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

Muhammad Shahbaz, Changyuan Gao, Lili Zhai, Fakhar Shahzad and Muhammad Rizwan Arshad

Complexity, 2020, vol. 2020, 1-13

Abstract:

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.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:2173765

DOI: 10.1155/2020/2173765

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