Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis

Cao, Guangming, Duan, Yanqing and Li, Gendao (2015) Linking Business Analytics to Decision Making Effectiveness: A Path Model Analysis. IEEE Transactions on Engineering Management, 62 (3). pp. 384-395. ISSN 0018-9391

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Abstract

While business analytics is being increasingly used to gain data-driven insights to support decision making, little research exists regarding the mechanism through which business analytics can be used to improve decision-making effectiveness (DME) at the organizational level. Drawing on the information processing view and contingency theory, this paper develops a research model linking business analytics to organizational DME. The research model is tested using structural equation modeling based on 740 responses collected from U.K. businesses. The key findings demonstrate that business analytics, through the mediation of a data-driven environment, positively influences information processing capability, which in turn has a positive effect on DME. The findings also demonstrate that the paths from business analytics to DME have no statistical differences between large and medium companies, but some differences between manufacturing and professional service industries. Our findings contribute to the business analytics literature by providing useful insights into business analytics applications and the facilitation of data-driven decision making. They also contribute to managers' knowledge and understanding by demonstrating how business analytics should be implemented to improve DME.

Item Type:	Article
Subjects:	N200 Management studies
Department:	Faculties > Business and Law > Newcastle Business School
Depositing User:	Becky Skoyles
Date Deposited:	15 Aug 2018 11:05
Last Modified:	19 Nov 2019 10:03
URI:	http://nrl.northumbria.ac.uk/id/eprint/35343

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