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Making strategy process intelligent with business intelligence: an empirical investigation

Arora, Sanjeev; Nangia, Vinay Kumar; Agrawal, Rajat

International journal of data analysis techniques and strategies. Volume 7:Number 1 (2015, March 2nd); pp 77-95 -- Inderscience

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  • Title:
    Making strategy process intelligent with business intelligence: an empirical investigation
  • Author: Arora, Sanjeev;
    Nangia, Vinay Kumar;
    Agrawal, Rajat
  • Found In: International journal of data analysis techniques and strategies. Volume 7:Number 1 (2015, March 2nd); pp 77-95
  • Journal Title: International journal of data analysis techniques and strategies
  • Subjects: Database searching--Periodicals; Electronic data processing--Periodicals; business intelligence--strategic management--strategy process--cluster analysis--binary logistic regression--automotive manufacturers--automotive component manufacturers--SPSS analysis; Dewey: 005
  • Rights: Licensed
  • Publication Details: Inderscience
  • Abstract:

    With the advancements in information systems, 'business intelligence (BI)' has gained considerable ground in business and strategic decision making. A number of studies show the relation between BI and business value, and provide ample evidence of uses of BI. The present study contributes to the growing body of literature on BI and strategic management by attempting to find out the relation between BI applications and tightness/looseness of strategy process structuring with the help of binary logistic regression. It is found from the empirical investigation that more use of BI systems increases the odds of tightly structured strategy process in a significant way, i.e., increased control systems, higher focus on goals and strategies, more objectivity in performance measurement, etc. Also some of the BI measures, like spreadsheet analytics, dashboards, etc. dominate the information systems but a need for improved systems is felt by businesses.


  • Identifier: System Number: ETOCvdc_100079621556.0x000001; Journal ISSN: 1755-8050
  • Publication Date: 2015
  • Physical Description: Electronic
  • UIN: ETOCvdc_100079621556.0x000001

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