A structural equation model for big data adoption in the healthcare supply chain
Dindayal Agrawal and
Jitender Madaan
International Journal of Productivity and Performance Management, 2021, vol. 72, issue 4, 917-942
Abstract:
Purpose - The purpose of this study is to examine the barriers to the implementation of big data (BD) in the healthcare supply chain (HSC). Design/methodology/approach - First, the barriers concerning BD adoption in the HSC were found by conducting a detailed literature survey and with the expert's opinion. Then the exploratory factor analysis (EFA) was employed to categorize the barriers. The obtained results are verified using the confirmatory factor analysis (CFA). Structural equation modeling (SEM) analysis gives the path diagram representing the interrelationship between latent variables and observed variables. Findings - The segregation of 13 barriers into three categories, namely “data governance perspective,” “technological and expertise perspective,” and “organizational and social perspective,” is performed using EFA. Three hypotheses are tested, and all are accepted. It can be concluded that the “data governance perspective” is positively related to “technological and expertise perspective” and “organizational and social perspective” factors. Also, the “technological and expertise perspective” is positively related to “organizational and social perspective.” Research limitations/implications - In literature, very few studies have been performed on finding the barriers to BD adoption in the HSC. The systematic methodology and statistical verification applied in this study empowers the healthcare organizations and policymakers in further decision-making. Originality/value - This paper is first of its kind to adopt an approach to classify barriers to BD implementation in the HSC into three distinct perspectives.
Keywords: Barriers; Big data (BD); Healthcare supply chain (HSC); Structural equation modeling (SEM); Exploratory factor analysis (EFA); Confirmatory factor analysis (CFA) (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:
Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eme:ijppmp:ijppm-12-2020-0667
DOI: 10.1108/IJPPM-12-2020-0667
Access Statistics for this article
International Journal of Productivity and Performance Management is currently edited by Dr Luisa Huatuco and Dr Nicky Shaw
More articles in International Journal of Productivity and Performance Management from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().