The impact of business intelligence system maturity on information quality

Information Research

Volumn 14, Issue 4, 2009, Pages

  Popovic A ^a   Coelho, P S ^b   Jaklic J ^a  

^a UNIVERSITY OF LJUBLJANA   (Slovenia)

^b NOVA INFORMATION MANAGEMENT SCHOOL NOVA IMS   (Portugal)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 77149152555     PISSN: None     EISSN: 13681613     Source Type: Journal    
DOI: None     Document Type: Article

References (81)

1. Al-Hakim, L. (Ed.) (2007). Information quality management: theory and applications. Hershey, PA: Idea Group Pub.
2. Arnold, V. (2006). Behavioural research opportunities: understanding the impact of enterprise systems. International Journal of Accounting Information Systems, 7(1), 7-17.
3. Bell, D. & Grimson, J. (1992). Distributed database systems. Reading, MA: Addison Wesley.
4. Bouzeghoub, M. & Lenzerini, M. (2001). Introduction to data extraction, cleaning and reconciliation: a special issue of Information Systems: An International Journal. Information Systems, 26(8), 535-536.
5. Chamoni, P. & Gluchowski, P. (2004). Integration trends in business intelligence systems: an empirical study based on the business intelligence maturity model. Wirtschaftsinformatik, 46(2), 119-128.
6. Chin, W.W. (1998). Issues and opinion on structure equation modeling. MIS Quarterly, 22(1), vii-xvi.
7. Coelho, P.S. & Esteves, S.P. (2007). The choice between a 5-point and a 10-point scale in the framework of customer satisfaction measurement. International Journal of Market Research, 49(3), 313-345.
8. Crump, N. (2002). Managing professional integration in an acute hospital - a socio-political analysis. International Journal of Public Sector Management, 15(2), 107-117.
9. Davenport, T.H. (1997). Information ecology: mastering the information and knowledge environment. Oxford: Oxford University Press.
10. Davenport, T.H. & Harris, J.G. (2007). Competing on analytics: the new science of winning. Boston, MA: Harvard Business School Press.
11. Davenport, T.H., Jarvenpaa, S.L. & Beers, M.C. (1996). Improving knowledge work processes. Sloan Management Review, 37(4), 53-66.
12. DeLone, W.H. & McLean, E.R. (2003). The DeLone and McLean model of information systems success: a ten-year update. Journal of Management Information Systems, 19(4), 9-30.
13. Deming, W.E. (1986). Out of the crisis: quality, productivity and competitive position. Cambridge: Cambridge University Press.
14. Elbashir, M.Z., Collier, P.A. & Davern, M.J. (2008). Measuring the effects of business intelligence systems: the relationship between business process and organizational performance. International Journal of Accounting Information Systems, 9(3), 135-153.
15. English, L.P. (1999). Improving data warehouse and business information quality: methods for reducing costs and increasing profits. New York, NY: John Wiley & Sons.
16. ® at http://www.webcitation.org/5lWCbyuWs)
17. Eppler, M.J. (2006). Managing information quality: increasing the value of information in knowledge-intensive products and processes (2nd ed.). Berlin: Springer.
18. Eppler, M.J., Helfert, M. & Gasser, U. (2004). Information quality: organizational, technological and legal perspectives. Studies in Communication Sciences, 4(2), 1-16.
19. Ferguson, B. & Lim, J.N.W. (2001). Incentives and clinical governance. Journal of Management in Medicine, 15(6), 463-487.
20. Fornell, C. & Larcker, D.F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50.
21. Forslund, H. (2007). Measuring information quality in the order fulfilment process. International Journal of Quality & Reliability Management, 24(5), 515-524.
22. Frolick, M.N. & Ariyachandra, T.R. (2006). Business performance management: one truth. Information Systems Management, 23(1), 41-48.
23. Gangadharan, G.R. & Swami, S.N. (2004). Business intelligence systems: design and implementation strategies. In V. Lužar-Stiffler and V.H. Dobrič (Ed.), Proceedings of 26th International Conference on Information Technology Interfaces (ITI 2004) (Vol. 1, pp. 139-144). Cavtat, Croatia. Zagreb, Croatia: University Computing Centre.
24. Gefen, D. & Straub, D. (2005). A practical guide to factorial validity using PLS-graph: tutorial and annotated example. Communications of the Association for Information Systems, 16(5), 91-109.
25. Gefen, D., Straub, D.W. & Boudreau, M.C. (2000). Structural equation modeling and regression: guidelines for research practice. Communications of the Association for Information Systems, 4(7), 1-80
