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Business Intelligence: An Analysis of the Literature 1

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This research collects, synthesizes, and analyzes 167 articles on a variety of topics closely related to business intelligence (BI) published from 1997 to 2006 in ten leading Information Systems (IS) journals. We found a generally increasing level of activity during the 10-year period and a focus on exploratory research methodologies. We noted that several methodologies were either underrepresented or absent from the pool of BI research. We also identified several subject areas that need further exploration.
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Information Systems Management, 25: 121–131
Copyright © Taylor & Francis Group, LLC
ISSN: 1058-0530 print/1934-8703 online
DOI: 10.1080/10580530801941512
UISM
Business Intelligence: An Analysis of the Literature
1
Business Intelligence: An Analysis of the Literature
Zack Jourdan, R. Kelly Rainer, and Thomas E. Marshall
Department of Management, College of Business,
Auburn University, Auburn, Alabama, USA
Abstract This research collects, synthesizes, and analyzes 167 articles on a variety of topics closely
related to business intelligence (BI) published from 1997 to 2006 in ten leading Information Systems (IS)
journals. We found a generally increasing level of activity during the 10-year period and a focus on explor-
atory research methodologies. We noted that several methodologies were either underrepresented or absent
from the pool of BI research. We also identified several subject areas that need further exploration.
Keywords business analytics, business intelligence, competitive intelligence, data
mining, data warehousing, literature review, content analysis
Managers and researchers alike have been working to
develop IS that provide business intelligence (BI). BI is
“both a process and a product.” The process is composed
of methods that organizations use to develop useful
information, or intelligence, that can help organizations
survive and thrive in the global economy. The product is
information that will allow organizations to predict the
behavior of their “competitors, suppliers, customers,
technologies, acquisitions, markets, products and ser-
vices, and the general business environment” with a degree
of certainty (Vedder, Vanecek, Guynes, & Cappel, 1999).
The stakes are high for organizations to develop
successful BI implementations. Winning companies,
such as Continental Airlines, have seen investments in BI
generate increases in revenue and produce cost savings
equivalent to a 1000% return on investment (ROI)
(Watson, Wixom, Hoffer, Anderson-Lehman, & Reynolds,
2006). On the other hand, losing companies have spent
more resources than their competitors with a smaller
ROI, all while watching their market share and customer
base continuously shrink (Gessner & Volonino, 2005).
Two related needs provide the motivation for this
paper. First, the growing body of BI research necessitates
a review of this literature with the intent of “identifying
critical knowledge gaps and thus motivate researchers to
close this breach” (Webster and Watson, 2002). Second,
as noted by Scandura & Williams (2000), in order for
research to advance, the methods used by researchers
must periodically be evaluated to provide insights into
the methods utilized and thus the methods which
should also be used in a given research field. This study
analyzes the BI literature and then proposes an agenda
for future research efforts.
The remainder of the paper is organized as follows.
Section 2 describes the approach to the analysis of BI
research, Section 3 contains the results of the research,
and Section 4 discusses the limitations of the project and
possible future research efforts.
Research Study
We examined the number and distribution of BI articles
published in leading journals, the methodologies
employed in BI research, and the research topics being
addressed in BI research. During our analysis, we identi-
fied gaps in the research which would allow us to
propose and discuss a research agenda that will facilitate
the progression of BI research (Webster & Watson, 2002).
We hope to paint a representative landscape of the
current BI literature base in order to influence the direc-
tion of future research efforts in this important field.
In order to examine the current state of research on
BI, we conducted a literature review and analysis in three
phases. First, we accumulated a representative pool of
Note: For the full bibliography of the 167 articles, please contact
Kelly Rainer at rainer@business.auburn.edu
1
For the full bibliography of the 167 articles, please contact Kelly
Rainer at rainer@business.auburn.edu
Address Corresspondence to R. Kelly Rainer, Jr., Department of
Management, College of Business, Auburn University, Auburn,
Alabama 36849, USA. E-mail: rainer@business.auburn.edu
122 Jourdan, Rainer, and Marshall
articles, then classified the articles by research method,
and finally classified the articles by research topic.
Accumulation of Article Pool
Because BI research is typically published in many
IS journals, we searched through a ten year period
(1997–2006) of ten leading IS journals. In order to decide
which journals to search, we chose four recent rankings
for IS journals (Rainer & Miller, 2005; Lowry, Romans, &
Curtis, 2004; Katerattanakul, Han, & Hong, 2003; Peffers &
Ya, 2003). By selecting the top journals in each of these
four rankings, we created a list of ten journals (see
Table 1). We deliberately did not search journals devoted
to one particular area of business intelligence because we
wanted general, mainstream journals, rather than
specialized journals.
We then used the ABI/INFORM database to search for
the research articles by searching the titles and abstracts
of each of the ten journals using phrases such as “busi-
ness analytics,” “business intelligence,” “data mining,”
and “data warehousing.” Because we were looking for
research articles in the topic area of BI, we eliminated
any result that was a book review or an editorial.
Classification by Research Strategy
Once we identified the articles, we examined the research
strategy used in each article and categorized it according
to that strategy. Due to the subjective nature of method
classification, we performed a content analysis of the arti-
cles. Figure 1 shows the process we followed, which was
adapted from Neuendorf (2002). First, we defined the
research method categories utilizing those presented in
Scandura and Williams (2000), who extended the research
strategies initially described by McGrath (1982). Specifi-
cally, we used nine categories of research strategies: For-
mal theory/literature reviews (FT/LR), sample survey,
laboratory experiment, experimental simulation, field
study (primary data), field study (secondary data), field
experiment, judgment task, and computer simulation. To
guard against the threats to reliability (Neuendorf, 2002),
we performed a pilot on unused articles, discussed the
results, and refined the definitions.
Each research strategy is defined by a specific design
approach and each is also associated with certain trade-
offs that researchers must make when designing a study.
These trade-offs are inherent flaws that limit the conclu-
sions that can be drawn from a particular research strat-
egy. These trade-offs refer to three aspects of a study that
can vary depending on the research strategy employed.
These variable aspects include: generalizability from the
sample to the target population (external validity); preci-
sion in measurement and control of behavioral variables
(internal and construct validity); and the issue of realism
of context (Scandura & Williams, 2000).
Cook and Campbell (1976) stated that a study has
generalizability when the study has external validity
across times, settings, and individuals. Formal theory/
literature reviews and sample surveys have a high degree
of generalizability by establishing the relationship
between two constructs and illustrating that this rela-
tionship has external validity. A research strategy that
has low external validity but high internal validity is the
laboratory experiment. In the laboratory experiment,
where the degree of precision of measurement is high,
cause and effect relationships may be determined, but
these relationships may not be generalizable for other
Table 1. Journals in Study
#Journal TitleAcronym
1 MIS Quarterly MISQ
2 Information Systems Research ISR
3 Communications of the ACM CACM
4 Journal of Management Information Systems JMIS
5Management Science MS
6 Journal of the ACM JACM
7 European Journal of Information Systems EJIS
8 IEEE Transactions on Software Engineering IEEETSE
9 Information & Management I&M
10 Harvard Business Review HBR
Figure 1. Overview of literature analysis.
