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An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry

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  • Cheon-Pyo Lee
  • Jung P Shim

Abstract

This study examines the radio frequency identification (RFID) adoption decision process and proposes a model predicting the likelihood of adopting RFID within organizations in the healthcare industry. A considerable number of studies have been conducted regarding organizational information technology (IT) adoption, but the nature of the organizational IT adoption process is still not well understood. Especially, although there are a number of variables and categories that have been found empirically to be related to adoption behavior, there is little in the way of evidence to suggest the origin or motivation behind the adoption. Thus, this study investigates the underlying motivations and driving forces behind the adoption of RFID using the theory of technology-push and need-pull. In this study, an organizational RFID adoption model is proposed and empirically tested by a survey using a sample of 126 senior executives in U.S. hospitals. The model posits that three categories of factors, technology push, need pull, and presence of champions, determine the likelihood of adopting RFID within organizations. This study also found that the relationships between those three categories and the likelihood of adopting RFID are strengthened or weakened by organizational readiness.

Suggested Citation

  • Cheon-Pyo Lee & Jung P Shim, 2007. "An exploratory study of radio frequency identification (RFID) adoption in the healthcare industry," European Journal of Information Systems, Taylor & Francis Journals, vol. 16(6), pages 712-724, December.
    • Handle: RePEc:taf:tjisxx:v:16:y:2007:i:6:p:712-724
    DOI: 10.1057/palgrave.ejis.3000716
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    Cited by:

    1. Hashimy, Loha & Geetika, Jain & Grifell-Tatje, Emili, 2023. "Determinants of Blockchain Adoption as Decentralized Business Model by Spanish Firms: – An Innovation Theory Perspective," MPRA Paper 119903, University Library of Munich, Germany.
    2. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Kumar, Ajay & Gupta, Shivam & Sengupta, Pooja, 2023. "Rethinking of firm innovation capability: Examining the moderating role of leadership ability on a new business model," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    3. 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).
    4. Gupta, Shivam & Modgil, Sachin & Choi, Tsan-Ming & Kumar, Ajay & Antony, Jiju, 2023. "Influences of artificial intelligence and blockchain technology on financial resilience of supply chains," International Journal of Production Economics, Elsevier, vol. 261(C).
    5. Vijendra Kumar & Hazi Md. Azamathulla & Kul Vaibhav Sharma & Darshan J. Mehta & Kiran Tota Maharaj, 2023. "The State of the Art in Deep Learning Applications, Challenges, and Future Prospects: A Comprehensive Review of Flood Forecasting and Management," Sustainability, MDPI, vol. 15(13), pages 1-33, July.
    6. Sheshadri Chatterjee & Ranjan Chaudhuri & Demetris Vrontis, 2023. "Role of fake news and misinformation in supply chain disruption: impact of technology competency as moderator," Annals of Operations Research, Springer, vol. 327(2), pages 659-682, August.
    7. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.
    8. 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.

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