Hello everyone, I have an Assignment for you today. This assignment must be DONE by Wednesday, February 19, 2020,no later than 10 pm. By the way, I need this assignment to be PLAGIARISM FREE & a Spell Check when completed. Make sure you READ the instructions CAREFULLY. Now without further ado, the instructions to the assignments are below:

Instructions

Choose one of the scenarios below (A or B) to complete the assignment.

Scenario A

You are the business owner of a local small engine repair shop, and you have been thinking about implementing a knowledge management system for your customer service technicians. You are thinking about this because there are times when some of your technicians know how to fix certain engine problems and others do not. Providing a central knowledge repository could help share troubleshooting and repair knowledge among your technicians.

Scenario B

You are the business owner of a local cleaning service, and you have been thinking about implementing a knowledge management system for your cleaning technicians, especially for those who troubleshoot and solve cleaning problems, such as removing certain carpet and water stains, addressing mold, and selecting the proper tools and products to use for other types of cleaning issues. You are thinking about this because there are times when some of your cleaning technicians know how to properly clean carpets and others do not. Providing a central knowledge repository could help share cleaning knowledge among your cleaning technicians.

After you chose your scenario (A or B), compose a paper that addresses the elements listed below.

• Explain the role of knowledge management systems.
• Explain what is meant by expert systems.
• Explain what is meant by content management systems.
• Discuss how the business in the selected scenario could benefit from an expert system and a content management system, and provide two examples for each type of system.
• Discuss how the business in the selected scenario could benefit from business intelligence, and provide two examples of these benefits.
• Discuss how the business in the selected scenario can use social media to not only obtain information and knowledge but to share it as well, and provide two examples of how the business might use social media information systems.

Your paper must be at least two pages in length (not counting the title and reference pages), and you must also use at least two scholarly sources, one of which must come from the CSU Online Library. Any information from a source must be cited and referenced in APA format, and your paper must be formatted in accordance to APA guidelines.

By the way, I’ve attached a Study Guide & several articles (for you to pick from) from the CSU Library to help you with this assignment! You can choose one or more articles from below or take one article below and pick another article from a different source.

BBA 3551, Information Systems Management

Course Learning Outcomes for Unit V

Upon completion of this unit, students should be able to:

2. Explain the similarities and differences of personal knowledge management tools.
2.1 Examine how social media, an information gathering tool, has shaped knowledge and business

intelligence techniques.

5. Evaluate the approaches to developing organizational knowledge management strategies.
5.1 Examine how business intelligence systems are used to manage organizational knowledge.
5.2 Discuss how expert systems and content management systems can benefit a business.

Course/Unit
Learning Outcomes

Learning Activity

2.1
Unit Lesson
Chapter 8
Unit V Scholarly Activity

5.1
Unit Lesson
Chapter 9
Unit V Scholarly Activity

5.2
Unit Lesson
Chapter 9
Unit V Scholarly Activity

Reading Assignment

The following sections from Chapters 8 and 9, which are located in the textbook in uCertify, are not required
for this unit, but the sections still contain beneficial information. You are highly encouraged to read them.

Chapter 8: Social Media Information Systems, Q8-1 – Q8-7

Chapter 9: Business Intelligence Systems, Q9-1 – Q9-8

Unit Lesson

In Unit IV, we discussed the cloud and how the cloud works as well as the types of business processes and
the importance of enterprise resource planning (ERP) systems. In this unit, we will discuss social media
information systems (IS), some innovative applications for social media IS, and the unique applications of
social networking. You will also learn about some practical applications for business intelligence systems,
specifically reporting, the use of animation for reporting on a mobile device, and the advantages of storing
data in the cloud.

After reading the Unit V Reading Assignment, refer back to the Augmented Reality Exercise System (ARES)
scenario at the beginning of Chapter 9. We are introduced to wellness programs, an increasing trend as more
and more people, especially millennials, express the desire to live healthier lives (Hamstra, 2017). Following
this trend are wellness programs sponsored by employers who are looking to help their employees develop
and maintain a healthy lifestyle. Many of the wellness programs can be accessed through mobile applications
(apps), which is a convenient way to review one’s overall health and fitness. One of the most common ways
to monitor one’s health or fitness related metrics is remotely, and some insurance providers offer discounts or
prizes as incentives to their subscribers for participating in an exercise program and recording their metrics
online (“Anthem Blue Cross,” 2017; Satter, 2017).

UNIT V STUDY GUIDE

Social Media Information Systems
and Business Intelligence Systems

BBA 3551, Information Systems Management 2

UNIT x STUDY GUIDE

Title

Because this information is being served from the cloud, it can be accessible by doctors, patients, health
clubs, employers, insurance companies, and others. This is an excellent way to monitor a person’s progress
in a fitness or weight loss program. For example, a physician can monitor a patient recovering from heart
surgery by reviewing the recorded metrics.

Examples of Business Intelligence Systems and Social Media IS

Business intelligence systems take
structured and unstructured data to
produce a source of collective
knowledge that can be used in data
analytics (Kroenke & Boyle, 2017). An
example of this type of technology is in
the use of exercise equipment over the
cloud. In order to maintain an exercise
regimen during extreme weather
conditions, such as high temperatures
during the summer and extreme cold
temperatures during the winter months,
one can purchase a treadmill to help
maintain exercise goals (such as a
minimum of 30 minutes, 5 days per
week). Most exercise equipment has
the ability to download and store
exercise programs to help improve
endurance, increase heart rate,
increase metabolism, and provide other
health advantages. For example, some
treadmills can download virtual

environments that simulate a specific setting such as running a marathon or running along a nature path.
Other programs that exercise equipment can provide are training workouts with specific goals in mind such as
maximum fat burns to help burn calories or cardio exercises to increase blood circulation and to help improve
heart health (Figure 1).

Most computerized exercise equipment, including treadmills, can synchronize with an online app that uploads
activity metrics and translates them into meaningful data such as graphs and charts. This also enables the
treadmill to download user-personalized workouts, track workout results, and track the user’s progress
against other users.

The user can then monitor his or her progress via a mobile app on a compatible phone (e.g., iPhone, Android)
and on the web from a laptop or tablet. In addition to the fitness training programs, there is also a social
community where users can provide motivation to one another.

Social networking allows people to connect with one another and to share content. This network of users is
supported by a social media IS. In the fitness example used above, users can view their fitness information in
the form of digital reports on a mobile device such as a smartphone or tablet. This data is stored on the cloud
so it can be retrieved from just about any computer device. These reports are generated with reporting
applications that use business intelligence. Business intelligence is defined as the manipulation or translation
of data into meaningful information. The purpose of business intelligence reports is to provide useful
information from which observations can be made. This data is obtained when basic reporting operations are
performed such as sorting, filtering, grouping, calculating, and formatting (Kroenke & Boyle, 2017).

Figure 1: Many treadmills use different programs to help users collect data
and maintain goals.
(Sport-Tiedje GmbH, 2017)

BBA 3551, Information Systems Management 3

UNIT x STUDY GUIDE
Title

Microsoft (MS) Excel is one application that can perform all of the basic reporting operations to create reports.
If you are familiar with MS Excel, then you know that you can use this software to create statements such as
monthly sales reports. This is one example of how you can use
reporting applications to develop business intelligence. Therefore,
by evaluating several monthly sales reports, you can gauge how
well the business is doing or not doing. You can also predict future
trends for products or services. If sales for a certain appliance is
dropping, this tells you to reduce the number of reorders, look for
a new or upgraded version of that appliance, or perhaps
discontinue the product altogether.

Let’s look at another example such as a fitness tracker (Figure 2).
There are several brands of fitness trackers, but they all have one
thing in common—to help you keep track of fitness metrics. As
you go about your day, the fitness tracker records physical activity
and stores that data on the device. In order to access that data
from another device, such as a tablet or computer, you will need
to synchronize the device to the fitness application. You
synchronize the data from your fitness tracker to the fitness
application via the cloud. Depending on the brand, the device
could be recording the number of steps you take, your heart rate,
calories burned, and even your sleep patterns. Because this
information is stored on the cloud, you can view the data from just about any computing device such as a
mobile phone, tablet, or computer. You can then review the statistics in a variety of visual forms such as
graphs, charts and even animated diagrams. These reports help you to analyze the data, detect patterns, and
identify potential health issues. Users can also share these statistics on the web in social fitness networks or
chat with health experts or fitness trainers.

Privacy and Security

The availability of cheap cloud processing makes processing consumer data easier and less expensive every
day. The result is more and more data, and that data is processed by more and more sophisticated
algorithms. George Orwell’s book 1984, which was science fiction when published, is becoming closer to
reality. This dystopian story takes place in 1984, where society is ruled by a totalitarian government that
manipulated media to control its population (Fitzpatrick, 2013). North Korea is a good example of a totalitarian
government because the country is isolated from the rest of the world, and its people are oppressed by the
regime of Kim Jong Un through the use of various media such as newspapers, posters, radio, and television.
The Internet, taken for granted by many citizens in the United States, is virtually unheard of in North Korea.
Even for those few who may have access to the Internet, only a few (approved) websites are provided to its
people by the North Korean propaganda bureau. North Korean citizens who are accused of violating its
censorship laws are met with harsh punishment such as jail time, reprogramming, and even death (Kim,
2010).

In North Korea, the government uses mass surveillance to censor its people, severely restricting them of
basic freedoms. There are four basic freedoms in the first amendment of the U.S. Constitution:

Congress shall make no law respecting an establishment of religion, or prohibiting the free exercise
thereof; or abridging the freedom of speech, or of the press; or the right of the people peaceably to
assemble, and to petition the government for a redress of grievance. (National Archives, n.d., para. 6)

In the United States, organizations routinely use surveillance to monitor its employees, and the U.S.
government also uses surveillance to monitor its citizens. So, what is the difference between surveillance in
the United States when compared to North Korea? The difference is that U.S. citizens, unlike the citizens of
North Korea, are protected by the Privacy Protection Act, the Electronic Communications Privacy Act (ECPA),
and the Communications Assistance for Law Enforcement Act, coupled with the fourth amendment of the U.S.
Constitution (Hannon, 2017). So, what does all of this mean? This means that government officials can
monitor electronic communications for potential criminal activity while protecting the privacy of U.S. citizens.

What does this mean for the data that is stored in the cloud? If we use fitness trackers and store information
in the cloud, is our information safe from hackers? If we participate in social networks, what are the risks?

Figure 2: Fitness trackers use technology to
record data.
(BMoreliff, 2016)

BBA 3551, Information Systems Management 4

UNIT x STUDY GUIDE
Title

These are questions we should ask ourselves in regard to expectations of privacy.

In this unit, we examined how social media can be used as an information-gathering tool to increase
knowledge and business intelligence through the use of social media IS. We also examined the expectations
of privacy when using social media. Even though electronic media is monitored by the U.S. government, it is
only to detect and identify potential criminal activity.

References

Anthem Blue Cross unveils engage platform. (2017). Health & Beauty Close – Up, Retrieved from

https://search-proquest-
com.libraryresources.columbiasouthern.edu/docview/1953795804?accountid=33337

BMorellif. (2016). Withings Pulse O2 fitness tracker [Image]. Retrieved from

https://commons.wikimedia.org/wiki/File:Withings_Pulse_O2_fitness_tracker

Fitzpatrick, S. (2013, August). Orwell’s 1984: Are we there yet? Crisis Magazine. Retrieved from

http://www.crisismagazine.com/2013/orwells-1984-are-we-there-yet

Hamstra, M. (2017). Center of the storm. Supermarket News. Retrieved from https://search-proquest-

com.libraryresources.columbiasouthern.edu/docview/1951884195?accountid=33337

Hannon, M. J. (2017). The importance of metadata in digital evidence for legal practitioners. Computer and

Internet Lawyer, 34(10), 1–19. Retrieved from https://search-proquest-
com.libraryresources.columbiasouthern.edu/docview/1941694965?accountid=33337

Kim, S. Y. (2010). Illusive utopia: Theater, film, and everyday performance in North Korea. Ann Arbor, MI:

University of Michigan Press.

Kroenke, D. M., & Boyle, R. J. (2017). Using MIS (10th ed.). New York, NY: Pearson.

National Archives. (n.d.). The Bill of Rights: A transcription. Retrieved from

https://www.archives.gov/founding-docs/constitution-transcript

Satter, M. Y. (2017). After rosy Q3, anthem to launch digital health platform. Benefits Selling. Retrieved from

https://search-proquest-
com.libraryresources.columbiasouthern.edu/docview/1955393869?accountid=33337

Sport-Tiedje GmbH. (2017). Cardiostrong treadmill TX50 [Image]. Retrieved from

https://commons.wikimedia.org/wiki/File:Cardiostrong_treadmill_TX50

Suggested Reading

The following sections from Chapters 8 and 9, which are located in the textbook in uCertify, are not required
for this unit, but the sections still contain beneficial information. You are highly encouraged to read them.

Chapter 8: Social Media Information Systems, Q8-8

Chapter 9: Business Intelligence Systems, Q9-9

Learning Activities (Nongraded)

Nongraded Learning Activities are provided to aid students in their course of study. You do not have to submit
them. If you have questions, contact your instructor for further guidance and information.

BBA 3551, Information Systems Management 5

UNIT x STUDY GUIDE
Title

To test your knowledge of the material covered in this unit, complete the activities listed below.

 Chapter 8 Active Review

 Chapter 8 Using Your Knowledge

 Chapter 8 Collaboration Exercise

 Chapter 8 Review Questions

 Chapter 8 Cards

 Chapter 9 Active Review

 Chapter 9 Using Your Knowledge

 Chapter 9 Collaboration Exercise

 Chapter 9 Review Questions

 Chapter 9 Cards

The activities are located within the chapter readings in uCertify. The Chapter 8 and Chapter 9 Active Review
sections, Using Your Knowledge sections, Collaboration Exercises, and Review Questions are located at the
end of each chapter. The cards can be accessed by clicking on the Cards icon within uCertify, which is
located to the right of the chapter title, and the icon in uCertify resembles the image shown below.

SystemsResearchandBehavioralScience
Syst. Res.23,177 1̂90 (2006)
PublishedonlineinWiley InterScience (www.interscience.wiley.com)
DOI:10.1002/sres.752

& ResearchPaper

Knowledge Management in OSS — an
Enterprise Information System for the
Telecommunications Industry

Jiayin Qi1*, Li Da Xu2, Huaying Shu1 and Huaizu Li3

1School of Economics and Management, Beijing University of Posts and Telecommunications, Beijing, China
2Department of Information Technology and Decision Sciences, Old Dominion University, Norfolk,
Virginia, USA
3School of Management, Xian Jiaotong University, Xian, China

Knowledge management in Enterprise Information Systems (EIS) has become one of the
hottest research topics in the last few years. Operations Support Systems (OSS) is one kind
of EIS, which is becoming increasingly popular in the telecommunications industry.
However, the academic research on knowledge management in OSS is sparse. In this
paper, a knowledge management system for OSS is proposed in the framework of systems
theory. Knowledge, knowledge management, organization and information technology
are the four main interactive elements in the knowledge management system. The paper
proposes that each subsystem of the OSS is to be equipped with knowledge management
capacity, and the knowledge management of the OSS is to be realized through its
subsystems.

Copyright # 2006 John Wiley & Sons, Ltd.

Keywords enterprise information systems; ERP; operations support systems; knowledge
management; management information systems

INTRODUCTIO

N

In recent years, the topic of knowledge economy
has attracted much research interest. As a result,
a substantial number of researches have been
conducted on knowledge management from
both theoretical and empirical perspectives.
Studies show that effective knowledge manage-
ment has a positive effect on enterprise perfor-

mance and competitive advantage (Ahn and
Chang, 2004; Chuang, 2004; Joshi and Sharma,
2004; Tzokas and Saren, 2004; Badii and Sharif,
2003; Cavusgil et al., 2003; Choi and Lee, 2002).
For this reason, more and more enterprises have
emphasized the importance of knowledge man-
agement. Most of them have acquired enterprise
information systems (EIS) such as ERP as an
integrated platform with intended applications
in knowledge management.

Operations Support Systems (OSS) is a main-
stream technology which supports large-scale
network operation, maintenance and management.

Copyright # 2006 John Wiley & Sons, Ltd.

* Correspondence to: Jiayin Qi, School of Economics and Management,
Beijing University of Posts and Telecommunications, Beijing 100876,
China. E-mail: ssqjy@263.net

It was put forward by TeleManagement Forum
(TMF), an international organization that has
been contributing to the information and com-
munications services industry for over 15 years.
So far OSS has been increasingly adopted by
telecom industry with NGOSS (New Generation
Operations and Software Systems) as its next
generation product. If ERP systems are the EI

S

mainly help manufacturing industry achieve
competitive edge in the global market, OSS plays
a similar role in the telecom industry.

Telecommunications industry is a very specific
high-tech service industry. The main feature of
the telecommunications industry is its tight
integration of business process and IT applica-
tions; it is very important to use IT to promote its
competitiveness. OSS is generally considered as
a basic EIS which can also support knowledge
management. OSS market and applications are
growing. Taking the Asia Pacific market as an
example, it generated $8.8 billion of revenues in
2002. Revenues show an increasing trend and
the market for OSS is expected to grow at a
steady pace. The compound annual growth rate
(CAGR) of the revenues for the period 2001–2007
is forecasted to be 6.27 per cent. Industry reven-
ues are forecasted to rise to $11.87 billion by the
year 2007.

Although OSS has been acquired by many
telecom companies, the shortage of scholastic
research on OSS is obvious (Li et al., 2003a).
IEEE Xplore provides full text access to IEEE
transactions, journals, magazines and conference
proceedings since 1998, plus select contents back
to 1950, and all the current IEEE standards. Most
of the academic publications in telecommuni-
cations are included in IEEE Xplore. Using
operations support systems as key word, our
search matched 189 of 1043417 documents. In
these 189 documents, there is only one paper
related to the word knowledge. Searching other
academic journals, such as Decision Support
systems, Expert Systems with Application, Knowl-
edge-Based Systems, Computers in Industry, Expert
Systems, Data & Knowledge Engineering, Advanced
Engineering Informatics, Logistic Information Man-
agement, Information & Management, Telecommu-
nications Policy from 2003 to 2005, no papers on
OSS are found. There are some whitepapers

about OSS at www.tmforum.org, but they are not
typical research papers.

Knowledge may not show its significant value
until it is embedded in software products or
business processes. Only then can its value be
fully utilized. OSS is the basic software platform
to support value chain management for the
telecom industry. OSS should be the enabling
tools to fulfil effective knowledge management.
How could this objective be achieved? The
purpose of this paper is to explore a possible
answer to the question.

The paper is organized as follows. ‘Knowledge
Management in Systems Perspectives’ section
presents the implication of knowledge manage-
ment in systems perspectives. The relationship
among data, information and knowledge, as well
as the relationship between knowledge manage-
ment and EIS is discussed. In ‘Overview of OSS
and Knowledge Management in OSS’ sections,
an overview of OSS and the knowledge manage-
ment in OSS is discussed. ‘Discussion and
Conclusion’ section provides a summary of the
paper and future research.

KNOWLEDGE MANAGEMENT IN
SYSTEMS PERSPECTIVES

A system is made up of a set of interacting
elements sharing a particular purpose within a
boundary. The interaction among elements forms
the structure of a system. Depending on its
boundary, a system can be an economic entity,
an inventory system, or a business organization.
Knowledge management is an element of the
organizational management system (Warfield,
1989). From the point of view of the concept of
whole, a knowledge management system pro-
motes the effective use of knowledge assets of an
enterprise as a whole over time, and is an impetus
to the performance of the enterprise.

Data, Information and Knowledge

Prior to discussing knowledge management, the
terms such as data, information and knowledge
must be defined. The following is a summary of

RESEARCH PAPER Syst. Res.

Copyright � 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177 1̂90 (2006)

178 Jiayin Qi et al.

the distinction between data, information and
knowledge:

Data are known facts that can be recorded and
that have implicit meaning (Elmasri and
Navathe, 2004). Information is data placed in a
meaningful and useful context after that has been
processed (O’Brien, 2005). Information is user-
aimed, providing values and existing in the eyes
of the beholder (Spiegler, 2003). Knowledge is
information synthesized and contextualized to
provide further value for human activities
(Pearlson and Saunders, 2004).

The relationship among data, information and
knowledge can be depicted as shown in Figure 1.
Data is the abstract description of objects and is
the raw material that is used to generate useful
information and knowledge. Information is a
flow of processed data after being processed.
Knowledge involves the capacity of gathering
and using information. Knowledge becomes
information when it is articulated or commu-
nicated to others in the form of text, computer
outputs, speech or written words (Alavi and
Leindner, 2001; Spiegler, 2003).

Data warehouse is a large-scale storage facility
for data. Knowledge warehousing is an exten-
sion of data warehousing to facilitate the captur-
ing and coding of knowledge and to enhance the
retrieval and sharing of knowledge across the
organization (Nemati et al., 2002). Online Analy-
tical Processing (OLAP) is a software application
used to explore the data in ways that are decision
oriented (Shi et al., 2005). Data mining (DM) tools

allow for the creation of well-defined transfer-
able information (Li and Xu, 2001; Li et al.,
2003b). Knowledge discovery (KD) process
agglomerates information found by such techni-
ques as DM in generating domain knowledge
(Bendoly, 2003).

Implication of Knowledge Management
in Systems Perspective

The implication of knowledge management has
been studied by many authors (Warfield, 1989).
Table 1 summarized the selected findings.

In this paper, knowledge management is
studied in terms of systems theory and the
perspectives listed in Table 1 will be synthe-
sized. It is emphasized in this paper that
knowledge management can be used to effec-
tively manage corporate knowledge assets
especially those knowledge in business pro-
cesses. Therefore, the objective of knowledge
management is considered to promote an
enterprise’s core competency. Such an objective
can be achieved with a systematic process of
creating, maintaining, employing, sharing and
renewing knowledge.

Knowledge Management Framework
in Systems Point of View

Viewing knowledge management as a man-
made system, the boundary of the system and

Data

Information

Knowledge

Data Processing: Organizing, storing,

calculating, Retrieving, Reporting

Information Processing: Reforming,

Quantification, Qualification, Clustering,

learning, Disseminating To be communicated

to others in the form

of text, computer

output, speech and

writing words etc.