Calculate
Intercoder
Reliability
Determine
Journal Pool
Extract
Articles from
Journals
Phase 1
Accumulation
of Article Pool
Phase 2
Categorization
by Research
Strategy
Delete
Irrelevant
Articles
Conceptualize/
Define Strategies
Create Codebook
& Coding Form
Develop Pilot &
Train Coders
Discuss
Disagreements
Refine Definitions
& Codebook
Calculate
Results
Resolve
Disagreements
using Arbitrator
Code by
Research
Strategy
Calculate
Intercoder
Reliability
Phase 3
Categorization
by BI Category
Conceptualize/
Define Categories
Create Codebook
& Coding Form
Develop Pilot &
Train Coders
Discuss
Disagreements
Refine Definitions
& Codebook
Calculate
Results
Resolve
Disagreements
using Arbitrator
Code by
Research
Category
Business Intelligence: An Analysis of the Literature 123
times, settings, and populations. While the formal theory/
literature reviews and sample surveys have a high degree
of generalizability and the laboratory experiment has a
high degree of precision of measurement, these strate-
gies have low degree of realism of context. The only two
strategies that maximize degree of realism of context are
field studies using either primary or secondary data
because the data is collected in a field setting (Scandura &
Williams, 2000).
The other four strategies maximize neither generaliz-
ability, nor degree of precision of measurement, nor
degree of realism of context. This point illustrates the
futility of using only one strategy when conducting IS
research. Because no one strategy can maximize all types
of validity, it is best for researchers to use a variety of
research strategies. Table 2 contains an overview of the
nine strategies and their ranking on the three strategy
tradeoffs (Scandura & Williams, 2000).
We then classified the articles independently as to
research strategy. We coded only a few articles at a time
to minimize coder fatigue and thus protect intercoder
reliability (Neuendorf, 2002). Upon completion of the
independent classification, we tabulated agreements and
disagreements, intercoder crude agreement (percent of
agreement), and intercoder reliability using Cohen’s
kappa (Cohen, 1960). The latter two calculations were
well within the acceptable ranges for intercoder crude
agreement and intercoder reliability (Neuendorf, 2002).
We calculated the reliability measures prior to discussing
disagreements as mandated by Weber (1990). If two
reviewers did not agree on how a particular article was
coded, a third reviewer arbitrated the discussion of how
the disputed article was to be coded. This process
resolved the disputes in all cases.
Classification by BI Research Topic
To classify articles by research topic, we held several
brainstorming and discussion sessions where we
attempted to identify BI topics with the intent to catego-
rize the diverse body of literature. In these discussion ses-
sions, we sought to synthesize the literature and provide
a better understanding of the current state of BI research.
Once we established the category definitions, we inde-
pendently placed each article in one BI category. As
before, we placed only a few articles at a time to mini-
mize coder fatigue and thus protect intercoder reliability
(Neuendorf, 2002). Upon completion of the classification
process, we tabulated agreements and disagreements,
intercoder crude agreement (percent of agreement), and
intercoder reliability using Cohen’s kappa (Cohen, 1960)
Table 2. Research Strategies*
Strategy Tradeoffs
R
esearch Strategy Description
Degree of Precision
of Measurement
Degree of Realism
of Context
Generalizability to
Target Population
Formal Theory/Literature
Reviews
Summarization of the literature in an area
of research in order to conceptualize models
for empirical testing.
Low Low Maximizes
Sample Survey The investigator tries to neutralize context
by asking for behaviors that are unrelated
to the context in which they are elicited.
Low Low Maximizes
Laboratory Experiment Participants are brought into an artificial
setting, usually one that will not
significantly impact the results.
Maximizes Low Low
Experimental Simulation A situation contrived by a researcher in
which there is an attempt to retain some
realism of context through use of
simulated situations or scenarios.
Moderate Moderate Low
Field study: Primary Data Investigates behavior in its natural setting.
Involves collection of data by researchers.
Low Maximizes Low
Field Study: Secondary Data Involves studies that use secondary data
(data collected by a person, agency, or
organization other than the researchers.
Low Maximizes Low
Field Experiment Collecting data in a field setting but
manipulating behavior variables.
Moderately high Moderately
high
Low
J
udgment Task Participants judge or rate behaviors.
Sampling is systematic vs. representative,
and the setting is contrived.
Moderately high Low Moderately high
Computer Simulation Involves artificial data creation or
simulation of a process.
Low Moderately
high
Moderately high
*Source: Scandura & Williams, 2000.
124 Jourdan, Rainer, and Marshall
for each category. Again, the latter two calculations were
well within the acceptable ranges (Neuendorf, 2002). We
again calculated the reliability measures prior to discussing
disagreements as mandated by Weber (1999). If two
reviewers did not agree on how a particular article was
coded, a third reviewer arbitrated the discussion of how
the disputed article was to be coded. This process also
resolved the disputes in all cases.
Results
Using the described search criteria within the selected
journals, we collected a total of 167 articles. (For the com-
plete list of articles in the sample, see Appendix A.) We
then analyzed the articles’ year of publication, journal,
and author.
Table 3 shows the number of articles per year in our
sample. With BI issues becoming more important to
researchers and practitioners, we see a generally increasing
trend in the number of articles per year.
In order to determine the degree to which leading IS
journals publish BI research, we calculated the percentage
of BI articles based on the total number of articles pub-
lished in each journal in the ten-year period. Calculating
this percentage involved several steps. First, three sample
issues of each journal were examined to determine the
average total number of articles per issue. We counted
any article within the journal over five pages in length to
eliminate editors’ notes, book reviews, and editorial
pages. Second, the number of articles per issue was
multiplied by the number of issues that each journal
publishes in a year. This number gave us the estimated
number of total articles for a journal during the ten-year
period we were studying. Finally, we calculated the
percentage of BI articles by dividing the actual number of
BI articles in a journal by the estimated total number of
articles in a journal. As shown in Table 4, some top IS
journals publish very little BI research while others
devote a substantial amount of space to this research.
Analysis of Research Strategies in BI Research
The categorization of the 167 articles according to the
nine research strategies produced the following results.
Ninety-four articles (56%) were classified as Formal Theory/
Literature review making it the most prevalent research
strategy. Other categories, in decreasing order, are Field
Study-Primary Data (21 articles), Field Study-Secondary
Data (19 articles), Sample Survey (13 articles), Computer
Simulation (10 articles), Lab Experiment (7 articles), and
Field Experiment (3 articles). No articles were classified
as either Experimental Simulation or Judgment Task.
Analysis of the research strategies over the ten year
period from 1997 to 2006 (Table 5) illustrates that Formal
Theory/Literature Review, Field Study—Primary Data,
Field Study – Secondary Data, and Sample Survey are rep-
resented in almost every year of the time frame. These
four strategies are exploratory in nature and indicate the
beginnings of a body of research (Scandura & Williams,
2000).
