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Data quality management, data usage experience and acquisition intention of big data analytics

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  • Kwon, Ohbyung
  • Lee, Namyeon
  • Shin, Bongsik

Abstract

Big data analytics associated with database searching, mining, and analysis can be seen as an innovative IT capability that can improve firm performance. Even though some leading companies are actively adopting big data analytics to strengthen market competition and to open up new business opportunities, many firms are still in the early stage of the adoption curve due to lack of understanding of and experience with big data. Hence, it is interesting and timely to understand issues relevant to big data adoption. In this study, a research model is proposed to explain the acquisition intention of big data analytics mainly from the theoretical perspectives of data quality management and data usage experience. Our empirical investigation reveals that a firm's intention for big data analytics can be positively affected by its competence in maintaining the quality of corporate data. Moreover, a firm's favorable experience (i.e., benefit perceptions) in utilizing external source data could encourage future acquisition of big data analytics. Surprisingly, a firm's favorable experience (i.e., benefit perceptions) in utilizing internal source data could hamper its adoption intention for big data analytics.

Suggested Citation

  • Kwon, Ohbyung & Lee, Namyeon & Shin, Bongsik, 2014. "Data quality management, data usage experience and acquisition intention of big data analytics," International Journal of Information Management, Elsevier, vol. 34(3), pages 387-394.
    • Handle: RePEc:eee:ininma:v:34:y:2014:i:3:p:387-394
    DOI: 10.1016/j.ijinfomgt.2014.02.002
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    7. Lismont, Jasmien & Vanthienen, Jan & Baesens, Bart & Lemahieu, Wilfried, 2017. "Defining analytics maturity indicators: A survey approach," International Journal of Information Management, Elsevier, vol. 37(3), pages 114-124.
    8. Gupta, Shivam & Kar, Arpan Kumar & Baabdullah, Abdullah & Al-Khowaiter, Wassan A.A., 2018. "Big data with cognitive computing: A review for the future," International Journal of Information Management, Elsevier, vol. 42(C), pages 78-89.
    9. Yonghong Ma & Xiaomeng Yang & Sen Qu & Lingkai Kong, 2022. "Research on the formation mechanism of big data technology cooperation networks: empirical evidence from China," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1273-1294, March.
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    12. 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.
    13. Haneem, Faizura & Kama, Nazri & Taskin, Nazim & Pauleen, David & Abu Bakar, Nur Azaliah, 2019. "Determinants of master data management adoption by local government organizations: An empirical study," International Journal of Information Management, Elsevier, vol. 45(C), pages 25-43.
    14. Farheen Naz & Anil Kumar & Abhijit Majumdar & Rohit Agrawal, 2022. "Is artificial intelligence an enabler of supply chain resiliency post COVID-19? An exploratory state-of-the-art review for future research," Operations Management Research, Springer, vol. 15(1), pages 378-398, June.
    15. Tugba Karaboga & Cemal Zehir & Ekrem Tatoglu & H. Aykut Karaboga & Abderaouf Bouguerra, 2023. "Big data analytics management capability and firm performance: the mediating role of data-driven culture," Review of Managerial Science, Springer, vol. 17(8), pages 2655-2684, November.
    16. Gandomi, Amir & Haider, Murtaza, 2015. "Beyond the hype: Big data concepts, methods, and analytics," International Journal of Information Management, Elsevier, vol. 35(2), pages 137-144.
    17. Maria Hoffmann Jensen & John Stouby Persson & Peter Axel Nielsen, 2023. "Measuring benefits from big data analytics projects: an action research study," Information Systems and e-Business Management, Springer, vol. 21(2), pages 323-352, June.
    18. Patrucco, Andrea S. & Marzi, Giacomo & Trabucchi, Daniel, 2023. "The role of absorptive capacity and big data analytics in strategic purchasing and supply chain management decisions," Technovation, Elsevier, vol. 126(C).
    19. Meng Wang & Yalin Qin & Jiaojiao Liu & Weidong Li, 2023. "Identifying personal physiological data risks to the Internet of Everything: the case of facial data breach risks," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-15, December.
    20. Jianmin Song & Senmao Xia & Demetris Vrontis & Arun Sukumar & Bing Liao & Qi Li & Kun Tian & Nengzhi Yao, 2022. "The Source of SMEs’ Competitive Performance in COVID-19: Matching Big Data Analytics Capability to Business Models," Information Systems Frontiers, Springer, vol. 24(4), pages 1167-1187, August.
    21. Nam, Dalwoo & Lee, Junyeong & Lee, Heeseok, 2019. "Business analytics use in CRM: A nomological net from IT competence to CRM performance," International Journal of Information Management, Elsevier, vol. 45(C), pages 233-245.

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