ABSORPTION OF INFORMATION PROVIDED BY BUSINESS
INTELLIGENCE SYSTEMS
The Effect of Information Quality on the Use of Information in Business Processes
Aleš Popovič
1
, Pedro Simões Coelho
2
and Jurij Jaklič
1
1
Department of Information Management, Faculty of Economics, University of Ljubljana
Kardeljeva pl. 17, 1000 Ljubljana, Slovenia
2
Institute for Statistics and Information Management, Universidade Nova de Lisboa
Campus de Campolide, 1070-312 Lisboa, Portugal
Keywords: Business Intelligence, Business Intelligence System Maturity, Content Quality, Information Quality, Media
Quality, Structural Equation Modelling, Use of Information.
Abstract: The fields of business intelligence and business intelligence systems have been gaining relative significance
in the scientific area of decision support and decision support systems. In order to better understand
mechanisms for providing benefits of business intelligence systems, this research establishes and
empirically tests a model of business intelligence systems’ maturity impact on the use of information in
organizational operational and managerial business processes, where this effect is mediated by information
quality. Based on empirical investigation from Slovenian medium and large-size organizations the proposed
structural model has been analyzed. The findings suggest that business intelligence system maturity
positively impacts both segments of information quality, yet the impact of business intelligence system
maturity on information media quality is greater than the impact on content quality. Moreover, the impact of
information content quality on the use of information is much larger than the impact of information media
quality. Consequently, when introducing business intelligence systems organizations clearly need to focus
more on information content quality issues than they do currently.
1 INTRODUCTION
For today’s organizations, in order to succeed, it is
important to understand how information technology
can create substantial and sustainable competitive
advantages. Peppard, Ward and Daniel (2007)
suggest that “with their information technology
investments, most organizations focus on
implementing the technology rather than on
realizing the expected business benefits”. A similar
pattern can be spotted in the field of business
intelligence systems (Williams and Williams, 2007,
Elbashir et al., 2008). This situation can be easily
attributed to the lack of ability of organizations to
view these investments in the context of the business
value creation process, which is binding for
organizations if they want benefits forthcoming.
The field of business intelligence shows very few
empirical studies regarding the realization of
benefits from business intelligence systems.
Findings from Jourdan et al. (2008) suggest benefits
derived from business intelligence systems have not
been adequately researched and thus need further
attention.
The quest for delivering business value via
business intelligence systems can be seen as a matter
of determining how an organization can use the
information provided through business intelligence
systems “to improve management processes (such as
planning, controlling, measuring, monitoring, and/or
changing) and/or to improve operational processes
(such as sales, order processing, purchasing)”
(Williams and Williams, 2007).
Decision-makers’ information-processing
characteristics contribute significantly in adopting
business intelligence systems. The greater the
capability of decision-makers to process the
provided information, the higher the probability will
be of the business intelligence systems being
adopted. This all depends on the absorption capacity,
which refers to the knowledge and ability of an
organization to judge and process certain
176
Popovi
ˇ
c A., Simões Coelho P. and Jakli
ˇ
c J. (2010).
ABSORPTION OF INFORMATION PROVIDED BY BUSINESS INTELLIGENCE SYSTEMS - The Effect of Information Quality on the Use of Information
in Business Processes.
In Proceedings of the 12th International Conference on Enterprise Information Systems - Databases and Information Systems Integration, pages
176-181
DOI: 10.5220/0002890901760181
Copyright
c
SciTePress
information in order to make as efficient as possible
use of the information within the organization
(Baldwin & Scott, 1987). It may be the case,
especially for small companies, that an organization
lacks the knowhow to process potentially valuable-
information adequately (Frambach, 1993).
The purpose of this research is therefore to come
up with a deeper understanding of the role of
business intelligence systems for providing quality
information and further on the impact of the
information quality on the use of information in
organizational business processes, i.e. on absorption
of quality information.
The outline of the paper is as follows: In Section
2 we present our research model, Section 3 aims to
present the methodological framework for the study,
while Section 4 deals with the testing of the
proposed research model and hypotheses. Section 5
concludes with a summary and a discussion of the
main findings, limitations and direction for future
work.
2 THE RESEARCH MODEL
Implementation of business intelligence systems first
of all addresses information goals, namely providing
high quality information for decision-makers.
Similarly Brown (2005) argues the value of business
intelligence systems is created by acting on the
information delivered through these systems.