BI Research Categories
As we analyzed the articles, five relatively distinct catego-
ries emerged. The Artificial Intelligence (AI) category consists
of algorithms and applications of AI. The applications of
the AI category addressed classification, prediction, web
mining and machine learning. The Benefits category
details how organizations have used data warehousing,
data mining, and/or an enterprise-wide BI systems to
achieve some measurable financial benefit. The Decision
category contains articles related to improving overall
decision making and includes such subjects as data
modeling, decision-making, and decision modeling. The
Implementation category covers project management issues
Table 3. Number of BI Articles
per Year
Year Number of BI Articles
1997 5
1998 6
1999 10
2000 24
2001 17
2002 9
2003 26
2004 18
2005 25
2006 27
T
a
bl
e
4
.
BI
A
rtic
l
es as a
P
ercentage o
f
T
ota
l
A
rtic
l
es
J
ournal Name BI Articles/Year Years Total %
European Journal of
Information Systems
18 20 10 200 9.00
MIS Quarterly 17 20 10 200 8.50
Information &
Management
21 32 10 320 6.56
Communications of
the ACM
51 84 10 840 6.07
Information Systems
Research
10 24 10 240 4.17
J
ournal of Management
Information Systems
12 44 10 440 2.73
J
ournal of the ACM 5 30 10 300 1.67
Management Science 19 120 10 1200 1.58
Harvard Business Review 10 90 10 900 1.11
IEEE Transactions on
Software Engineering
448 104800.83
Business Intelligence: An Analysis of the Literature 125
in a variety of BI contexts including data warehousing,
data mining, customer relationship management (CRM),
enterprise resource planning (ERP), knowledge manage-
ment systems (KMS), and eBusiness projects.
The final and most diverse category is Strategies. This
category focuses on how to apply BI tools and technolo-
gies in the modern business environment. The category
covers such diverse topics as improving internal perfor-
mance (i.e., enterprise agility, marketing, and integrat-
ing business functions), working with external partners
to improve collaboration in the supply chain, and provid-
ing the customer a better experience through customiza-
tion/personalization and customer relationship
management (CRM).
BI research covers diverse subjects ranging from prac-
tical applications of neural networks (Baesens, Rudy,
Mues, & Vanthienen, 2003), to end-user satisfaction
(Chen, Soliman, Mao, & Frolick, 2000), to the use of clus-
tering as a business strategy to gain a competitive advan-
tage (Porter, 1998). The researchers conducting BI
research have backgrounds as varied as marketing (Cui,
Wong, & Lui, 2006), management information systems
(Watson, Goodhue, & Wixom, 2002), and computer sci-
ence (Menzies, Chen, Hihn, & Lum, 2006). To integrate BI
research stemming from such disparate backgrounds, we
placed the BI literature into research categories. In Table 6,
the BI categories are displayed along with some of the
topics relevant to each category.
These five categories provided a subject-area classifi-
cation for all of the 167 articles in our research sample.
Fifty-nine articles were classified as Strategies, making
it the most prevalent BI category. This category was
followed by Artificial Intelligence (37 articles), Imple-
mentation (35 articles), and Decisions (26 articles).
These four research strategies accounted for 94% of the
articles in the sample. The category Benefits repre-
sented the remaining six per cent (10 articles). These
numbers illustrate the categories of BI research that
have received the most and least attention in the IS
journals.
BI Category vs. Research Strategy
An examination of BI category versus research strategy
(Table 7) identifies the research strategies used in articles
on the various BI topics. Articles on all five BI topics used
the FT/LR research strategy most often. Approximately
one-half the articles in the Artificial Intelligence,
Benefits, Decisions, and Implementation categories uti-
lized the FT/LR strategy. However, almost three-fourths of
the articles in the Strategies topic area used the FT/LR
strategy.
Other than FT/LR, the technology-focused category of
Artificial Intelligence used field-secondary and computer
simulations the most frequently. The technology-focused
category of Decisions used lab experiments most fre-
quently. On the other hand, the less technical categories
of Benefits, Implementation, and Strategies relied more
heavily on sample surveys and field studies using both
primary and secondary data.
The rationale for these findings is as follows. First,
the FT/LR strategy is the most appropriate strategy for
the early stages of research in any area. In these explor-
atory years of BI research, formal theory/literature
reviews are appropriate for theory formulation and
development. Second, researchers in business schools
may be more skilled in administering sample surveys
and performing field studies, and may not typically
employ strategies such as laboratory experiment,
Table 5. Research Strategy vs. Year
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Total
Formal Theory/Lit. Review3 4 61710 5 14 81314 94
Field Study-Primary 22 1132 241321
Field Study-Secondary 21 2 325419
Sample Survey 133 311113
Computer Simulation 11 222210
Lab Experiment 1 2 1 1 2 7
Field Experiment 21 3
Experimental Simulation 0
J
udgment Task 0
Total 5 6 10 24 17 9 26 18 25 27 167
Table 6. BI Categories
Category Topics
Number of
Articles
A
rtificial
Intelligence
Algorithms, Classification,
Machine Learning, Prediction,
Web Mining
37
Benefits Data Mining, Enterprise-wide IS 10
Decisions Data Modeling, Decision
Making, Decision Modeling
26
Implementation CRM, DM, DSS, DW, eBusiness,
ERP, KMS, Project Management
35
Strategies Collaboration, Competition,
Customization, Integration, etc.
59
126 Jourdan, Rainer, and Marshall
experimental simulation, judgment task, and computer
simulation. For example, the majority of articles found
in our search that used the computer simulation
research strategy were written by computer scientists
and computer engineers, while the majority of articles
using sample surveys were written by business research-
ers. Finally, organizations are often less likely to
commit to certain strategies (i.e., primary and second-
ary field studies and field experiments) because these
strategies are more expensive for the organizations.
These types of research strategies are very labor inten-
sive to the organization being studied because records
will need to be examined, personnel will need to be
interviewed, and senior managers will be required to
devote large amounts of their time to help facilitate the
research project.
Areas for Future Research in Business Intelligence
It is interesting that the Theory Formulation/Literature
Review research strategy remains the most prevalent
strategy after so many years. The focus of BI researchers
could shift to other research strategies. Also notable is
that the Survey research strategy has been used so little.
Perhaps additional survey research would provide value
to this field. In addition, the Benefits topic deserves more
attention. One of the reasons for so few articles pub-
lished in this area is probably the difficulty in quantify-
ing the benefits of improved decision making attributed
to BI systems.
Limitations and Directions for Future
Research
Our analysis of the BI literature is not without limita-
tions. Future literature reviews could search a broader
domain of research outlets. Further, future BI studies
should consider the research gaps that we have identi-
fied in light of generalizability, precision of measure,
and realism of context. Future efforts should also con-
sider the five BI categories with respect to the research
strategies.
In addition, much of the research in our sample
addresses new technologies and issues in BI without
attempting to explain the fundamental issues of infor-
mation systems research as it relates to BI. This is to be
expected in the exploratory stages of research in a sub-
ject area. However, our study indicates that enough
research in FT/LR has been done to begin formulating
guiding theories in BI research.
For researchers to continue to address important
questions in BI, future studies need to employ a wider
variety of research strategies. Scandura and Williams
(2000) stated that looking at research strategies
employed over time by triangulation in a given subject
area can provide useful insights into how theories are
developing. In addition to the lack of variety in research
strategy, very little triangulation has occurred during
the timeframe used to conduct this literature review.
