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Business Analytics-Enabled Decision Making Effectiveness through Knowledge Absorptive Capacity in Health Care

Lookup NU author(s): Dr Yichuan Wang

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by Emerald Group Publishing, 2017.

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Abstract

Purpose:Drawing on the resource-based theory and dynamic capability view, this study examines the mechanisms by which business analytics capabilities (i.e. the effective use of data aggregation, analytics and data interpretation tools) in healthcare units, indirectly influence decision making effectiveness through the mediating role of knowledge absorptive capacity. Design/Methodology/Approach:Using a survey method, this study collected data from hospitals in Taiwan. Of the 155 responses received, three were incomplete, giving a 35.84% response rate, with 152 valid data points. Structural equation modelling (SEM) was used to test the hypotheses. Findings:This study conceptualizes, operationalizes and measures the business analytics (BA) capability as a multi-dimensional construct which is formed by capturing the functionalities of BA systems in health care, leading to the conclusion that healthcare units are likely to obtain valuable knowledge through utilizing the data interpretation tools effectively. The effective use of data analysis and interpretation tools in healthcare units indirectly influences decision making effectiveness: an impact that is mediated by absorptive capacity. Originality/Value:This study adds value to the literature by conceptualizing BA capabilities in healthcare and demonstrating how knowledge absorption matters when implementing BA in the decision making process. The mediating role of absorptive capacity not only provides a mechanism by which BA can contribute to decision making practices, but also offers a new solution to the puzzle of the IT productivity paradox in healthcare settings.

Publication metadata

Author(s): Wang Y, Byrd TA

Publication type: Article

Publication status: Published

Journal: Journal of Knowledge Management

Year: 2017

Volume: 21

Issue: 3

Pages: 517-539

Print publication date: 10/04/2017

Acceptance date: 08/08/2016

Date deposited: 04/12/2016

ISSN (print): 1367-3270

ISSN (electronic): 1758-7484

Publisher: Emerald Group Publishing

URL: https://doi.org/10.1108/JKM-08-2015-0301

DOI: 10.1108/JKM-08-2015-0301

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