Assessment of an IT asset can be based upon a
maturity model, also known as stages theory, not
only to determine the current stage of implemented
IT but also to show its next step (Nolan, 1979).
There are many IT/IS maturity models dealing with
different aspects of maturity, namely technological,
organizational and process maturity. These maturity
models are quite general and their focus is not on the
key technological elements of business intelligence
systems. Moreover, according to Becker et al.
(2009) “maturity models inherently become obsolete
because of changing conditions, technological
progress or new scientific insights”. The fields of
business intelligence and business intelligence
systems are rapidly evolving thus requiring regular
validation and constant changes of maturity models.
In the current business environment, there is no
lack of business intelligence or business intelligence
systems maturity models (Williams & Williams,
2007), yet they are relatively few compared to
maturity models from other disciplines. What is
more, none of the models found in the literature
were empirically supported. Based on the reviewed
business intelligence and business intelligence
system maturity models we found no evidence of an
agreement on the business intelligence systems’
maturity concept (Popovič, Coelho and Jaklič,
2009). However, we can derive two main
emphasizes from the reviewed models. First, there is
an awareness of the importance of integrating large
amounts of data from disparate sources (Elbashir et
al., 2008) and an awareness of the need to cleanse
the data extracted from the sources (Bouzeghoub
and Lenzerini, 2001) within the field of business
intelligence systems. Second, organizations are
focusing on technologies (e.g. querying, online
analytical processing, reporting, data mining) for the
analysis of business data integrated from
heterogeneous source systems (Negash, 2004). On
this basis, we propose the first hypothesis:
H1: Business intelligence system maturity is
determined by data integration and analytics.
Petrini and Pozzebon (2009) suggest the role of
business intelligence systems is to create an
informational environment in which gathered
operational data can be analyzed to provide quality
information relevant to the decision-making process.
Although the field of information quality evaluation
has already been extensively researched (e.g. Slone,
2006, Lee et al., 2002), most of the proposed
information quality frameworks don’t address the
issue of information quality evaluation
comprehensively enough. For evaluating
information quality we adopted Eppler’s (2006)
information quality framework since it provides one
of the broadest and most thorough analyses of the
information quality evaluation criteria. The
framework in essence divides its criteria into two
segments: a) criteria dealing with information
content quality, which relates to actual information
itself, and b) criteria addressing information media
quality, which relates to whether delivery process
and infrastructure are adequate in quality. Eppler
(2006) further argues that technology mainly
influences media quality and has limited possibilities
of influencing content quality. Thus, we propose the
concept of information quality as involving two
dimensions that are both positively, yet differently
affected by the maturity of business intelligence
systems. In this context, hypotheses 2a, 2b and 2c
are put forward:
H2a: Business intelligence system maturity has a
positive impact on content quality.
H2b: Business intelligence system maturity has a
positive impact on media quality.
ABSORPTION OF INFORMATION PROVIDED BY BUSINESS INTELLIGENCE SYSTEMS - The Effect of
Information Quality on the Use of Information in Business Processes
177
H2c: Business intelligence system maturity has a
different positive impact on content quality and
media quality, with larger impact on media quality.
Mere availability of information does not
guarantee the information’s ultimate use
(Diamantopoulos et al., 2003). The limited previous
research suggests a positive relationship between
information quality and information use (Low and
Mohr, 2001, Deshpande and Zaltman, 1982), yet we
are not aware of any previous study empirically
analyzing separately the impact of content quality
and media quality on the use of information.
Moreover, while the use of information is closely
linked to the value that the available information
provides to knowledge workers for solving their
decision problems content quality appears to be of
greater importance than it is providing access to
information. Thus we put forward hypotheses 3a, 3b
and 3c:
H3a: Quality of information content has positive
impact on the use of information.
H3b: Quality of information media has positive
impact on the use of information.
H3c: Quality of information content and quality of
information media have different positive impacts on
the use of information.
3 METHODOLOGY
This study used a survey to obtain data measuring
business intelligence systems maturity, participants’
perceptions of information quality, and perception
about the use of information within business
processes. The questionnaire was developed by
building on the previous theoretical basis in order to
ensure content validity. Pre-testing was conducted
using a focus group involving 3 academics interested
in the field and 7 semi-structured interviews with
selected CIOs who were not interviewed later. This
was also used to assure face validity. We used a
structured questionnaire with a combination of 7-
point Likert scales and 7-point semantic
differentials.