26. Gibson, M., Arnott, D., Jagielska, I. & Melbourne, A. (2004). Evaluating the intangible benefits of business intelligence: review & research agenda. In Meredith, R. (Ed.), Proceedings of the 2004 IFIP WG 8.3 International Conference on Decision Support Systems (DSS2004): Decision Support in an Uncertain and Complex World (pp. 295-305). Caulfield East, Victoria, Australia: Monash University.
27. Grotz-Martin, S. (1976). Informations-qualität und Informations-akzeptanz in Entscheidungsprozessen: Theoretische Ansätze und ihre empirische Überprüfung. [Information quality and information acceptance in decision-making processes: theoretical beginnings and their empirical examination]. Unpublished doctoral dissertation. University of Saarbrücken, University of Saarbrücken, Germany.
28. Huang, K.T., Lee, J.Y. & Wang, R.Y. (1999). Quality information and knowledge. Upper Saddle River, NJ: Prentice Hall.
29. Hulland, J. (1999). Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strategic Management Journal, 20(2), 195-204.
30. Humphrey, W. S. (1989). Managing the software process. Reading, MA: Addison-Wesley.
31. Jhingran, A.D., Mattos, N. & Pirahesh, H. (2002). Information integration: a research agenda. IBM Systems Journal, 41(4), 555-562.
32. Jöreskog, K.G. (1970). A general method for analysis of covariance structure. Biometrika, 57(2), 239-251.
33. Kahn, B.K., Strong, D.M. & Wang, R.Y. (2002). Information quality benchmarks: product and service performance. Communications of the ACM, 45(4), 185.
34. Karimi, J., Gupta, Y.P. & Somers, T.M. (1996). Impact of competitive strategy and information technology maturity on firms' strategic response to globalization. Journal of Management Information Systems, 12(4), 55.
35. Khalil, O.E.M. & Elkordy, M.M. (2005). EIS information: use and quality determinants. Information Resources Management Journal, 18(2), 68-93.
36. Koronios, A. & Lin, S. (2007). Information quality in engineering asset management. In L. Al-Hakim, (Ed.), Information quality management: theory and applications (pp. 221-251). Hershey, PA: Idea Group Publishing.
37. Lee, Y.W., Strong, D.M., Kahn, B.K. & Wang, R.Y. (2002). AIMQ: a methodology for information quality assessment. Information & Management, 40(2), 133-146.
38. Lenzerini, M. (2002). Data integration: a theoretical perspective. In Lucian Popa (Ed.). Proceedings of the Twenty-first ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, June 3-5, Madison, Wisconsin, USA. ACM. New York, NY: ACM Press
39. Lesca, H. & Lesca, E. (1995). Gestion de l'information, qualité de l'information et performances de l'entreprise. [Information management, information quality and business performance.] Paris: Litec.
40. Lillrank, P. (2003). The quality of information. International Journal of Quality & Reliability Management, 20(6), 691-703.
41. Lindell, M.K. & Whitney, D.J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of Applied Psychology, 86(1), 114-121.
42. Low, G.S. & Mohr, J.J. (2001). Factors affecting the use of information in the evaluation of marketing communications productivity. Journal of the Academy of Marketing Science, 29(1), 70-88.
43. ® at http://www.webcitation.org/5lj5CPQBB)
44. McFarlan, F.W., McKenney, J.L. & Pyburn, P. (1983). The information archipelago: plotting a course. Harvard Business Review, 61(1), 145-156.
45. McGill, T., Hobbs, V. & Klobas, J. (2003). User-developed applications and information systems success: a test of DeLone and McLean's model. Information Resources Management Journal, 16(1), 24-45.
46. Moss, L.T. & Atre, S. (2003). Business intelligence roadmap: the complete project lifecycle for decision-support applications. Boston, MA: Addison-Wesley Professional.
47. Najjar, L. (2002). The impact of information quality and ergonomics on service quality in the banking industry. Unpublished doctoral dissertation. University of Nebraska, Lincoln, Nebraska, USA.
48. Negash, S. (2004). Business intelligence. Communications of the Association for Information Systems, 13, 177-195.
49. Negash, S. & Gray, P. (2008). Business intelligence. In F. Burstein & C. W. Holsapple (Eds.), Handbook on decision support systems 2 (pp. 175-193). Berlin, Heidelberg: Springer
50. Nolan, R.L. (1973). Managing the computer resource: a stage hypothesis. Communications of the ACM, 16(7), 399-405.
51. Nolan, R.L. (1979). Managing the crises in data processing. Harvard Business Review, 57(2), 115-126.
52. Nunnally, J.C. (1978). Psychometric theory (2nd ed.). New York, NY: McGraw-Hill.
