Identification and prioritization of critical socio-technical factors influencing big data analytics adoption in healthcare using Delphi method.
Saved in:
| Title: | Identification and prioritization of critical socio-technical factors influencing big data analytics adoption in healthcare using Delphi method. |
|---|---|
| Authors: | Salahuddin, Lizawati (AUTHOR), Wolseley, Nik Nurdini (AUTHOR), Mohd Aboobaider, Burhanuddin (AUTHOR), Raja Ikram, Raja Rina (AUTHOR), Hashim, Ummi Rabaah (AUTHOR), Mohamed Said, Mohd Shahrir (AUTHOR), Hassan, Noor Hafizah (AUTHOR) |
| Source: | Behaviour & Information Technology. Nov2025, Vol. 44 Issue 19, p4838-4852. 15p. |
| Subjects: | Consensus (Social sciences), Medical informatics, Research funding, Data analytics, Decision making, Judgment sampling, Descriptive statistics, System analysis, Organizational change, Statistics, Delphi method, Data quality |
| Abstract: | The healthcare sector faces significant challenges in adopting big data analytics, primarily due to the complex interplay of various socio-technical factors that influence successful implementation. While prior research has identified numerous factors affecting big data analytics adoption, there is limited understanding of their relative importance in the healthcare context. This study aims to identify and prioritise the critical socio-technical factors influencing big data analytics adoption in healthcare through expert consensus. A four-round Delphi study was conducted with ten experts representing diverse stakeholders including academics, data scientists, healthcare practitioners, and top management. The study employed a comprehensive framework encompassing person, technology, organisation, environment, and task dimensions. The results revealed that analytical skills, knowledge, and experience were the most critical person-related factors. In the technology dimension, system reliability, and data quality emerged as top priorities, while organisational support, funding, and infrastructure were identified as crucial organisational factors. The study also highlighted technological advancement and government regulations as key environmental factors, with task reliability emerging as the most significant task-related factor. The study offers a comprehensive understanding of the relative importance of various social-technical factors, thereby enabling more focused and effective big data analytics adoption strategies in healthcare settings. [ABSTRACT FROM AUTHOR] |
| Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | The healthcare sector faces significant challenges in adopting big data analytics, primarily due to the complex interplay of various socio-technical factors that influence successful implementation. While prior research has identified numerous factors affecting big data analytics adoption, there is limited understanding of their relative importance in the healthcare context. This study aims to identify and prioritise the critical socio-technical factors influencing big data analytics adoption in healthcare through expert consensus. A four-round Delphi study was conducted with ten experts representing diverse stakeholders including academics, data scientists, healthcare practitioners, and top management. The study employed a comprehensive framework encompassing person, technology, organisation, environment, and task dimensions. The results revealed that analytical skills, knowledge, and experience were the most critical person-related factors. In the technology dimension, system reliability, and data quality emerged as top priorities, while organisational support, funding, and infrastructure were identified as crucial organisational factors. The study also highlighted technological advancement and government regulations as key environmental factors, with task reliability emerging as the most significant task-related factor. The study offers a comprehensive understanding of the relative importance of various social-technical factors, thereby enabling more focused and effective big data analytics adoption strategies in healthcare settings. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 0144929X |
| DOI: | 10.1080/0144929X.2025.2494283 |