Figure 1. Data, information and knowledge

Syst. Res. RESEARCH PAPER

Copyright � 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177 1̂90 (2006)

Knowledge Management in OSS 179

the elements of the system needs to be deter-
mined. Obviously, the boundary of the knowl-
edge management system is the corporate
business environment, while the elements in
the system include knowledge architecture,
knowledge management process architecture,
organization architecture and IT architecture
(Kim et al., 2003). The other questions of interest
include the interaction among these elements,
the structure of the system, and the function of
the system.

Main Factors Influence Knowledge Management
Knowledge management system is a system to
effectively manage knowledge within an enter-
prise. Two main factors are considered influencing
the needs of practicing knowledge management.
The first factor is competition. If there is a tough
competition in a certain industry sector, managing
knowledge is generally in high demand. The other
factor is the volume of data. If there is a huge
volume of data that exist within an enterprise, the
data resource is available which can help convert
data into information as well as knowledge.

Elements of Knowledge Management System
Knowledge architecture, knowledge manage-
ment process architecture, organization architec-
ture and IT architecture are the four elements of
knowledge management system.

The so-called knowledge architecture is the
result of classifying organizational knowledge by
one or more dimensions. Fernandez et al. distin-
guished knowledge into human knowledge,
organizational knowledge, technological knowl-
edge and relational knowledge (Fernandez et al.,

2000). Human knowledge refers to the knowl-
edge acquired by a person that can increase
productivity and the contribution to the organi-
zation. It also includes other individual qualities
such as experience, judgement and intelligence.
A firm’s organizational knowledge includes
its norms and business guidelines, corporate cul-
ture, organizational procedures, as well as strate-
gic alliance. Technological knowledge includes
knowledge related to the access, use and innova-
tion of production techniques and technology
(Xu et al., 2005a,b). The relational knowledge
consists of the potential derived from the
intangible resources related to marketplace,
such as brands, customer loyalty, long-term
customer relationship, distribution channels, etc
(Kanjanasanpetch and Igel, 2003).

The knowledge management process architec-
ture defines a variety of processes involved in the
life cycle of knowledge, from its creation to
termination. Knowledge creation process, know-
ledge maintenance process, knowledge distribu-
tion process and knowledge review and revision
process are the four steps in the entire knowledge
management process (Bhatt et al., 2005). Creativ-
ity refers to the ability to originate novel
and useful ideas and solution (Marakas, 2003).
An organization creates knowledge through
its employees who are equipped with knowledge
and generate new ideas by breaking down
business thinking that is no longer viable
(Argyris and Schon, 1996; Lynn et al., 1996).
Knowledge maintenance refers to making use of
existing ‘discovered’ knowledge (Bhatt et al.,
2005). Knowledge distribution means the sharing
of knowledge across the organization. Knowledge

Table 1. Existing research on the implication of knowledge management

Author Perspective Implication

Siemieniuch and Sinclair (2004) Process Systematic process of applying expertise
Kwan and Balasubramanian (2003)
Wang and Ariguzo (2004)
Mesaric (2004)
Fowler and Pryke (2003) Capability Building core competencies through know-how
Badii and Sharif (2003)
Tzokas and Saren (2004)
Nemati et al. (2002) Relationship Converting information to knowledge

RESEARCH PAPER Syst. Res.
Copyright � 2006 John Wiley & Sons, Ltd. Syst. Res. 23,177 1̂90 (2006)

180 Jiayin Qi et al.

review and revision is the modification and
version management of knowledge.

The organization architecture designs organi-
zational structure. Organizational structure defi-
nes the role of each knowledge management team
that is responsible for performing or supporting
knowledge management process.

The IT architecture is a technical infrastructure
for knowledge management. It defines compo-
nents of knowledge management system and
their relationships.

Interactions Among the Elements in
Knowledge Management System
The four elements in knowledge management
system are interrelated to each other. Knowledge
management system can not attain its purpose
without any one of the elements.

The knowledge architecture is the base of the
knowledge management process. The knowl-
edge management process consists of the main
activities in knowledge management. The orga-
nization architecture is responsible for perform-
ing or supporting knowledge management
process. IT architecture is a facilitator for enhan-
cing dynamic capabilities through knowledge
management (Sher and Lee, 2004).

Structure of Knowledge Management System
According to the interaction among the elements
in knowledge management system, the structure
of knowledge management system is shown in
Figure 2.

Both theoretical and empirical researches
have shown that knowledge management can
play a key role in creating sustainable competi-
tive advantages for corporations. In which, the
organization architecture is the guarantee of
knowledge architecture, knowledge manage-
ment process architecture and IT architecture.
Right organization architecture has positive
effects on the other three elements. On the other
hand, knowledge architecture, knowledge man-
agement process, and IT architecture all have
impacts on organization architecture. Organiza-
tion architecture has to be adapted to meet the
needs from the three elements too. Knowledge
architecture is the base of knowledge manage-
ment process. The fundamental function of the
knowledge management system is to improve
the business process and to achieve superior
business performance through effective knowl-
edge management process.

Enterprise Information Systems and
Knowledge Management

Enterprise information system (EIS) is an inte-
grated information system seeking to integrate
every single business process and function in
the enterprise to present a holistic view of the
business with a single IT architecture. It is a
powerful and integrated enterprise-level IT archi-
tecture that is also designed to facilitate knowl-
edge management within an enterprise. The

Knowledge Management system

Organization

Architecture

Knowledge
Architecture
Knowledge

Management

Process

Corporation’s

business

operation

Corporation

with superior

performance

Input
Output

IT Architecture

Figure 2. The framework of knowledge management system

Syst. Res. RESEARCH PAPER
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Knowledge Management in OSS 181

characteristics of EIS include (Ross and Vital,
2000):

An EIS is composed of a suite of different
modules. Typical modules include accounting,
human resource, manufacturing, logistics, custo-
mer relationship management, etc. An enterprise
can get its EIS solution through integrating a
number of modules.

Each module is business process-specific. The
use of EIS is associated with business process re-
engineering to optimize business processes.

An EIS creates an enterprise-wide transaction
structure by integrating modules, data storing/
retrieving processes, and management and ana-
lysis functionality.

An EIS is not just a software system; it repre-
sents a new kind of managerial thinking. A
successful implementation of ERP is not only
related to software selection, but also enterprise
strategy, enterprise culture, business process
reengineering (BPR), top management support,
training and others.

Considering the relationship between knowl-
edge architecture, knowledge process architec-
ture, organization architecture, IT architecture,
and enterprise operations, an EIS supports
knowledge management that encompasses all
types of knowledge in business operations. The
support provided by an EIS to an enterprise’
knowledge management is embodied in each
module for specific knowledge management.
Each module associates with a specific type of
business process, which corresponds to a specific
knowledge management. The knowledge man-
agement of the entire enterprise is realized
through the integration of individual knowledge
management module.

OVERVIEW OF OSS

Evolution of OSS

In the 1980s, the basic standard of OSS was
determined. The main usage is to manage net-
works. In the beginning of 1990s, OSS standard
has placed emphasis on both network systems
and network management. A substantial amount
of work has been completed by the International

Telecommunications Union (ITU) and the Inter-
national Organization for Standardization (ISO).
The representative standards of OSS are Tele-
communications Management Network (TMN)
and Simple Network Management Protocol
(SNMP). In recent years, the next generation
network (NGN) is coming ever closer. NGN is a
high speed multi-service packet data network
capable of supporting the traditional functions
of voice networks, data networks/internets
and even mobility by providing quality-assured
transmission, switching and services over IP and
ATM cores. The competitiveness is in managing
service, not managing network resources. Thus
the OSS has shifted from network-oriented to
service-oriented. During the process of develop-
ing OSS standards, support has been provided
by service providers (SP), network operation
providers, equipment manufacturing enter-
prises, and software suppliers.

Definition of OSS

OSS stands for Operations Support Systems. OSS
is a common term for the collection of all the
support systems required to run a telecom
operator’s business. OSS is consisted of four
subsystems: Operation Support System (OSS),
Business Support System (BSS), Resource Sup-
port System (RSS), and System Support System
(SSS). The functions of OSS consist of activation,
inventory management, fault management, and
workforce management, etc. BSS includes custo-
mer care, multi-service provisioning, service
assurance, and billing, etc. RSS handles network
resource management, operation information
management, customer basic information man-
agement and customer service information, etc.
SSS deals with log file, system parameters, etc.
Figure 3 provides a framework of OSS in which
OSS and BSS are the main functions.

The main functions of OSS include,

* Customer care: provide an interface to the
customers for all issues related to customer
order, sales, billing, and problem handling.

* Multi-service provision: activate instances of
service for particular customers.

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182 Jiayin Qi et al.

* Service assurance: monitor and uphold the
quality of the delivered services.

* Billing: charge for the service.
* Planning and administration: plan, design and

administer the services and infrastructures.

* EAI (Enterprise Application Integration):
automate the exchange of data between inter-
nal applications.

* Activation: execute a service in an optimal and
well-defined order

Customer/Market

OSS
BSS

Customer Care

Multiservice
Provision

Service Assurance

Billing

& Planning
Administration

Activation

Inventory
Management

Fault Management

Workforce

Management

RSS

Network resource

management

informationOperation

management

basic

Customer

information management

serviceCustomer

information management

SSS

User Management

System monitoring system parameters Versioning

Backuping Log file

EAI

Figure 3. OSS structure

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Knowledge Management in OSS 183

* Inventory management: keep track of the
equipment such as where it is, how it is confi-
gured, and its status.

* Fault management: handle alarms.
* Workforce management: manage and sche-

dule teams of technicians, installers and engi-
neers.

In this paper, a network operator is defined as
a telecommunication service provider with a
network infrastructure and provides multiple
services. It could be a network, a fixed-line access
network of any kind, or a mobile 2/2.5/3G
mobile network. This type of network operator is
named as telecom operator throughout the
paper. Of course, the research is related to Ser-
vice Provider (SP) and Content Provider (CP)
with no infrastructure of their own although
their tasks are simpler since they only manage
services and IT infrastructure.

TOM and OSS

OSS is intended to cover TOM (Telecom Opera-
tions Map) provided by the organization
TMForum. TOM model focuses on the opera-
tional processes within the telecommunication
industry. It was designed as a blueprint for pro-
cess direction and a starting point for developing
and integrating OSS. The relationship between
TOM and OSS is shown in Figure 4.

FAB (fulfilment, assurance and billing) is the
core area of operations for telecom operators. FAB
defines the process for fulfilling an order, assuring
the defined level of performance and facilitating
billing for the services provided. FAB is carried
out through the following vertical processes:

Customer interface management process: It is
responsible for the dialogue with customer.

Customer care process: It deals with the custo-
mer needs, ways to identify the needs and how to
achieve it.

Service/product development and operation
process: It handles how the service is offered and
how to achieve it.

Network and systems management process: It
handles resources required for achieving the
service offered to the customer.

Features of OSS

OSS is a kind of EIS, which is applied to tele-
communications industry. Corresponding to the
characteristics of EIS, OSS’ characteristics can be
described as,

The key idea of OSS is the modularization of
telecommunications operation management. Tel-
ecom operators face a lot of uncertainty. The
appearance of new services is very quick. The
modular design of OSS is considered a necessity
(Wade, 2000).

OSS realizes the end-to-end customer business
operation processes. TOM is an important refer-
ence function model for OSS planning. The TOM
model contains a detailed description of the most
important processes involved in running a telecom
operator’s operation. Service fulfilment, service
assurance, and billing are the three basic customer
business operation processes. OSS implementation
will inevitably consider business process reengi-
neering (Wade, 2000; Huang et al., 2003).

OSS is a highly integrated software architec-
ture. Integrating multi-sections’ businesses in a
single software platform efficiently for improv-
ing customer service is one of the aims of OSS.
This task requires a high level of integration
among each subsystem.

OSS is not just a software system, but also
represents managerial thinking. Using TOM as
an important reference model, OSS encourages
telecom operators pay more attention to the
customers rather than just do billing as in the
past (Walsh, 1998).

Generally speaking, OSS can work not only for
telecom operators, but also for those other enter-
prises with characteristics resemble to that of
telecom operators with special network resources,
special service flow, and value chain based on
these network resources and service flows; for
example, large power plants (Feng et al., 2001),
traffic management (Takahashi, 1998), and others
(Miyamoto et al., 1997; Sherif and Ho, 2000).

Objectives of OSS

As for the motivation for OSS’ implementation,
there are six main reasons (Schroter, 1998):

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184 Jiayin Qi et al.

(1) rapid development and deployment of new
services (Everitt and Virgin, 1996); (2) cost reduc-
tion through operation automation; (3) business
process integration (Xia and Rao, 1997);
(4) uniform software platform (Furley, 1996;
Appel and Polosky, 1988); (5) customer service

level improvement (Giannelli et al., 1990);
(6) efficient network resource and customer
resource management (Appel and Polosky,
1988; Kittel et al., 2000).

The objective of OSS is to achieve superior
performance, which is embodied in higher

TOM

Customer

Customer Interface Management process

Customer Care Process

Service/Product Development and Operations Process

Physical Network and Information

Technology

s
el

a
S

r
ed

r

O

g
ni

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na

H

me

lb
or

P

g
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l d
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e

mo
ts

u
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so
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tn
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me
ga

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ci
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n
oi

tc
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Network and Systems Management Process

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P
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Figure 4. TOM and OSS

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Knowledge Management in OSS 185

average revenue per user (ARPU), better ser-
vices, higher customer satisfaction, and improv-
ed asset utilization, etc.

KNOWLEDGE MANAGEMENT IN OSS

Business Environment in
Telecommunications Industry

The telecommunications environment can be
characterized by its inherent distributive, contin-
uous expansion in the size of network, and
the particular importance of fault-tolerance
requirement. These characteristics are reflected
in the design of software systems. Software sys-
tems in telecommunications have to cope with the
universe of telecommunications protocols, numer-
ous hardware platforms, and network architec-
tures (Cselényi et al., 1998). The characteristics of
telecommunications software systems include
high software cost, concurrency, distributivity,
reliability, diversity and complexity (Patel, 2002).

Except the above-mentioned industry charac-
teristics, telecom operators are facing more and
more challenges nowadays. Factors such as
globalization and technology innovation repre-
sent radical challenges to telecom operators. They
must be more and more competitive to survive.

Today’s telecommunication market introduces
more competitions; meanwhile offers more choi-
ces for customers, lower price and the pressure to
improve service quality for operators. As the
previous monopoly situation is no longer exist,
new entrants come into the market. In emerging
economy, state-owned operators are fully or
partially privatized in order to survive better
(Stienstra et al., 2004).

Globalization promotes the domestic competi-
tion. Global telecommunication market gives
opportunities to some operators because of the
economies of scale in telecommunication net-
work, such as BT and Vodafone. It also brings
radical domestic competition since more new
entrants enter to the market.

Internet technology causes an extraordinary
growth of the Internet and IP services and
applications. Customers are increasingly free
to choose different service components from

different vendors and assemble their own solu-
tion (Li and Whalley 2004).

Industry deregulation, globalization, and IP
make the telecommunication industry full of
intensified competition. The telecommunication
market involves a shift from a stable market to an
increasingly user-driven market place. The suc-
cess of a telecom operator will entirely depend
on the operator’s ability to create services and
applications that are embraced by the users.

Same as the success brought by knowledge
management to the manufacturing sector, know-
ledge management is increasingly helping the
telecomm sector to keep sustainable competi-
tiveness and competency.

Knowledge Management in BSS

BSS focuses on developing the core business by
defining marketing and offering strategies, new
products implementation and managing existing
products. Customer interface management pro-
cess and customer care process are the two major
aspects involved in BSS. Dialogue carrying, ser-
vice ordering, service activation, trouble admin-
istration, and billing account review make up all
the activities in BSS.

Staff knowledge, organizational knowledge,
and relational knowledge form the know-
ledge architecture of BSS. There is a plenty of
staff knowledge involved such as sales staff’s
experience. There are also rich organizational
knowledge existing in the customer interface
management process and customer care process.
Deeper customer knowledge can give rise carriers
an edge in developing pricing models (Limbach,
2004). In addition, relational knowledge exists in
BSS such as reputation, brands, customer loyalty
and distribution channel knowledge. Those are
the important factors influencing CRM.

Successful sales experiences can be acquired
and shared among the employees in the sales
and marketing department. Replication, imitation,
elicitation and innovation will be the main acti-
vities for knowledge creation. Some knowledge on
routine problems, success experience, standard
business process, can be considered as existing
‘discovered’ knowledge to be maintained and
reused. Sharing of existing knowledge distributes

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186 Jiayin Qi et al.

knowledge at the organizational level. Due to the
fact that the telecommunications industry changes
rapidly, new services, new regulation policies,
new market environments, all require continual
revision of existing knowledge.

BSS is at the front-end in serving customers
for telecom operators. Due to the competition
in telecommunications industry, organizational
structures have increasingly been adjusted to
customer-oriented. All of these request organi-
zational knowledge process.

Telephone call centre, interactive voice
response (IVR), computer telephone integration
(CTI), predictive dialers, wireless agents, e-mail,
web self service, text chat and web collaboration
make up the technology to complete customer
communication. IP based call centre, operational
CRM and interactive CRM, billing system, and
performance management are sets of software to
support the business operation process. The
integration of these technologies and sets of
software forms the IT architecture of BSS.

Knowledge Management in OSS

OSS focuses on planning, developing and
delivering services and products in operation
domain. Service/product development and
operation process are the operational processes.
OSS deals with service generation and network
resource planning.

Human knowledge, organizational knowledge,
technological knowledge and relational knowl-
edge are all involved in OSS. Those previous
service cases, as well as proven cross-selling rules
are human knowledge. How to organize service/
product development, operation process, and
network, is considered as organizational knowl-
edge. In addition, culture, regulations, and
partnerships are considered as organizational
knowledge as well. There are many innovative
techniques and skills involved with these which
are considered as technological knowledge. Inter-
estingly, the greater the scope of services offered,
and the greater the range of quality and price
options, the more efficient (and cost efficient) the
use of the network resources. Service innovation
is a key factor for revenue growth of a telecom
company. For designing a successful marketing

strategy, some intangible resources will inevitably
be used. And a successful strategy will also create
new intangible resources. These intangible
resources are relational knowledge.

Knowledge can be created from studying
previous successful service offering. The enligh-
tening effect can create new types of human,
organizational, technological and relational
knowledge. All of the knowledge can be acquired
and reused. Sharing such knowledge can further
diffuse knowledge across the enterprise.

OSS is operated at the back end which
provides decision support for BSS. Knowledge
sharing and creating are essential to such deci-
sion support function. For reducing ‘noise’ and
eliminating barriers across sectors, smooth com-
munication is required. Organizational structure,
based on traditional command and control, must
shift to an open and collaborative structure.

The analytical CRM is an outstanding compo-
nent to support service/product development
process. Decision support system (DSS) and
expert system (ES) are both common tools.

Knowledge Management in RSS

RSS focuses on planning, developing and deli-
vering resources needed to support services and
products in the operations domain. Network and
systems management process is the operational
process in RSS.

Human knowledge, organizational knowledge
and technological knowledge are the main types
of knowledge. Those previous network resource
planning cases and the accumulated network
resource management strategy form the major
human knowledge. Database, data marts and data
warehouses about services and products represent
the major organizational knowledge. Some inno-
vation techniques are technological knowledge.

Organizational structure has influence on RSS,
but the degree of influence is much weaker than
that to OSS and BSS. Database, data mart and data
warehouse are the three data storages in RSS.

Knowledge Management in SSS

SSS is of significance to OSS as an EIS. A variety of
technological knowledge is involved with this

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Knowledge Management in OSS 187

system including operating systems methods and
techniques. In general, organizational structure
has relatively minor influence on it.

Summary

BSS, OSS, RSS and SSS are integrated into a single
OSS through system integrator (SI) software.
Knowledge management varies among different
components in OSS. BSS and OSS involve with
types of knowledge throughout the entire knowl-

edge management process, requiring organiza-
tional learning, open organization structure
and certain IT architecture. RSS involves human
knowledge, organizational knowledge and tech-
nological knowledge. Organizational structure
has a less significant influence on it. Data mani-
pulation tools are needed. Technological knowl-
edge is the main type of knowledge involved in
SSS for which organizational structure has minor
effect on it. The summary of knowledge manage-
ment in OSS is described in Table 2.

Table 2. Summary of knowledge management in OSS

OSS\ Knowledge Knowledge Organization IT Function
KM Architecture Management Architecture Architecture

Process

BSS Human knowledge, Create, maintain, Team management, Call centre To provide
organizational distribute and revise project manager, CTI, operational customer
knowledge, knowledge to support communicate with CRM, interactive service
relational customer interface
knowledge management process software vender CRM, billing

effectively

and customer care system, etc
process

OSS Human knowledge, Create, maintain, Team management, Analytical CRM, To support the
organizational distribute and revise project manager, DSS, etc. customer service
knowledge, knowledge to support communicate with provision
technological service/product software vender effectively
knowledge, development and
relational operation process
knowledge

RSS Human knowledge, Create, maintain, Team management Database, data To support the
organizational distribute knowledge mart, warehouse, above activities
knowledge, to support network etc. effectively
technological and systems
knowledge management process

SSS Technological Revise knowledge to Team management OS, such as Unix To support the
knowledge support OSS’ regular operation etc. above activities

effectively

OSS Human knowledge, Create, maintain, Team management, Enterprise To gain superior
In knowledge, distribute and revise project manager, Information advantage
general knowledge, knowledge to support communicate with Systems (EIS) through

organizational the horizontal business software effectively
knowledge, process of fulfilment vendor providing
technological assurance and billing end-to-end
knowledge, (FAB) customer service
relational
knowledge

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188 Jiayin Qi et al.

DISCUSSION AND CONCLUSION

An integrated OSS is a combination of applica-
tions that interact with each other to enable sup-
port, administration and management of services
for telecom industry. It includes systems that
manage the networking infrastructure, planning
tools, billing systems, customer care, trouble
management tools and the like. It is the funda-
mental integrated software platform for telecom
operators. It is an EIS used in the telecommuni-
cations industry.

