Big data analytics adoption: A case study in a large South African telecommunications organisation

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.

[1]  Michail N. Giannakos,et al.  Big data analytics capabilities: a systematic literature review and research agenda , 2017, Information Systems and e-Business Management.

[2]  Dianne Hall,et al.  Understanding the Factors Affecting the Organizational Adoption of Big Data , 2018, J. Comput. Inf. Syst..

[3]  Jan vom Brocke,et al.  How Big Data Analytics Enables Service Innovation: Materiality, Affordance, and the Individualization of Service , 2018, J. Manag. Inf. Syst..

[4]  C. Czarnecki,et al.  Understanding Today’s Telecommunications Industry , 2017 .

[5]  Christian Czarnecki,et al.  Reference Architecture for the Telecommunications Industry , 2017 .

[6]  Shahriar Akter,et al.  Big data analytics and firm performance: Effects of dynamic capabilities , 2017 .

[7]  Jean-Paul Van Belle,et al.  Big Data capabilities and readiness of South African retail organisations , 2016, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence).

[8]  Jun Zhu,et al.  A Framework-Based Approach to Utility Big Data Analytics , 2016, IEEE Transactions on Power Systems.

[9]  Carl Niklas Fredriksson Model of Big Data Failure: Review of Information System Failure , 2016 .

[10]  Roger H. L. Chiang,et al.  Big Data Research in Information Systems: Toward an Inclusive Research Agenda , 2016, J. Assoc. Inf. Syst..

[11]  K. F. Hashim,et al.  INNOVATION TRAITS FOR BUSINESS INTELLIGENCE SUCCESSFUL DEPLOYMENT , 2016 .

[12]  Lech J. Janczewski,et al.  Adoption of Big Data Solutions: A study on its security determinants using Sec-TOE Framework , 2016, CONF-IRM.

[13]  Yu-Wen Huang,et al.  Factors Influencing Business Intelligence Systems Implementation Success in the Enterprises , 2016, PACIS.

[14]  Osden Jokonya,et al.  Towards a Conceptual Framework for Big Data Adoption in Organizations , 2015, 2015 International Conference on Cloud Computing and Big Data (CCBD).

[15]  Morgan Swink,et al.  How the Use of Big Data Analytics Affects Value Creation in Supply Chain Management , 2015, J. Manag. Inf. Syst..

[16]  Irwin Brown,et al.  Challenges to the Organisational Adoption of Big Data Analytics: A Case Study in the South African Telecommunications Industry , 2015, SAICSIT '15.

[17]  Qiang Yang,et al.  Differential Privacy in Telco Big Data Platform , 2015, Proc. VLDB Endow..

[18]  Rick Kazman,et al.  Demystifying Big Data Adoption: Beyond IT Fashion and Relative Advantage , 2015 .

[19]  Ahsan Habib,et al.  Big Data Use Case Domains for Telecom Operators , 2015, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity).

[20]  Stanislav Nedev,et al.  Exploring the factors influencing the adoption of Cloud computing and the challenges faced by the business , 2014 .

[21]  Chrisna Jooste,et al.  Usability evaluation for Business Intelligence applications: a user support perspective , 2014, South Afr. Comput. J..

[22]  Paridhi Verma,et al.  Techniques for Surviving the Mobile Data Explosion: Verma/Techniques for Surviving the Mobile Data Explosion , 2014 .

[23]  Leon A. Kappelman,et al.  The 2014 SIM IT Key Issues and Trends Study , 2014, MIS Q. Executive.

[24]  R. Yin Validity and generalization in future case study evaluations , 2013 .

[25]  Tiago Oliveira,et al.  Literature Review of Information Technology Adoption Models at Firm Level , 2011 .

[26]  P. Curșeu,et al.  Effective decision-making : The role of cognitive complexity in strategic decisions , 2009 .

[27]  Philip E. T. Lewis,et al.  Research Methods for Business Students (5th edn) , 2007 .

[28]  Philip E. T. Lewis,et al.  Research Methods for Business Students , 2006 .

[29]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[30]  E. Rogers,et al.  Diffusion of innovations , 1964, Encyclopedia of Sport Management.

[31]  Izak Benbasat,et al.  Research Report: Empirical Test of an EDI Adoption Model , 2001, Inf. Syst. Res..

[32]  Michael D. Myers,et al.  A Set of Principles for Conducting and Evaluating Interpretive Field Studies in Information Systems , 1999, MIS Q..

[33]  Earl R. Babbie,et al.  The practice of social research , 1969 .