Τhe impact of business intelligence tools on performance : a user satisfaction paradox?
Part of : International journal of economic sciences and applied research ; Vol.5, No.3, 2012, pages 7-32
Issue:
Pages:
7-32
Abstract:
While Business Intelligence (BI) initiatives have been a top-priority of CIOs around the world for several years, accounting for billions of USD of IT investments per annum (IDC), academic research on the actual benefits derived from BI tools and the drivers of these benefits remain sparse.This paper reports the findings ofan exploratory, cross-sectionalfield study investigating the factors that define and drive benefits associated with the deployment of dedicated BI tools.BI is broadly defined as an analytical process which transforms fragmented data of enterprises and markets into action-oriented information or knowledge about objectives, opportunities and positions of an organization; BI tools are software products primarily designed and deployed to support this analytical process (e.g. data warehouse software, data mining software, digital dashboards applications).Building upon DeLoneand McLean ’s (1992; 2002; 2003) information systems success model, we develop, test and refine a BI quality and performance model adapted for the specific purpose, application, user group and technology of BI tools. The ultimate performance predictors in this model are user satisfaction and the impact of BI tools on managerial decision quality, both of which are determined by data quality.Partial Least Square (PLS) modeling is used to analyze data collected in a survey administered to IT executives of large Australian Stock Exchange (ASX) listed companies.The results confirm some of the theoretical relationships established in - especially the original - DeLone-McLean model in the specific context of BI. More importantly, the results also confirm the important role of explicit BI management as antecedent of benefits derived from BI tools, and the key impact of data quality on managerial decision making and organizational performance.However, the results also reveal a ‘user satisfaction paradox’: In contrast to the predictions derived from the DeLone-McLean model, organizational performance is negatively associated with user satisfaction with BI tools. Financial performance data collected for ex-post verification of this unexpected result confirm this paradox. We discuss Bl-specfic interpretations of these unexpectedfindings and provide avenues for future research.
Subject (LC):
Keywords:
Business Intelligence (Bl), information systems success, data quality, user satisfaction, IT impact analysis
Notes:
Περιέχει σχήματα, πίνακες και βιβλιογραφία
References (1):
- Arend, R. J., 2003, ‘Revisiting the logical and research considerations of competitiveadvantage’, Strategic Management Journal, 24, 3, pp. 279-284.Baars, H. and Kemper, H.-G., 2008, ‘Management support with structured and unstructureddata - an integrated business intelligence framework \ Information Systems Management,25,2, pp. 132-148.Bagozzi, R. R, 1980, Causal Methods in Marketing, New York, John Wiley and Sons.Baron, R. M. and Kenny, D. A., 1986, ‘The moderator-mediator variable distinction insocial psychological research: Conceptual, strategic, and statistical considerations’,Journal of Personality and Social Psychology, 51, pp. 1173-1182.Bollen, K. A. and Stine, R., 1990, ‘Direct and Indirect Effects: Classical and BootstrapEstimates of Variability ’, Sociological Methodology, 20, pp. 115-140.Chamoni, P. and Gluchowski, R, 2004, Tntegrationstrends bei Business-Intelligence-Systemen’, Wirtschaftsinformatik, 46, 2, pp. 119-128.Chen, L.-d., Soliman, K. S., Mao, E. and Frolick, Μ. N., 2000, ‘Measuring user satisfactionwith data warehouses: an exploratory study’, Information & Management, 37, 3, pp.103-110.Chin, W. W., 1998, ‘The Partial Least Squares Approach to Structural Equation Modeling’,Modern Methods for Business Research, G. A. Marcoulides, Ed/'Eds., Mahwah, NJ,Lawrence Erlbaum Associates, pp. 195-336.Chin, W. W. and Dibbern, J., 2010, ‘An Introduction to a permutation based procedure forMulti-Group PLS Analysis: Results of Tests of differences on simulated data an a crosscultural analysis of the sourcing of Information System Services between Germnay andthe USA’, Handbook of Partial Least Square Concepts, Methods and Applications, V.E. Vinzi, W. W. Chin, J. Henseler and H. Wang, EdAEds., Berlin Heidelberg, Springer-Verlag.Clark, J., T.D, Jones, M. C. and Armstrong, C. P, 2007, ‘The dynamic structure ofmanagement support systems: theory development, research focus and direction ’, MISQuarterly, 31, 3, pp. 579-615.Cox, C., 2010, ‘Balancing decision speed and decision