Although there are some researches on the
knowledge management in EIS, especially in ERP,
there are only limited researches on knowledge
management in OSS. In this paper, an overview of
knowledge management in OSS and its frame-
work is provided. Future research will be focusing
on knowledge management in OSS implementa-
tion, key knowledge management techniques in
OSS (Liao, 2003; Sher and Lee, 2004), knowledge
version management in OSS, etc.

In this paper, a knowledge management model
in OSS with systems point of view is proposed.
A knowledge management framework for OSS in
systems perspectives is also developed.

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p. 247-262, July/Sept. 2009

A Proposed Architecture for Implementing a Knowledge
Management System in the Brazilian National Cancer Institute

José Geraldo Pereira Barbosa *
E-mail address: jose.geraldo@estacio.br
Mestrado em Administração e Desenvolvimento Empresarial/Universidade Estácio de Sá
Rio de Janeiro, RJ, Brazil.

Antônio Augusto Gonçalves
E-mail address: augusto@inca.gov.br
Instituto Nacional do Câncer – INCA
Rio de Janeiro, RJ, Brazil.

Vera Simonetti
E-mail address: vera.simonetti@estacio.br
Mestrado em Administração e Desenvolvimento Empresarial/Universidade Estácio de Sá
Rio de Janeiro, RJ, Brazil.

Altino Ribeiro Leitão
E-mail address: altino@inca.gov.br
Instituto Nacional do Câncer – INCA
Rio de Janeiro, RJ, Brazil.

ABSTRACT

Because their services are based decisively on the collection, analysis and exchange of clinical information or
knowledge, within and across organizational boundaries, knowledge management has exceptional application
and importance to health care organizations. This article proposes a conceptual framework for a knowledge
management system, which is expected to support both hospitals and the oncology network in Brazil. Under this
holistic single-case study, triangulation of multiple sources of data collection was used by means of archival
records, documents and participant observation, as two of the authors were serving as INCA staff members, thus
gaining access to the event and its documentation and being able to perceive reality from an insider point of
view. The benefits derived from the present status of the ongoing implementation, so far, are: (i) speediness of
cancer diagnosis and enhanced quality of both diagnosis and data used in epidemiological studies; (ii) reduction
in treatment costs; (iii) relief of INCA’S labor shortage; (iii) improved management performance; (iv) better use
of installed capacity; (v) easiness of massive (explicit) knowledge transference among the members of the
network; and (vi) increase in organizational capacity of knowledge retention (institutionalization of procedures).

Key words: knowledge management; information system; health care; hospital management.

Received 26 October 2007; received in revised form 27 February 2009.

Copyright © 2009 Brazilian Administration Review. All rights reserved, including rights for
translation. Parts of this work may be quoted without prior knowledge on the condition that
the source is identified.

* Corresponding author: José Geraldo Pereira Barbosa
Av. Presidente Vargas 642, 22O andar, Rio de Janeiro, RJ, 20071-001, Brazil.

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INTRODUCTION

Brazil currently has a complex cancer scenario. General incidence and mortality rates are elevated,
with the particularly high incidence of prostate cancer in men and breast cancer in women. Theses
cases have been responsible for over one hundred thousand deaths per year. There are approximately
480,000 new diagnoses of cancer each year in Brazil, and the vast majority of these patients have had
some contact with hospital services. However, research also shows that in several regions of the
country long waiting lists for diagnostics and treatments have become commonplace, which leads to a
situation of people being diagnosed with cancer at a very advanced stage (National Cancer Institute
[INCA], 2008). These sad findings have moved the national health care ministry in recent years to
initiate a series of challenging reforms in order to implement early diagnosing clinical procedures. The
management of cancer treatment is a long and complex process and the reduction of the patient’s
waiting time to start cancer treatment plays an increasingly important role. Therefore, any environment
focusing on the accessibility to the treatment of a chronic illness like cancer should make every effort
to avoid medical errors and fragmentation of care delivery.

The huge number of cancer cases in Brazil means that information is highly sought after by patients
and the clinicians involved in their care and those responsible for cancer services, which opens an
opportunity window for implementing hospital information systems [HIS]. Hoping to bring together
people involved in care planning and delivering – clinicians, managers and patient representatives, the
Instituto Nacional de Cancer (National Cancer Institute [INCA]) has been implementing an oncology
care network. The network, as conceived by the INCA, is a partnership of both private and public
cancer care delivery organizations, whose success will depend heavily on the collection, analysis and
exchange of clinical and managerial information or knowledge within and across the partners’
organizational boundaries. Its intention is to generate valuable information by answering the requests
from patients, government and regulatory bodies with regard to clinical and medical services.
Moreover, the integration of key data would help the evaluation of medical procedures and protocols,
streamlining the organizational processes and bringing improvements to cancer treatment.
Specifically, the network’s objectives are as follows: (i) to improve access to information and
knowledge at all levels (physicians, hospital administrators, patients); (ii) to create a community of
cancer practice knowledge; (iii) to develop an environment of easy and friendly access to relevant
information; and (iv) to collaborate with the decision-making process related to cancer care delivery.

In fact, the oncology care network is increasingly becoming a knowledge-based community of
health services and patients themselves that share their knowledge, helping reduce administrative
bottlenecks and improve the quality of care. As a result, an environment that helps translate
information into knowledge is under construction and constitutes, in itself, a driver for quality
improvements.

All the above comments bring to mind the need for a knowledge management system that will
identify, capture, structure, share and apply an individual’s or organization’s knowledge, which will
result in a competitive advantage and create sources of sustainable development, according to Nonaka
and Takeuchi (1995). Therefore, the purpose of this study is to propose a conceptual framework for a
knowledge management system in the Brazilian National Cancer Institute. To accomplish this,
knowledge management [KM] enabled health care system is envisioned that will integrate clinical,
administrative and financial processes in health care through a common technical architecture, as well
as provide a decision support infrastructure for general decision-making.

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KNOWLEDGE MANAGEMENT

Knowledge management is associated with intellectual capital and the process of creation and
diffusion of knowledge embedded in business processes (Wigg, 1993), and constitutes a critical
success factor in the current challenging and innovative business scenario. The main premise of
knowledge management is the recognition of knowledge as the central point of organizational
performance (Drucker, 1993).

In order to enhance organizational performance and create value, knowledge management includes
all the processes that deal with the creation, structuring, dissemination and application of knowledge.
In the words of Hedlund (1994), these processes can be analyzed at various levels: the individual,
group or organization. For Nonaka and Takeuchi (1995), knowledge management is predicated on
shared learning, collaboration and the sharing of knowledge at the strategic organizational level.
According to Davenport and Prusak (1998), knowledge management not only involves the production
of information but also the capture, transmission and analysis of data, as well as the communication of
information based on or derived from the data to those who can work on it.

The Nature of Knowledge

Concerning knowledge itself, there are two main kinds: tacit and explicit. Tacit knowledge is the
personal, unarticulated, unexpressed knowledge possessed by an individual. It is the knowledge and
expertise that a person has gained over the years through experience, by interacting with others, and
through a process of trial and error. This knowledge lies in the individual’s brain or in his personal
notes, computer files or desk drawers. It has never been completely articulated, recorded, documented
or written down in a comprehensive format. Generally found in non-structured form, such as an
individual’s ideas, insights, values, experiences and judgments, it is more difficult to identify and to
manage. Thus, it needs to be structured before it may be stored and processed. On the other hand,
knowledge, sometimes, may be explicated, codified and set down in manuals, written procedures,
records, notes, graphic representations, audio and visual materials. Stored in databases, explicit
knowledge is suitable for access and processing (Nonaka & Takeuchi, 1995).

Reber, Nonaka and Takeuchi (1992 as cited in Spender, 1996) picture explicit knowledge as the
small tip of a huge iceberg of pre-conscious collective knowledge. The major part of it would be
formed by tacit knowledge, invisible and completely incorporated into social identity and practice. An
individual will understand a message completely only if he understands the body of his organization
collective tacit knowledge. In other words, the physical reality is socially built. By similar reasoning,
Spender (1996) concludes that interpretation (knowledge) of experience comes from the interaction
among the variables that surround the environment and the individual’s perception enhanced by
experience.

According to Tsoukas (1996), people are co-producers of their own reality, and this will help them
to form their attitudes and behaviours. Attitudes are norms and values that the individual perceives as
favourable or not, and the behaviours are the expression of these attitudes which can be shown as
coherent or incoherent towards its related attitude. Tacit knowledge is one of the main foundations of
the individual’s attitudes. Therefore, detecting tacit knowledge is complicated by the fact that the
individual has the autonomy to decide what and how much should be transmitted to others.

Almost all activities require some combination of explicit and tacit knowledge, and effective
knowledge management is the one that captures both of them. In fact, the real challenges to knowledge
management lie in being able to identify and capture tacit knowledge so that it can be retrieved when
needed. While explicit knowledge is easy to record and transfer, tacit knowledge is difficult to
identify, capture, and transmit. Although converting tacit knowledge to explicit knowledge is difficult,
it is not impossible. The employee’s tacit knowledge is generally transmitted under the form of

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organizational best practices, which are often documented and put into a network, which is
subsequently accessed and used by other employees.

For Spender (1996), the big challenge of management is to distinguish the systemic activities that
really increase the collective tacit knowledge from those that only generate local and limited
knowledge. In the opinion of Hayek (1992 as cited in Spender, 1996), good managers know how to
use organizational knowledge efficiently, the kind of knowledge that, although incorporated into
people and processes, does not belong totally to any collaborator. So managers must build an
organizational space that facilitates the selection of and interaction among different tacit and explicit
knowledge available to the organization, both within its internal and external environment.

The Strategic Value of Knowledge

The health care industry itself is increasingly becoming a knowledge-based community that is
connected to hospitals, clinics and patients for sharing knowledge, reducing administrative costs and
improving the quality of care. Thus, the success of health care depends critically on the collection,
analysis and exchange of clinical information or knowledge within and across organizational
boundaries. It is recognized that the spread of new practices is shaped by multiple influences. In this
process, physicians play an important role. They can be thought of in terms of very sophisticated
knowledge workers (Wickramasinghe, 2000). Like others, (i) physicians “make sense” of this wealth
of knowledge (Borghoff & Pareschi, 1998); (ii) they own the means of production, e. g., their
specialized knowledge; (iii) they possess specialized skills and training, which they have acquired by
investing significant resources towards their education; and (iv) they make decisions that have far-
reaching consequences both for their organizations and their patients (Wickramasinghe, 2000).

Thus, it is the interchange of knowledge that represents the significant change in the present way of
managing knowledge in comparison to the early days of managing knowledge. It means that an
organization’s body of knowledge is considered an asset (intellectual capital) only when shared by its
employees. Ultimately, this implies seeing the organization as a distributed knowledge system, where
managers have to deal with the central question of how to stimulate experts and workers to share their
knowledge without facing resistance and insecurity. The main point is how to create knowledge and
exchange it among partners. The traditional dichotomy of acquiring information either in reactive
mode, to support a specific decision, or in proactive mode, to scan and monitor the environment to
detect problems, is not preemptive.

For Grant (1996), the following conditions, when present in the organization, would be primary for
transforming knowledge into value: the first is the organizational capacity of disseminating explicit
knowledge and of auditing the application of tacit knowledge. The second condition is the capacity of
the workforce to fully understand the received knowledge, what is facilitated by a common
organizational language. The third factor stems from the organization being able to recognize the
collaborator who is responsible for the knowledge creation and to compensate his effort adequately.
This is not an easy task because, although incorporated into the workforce, most of the knowledge is
generated within the boundaries of the workplace and refer to it specifically. The fourth condition
derives from the limited human capacity to acquire, store and process knowledge, the focus of the
rationality thesis proposed by Simon, Egidi and Marris (1992). That would require the division of
knowledge among specialized organizational areas. And finally, the last condition presumes that
knowledge is the critical input of a production system and the one which generates value into
products.

Sveiby (2001) conceives the body of intangible assets of an organization as formed by the
competence of the professional staff and the internal and external organizational structure. In this
author’s words, the internal structure is the one filled with management staff and information systems,
basically, and its main function is to support the professional staff when delivering services. Secondly,
the internal structure, viewed as the conduction wire connecting the organizational body of knowledge,
would be used to facilitate the transfer of tacit knowledge among the professional staff. One of the

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most important facets of this process is to provide professionals with the elements for an efficient
conversion of knowledge. By conversion of knowledge, Sveiby (1997) understands those activities of
(i) gathering information (explicit knowledge) about a potential problem, (ii) applying and
transforming it into tacit knowledge, through practice, and finally presenting the problem’s solution in
an explicit form. It must be kept in mind that this explicit knowledge will be impregnated, in the
subtlest ways, by the individuals’ attitudes.

INFORMATION SYSTEMS [IS]

According to Moraes, Silva and Cunha (2004), organizations should make efforts to implement
friendly manageable learning environments and to promote learning by doing because these initiatives
improve performance without increasing education and training costs. There is a rising recognition
that more extensive use of information technologies could do more to improve the performance of
health care systems, within the bounds of appropriate measures to protect the confidentiality of private
health information. Indeed, the remarkable restructuring of cancer care institutions, from independent
local units into regional and national integrated health care delivery organizations, has required a
change in the role performed by information systems. On the other hand, it is important to emphasize
that, like other complex applications, the design of an IS-based oncology network represents a
challenge to health care managers and public authorities.

The Benefits and Limitations

Fleury and Fleury (2006) argue that knowledge is managed by means of organizational learning
processes which can be seen in three dimensions: acquisition and development of knowledge,
dissemination of knowledge and organizational memory building. An important point, raised by
Fleury and Fleury (2001), is the issue of knowledge transfer from individuals to teams, and from these
to the entire organization. According to the authors, while the individual learning process requires
from management the comprehension of positive and negative feelings of employees, the team
learning process requires the mixing, interpretation and integration of individual knowledge and
beliefs into shared collective systems. Regarding the organizational level, individual and team
knowledge may be institutionalized in several ways: structure, procedures, rules and symbolic
elements. Organizational memory is developed to store and retrieve information, e. g., data related to
past experiences, both successful and non-successful, will be easily available to employees. It is not
difficult to perceive that both knowledge dissemination and memory building are substantially
enhanced by information systems. The centralized databases where knowledge is codified and stored
and made available to employees are very effective, especially in the case of explicit knowledge.

The question of information distribution is not a trivial one. Although information technology [IT]
tools, such as databases, intranet and e-mail are helpful, Davenport, Harris and Kohli (2001, p. 71)
warn that:

…distribution involves more than just how to send knowledge. There is also the question of what
kind and how much to send. Several firms try to limit the information and knowledge to those who
interact with clients. One firm uses software technologies that filter the knowledge according to a
user’s predefined categories of importance. The filters select relevant content and distribute the
appropriate parts to the appropriate people. Another firm defines what types of information and
queries are appropriate for a particular communication method (e-mail, voice-mail). Once it
establishes a norm for each communication type, it holds formal training sessions to educate
employees on communication protocols and norms.

Terra (2004) shows that advancements in communication technologies and information systems are
affecting in a significant way the processes of generation, diffusion and storing of organizational

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knowledge. Based on the results of his research with 428 Brazilian companies, the author argues that
in the 166 companies named by him as ‘learning companies’, one can find (i) information systems that
allow efficient communication throughout the whole organization, (ii) employees with wide access to
database and organizational knowledge and (iii) incentives for a systematic documentation of
organizational knowledge. According to Terra (2004), investments in the infrastructure technology
help knowledge management in three aspects:

. Storing of reference materials: codified management can be easily accessed, avoiding efforts
duplication;

. Elaboration of expertise maps: database containing descriptions of individual competences inside
and outside the organizational space, making the sharing of tacit knowledge easier;

. Just in time knowledge: tools that reduce time and space barriers to knowledge access
(videoconference, distance learning etc).

In terms of massive routine and explicit knowledge transfer, Anand, Glick and Manz (2002) point
out that IT based systems are the best way of dealing with it. This kind of data transfer is usually
necessary to support relatively structured decisions where cause and effect relations are well known.
Using the same line of reasoning, Gupta and Govinjaradan (2000, p. 72) affirm “IT is the only viable
mechanism to connect efficiently large numbers of geographically dispersed people”. According to
these authors, intellectual capital is the individual and organizational knowledge stock multiplied by
the speed at which it is circulated inside the organization (or network), which highlights the role of IT
systems as the speed accelerator.

According to Sveiby (2001), the implementation of Intranet, management information systems and
data bases are important initiatives to transfer individual knowledge to the internal structure. On the
other hand, specialist systems, such as the cancer diagnosis system of the INCA, improved man-
machine interface, simulation environment and interactive learning via Internet help translate
organizational learning to the employees.

The process of knowledge conversion, the so called spiral of knowledge in the words of Nonaka
and Takeuchi (1995), is comprised of four steps: socialization, externalization, combination and
internalization. The combination step is the one dealing with the creation of systemic knowledge by
means of new combinations of accumulated knowledge. According these authors, databases and
Internet not only help to systematize concepts but also make the flow of new ideas easy.

On the other hand, knowledge management is not merely a question of sophistication of IT
infrastructure. Concerning the obstacles to the transfer of best practices (tacit knowledge) among
organization co-workers, Szulanski (1996) enumerates the main ones: (i) an inadequate absorption
capacity by the knowledge receiver, (ii) lack of knowledge about the production factors involved in
the practices, as well as the interaction process among these factors, and (iii) the lack of
comprehension, by the receiver, of the organizational context where the practice will be applied, and
finally (iv) the lack of empathy between the provider and receiver of knowledge. Indeed, Szulanski
(1996) lists several reasons that may make people reluctant to accept a system that encourages
knowledge transfer: loss of the power warranted by individual property of specialized knowledge; not
being awarded by the transference of knowledge; spending time and energy in transference and the
‘non-invented here’ (NIH) syndrome that makes people impermeable to knowledge coming from
outside their organization. Compounding these problems there is the fact that people will have to
acquire new competences to move around the paraphernalia of new IT-based procedures and tools. It
is easy to see that those obstacles are beyond IT capability.

Recent research conducted by Meister (2003 as cited in Bertucci, 2005) in 8 private hospitals located
in Belo Horizonte, a Brazilian city, shows a low level of research and development [R&D] activities,
intellectual production and training and development [T&D] activities in the majority of private
hospitals. Again, the implementation of IT-based knowledge system may help alleviate some of those
problems but will not be a substitute for persistent low funding of research and training.

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It is also important that the manager focuses his attention on knowledge management processes and
structures that directly support the strategic initiatives of organizations, such as cancer research, as in
the case of the INCA. As Zack (2003, p. 70) argues:

Knowledge management has gotten a bad rap lately, but much of it can be attributed to the fact that
most KM initiatives are not focused on strategic knowledge. An organization that defines its strategy
in terms of knowledge and identifies the strategic knowledge leverage points will know where to
focus its KM efforts, will get a long-term return on its investment, and will best the KM efforts of
competitors.

Similarly, Hammer, Leonard and Davenport (2004, p. 17) mention that “technology has immensely
improved access to, and transmission of, information, but it cannot create shortcuts to the most
valuable kinds of knowledge. That dilemma explains much about why organizations still have trouble
managing knowledge”. The authors are clearly speaking of individual and collective tacit knowledge.

A final point to be mentioned concerns the obstacles imposed by organizational culture to strategic
changes such as the implementation of IT-based knowledge management in the INCA. Mintzberg,
Ahlstrand and Lampel (2005) define organizational culture as the body of shared beliefs which are
reflected in traditions, habits, stories, symbols, products etc. Acting as a perceptive filter or lens,
culture interferes with thinking styles (decision making, analysis procedures etc) of the acculturated
people. According to Mintzberg et al. (2005, p. 268), “culture and especially ideology do not
encourage strategic change so much as the perpetuation of existing strategy; at best, they tend to
promote shifts in position within the organization’s overall strategic perspective”. Lorsch (1986)
argues that introducing and nurturing the values of innovation and flexibility in the organization is an
effective way of promoting acceptance of changes. He also mentions that managers should be
submitted to a cultural auditing in order to identify their shared beliefs. The author suggests that these
shared beliefs must be made visible around the organization as a way to make people aware of their
possible prejudices. Naturally, special attention should be paid to the prejudices against IT knowledge
management. However, Hernandez and Caldas (2001) warn that some managers sometimes use
culture resistance as an excuse for problems arising from ill designed change processes. Contrary to
the classical prejudice that human beings are naturally resistant to changes, these authors argue that
human beings resist loss but desire change. For the authors, it is important to treat resistance to
changes from both collective and individual perspectives. Resistance, depending on several situational
and perceptual factors, varies from person to person.

The Foundations of the Proposed Knowledge Management System

Healthcare organizations generate a massive amount of data, such as electronic medical records,
clinical trial data, hospital records and administrative reports, gathered from internal and external
sources, such as clinical practices, hospital information systems, and cancer registries. Usually, this
huge collection effort is incomplete because data are rarely transformed into a strategic decision-
support resource. For this purpose, the emergence of knowledge management tools, such as Data
Mining [DM], represents an opportunity to convert raw data into knowledge (Cheah & Abidi, 1999).
Knowledge management in healthcare can be regarded as the confluence of concepts and techniques to
facilitate the creation, identification, acquisition, development, dissemination and utilization of a
healthcare enterprise’s knowledge assets (O’Leary, 1998).

The proposed knowledge management system incorporates four steps: creating, structuring ,
sharing and applying. Figure 1 presents a short list of procedures and tools included in each step.
This knowledge process is based on Bose (2003), who describes knowledge as a process that extracts,
transforms and disseminates information to be shared and reused by the entire organization.
Additionally, the process includes the contribution of Davenport et al. (2001) when he describes the
four major goals of the knowledge management systems: to create knowledge replacement, to increase
the access of knowledge, to improve the knowledge environment and to manage knowledge as an
intangible asset.

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Figure 1: The Knowledge Process

Source: adapted from Bose (2003, p. 63).

The creating step includes knowledge acquisition and knowledge exhibition. As seen before,
knowledge comes from different sources, such as clinical learning outcomes, best practices and
innovative procedures. The process for acquiring knowledge, from both internal and external sources,
is highly dependent on the hospital staff involved in the cancer treatment. Knowledge exhibition is
understood to be the formal process representation methods developed by the organization.

The structuring step involves defining, storing, indexing and linking documents and digital images.
Mapping existing knowledge, in terms of context and importance, helps classify the knowledge into
taxonomies. Storing the knowledge in appropriate repositories such as the yellow pages of expertise,
clinical guidelines, protocols and best practices, may then be done.