This absence of coordinated theory development causes
the research in BI to appear haphazard and unfocused.
However, the good news is that many of the categories
and research strategies in BI research are open for future
research efforts. We hope that this research analysis has
laid the foundation for such efforts that will enhance
the IS body of knowledge and theoretical progression
relative to BI.
Author Bios
Zack Jourdan is an Assistant Professor of Management
Information Systems at Georgia Southwestern University.
He is a doctoral candidate at Auburn University. His
current research interests include various aspects of
information security and risk analysis.
R. Kelly Rainer, Jr. is George Phillips Privett Professor of
Management Information Systems at Auburn University,
Auburn, Alabama. He received his Ph.D from the University
of Georgia. His current research interests include infor-
mation security and risk analysis, and healthcare infor-
mation systems. His most recent book (co-authored with
Efraim Turban) is Introduction to Information Systems, 2007,
John Wiley and Sons.
Thomas E. Marshall is an Associate Professor of
Management Information Systems at Auburn University,
Table 7. BI Category vs. Research Strategy
FT/LR Survey Lab Exp. Exp. Sim. Field - Pri. Field - Sec. Field Exp. Judgment Task Comp. Sim. Total
A
rtificial Intelligence 17 1 10 2 7 37
Benefits 5 2 1 2 10
Decisions 12 1 7 2 3 1 26
Implementation 17 6 10 2 35
Strategies 43 3 8 2 1 2 59
Total 94 13 7 0 21 19 3 0 10 167
Business Intelligence: An Analysis of the Literature 127
Auburn, Alabama. He received his Ph.D from the Univer-
sity of North Texas. His current research interests include
information security, various aspects of database
management, and geographical information systems.
References
Chen, L., Soliman, K. S., Mao, E., & Frolick, M. N. (2000). Measuring
User Satisfaction with Data Warehouses: An exploratory
study. Information & Management, 37(3), 103–110.
Cohen, J. (1960). A Coefficient of Agreement for Nominal Scales.
Educational and Psychological Measurement, 20(1), 37–46.
Cook, T. D., & Campell, D. T. (1976). The Design and Conduct of
Quasi-experiments and True Experiments in Field Settings. In
Handbook of Industrial and Organizational Psychology, M.D.
Dunnette, Ed. Chicago, IL: Rand McNally.
Cui, G., Wong, M. L., & Lui, H. (2006). Machine Learning for Direct
Marketing Response Models: Bayesian network with evolution-
ary programming. Management Science, 52(4), 597–612.
Cumbie, B. A., Jourdan, S. Z., Peachey, T. A., Dugo, T. M. &
Craighead, C. W. (2005). Enterprise Resource Planning
Research: Where Are We Now and Where Should We Go
from Here? Journal of Information Technology Theory & Applica-
tion, 7(2), 21–36.
Gessner, G. H. & Volonino, L. (2005). Quick Response Improves
Return on Business Intelligence Investments. Information
Systems Management, 22(3), 66–74.
Katerattanakul, P., Han, B., & Hong, S. (2003). Objective quality
ranking of computing journals. Communications of the ACM,
46(10), 111–114.
Lowry, P., Romans, D., & Curtis, A. (2004). Global journal
prestige and supporting disciplines: A scientometric study of
information systems journals. Journal of the Association of
Information Systems, 5(2), 29–75.
McGrath, J. (1982). Dilemmatics: The study of research choices
and dilemmas. In Judgment Calls in Research, J. E. McGrath,
J. Martin, & R. A. Kilka, Eds. Beverly Hills, CA: SAGE
Publications.
Neuendorf, K. A. The Content Analysis Guidebook. Thousand Oaks,
CA: SAGE Publications.
Peffers, K., & Ya, T. (2003). Identifying and Evaluating the Uni-
verse of Outlets for Information Systems Research: Ranking
the journals. JITTA: Journal of Information Technology Theory and
Application, 5(1), 63–84.
Porter, M. (1998). Clusters and the New Economics of Competi-
tion. Harvard Business Review, 76(6), 77–90.
Rainer, R. K., Jr. and Miller, M. (2005). Examining differences
across journal rankings. Communications of the ACM, (48:2), 91–94.
Scandura, T. A., and Williams, E. A. (2000). Research Methodol-
ogy in Management: Current practices, trends, and implica-
tions for future research. Academy of Management Journal, 43(6),
1248–1264.
Vedder, R. G., Vanecek, M. T., Guynes, C. S., & Cappel, J. J. (1999).
CEO and CIO Perspectives on Competitive Intelligence.
Communications of the ACM, 42(8), 108–116.
Watson, H. J., Goodhue, D. L., & Wixom, B. H. (2002). The Bene-
fits of Data Warehousing: Why some organizations realize
exceptional payoffs. Information & Management, 39(6), 491–502.
Watson, H. J., Wixom, B. H., Hoffer, J. A., Anderson-Lehman, R., &
Reynolds, A. M. (2006). Real-Time Business Intelligence: Best
practices at Continental Airlines. Information Systems Manage-
ment, 23(1), 7–18.
Weber, R. H., (1990). Basic Content Analysis (2
nd
ed.) Thousand
Oaks, CA: Sage Publications.
Webster, J., & Watson, R. T. (2002) Analyzing the Past to Prepare
for the Future: Writing a literature review. MIS Quarterly, 26(2),
xiii–xxiii.
Appendix A. Complete List of BI Articles
Abramson, C., Currim, I. S., & Sarin, R. (2005). An Experimental
Investigation of the Impact of Information on Competitive
Decision Making. Management Science, 51(2), 195–207.
Adam, N. R., Atluri, V., & Adiwijaya, I. (2000). SI in Digital Libraries.
Communications of the ACM, 43(6), 64–72.
Adomavicius, G. & Tuzhilin, A. (2005). Personalization Technolo-
gies: A process-oriented perspective. Communications of the
ACM, 48(10), 83–90.
Alavi, M. & Leidner, D. E. (2001). Review: Knowledge manage-
ment and knowledge management systems: Conceptual foun-
dations and research issues. MIS Quarterly, 25(1), 107–136.
Albert, T. C., Goes, P. B., & Gupta, A. (2004). GIST: A model for
design and management of content and interactivity of
customer-centric web sites. MIS Quarterly, 28(2), 161–182.
Allmendinger, G. & Lombreglia, R. (2005). Four Strategies for the
Age of Smart Services. Harvard Business Review, 83(10), 131–145.
Anand, B. & Galetovic, A. (2004). How Market Smarts can Protect
Property Rights. Harvard Business Review, 82(12), 73–79.
Andriole, S. J. (2006). The Collaborate/Integrate Business Tech-
nology Strategy. Communications of the ACM, 49(5), 85–90.
Apte, C., Liu, B., Pednault, E. P. D., & Smyth, P. (2002). Business
Applications of Data Mining. Communications of the ACM,
45(8), 49–53.
Arnott, D. (2004). Decision Support System Evolution: Frame-
work, case study, and research agenda. European Journal of
Information Systems, 13(4), 247–259.