quality: Assessing the impact ofBusiness Intelligence Systems in high velocity environments ’, Faculty of the College ofBusiness Administration, United States - California, TUI University, Ph.D.Davenport, T. H., 2010, ‘Business Intelligence and Organizational Decisions ’, InternationalJournalofBusiness Intelligence Research, 1, 1, pp. 1-12.DeLone, W. H. and McLean, E. R., 1992, ‘Information systems success: The quest for thedependent variable’, Information Systems Research, 3, 1, pp. 60-95.DeLone, W. H. and McLean, E. R., 2002, ‘Information systems success revisited’,System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii InternationalConference on System Sciences - 2002.DeLone, W. H. and McLean, E. R., 2003, ‘The DeLone and McLean Model of InformationSystems Success: A Ten-Year Update ’, Journal of Management Information Systems,19, 4, pp. 9-30.Dodson, G., Arnott, D. and Pervan, G., 2008, ‘The use of Business Intelligence Systems inAustralia ’, ACIS 2008 Proceedings, Christchurch, New Zealand.Elbashir, Μ. Z., Collier, P. A. and Davern, M. J., 2008, ‘Measuring the effects of businessintelligence systems: The relationship between business process and organizationalperformance ’, International Journal of Accounting Information Systems, 9, 3, pp. 135-153.Ellis, D., 2004, ‘Data Mining and Business Intelligence: Where will it lead us?’, InfotechUpdate, 13, 6, pp 1-3.Foley, E. and Manon, G., 2010, ‘What is Business Intelligence?’, International Journal ofBusiness Intelligence Research, 1, 4, pp. 1-28.Fomell, C. and Larcker, D. F., 1981, ‘Evaluating Structural Equation Models withUnobservable Variables and Measurement Error’, Journal of Marketing Research, 18,1, pp. 39-50.Foster, S., Hawking, P. and Stein, A., 2005, ‘Business intelligence solution evolution:adoption and use’, Business Intelligence Journal, 10, 4, pp. 44-54.Gardner, S. R., 1998, ‘Buildingthe Data Warehouse’, Association for Computing Machinery.Communications of the ACM, 41,9, pp. 52-60.Garg, V. K., Walters, B. A. and Priem, R. L., 2003, ‘Chief executive scanning emphases,environmental dynamism, and manufacturing performance ’, Strategic ManagementJournal, 24, pp. 725-744.Gonzales, M. L., 2011, ‘Success factors for business intelligence and data warehousingmaturity and competitive advantage’, Business Intelligence Journal, 16, 1, pp. 22-29.Govindarajan, V. J. and Fisher, J., 1990, ‘Strategy, control systems and resource sharing:effects on business-unit performance’, Academy of Management Journal, 33, pp. 259-285.Hertel, G., Schroer, J., Batinic, B. and Naumann, S., 2008, ‘Do Shy People Prefer to SendE-Mail?’,Social Psychology, 39, 4, pp. 231-243.Hocevar, B. and Jaklic, J., 2010, ‘Assessing benefits of business intelligence systems - acase study’, Management, 15, 1, pp. 87-120.Hugh, J. W., Dorothea, L. A., Daniel, C., David, P. and Dominic, T., 2004, ‘Data warehousingROI: justifying and assessing a data warehouse’, Business Intelligence Journal, 9,2, pp. 6-17.Hulland, J., 1999, ‘The use of partial least square (PLS) in strategic management research:a review of four recent studies’, Strategic Management Journal, 20, 2, pp. 195-204.Hwang, H. G., Ku, C.-Y., Yen, D. C. and Cheng, C.-C., 2004, ‘Critical factors influencingthe adoption of data warehouse technology: a study of the banking industry in Taiwan’,Decision Support Systems, 37, 1, pp. 1-21.Hwang, Μ. I. and Xu, H., 2008, ‘A Structural Model of Data Warehousing Success’,Journal of Computer Information Systems, Iss Fall 2008, pp. 48-56.Imhoff, C., 2005, ‘What Do Customers Really Want?’, DM Review, 15, 5, pp. 12-68.Imhoff, C. and White, C., 2010, ‘Business Intelligence and Collaboration: A NaturalMarriage’, Business Intelligence Journal, 15, 3, pp. 44-48.Inmon, W. H., 2000, ‘The data warehouse environment: quantifying cost justification andReturn on Investment’, Microsoft Corporation and Billinmon.com 11c.Inmon, W. H., 2004, ‘The logical data warehouse: delving into the mysteries of the logicaland physical worlds’, DM Review Online, June 2004.Johansson, J. K. and Yip, G. S., 1994, ‘Exploiting globalization potential: U.S. and Japanesestrategies’, Strategic Management Journal, 15, pp. 579-601.Kaplan, R. S. and Norton, D. R, 1996, The Balanced Scorecard, Boston, MA.Lönnqvist, A. and Pirttimäki, V., 2006, ‘The measurement of business intelligence’,Information Systems Management, 23, 1, pp. 32-40.Luhn, H. R, 1958, ‘A Business Intelligence System’, IBM Journal, 2, 4, pp. 314-319.March, S. T. and Hevner, A. R., 2007, ‘Integrated decision support systems: A datawarehousing perspective’, Decision Support Systems, 43, 3, pp. 1031-1043.Nelson, R. R., Todd, P. A. and Wixom, B. H., 2005, ‘Antecedents of Information andSystem Quality: An