Analyzing IT business values - A Dominance based Rough Sets Approach perspective

The impact of information technology (IT) on the business value of a cooperation has been an active research area for more than two decades. Although it is widely agreed that IT has a positive impact on the business values of cooperations an in-depth understanding of the underlying structures is still missing. Especially due to the huge investments in IT, there is still a need to better understand how IT influences the performance of cooperations and business values. Generally, the data collected in IT business value research to be quantitative as well as of qualitative nature. While quantitative data can be examined by classic econometric methods the analysis of qualitative data requires special methods. In the case of ordinal data DRSA - Dominance based Rough Sets Approach has been proposed. DRSA can be applied to induce rules out of a decision table containing ordinal data. This method has already successfully applied to such diverse areas like customer relationship management and satisfaction analysis, or the technical diagnostic of a fleet of vehicles besides others. In this article we apply it for the first time to the analysis of IT business value. We use ordinal data of a survey on IT management strategies of Australian firms conducted by the Australian Department of Communications, Information Technology and the Arts. The induces rules are interpreted and provide important insights into the impact of information technology on the business values of cooperations. Furthermore our study shows the potential of DRSA for information systems research where questionnaire are a widely applied technique to collect ordinal data.

[1]  Salvatore Greco,et al.  Customer satisfaction analysis based on rough set approach , 2007 .

[2]  Sinan Aral,et al.  I.T. Assets, Organizational Capabilities and Firm Performance: Do Resource Allocations and Organizational Differences Explain Performance Variation? , 2007 .

[3]  Wojciech Kotlowski,et al.  Statistical Model for Rough Set Approach to Multicriteria Classification , 2007, PKDD.

[4]  Sandra E. Black,et al.  How to Compete: The Impact of Workplace Practices and Information Technology on Productivity , 1997, Review of Economics and Statistics.

[5]  Salvatore Greco,et al.  Rough Set Approach to Customer Satisfaction Analysis , 2006, RSCTC.

[6]  Roman Słowiński,et al.  Dominance-Based Rough Set Approach to Decision Involving Multiple Decision Makers , 2006, RSCTC.

[7]  N. Carr IT doesn't matter , 2003, IEEE Engineering Management Review.

[8]  Kevin J. Stiroh,et al.  Reassessing the Impact of IT in the Production Function: A Meta-Analysis and Sensitivity Tests , 2002 .

[9]  Salvatore Greco,et al.  Variable Consistency Model of Dominance-Based Rough Sets Approach , 2000, Rough Sets and Current Trends in Computing.

[10]  Salvatore Greco,et al.  Dominance-Based Rough Set Approach for Decision Analysis - A Tutorial , 2008, RSKT.

[11]  Maryellen R. Kelley,et al.  Productivity and Information Technology: The Elusive Connection , 1994 .

[12]  James J. H. Liou,et al.  A novel decision rules approach for customer relationship management of the airline market , 2009, Expert Syst. Appl..

[13]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[14]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[15]  Jacek Zak,et al.  Technical diagnostic of a fleet of vehicles using rough set theory , 2009, Eur. J. Oper. Res..

[16]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[17]  Nicholas G. Carr,et al.  Does IT Matter? Information Technology and the Corrosion of Competitive Advantage , 2004 .

[18]  Rajiv Kohli,et al.  Measuring Information Technology Payoff: A Meta - Analysis of Structural Variables in Firm - Level Empirical Research , 2003, Inf. Syst. Res..

[19]  E. Brynjolfsson,et al.  Paradox Lost? Firm-Level Evidence on the Returns to Information Systems Spending , 1996 .

[20]  Lesley Pek Wee Land,et al.  Complementary Organizational Mechanisms: A Case Study on Information Technology Business Value , 2010, ECIS.

[21]  R. Mark Sirkin,et al.  Statistics for the Social Sciences , 1994 .

[22]  Yuan Li,et al.  A rough set approach to knowledge discovery in analyzing competitive advantages of firms , 2009, Ann. Oper. Res..

[23]  Salvatore Greco,et al.  Rough sets theory for multicriteria decision analysis , 2001, Eur. J. Oper. Res..

[24]  Joseph G. Davis,et al.  The Economic Contribution of Software: An Alternative Perspective on the Productivity Paradox , 2004, ICIS.

[25]  Thomas Hempell,et al.  Ict, Innovation and Business Performance in Services: Evidence for Germany and the Netherlands , 2004 .

[26]  Kenneth L. Kraemer,et al.  Review: Information Technology and Organizational Performance: An Integrative Model of IT Business Value , 2004, MIS Q..

[27]  Paul P. Tallon,et al.  Investigating the relationship between strategic alignment and IT business value: the discovery of a paradox , 2003 .

[28]  Kenneth L. Kraemer,et al.  Executives’ Perceptions of the Business Value of Information Technology: A Process-Oriented Approach , 2000, J. Manag. Inf. Syst..

[29]  Joseph G. Davis,et al.  Augmenting productivity analysis with data mining: An application on IT business value , 2009, Expert Syst. Appl..

[30]  Salvatore Greco,et al.  Measuring Attractiveness of Rules from the Viewpoint of Knowledge Representation, Prediction and Efficiency of Intervention , 2005, AWIC.

[31]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.