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Business analytics adoption process: An innovation diffusion perspective

Author

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  • Nam, Dalwoo
  • Lee, Junyeong
  • Lee, Heeseok

Abstract

Although business analytics (BA) have been increasingly adopted into businesses, there is limited empirical research examining the drivers of each stage of BA adoption in organizations. Drawing upon technological-organizational-environmental framework and innovation diffusion process, we developed an integrative model to examine BA adoption processes and tested with 170 Korean firms. The analysis shows data-related technological characteristics derive all stages of BA adoption: initiation, adoption and assimilation. While organizational characteristics are associated with adoption and assimilation stage, only competition intensity in environmental characteristics is associated with initiation stage. Our findings help practitioners and researchers to understand what factors can enable companies to adopt BA in each stage.

Suggested Citation

  • Nam, Dalwoo & Lee, Junyeong & Lee, Heeseok, 2019. "Business analytics adoption process: An innovation diffusion perspective," International Journal of Information Management, Elsevier, vol. 49(C), pages 411-423.
    • Handle: RePEc:eee:ininma:v:49:y:2019:i:c:p:411-423
    DOI: 10.1016/j.ijinfomgt.2019.07.017
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    Cited by:

    1. Showimy Aldossari & Umi Asma’ Mokhtar & Ahmad Tarmizi Abdul Ghani, 2023. "Factor Influencing the Adoption of Big Data Analytics: A Systematic Literature and Experts Review," SAGE Open, , vol. 13(4), pages 21582440231, December.
    2. James A. Cunningham & Nadja Damij & Dolores Modic & Femi Olan, 2023. "MSME technology adoption, entrepreneurial mindset and value creation: a configurational approach," The Journal of Technology Transfer, Springer, vol. 48(5), pages 1574-1598, October.
    3. Lodemann, Sebastian & Kersten, Wolfgang, 2021. "Supply chain analytics implementation: A TOE perspective," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Ringle, Christian M. & Blecker, Thorsten (ed.), Adapting to the Future: How Digitalization Shapes Sustainable Logistics and Resilient Supply Chain Management. Proceedings of the Hamburg Internationa, volume 31, pages 411-434, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    4. Perdana, Arif & Lee, Hwee Hoon & Arisandi, Desi & Koh, SzeKee, 2022. "Accelerating data analytics adoption in small and mid-size enterprises: A Singapore context," Technology in Society, Elsevier, vol. 69(C).
    5. Kalaitzi, Dimitra & Tsolakis, Naoum, 2022. "Supply chain analytics adoption: Determinants and impacts on organisational performance and competitive advantage," International Journal of Production Economics, Elsevier, vol. 248(C).
    6. Xiaohui Liu & Na Jiang & Mengyao Fu & Zhao Cai & Eric T. K. Lim & Chee-Wee Tan, 2023. "What Piques Users’ Curiosity on Open Innovation Platforms? An Analysis Based on Mobile App Stores," Information Systems Frontiers, Springer, vol. 25(4), pages 1639-1660, August.
    7. Lutfi, Abdalwali & Alrawad, Mahmaod & Alsyouf, Adi & Almaiah, Mohammed Amin & Al-Khasawneh, Ahmad & Al-Khasawneh, Akif Lutfi & Alshira'h, Ahmad Farhan & Alshirah, Malek Hamed & Saad, Mohamed & Ibrahim, 2023. "Drivers and impact of big data analytic adoption in the retail industry: A quantitative investigation applying structural equation modeling," Journal of Retailing and Consumer Services, Elsevier, vol. 70(C).
    8. Muhammad Ovais Ahmad & Iftikhar Ahmad & Nripendra P. Rana & Iqra Sadaf Khan, 2023. "An Empirical Investigation on Business Analytics in Software and Systems Development Projects," Information Systems Frontiers, Springer, vol. 25(2), pages 917-927, April.

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