Resumo

WALKER, Richard S.  e  BROWN, Irwin. Big data analytics adoption: A case study in a large South African telecommunications organisation. SAJIM (Online) [online]. 2019, vol.21, n.1, pp.1-10. ISSN 1560-683X.  http://dx.doi.org/10.4102/sajim.v21i1.1079.

BACKGROUND: Big data analytics (BDA) offers a frontier of opportunities across all industries enabling improvements in marketing, customer service and product development. The adoption process for BDA is often challenging for organisations, given the complexities associated with it. OBJECTIVE: The objective of this study was hence to understand factors that influence the BDA adoption process in organisations. The technology-organisation-environment framework was combined with factors from a Big Data Adoption model and used as a foundation for the study. METHOD: A case study research strategy was performed on a large telecommunication organisation. Themes were identified which provided rich explanations into the factors influencing the BDA adoption process in organisations. RESULTS: Five technological factors were confirmed to influence the BDA adoption process. These were: (1) relative advantage, (2) complexity, (3) compatibility, (4) trialability and (5) data quality. Four organisational factors were confirmed to influence the BDA adoption process. These were: (1) top management support, (2) human resource expertise, (3) business and information technology (IT) alignment and (4) organisation size. Five environmental factors were confirmed to influence the BDA adoption process. These were: (1) competitive pressure, (2) data privacy, (3) vendor support, (4) IT fashion and (5) regulatory requirements. Two factors were confirmed as influencing an organisations' ability to move from intention to adopt BDA to actual deployment. These were: (1) complexity tolerance and (2) paradigm shifts. CONCLUSION: This study provided evidence that organisations that have a high tolerance for complexity are more likely to move rapidly from intention to adopt BDA to actual deployment and effectively reduce the deployment gap.

Palavras-chave : Big data analytics; adoption process; telecommunications; complexity tolerance; deployment gap.

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