analytics (BDA) by future managers of companies. Design/methodology/approach: The sample comprised 364 business students from a public university in Brazil. The methodology had a quantitative approach, with the use of structural equation modeling. Findings: This paper presented a robust model with a high explanatory factor for the intention to use BDA, in which the elements of positive influence on the intention to use are expected performance, social influence and cost–benefit, and the negative influence factor is resistance to use. Research limitations/implications: Research on BDA has improved the understanding of the phenomenon, mostly emphasizing the technical dimensions of BDA and underestimating organizational and human dimensions. This research contributed to the literature by presenting new insights into these organizational and human aspects by presenting influencing factors for future managers. User resistance is a variable that can incorporate technology adoption theories in BDA. Practical implications: The results present a positive perception of future managers in the decision on financial resources in the acquisition of new technologies and enable managers to improve planning, investment and choice of technologies while presenting insights from the next generation. Issues regarding privacy, security and ethical aspects are key to minimizing user resistance. Originality/value: This paper fills a significant research gap on the adoption of BDA, presenting the perception of future managers on fundamental aspects of adoption in a developing country. In addition, the research offers a theoretical model with new latent variables for a current and relevant topic.
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