Abstract
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.
Highlights
Big data analytics (BDA) is emerging as a hot topic amongst scholars and practitioners (Wamba et al 2017) and is considered to be the most important technology disruption since the rise of the Internet (Chen, Preston & Swink 2015a)
Studies in BDA adoption usually focus on the adoption intentions that relate to the initiation stage and very few on the adoption decision stage that leads to deployment (Chen, Kazman & Matthes 2015b)
This study aims to explain the factors influencing the adoption process of BDA in organisations by combining the TOE framework with factors from the Big Data Adoption model of Chen et al (2015b) to provide insights into the deployment gap
Summary
Big data analytics (BDA) is emerging as a hot topic amongst scholars and practitioners (Wamba et al 2017) and is considered to be the most important technology disruption since the rise of the Internet (Chen, Preston & Swink 2015a). A ‘limbo stage’ is observed in BDA adoption where organisations http://www.sajim.co.za signal an intention to adopt but remain in an experimental stage for years. This is known as the deployment gap (Chen et al 2015b). This study aims to explain the factors influencing the adoption process of BDA in organisations by combining the TOE framework with factors from the Big Data Adoption model of Chen et al (2015b) to provide insights into the deployment gap. The adoption process for BDA is often challenging for organisations, given the complexities associated with it
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