Empirical Examination within the context of data warehousing ’,Journal of Management Information Systems, 21, 4, pp. 199-235.Nunnally, J. C., 1978, Psychometric Theory, New York, McGraw-Hill.Petter, S. and McLean, E. R.,2009, ‘A meta-analytic assessment of the DeLone and McLeanIS success model: An examination of IS success at the individual level’, Information &Management, 46, 3, pp. 159-166.Power, D. J., 2003, ‘A Brief History of Decision Support Systems’, Retrieved May 31,2003, from DSSResources.COM/history/dsshistory2.8.html.Rekom, P. E., 2000, Data Warehouse: A Case study of the factors effecting user satisfaction.Faculty of the Rossier School of Education. California, University of Southern California.Doctor of Education: 287.Research, G., 2011, Gartner Quarterly IT Spending Forecast.Ringle, C. M., Wende, S. and Will, S., 2005, SmartPLS2.0 (M3), Hamburg.Rumelt, R. R, 1987, ‘Theory, strategy and entreprenuership’, The competitive challenge, D.J.Teece, Ed.AEds., Cambridge, Ballinger, pp. 137-158.Sammon, D. and Adam, F., 2005, ‘Towards a model of organisational prerequisites forenterprise-wide systems integration: Examining ERP and data warehousing’, Journal ofEnterprise Information Management, 18, 4, pp. 458.Sammon, D., Adam, F. and Carton, F., 2003, ‘Benefit realisation through ERP: The re-emergence of data warehousing ’, Electronic Journal of Information Systems Evaluation,6, 2, pp. 155-163Sandler, D., 2008, ‘Four elements of successful data quality programs ’, Business IntelligenceJournal, 13, 4, pp. 22-29.Schumpeter, J. A., 1950, Capitalism, socialism and democracy, New York, Harper andRow.Shankaranarayanan, G. and Even, A., 2004, ‘Managing metadata in data warehouses:Pitfalls and possibilities ’, Communications ofAIS, 2004, 14, pp. 247-274.Shrout, P. E. and Bolger, N., 2002, ‘Mediation in Experimental an Nonexperimental Studies:New Procedures and Recommendations’, Psychological Methods, 7, 4, pp. 422-445.Slater, S. F. and Olson, E. M., 2000, ‘Strategy type and performance: the influence of salesforce management’, Strategic Management Journal, 21, pp. 813-829.Solomon, M. D., 2005, ‘Ensuring a successful data warehouse initiative’, InformationSystems Management, 22, 1, pp. 26-36.Swartz, N., 2007, ‘Gartner Warns Firms of'Dirty Data”, Information Management Journal,41,3, pp. 6-6.TDWI-Research, 2008, 2008 TDWI BI Benchmark Report - Organizational andPerformance Metrics for Bl Teams.Tippins, M. J. and Sohi, R., 2003, ‘IT competency and firm performance: is organizationallearning a missing link?’, Strategic Management Journal, 24, pp. 745-761.Tseng, F. S. C. and Chou, A. Y. H., 2006, ‘The concept of document warehousing formulti-dimensional modeling of textual-based business intelligence’, Decision SupportSystems, 42, 2, pp. 727-744.Turban, E. and Volonino, L., 2011, Information Technology for Management, Wiley.Watson, H. J., 2001, ‘Current practices in data warehousing’, Information SystemsManagement, 18, 1, pp. 47-55.Watson, H. J., 2009, ‘Tutorial: Business Intelligence-Past, Present and Future’,Communications of the Association for Information Systems, 25, pp. 487-510.Watson, H. J., 2010, ‘The More Things Change, The More They Remain the Same’,Business Intelligence Journal, 15, 3, pp. 4-6.Watson, H. J., Goodhue, D. L. and Wixom, B. H., 2002, ‘The benefits of data warehousing:why some organizations realize exceptional payoffs’, Information & Management, 39,6, pp. 491-502.Watson, H. J. and Wixom, B. H., 2010, ‘The ΒΙ-based Organization’, International JournalofBusiness Intelligence Research, 1, 1, pp. 13-28.Williams, S., 2004, ‘Delivering strategic business value’, Strategic Finance, 86, 2, pp. 41-48.Wixom, B. H., 2004, ‘Business Intelligence Software for the classroom: microstrategyresource on the Teradata University Network’, Communications of AIS, 2004, 14, pp.234-246.Wixom, B. H., Watson, H., Reynolds, A. and Hoffer, J., 2008, ‘Continental Airlinescontinues to soar with Business Intelligence’, Information Systems Management, 25,2, pp. 102-112.Wixom, B. H. and Watson, H. J., 2001, ‘An empirical investigation of the factors affectingdata warehousing success’, MIS Qua rterly, 25, 1, pp. 17-41.Wold, H., 1982, ‘Soft modeling: the basic design and some extensions ’, Systems underindirect observations: causality, structure, prediction, K. G. Joreskog and H. Wold,Ed.AEds., Amsterdam, North-Hollamd.1-54.Yong-Tae, R, 2006, ‘An empirical investigation of the effects of data warehousing ondecision performance’, Information & Management, 43, 1, pp. 51-61.Zeng, Y. C., Roger, H. L. and Yen, D. C., 2003, ‘Enterprise integration with advancedinformation technologies: ERP and data warehousing’, Information Management &Computer Security, 11, pp. 115.