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Business Intelligence: An Analysis of the Literature
Zack Jourdan
a, R. Kelly Rainer
a& Thomas E. Marshall
aa
Department of Management, College of Business, Auburn University, Auburn, Alabama, USA
Available online: 07 Apr 2008
To cite this article: Zack Jourdan, R. Kelly Rainer & Thomas E. Marshall (2008): Business Intelligence: An Analysis of the
Literature , Information Systems Management, 25:2, 121-131
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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
1For 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
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,” “busi“busi-ness 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 Title Acronym
1 MIS Quarterly MISQ
2 Information Systems Research ISR
3 Communications of the ACM CACM
4 Journal of Management Information Systems JMIS
5 Management 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
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
Research 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
Judgment 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.
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
Table 4.
BI Articles as a Percentage of Total Articles
Journal 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 Journal of Management Information Systems 12 44 10 440 2.73
Journal 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
4 48 10 480 0.83
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), workintegrat-ing 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. Review 3 4 6 17 10 5 14 8 13 14 94
Field Study-Primary 2 2 1 1 3 2 2 4 1 3 21 Field Study-Secondary 2 1 2 3 2 5 4 19 Sample Survey 1 3 3 3 1 1 1 13 Computer Simulation 1 1 2 2 2 2 10 Lab Experiment 1 2 1 1 2 7 Field Experiment 2 1 3 Experimental Simulation 0 Judgment Task 0 Total 5 6 10 24 17 9 26 18 25 27 167
Table 6.
BI Categories
Category Topics Number of Articles Artificial Intelligence Algorithms, Classification, Machine Learning, Prediction, Web Mining37
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
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 makquantify-ing 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
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
Artificial 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
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.
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