Based on the reviewed business intelligence and
business intelligence systems’ maturity models we
modeled the business intelligence system maturity
concept as a second-order construct formed by two
first-order factors: data integration and analytics.
The data integration construct is supported by the
findings of Lenzerini (2002). Within the analytics
construct we look at the different types of analyses
the business intelligence system enables. We
selected those indicators most used in previous
works: paper reports (TDWI, 2005), ad-hoc reports
(Claraview, 2005), online analytical processing
(‘OLAP’) (Davenport and Harris, 2007), data
mining (TDWI, 2005), dashboards, key performance
indicators (‘KPIs’) and alerts (Davenport and Harris,
2007).
To measure information quality we adopted 11
previously validated information quality criteria
indicators from the Eppler’s framework (Eppler,
2006).
For measuring use of information in business
processes we used indicators available in reviewed
literature and those obtained from the pilot study.
Davenport (1993) and Choo (1996) suggest
available information in organizational processes
pinpoints problems regarding process execution.
Furthermore, information actively supports
continuous process improvement programs
(Davenport, 1993) and business process change
initiatives (Davenport and Short, 2003).
The target population for this study were
Slovenian medium and large size organizations
(1,329). Empirical data for this research were
collected by means of paper and Web-based survey.
Questionnaires were addressed to CIOs and senior
managers estimated as having adequate knowledge
of business intelligence systems, the quality of
available information for decision-making and the
use of information in business processes. The final
response rate was 13.6%.
4 RESULTS
Data analysis was carried out using a form of struc-
tural equation modelling (‘SEM’). For the estimation
of the model we employed SEM-PLS (Structural
Equation Models by Partial Least Squares) (Ringle,
Wende and Will, 2007), also known as PLS Path
Modelling (‘PLS’).
Figure 1 shows the results of testing the
measurement model in the final run. Without
exception, latent variable composite reliabilities
show a high internal consistency of indicators
measuring each construct and thus confirming
construct reliability. The average variance extracted
(‘AVE’) demonstrates a convergent validity of the
constructs. Reliability and convergent validity of the
measurement model was also confirmed by
computing standardized loadings for indicators and
bootstrap t-statistics for their significance. All
standardized loadings confirmed a high convergent
ICEIS 2010 - 12th International Conference on Enterprise Information Systems
178
validity of the measurement model.
To assess discriminant validity, the following
two procedures were used: 1) a comparison of item
cross loadings to construct correlations, and 2)
determining whether each latent variable shares
more variance with its own measurement variables
or with other constructs. All the item loadings met
the requirements of the first procedure in the
assessment of discriminant validity and all the
constructs showed evidence for acceptable validity.
A bootstrapping with 1,000 samples has been
conducted to test the hypothesized relationships
between the constructs. As shown in Figure 1, the
standardized path coefficients range from 0.198 to
0.674 while the R
2
is moderate, i.e. between 0.205
and 0.349 (Chin, 1998), for all endogenous
constructs. We can see that 30% of the variance in
media quality is explained by the influence of
business intelligence system maturity, while 20% of
the variance in content quality is explained by the
influence of business intelligence system maturity.
Moreover, the influence of media quality and
content quality explain about 35% of the variance in
the use of information in business processes.
As indicated by the path loadings, business
intelligence system maturity has significant direct
and different positive influences on content quality
(
ˆ
= 0.453, p< 0.001) and media quality (
ˆ
=
0.549, p < 0.001). The t-statistic for the difference of
the two impacts is 2.2 with p < 0.05 hence
confirming that the two hypothesized impacts are
indeed different. These results thus confirm our
theoretical expectation and provide support for H2a,
H2b, and H2c. To derive additional relevant
information, sub-dimensions of the second-order
construct (business intelligence system maturity)
were also examined. As evident from the path
loadings of data integration and analytics, each of
these two dimensions of business intelligence
system maturity is significant (p < 0.001) and of
moderate to high magnitude (
ˆ
= 0.488 and
ˆ
=
0.674), supporting H1 as conceptualization of the
dependent construct as a second-order structure.
