Challenges Faced by Healthcare Supply Chain for The Adoption of Big Data

Abstract

This study aims to analyse the obstacles to initiate big data in the healthcare supply chain. The report pinpoints roadblocks and presents immense insights to healthcare firms trying to leverage the potential of such Big Data (BD) technology to enhance their supply chain operations. A literature analysis was conducted in combination with consulting experts in order to identify the major impediments to BD adoption among the HSC. Then EFA was used to group the barrier factors into different categories. Results generated from the EFA were then validated using confirmatory factor analysis (CFA) to verify correct and precise results. Moreover, structural equation modelling analysis was used to determine the relationship between latent and observable variables and to develop a conclusive path diagram for further study. The research effectively classified the 13 identified barriers into three main categories: EFA was used to classify these groups and the interrelations among these groups were considered. Three hypotheses was evaluated with a result that all were valid. In particular, the study verified that the Data Governance Perspective was positively related to the Technological and Expertise Perspective as well as the Organizational and Social Perspective. Furthermore, a good correlation existed between the Technological and Expertise Perspective, and the Organizational and Social Perspective. However, most research focused on obstacles to BD acceptance in the HSC has failed to be conducted. This research uses a methodical technique and statistical verification creating a firm basis for future studies in this field. In particular, the results provide benefits for healthcare organizations and policymakers in making informed decisions regarding the deployment of BD technologies in ways that address possible problems and refine their decision-making processes. In this paper, the obstacles to BD implementation in the healthcare supply chain are uniquely categorized into three different frames of reference reflected on the understanding of the issues and providing unique insights to overcome the obstacles of BD implementation in future healthcare operations.