The sharing step concerns the diffusion of knowledge and collaboration among co-workers,
resulting from transfer and dissemination of best practices. Knowledge sharing is accomplished by
different means such as training, intranet, groupware, extranets, communities of practice,
benchmarking and cross-functional teams.

The last step, applying, involves activities related to decision-making support, problem-solving,
developing competency and teamwork, improving productivity, establishing communities of interest,
using process workflow, customer support and training to encourage people to speed up the process.

Decision Support
System (DSS)

Data Mining (DM)

APPLYING
Data acquisition
Identification of Best
Practices

CREATING

Groupware
Extranet
Intranet

SHARING
Clinical Guidelines
Protocols
Data Storage

STRUCTURING

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METHOD

Research Design

This study was developed through a qualitative research design to present a descriptive analysis of
the conceptual framework of the knowledge management system implemented into the National
Cancer Institute (Instituto Nacional de Cancer [INCA]). This system is expected to support both
hospitals and the national oncology network, the implementation of which has been placed in the
hands of the INCA. The INCA was chosen as a unit of analysis in order to take advantage of the
professional experience of two of the authors while working at its Information Technology Division.
The first author is the INCA’s Chief Information Office [CIO] and associate professor at the graduate
program in Business Administration that has been conducting this study. The second and third authors
are full professors at the same program while the fourth is the systems manager of the INCA´s
Information Technology Division.

This holistic single-case study intends to contribute to the knowledge of organizational phenomena,
presenting a contemporary description of the system implemented, through an empirical inquiry,
answering the questions what, who, where and how (Cooper & Schindler, 2003; Yin, 2003).

Data Collection

Triangulation of multiple sources of data collection was used by means of archival records,
documents and participant observation, as two authors were serving as INCA staff members,
thereby gaining access to the event and its documentation and being able to perceive reality from an
insider point of view, characterizing a comprehensive research strategy, in the words of Yin (2003).

The following table summarizes the sourcing procedures:

Table 1: Data Collection Methods

Source of data Description

Archival Records • Clinical data

• Cancer registries

• Patient admission data (demography, rate etc)

Documents • Hospital Information System manuals and user guides;

• Hospital organizational routines (administration, medical care, operations etc)

• Medical research literature

Interviews • 20 interviews with hospital and administrative staff (nurses, doctors, technicians,

coordinators, managers)

Participant

observation

As stated above, two of the researchers (first and fourth) have strong connections with the

problem under investigation, which required careful attention in order to avoid biases. On

the other hand, this fact enabled access to tacit knowledge through the observation of

people directly involved in the problem (physicians, technicians, system analysts etc).

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Data Analysis

The information was organized in a sequential scheme, under a descriptive approach, crossing
information from records and documents with evidence from observation. Following this, the
proposed knowledge management system used at the INCA was analyzed through the lens of Bose
(2003) and Davenport (2006), Davenport and Prusak (1998), Davenport et al. (2001) and Hammer et
al. (2004), seeking converging evidence of their theoretical outlines with the proposed system.

THE PROPOSED ARCHITECTURE

In most cancer hospitals, multidisciplinary committees discuss their patients’ clinical approach. The
medical experts go to different sources of information in order to make their decisions. First, they
check whether their patients comply with existing guidelines. On the other hand, they can also select
their therapeutic decisions based on the cases of patients that have undergone similar treatment in the
past.

To support the physicians’ activities, several tools, such as tracking mechanisms for keeping the
longitudinal patient history, on-line tools for gathering clinical information and the traditional medical
record, are used. Most of these are patient-centric and make the hospital environment amenable to the
kind of knowledge management system framework, such as the one presented in Figure 2. It can be seen
that Figure 2, being based mainly on the recommendations of Bose (2003) and Davenport et al. (2001),
incorporates components and associated activities related to all four steps presented in Figure 1.

Figure 2: Proposed Framework for Cancer Knowledge Management

Source: adapted from Bose (2003, p. 68).

Clinical
Data

HIS CancerRegistries

Knowledge Management Engine

DSS DM

KDW

Groupware, Intranet, Extranet

Clinical
Analysis

Capacity
Analysis

1. Creating

2. Structuring

3. Sharing

4. Applying

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257

The Main Features of the Proposed Architecture

Creating

Clinical Data

The clinical data, to be incorporated into the Knowledge Data Warehouse [KDW], come from HIS
(patients demography) and from physicians, nurses and other health care providers, and fall into three
general categories: (i) the historical information that the patient provides; (ii) the information obtained
from physical examination; and (iii) the results of the tests or procedures performed on the patient

Hospital Information System [HIS]

The goal of a hospital information system is to use computers and communications equipment to
collect, store, process, retrieve, and communicate, e. g., only administrative information for all
hospital activities, and, at the same time, to satisfy the functional requirements of all authorized users.
The system is comprised of patient-oriented modules (admission, discharge, order entry, radiology
modules and laboratory modules) and administrative modules (finance and billing, management
information and decision-support module). In the case of the INCA, Clinical Data is not included in
the HIS since this specific module was customized to fit INCA requirements.

Cancer Registries

The oncology care network aims to develop a national network of cancer registries, which will
collect information about patients with cancer from public and private hospitals. Data will be stored
and updated to produce a history of all cancer patients, which includes primary treatments, stage of
diagnosis, length of survival and subsequent cancers. No matter the patient’s status – inpatient, day
case or outpatient – cancer registries will collect treatment details. Therefore, it is expected that these
registries will play an important role in auditing the quality of cancer services, reducing waiting lists
and improving the patient’s experience.

Structuring

Knowledge Data Warehouse

Data Warehouses act as a repository for current and historical operational data. The health care
industry has a poor record in terms of standardization, so data are widely used (and misused) in an ad
hoc manner. The knowledge data warehouse [KDW] allows the information to be presented in several
formats and to be distributed more widely in communities of practice. At the same time, Online
Analytic Processing [OLAP] functionality can be used to gain a deeper understanding of specific
health care issues. For Bose (2003), the Knowledge Data Warehouse [KDW] provides the means for
business intelligence through ad hoc and managed query environment, OLAP support, statistical
analysis tools, knowledge mining and access to Decision Support Systems [DSS] applications.

Intranet, Extranet, Groupware

By using Intranet/Extranet as a secure access portal, the environment allows for secure, selective
sharing of key information, such as test results, follow-up care and support groups. This strategy
bridges the gap between what the doctor and the manager knows. The key benefits of e-health strategy
adoption and groupware employment are enhanced collaboration between physician and manager,
optimization of physicians’ work and empowered managers, all of them using sophisticated, cost-
effective web applications and architecture. According to Bose (2003), executives in leading health
care organizations are increasingly recognizing that in order to maintain or gain competitive

José Geraldo Pereira Barbosa, Antônio Augusto Gonçalves, Vera Simonetti, Altino Ribeiro Leitão

BAR, Curitiba, v. 6, n. 3, art. 5, p. 247-262, July/Sept. 2009 www.anpad.org.br/bar

258

advantages, organizational knowledge not only needs to be managed for, but also integrated with, their
corporate systems.

Sharing

Knowledge Management Engine (DSS and DM)

The Data Mining [DM] and Decision Support System [DSS] are among the tools used by physicians
to gain access to KDW. These tools provide the means for business intelligence through ad hoc and
managed query environment, statistical analysis tools, knowledge mining (answers that lead to new
questions), and access to decision support software at all hierarchical levels. Goebel and Gruenwald
(1999) argue that knowledge-mining tools are used to recognize patterns and relationships that may be
valuable for building models that support clinical analysis and capacity analysis. These tools improve
the decision-making process by providing new information that otherwise users would not have been
able to access on a timely basis. The newly extracted knowledge needs to be inserted into one or more
bases to keep them continuously up-to-date and to be of good use for the practice of evidence-based
medicine. The data visualization techniques that facilitate the interpretation process of new knowledge
can be used in conjunction with the knowledge data warehouse.

Concerning DSS, its most powerful feature is the drill-down capability, which allows users to have
access to detailed information, allowing users also to drill up and across. Therefore, the users have
unprecedented capabilities to capture, analyze and present data. Physicians, through the experience of
using such tools and techniques, gain new knowledge related to their health care area. Specific
decision support systems are built using data extracted from various data sources and models.

The data and knowledge necessary for decision-making are spread around the organization. The
DSS is programmed to compare the patient’s case to the corresponding guideline, then to other cases,
and retrieve similar cases. In other words, the system is also designed to be a data warehouse. Thus,
the decision-making process itself results in enhanced understanding of the problem process,
generating new knowledge, indicating the interdependence between the decision-making and
knowledge creation processes.

The Knowledge Management Engine consolidates knowledge from multiple source systems and is
capable of presenting different visions of data in order to match the specific requirements of different
user segments (e.g. disease, clinical areas, geography). Those visions (data marts) must remain
consistent to each other to ensure final report consistency. Furthermore, they must be designed to
match the type of analysis required by target users — online analytical processing [OLAP], querying
etc. Overall, the access tools provide the means for business intelligence through ad hoc and managed
query environment, OLAP support, statistical analysis tools, knowledge mining and access to DSS
applications at all levels.

Applying

Clinical Analysis

Important beneficiaries of the proposed knowledge management are the activities related to clinical
analysis, such as epidemiological and disease analysis and their correspondent management. The
frequently mined knowledge required by these activities comes under the form of associations, classes
(groups with particular profiles), clusters (groups of instances with the same characteristics),
sequences (events linked over a period of time), exceptions (unusual knowledge), forecasts
(estimations of future values of attributes), text (e-mails, news) and Web documents.

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259

Capacity Analysis

Another beneficiary is the area of capacity analysis that involves comprehensive analysis of patient
treatment flow and the availability of capacity (people, machines, etc.) to deal with demand, which
often fluctuates. In the course of this kind of analysis, mining of knowledge has frequently been used
to help identify patient populations, lab utilization, operating room utilization and patient flow.
Davenport (2006) mentions that the knowledge management system is a highly supportive
environment for business process analyses.

As seen above, this framework is instrumental to the building of an environment for managing
cancer information that incorporates the patient’s treatment flow, epidemiological aspects and analysis
of installed capacity. These features allow an overview of the national oncology practice which in
several ways is much more accurate than viewing each hospital separately.

Current Status of the System Implementation

In recent years, the INCA has been investing heavily in information technology in order to create
favorable conditions for the implementation of the above proposal. A communication infrastructure
was established and an information architecture, in which the Hospital Information System [HIS] is
one of the most important bases, was implemented. Furthermore, a significant amount of resources has
been assigned to the training of managers and operational staff to work in a web environment.

The operational systems, which are data sources to the data warehouse (KDW), have already been
implemented. The electronic medical record [EMR] is fully operational in all of the INCA’s hospital
units and the physicians have been accessing the patient information in real time in the web
environment. The Hospital Information System [HIS], the Cancer Registries and Clinical Data are
fully implemented. Concerning the Cancer Registries, they have been implemented in more than 100
hospitals around the country and have been generating data to feed the INCA’s data warehouse
(KDW). Currently in implementation are the Data Mining tools to be used to treat the collected data
supporting the epidemiological analysis (clinical and capacity).

FINAL CONSIDERATIONS

This study presented the capabilities, the technical infrastructure and the decision support
architecture to be incorporated into the proposed knowledge management enabled health care
management system. The four steps of the framework proposed, i.e., creating, structuring , sharing
and applying, represent the pillars of a knowledge structure which could promote a security
environment for individuals to express their attitudes through coherent behaviors, facilitating the
dissemination of tacit knowledge, issues which have been studied in detail by Davenport (2006),
Davenport and Prusak (1998), Davenport et al. (2001) and Hammer et al. (2004).

The benefits derived from the present status of the implementation are reflected mainly on the
easiness of knowledge access and on the increase of operational efficiency, not forgetting patient focus
and satisfaction, as well as enhanced knowledge transfer and diffusion processes. Specifically, the
main results, so far, are:

. Speediness of cancer diagnosis and enhanced quality of both diagnosis and data used in
epidemiological studies;

. Reduction in treatment costs;

. Relief of the INCA’S labor shortage;

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260

. Improved management performance;

. Better use of installed capacity;

. Easiness of massive (explicit) knowledge transference if we think of the dozens of medical records
daily exchanged among the members of the network; and

. Increase in organizational capacity of knowledge retention (institutionalization of procedures).

The implementation has also demonstrated that a clear understanding of the knowledge management
process by professionals and administrative staff is imperative, as mentioned by Lorsch (1986) and
Hernandez and Caldas (2001), who also indicate several practical measures to overcome cultural
barriers to strategic change. Resistance against the new system on the part of some physicians has
been overcome by the development of user-friendly web interfaces, as mentioned by Moraes et al.
(2004). An additional facilitator has been the enthusiastic adhesion of young physicians who are used
to navigating the web environment.

Above all, the ongoing implementation has revealed that this kind of initiative is most likely to be
successful in health care organizations that value organizational learning, pursue strategic goals,
nurture a culture of knowledge sharing, accept new challenges, try original approaches and have the
ability to exploit the power of information technology. It is not less important to stress that
transforming an oncology care network into a knowledge-based community of health services will
require the participation of all players: hospitals, clinics, physicians and communities.

Therefore, the sources of data collection used in this study could be strengthened by using semi-
structured interviews with some staff members, focusing on issues that cannot be examined through
the other sources used. Staff participation could provide important insights into the methodology
implementation, helping to corroborate previous findings from the other sources through verbal
responses.

Finally, Bose (2003) warns that future research on privacy and confidentiality issues of health care
knowledge, including the lifetime health care record of patients, is fundamental to the widespread
adoption of a health care management system. Significant privacy and confidentiality issues emerge
when knowledge from widely disparate sources is brought together and made available in electronic
forms.

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S A F E T Y SYSTEMS

Steps to Designing
a S a fe ty M a n a g e m e n t S y s te m
B e i n g p r o a c t i v e a n d p r e d i c t i v e , i n s t e a d o f r e a c t i v e , is k e y t o s u c c e s s
By Susan E. S a g a rra

T h e a n n u a l A M R

S a f e t y C o m p e t it io n

s im u la t e s s it u a t io n s

E M S p r o fe s s io n a ls

e n c o u n t e r e v e r y day.

E
MS agencies have protocols and policies
in place to ensure proper care and safety
of patients. Agencies can also include
p ro g ram s th a t make EMS providers
more m indful of their own safety and the
behaviors that impact that safety. Developing a safety

management system (SMS) can assist field providers
and their supervisors in being proactive, and possibly
even predictive, instead of reactive, to the hazardous
situations they face in the field.

The creation of an SMS can become an integral
p art of the agency’s operational procedures, but it
takes a com m itm ent from leadership to adm inistra­
tive staff says Ron Thackery, senior vice president
of professional services & integration for American
Medical Response (AMR).

During the recent EMS World Expo held Septem­
ber 15-19 in Las Vegas, NV, Thackery and others
presented ways to create an SMS as part of the EMS
Safety Officer workshop that debuted this year.

According to Thackery everyone in an agency must
understand the role of safety in order to implement
and manage a program. He cautions, however, that
one size does not fit all. Leadership, with strong input
from employees, m ust analyze individual agency
needs and prioritize based on w hat is realistic to
implement. “You can’t boil the ocean,” says Thack­
ery. “You might have a list of 10 things you want to
do but realistically, you can’t do all of them. You have
to boil it down to something manageable. Agencies
have to pick th eir top needs and be com m itted to
implementing the most im portant ones.”

2 6 NO VEMBER 2015 | E M S W O R L D . c o m

S A F E T Y SYSTEMS

Thackery and his colleagues at AMR
have extensive knowledge in integrating
safety into every aspect of providers’ duties.
Thackery is responsible for risk manage­
m ent and safety, w ith previous executive
oversight for fleet a d m in istra tio n and
clinical services. He serves on the board
of directors for the National Safety C oun­

cil and chairs the Professional Standards
and Research Com m ittee of the American
Ambulance Association.

“There are four com ponents to a safety
management system,” says Thackery. “The
three prim ary ones are safety risk manage­
ment, safety leadership and safety assur­
ance. The fourth is safety promotion, which

is the glue th a t holds it all together. T hat
part is how you communicate to the people
in the field to behave safely. If you remember
the TV show Hill Street Blues, at the end of
every roll call they were told, ‘Let’s be care­
ful out there.’ That built a culture of safety
in every show. T hat kind of culture can be
built w ithin any agency.”

Thackery says the message can vary. In
some places the chief or board president
signs a mission statement or pledge that the
agency is com m itted to safety. O thers may
have the entire staff sign the pledge. The
statem ent may be fram ed and placed in a
heavily traveled area of the agency.

“It’s a pledge saying, ‘This is why I work
safe,”’ he says. “A nother idea is to have the
staff provide photos of whatever is impor­
ta n t to them in life -a spouse, kids, a pet. It
sends a message that they are committed to
work safely because of the rem inders th at
are posted. It helps build a culture among
the staff because people also learn things
about each other. Then it is up to leadership
to develop policies and program s of what
they expect them to do to behave safely.”

In most agencies:

100%
of providers have

knowledge of safety issues;

of supervisors have
knowledge of safety issues;

of top m anagem ent have
knowledge of safety issues.

D id y o u k n o w t h a t 7 4 %
o f E M S w o r k e r d e a t h s a r e
t r a n s p o r t a t io n – r e la t e d ? *

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Thackery says that the message needs
to be changed on a regular basis: “People
might become numb to the message. Some
organizations update the photos every six
months or every year. I try to push people
to have something personal and to update
with new photos all the time. And really,
people who are committed will keep updat­
ing it, and leadership must make sure it
happens.”

Thackery says the aviation industry
implemented safety management systems
in the 1970s. The EMS industry is mim­
icking the aviation industry’s research and
policies for its programming.

“It became popular in the EMS profes­
sion in the past decade because the air-med
side kept having crashes,” says Thackery.
“We decided we needed to have a frame­
work with a tie to EMS. Agencies have a
process and policies, a way to manage their
dispatching, billing, etc. It makes sense to
include a program and policies for safety.
When they do it, it just becomes a part of
the overall way they run the agency.”

The concept is rooted in behavior-based
safety and relies heavily on Herbert Hein­
rich’s Pyramid of Safety. Heinrich was an
industrial safety pioneer from the 1930s
who worked for Travelers Insurance Co.
Heinrich’s research is claimed as the basis
for the theory of behavior-based safety,
which holds that as many as 95% of all
workplace accidents are caused by unsafe
acts. Heinrich came to this conclusion after
reviewing thousands of accident reports.

According to Thackery, changing behav­
iors to create a culture of safety in the EMS
industry requires a commitment to improve
provider safety, which results in improved
patient safety and, ultimately, improved
community safety. The foundation of an
SMS includes fostering a culture of safety,
coordinated support and resources, safety
data, education initiatives, safety standards
and requirements for reporting and investi­
gating incidents that affect safety.

An SMS should examine how paramed­
ics are lifting patients, driving ambulanc­
es, fatigue levels (analyzing hours of ser­
vice), infection control protocols, hazmat
responses, machinery operations and work­
space ergonomics. Thackery also says an

F o r M o r e I n f o r m a t i o n C irc le 2 5 o n R e a d e r S e rv ic e C a rd

E M S W O R L D . c o m | NOVEMBER 2015 2 9

“A LOT OF AGENCIES MANAGE
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SMS can focus on environm ental sustain­
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ness for duty, physical agility testing, drug/
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employees, but also communicable diseases

ABOUT THE AUTHOR

Susan E. Sagarra is a writer,
editor and book author based
in St. Louis, MO.

in the community, such as Ebola).
Thackery says there are three approaches

to managing safety issues:
1. Reactive (past): Respond to events that

have already occurred, such as incidents and
accidents;

2. Proactive (present): Actively identify
hazards through the analysis of the orga­
nization’s processes;

3. Predictive (future): Analyze system
processes and cu rre n t en v iro n m en t to
identify potential future problems.

“A lot of agencies manage safety in a reac­

A M R h o ld s a n a n n u a l e v e n t In D e n v e r

w h e r e p ro v id e r s c o m p e t e in a s a f e t y

a n d s k ills c o m p e t it io n t h a t s im u la t e s

s it u a t io n s E M S p ro fe s s io n a ls e n c o u n t e r

e v e r y d a y . “ T h e A M R N a t io n a l S a f e t y

C o m p e t it io n is n o t o n ly a v e r y p r e s tig io u s

e v e n t f o r o u r c a r e g iv e r s , i t a ls o in s tills

a c u lt u r e o f s a f e t y a n d an e m p h a s is on

c lin ic a l e x c e lle n c e ,” s a y s T h a c k e r y .

“ T h e c o m p e t it io n in c lu d e s a t im e d d r iv in g

c o u rs e t h a t m e a s u r e s t h e i r a b ilit ie s

t o s a f e ly a n d e f f i c i e n t l y o p e r a t e an

a m b u la n c e , t w o v e r y d i f f i c u l t p a t ie n t

e n c o u n te r s a n d a c lin ic a l s k ills s e c t io n .”

W h i l e t h e c re w s h a v e a lo t o f f u n a n d

t r u l y e n jo y t h e c o m p e t it io n , t h e y also

u n d e r s t a n d t h e im p o r t a n c e o f fo c u s in g

o n s a f e t y a n d c lin ic a l e x c e lle n c e . “ T h e

c re w s a ll t a k e h o m e s o m e t h in g t h e y

h a v e le a r n e d a n d t h e n t h e y p ass i t o n to

t h e o t h e r t e a m m e m b e r s in t h e i r loc al

o p e r a t io n ,” s a y s T h a c k e r y . “ I t ’s t h a t

t y p e o f c o m m it m e n t t o le a r n in g a n d

e x c e lle n c e t h a t e n s u re s o u r c re w s p r o v id e

o u t s t a n d in g p a t i e n t c a re e v e r y d a y .”

tive mode,” says Thackery. “An employee
gets injured and the agency reacts. But they
can develop systems to be pro-active or even
predictive.”

However, that requires leadership to be
more in tune with what is affecting safety
w ithin the agency. Thackery says th a t in
most agencies, the paramedics and EMTs
have 100% knowledge of problem s and
safety issues; supervisors have 74% knowl­
edge; mid-level m anagem ent has just 9%
knowledge; and top management has just
4% knowledge.

