IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v26y2024i3d10.1007_s10796-023-10399-1.html
   My bibliography  Save this article

Motivators and Inhibitors for Business Analytics Adoption from the Cross-Cultural Perspectives: A Data Mining Approach

Author

Listed:
  • Hokey Min

    (Bowling Green State University)

  • Bih-Ru Lea

    (Missouri University of Science and Technology)

Abstract

In the increasingly knowledge-based world economy, the multinational firm’s success often hinges on its business intelligence capability nurtured by business analytics (BA). Despite the growing recognition of BA's role in enhancing the firm’s intellectual capital and subsequent competitiveness, it is still unknown what truly motivates and inhibits BA adoption. This study aims to identify key influencing factors for BA adoption such as organizational characteristics, information security/privacy, and information technology maturity (knowledge level). In so doing, this study employed data mining and data visualization techniques to develop specific patterns of BA adoption practices based on a combined sample of 224 Korean firms and 106 U.S. firms representing various industry sectors. This study is one of the first attempts to develop practical guidelines for the successful implementation of BA based on the cross-national study of BA practices among both Korean and U.S. firms.

Suggested Citation

  • Hokey Min & Bih-Ru Lea, 2024. "Motivators and Inhibitors for Business Analytics Adoption from the Cross-Cultural Perspectives: A Data Mining Approach," Information Systems Frontiers, Springer, vol. 26(3), pages 1041-1062, June.
    • Handle: RePEc:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-023-10399-1
    DOI: 10.1007/s10796-023-10399-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-023-10399-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-023-10399-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Akter, Shahriar & Motamarri, Saradhi & Hani, Umme & Shams, Riad & Fernando, Mario & Mohiuddin Babu, Mujahid & Ning Shen, Kathy, 2020. "Building dynamic service analytics capabilities for the digital marketplace," Journal of Business Research, Elsevier, vol. 118(C), pages 177-188.
    2. Robert D. Dewar & Jane E. Dutton, 1986. "The Adoption of Radical and Incremental Innovations: An Empirical Analysis," Management Science, INFORMS, vol. 32(11), pages 1422-1433, November.
    3. Anandhi S. Bharadwaj & Sundar G. Bharadwaj & Benn R. Konsynski, 1999. "Information Technology Effects on Firm Performance as Measured by Tobin's q," Management Science, INFORMS, vol. 45(7), pages 1008-1024, July.
    4. Akter, Shahriar & Gunasekaran, Angappa & Wamba, Samuel Fosso & Babu, Mujahid Mohiuddin & Hani, Umme, 2020. "Reshaping competitive advantages with analytics capabilities in service systems," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
    5. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    6. Ravi Srinivasan & Morgan Swink, 2018. "An Investigation of Visibility and Flexibility as Complements to Supply Chain Analytics: An Organizational Information Processing Theory Perspective," Production and Operations Management, Production and Operations Management Society, vol. 27(10), pages 1849-1867, October.
    7. Milanović Marina & Stamenković Milan, 2016. "CHAID Decision Tree: Methodological Frame and Application," Economic Themes, Sciendo, vol. 54(4), pages 563-586, December.
    8. Rajeev Sharma & Sunil Mithas & Atreyi Kankanhalli, 2014. "Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations," European Journal of Information Systems, Taylor & Francis Journals, vol. 23(4), pages 433-441, July.
    9. Kieran Mathieson, 1991. "Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior," Information Systems Research, INFORMS, vol. 2(3), pages 173-191, September.
    10. Kevin Zhu & Shutao Dong & Sean Xin Xu & Kenneth L Kraemer, 2006. "Innovation diffusion in global contexts: determinants of post-adoption digital transformation of European companies," European Journal of Information Systems, Taylor & Francis Journals, vol. 15(6), pages 601-616, December.
    11. Côrte-Real, Nadine & Oliveira, Tiago & Ruivo, Pedro, 2017. "Assessing business value of Big Data Analytics in European firms," Journal of Business Research, Elsevier, vol. 70(C), pages 379-390.
    12. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    13. Hokey Min & Hey-Young Joo & Seok-Beom Choi, 2021. "Success Factors Affecting the Intention to Use Business Analytics: An Empirical Study," Journal of Business Analytics, Taylor & Francis Journals, vol. 4(2), pages 77-90, July.
    14. Ali Tarhini & Nalin Asanka Gamagedara Arachchilage & Ra'ed Masa'deh & Muhammad Sharif Abbasi, 2015. "A Critical Review of Theories and Models of Technology Adoption and Acceptance in Information System Research," International Journal of Technology Diffusion (IJTD), IGI Global, vol. 6(4), pages 58-77, October.
    15. Thong, J. Y. L. & Yap, C. S., 1995. "CEO characteristics, organizational characteristics and information technology adoption in small businesses," Omega, Elsevier, vol. 23(4), pages 429-442, August.
