Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare

Moon-Koo, Kim; Park, Jong‐Hyun

doi:10.1177/0266666916652671

Cited by 44 publications

(29 citation statements)

References 32 publications

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“…e literature mostly highlights the importance, challenges, and benefits of BDA in healthcare organizations [3,19,20]. Only a few studies discussed the adoption factors at a very basic level [4,21,22].…”

Section: Complexitymentioning

confidence: 99%

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

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The big data analytics (BDA) has dragged tremendous attention in healthcare organizations. Healthcare organizations are investing substantial money and time in big data analytics and want to adopt it to get potential benefits. Thus, this study proposes a BDA adoption model in healthcare organizations to explore the critical factors that can influence its adoption process. The study extends the technology acceptance model (TAM) with the self-efficacy as an external factor and also includes gender and resistance to change (RTC) as moderators to strengthen the research model. The proposed research model has been tested on 283 valid responses which were collected through a structured survey, by applying structural equation modeling. Our results portray that self-efficacy is a strong predictor of intention to use BDA along with other TAM factors. Moreover, it is confirmed by the results that RTC dampens the positive relationship between intention to use and actual use of BDA in healthcare organizations. The outcomes revealed that male employees as compared to female employees are dominant towards the positive intention to use BDA. Furthermore, females create more RTC than males while adopting BDA in healthcare organizations. Theoretical and practical implications, limitations, and future research directions also underlined in this study.

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Section: Complexitymentioning

confidence: 99%

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

et al. 2020

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“…Few studies have described the importance of BDA in healthcare [4,12,13], although studies have investigated the technological aspects and required qualifications for big data in healthcare [14][15][16][17][18]. Previous studies focused on technological and policy issues and not on adoption factors, such as security, trust, and fitness of technology for the tasks required to manage BDA in healthcare [13]. According to Dishaw [19], the technology acceptance model (TAM) and task-technology fit (TTF) provide better outputs than either TAM or TTF alone in the adoption of information technology systems.…”

mentioning

confidence: 99%

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

et al. 2019

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IntroductionBig data analytics (BDA) is a course of action to examine large and complex data sets (i.e., big data) and select veiled information that can help organizations with efficient decision making [1]. The volume of data related to healthcare organizations has grown dramatically in past years and is expected to increase in coming years due to the use of innovative technologies [2]. Meanwhile, healthcare reimbursement methods are changing, and pay for performance is an emerging factor in the current healthcare environment. Recently, healthcare organizations have only focused on profit and have neglected to acquire the essential tools, infrastructure, and technologies for effective control of big data to ensure citizens' health care [3,4]. Big data incorporates features such as variety, velocity, and veracity. BDA techniques can be applied to the massive amount of prevailing patient-related medical information to analyze outcomes for improvement of the healthcare sector [5,6]. Using BDA in the healthcare sector will help inform each physician of the medical histories of individuals and the population and enable appropriate Abstract Big data analytics is gaining substantial attention due to its innovative contribution to decision making and strategic development across the healthcare field. Therefore, this study explored the adoption mechanism of big data analytics in healthcare organizations to inspect elements correlated to behavioral intention using the technology acceptance model and task-technology fit paradigm. Using a survey questionnaire, we analyzed 224 valid responses in AMOS v21 to test the hypotheses. Our results posit that the credentials of the technology acceptance model together with task-technology fit contribute substantially to the enhancement of behavioral intentions to use the big data analytics system in healthcare, ultimately leading towards actual use. Meanwhile, trust in and security of the information system also positively influenced the behavioral intention for use. Employee resistance to change is a key factor underlying failure of the innovative system in organizations and has been proven in this study to negatively moderate the relationship between intention to use and actual use of big data analytics in healthcare. Our results can be implemented by healthcare organizations to develop an understanding of the implementation of big data analytics and to promote psychological empowerment of employees to accept this innovative system. which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

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“…[5] [25] • Data Governance • The governance of big data includes formulating data policy, data integration and data management.…”

Section: Findings and Discussionmentioning

confidence: 99%

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

Adrian¹,

Abdullah²,

Atan³

et al. 2018

IJIMAI

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Identifying and prioritizing critical factors for promoting the implementation and usage of big data in healthcare

Cited by 44 publications

References 32 publications

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Moderating Effects of Gender and Resistance to Change on the Adoption of Big Data Analytics in Healthcare

Investigating the adoption of big data analytics in healthcare: the moderating role of resistance to change

Conceptual Model Development of Big Data Analytics Implementation Assessment Effect on Decision-Making

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