3 0 N O V E M B E R 2 0 1 5 | E M S W O R L D . c o m

So how does a n EMS agency go a b o u t
an aly z in g its n eed s an d th e n im p le m e n t­
ing an d m a in ta in in g a safety m anagem ent
system ? T hackery said agencies should fol­
low th e ideas in th e fo u r pillars o f an SMS:

S a fe ty P o licy : E sta b lis h e s
senior m anagem ent’s com m it­
m e n t to c o n tin u a lly im prove
safety, d efining th e m ethods,
processes an d o rg an izatio n al
s t r u c t u r e n e e d e d to m e e t

safety goals. It requires:
• C o m m itm e n t o f th e te a m to achieve

high sta n d a rd s and com pliance. T h is also
involves eth ical decision-m aking an d p ro ­
m oting a c u ltu re o f safety.

• T ran sp aren cy in m anaging safety.
• D o cu m en te d policies/processes.
• O p en rep o rtin g .
• Any policy, program or initiative m ust

pass tw o tests: W ill it mitigate risk if used as
designed, and will the system adversely impact
productivity, safety, efficiency and privacy?2 S a fety R isk M an agem en t: D eterm in es th e need for, and ad eq u acy of, new o r revised

risk controls. The agency m ust
id en tify h a z a rd s an d assess,
analyze an d control th e risks.3 Safety A ssurance: Evaluates th e c o n tin u e d e ffectiv en ess of im p le m e n te d risk c o n tro l
stra te g ie s an d s u p p o r ts th e
identification of new hazards.
T h is e n s u r e s re s u lts m e e t

e x p e c ta tio n s an d co m pliance, a n d facili­
ta te s in fo rm a tio n g a th e rin g (via a u d its/
evaluations, em ployee re p o rtin g an d data
analysis). Periodic assessm ent of th e system
is required.

4 Safety P rom otion : Includes tra in in g , c o m m u n icatio n and o th e r actions to create a posi-
tive safety c u ltu re w ith in all

© f l l l P levels of th e w orkforce. T h is
phase is th e glue th a t “b o n d s”

all safety activities. It involves advocating
for a s tro n g safety c u ltu re ; c o m m u n ic a ­
tio n (includes aw areness, lessons learned,
social m edia); tra in in g an d education; and
elic itin g in p u t, ideas a n d feed b ack from
everyone.

“T h e four categories give agencies th e
ab ility to p u t to g e th e r th e ir ow n SM S,”
says Thackery. “T hey need to th in k ab o u t
w hat they w ant to do in th e ir ow n agencies.
T hey also have to build a process so th a t
people are w illing to com e fo rw ard ab o u t
things they see and get employees to become
engaged in o rder for it to w ork.” ®

MAKE SURE YOUR NEXT
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In fla ta b le liftin g cushions are p ro v e n to:

• R educe risk o f back in ju ry • P ro te c t w o rk e rs

• R educe co sts • M aintain p a tie n t d ig n ity

Emergency services in th e UK, A ustralia, Canada and
th e USA are using liftin g cushions. To fin d o u t w h y
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M a n g a r
□ m a n g a r i n t Q M a n g a r L if tin g C u s h io n s E M S

F o r M o r e In f o r m a t i o n C irc le 2 6 o n R e a d e r S e r v ic e C a rd

E M S W O R L D . c o m | NOVEMBER 2015 31

mailto:info@mangarusa.com

Copyright of EMS World is the property of Cygnus Business Media and its content may not
be copied or emailed to multiple sites or posted to a listserv without the copyright holder’s
express written permission. However, users may print, download, or email articles for
individual use.

Economic REsEaRch-Ekonomska istRaživanja, 2017
voL. 30, no. 1, 911–921
https://doi.org/10.1080/1331677X.2017.1311232

If we implement it, will they come? User resistance in post-
acceptance usage behaviour within a business intelligence
systems context

Aleš Popovič 

Faculty of Economics, University of Ljubljana, Ljubljana, slovenia

ABSTRACT
The aim of this article is to examine individual, corporate and
technology-related factors that shape user resistance in business
intelligence systems (BIS) post-acceptance usage behaviour. The
author develops a conceptual framework and a series of propositions,
grounded on previous studies of user resistance to information systems
(IS) and post-acceptance usage. The framework proposes that three
individual-level variables (loss of power, change in decision-making
approach, change of job or job skills), four corporate-level variables
(information culture, communication, user training, service quality)
and a technology-related variable (system issues) can be attributed
to fuel user resistance towards BIS post-acceptance usage stages.
A series of propositions is offered that aims to stimulate empirical
research in this topical subject. Despite wide acknowledgement of
the importance of user resistance for IS implementation success, this
area has been under-researched in the field of BIS. This article draws
insights from theoretical and empirical studies to shed some light on
this area. A framework is presented which transcends previous works
on user resistance to IS by looking at the context of BIS use within the
voluntary use environment.

1. Introduction

Today’s increasing market pressures and environmental uncertainty call for implementation
and utilisation of information systems (IS) capabilities having the potential of providing
high quality information to inform decision-making to achieve firm performance (Popovič,
Hackney, Coelho, & Jaklič, 2012). Recognised as one of the four major technological trends
in the 2010s, business intelligence systems (BIS) have been of great interest to several indus-
tries (Chen, Chiang, & Storey, 2012; Işık, Jones, & Sidorova, 2013). These systems are most
commonly identified as technological solutions that provide users with timely access, effec-
tive analysis and intuitive presentation of the information from enterprise-wide IS (Popovič
et al., 2012). BIS are considered enterprise-wide solutions that have some noteworthy

KEYWORDS
Business intelligence
systems; post-acceptance
use; user resistance

JELS CLASSIFICATIONS
D80; o14; o32

ARTICLE HISTORY
Received 30 july 2015
accepted 20 july 2016

© 2017 the author(s). Published by informa Uk Limited, trading as taylor & Francis Group.
this is an open access article distributed under the terms of the creative commons attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

CONTACT aleš Popovič ales.popovic@ef.uni-lj.si

OPEN ACCESS

http://orcid.org/0000-0002-6924-6119

http://creativecommons.org/licenses/by/4.0/

http://creativecommons.org/licenses/by/4.0/

mailto: ales.popovic@ef.uni-lj.si

http://www.tandfonline.com

http://crossmark.crossref.org/dialog/?doi=10.1080/1331677X.2017.1311232&domain=pdf

912 A. POPOVIČ

features that distinguish them from traditional IS (for a detailed overview, see Popovič et al.,
2012): first, while traditional systems are application-oriented, BIS are data oriented. Second,
the structuredness of the business process where BIS are used is generally lower compared
to the process where operational IS are used. Third, for operational IS, information needs
are identified at the process level whereas for BIS these needs are identified in the context of
process and performance management. Moreover, the methods for identifying information
needs for operational IS are well established while for BIS they are less established. Next,
the level of voluntariness of IS use is higher in the context of BIS compared to operational
IS. Furthermore, operational IS are integrated at the process level and employ data sources
mostly from within the process. BIS, on the other hand, are integrated at the enterprise level
with additional data sources being used. Last, but not least, the level of required reliability
of the IS much higher with operational IS than with BIS.

BIS primarily support analytical decision-making in knowledge-intensive activities, par-
ticularly on the tactical and strategic levels. Nevertheless, the success of BIS continued use
depends largely on the extent and ways in which users are willing to embed them in their
decision-making routines (Grublješič & Jaklič, 2013).

There is broad agreement that acceptance and resistance are vital determinants in IS
acceptance and usage (Klaus & Blanton, 2010; van Offenbeek, Boonstra, & Seo, 2013).
Moreover, van Offenbeek et al. also observe that ‘IS research focusing on resistance is much
scarcer than that on acceptance’ (2013, p. 435). User resistance is defined as an individual’s
behavioural reactions indicating reluctance to a situation perceived as being negative, as
a threat or as a stressful sensation (Lapointe & Rivard, 2005; Meissonier & Houzé, 2010).
Among key issues linked with IS implementation failures, users’ resistance is one of the
most salient as it is associated with human resistance to change (Jiang, Muhanna, & Klein,
2000). Users can expound resistance towards IS actively or passively (Meissonier & Houzé,
2010), at both the group level and the individual level (Lapointe & Rivard, 2005; Markus,
1983). This work focuses on active user resistance at the individual level.

A typical IS implementation in a firm generally evolves through six stages, from initiation
through adoption, adaptation, acceptance and routinisation, to infusion (Cooper & Zmud,
1990). While the first three stages primarily deal with activities at global levels (e.g., firm or
departmental levels), the latter three stages are manifested also at micro levels (Li, Hsieh, &
Rai, 2013). At an individual level, following the commitment to IS use, routinisation depicts
the state in which IS use is integrated as a normal part of the users’ work processes, whereas
infusion refers to embedding IS deeply and comprehensively in work processes (Cooper
& Zmud, 1990). Routinisation and infusion have been typically considered together as the
post-acceptance stage (Po-An Hsieh & Wang, 2007).

From a BIS implementation success perspective, resistance towards BIS use draws atten-
tion from academia for two main reasons, i.e., a largely voluntary usage context (Popovič
et al., 2012) and the ability to address a wide range of evolving user information needs
(Yeoh & Koronios, 2010). Moreover, evidence suggests that enterprise-wide decision sup-
port systems and operational IS are resisted for different reasons (Jiang et al., 2000), thus
suggesting that promotion of IS acceptance also differs. In IS research, an often-emphasised
aspect of IS usage behaviour in the post-acceptance stage is the degree of perceived volitional
control that users have over IS usage. Voluntary and mandatory usage contexts have been
previously highlighted as important factors affecting IS acceptance and usage (Karahanna,
Straub, & Chervany, 1999; Venkatesh & Davis, 2000). Moreover, IS literature often argues

ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 913

that meeting or exceeding user information needs reinforces user IS satisfaction, yet failing
to meet current and future user information needs leads to dissatisfaction with an IS and
ultimately results in discontinued use (Bhattacherjee, 2001). Focusing on practice, managers
are, more than ever before, concerned about enabling well-informed decisions through BIS
utilisation (Chen et al., 2012) to organise internal activities, steer inter-firm relationships and
reconfigure internal and external resources to sense and respond to market opportunities
with agility (Dinter, 2013; Işık et al., 2013). Understanding the forces fuelling the resistance
towards BIS use is, therefore, important for firms that rely profoundly on BIS to achieve
their performance goals.

Drawing upon extant models of resistance to IT (for an overview of these models, see
Lapointe & Rivard, 2005), this article presents a conceptual framework of post-acceptance
user resistance to BIS. The resulting conceptual framework attempts to open the black box
of a long-avoided side of BIS implementation success, namely user resistance to BIS. We
encourage scholars to empirically test our propositions in different contexts to advance
theory in this IS domain.

2. User resistance in enterprise-wide system implementation

User resistance has been reported as a significant motive for system implementation failures
(Barker & Frolick, 2003; Robey, Ross, & Boudreau, 2002). Studies of enterprise-wide IS
implementation failures indicate that a better understanding of user resistance is needed
(Klaus & Blanton, 2010; Shang & Su, 2004) so as to apply appropriate promotion strategies
for the implemented system (Jiang et al., 2000). User resistance must be curtailed in order
to realise benefits from enterprise-wide IS and reduce the risk of failure (Klaus & Blanton,
2010).

Prior IT resistance studies have explored certain areas of resistance and can partially
explain user resistance in IS implementations. Resistance determinants previously examined
in this literature include job insecurity (Krovi, 1993), loss of power (Smith & McKeen, 1992),
lack of appropriate communication (Marakas & Hornik, 1996), mismatch of the system
with the organisational goals (Gosain, 2004), perceived adoption risk (Zmud, 1979), pro-
cess changes (O’Leary, 2000), lack of understanding (Joshi, 1991) and other issues (Shang
& Su, 2004).

Markus (1983) identifies three viewpoints for investigating user resistance: (1)
system-oriented; (2) people-oriented; and (3) interaction-oriented. The system-oriented
perspective suggests that resistance occurs because of technology-related factors (e.g.,
user interface, performance, ease of use). The people-oriented perspective suggests that
user resistance occurs because of individual or group factors (e.g., traits, attitude towards
the technology). The interaction-oriented perspective suggests that perceived social losses
caused by interaction between people and the technology affect resistance (e.g., changing
power relationships, job structure) (Jiang et al., 2000; Markus, 1983).

3. Determinants of user resistance in post-acceptance usage behaviour
within a business intelligence systems context

This study develops a framework of user resistance in BIS post-acceptance use that expands
on Markus’s (1983) viewpoints to take into consideration BIS specifics. The factors that shape

914 A. POPOVIČ

user resistance in BIS post-acceptance use are, for the purpose of this study, grouped into
three broad categories: corporate determinants, individual determinants and technological
determinants. Through corporate-level factors, the firm itself is seen as the main factor that
fuels users’ resistance, whereas by individual-level factors this work identifies characteris-
tics that relate to the individual as additional determinants of user resistance. Technology
factors relate to the characteristics of the system and its output that draw users towards
resisting post-acceptance use. Such categorisation allows better fit of previously proposed
perspectives with the context under study, namely BIS.

3.1. Corporate-level factors

Organisational culture continues to be cited as an important factor in the success or failure
of IS implementation projects (Cooper, 1994; Jackson, 2011; Leidner & Kayworth, 2006).
On such a subset of organisational culture, particularly relevant for the BIS post-acceptance
context is information culture. Within it, the value and utility of information in achieving
operational and strategic success is recognised and information forms the basis for organisa-
tional decision-making (Curry & Moore, 2003). Early research has established a developed
information culture to be positively associated with organisational practices, such as infor-
mation utilisation, that lead to successful firm performance (Ginman, 1988). To foster the
development of information culture, firms must nurture such values, norms and practices
that have an impact on how information is perceived, created and used (Choo, Bergeron,
Detlor, & Heaton, 2008; G. Oliver, 2003). These information behaviours, norms and values
echo the firm’s environment towards the use of information in a trustful manner, the will-
ingness to use and trust institutionalised information over informal sources, the extent to
which information about performance is continuously presented to people to manage and
monitor their performance, the openness in reporting and presentation of information,
the willingness to provide others with information in an appropriate and collaborative way,
and the active concern to think about how to obtain and apply new information in order to
respond quickly to business changes and to promote innovation in products and services
(Choo et al., 2008; Hwang, Kettinger, & Yi, 2013). Once a BIS is deeply embedded into work
activities and decision-making, the lack of appropriate cultural values, as depicted above, can
decouple users’ motivation towards post-acceptance BIS use from the understanding of the
importance and value BIS bring to firms (Li et al., 2013), resulting in increased likelihood
of user resistance. I, hence, propose that:

P1. Low levels or absence of information culture will increase the chance of user resistance towards
BIS use.

Through the lens of psychological contract theory, Klaus and Blanton (2010) emphasise
communication as an important corporate-level determinant of user resistance in the
implementation of enterprise-wide systems. Specifically, a common part of the psychological
contract between employees and the firm is a belief that management will keep employees
informed (Guest & Conway, 2002). Yet, in an enterprise-wide implementation, a lack of
communication often exists, not informing the users about the benefits of the system and
the reasons for change (D. Oliver & Romm, 2002). However, by communicating a clear
vision, the psychological contract of users becomes more aligned with the reality of the
firm (Klaus & Blanton, 2010). Moreover, frequent communication in post-acceptance stage

ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 915

leads to the psychological contract changing incrementally, which is less likely to lead to
a psychological contract breach in the form of user resistance. Therefore, the following
proposition is suggested:

P2. It is expected that lack of communication from management will lead to user resistance
towards BIS use.

Training is another organisational issue relevant for deriving the most value from post-ac-
ceptance BIS use. Prior IS research looking at the causes of user resistance to IS implemen-
tation suggests training can be tricky when users perceive training to be a waste of time,
that trainers are inept, the timing of training is unsuitable, or if there is a lack of training
(Kim & Kankanhalli, 2009; Klaus & Blanton, 2010; Krovi, 1993). Users may expect that
suitable training will complement new job requirements while problematic training may
be considered a cause for resistance (Hirschheim & Newman, 1988). Hence, the following
is proposed:

P3. Lack of appropriate training will likely lead to user resistance towards BIS use.

Last, but not least, IT adoption literature emphasises IS service quality as an important factor
when considering continued IS use and IS effectiveness and has been extensively investigated
over the past three decades (Pitt, Watson, & Kavan, 1995). DeLone and McLean (Petter,
DeLone, & McLean, 2013) extended their IS success model by highlighting the importance
of service quality in IS success. Service quality is often regarded as a multidimensional con-
struct that mimics responsiveness (i.e., willingness to help customers and provide prompt
service and help when needed), assurance (i.e., knowledge and courteousness of support
staff and their ability to inspire confidence), empathy (i.e., individualised attention the sup-
port staff gives to BIS users) and reliability (i.e., the ability to perform the promised service
dependably and accurately) (Pitt et al., 1995; Xu, Benbasat, & Cenfetelli, 2013). When users
perceive service quality to be at insufficient levels, they may put less trust in BIS solutions
to readily service their information needs. Users may expect that good service quality will
complement their BIS use experience while deprived service quality may be contemplated
as a cause for resistance (Pitt et al., 1995). I, hence, propose that:

P4. Lack of appropriate BIS service quality will likely lead to user resistance towards BIS use.

3.2. Individual-level factors

Resistance theory suggests that for resistance of an individual to occur, some threat has to be
perceived. The IS-business relationship literature and resistance literature have long empha-
sised perceived loss of power as a significant determinant of user resistance towards IT use
(e.g., Jiang et al., 2000; Lapointe & Rivard, 2005; Smith & McKeen, 1992). From a decision
support system perspective, integrating the system in work processes will likely result in
increased access to information and information sharing that, in turn, draws control over
information and decision influence away from certain individuals (Borum & Christiansen,
1993; Kim & Kankanhalli, 2009; Robey, 1987). Although IT-enabled information sharing
is generally regarded as a positive contributor to performance (Mithas, Ramasubbu, &
Sambamurthy, 2011), it can also be potentially perceived as curbing an individual’s posi-
tion of control within the decision-making process and further breaking down established

916 A. POPOVIČ

monopolies linking to the dissolution of existing power structures (Griffiths & Light, 2006).
I therefore formulate the following proposition:

P5. If an individual perceives the threat of losing power as a result of routinisation and infusion
of BIS in business processes, user resistance towards BIS use will form.

Another issue concerning the embeddedness of enterprise-wide IS into organisational pro-
cesses is the change in decision-making approach (Smith & McKeen, 1992) they bring
about. There might be problematic changes to existing decision-making approaches and new
approaches not working as expected. Integrating a BIS into some processes may fit some
firms well and not others, forcing them to change their organisational structure to fit the
technology. BIS not fitting well with organisational processes might spark an affective and
emotional experience of disappointment, frustration or even resentment (Klaus & Blanton,
2010), leading to resistance towards employing BIS in the latter stages of IS implementation.
Hence, the following research proposition is suggested:

P6. Changes in existing decision-making approaches resulting from BIS routinisation and infusion
in organisational business processes will spark user resistance towards BIS use if there is lack of
fit between the technology and the processes.

Adoption and further deep integration of BIS in work processes has been suggested to also
have an important impact on decision-makers’ jobs and/or skills (Laursen & Thorlund,
2010). BIS often require that users perform different job tasks or develop new skills and
new ways of thinking for the job. Since employees have certain expectations of their jobs,
a considerable job or job skills change is likely to be considered a burden by the employees
(Jiang et al., 2000; Markus, 1983). Change of job/job skills in post-adoptive IT usage has
been further fought against by users since frequently performed behaviours tend to become
habitual and automatic over time and are hard to adjust (Limayem, Hirt, & Cheung, 2007).
Consistent with this reasoning, it is proposed that:

P7. If an individual perceives the threat of considerable change of job or job skills due to routini-
sation and infusion of BIS in his/her work processes, user resistance towards BIS use will arise.

3.3. Technology-related factors

While corporate-level and individual-level factors contribute to a large portion of user
resistance towards BIS use in post-acceptance stages, technological issues also need to
be considered when evaluating user resistance at the implementation phases. Specifically,
prior studies of enterprise-wide IT implementation success emphasise system issues as a
significant determinant of user resistance (e.g., Hirschheim & Newman, 1988; Markus, 1983;
Martinko, Zmud, & Henry, 1996). It is important to note, however, that system issues have
been considered both through the technological aspect, i.e., the desirable characteristics of
the IS (Petter et al., 2013), as well as in regard to the quality of the information the system
provides, i.e., the desirable characteristics of the IS outputs (Petter et al., 2013). Although
the quality of an IS and its outputs are assessed at the earlier implementation phases (e.g.,
at the acceptance stage), additional challenges may surface at a continuous, deeper level
of use, once the IS gets applied to the full extent of anticipated use (Martinko et al., 1996;
Zhu, Li, Wang, & Chen, 2010). While most of the quality-related problems are accounted
for and addressed, some hidden problems might only appear as the depth and range of
use develops. When BIS become comprehensively integrated into business processes and

ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 917

decision-making routines, their ability to efficiently integrate data from various sources,
provide access to it, perform reliably and deliver relevant information that is complete,
accurate, current and in the desired format tend to become important characteristics of BIS
value (Işık et al., 2013; Popovič et al., 2012). Failing to meet user needs and requirements
in the post-acceptance stage will likely result in user frustration and a decrease in use com-
mitment (Kim & Kankanhalli, 2009). In the BIS context, this is particularly relevant since
user needs are deeply knotted with process management and performance management
practices (Popovič et al., 2012). This leads us to formulate the final proposition:

P8. System issues promote the development of user resistance towards BIS post-acceptance use.

The conceptual framework is presented in Figure 1.

4. Discussion

In developing the proposed framework, the focus has been placed on theory development
rather than theory testing. I believe that earlier presented propositions will serve as the basis
to stimulate empirical studies and move this field of research forward. Researchers are urged
to test these propositions across different firms, yet a series of potential challenges need to
be considered. First, researchers need to ensure that the antecedent condition is met (that
respondents are aware of the BIS post-acceptance stage) when testing the propositions in
practice. One way forward would be through a free association technique to reveal the
phases that come to individuals’ minds when they think of the post-acceptance stage as

Figure 1. conceptual model of the influence of individual, corporate and technology-related factors on
user resistance in post-acceptance business intelligence systems use. source: author’s own.