Arrañada, B. & Vázquez, X. (2006). When Your Contract Manu-
facturer Becomes Your Competitor. Harvard Business Review,
84(9), 135–144.
Baesens, B., Rudy, S., Mues, C., & Vanthienen, J. (2003). Using
Neural Network Rule Extraction and Decision Tables for
Credit-Risk Evaluation. Management Science, 49(3), 312–329.
Bakos, J. Y. (1997). Reducing Buyer Search Costs: Implications
for electronic marketplaces. Management Science, 43(12),
1676–1692.
Ballou, D. P. & Tayi, G. K. (1999). Enhancing Data Quality in
Data Warehousing Environments. Communications of the ACM,
42(1), 73–78.
Bendoly, E. (2003). Theory and Support for Process Frameworks
of Knowledge Discovery and Data Mining from ERP Systems.
Information & Management, 40(7), 639–647.
Bharadwaj, A. S. (2000). A Resource-Based Perspective on Infor-
mation Technology Capability and Firm Performance: An
empirical investigation. MIS Quarterly, 24(1), 169–196.
Bontempo, C. & Zagelow, G. (1998). The IBM Data Warehouse
Architecture. Communications of the ACM, 41(9), 38–48.
128 Jourdan, Rainer, and Marshall
Boonstra, A. (2003). Structure and Analysis of IS Decision-Making
Processes. European Journal of Information Systems, 12(3), 195–209.
Bose, I. & Mahapatra, R. K. (2001). Business Data Mining:
A machine learning perspective. Information & Management,
39(3), 211–225.
Bose, I. & Pal, R. (2005). Auto-ID: Managing anything, anywhere,
anytime, in the supply chain. Communications of the ACM, 48(8),
100–106.
Bose, I. (2006). Deciding the Financial Health of Dot-Coms Using
Rough Sets. Information & Management, 43(7), 835–846.
Bradley, P., Gehrke, J., Ramakrishnan, R., & Srikant, R. (2002).
Scaling Mining Algorithms to Large Databases. Communica-
tions of the ACM, 45(8), 38–43.
Chandra, J., March, S.T., Mukherjee, S., Pape, W., Ramesh, R.,
Rao, H. R., & Waddoups, R O. (2000). Information Systems
Frontiers. Communications of the ACM, 43(1), 71–79.
Chen, L., Soliman, K.S., Mao, E., & Frolick, M.N. (2000). Measuring
User Satisfaction with Data Warehouses: An exploratory
study. Information & Management, 37(3), 103–110.
Chenoworth, T., Schuff, D., & St. Louis, R. (2003). A Method for
Developing Dimensional Data Marts. Communications of the
ACM, 46(12), 93–98.
Chiasson, M. W. & Davidson, E. (2005). Taking Industry Seriously
in Information System Research. MIS Quarterly, 29(4), 591–605.
Chiu, C. (2003). Towards Integrating Hypermedia and Informa-
tion Systems on the Web. Information & Management, 40(3),
165–175.
Choudhury, V. & Sampler, J. L. (1997). Information Specificity
and Environmental Scanning: An Economic Perspective. MIS
Quarterly, 21(1), 25–53.
Chung, W., Chen, H., & Nunamaker, J. F., Jr. (2005). A Visual
Framework for Knowledge Discovery on the Web: An empirical
study of business intelligence exploration. Journal of Manage-
ment Information Systems, 21(4), 57–84.
Churilov, L., Bagirov, A., Schwartz. D., Smith, K., & Dally, M.
(2005). Data Mining with Combined Use of Optimization
Techniques and Self-Organizing Maps for Improving Risk
Grouping Rules: Application to prostate cancer patients.
Journal of Management Information Systems, 21(4), 85–100.
Cingil, I., Dogac, A., & Azgin, A. (2000). A Broader Approach to
Personalization. Communications of the ACM, 43(8)
, 136–141.
Clemons, E. K., Gu, B., & Lang, K. R. (2003). Newly Vulnerable
Markets in an Age of Pure Information Products: An analysis
of online music and online news. Journal of Management Infor-
mation Systems, 19(3), 17–41.
Coltman, T., Devinney, T. M., Latukefu, A. S., & Midgley, D. F.
(2002). Keeping E-Business in Perspective. Communications of the
ACM, 45(8), 69–73.
Cooper, B. L., Watson, H. J., Wixom, B .H., & Goodhue, D. L.
(2000). Data Warehousing Supports Corporate Strategy at
First American Corporation. MIS Quarterly, 24(4), 547–567.
Cui, G., Wong, M. L., & Lui, H. (2006). Machine Learning
for Direct Marketing Response Models: Bayesian network with
evolutionary programming. Management Science, 52(4), 597–612.
Daniel, E. M. & Wilson, H. N. (2003). The Role of Dynamic
Capabilities in E-Business Transformation. European Journal of
Information Systems, 12(4), 282–296.
Dennis, A. R., Wixom, B. H., & Vandenberg, R. J. (2001). Under-
standing Fit and Appropriation Effects in Group Support
Systems via Meta-Analysis. MIS Quarterly, 25(2), 167–193.
Dewan, R., Jing, B., & Seidmann, A. (2003). Product Customiza-
tion and Price Competition on the Internet. Management Sci-
ence, 49(8), 1055–1070.
Dreiling, A., Rosemann, M., van der Aalst, W., Heuser, L., &
Schulz, K. (2006). Model-Based Software Configuration:
Patterns and Languages. European Journal of Information Systems,
15(6), 583–600.
Earl, M. (2001). Knowledge Management Strategies: Toward a tax-
onomy. Journal of Management Information Systems, 18(1), 215–233.
Eick. S. G. (2001). Visualizing Online Activity. Communications of
the ACM, 44(8), 45–50.
Fadlalla, A. (2005). An Experimental Investigation of the Impact
of Aggregation on the Performance of Data Mining with
Logistic Regression. Information & Management, 42(5), 695–707.
Fan, W., Wallace, L., Rich, S., & Zhang, Z. (2006). Tapping the
Power of Text Mining. Communications of the ACM, 49(9), 77–82.
Feelders, A., Daniels, H., & Holsheimer, M. (2000). Methodological
and Practical Aspects of Data Mining. Information & Manage-
ment, 37(5), 271–281.
Ferson, W. E., Heuson, A., & Su, T. (2005). Weak-Form and
Semi-Strong-Form Stock Return Predictability Revisited.
Management Science, 51(10), 1582–1592.
Fingar, P. (2000). Component-Based Framework for E-Commerce.
Communications of the ACM, 43(10), 61–66.
Fisher, C. W., Chengalur-Smith, I., & Ballou, D. P. (2003). The
Impact of Experience and Time on the Use of Data Quality
Information in Decision Making. Information Systems Research,
14(2), 170–188.
Flajolet, P., Szpankowski, W., & Vallée, B. (2006). Hidden Word
Statistics. Journal of the ACM, 53(1), 147–183.
Ganapathy, S., Ranganathan, C., & Sankaranarayanan, B. (2004).
Visualization Strategies and Tools for Enhancing Customer Rela-
tionship Management. Communications of the ACM, 47(11), 93–99.
