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Exploring the Effects of Data-Driven Hospital Operations on Operational Performance From the Resource Orchestration Theory Perspective
Ieee Transactions on Engineering Management ; : 13, 2021.
Article in English | Web of Science | ID: covidwho-1583754
ABSTRACT
In the big data era, managing data-driven hospital operations have become one of the most important tasks for healthcare executives, increasing responsiveness to exceptional disruptions such as those caused by the COVID-19 pandemic. However, they are still facing the challenges of how best to orchestrate the digital medical resources for improving operational performance such as cost, delivery, and quality. Therefore, drawing upon resource orchestration theory, this article investigates how hospitals orchestrate data-driven culture (DDC) and digital technology orientation (DTO) to develop big data analytics capability (BDAC) for operational performance improvement. Survey data were collected from 105 hospitals in China and analyzed using structural equation modeling and ordinary least square regression. The results show that DDC has a significant positive impact on DTO. More interestingly, there is no significant interaction effect between DDC and DTO, indicating that DDC and DTO affect BDAC independently, and not synergistically. The results further reveal that BDAC fully mediates the DTO-operational performance relationship. The findings offer useful and timely guidance on how healthcare executives can manage data-driven hospital operations to improve operational performance during and post the COVID-19 pandemic.
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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Experimental Studies Language: English Journal: Ieee Transactions on Engineering Management Year: 2021 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Web of Science Type of study: Experimental Studies Language: English Journal: Ieee Transactions on Engineering Management Year: 2021 Document Type: Article