918 A. POPOVIČ

defined in this work. A second challenge is that it might be argued that representing a firm
as a closed system (Scott, 2003) does not properly capture the fact that firms are ‘open to
and dependent on flows of personnel, resources, and information from outside’ (Scott, 2003,
p. 28). While it has been suggested that various environmental conditions may have an
effect on user resistance (Rivard & Lapointe, 2010), I contend that the aim of the proposed
framework is to provide an initial understanding of the effect of individual, corporate and
technology-related factors on user resistance to BIS rather than to provide a comprehensive
explanation of the determinants of user resistance to BIS.

Nevertheless, the proposed framework also raises a series of opportunities. For instance,
the dynamic and complex nature of individual, corporate and technology-related charac-
teristics influencing the IS post-acceptance stage suggests that user resistance, as depicted
in our framework, may change over time. Therefore, future studies may seek to track this
influence through the use of rich and longitudinal data. Also, in some cases, where volun-
tary IS use is shifting more towards mandatory use, effect size of individual and corporate
factors will be smaller than in firms with a prevailing voluntary nature of IS use (Karahanna
et al., 1999). Last, but not least, recognising the proposed set of factors is not exhaustive,
further research may incorporate additional determinants that shape user resistance (e.g.,
country-level determinants).

The proposed work also has several implications for practice. If a firm’s management aims
to take a proactive approach for regular BIS use among decision-makers and act as a BIS
ambassador, it should convey its support through providing an auspicious environment for
BIS. The degree of user resistance to IT is effectively controlled by establishing a milieu of
implementers’ responsiveness to resistance (Rivard & Lapointe, 2012) that is backed up by
pertinent strategies (Jiang et al., 2000). If a firm plays up the significance of continued BIS
use for firm performance, users will be more likely to establish a link between the value of
BIS and decision outcomes. For firms, it is sensible to pay greater attention to issues relating
to power, social status and job security when implementing BIS. When considering ways of
promoting BIS, ‘participative’ strategies – where users are actively involved in the process
and in making decisions – were previously identified as being most desirable; in contrast,
‘direct management’ methods, such as arranging job transfers and giving separation pay,
were viewed negatively by employees (Jiang et al., 2000). In addition, a sensible user training
strategy, such as conducting orientation sessions and retraining employees to successfully
use BIS, are also important.

Furthermore, lessons learnt from user resistance to BIS can be leveraged to accommo-
date a higher level of employee resistance, namely resistance to organisation-wide change.
The ability of BIS to tie various levels of decision-making and performance make them an
almost ideal candidate to identify resistance risk factors, analyse them and set up appro-
priate actions to ensure that resistance does not result in major organisational disruptions.

5. Conclusion

The effect of user resistance on IS implementation success is one of the most intriguing,
yet still inadequately addressed fields in IS research. Recently, the need to explore user
resistance factors in different IS contexts and within different IS implementation stages
has been increasingly highlighted. However, user resistance to IT in the post-acceptance
BIS usage behaviour context, has received limited attention. To address this gap, this work

ECONOMIC RESEARCH-EKONOMSKA ISTRAŽIVANJA 919

contributes to the understanding of enterprise-wide IS resistance by offering a conceptual
framework that sheds more light on the resistance factors influencing post-acceptance BIS
usage behaviour. The article identifies individual, corporate and technological determinants
that influence user resistance, and offers a series of relevant propositions grounded in pre-
vious conceptual and empirical studies in IS success, technology acceptance and resistance
to IT literature. On a practical side, several possible strategies to mitigate resistance are also
proposed and discussed.

Disclosure statement

No potential conflict of interest was reported by the author.

Funding

This work was supported by Javna Agencija za Raziskovalno Dejavnost RS [grant number J5-7287];
and the Croatian Science Foundation [grant number IP-2014-09-3729].

ORCID

Aleš Popovič   http://orcid.org/0000-0002-6924-6119

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• Abstract

1. Introduction
2. User resistance in enterprise-wide system implementation

• 3. Determinants of user resistance in post-acceptance usage behaviour within a business intelligence systems context

3.1. Corporate-level factors
3.2. Individual-level factors
3.3. Technology-related factors
4. Discussion
5. Conclusion
Disclosure statement
Funding
References

Mercadosy Negocios

1665-7039 impreso

2594-0163 electrónico
Vol. 1, Núm. 41, enero-junio (2020)

Mercados y Negocios por Departamento Mercadotecnia y Negocios Internacionales. Universidad de Guadalajara se distribuye bajo una Licencia Creative Commons Atribución-NoComercial 4.0 Internacional.
Basada en una obra en http://revistascientificas.udg.mx/index.php/MYN/.

Data and Business Intelligence Systems for Competitive Advantage:

prospects, challenges, and real-world applications

Sistemas de datos e inteligencia empresarial para una ventaja competitiva:
perspectivas, desafíos y aplicaciones del mundo real

Mohamed Djerdjouri

State University of New York at Plattsburgh
(New York)

djerdjm@plattsburgh.edu

Received: October 12th, 2019
Accepted: December 9th, 2019

ABSTRACT

This paper is intended as a short introduction to Business Intelligence (BI) and Analytics
systems. The main aim of the paper is to raise awareness of organizations in the developing
world, about the benefits of these technologies and the crucial role they play in the survival
and competitiveness of the firm in the complex and turbulent global market. For many years,
many small and medium-sized businesses (SMBs) have not followed large organizations in
the implementation of BI technologies. The main reason stated by SMBs is the complexity
and high cost of deploying and managing BI systems. However, according to recent IT
industry survey of SMBs executives, they now realize the crucial role BI systems play in the
company’s performance, and competitiveness and they are now increasingly investing in and
implementing BI technologies.

Keywords: SMBs, turbulent global market, managing BI systems, IT industry.

Jel Code: M15.

Big Data and Business Intelligence Systems for Competitive Advantage: prospects,
challenges, and real-world applications

MERCADOS y Negocios

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RESUMEN

El objetivo principal del documento es sensibilizar a las organizaciones en desarrollo sobre
los beneficios de la tecnología y el papel crucial que desempeña en la supervivencia y
competitividad de la empresa ante el complejo y turbulento mercado global. Durante muchos
años, las pequeñas y medianas empresas (PYMES) no han seguido a las organizaciones
grandes en la implementación del modelo Business Intelligence (BI). La razón principal
declarada por las pymes es la complejidad y el alto costo de implementar y administrar
sistemas de BI. Sin embargo, según una encuesta reciente de la industria de TI a los ejecutivos
de la PYME, ahora se dan cuenta del papel crucial que juegan los sistemas de BI en el
rendimiento y la competitividad de la empresa y ahora están invirtiendo cada vez más en su
implementación.

Palabras clave: PYME, turbulento mercado global, gestión del Business Intelligence,
Industria IT.

Código Jel: M15.

Djerdjouri, M.

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INTRODUCCIÓN

Second to its people, a company’s most valuable asset is information. Information is a critical
resource for any organization. In this rapidly changing global market, consumers are now
demanding quicker, more efficient service from businesses. To stay competitive, companies
must meet or exceed the expectations of consumers. Moreover, the world has witnessed an
information explosion. Data is being generated at a very high pace, and more and more of
this Data is unstructured, which makes its analysis challenging to say the least. Nowadays
Data is seen as a new class of economic assets, just like currency or gold.

Figure 1
The Information Explosion

(zettabyte = unit of information equal to one sextillion (1021) or, strictly, 270 bytes)

Source: Own elaboration.

So to stay competitive and to improve its own performance, a company must make decisions,
often promptly, based on timely and accurate information. To this end, many leading
innovative companies are adopting and relying on Business Intelligence systems to stay
ahead of trends and future events. Also, Business Intelligence (BI) expedites decision
making. This, in turn, helps companies to act quickly and correctly on information before
competing businesses do. The result of all this is a competitively superior performance for
the company, which allows for an appropriate and timely response to customer problems and
primary concerns.

The ultimate achievement is improved customer experience. BI refers to technologies,
applications and approaches practices for the collection, integration, analysis, and
presentation of business information (Hedgebeth, 2007). BI helps managers gain insights into
their own business as well as into the market in general, and it provides them with valuable
facts and information that improves the quality of their decisions. (Chaudhuri, Dayal &
Narasayya, 2011)

Big Data and Business Intelligence Systems for Competitive Advantage: prospects,
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Analytics, on the other hand, is defined as the scientific process of transforming data into
insight for making better decisions. A sound BI system provides the decision-maker with
valuable information, at the appropriate time and in the right format. The ability to mine and
analyze big data gives organizations deeper and richer insights into business patterns and
trends, helping drive operational efficiencies and competitive advantage in manufacturing,
security, marketing, and IT (Ghasemghaei, 2019). Sun and Wang (2017) state that big data
have become a strategic resource for industry, business, and national security. Moreover, Sun
and Wang (2017) affirm that data nowadays have also become a strategic enabler of
exploring business insights and the economics of services.

Figure 2
Data mining

Source: Own elaboration.

BI systems merge data with different formats and from various sources and gather it into
data warehouses or data marts. Then they use Analytics to process these data to provide
historical, current and predictive outlook of business operations and the market in which
they operate. The information is usually presented through a dashboard or analytics
interface. BI software makes analysis and report-making much faster and more reliable.

In her article, Loshin (2012) reported that BI is used to understand and improve
performance and to cut costs and identify new business opportunities, this can include,
among many other things:

o Analyzing customer behaviors, buying patterns, and sales trends
o Identifying opportunities to reduce costs
o Measuring, tracking and predicting sales and financial performance
o Budgeting and financial planning and forecasting
o Tracking the performance of marketing campaigns
o Optimizing processes and operational performance
o Improving delivery and supply chain effectiveness
o Web and e-commerce analytics
o Customer relationship management
o Risk analysis
o Strategic value driver análisis

Djerdjouri, M.

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Jennifer Lonoff Schiff (2013), reports that CIO.com surveyed a sample of BI experts and
IT executives about the benefits of investing in BI systems. The consensus among these
experts is that BI improves the bottom line of businesses. And the fundamental reasons
for that are that BI helps organizations: – Get fast answers to critical business questions;
align business activities with corporate strategy; empower employees; reduce time spent
on data entry and manipulation; gain insights into customers; benchmark sales channel
partners; identify areas for cost-cutting; and boost productivity.

BI simplifies information discovery and analysis, making it possible for decision-makers
at all levels of an organization to quickly and more easily access, understand, analyze,
collaborate, and act on information, anytime and anywhere. BI helps move from just
consuming information to developing in-depth contextual knowledge about that
information. By tying strategy to metrics, organizations can gain competitive advantage
by making better decisions faster, at all levels of the organization. BI is the capability
that transforms data into meaningful, actionable information.

BI software consolidates data from different sources and assembles it in “data
warehouses” or “data marts” that eliminate distinctions in data formats. It then presents
the results through a reporting, analytics or dashboard interface. BI software thus serves
as a common platform for shared, company-wide insight. BI software makes analysis
and report making much faster and more reliable.

TECHNOLOGY AND TOOLS

A typical architecture for supporting BI within a firm is shown in figure 3 below. A BI
architecture is a framework for organizing the data, information management, and
technology components that are used to build BI systems for reporting and data analytics.
The underlying BI architecture plays a vital role in BI projects because it affects the
development and implementation of timely decisions. The data over which BI tasks are
performed are typically loaded into a repository called the data warehouse that is managed
by one or multiple data warehouse servers. The data often comes from different sources,
operational databases across departments within the firm, as well as external sources. The
data have different formats and structures. Also, both structured and unstructured data may
be used. All these data need to be standardized and integrated in preparation for BI tasks. The
technologies for preparing the data for BI are known as Extract-Transform-Load (ETL) tools.
Also, a popular engine tool for storing and querying data warehouses is Relational Database
Management Systems (RDBMS). Large data warehouses usually deploy parallel RDBMS
engines so that SQL queries can be executed over large volumes of data.

Big Data and Business Intelligence Systems for Competitive Advantage: prospects,
challenges, and real-world applications

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Figure 3

Typical Business Intelligence (BI) architecture

Source: Own elaboration.

The technology components, referred to as BI tools in figure 4 above, are used to present
information to business users and enable them to analyze the data. This includes the BI tools
(or BI software suite) to be used within an organization as well as the supporting IT
infrastructure such as hardware, database software, and networking devices. There are
various types of BI applications that can be built into an architecture: – reporting, ad hoc
query, and data visualization tools, as well as online analytical processing (OLAP) software,
dashboards, performance scorecards, data mining engines, and web analytics, to name a few.

Figure 4
Data Integration Architecture

Source: Own elaboration

Reporting tools are an essential way to present data and easily convey the results of analysis.
BI users are increasingly business users who need quick, easy-to-understand displays of
information (Mikalef et al., 2019). And report writers allow users to design and generate
custom reports Ad hoc query tool is an end-user tool that accepts an English-like or point-
and-click request for data and constructs an ad hoc query to retrieve the desired result.
Visualization tools: help users create advanced graphical representations of data via simple
user interfaces. This tool help users uncover patterns, outliers, and relevant facts. Online

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Analytical Processing (OLAP) tools enable users to analyze different dimensions of
multidimensional data. The OLAP server understands how data is organized in the database
and uses special functions for analyzing the data. Examples of analysis tools are time series
and trend analysis.

Dashboards typically highlight key performance indicators (KPIs), which help managers
focus on the metrics that are most important to them. Dashboards are often browser-based,
making them easily accessible by anyone with permission. Performance scorecards attach a
numerical weight to performance and map progress toward goals. Think of it as dashboards
taken one step further. Scorecards are an effective way to keep tabs on key metrics.

Data mining tools allow users to analyze data from many different dimensions or angles,
categorize it, and summarize the relationships identified. Technically, data mining is the
process of finding correlations or patterns among dozens of fields in large relational databases
Web analytics tools enable users to understand how visitors to a company’s website interact
with the pages (Imhoff, Galemmo & Geiger, 2003; Shen, 2013). They perform the
measurement, collection, analysis, and reporting of Web data for purposes of understanding
and optimizing Web usage. They are also used for business and market research, and to assess
and improve the effectiveness of a web site.

BENEFITS

A well-implemented BI strategy can deliver real insight for an organization. BI systems help
the organization make better decisions with higher speed and confidence; recognize and
maximize the firm’s strengths; shorten marketing efforts; improve customer relationships;
align effort with the firm’s strategy and improve revenues and profits (Williams & Williams,
2010).

Moreover, BI systems help firms quantify the value of relationships with suppliers and
customers, and this gives them more leverage during negotiations. Jennifer Lonoff Schiff
(2013) reports that in a survey of executives of “500” companies, they revealed a variety of
benefits these firms, the main ones include: Eliminate guesswork; get faster answers to your
business questions; get key business metrics reports when and where you need them; gain
insight into customer behavior; identify cross-selling and up-selling opportunities; learn how
to streamline operations; improve efficiency; learn what your real manufacturing costs are;
manage inventory better and; see where your business has been, where it is now and where
it is going.

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Without business intelligence, a firm runs the risk of making critical decisions based on either
insufficient or inaccurate information. Robert Eugene Miller (2013) also reports that
executives that a well-implemented BI strategy helps firms in the following ways:
– Quickly identify and respond to business trends
– Empowered staff using timely, meaningful information and trend reports
– Easily create in-depth financial, operations, customer, and vendor reports
– Efficiently view, manipulate, analyze, and distribute reports using many familiar tools
– Extract up-to-the-minute high-level summaries, account groupings, or detail transactions
– Consolidate data from multiple companies, divisions, and databases
– Minimize manual and repetitive work

It is reported in the literature that successful implementation and usage of BI has shown
excellent results in all sectors of the economy- healthcare, e-commerce, government,
industry, etc. On average, companies have reported an ROI of $10.66 for every dollar spent
on business intelligence/analytics. Real-world applications in different sectors of the
economy will be presented in section 5 below.

CHALLENGES

According to the Garner Analytics firm research, 70% to 80% of corporate BI projects fail.
Firms encounter many challenges when developing and implementing a BI strategy. The two
main ones are: user resistance for adoption, Poor data quality, and Others challenges

User resistance for adoption
Like for any new IT system, user resistance is one significant barrier to BI success. Users
resist changing the way they do things unless their current methods are tedious and time-
consuming. Also, many firms make the mistake of believing that if they implement the
system first, people will use it (build it, and they will come cliché). The way around this
pitfall is for the firm to involve all the stakeholders from the beginning o the project and
throughout the implementation process. Users should define what they really need from a BI
project. When the implementation ends, the majority of the users will already be familiar
with the system and know how to use it. They also feel empowered when their suggestions
are implemented. Thus to ensure success, the firm must high rates of user adoption.

Poor data quality
Without the collection, storage, and access to reliable data, a firm cannot get any valuable
and accurate insights into their business and the business environment. Data is the most
essential component of any BI system. The main challenge here is for the firm to make sure
the data stores and data warehouses are in good working order before they can begin
extracting and acting on insights. The risk is that if that is not done correctly, critical and

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strategic decisions will be made based on unreliable information. The firm must establish
and maintain an appropriate level of data quality to feed into the BI system.

Others challenges
The other challenges include breaking down departmental knowledge silos; integrating the
BI tool with other operational, performance management and transactional system;
transforming the workplace from a culture of ‘gut feel’ to one of data-based decision-making;
securing executive sponsorship and necessary financial backing,

Finally, measuring the performance of BI is a significant challenge and can be problematic.
The firm should develop and employ a set of key metrics to help evaluate performance and
return on investment. In practice, many firms use metrics such as the time it takes to answer
user queries, the depth, and usability of the information obtained from the BI tool and, the
number and quality of decisions made as a result of insights generated via the BI tool

BUSINESS AND GOVERNMENT APPLICATIONS

Proper implementation of BI technologies can reap many benefits for the firm. Excellent
results have been reported across all sectors of the economy: healthcare, government, and
industry. It is estimated that for each dollar spent in BI technologies and Analytics
technology, there is, on average a ten dollars return on investment. In this section, a few
successful implementations of BI will be presented. The summaries below are “literally”
taken from the articles in which the cases were published.

New York State Department of Taxation and Finance: Using Business Intelligence to
improve tax revenues and citizen equity (IBM Smarter Planet Leadership Series, 2011)
The New York State Department of Taxation and Finance resolved to make its processes
more data-driven. The Tax Audits department has a team of 1600 auditors. Research has
shown that more than half of U.S. taxpayers willing to take liberties with their taxes when
they sense that the government lacks the information to catch them. The core of the deterrent
is the incorporation of more data sources–combined with the use of predictive intelligence
capabilities–to accurately identify potentially questionable returns.

The main flaw with the current process (“pay and then chase”) was that the problems were
often detected only after refund checks had been sent and cashed. Also, the process was time-
consuming, drained valuable resource and was often fruitless. The department wanted to
change the process to catch and rectify such refunds before they were sent out.

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The system: The New York State Department of Taxation and Finance achieved this goal by
developing a BI system called Case Identification and Selection System (CISS). The system
is not merely used to search for questionable returns patterns with historical data stored in
the department’s warehouse.

The analytics are embedded directly into the mainstream return process. The department uses
predictive intelligence to determine dynamically when to process a refund request and when
to set it aside for further analysis or to reject the refund directly. In a nutshell, the system
compares each open case with profiles of past similar cases to recommend which cases
should be pursued and through which means, to maximize the overall amount of revenue
collected. The results were outstanding. The New York State Taxation and Finance
Department reported the following critical results and benefits:
– $1.2 billion reduction in improper or questionable refunds paid from the State of New
York’s coffers, plus another $400 million reduction projected in 2011
– Dramatic reduction in the costs and inefficiencies associated with “pay and chase” policies
– $100 million increase in delinquent tax collections through the use of optimization
algorithms
– Over a 350% increase in criminal tax fraud investigations due to greater interdepartmental
collaboration on cases.

Business Intelligence and Analytics in Politics: The Real story behind President
OBAMA Election Victory (Siegel, 2013)
Barack Obama’s 2012 campaign for a second term employed more than 50 Business
Intelligence/Analytics experts. The traditional political campaigns up to now spent large
amounts of money focusing on trying to sway swing voters in swing states. The Obama
campaign management hired a multi-disciplinary team of statisticians, predictive modelers,
data-mining experts, mathematicians, software programmers, and quantitative analysts. It
eventually built an entire Business Intelligence/Analytics department five times as large as
that of its 2008 campaign.

What the Obama BI team realized is that presidential campaigns must focus even more
narrowly than that. They applied predictive analytics (BI technology) that pinpoints truly
persuadable voters. The BI team moved beyond simple poll analysis. Its real power came
from in trying to influence the future rather than to speculate on it. Forecasting calculates an
aggregate view for each US state, whereas predictive analytics (BI technology) delivers
predictions for each individual voter.

During the six months leading up to the election, the Obama team launched a full-scale and
all-front campaign, leveraging Web, mobile, TV, call, social media, and analytics to directly
micro-target potential voters and donors with tailored messages. Instead of focusing on just

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“swing” voters (mostly independent voters who have not made up their minds and are
persuadable to vote one way or another “swingable.” The Obama BI team realized that a
“persuadable voter” (swingable) is a person who will be influenced to vote for the candidate
by a call, a door knock, flyer, or TV ad?

The benefits: The Obama BI team predicted an entirely new thing. Beyond predicting which
a constituent was destined to vote, they also predicted whether each individual voter would
be persuaded by campaign contact. The best way to do persuasion is to predict it. Beyond
identifying voters who will come out for Obama if contacted, the BI models had to
distinguish those voters who would come out for Obama in any case as well as those who
were at risk of being turned off by campaign contact and switching over to for vote for the
opponent.

The necessity was to learn to discriminate, voter by voter, whether contact would persuade.
There were only four especially close states in the 2012 election. Only Florida, North
Carolina, Ohio, and Virginia were decided by less than 5 percentage points. The smallest
number in 30 years (Reagan vs Mondale).

