Cited by 134Big Data and Business IntelligenceBig Data Technologies and ApplicationsInformation Technology Governance and Strategy

The Assessment of Big Data Adoption Readiness with a Technology–Organization–Environment Framework: A Perspective towards Healthcare Employees

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Ghaleb, Ebrahim A. A., Dominic, P. D. D., Fati, Suliman Mohamed, Muneer, Amgad, Ali, Rao Faizan·2021·Sustainability

Abstract

Big data is rapidly being seen as a new frontier for improving organizational performance. However, it is still in its early phases of implementation in developing countries’ healthcare organizations. As data-driven insights become critical competitive advantages, it is critical to ascertain which elements influence an organization’s decision to adopt big data. The aim of this study is to propose and empirically test a theoretical framework based on technology–organization–environment (TOE) factors to identify the level of readiness of big data adoption in developing countries’ healthcare organizations. The framework empirically tested 302 Malaysian healthcare employees. The structural equation modeling was used to analyze the collected data. The results of the study demonstrated that technology, organization, and environment factors can significantly contribute towards big data adoption in healthcare organizations. However, the complexity of technology factors has shown less support for the notion. For technology practitioners, this study showed how to enhance big data adoption in healthcare organizations through TOE factors.

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  1. [5]5. Ghaleb, E.A.; Dominic, P.; Mohamed, I.; Almaghthawi, A.; AL-Ashmori, A. Factors Affecting the Quality on the Health Information System users among the Yemeni Hospitals. Solid State Technol. 2020 , 63 , 9202-9209.
  2. [11]11. Ali, R.F.; Dominic, P.; Ali, S.E.A.; Naseer, S. Information Security Behavior of IT Professionals (Role of Polices and Compliance). Solid State Technol. 2020 , 63 , 21601-21608.
  3. [18]18. Gahleb, E.; Mohamed, I. Health information system success framework based on user requirements perspective. J. Theor. Appl. Inf. Technol. 2018 , 96 , 3740-3746.
  4. [30]30. Jeremiah, P.; Samy, G.N.; Ponkoodalingam, K.; Shanmugam, B.; Maarop, N. Unravelling the Ubiquitous Information Security Compliance Conundrum Among Practitioners in Private Healthcare Organisations Within Malaysia. Psychol. Educ. J. 2020 , 57 , 3585-3600.
  5. [44]44. Bahl, S.; Singh, R.P.; Javaid, M.; Khan, I.H.; Vaishya, R.; Suman, R. Telemedicine technologies for confronting COVID-19 pandemic: Areview. J. Ind. Integr. Manag. 2020 , 5 . [CrossRef]
  6. [56]56. Christodoulakis, A.; Karanikas, H.; Billiris, A.; Thireos, E.; Pelekis, N. 'Big data' in health care. Arch. Hell. Med. Arheia Ellenikes Iatr. 2016 , 33 , 490-496.
  7. [63]63. Piai, S.; Claps, M. Bigger data for better healthcare. IDC Health Insights 2013 , 8 , 1-24.
  8. [70]70. Lee, S.W. Research on Determinants for Big Data System Adoption in Organization. Ph.D. Thesis, Graduate School of Sungkyunkwan University Seoul, Seoul, Korea, August 2016.
  9. [75]75. Dedrick, J.; West, J. Why firms adopt open source platforms: A grounded theory of innovation and standards adoption. In Standard Making: A Critical Research Frontier for Information Systems ; Management Information Systems Research Center, University of Minnesota: Minneapolis, MN, USA; Saint Paul, MN, USA, 2003; pp. 236-257.
  10. [79]79. Kim, J.K. A Study on the Usage Intention of Category Types in the Mobile Application Based on the Technology Readiness and Acceptance Model. Ph.D. Thesis, Kongju National University, Gongju, Korea, 2013.
  11. [93]93. Rogers, E. The Diffusion of Innovations , 4th ed.; The Free Press, Simon and Schuster: New York, NY, USA, 1995.
  12. [121]121. Rogers, E.M.; Singhal, A.; Quinlan, M.M. Diffusion of Innovations ; Routledge: London, UK, 2014.
  13. [126]126. Kandil, A.M.N.A.; Ragheb, M.A.; Ragab, A.A.; Farouk, M. Examining the effect of TOE model on cloud computing adoption in Egypt. Bus. Manag. Rev. 2018 , 9 , 113-123.
  14. [159]159. Amini, M.; Sadat Safavi, N.; Mirzaeyan Bahnamiri, R.; Mirzaei Omran, M.; Amini, M. Development of an instrument for assessing the impact of environmental context on adoption of cloud computing for small and medium enterprises. Aust. J. Basic Appl. Sci. AJBAS 2014 , 8 , 129-135.
  15. [165]165. Hair, J.F., Jr.; Hult, G.T.M.; Ringle, C.; Sarstedt, M. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) ; Sage Publications: Thousand Oaks, CA, USA, 2016.
  16. [177]177. Hair, J.F., Jr.; Sarstedt, M.; Hopkins, L.; Kuppelwieser, V.G. Partial least squares structural equation modeling (PLS-SEM). Eur. Bus. Rev. 2014 , 26 , 106-121. [CrossRef]

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Authors
Ghaleb, E. A. A., Dominic, P. D. D., Fati, S. M., Muneer, A., & Ali, R. F.
Affiliations
  • Universiti Teknologi Petronas (MY)
  • Prince Sultan University (SA)
Corresponding author
  • Ghaleb, Ebrahim A. A.
  • Ali, Rao Faizan
Venue
Sustainability
Publisher
MDPI AG
Published
2021-07-27
Volume
13
Issue
15
Pages
8379
ISSN
2071-1050 (ISSN-L)
OpenAlex
W3187019838
Cited by
134
References
192
Citations by year
2026: 23 · 2025: 34 · 2024: 32 · 2023: 27 · 2022: 15 · 2021: 3
FWCI
19.02
Type
journal article
Language
en
Open access
Locations
  • Sustainability (publishedVersion, cc-by)
  • RePEc: Research Papers in Economics (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
Prince Sultan University (Funding Agency PSU-Funding number 0000)
Topics
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Big Data and Business Intelligence
  • Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
  • Social Sciences › Business, Management and Accounting › Management Information Systems › Information Technology Governance and Strategy
Keywords
big data adoption, technology-organization-environment framework, healthcare organizations, healthcare workers, adoption readiness, technological complexity, developing countries

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