ABSORPTION OF INFORMATION PROVIDED BY BUSINESS

INTELLIGENCE SYSTEMS

The Effect of Information Quality on the Use of Information in Business Processes

Aleš Popovič

, Pedro Simões Coelho

and Jurij Jaklič

Department of Information Management, Faculty of Economics, University of Ljubljana

Kardeljeva pl. 17, 1000 Ljubljana, Slovenia

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

c A., Simões Coelho P. and Jakli

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

 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

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

(



= 0.453, p< 0.001) and media quality (



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 (



= 0.488 and



0.674), supporting H1 as conceptualization of the

dependent construct as a second-order structure.

Results also showed content quality (



= 0.440,

p < 0.001) and media quality (



= 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

= 0

DATA

INTEGRATION

DI2

= 1

BUSINESS

INTELLIGENCE

SYSTEM

= 0

ANALYTICS

= 0.205

CONTENT

QUALITY

= 0.301

MEDIA

QUALITY

MQ3

MQ2

CQ1

CQ3

CQ4

MQ1

CQ5

CQ7

= 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).

REFERENCES

Baldwin, W.L. & Scott, J.T. (1987). Market Structure and

chronological Change, Harwood Academic Publishers

GmbH, Chur, Switzerland.

Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009).

Developing Maturity Models for IT Management.

Business & Information Systems Engineering, 1, 213-

222.

Bouzeghoub, M. & Lenzerini, M. (2001). Introduction to:

data extraction, cleaning, and reconciliation a special

issue of Information Systems, An International

Journal. Information Systems, 26, 535-536.

Brown, A. (2005). IS Evaluation in Practice. The

Electronic Journal Information Systems Evaluation, 8,

169-178.

Chin, W. W. (1998). Issues and opinion on structure

equation modeling. MIS Quarterly, 22, vii-xvi.

Choo, C. W. (1996). The knowing organization: How

organizations use information to construct meaning,

ICEIS 2010 - 12th International Conference on Enterprise Information Systems

180

create knowledge and make decisions. International

Journal of Information Management, 16, 329-340.

Choo, C. W. (2002). Information management for the

intelligent organization: the art of scanning the

environment, Medford, NJ, Information Today, Inc.

Claraview. (2005). Business Intelligence Maturity Model

[Online]. Retreived from: http://www.claraview.com

[Accessed January 20, 2007].

Davenport, T. H. (1993). Process Innovation:

Reengineering Work Through Information

Technology, Boston, Harvard Business School Press.

Davenport, T. H. & Harris, J. G. (2007). Competing on

analytics: the new science of winning, Harvard

Business School Press.

Davenport, T. H. & Short, J. E. (2003). Information

technology and business process redesign. Operations

Management: Critical Perspectives on Business and

Management, 1, 1-27.

Deshpande, R. & Zaltman, G. (1982). Factors Affecting

the Use of Market Research Information: A Path

Analysis. Journal of Marketing Research, 19, 14-31.

Diamantopoulos, A., Souchon, A. L., Durden, G. R.,

Axinn, C. N. & Holzmuller, H. H. (2003). Towards an

understanding of cross-national similarities and

differences in export information utilization: A

perceptual mapping approach. International Marketing

Review, 20, 17-43.

Elbashir, M. Z., Collier, P. A. & Davern, M. J. (2008).

Measuring the effects of business intelligence systems:

The relationship between business process and

organizational performance. International Journal of

Accounting Information Systems, 9, 135-153.

Eppler, M. J. (2006). Managing Information Quality:

Increasing the Value of Information in Knowledge-

Intensive Products and Processes, Springer.

Frambach, R. T. (1993). An integrated model of

organizational adoption and diffusion of innovations.

European Journal of Marketing, 27, 22-41.

Jourdan, Z., Rainer, R. K. & Marshall, T. E. (2008).

Business Intelligence: An Analysis of the Literature.

Information Systems Management, 25, 121-131.

Khalil, O. E. M. & Elkordy, M. M. (2005). EIS

Information: Use and Quality Determinants.

Information Resources Management Journal, 18, 68-

93.

Lee, Y. W., Strong, D. M., Kahn, B. K. & Wang, R. Y.

(2002). AIMQ: a methodology for information quality

assessment. Information & Management, 40, 133-146.

Lenzerini, M. (2002). Data integration: a theoretical

perspective. In: Popa, L., Abiteboul, S. & Kolaitis, P.

G. (eds.) Proceedings of the 21st ACM SIGMOD-

SIGACT-SIGART symposium on Principles of

database systems. New York, NY, USA: ACM.

Low, G. S. & Mohr, J. J. 2001. Factors Affecting the Use

of Information in the Evaluation of Marketing

Communications Productivity. Journal of the

Academy of Marketing Science, 29, 70-88.

Negash, S. (2004). Business Intelligence. Communications

of the Association for Information Systems, 13, 177-

195.

Nolan, R. L. (1979). Managing the crises in data

processing. Harvard Business Review, 57, 115-126.

Peppard, J., Ward, J. & Daniel, E. (2007). Managing the

Realization of Business Benefits from IT Investments.

MIS Quarterly Executive, 6, 1-11.

Petrini, M. & Pozzebon, M. (2009). Managing

sustainability with the support of business intelligence:

Integrating socio-environmental indicators and

organisational context. The Journal of Strategic

Information Systems, In Press, Corrected Proof.

Popovič, A., Coelho, P. S. & Jaklič, J. (2009). The impact

of business intelligence system maturity on

information quality. Information Research, 14.

Ringle, C. M., Wende, S. & Will, A. (2007). SmartPLS 2.0

M3. University of Hamburg.

Slone, J. P. (2006). Information Quality Strategy: an

Empirical Investigation of the Relationship between

Information Quality Improvements and

Organizational Outcomes. Doctoral Dissertation,

Capella University.

TDWI. (2005). 2005 TDWI Poster: Business Intelligence

Maturity Model [Online]. Renton, WA: The Data

Warehousing Institute. Retreived from:

http://tdwi.org/Publications/display.aspx?ID=7288

[Accessed November 10, 2009].

Williams, S. & Williams, N. (2007). The Profit Impact of

Business Intelligence, Morgan Kaufmann.

ABSORPTION OF INFORMATION PROVIDED BY BUSINESS INTELLIGENCE SYSTEMS - The Effect of

Information Quality on the Use of Information in Business Processes

181