Results also showed content quality (
ˆ
= 0.440,
p < 0.001) and media quality (
ˆ
= 0.198, p < 0.05)
have direct and different positive impact on the use
of information, with the content quality impact on
the use of information to be significantly larger than
the one originated by media quality. The t-statistic
for the difference of the two impacts is 2.14 with p <
0.05 thus confirming that the two hypothesized
impacts are different. These results hence support
H3a, H3b, and H3c.
5 CONCLUSIONS AND
LIMITATIONS
This study suggests business intelligence systems
maturity positively impacts information quality.
More precisely, results reveal that a higher level of
business intelligence system maturity has a
significant positive impact on both segments of
information quality, namely information content
quality and information media quality, as they were
conceptualized in our model.
Even if both information quality segments are
obviously addressed with the implementation of
business intelligence systems, one may expect that
projects dealing with implementation of business
intelligence systems are focused more on issues
related to the main information quality issues in
knowledge-intensive activities, i.e. content quality
issues. This means that the implementation of such
systems should affect more content quality than
media quality. The results show that the
implementation of business intelligence systems
indeed differently impacts the two dimensions of
information quality: business intelligence systems
maturity affects media quality more than content
quality. It appears as organizations implementing
business intelligence systems give less emphasis to
the quality of information content and rather call
attention to the information media quality. It seems
that organizations avoid more demanding data
management approaches that would lead to the
higher content quality of the information provided
by their business intelligence systems (Popovič et
al., 2009).
Literature (e.g. Khalil and Elkordy, 2005)
suggests that information of higher perceived quality
will be used more frequently than will those of lower
perceived quality. The results of this study conform
to the above literature and additionally provide two
important insights into the impact of the two
information quality segments on the use of
information as they were conceptualized in our
model. First, considering the impacts of content
quality and media quality on the use of information
as proposed in the model it shows that both
information quality segments have positive impact
on the use of information. From the results it also
appears that quality of information content is
substantially more important to the use of
information than it is the information media quality.
This is an interesting finding since it shows the
gap between the media quality provided by business
intelligence systems and the information quality
needs of knowledge workers when using informa-
ABSORPTION OF INFORMATION PROVIDED BY BUSINESS INTELLIGENCE SYSTEMS - The Effect of
Information Quality on the Use of Information in Business Processes
179
DI1
R
2
= 0
DATA
INTEGRATION
DI2
R
2
= 1
BUSINESS
INTELLIGENCE
SYSTEM
R
2
= 0
ANALYTICS
A
3
R
2
= 0.205
CONTENT
QUALITY
R
2
= 0.301
MEDIA
QUALITY
MQ3
MQ2
CQ1
CQ3
CQ4
A
4
A
5
A
6
MQ1
CQ5
CQ7
R
2
= 0.349
USE OF
INFORMATION
IN BUSINESS
UI2
UI4
UI6
UI5
UI7
UI8
UI9
UI3
0.907
0.887
0.912
0.908
0.642
0.662
0.838
0.774
0.779
0.785
0.721
0.836
0.646
0.773
0.739
0.761
0.649
0.677
0.727
0.737
0.794
0.697
0.488
(15.645)
CQ2
0.632
0.453
(8.121)
0.674
(22.489)
0.549
(11.984)
0.198
(2.393)**
0.440
(6.377)
Note: All values significant at p<.001 level (N=181), except ** (significant at p<.05 level (N=181)). Values in parenthesis are bootstrap t-
values.
Figure 1: Final baseline model of business intelligence system maturity impact on the use of information.
tion. While the implementation of business
intelligence systems contributes above all to faster
access to information, easier querying and analysis,
and a higher level of interactivity, it is important to
understand that the major problems of providing
quality information for knowledge-intensive
activities relate to information content quality, not
media quality. Thus it is necessary to define as
accurately as possible knowledge workers’ needs.
This is a difficult task due to the non-routine and
creative nature of knowledge workers’ work.
However, contemporary managerial concepts, such
as business performance management, enable better
definition of information needs in managerial
processes by connecting business strategies with
business process management.
A limitation of this research is the cross-sectional
nature of the data gathered. In fact, although the
conceptual and measurement model is well
supported by theoretical assumptions and previous
research findings, the ability to draw conclusions
through our causal model would be strengthened
with the availability of longitudinal data. For this
reason, in future research other designs such as
experimental and longitudinal designs should be
tested.
ACKNOWLEDGEMENTS
The authors acknowledge the financial support from
the state budget by the Slovenian Research Agency
(project No. J5-2105).
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