    16. Akter, Shahriar & Wamba, Samuel Fosso & Gunasekaran, Angappa & Dubey, Rameshwar & Childe, Stephen J., 2016. "How to improve firm performance using big data analytics capability and business strategy alignment?," International Journal of Production Economics, Elsevier, vol. 182(C), pages 113-131.
    17. Thuyuyen H. Nguyen & Michael Newby & Michael J. Macaulay, 2015. "Information Technology Adoption in Small Business: Confirmation of a Proposed Framework," Journal of Small Business Management, Taylor & Francis Journals, vol. 53(1), pages 207-227, January.
    18. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    19. Bongsug Kevin Chae & David L. Olson, 2013. "Business Analytics For Supply Chain: A Dynamic-Capabilities Framework," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 12(01), pages 9-26.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Oesterreich, Thuy Duong & Anton, Eduard & Teuteberg, Frank & Dwivedi, Yogesh K, 2022. "The role of the social and technical factors in creating business value from big data analytics: A meta-analysis," Journal of Business Research, Elsevier, vol. 153(C), pages 128-149.
    2. Osama Musa Ali Al-Darras & Cem Tanova, 2022. "From Big Data Analytics to Organizational Agility: What Is the Mechanism?," SAGE Open, , vol. 12(2), pages 21582440221, June.
    3. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    4. Kim, Jaemin & Dibrell, Clay & Kraft, Ellen & Marshall, David, 2021. "Data analytics and performance: The moderating role of intuition-based HR management in major league baseball," Journal of Business Research, Elsevier, vol. 122(C), pages 204-216.
    5. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    6. Philipp Korherr & Dominik Kanbach, 2023. "Human-related capabilities in big data analytics: a taxonomy of human factors with impact on firm performance," Review of Managerial Science, Springer, vol. 17(6), pages 1943-1970, August.
    7. Luminița Hurbean & Florin Militaru & Mihaela Muntean & Doina Danaiata, 2023. "The Impact of Business Intelligence and Analytics Adoption on Decision Making Effectiveness and Managerial Work Performance," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 70(SI), pages 43-54, February.
    8. Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis, 2024. "Does data-driven culture impact innovation and performance of a firm? An empirical examination," Annals of Operations Research, Springer, vol. 333(2), pages 601-626, February.
    9. Sabeen Hussain Bhatti & Wan Mohd Hirwani Wan Hussain & Jabran Khan & Shahbaz Sultan & Alberto Ferraris, 2024. "Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?," Annals of Operations Research, Springer, vol. 333(2), pages 799-824, February.
    10. Mihai BOGDAN & Anca BORZA, 2019. "Big Data Analytics and Organizational Performance: A Meta-Analysis Study," Management and Economics Review, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 4(2), pages 1-13, June.
    11. Mihai BOGDAN & Anca BORZA, 2020. "Big Data Analytics And Firm Performance: A Text Mining Approach," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 14(1), pages 549-560, November.
    12. Ashrafi, Amir & Zare Ravasan, Ahad & Trkman, Peter & Afshari, Samira, 2019. "The role of business analytics capabilities in bolstering firms’ agility and performance," International Journal of Information Management, Elsevier, vol. 47(C), pages 1-15.
    13. Mujahid Mohiuddin Babu & Mahfuzur Rahman & Ashraful Alam & Bidit Lal Dey, 2024. "Exploring big data-driven innovation in the manufacturing sector: evidence from UK firms," Annals of Operations Research, Springer, vol. 333(2), pages 689-716, February.
    14. Rodepeter, Elisa & Gschnaidtner, Christoph & Hottenrott, Hanna, 2023. "Big data and start-up performance," ZEW Discussion Papers 23-061, ZEW - Leibniz Centre for European Economic Research.
    15. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Gupta, Shivam & Sivarajah, Uthayasankar & Bag, Surajit, 2023. "Assessing the impact of big data analytics on decision-making processes, forecasting, and performance of a firm," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    16. Ashaari, Mohamed Azlan & Singh, Karpal Singh Dara & Abbasi, Ghazanfar Ali & Amran, Azlan & Liebana-Cabanillas, Francisco J., 2021. "Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    17. Dignity Paradza & Olawande Daramola, 2021. "Business Intelligence and Business Value in Organisations: A Systematic Literature Review," Sustainability, MDPI, vol. 13(20), pages 1-27, October.
    18. Sanjay Kumar Tyagi & Sujeet Kumar Sharma & R. Krishankumar & K. S. Ravichandran, 2022. "An extension of interpretive structural modeling using linguistic term sets for business decision-making," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1158-1177, September.
    19. Burger, Katharina & White, Leroy & Yearworth, Mike, 2019. "Developing a smart operational research with hybrid practice theories," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1137-1150.
    20. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "Towards a business analytics capability for the circular economy," Technological Forecasting and Social Change, Elsevier, vol. 171(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:infosf:v:26:y:2024:i:3:d:10.1007_s10796-023-10399-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.