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dc.contributor.authorCao, Guangmingen
dc.contributor.authorDuan, Yanqingen
dc.contributor.authorLi, Gendaoen
dc.date.accessioned2015-07-14T12:03:43Zen
dc.date.available2015-07-14T12:03:43Zen
dc.date.issued2015-06-24en
dc.identifier.citationCao G, Duan Y, Li G (2015) 'Linking business analytics to decision making effectiveness: a path model analysis', IEEE Transactions on Engineering Management, 62 (3), pp.384-385.en
dc.identifier.issn0018-9391en
dc.identifier.doi10.1109/TEM.2015.2441875en
dc.identifier.urihttp://hdl.handle.net/10547/560379en
dc.description.abstractWhile 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 manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DME
dc.language.isoenen
dc.publisherIEEEen
dc.relation.urlhttp://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=7132744en
dc.subjectbusiness analyticsen
dc.subjectinformation processing capabilityen
dc.subjectdecision-making effectivenessen
dc.subjectinformation processing viewen
dc.subjectcontingency theoryen
dc.subjectdata-driven environmenten
dc.subjectN210 Management Techniquesen
dc.subjectdecision makingen
dc.subjectinformation processingen
dc.titleLinking business analytics to decision making effectiveness: a path model analysisen
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalIEEE Transactions on Engineering Managementen
html.description.abstractWhile 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 manager's knowledge and understanding by demonstrating how business analytics should be implemented to improve DME


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