Gardner, S. R. (1998). Building the Data Warehouse. Communica-
tions of the ACM, 41(9), 52–60.
Gasson, S. (2006). A Genealogical Study of Boundary-Spanning IS
Design. European Journal of Information Systems, 15(1), 26–41.
Geoffrion, A. M. & Krishnan, R. (2003). E-Business and Manage-
ment Science: Mutual impacts (Part 1 of 2). Management
Science, 49(10), 1275–1286.
Geoffrion, A. M. & Krishnan, R. (2003). E-Business and Manage-
ment Science: Mutual impacts (Part 2 of 2). Management
Science, 49(11), 1445–1456.
Glassey, K. (1998). Seducing the End User. Communications of the
ACM, 41(9), 62–69.
Gold, A. H., Malhotra, A., & Segars, A. H. (2001). Knowledge
Management: An organizational capabilities perspective.
Journal of Management Information Systems, 18(1), 185–214.
Gorla, N. (2003). Features to Consider in a Data Warehousing
System. Communications of the ACM, 46(11), 111–115.
Gregg, D. G. & Walczak, S. (2006). Adaptive Web Information
Extraction. Communications of the ACM, 49(5), 78–84.
Grimson, J., Grimson, W., & Hasselbring, W. (2000). The SI Chal-
lenge in Health Care. Communications of the ACM, 43(6), 49–55.
Grover, V. & Ramanlal, P. (1999). Six Myths of Information
and Market: Information technology networks, electronic
commerce, and the battle for consumer surplus. MIS Quarterly,
23(4), 465–495.
Grover, V. & Vaswani, P. (2000). Partnership in the U.S. Telecom-
munications Industry.
Communications of the ACM, 43(2), 80–89.
Business Intelligence: An Analysis of the Literature 129
Guan, T. & Wong, K. F. (2003). Nstar: An interactive tool for local
web search. Information & Management, 41(2), 213–225.
Harding, D. & Rovit, S. (2004). Building Deals on Bedrock.
Harvard Business Review, 82(9), 121–128.
Hasselbring, W. (2000). Information System Integration. Commu-
nications of the ACM, 43(6), 33–38.
Hirji, K.K. (2001). Exploring Data Mining Implementation.
Communications of the ACM, 44(7), 87–93.
Horton, K. S. & Wood-Harper, T. A. (2006). The Shaping of I.T.
Trajectories: Evidence from the U.K. Public Sector. European
Journal of Information Systems, 15(2), 214–224.
Hosanagar, K., Krishnan, R., Chuang, J., & Choudhary, V. (2005).
Pricing and Resource Allocation in Caching Services with
Multiple Levels of Quality of Service. Management Science,
51(12), 1844–1859.
Hui, S. C. & Jha, G. (2000). Data Mining for Customer Service
Support. Information & Management, 38(1), 1–13.
Indyk, P. (2006). Stable Distribution, Pseudorandom Generators,
Embedding, and Data Stream Computation. Journal of the ACM,
53(3), 307–323.
Irani, Z., Sharif, A. M., & Love, P. E. D. (2001). Transforming Fail-
ure into Success Through Organisational Learning: An analy-
sis of a manufacturing information system. European Journal of
Information Systems, 10(1), 55–66.
Jain, K. & Vazirani, V. V. (2001). Approximation Algorithms for
Metric Facility Location and k-Median Problems Using the
Primal-Dual Schema and Lagrangian Relaxation. Journal of the
ACM, 48(2), 274–296.
Jones, Q., Ravid, G., & Rafaeli, S. (2004). Information Overload
and the Message Dynamics of Online Interaction Spaces: A
theoretical model and empirical exploration. Information
Systems Research, 15(2), 194–210.
Jukic, N. (2006). Modeling Strategies and Alternatives for Data
Warehousing Projects. Communications of the ACM, 49(4), 83–88.
Kalvenes, J. & Basu, A. (2006). Design of Robust Business-to-Business
Electronic Marketplace with Guaranteed Privacy. Management
Science, 52(11), 1721–1736.
Keim, D. A. (2001). Visual Exploration of Large Data Sets. Commu-
nications of the ACM, 44(8), 39–44.
Kiang, M. Y. & Kumar, A. (2001). An Evaluation of Self-Organization
Map Networks as a Robust Alternative to Factor Analysis in
Data Mining Applications. Information Systems Research, 12(2),
177–194.
Kim, W. C. & Mauborgne, R. (1997). Value Innovation: The strategic
logic of high growth. Harvard Business Review, 75(1), 172–180.
Kim, Y., Street, W. N., Russell, G. J., & Menczer, F. (2005).
Customer Targeting: A neural network approach guided by
genetic algorithms. Management Science, 51(2), 264–276.
Kleinberg, J., Papadimitriou, C., & Raghavan, P. (2004). Segmen-
tation Problems. Journal of the ACM, 51(2), 263–280.
Kudyba, S. & Diwan, R. (2002). Research Report: Increasing
returns to information technology. Information Systems
Research, 13(1), 104–111.
Kumar, N. & Benbasat, I. (2004). The Effect of Relationship
Encoding, Task Type, and Complexity in Information Repre-
sentation: An empirical evaluation of 2D and 3D line graphs.
MIS Quarterly, 28(2), 255–281.
Li, X. & Sarkar, S. (2006). Privacy Protection in Data Mining: A
perturbation approach for categorical data. Information
Systems Research, 17(3), 254–270.
Lim, A., Rodrigues, B., & Zhang, X. (2004). Metaheuristics with
Local Search Techniques for Retail Shelf-Space Optimization.
Management Science, 50(1), 117–131.
Lin, Q., Chen, Y., Chen, J., & Chen, Y. (2003). Mining Inter-
Organizational Retailing Knowledge for an Alliance Formed
by Competitive Firms. Information & Management, 40(5), 431–442.
Little, J. D. C. (2004). Models and Managers: The concept of a
decision calculus. Management Science, 50(12), 1841–1853.
Little, R. G., Jr., & Gibson, M. L. (2003). Perceived Influences on
Implementing Data Warehousing. IEEE Transactions on Software
Engineering, 29(4), 290–296.
Liu, D. & Shih, Y. (2005). Integrating AHP and Data Mining for
Product Recommendation Based on Customer Lifetime Value.
Information & Management, 42(3), 387–400.
Looney, C. A. & Chatterjee, D. (2002). Web-Enabled Transformation
of the Brokerage Industry. Communications of the ACM, 45(8), 75–81.
MacKay, N., Parent, M., & Gemino, A. (2004). A Model of Elec-
tronic Commerce Adoption by Small Voluntary Organization.
European Journal of Information Systems, 13(2)
, 147–159.
Maes, P., Guttman, R. H., & Moukas, A. G. (1999). Agents that Buy
and Sell. Communications of the ACM, 42(3), 81–91.
Magnusson, C., Arppe, A., Eklund, T., Back, B., Vanharanta, H., &
Visa, A. (2005). The Language of Quarterly Reports as an
Indicator of Change in the Company’s Financial Status.
Information & Management, 42(4), 561–574.
