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

Guangming Cao, Yanqing Duan, Gendao Li

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)384-395
JournalIEEE Transactions on Engineering Management
Volume62
Issue number3
DOIs
Publication statusPublished - Aug 2015
Externally publishedYes

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