53. O'Reilly, C.A. (1982). Variations in decision makers' use of information sources: the impact of quality and accessibility of information. The Academy of Management Journal, 25(4), 756-771.
54. ® at http://www.webcitation.org/5lj5VtJyg)
55. Paré, G. & Sicotte, C. (2001). Information technology sophistication in health care: an instrument validation study among Canadian hospitals. International Journal of Medical Informatics, 63(3), 205-223.
56. Park, C.W. (2007). Bad news reporting on troubled IT projects: the role of personal, situational, and organizational factors. Unpublished doctoral dissertation. Georgia State University Atlanta, Georgia, USA.
57. Paulk, M.C., Weber, C.V., Curtis, B. & Chrissis, M.B. (1995). The capability maturity model: guidelines for improving the software process. Boston, MA: Addison-Wesley.
58. Petter, S., DeLone, W. & McLean, E. (2008). Measuring information systems success: models, dimensions, measures and interrelationships. European Journal of Information Systems, 17(3), 236-263.
59. Pipino, L.L., Lee, Y.W. & Wang, R.Y. (2002). Data quality assessment. Communications of the ACM, 45(4), 211-218.
60. Raghunathan, S. (1999). Impact of information quality and decision-maker quality on decision quality: a theoretical model and simulation analysis. Decision Support Systems, 26(4), 275-286.
61. Rahm, E. & Do, H. (2000). Data cleaning: problems and current approaches. IEEE Data Engineering Bulletin, 23(4), 3-13.
62. Ruzevicius, J. & Gedminaite, A. (2007). Business information quality and its assessment. Engineering Economics, 52(2), 18-25.
63. Salaun, Y. & Flores, K. (2001). Information quality: meeting the needs of the consumer. International Journal of Information Management, 21(1), 21-37.
64. ® at http://www.webcitation.org/5lj5vcHhL)
65. Seddon, P.B. & Kiew, M.-Y. (1994). A partial test and development of the Delone and McLean model of IS success. In Proceedings of the 15th International Conference on Information Systems, (pp. 99-110). Atlanta, GA: Association for Information Systems.
66. Slone, J. P. (2006). Information quality strategy: an empirical investigation of the relationship between information quality improvements and organizational outcomes. Unpublished doctoral dissertation. Capella University, Minneapolis, Minnesota, USA
67. Spahn, M., Kleb, J., Grimm, S. & Scheidl, S. (2008). Supporting business intelligence by providing ontology-based end-user information self-service. In A. Duke, M. Hepp, K. Bontcheva & M. Vilain (Eds.), Proceedings of the first international workshop on ontology-supported business intelligence, Karlsruhe, Germany (Vol. 308, pp. 1-12). New York, NY: ACM Press.
68. Strong, D.M., Lee, Y.W. & Wang, R.Y. (1997). Data quality in context. Communications of the ACM, 40(5), 103-110.
69. ® at http://www.webcitation.org/5lj71KbAa)
70. Teo, H.H., Wei, K.K. & Benbasat, I. (2003). Predicting intention to adopt interorganizational linkages: an institutional perspective. MIS Quarterly, 27(1), 19-49.
71. Thierauf, R.J. (2001). Effective business intelligence systems. Westport, CT: Quorum Books.
72. Turban, E., Sharda, R., Aronson, J.E. & King, D. (2008). Business intelligence: a managerial approach. Upper Saddle River, NJ: Prentice Hall.
73. van der Pijl, G.J. (1994). Measuring the strategic dimensions of the quality of information. The Journal of Strategic Information Systems, 3(3), 179-190.
74. Wang, R.Y., Storey, V.C. & Firth, C.P. (1995). A framework for analysis of data quality research. IEEE Transactions on Knowledge and Data Engineering, 7(4), 623-640.
75. Watson, H.J., Goodhue, D.L. & Wixom, B.H. (2002). The benefits of data warehousing: why some organizations realize exceptional payoffs. Information & Management, 39(6), 491-502.
76. Werts, C.E., Linn, R.L. & Jöreskog, K.G. (1974). Intraclass reliability estimates: testing structural assumptions. Educational and Psychological Measurement, 34(1), 25-34.
77. Williams, S. (2004). Delivering strategic business value. Strategic Finance, 86(2), 40-48.
78. Williams, S. & Williams, N. (2003). The business value of business intelligence. Journal of Data Warehousing, 8(4), 1-11.
79. Williams, S. & Williams, N. (2007). The profit impact of business intelligence. Amsterdam: Morgan Kaufmann.
80. Zhu, K., Kraemer, K.L., Gurbaxani, V. & Xu, S. (2006). Migration to open-standard interorganizational systems: network effects, switching costs and path dependency. MIS Quarterly, 30 (Special Issue on Standards), 515-539.
81. ® at http://www.webcitation.org/5lj9ax2nU)