Makadok, R. & Barney, J. B. (2001). Strategic Factor Market Intel-
ligence: An application of information economics to strategy
formulation and competitor intelligence. Management Science,
47(12), 1621–1638.
Malhorta, A., Gosain, S., & El Sawy, O. A. (2005). Absorption
Capacity Configurations in Supply Chains: Gearing for
partner-enabled market knowledge creation. MIS Quarterly,
29(1), 145–187.
Marble, R. P. (2004). Technological Switchbacks: The transition
to Western information system in privatised firms of the
former East Germany. European Journal of Information Systems,
13(2), 115–132.
Markus, M. L. (2001). Toward a Theory of Knowledge Reuse:
Types of knowledge reuse situations and factors in reuse
success. Journal of Management Information Systems, 18(1), 57–93.
McCarthy, J. (2000). Phenomenal Data Mining. Communications of
the ACM, 43(8), 75–79.
McGahan, A. (2004). How Industries Change. Harvard Business
Review, 82(10), 87–94.
McNulty, E. (2003). They Bought In. Now They Want to Bail Out..
Harvard Business Review, 81(12), 28–38.
Melville, N., Kraemer, K., & Gurbaxani, V. (2004). Review: Infor-
mation technology and organizational performance: An
integrative model of IT business value. MIS Quarterly, 28(2),
283–322.
Mendonça, M. G. & Basili, V. R. (2000). Validation of an Approach
for Improving Existing Measurement Framework. IEEE Trans-
actions on Software Engineering, 26(6), 484–499.
Mennecke, B. E., Crossland, M.D., & Killingsworth, B.L. (2000). Is
a Map More than a Picture? The role of SDSS technology, sub-
ject characteristics, and problem complexity on map reading
and problem solving. MIS Quarterly, 24(4), 601–629.
Menon, S., Sarkar, S., & Mukherjee, S. (2005). Maximization
Accuracy of Shared Databases when Concealing Sensitive Pat-
terns. Information Systems Research, 16(3), 256–270.
130 Jourdan, Rainer, and Marshall
Menzies, T., Chen, Z., Hihn, J., & Lum, K. (2006). Selecting Best
Practices for Effort Estimation. IEEE Transactions on Software
Engineering, 32(11), 883–895.
Merali, Y. (2002). The Role of Boundaries in Knowledge
Processes. European Journal of Information Systems, 11(1), 47–60.
Mitchell, T. M. (1999). Machine Learning and Data Mining.
Communications of the ACM, 42(11), 31–36.
Mobasher, B., Cooley, R., & Srivastava, J. (2000). Automatic
Personalization Based on Web Usage Mining. Communications
of the ACM, 43(8), 142–151.
Murthi, B.P. & Sarkar, S. (2003). The Role of the Management Sci-
ence in Research on Personalization. Management Science,
49(10), 1344–1362.
Nelson, R. R., Todd, P. A., & Wixom, B. H. (2005). Antecedents of
Information and System Quality: An Empirical Examination
Within the Context of Data Warehousing. Journal of Manage-
ment Information Systems, 21(4), 199–235.
Orr, K. (1998). Data Quality and Systems. Communications of the
ACM, 41(2), 66–71.
Osei-Bryson, K. & Ko, M. (2004). Exploring the Relationship
between Information Technology Investments and Firm
Performance using Regression Splines Analysis. Information &
Management, 42(1), 1–13.
Overby, E., Bharadwaj, A., & Sambamurthy, V. (2006). Enterprise
Agility and the Enabling Role of Information Technology.
European Journal of Information Systems, 15(2), 120–131.
Padmanabhan, B. & Tuzhilin, A. (2003). On the Use of Optimiza-
tion for Data Mining: Theoretical interactions and eCRM
opportunities. Management Science, 49(10), 1327–1343.
Padmanabhan, B., Zheng, Z., & Kimbrough, S. O. (2006). An
Empirical Analysis of the Value of Complete Information for
eCRM Models. MIS Quarterly, 30(2), 247–267.
Pan, S. L. & Lee, J. (2003). Using e-CRM for a Unified View of the
Customer. Communications of the ACM, 46(4), 95–99.
Park, Y. (2006). An Empirical Investigation of the Effects of Data
Warehousing on Decision Performance. Information & Manage-
ment, 43(1), 51–61.
Pendharkar, P. (2004). An Exploratory Study of Object-Oriented
Software Component Size Determinants and the Application
of Regression Tree Forecasting Models. Information & Manage-
ment, 42(1)
, 61–73.
Perkowitz, M. & Etzioni, O. (2000). Adaptive Web Sites. Communi-
cations of the ACM, 43(8), 152–158.
Piccoli, G. & Ives, B. (2005). Review: IT-Dependent strategic initia-
tives and sustained competitive advantage: A review and
synthesis of the literature. MIS Quarterly, 29(4), 747–776.
Porter, M. (1998). Clusters and the New Economics of Competi-
tion. Harvard Business Review, 76(6), 77–90.
Prieto, I. M. & Easterby-Smith, M. (2006). Dynamic Capabilities
and the Role of Organizational Knowledge: An exploration.
European Journal of Information Systems, 15(5), 500–510.
Prokesch, S. (1997). Unleashing the Power of Learning: An inter-
view with British Petroleum’s John Browne. Harvard Business
Review, 75(5), 147–168.
Puschmann, T. & Alt, R. (2005). Developing an Integration Archi-
tecture for Process Portals. European Journal of Information
Systems, 14(2), 121–164.
Rittgen, P. (2006). A Language-Mapping Approach to Action-
Oriented Development of Information Systems. European
Journal of Information Systems, 15(1), 70–81.
Ross, R., Subrahmanian, V. S., & Grant, J. (2005). Aggregate
Operators in Probabilistic Databases. Journal of the ACM, 52(1),
54–101.
Rundensteiner, E. A., Koeller, A., & Zhang, X. (2000). Maintaining
Data Warehouses over Changing Information Sources.
Communications of the ACM, 43(6), 57–62.
Sackmann, S., Strüker, J., & Accorsi, R. (2006). Personalization in
Privacy-Aware Highly Dynamic Systems. Communications of the
ACM, 49(9), 32–38.
Sambamurthy, V., & Zmud, R. W. (2000). Research Commentary:
The organizing logic for an enterprise”s IT activities in the
digital era - A prognosis of practice and a call for research.
Information Systems Research, 11(2), 105–114.
Sambamurthy, V., Bharadwaj, A., & Grover, V. (2003). Shaping
Agility through Digital Options: Reconceptualizing the role
of information technology in contemporary firms. MIS
Quarterly, 27(2), 237–263.
Sauter, V. L. (1999). Intuitive Decision-Making. Communications of
the ACM, 42(6), 109–115.
Schonberg, E., Cofino, T., Hoch, R., Podlaseck, M., & Spraragen,
S.L. (2000). Measuring Success. Communications of the ACM, 43(8),
53–57.
Sen, A., & Sinha, A. P. (2005). A Comparison of Data
Warehousing Methodologies. Communications of the ACM
,
48(3), 79–84.
Sen, A., Dacin, P. A., & Pattichis, C. (2006). Current Trends in
Web Data Analysis. Communications of the ACM, 49(11), 85–91.