The results: More voters were convinced to choose Obama, in comparison to traditional
campaign targeting. Most people predicted the election to be very close, but in fact, Obama
won a decisive victory. Obama got 51.1 percent of the popular vote to Mitt Romney’s 47.2
percent, a four-point margin. Moreover, President Obama won 26 states and the District of
Columbia, and he also won 332 electoral votes against 206 for Romney (It takes 270
electorate votes to win the Presidential election). It is widely believed that the use of
BI/Analytics by Obama’s Campaign led to the landslide victory by Barack Obama. (Scherer,
2012)

Improving Financial Reserve Management in the Insurance Industry (Microsoft, 2019)
EM Insurance company located in the state of Iowa employs more than 2100 people. With
assets of approximately $3 billion, it sells its products through independent insurance
agencies throughout the United States. EMC Insurances Companies struggled with
pinpointing the right amount of money to hold in reserve against potential case payouts;
keeping too much or too little could be disadvantageous to the firm’s performance.

After experience a run-up in reserves, EMC took steps to improve its financial reserve
management. The company had a great deal of insurance claim data but a limited ability to
analyze the information. Unexpected fluctuations of financial reserves prompted EMC to use
BI technologies to uncover anomalies, correlations, relationships, and patterns hidden within
the firm’s warehouse of claim data. The BI system included predictive modeling for
improved claim outcomes.

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Results/Benefits: The company can identify casualty and worker’s compensation claims that
are likely to have a negative outcome. There is also an apparent enhancement of the accuracy
and reliability of data. Executive decision making is supported with improved analysis.
Expenses are now more effectively controlled.

There are many more success stories in business and government of organizations which
decision process and quality improved significantly with the appropriate implementation of
BI technologies. The main benefit for these organizations was the improvement of their
competitiveness in the Marketplace.

The Gartner report (2019) mentioned that the benefits of fact-based decision-making are clear
to business managers in a broad range of disciplines, including marketing, sales, supply chain
management, manufacturing, engineering, risk management, and finance and HR. Significant
changes are imminent to the world of BI and analytics, including the dominance of data
discovery techniques, more extensive use of real-time streaming event data, and the eventual
acceleration in BI and analytics spending when big data finally matures, said Roy Schulte,
vice president, and distinguished analyst at Gartner. As the cost of acquiring, storing and
managing data continues to fall, companies are finding it practical to apply BI and analytics
in a more extensive range of situations. Nowadays thousands of businesses in all sizes, in all
industries, all around the world are implementing and utilizing Strategic Business
Intelligence (Stackpole, 2011).

The Chief Information Officers focus on BI, and analytics looks set to continue through 2017,
according to Gartner (2013). Gartner’s user surveys show that “improved decision making”
is the key driver of BI purchases. Capabilities that will evolve BI from an information
delivery system to a decision platform will increase the value of BI and drive its growth
(Gartner Report, 2011 and 2019).

CONCLUSION

According to the 2019 Gartner report, by 2020, the number of data and analytics experts in
business units will grow at three times the rate of experts in IT departments, and by 2021,
analytics and BI adoption will increase from 35% of employees to over 50%, including new
classes of users, particularly front-office workers.

BI is essential for the firm’s growth and decision-making. It gives companies a more
structured way to look at data while providing in-depth interpretations. It aids decision
making via real-time, interactive access to and analysis of vital corporate information. The

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business and technological advances promised by BI are still being developed, explored, and
enhanced.

For many years, many small and medium-sized businesses (SMBs) have not followed large
organizations in the implementation of BI technologies. The main reason stated by SMBs is
the complexity and high cost of deploying and managing BI systems. However, according
to recent IT industry survey of SMBs executives, they now realize the crucial role BI systems
play in the company’s performance, and competitiveness and they are now increasingly
investing in and implementing BI technologies.

In the majority of developing economies, firms face much more significant and numerous
challenges because most organizations do not have access to the latest technologies.
However, the biggest obstacle to implementing BI systems stems from the lack of reliable
and quality data. As mentioned earlier in this paper, data is the lifeblood of BI systems.
Today’s data-driven business culture has given organizations new resources and competitive
advantages through the integration of data into everyday operations and strategic business
decisions.

However, the managerial culture should change to adopt more a data-driven decision-making
process. Organizations should realize the importance of collecting, storing, and analyzing
internal as well as external data to harness the information obtained from BI systems and
Analytics to improve business processes, uncover insights into customer buying patterns,
internal cots, revenues, and profitability trends and of other critical business issues.

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G00210036. Gartner.

Gartner. (2019). Gartner market trends report: how to win as wan edge and security

converge into secure access service edge. Core Research Note G0035476. Gartner.

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Loshin, D. (2012). Business intelligence: the savvy manager’s guide. Massachusetts: Morgan

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Scherer, M. (2012) Inside the Secret World of the Data Crunchers Who Helped Obama Win.

Time, Nov. 7, 2012.

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intelligence and analytics. SearchBusinessAnalytics.com.

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Universitario de Ciencias Economico Administrativas and its content may not be copied or
emailed to multiple sites or posted to a listserv without the copyright holder’s express written
permission. However, users may print, download, or email articles for individual use.

Business intelligence for social media interaction in the
travel industry in Indonesia

Michael Yuliantoa, Abba Suganda Girsanga* and Reinert Yosua Rumagita

a Computer Science Department, BINUS Graduate Program-Master of Computer Science, Bina
Nusantara University, Jakarta, Indonesia 1148

Corresponding author (*): mailto:agirsang@binus.edu

Received 14 May 2018 Accepted 25 July 2018

ABSTRACT Electronic ticket (eticket) provider services are growing fast in Indonesia, making
the competition between companies increasingly intense. Moreover, most of them have the same
service or feature for serving their customers. To get back the feedback of their customers, many
companies use social media (Facebook and Twitter) for marketing activity or communicating
directly with their customers. The development of current technology allows the company to
take data from social media. Thus, many companies take social media data for analyses. This
study proposed developing a data warehouse to analyze data in social media such as likes,
comments, and sentiment. Since the sentiment is not provided directly from social media data,
this study uses lexicon based classification to categorize the sentiment of users’ comments. This
data warehouse provides business intelligence to see the performance of the company based on
their social media data. The data warehouse is built using three travel companies in Indonesia.
As a result, this data warehouse provides the comparison of the performance based on the social
media data.

KEYWORDS Business intelligence, lexicon based classification, sentiment analysis, social
media

1. INTRODUCTION
The development of air transportation and
airlines in Indonesia is increasing. This is
marked by the growing number of airlines that
have sprung up by offering both domestic and
international travel routes that make the
competition more competitive. With
competitive competition, many airlines offer
promotions that can be an attraction for
consumers. This is certainly a great
opportunity for business people to use
information technology. The development of
telecommunication and computer technology
led to changes in the pattern of instant
purchasing, online reservations, and the
ticketing process, which in the aviation world

is often called the online system or electronic
ticketing (Atmadjati, 2012).

In Indonesia electronic ticketing providers
are becoming more common, so competition is
increasing. Because business competition
requires price matching, companies must
compete to attract consumers as much as
possible in order to survive. Many companies
use media for marketing. This includes social
media, like Facebook and Twitter. With social
media, customers can easily contact the
company (customer service). Businesses start
looking at such technologies as effective
mechanisms to interact more with their
customers (Ali Abdallah Alalwan, et al. 2017).

Social media has become the largest data
source of public opinion (Shuyuan Deng, 2017).

Journal of Intelligence Studies in Business
Vol. 8, No. 2 (2018) pp. 77-84
Open Access: Freely available at: https://ojs.hh.se/

78
Indonesia has the fourth most Facebook users
in the world. Therefore, this study focuses on
the relationship of social media use, namely
Facebook and Twitter, to see the interaction
between companies and consumers.

Data that exist in social media can help us
to do the analysis to help companies get
feedback from consumers. The data that can be
retrieved include “like, comment, and share”
information. Sentiment analysis can be used to
process comments in order to get feedback on
the nature of the comment, good or bad (He,
Zha, & Li, 2013). Poor comments can be used
as advice and input for the company in the
future (Saragih & Girsang, 2017).

In this study, using existing data in social
media Facebook and Twitter is expected to
create business intelligence that can help
analyze travel business companies in
Indonesia with social media data interaction.

2. CONCEPTUAL BACKGROUND
In this chapter, we examine the concept and
characteristics of business intelligence and
sentiment analysis using lexicon based
classification.
2.1 Business Intelligence
Business information and business analysis in
the context of business processes are the key
that leads to decision-making and actions that
lead to improved business performance.
Business intelligence can be defined as “a set of
mathematical models and analytical
methodologies used to exploit the data
available to produce information and
knowledge useful for complex decision-making
processes” (Vercellis,2006, Williams, S., and
Williams, N, 2006).

Advantages of business intelligence:

• Effective decisions: Business

intelligence applications allow users to
use more reliable information and
knowledge. The result is a decision
maker can make better decisions and
match goals with the help of business
intelligence.

• Timely decision: Dynamic, where
decisions can be taken quickly. The
result obtained by the organization is
that the organization will have the
ability to react continuously in
accordance with the movements of
competitors and to change when there
are important new market
circumstances.

• Increase Profits: Business intelligence
can help business clients to evaluate
customer value and desire for short-
term profits and to use the knowledge
used to differentiate between profitable
customers and non-profitable
customers.

• Reduced costs: Reducing the
investment needed to use sales,
business intelligence can be used to
assist in evaluating the organization’s
costs.

• Develop Customer Relationship
Management (CRM): This is essentially
a business intelligence application that
applies customer information collection
analysis to provide responsible
customer service responsibilities that
have been developed.

• Reduce the Risk: Applying the business
intelligence method to enter data can
develop a credit risk analysis, looking
at the analysis of consumer activity,
producers, and reliability can provide
insight into how to shorten the supply
chain

2.2 Sentiment Analysis
Sentiment analysis or opinion mining is a
process of understanding, extracting and
processing textual data automatically to get
sentiment information contained in an opinion
sentence. Sentiment analysis is done to view
opinions or opinion tendency of a problem or
object by someone. Sentiment analysis can be
distinguished based on the data source, some of
the level that is often used in research
sentiment analysis is sentiment analysis at
document level and sentiment analysis at
sentence level (Bo,P et al. 2002)

The lexicon-based approach depends on the
words in the opinion (sentiment), specifically
words that usually expresses a positive
sentiment or negative sentiment. Words that
describe the desired state (e.g. great, good)
have positive polarity, whereas the words
describing the unwanted state have negative
polarity (e.g. bad, horrible). One common
approach used in performing sentiment
analysis is using a dictionary based approach.
Because this research is based on Indonesia,
the dictionary will use Indonesian words.
Figure 1 is a positive dictionary and Figure 2 is
a negative dictionary.

3. METHODOLOGY

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Research conducted begins based on the
interest of the writer about the data that exist
on social media.

Therefore, through this research, the author
wants to create a data warehouse for social
media data in order to perform analyses
related to social media interactions. These
include an analysis of how actively the
company replies or communicates with its
customers on social media such as Facebook or
Twitter.

3.1 Crawling Data
Data retrieval is done from selected social
media platforms such as Facebook and Twitter
via the social media API available on each
platform.

Data retrieval is done periodically by
crawlers. The data is taken every Wednesday
and Saturday. This is done because the data
provided by the Twitter API only retrieves data
up to seven days old. For example, data
retrieved on October 18, 2017 from Twitter can
only go back as early as October 11, 2017. Data
before that date cannot be retrieved.

From the data that was regularly taken by
the crawler, was stored on in the form of excel
files.

The types of data stored on each social
media platform are different:

• Facebook: post, comment, reply, like
• Twitter: tweet, retweet, mention

Crawling data in this research uses Rstudio,

for crawling Facebook the Library Rfacebook
was used and for Twitter, TwitterR was used.

3.1.1 Crawling Facebook
In this research, will use three months of data,
from September 2017 to December 2017 from
three companies. The pseudocode used to get
data using Rfacebook in Rstudio was:

– Load Rfacebook
– Connect to Facebook API using

fbOauth
– Get Paget from Official Facebook Page

using function GetPage
– Get all post in Page use GetPost
– Get Like and Comment from Post

(post$Likes & post$Comments)
– Get Like and Reply from Comment

using getCommentReplies
– Export to Csv format

3.1.2 Crawling Twitter
TwitterR uses the Twitter API to get the data.
Because of this, there is a seven day limitation
from the day we request data. The pseudocode
to get the data using TwitterR in Rstudio was:

– Load TwitterR
– Connect to Twitter API using

setup_twitter_oauth
– Search @from Twitter@ example

from:traveloka
– Search “@” example @traveloka
– Search “to” tweet example to:traveloka
– Export to csv format

3.2 Sentiment Analysis
3.2.1 Preprocessing

Preprocessing data data comments from
Facebook and Twitter social media is done by
preprocessing before sentiment analysis.
Figure 4 shows the preprocessing stages.

The first step is case folding. Case folding is
the process of converting words into lowercase.
The purpose of turning words into lowercase is
to eliminate case sensitive errors. The next
step is to filter the sentence. Written words are

Figure 1 Positive dictionary.

Figure 2 Negative dictionary.

Figure 3 Methodology

80

punctuation, number, and website address.
The process of separating sentences into
individual words is usually called tokenization.
The easiest way to turn a sentence into words
is to separate them with spaces. Stemming is
the process of converting words into basic
words.

3.2.2 Lexicon Based Algorithm
The lexicon algorithm converts data via a
function that will process every sentence in the
data source. Figure 5 is the pseudocode for the
sentiment analysis using the lexicon based
algorithm (Chopra and Bhatia, 2016).

1. Enter the text as input.

2. Divide this paragraph into tokens and store the words in
an array list.
3. Select the first word from array list.

4. Fetch the words of database in second array named as
database array.
5. Check whether selected paragraph word matched with
each word of database array.
(i) If match found

(a) Find the sentiment of word from database whether it is
positive/negative or neutral.

(b) Find the exact position of word in the paragraph.

(c) Highlight the word according to their sentiment; make it
green if it is positive, red if it is negative and blue if it is
neutral.
(d) Calculate the score of sentence.

(e) Store the results in database.

(ii) Else match not found

(a) Select next word from the array

(b) Go to step 5.

6. Display the result to the user.

7. Plot the graph according to the results.

Figure 5 Pseudocode for the sentiment analysis using the
lexicon based algorithm.

4. RESULTS
Result from the methodology above are shown
in Figure 6. There are two table facts and five

dimension tables. The two fact tables are: the
fact company activity and fact user activity.
The five dimension tables are: dim user, dim
sentiment, dim company, dim media social,
and dim time.

Dashboard admin activity consists of four
reports (Figure 7). The first report is the report
of admin activity trends during the month, the
second report provides an overview of the
activities undertaken by the admin, the third
report is a report of activity per day while the
latter is an hourly activity. Uniquely by using
the business intelligence program tableau all
existing reports can affect each other, for
example when we click on the first report graph
on the line Traveloka and September all
reports on this page will show Facebook
Traveloka data in September.

Dashboard user activity consists of five
reports (Figure 8). The first report is the report
of user activity trends during the month, the
second report is sentiment analysis report, the
third report is the most active user in social
media, the fourth is user activity by day and
the last is an activity report by hour. With this
dashboard we can analyze who is active during
the month or day or time we choose in the
dashboard.

On the dashboard the activity of the
companies assesed can be seen. Facebook social
media shows that the company Pegi Pegi is the
most active compared to other companies. In
September it was found that Pegi-Pegi made a
ocial media strategy change, which can be seen
in October with a rise of almost 368.81%. The
company, Ticket, had the lowest activity. In
this company there is even a decline in October
and December.

On Twitter, Traveloka has the most activity
compared to other companies. Traveloka has
more than 1,000 activities per month. Other
companies have almost 10 times less activity
than Traveloka. Pegi-Pegi and Ticket had an

Figure 4 Preprocessing stages.

81

increase in November and December. In
November there was a decrease in activity.
Figure 9 summarizes the company activity on
social media.

The most frequent Facebook activity by
companies is reply to comments from
customers. This was most frequently done by
Traveloka, followed by Pegi-Pegi and Ticket. At
Pegi-Pegi the most most common activity was

liking comments from its customers. Figure 10
shows activity by hour.

The companies’ Facebook and Twitter
activity peaked at 16:00-16:59. Traveloka’s
activity peaked at 19.00 – 19.59 while Pegi-Pegi
was most active at 16.00 – 16.59 and Ticket was
most active at 12.00 – 12.59 (Figure 11).

Research conducted during four months of
social media data collection on Facebook and
Twitter, obtained 28,445 comments and

Figure 6 Star schema.

Figure 7 Dashboard company activity.

82

2,379,107 liked statuses by the users (Figure
12). This figure is very high, and reflects how
enthusiastic the users with activities
performed by the company. On social media
Facebook, Traveloka has more enthusiastic
users than the other two companies, this is
evidenced by the existence of 1,386,318 user
activity data points, of which 942,769 activities
occurred in October. When viewed in more
detail, Pegi-Pegi has more active users than
Traveloka in the last two months (November
and December).

From 28,445 comments, Traveloka has the
most negative sentiment with an average of
14.26% negative, 34.51% positive sentiment
and 51.23% neutral sentiment on Twitter.
Tickets have the best positive analytical
sentiment with a value of 44.05%, compared
with negative sentiment which is only 14.10%
and a neutral value of 41.85%. Figure 13 shows

the results of the lexicon-based sentiment
analysis.

The last four months’ data got the names of
users who most actively made comments or
liked a status or comment. In every form of
social media there were users who engaged in
more than 100 activities in the last 4 months
(Figure 14). On Traveloka, the top ten people
engaging had an average activity of 200
interactions, while Pegi-Pegi had an average of
168 activities and Ticket has the lowest
average of 84.

5. RECOMMENDIATION
From the dashboard analysis various
recommendations for companies studied were
obtained.
5.1 Traveloka
On Facebook social media needs to be improved
again because from November there was a
significant decline (23%) compared to the
previous month. At 19.00 – 19.59 the activities
of the Traveloka are recommended to have
more human resources in order to help solve
customer problems.

Figure 8 Dashboard user activity.

Figure 9 Summary company activity. Figure 10 Detail company activity.

83

5.2 Ticket
On Facebook, social media needs to be
improved. In September there were 94
activities, but this declined considerably to 74
activities in December. On Twitter,
engagement should be improved again as
compared to Traveloka, as the activity of Ticket
is lagging behind. For Twitter we suggest
human resources should be available in the
early hours, as in December at 00.00 – 07.00
there are only seven activities, compared with

user activity on Ticket’s Twitter feed of as
much as 85 activities.

5.3 Pegi-Pegi
For Twitter, we suggest increased human
resources in early hours. In December at 00.00
– 07.00 there were 55 activities only compared
with user activity on Twitter Pegi – Pegi as
many as 244 activities.

6. CONCLUSION
Based on the results of the research, there are
several conclusions. By using business
intelligence conducted in this research,
Traveloka has the most interaction in social
media, as compared with Pegi-Pegi and
Ticket.com.

This research provides some suggestions for
the development of business intelligence for
social media interaction. The classification
accuracy can be further improved by using
algorithms and machine learning such as naive
baise classification and in the future data could
also be analyzed to include emoticons for more
complete information from Facebook.

Figure 11 Detail company activity in hour.

Figure 12 Summary user activity.

Figure 13 Sentiment analysis.

84

7. REFERENCES

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M., & Williams, H. E. (2007). Stemming
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Alalwan, A. A., Rana, N. P., Dwivedi, Y. K., &
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Atmadjati, A. (2012). Era Maskapai Saat Ini.
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Barlow, J., & Maul, D. (2000). Emotional Value:
Creating Strong Bonds with Your Customers.
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Bo, P., Lee, L., & Vaithyanathan, S. (2002).
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Budiwati, S. D., & Setiawan, N. N. (2018).
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Chopra, F. K., & Bhatia, R. (2016). Sentiment
Analyzing by Dictionary based Approach.
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Deng, S., Sinha, A. P., & Zhao, H. (2017).
Adapting sentiment lexicons to domain-

specific social media texts. Decision Support
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Girsang, A. S., & Prakoso, C. W. (2017). Data
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Access Service at a Telecommunication
Company. International Journal on
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He, W., Zha, S., & Li, L. (2013). Social media
competitive analysis and text mining: A Case
study in the pizza Industry. Internasional
Journal of Information Management, 462-472.

Moro, S., Rita, P., & Vala, B. (2016). Predicting
social media performance metrics and
evaluation of the impact on brand building: A
data mining approach. Journal of Business
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Ray, P., & Chakrabarti, A. (2017). Twitter
sentiment analysis for product review using
lexicon method. International Conference on
Data Management, Analytics and Innovation
(ICDMAI), 211-216.

Saragih, M. H., & Girsang, A. S. (2017).
Sentiment analysis of customer engagement
on social media in transport online.
Sustainable Information Engineering and
Technology (SIET), 24-29.

Vercellis, C. (2009). Business Intelligence: Data
Mining and Optimization for Decision
Making. Politecnico di Milano: Wiley.

Williams, S., & Williams, N. (2006). The Profit
Impact of Business Intelligence. San
Francisco: Morgan Kaufmann.

Figure 14 Most active users.

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Social business intelligence: Review and research
directions

Helena Giotia, Stavros T. Ponisb* and Nikolaos Panayiotoub

a Hellenic Open University, Greece
b School of Mechanical Engineering, Section of Industrial Management and Operations
Research, National Technical University Athens, Greece

Corresponding author (*): staponis@central.ntua.gr

Received 14 June 2018 Accepted 20 August 2018

ABSTRACT Social business intelligence (SBI) is a rather novel discipline, emerged in the
academic and business literature as a result of the convergence of two distinct research
domains: business intelligence (BI) and social media. Traditional BI scientists and practitioners,
after an inevitable initial shock, are currently discovering and acknowledge the potential of user
generated content (UGD) published in social media as an invaluable and inexhaustible source
of information capable of supporting a wide range of business activities. The confluence of these
two emerging domains is already producing new added value organizational processes and
enhanced business capabilities utilized by companies all over the world to effectively harness
social media data and analyze them in order to produce added value information such as
customer profiles and demographics, search habits, and social behaviors. Currently the SBI
domain is largely uncharted, characterized by controversial definitions of terms and concepts,
fragmented and isolated research efforts, obstacles created by proprietary data, systems and
technologies that are not mature yet. This paper aspires to be one of the few -to our knowledge-
contemporary efforts to explore the SBI scientific field, clarify definitions and concepts,
structure the documented research efforts in the area and finally formulate an agenda of future
research based on the identification of current research shortcomings and limitations.

