EFFECTIVENESS OF BUSINESS INTELLIGENCE TECHNOLOGY ABSORPTIVE CAPACITY AND INNOVATION COMPETENCY OF UNIVERSITY STAFF, CASE OF UGANDA CHRISTIAN UNIVERSITY MBALE CAMPUS
DOI:
https://doi.org/10.47672/ejt.223Keywords:
Business Intelligence Technology (BIT), absorptive capacity, innovative competence of University staffsAbstract
Purpose: the purpose of the study was to explore the extent of business intelligence technology on absorptive capacity, the level of innovative competence of University staffs, the relationship between the extent of business intelligence technology, absorptive capacity, the level of innovative competence among University staff, formulated development programs based on the study findings and established profile of respondents in terms of age, gender, educational level and length of service.
Methodology: A descriptive cross sectional survey design guided the study and primary data was collected using structured questionnaires. Respondents classified into strata from which they were chosen randomly. The study population was 150 and a sample size of 108 got using sloven's formula for generating sample size. Research data was organized according to research questions and by category of respondents of the study. The results were analyzed using SPSS. The responses to different questions were quantified into frequencies mean, translated into percentages and ranks and presented in tables.
Results: The study revealed the following findings; majority of respondents were male, degree holders and most of the respondents served below 3 years. The extent of business intelligence technology Level ranged from high, moderate to very low, majority of staff's level of innovative competence high and there was a significant relationship between business intelligence technology, absorptive capacity and level of innovative competence and the null hypothesis was rejected.
Unique contribution to theory, practice and policy: The study recommends that the university's top management needs to support the staff in various ways that will not only build their absorptive capacity but improve upon their skills and competencies in preparation for adoption of business intelligence technology (BIT) in the university.
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References
Amabile et al. (1996) (Amabile, T. M., R. Conti, et al. (1996). "Assessing the work environment for creativity." Academy of Management Journal 39(5): 1154-1184) propose:
Arpan Kumar Kar, Ashis Kumar Pani and Supriya Kumar DeKumar Kar A., Kumar Pani A., Kumar De S.
A Study on Using Business Intelligence for Improving Marketing Efforts Business Intelligence Journal - July, 2010 Vol.3 No.2 150.
Barras, R. (1984). "Towards a theory of innovation in services". Research Policy 15: 161-73.
Byrd, Jacqueline (2003). The Innovation Equation - Building Creativity & Risk Taking in your
Organization. San Francisco, CA: Jossey- Bass/Pfeiffer - Aprint.
Choo, C. W. 1995. Information Management for the Intelligent Organization: The Art of Scanning the Environment. Medford (N.J.), Information Today Inc.
Cohen and Levinthal (1990), "Absorptive capacity: A new perspective on learning and innovation", Administrative Science Quarterly, Volume 35, Issue 1 pg. 128-152
Cabral, Regis (1998). "Refining the Cabral-Dahab Science Park Management Paradigm". Int. J. Technology Management, 16 (8): 813-818.
Cabral, Regis (2003). "Development, Science and". In Heilbron, J.. The Oxford Companion to the History of Modern Science
Collins, R. J. 1997. Better Business Intelligence. How to Learn More about Your Competitors.
Management Books, Astron On-Line, Letchworth
Cohen and Levinthal (1989), "Innovation and learning: The two faces of R&D", The Economic Journal, Volume 99, September pg. 569-596.
Davenport, T. H. 1993. Process Innovation: Reengineering Work through Information Technology. Harvard Business School Press, Boston, Massachusetts.
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.
E. Turban, J. E. Aronson, T. P. Liang, R. Sharda, Decision Support and Business Intelligence Systems,8th edition, Pearson Prentice Hall, 2007.
Eppler, M.J. (2006). Managing information quality: increasing the value of information in knowledge intensive products and processes (2nd ed.). Berlin: Springer.
Hannula, M. 2001. Onko tietoyhteiskunnassa tilaa tuottavuusajattelulle Tyoelaman tutkimus 2/2001, pp. 26 - 29. (In Finnish.)
International Conference & Exhibit, March 22 - 25, Boston, Massachusetts, USA. Hervonen, A. 2004.
Johdanto kurssiin sekä Business Intelligence -terminologiaan. Handout. Helsinki School of Economics. (In Finnish.) Journal, Volume 99, September pg. 569-596.
& 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.
Lesca, H. & Lesca, E. (1995). Gestion de l'information, qualit de l'information et performances del'entreprise. [Information management, information quality and business performance.] Paris: Litec. LB; Verron Haynes (2010) (notes)
McGee, J. V. & Prusak, L. 1993. Managing Information Strategically. John Wiley & Sons,New York.Moss,
M. Shariat, R. Jr. Hightower, "Conceptualizing Business Intelligence Architecture", Marketing Management Journal, Volume 17, Issue 2, pp. 40 -46, 2007.