Shanks, G. & Darke, P. (1999). Understanding Corporate Data
Models. Information & Management, 35(1), 19–30.
Sheng, Y. P., Mykytyn, P. P., Jr., & Litecky, C. R. (2005). Competitor
Analysis and Its Defenses in the E-Marketplace. Communica-
tions of the ACM, 48(8), 107–112.
Shin, B. (2002). A Case of Data Warehousing Project Manage-
ment. Information & Management, 39(7), 581–592.
Simester, D. I., Sun, P., & Tsitsiklis, J. N. (2006). Dynamic Catalog
Mailing Policies. Management Science, 52(5), 683–696.
Simon, S. J. (2001). The Art of Military Logistics. Communications
of the ACM, 44(6), 62–66.
Singh, N. (2003). Emerging Technologies to Support Supply
Chain Management. Communications of the ACM, 46(9), 243–247.
Sinha, A. P. & May, J. H. (2005). Evaluating and Tuning Predic-
tive Data Mining Models Using Receiver Operating Charac-
teristic Curves. Journal of Management Information Systems, 21(3),
249–280.
Smyth, P., Pregibon, D., & Faloutsos, C. (2002). Data-Driven
Evolution of Data Mining Algorithms. Communications of the
ACM, 45(8), 33–37.
Spangler, W. E., May, J. H., & Vargas, L. G. (1999). Choosing
Data-Mining Methods for Multiple Classification: Representa-
tional and performance measurement implications for
decision support. Journal of Management Information Systems,
16(1), 37–62.
Spangler, W. E., Gal-Or, M., & May, J. H. (2003). Using Data
Mining to Profile TV Viewers. Communications of the ACM,
46(12), 67–72.
Speier, C. & Morris, M. G. (2003). The Influence of Query Inter-
face Design on Decision-Making Performance. MIS Quarterly,
27(3), 397–423.
Spiegler, I. (2003). Technology and Knowledge: Bridging a
“generating” gap. Information & Management, 40(6), 533–539.
Business Intelligence: An Analysis of the Literature 131
Spiliopoulou, M. (2000). Web Usage Mining for Web Site Evalua-
tion. Communications of the ACM, 43(8), 127–134.
Stalk, G., Jr. & Lachenauer, R. (2004). Hardball: Five killer strate-
gies for trouncing the competition. Harvard Business Review,
82(4), 63–71.
Subirana, B. & Bain, M. (2006). Legal Programming. Communica-
tions of the ACM, 49(9), 57–62.
Subramanian, A., Smith, L. D., Nelson, A. C., Campbell, J. F., &
Bird, D. A. (1997). Strategic Planning for Data Warehousing.
Information & Management, 33(2), 99–113.
Sung, T. K., Chang, N., & Lee, G. (1999). Dynamics of Modeling in
Data Mining: Interpretive approach to bankruptcy predic-
tion. Journal of Management Information Systems, 16(1), 63–85.
Tam, K. Y., & Ho, S. Y. (2005). Web Personalization as a Persua-
sion Strategy: An elaboration likelihood model perspective.
Information Systems Research, 16(3), 271–291.
Taylor, W. A. (2004). Computer-Mediated Knowledge Sharing
and Individual User Differences: An exploratory study.
European Journal of Information Systems, 13(1), 52–64.
Thatcher, M. E. & Clemons, E. K. (2000). Managing the Cost of
Informational Privacy: Pure bundling as a strategy in the
individual health insurance market. Journal of Management
Information Systems, 17(2), 29–57.
Thomas, H. & Datta, A. (2001). A Conceptual Model and Algebra
for On-Line Analytical Processing in Decision Support
Databases. Information Systems Research, 12(1), 83–102.
Todd, P. & Benbasat, I. (1999). Evaluating the Impact of DSS,
Cognitive Effort, and Incentives on Strategy Selection.
Information Systems Research, 10(4), 356–374.
Tuomi, I. (2000). Data is More Than Knowledge: Implications of
the reversed knowledge hierarchy for knowledge manage-
ment and organization memory. Journal of Management
Information Systems, 16(3), 103–177.
van Oosterhout, M., Waarts, E., & van Hillegersberg, J. (2006).
Change Factors Requiring Agility and Implications for IT.
European Journal of Information Systems, 15(2), 132–145.
Vedder, R. G., Vanecek, M. T., Guynes, C. S., & Cappel, J. J. (1999).
CEO and CIO Perspectives on Competitive Intelligence.
Communications of the ACM, 42(8), 108–116.
Watson, H. J. & Haley, B. J. (1998). Managerial Considerations.
Communications of the ACM, 41(9), 32–37.
Watson, H. J., Goodhue, D. L., & Wixom, B. H. (2002). The Bene-
fits of Data Warehousing: Why some organizations realize
exceptional payoffs. Information & Management,
39(6), 491–502.
Weick, K. E. (2006). The Role of Imagination in the Organizing
of Knowledge. European Journal of Information Systems, 15(5),
446–452.
Welty, B. & Becerra-Fernandez, I. (2001). Managing Trust and
Commitment in Collaborative Supply Chain Relationships.
Communications of the ACM, 44(6), 67–73.
Wixom, B. H. & Watson, H. J. (2001). An Empirical Investigation
of the Factors Affecting Data Warehousing Success. MIS
Quarterly, 25(1), 17–41.
Wood, C. A. & Ow, T. T. (2005). Webview: An SQL extension for
joining corporate data to data derived from the web. Commu-
nications of the ACM, 48(9), 99–104.
Xanthopulos, Z., Melachrinoudis, E., & Solomon, M. M. (2000).
Interactive Multiobjective Group Decision Making with Inter-
val Parameters. Management Science, 46(12), 1585–1601.
Xu, X. M., Kaye, G. R., & Duan, Y. (2003). UK Executives. Vision on
Business Environment for Information Scanning: A cross
industry study. Information & Management, 40(5), 381–389.
Zheng, Z. & Padmanabhan, B. (2006). Selective Acquiring Customer
Information: A new data acquisition problem and an active
learning-based solution. Management Science, 52(5), 697–712.
Zhu, K., Kraemer, K., & Xu, S. (2003). Electronic Business Adop-
tion by European Firms: A Cross-Country Assessment of the
Facilitators and Inhibitors. European Journal of Information
Systems, 12(4), 251–268.
Zimmermann, T., Wei berger, P., Diehl, S., & Zeller, A. (2005).
Mining Version Histories to Guide Software Changes. IEEE
Transactions on Software Engineering, 31(6), 429–445.
... This information can identify the weaknesses and strength of the companies. Organisational performance is one of the most important structures in management research and is undoubtedly the most important criterion for measuring success in all organisations (service and non-services) (Jourdan et al., 2008). In general, organisational performance indicators categorised as objective and subjective. ...
... The business intelligent systems collect and analyze customer data and help in prediction for business improvements and overall organizational performance [3]. These systems rely on online analytical processing (OLAP) and data mining techniques [4] to provide better customer services. Data mining plays an essential role in the success of a business intelligence system, due to the fact that it deals with several issues related to data including heterogeneity, immense volume, the speed at which data is being produced, and many more [5]. ...
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