Business Intelligence & Organisational Change Research Proposal

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2003, pg. 3191). While the human analysis component of this definition is important, very little research has looked at it in any level of detail. Combining the various BI definitions we will use the following definition for BI in this research: Business Intelligence consists of the use of analytical technologies and data stores by people in an organization to analyze business problems and produce related business recommendations to improve business performance. The key technologies that make up the technological components of BI are data warehousing, and related extraction transformation and load (ETL) tools; analysis tools, including statistical analysis and online analytical processing (OLAP) tools; and reporting/visualization tools. Based on this definition it becomes clear that the people/organizational component of BI is as important as the technological.

Since BI is a relatively new topic, research specifically referring to BI is still sparse. However, the volume of DSS research is much larger. BI is considered a subset of DSS research by some (Arnott et al. 2005; Arnott et al. 2008), while others have suggested that DSS is a component of BI (Negash et al. 2003; Rajesh 2008). However you look at it, BI related research is still one of the least studied areas of DSS. BI related research accounted for only 7% of all of the DSS articles published between 1990 and 2004 (Arnott et al. 2008). We will examine the key research relative to DSS success and the major BI research that does exist. Research in DSS and BI can be categorized into four main areas; effectiveness, tools and technologies, algorithms and data mining, and organizational impacts. The next sections will examine research in each of these areas.

RESEARCH METHODOLOGY

This research will use a retroductive methodology. Retroduction "refers to the process of building hypothetical models of structures and mechanisms that are assumed to produce empirical phenomena" (Blaikie 2007, pg 83). Bhaskar (1998) suggests that retroduction is the appropriate method to use when exploring social phenomena from a realist perspective. Retroduction starts with a model of the constructs to be studied. These models are then tested to determine if they represent the reality that the researcher is attempting to uncover. These tests are then used to modify the model based on understanding developed through testing. Finally a new model is postulated based on the revisions suggested by the evidence discovered during testing (Blaikie 2007).

This research will seek to understand the specific competencies that would allow an organization to gain business value from business intelligence systems. The question being studied is concerned with "how" and "why" questions regarding the relationship between competencies and BI. These types of questions can most effectively be addressed, especially in the early phases of a stream of research, via a case study approach (Benbasat et al. 1987; Yin 2003). A case study approach also allows one to examine a real world phenomenon in detail in a real life setting. Such an examination can lead to insights that could subsequently be used to develop generalized theories about the phenomenon in question.

Data Analysis Approach

In order to ensure validity of the data collected in this case study it will be necessary to follow a structured approach to data analysis. While the data collection will be done from the perspective of existing theory, it will still be important to structure analysis that ensures a deep understanding of underlying meanings. This will be accomplished by using the techniques of open and axial coding (Strauss et al. 1998). Open coding is the process by which "concepts are identified and their properties and dimensions are discovered in data" (Strauss et al. 1998, pg 102). Through this process underlying themes embodied in the interviews should emerge. These themes will be evaluated within the theoretical constructs of individual know-how and skills and purposeful heedful interactions. Once themes have emerged from the data, axial coding will be performed to align emergent themes within the overall theoretical model. Axial coding relates the categories identified through open coding "to subcategories along the lines of their properties and dimensions" (Strauss et al. 1998, pg 124). From these coding steps should emerge a model of competencies that will begin to explain successful BI.

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As the coding of data will be performed new concepts may emerge that are not necessarily represented in the original theoretical model. These concepts will be explored by referring back to the literature to examine whether they have been studied. This iterative review of the research data and associated literature should provide insights that can be used to develop a theoretical model (Strauss et al. 1998).

Developing an Assessment Tool

An additional goal of this research will be to develop a preliminary assessment tool that can be used to determine an organization-s level of competence for BI. The tool will be created using the individual elements of competence that emerge from the coding of the interview data. These elements should represent the various items that will be identified as antecedents to BI competence in our evaluation. Respondents will be asked to evaluate each of the elements in terms of the status of their organization on each item and the importance of each item. This process will allow the evaluation of the potential for this tool to be used by an organization to determine their current likelihood of attaining BI benefits and provide direction for steps to take to increase their BI success. Evaluating how respondents use this tool can provide insight into how it can be used and its potential impact.

PROPOSED REFERENCES

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Ahn, H., and Kim, K.-j. "Using genetic algorithms to optimize nearest neighbors for data mining," Annals of Operations Research (163:1) 2008, pp 5-18.

Alavi, M., and Joachimsthaler, E.A. "REVISITING DSS IMPLEMENTATION RESEARCH - a METAANALYSIS of the LITERATURE and SUGGESTIONS for RESEARCHERS," MIS Quarterly (16:1), Mar 1992, pp 95-116.

Alavi, M., and Leidner, D.E. "Review: Knowledge Management and Knowledge Management Systems: Conceptual Foundations ans Research Issues," MIS Quarterly (25:1), March 2001, pp 107-136.

Allport, G.W. "The psychology of participation," Psychological Review (52:3) 1945, pp 117-132. Alter, S.L. "A Study of Computer Aided Decision Making in Organizations," MIT, Cambridge, MA, 1975.

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Amidon, S.R. "WRITING the LEARNING ORGANIZATION," Business Communication Quarterly (68:4) 2005, pp 406-428.

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Apte, C., Bing, L., Pednault, E.P.D., and Smyth, P. "Business APPLICATIONS of DATA MINING," in: Communications of the ACM, Association for Computing Machinery, 2002, p. 49.

Argyris, C. Strategy, Change and Defensive Routines Pitman Publishing Limited, Marshfield, MA, 1985. Argyris, C. "Teaching Smart People How to Learn," Harvard Business Review Reflections (4:2) 1991, pp 4-15.

Argyris, C. On Organizational Learning Blackwell Business, Cambridge, MA, 1992. Argyris, C. "Learning and Teaching: A theory of Action Perspective," Journal of Management Education (21:1) 1997, pp 9-26.

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Arnold, V., Clark, N., Collier, P.A., Leech, S.A., and Sutton, S.G. "THE DIFFERENTIAL USE and EFFECT of KNOWLEDGE-BASED SYSTEM EXPLANATIONS in NOVICE and EXPERT JUDGMENT DECISIONS," MIS Quarterly (30:1) 2006, pp 79-97.

Arnott, D., and Pervan, G. "A critical analysis of decision support systems research," Journal of Information Technology (20:2) 2005, pp 67-87.

Arnott, D., and Pervan, G. "Eight key issues for the decision support systems discipline," Decision Support Systems (44:3) 2008, pp 657-672.

Azvine, B., Cui, Z., and Nauck, D.D. "Towards real-time business intelligence," BT Technology Journal • Vol 23 No 3 • July 2005 (23:3) 2005, pp 214-225.

Bajema, C.J. "A Note on the Interrelations among Intellectual Ability, Educational Attainment, and Occupational Achievement: A Follow-up Study of a Male Kalamazoo Public School Population," Sociology of Education (41:3), Summer68 1968, pp 317-319.

Ballou, D.P., and Tayi, G.K. "Enhancing data quality.....

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