M. E. Porter, "Competitive Strategy"”Techniques for Analyzing Industries and Competitors", NewYork: Free Press. 1980 New York: Oxford University Press. pp. 205-207.
Pollard, A. 1999. Competitor Intelligence, Strategy, Tools and Techniques for Competitor Advantage. Financial Times, Prentice Hall Publishing, London.
Raven, J., & Stephenson, J. (Eds.). (2001). Competency in the Learning Society. New York: Peter Lang
Sitra. 1998. Elämän laatu, osaaminen ja kilpailijakyky. The Finnish Fund for Research and
Development. Hakapaino Oy, Helsinki. (In Finnish.)
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>
Vitt, E., Luckevich, M. & Misner, S. 2002. Business Intelligence: Making Better Decisions Faster. Microsoft Press, Washington
Viva Business Intelligence Inc. 1998. Developing a Business Intelligence Process: Viva Business Intelligence - Cycle Approach. Pro-How Paper, Helsinki. Vol. 2
Zahra and George (2002), "Absorptive Capacity: A Review,Reconceptualization, and Extention",
Academy of Management Review,Volume 27, Issue 2,pg.185-203 ACM, SIGMOD Intl. Conf. Management of Data, Washington.
Brooks NAL (1989). Marketing technology: new marketing information systems enhance service and profitability. Bank Administration. 65(5): 52-54.
Comanor WS, Wilson TA (1967). Advertising, Market Structure, and Performance. Review of economic statistics. 49 (4): 423-440.
DeSarbo WS, Hildebrand DK (1980). A Marketer's Guide to Log-Linear Models for Qualitative Data Analysis. Journal of Marketing. 44 (summer): 40-
Duda RO, Hart PE, Stork DG (2001). Pattern Classification. Wiley publications. Dunham MH (2003). Data mining: Introductory and advanced topics. Pearson Education.
Gartner Research (2008). Findings: Social Network Analysis Is coming into the Limelight. ID Number: G00157280.
Green PE (1978). An AID/Logic Procedure for Analyzing Large Multi-way Contingency Tables. Journal of Marketing Research. 42(4): 92-100.
Gronroos C (1990). Service Management and Marketing: Managing the Moments of Truth in Service
Competition. Lexington Books, Lexington, MA. 2010 149 Haeckel SH, Nolan RH (1993). Managing by wire. Harvard Business Review. pp. 122 - 132.
Han J, Kamber M (2001). Data Mining: Concepts and Techniques. 2nd edition, Morgan Kaufmann Publishers.
Holland PC, Naude P (2004). The metamorphosis of marketing into a problem. The Journal of Business & Industrial Marketing. 19(3): 167-178.
Holsheimer M, Kersten M, Mannila H, Toivonen H. (1995). A perspective on databases and data mining. In 1st Int. Conf. Knowledge Discovery and Data Mining.
Houtsma M & Swami A (1995). Set-oriented mining of association rules in relational databases. Proceedings, 11th Int. Conf. Data Engineering.
Kaldor N, Silverman R (1948). A Statistical Analysis of Advertising Expenditures and of the Revenue of the Press. Cambridge University Press.
Kotler P, Keller KL (2006). Marketing Management. 12th Edition, Prentice hall, New York.
Levin N, Zahavi J (1999). Continuous predictive modeling - A comparative analysis. Journal of Direct Marketing. 12(2): 5-22.
Moller KE (1994). Inter-organizational marketing exchange: Meta-theoretical analysis of currentresearch approaches. Research Traditions in Marketing. Kluwer Academic Publishers, Boston.
Moriarty RT, Swartz GS (1989). Automation to boost sales and marketing. Harvard Business Review. 67(1): 100-108.
Naude P, Holland CP (1996). Business to business marketing. Relationship marketing, theory and practice. Paul Chapman Series, 40-54.
Peacock PR (1998a). Data mining in marketing: Part 1. Mmarketing Management. 6(4): 8-19.
Peacock PR (1998b). Data mining in marketing: Part 2. Marketing Management. 7(1): 14-26.
Perreault WD, Barkswale HC (1980). A Model-Free Approach for Analysis of Complex Contingency Data in Survey Research. Journal of Marketing Research. 17: 503-515.
Rayport JR, Sviokla JJ (1995). Exploiting the virtual value chain. Harvard Business Review. Nov-Dec: 75-85.
Sheth JN, Gardner DM, Garrett DE (1988). Marketing Theory: Evolution and Evaluation. John Wiley & Sons, New York.
Telser GL (1961). How muchdoes it Pay Whom to Advertise. American Economic Review. 51: 194-205.
The World Advertising Research Center (2001). World advertising expenditure. International Journal of Advertising. 20: 266 - 268.
Webster FE Jr. (1992). The changing role of marketing in the corporation. Journal of Marketing. 56(4): 1-17.Business Intelligence Journal - July, 2010 Vol.3 No.
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