KEYWORDS Βig data, business intelligence, review, social business intelligence, social media

1. INTRODUCTION
In the last decade, business intelligence (BI)
has proved, beyond any doubt, that it is a
rapidly expanding domain in both research and
business terms with the number of BI related
scientific publications and organizations
embracing BI methodologies, techniques, tools
and platforms rapidly increasing year by year.
This remarkable growth is directly connected
with the abundance of customer/user data as a
result of increased bandwidth, technological
advancements in information systems and
mobile applications and the explosion of user
generated content mostly materialized by

social media and other Web 2.0 platforms.
Nowadays, social media and BI are converging
faster than ever before. The confluence of these
two emerging domains is already producing
new added value organizational processes and
enhanced business capabilities utilized by
companies all over the world to effectively
harness social media data and analyze them in
order to produce added value information such
as customer profiles and demographics, search
habits, and social behaviors.

This point of convergence is exactly the
scientific area where this paper sets its focus
and research efforts, i.e. social business

Journal of Intelligence Studies in Business
Vol. 8, No. 2 (2018) pp. 23-42
Open Access: Freely available at: https://ojs.hh.se/

24
intelligence (SBI), a very new concept trying to
capture this transformation of BI systems in
the era of Big Data and amidst the social media
revolution. This paper constitutes the third -to
our knowledge- effort to explore the SBI
scientific field, clarify definitions and concepts,
structure the documented research efforts in
the area and finally formulate an agenda of
future research based on the identification of
current research shortcomings and limitations.

In doing so, this paper follows a structured
literature review approach utilizing data from
one of the most established academic databases
in the world, i.e. Elsevier’s SCOPUS, and
imposing a ‘search and filter’ process based on
a carefully selected set of inclusion and
exclusion criteria described in detail in the next
section. The collected papers were studied
thoroughly with the objective to initially
eliminate duplicates and critically exclude
papers dealing with SBI superficially,
fragmentally or not at all. At the end of the
literature scrutiny process, 83 papers were
selected for further in-depth, full-text
examination with the objective to provide the
reader with an overview of the main themes
and trends covered by the relevant SBI
literature. The review process imposed on the
83 papers included in the final review sample
produced several interesting findings
regarding the current structure of the domain
and the necessary prioritization of the research
activities for the future.

The remainder of this paper is structured as
follows. The next section provides a brief
theoretical background of the two domains
under study, i.e. BI and social media. It aims to
provide the necessary information for
understanding the importance of Big Data for
BI and the potential impact and
transformative nature that social media have
on existing BI research and practice with a
special focus on User Generated Content
(UGC) and trends related to specific social
media platforms. In Section 3, the
methodological approach to the review and the
results of the selection process are presented,
followed by the review of the selected papers
and the synthesis and taxonomization of the
identified research efforts in Section 4. Finally,
Section 5 attempts a critical discussion of the
review findings in Section 4 and concludes with
a proposed SBI domain research taxonomy and
a suggested list of priorities and directions for
future research.

2. BACKGROUND

Business intelligence (BI) is an “umbrella”
term including a wide range of processes,
applications and technologies through which
various data sources can be gathered, stored,
accessed, and analysed in order to gain
meaningful information crucial for decision-
making (Olszak, 2016). The term, although
growing in popularity recently, was first
introduced more than seven decades ago to
describe “an intelligence system utilising data-
processing machines for auto-abstracting, auto-
encoding and profiling of action points in an
organisation” (Luhn, 1958).

However, only recently it turned to a
prevailing field for academics and practitioners
and a leading commercial concern for most
business organisations. According to Chen et
al. (2012), there are several reasons explaining
this incremented popularity. On the one hand,
there is a great opportunity from the rapid
expansion of readily available web data sources
and on the other, BI tools are becoming more
sophisticated, easier to use and find
applications in many business processes.
Meanwhile, intensive competition and global
economic pressures set the success barrier too
high, leading companies to a continuous fight
for improvement, better quality of service
(QoS) and more productive operations.

Chaudhuri et al. (2011) underline the
declining cost of data storage and acquisition
as an additional reason for the extensive
proliferation of BI systems. The same applies
to hardware, which is becoming more
technologically advanced and less expensive,
allowing for more powerful architecture of data
warehouses.

The implementation of BI provides modern
organisations, even SMEs (Ponis et al., 2013),
with the ability to achieve timely and quality
decision-making, which constitutes a crucial
prerequisite to build a stable competitive
advantage. Upon the effective aggregation of
“intelligent” data regarding the internal and
external business environment, executives are
able to take proactive actions preparing their
firms for future economic trends and
conditions. According to Ranjan (2009), BI is
like a “crystal ball” in the hands of managers,
revealing the best course of action depending
on five major parameters: the company’s
position in relation to its rivals; the overall
strengths and weaknesses of the company;
current and future market demographic and
economic trends; social, political and
regulatory environment; competitions’

25
decisions and strategy and finally, customer
preferences and purchasing patterns.

Beyond any shadow of doubt, the business
landscape in the era of a fast paced and
intensively competitive environment is
dominated by the struggle to proactively
respond to changes, satisfy the increasingly
demanding customer needs and timely decision
making on the best courses to action. BI and
sophisticated analytics provide contemporary
enterprises with the tools, methods and
corporate mentality required to survive the
hard business arena and maintain profitable
relationships with the whole value chain
surrounding their activities.

The concept of participation, on which Web
2.0 is based, has also great economic
implications and opens up significant new
potentials for enterprises (Tziralis et al., 2009).
In this very demanding and fiercely
competitive environment, businesses have
found a powerful ally in the face of social media
applications and their fast-paced advancement
and prevalence in the business and internet
ecosystem. Social media are online platforms
through which users can communicate, share
content and connect with each other. Since
their first appearance in the early 2000s, social
media are constantly increasing in numbers,
types and popularity. According to the
academic literature, social media constitute a
reasonable aftereffect of Web 2.0, an argument
that is summarised in Kaplan and Haenlein’s
(2010) definition: “Social media is a group of
Internet-based applications that build on the
ideological and technological foundations of
Web 2.0, which allows the creation and
exchange of user-generated content”. However,
what clearly distinguishes social media from
other Web 2.0 applications is the element of
social connectivity on a personal level. Within
such sites, users pre-select their connections
and own privacy control over the content they
share (Heijnen et al., 2013).

When it comes to classification, there is no
systematic way in which social media can be
categorised. Indicatively, Diamantopoulou et
al. (2010), propose a rational social media
segmentation based on their major activity (i.e.
communication, collaboration, share, rate and
opinions’ expression) and purpose of use
(leisure, work/business, democratic
engagement). Kaplan and Haenlein (2010),
however, suggest a matrix categorisation
consisting of two social media dimensions,
namely; self-presentation/ self-disclosure and
social presence/media richness.

The outburst of social media and their
increasing popularity has led to an era of fast
and immense internet data generation.
Consequently, the notion of social media
analytics and its utilisation in BI systems has
become a dominant trend in the
entrepreneurial world, due to its huge
potential in added-value applications (Fan et
al., 2015). In the next sections an attempt to
structure the current research domain on the
intersection of these two disciplines is made,
following a systematic literature review
approach.

3. RESEARCH METHOD
According to Hart (1998) a literature review is
an objective, thorough summary and critical
analysis of the relevant available research
literature on the topic being studied. A review
of prior and relative literature of a scientific
area is an essential feature of academic
progress and theory development, since it
creates a solid foundation for understanding
the current research status quo, while at the
same time highlights underdeveloped or
unexplored areas as candidates for future
research. The literature review should contain
processed information from all available
sources, be unbiased to the highest possible
extent, be free from jargon terminology and
supported by a well-defined and consistent
search and selection strategy (Hart, 1998).

This review examines literature
contributions directly addressing SBI, i.e. the
use of social media for BI purposes between
2006 and 2016. Expanding the search before
2006 was deemed unproductive since the
advent of social media in its current form is
connected with the launch of Facebook in 2004.
Previous efforts, like Friendster and MySpace,
are not taken into consideration in this study,
since they never managed to establish their
social media presence and were either defunct
(Friendster) or forced to pivot their offerings
(MySpace).

In this paper, we utilize a systematic
literature review (SLR) approach, which is a
trustworthy, rigorous and auditable
methodology for evaluating and interpreting
all available research relevant to a particular
research question, topic, area or phenomenon
of interest (Keele, 2007). The selection process
was straightforward. Initially, it was decided
that the SCOPUS academic database was
adequate in order to provide this study with a
representative list of relevant contributions,
within the context of this paper. Second, the

26
list of keywords was kept to a representative
minimum by using the strings: “social business
intelligence”, “social media AND business
intelligence”. The keywords were applied to the
title, abstract and keywords sections of
scrutinized publications included in the
SCOPUS database. The search includes
publications in scientific journals, peer-
reviewed conference proceedings and book
chapters. The research focus of our approach
led us to the decision to eliminate books and
editorial reviews. We decided not to exclude
publications in peer-reviewed conference
proceedings, since SBI is a rather new and
emerging scientific area and will be populated
by more than a few first stage publications in
the conference dissemination channel. Other
types of publications such as notes and short
surveys, are also excluded from the study.

The keyword search described above
returned 131 papers published from 2006 to

2016, as shown in Table 1 and Table 2 below.
These initial results show that contributions
using SBI as a term are scarce (14) implying
that, indeed, SBI is a scientific area in its
infancy.

The collected papers were studied
thoroughly with the objective to eliminate
duplicates and then critically exclude the ones
dealing with SBI issue superficially,
fragmentally or not at all, in the case of the
publications included in Table 3. Contributions
that were included in the initial sample
fulfilling the keyword string criteria but not
directly dealing with the study subject were
excluded from the database. Finally, a sum of
83 relevant papers was selected for in-depth,
full-text examination with the objective to
provide the reader with an overview of the
main themes and trends covered by the
relevant SBI literature.

Table 1 Search results for keyword string ‘social business intelligence’.

Table 2 Search results for keyword string “social media AND business intelligence”.

Year of Publication
Source Type

20
06

20
07

20
08

20
09

20
10

20
11

20
12

20
13

20
14

20
15

20
16

Journal Paper 1 1 2 5 6 8 12 16
Conference Paper 1 2 5 8 8 11 13 1

1

Book Chapter 2 3 1 1
Sum = 117 papers 2 3 7 15 17 20 25 28

4. LITERATURE REVIEW
4.1 Descriptive Analysis
As stated in the previous section, the main
body of literature identified comprises 83
papers. While 2006 is the first year of
publication where contributions were sought,
the first published papers found were from the
year 2010, further validating the decision not

to extend the study period prior to 2006. The
allocation of the publications within the
researched period (2006-2016) is presented in
Figure 1.

The allocation of papers in the three source
types, i.e. journal papers, papers in conference
proceedings and book chapters, is presented in
Figure 2. It is noted that contrary to what was
expected there seems to be an even distribution
between journal papers (44.6%) and conference

Year of Publication
Source Type
20
06

20
07

20
08

20
09

20
10

20
11

20
12

20
13

20
14

20
15

20
16

Journal Paper 1
Conference Paper 2 1 1 4 2
Book Chapter 1 2
Sum = 14 papers 2 1 2 7 2

27

proceedings (44.6%), with book chapters
corresponding to the smallest share (10.8%) of
the work published on SBI between 2006 and
2016.

A closer look in the data set shows that a
significant part of the publications (14
documents or 16.9%) originate from the
Institute of Electrical and Electronics Engineer
(IEEE) association (10 conference papers, 3
Journal papers and 1 book chapter). This is no
surprise, since IEEE is a leading organization
with a wide scientific area coverage including
the technical background and information
systems infrastructure necessary for SBI to be
facilitated in companies and other
organizations. In the same direction,
Association for Computing Machinery (ACM)
leads the item count when it comes to
publications in peer reviewed scientific
journals, with its cross-discipline journal
entitled “ACM Transactions on

Management

Information Systems” leading the relative list
with four publications. In Table 3, the number
of papers per journal in the dataset is
presented. The journals are presented in four
categories depending on their main focus and
thematic interest, i.e. information systems in
management, information and computer

science, social networks and miscellaneous. It
is interesting to note the absence of any special
issues dedicated to SBI and the scarcity of
papers’ appearance in more specific domains,
such as social networks and journals.
4.2 Thematic Analysis
SBI, as evidenced from the descriptive
statistics in the previous section, is a relatively
new area, with the first publications referring
explicitly to the term dating back to 2010. For
those critically standing before the rapid
emergence of the subject, SBI is nothing but
the next logical step of BI evolution, providing
enhanced collaborative capability in the
decision-making process of an organization by
adding the analytical capability pertaining to
social media. For others, SBI is a BI paradigm
revolution, especially when combined with the
emergence of Big Data and the ever increasing
variety, volume and velocity with which they
arrive in front of business systems’ queues for
further processing in order to effectively
support decision making. Needless to say that
this duality of perspectives, coupled with the
initial triggering of the term from the IT
business area, has led to a plurality of terms
describing SBI, still serving different business
needs or marketing of IT products, thus
creating confusion and reduced clarity on its
definition. On the academic front, Dinter &
Lorenz (2012), who according to our knowledge
provide the single academic reference
attempting to develop a framework of research
in the SBI area, along the same lines as this
paper, argue that lack of definition clarity for
SBI might lie in the ongoing use of diverse
related terms, coming mostly from industry
literature freely accessible on the web. Zeng et
al. (2010) provide one of the few available
definitions of the term as a set of tools and
techniques that “derive actionable information
from social media in context rich application
settings, in order to develop corresponding
decision-making frameworks and provide
architectural designs and solution frameworks
for existing and new business applications”. In
this paper, the term SBI is explored in
literature and used as the term of focus for the
following study.

In that direction, thematic analysis of
available literature in the SBI research field is
organized into the four following distinct
sections: the use of social media data in BI
systems, SBI tools and techniques, BI
applications in prevalent social media and
finally, industry-specific SBI applications.

Figure 2 Distribution of publications per year across the
study period.

Figure 1 Allocation of publications in source types.

Table 3 Number of papers per scientific peer-reviewed journal.

Journal Title # of Publications

Main
Thematic

Focus
ACM Transactions on Management Information Systems 4

Information
Systems in

Management

Decision Support Systems 1
Journal of Enterprise Information Management 1
Journal of the University of Pardubice 1
Management Decision 1
Production and Operations Management 1
Technology Analysis & Strategic Management 1
Journal of Decision Systems 1
Journal of Destination Marketing & Management 1
International Journal of Services Technology and
Management

1

Intelligent Systems in Accounting, Finance and Management 1
Procedia Manufacturing 1
15
Information Systems 1

Information &
Computer

Science

International Journal of Computer Technology and
Applications

1

Information Systems Frontiers 1
Frontiers in Artificial Intelligence and Applications 1
IEEE Computer Graphics and Applications 1
IEEE Transactions on Knowledge and Data Engineering 1
International Journal of Engineering & Technology 1
IEEE Transactions on Visualization and Computer Graphics 1
KSII Transactions on Internet and Information Systems 1
Knowledge and Information Systems 1
Scandinavian Journal of Information Systems 1
Knowledge-Based Systems 1
Mobile Networks and Applications 1
Sensors 1
Information Visualization 1
Journal of Computer Information Systems 1
Procedia Computer Science 1
17
Journal of Internet Social Networking & Virtual
Communities

1

Social Networks International Journal of Sociotechnology and Knowledge
Development

1

Social Network Analysis and Mining 1
3
The Decision Sciences Journal of Innovative Education 1 Miscellaneous The Scientific World Journal 1
2

4.2.1 The use of social media data in

BI systems
Meredith and O’Donnell (2010; 2011) and
Sathyanarayana et al. (2012) were among the
first to detect the value of social networks in BI

systems, beyond sales and marketing
applications. They developed a framework to
classify the social media functions that foster
the Web 2.0 core concepts of user collaboration
and contribution, and used it in order to exploit
how it can “create more effective and ‘active’ BI
applications”. Shroff et al. (2011) introduced

29
the term “Enterprise Information Fusion” to
describe an emerging BI need across multiple
industries, such as manufacturing, insurance
and retail. The term includes the publicly
available data, derived from social media, that
can potentially be of immense business value
for the enterprise ecosystem. On the same
account, Ruhi (2014) attempted to outline the
undeniable value of social media analytics,
incorporated in a BI perspective. As he
explains, the advantage of social media
analytics in the business environment is
twofold, as it can help organisations “formulate
and implement measurement techniques for
deriving insights from social media
interactions” and, alongside “evaluate the
success of their own social media initiatives”.
Wongthongtham and Abu-Salin (2015)
emphasise the need of evaluation of traditional
BI warehouses, which are more focused on
handling structured internal enterprise data,
in order to support the tremendous volume of
valuable, yet unstructured, social media
information, such as customer reviews and
brand-related posts. Finally, Ram et al. (2016)
conducted a survey in IT consultants and
managers in various industry sectors of China,
in order to prove the paradigm shift that social
media have dictated in business strategies
globally. With a semi-structured
questionnaire, they managed to identify the
critical issues in creating value through Big
Data and social media analytics for BI systems.

Alongside the prevalence of social media in
BI applications, several traditional business
terms were redefined in academic literature in
order to incorporate the new social trends, i.e.
social customer relationship management
(SCRM), digital marketing (Luo et al., 2015),
voice of customer (VoC) and voice of market
(VoM). Bachmann and Kantorova (2016)
separate the original concept of CRM “based
rather on face-to-face and offline
communication in the physical environment”,
from social CRM which is mainly conducted
“through social networks and relationships
within online communities”. Beverungen et al.
(2014) argue that the global penetration of
social networks constitutes a fertile ground for
novel CRM strategies (Rosemann et al., 2012).
After introducing the social CRM emerging
field of research, the authors propose a
framework to exploit Facebook data in CRM
strategies and testify its applicability by
building management reports for the retail
industry. Berlanga et al. (2014, 2016) use
advances in opinion mining techniques and

sentiment analysis to describe the new
opportunities arising in the VoM and VoC
concepts in BI applications. As they explain,
organisations can take advantage of the wealth
of sentiment data in massive social media (e.g.,
social networks, product review blogs, forums)
to ‘listen’ to their customers’ needs and extract
valuable business insights. In the same
context, Lotfy et al. (2016) propose a
framework to integrate customer opinion
streams extracted from social media, with pre-
existing corporate data, so as to constitute an
integrated data warehouse. According to them,
such a multidimensional data base “can
perform advanced analytical tasks and lead to
better insights that would not have been
possible to gain without this integration”.

Chan et al. (2015) deliberately examine the
challenges faced by contemporary BI systems,
associated with user-generated content (UGC)
derived from social media communication
channels. According to them, the available
social data is not fully exploited for three main
reasons: its unstructured format, its subjective
nature and tremendous volume. On that
account, they propose a systematic approach to
social media data analysis, which
counterbalances the aforementioned
challenges and captures the real value of online
social content for BI applications. Likewise,
Tayouri (2015) also draws attention to risks
associated with social media in the corporate
environment, highlighting cyber security
issues, such as fraud through social media
activities, leakage of sensitive business
information and damages to a firm’s
reputation. Hence, he suggests a consistent
cyber security training framework supported
by social media site monitoring tools, able to
assist companies in building a robust SBI
strategy by keeping track of correlated
malicious activities and threats.

4.2.2 SBI Tools and Techniques
SBI tools and techniques is a predominant
research field in academic literature. Data
visualization tools; online analytical processing
(OLAP); UGC and natural language processing
(NLP) techniques; sentiment and opinion
mining in the social media context; and user
profiling and personalized marketing tools are
some of the core thematic areas associated with
contemporary SBI practices.

4.3 Visual Analytics
Zimmerman et al. (2015), Zimmerman &
Vatrapu (2015) and Sigman et al., (2016)

30
highlight the importance of visual analytics
(VA) toolkits in assisting the interpretation of
the unstructured data derived from social
media into meaningful business or educational
insights. Their research project provides a
series of visual dashboards able to
comprehensively project the analytics related
to a brand and its marketing campaigns
outcomes. The technical architecture and the
specific characteristics of this set of dashboards
is further explained and defined in ‘Social
Newsroom’, a prototype VA tool for SBI, which
was developed to “provide practitioners with
user interfaces that can assist them in
interpreting social media data and taking
decisive actions” (Zimmerman et al., 2015). Lu
et al. (2014) proposed a VA toolkit able to
handle “noisy, unstructured data and use it for
trend analysis and prediction” in sales
forecasting and advertisement analysis. Their
data visualization tool was successfully applied
in Twitter users, in order to predict movie
revenue and ratings. Moreover, Pu et al. (2016)
focused on the valuable geo-location data
available in social media applications by
introducing the ‘Social Check-in
Fingerprinting (Sci-Fin)’ tool which offers
organisations “the opportunity to capture and
analyze users’ spatial and temporal behaviors”
through social network check-in data.
Respectively, Wen et al. (2016) suggested an
alternative VA system, called ‘SocialRadius’
that can interactively explore spatio-temporal
features and check-in activities, in a variety of
applications, ranging from BI applications to
transportation and information
recommendation systems. Meanwhile, Kucher
et al. (2014; 2016) presented a VA tool for social
media textual data that “can be used to
investigate stance phenomena and to refine the
so-called stance markers collection” with
respect to sentiment and certainty. Lastly, Wu
et al. (2014) introduced ‘OpinionFlow’ a VA
system detecting opinion propagation patterns
and providing gleaned insights in government
and BI applications.

4.4 OLAP techniques, UGC and NLP
tools

Gallinucci et al. (2013; 2015) defined SBI as the
“discipline of combining corporate data with
user-generated content (UGC) to let decision-
makers improve their business, based on the
trends perceived from the environment”. In
order to enable contextual topics extraction
and aggregation at different levels, they
introduced ‘Meta-Stars’, a model based on UGC

and real-time OLAP techniques. Golfarelli
(2014) demonstrated an empirical application
of a prototype demo of the model in real-world
marketing campaigns, in order to prove its
technical robustness and methodology. He
furthermore presented the available OLAP
solutions, for UGC analysis, that enable
decision-makers analyze their business
environment based on trends perceived from
social media. Lin and Goh (2011) proposed a
least-square (LS) algorithm to model sales
performance and business value derived from
social network data, by emphasizing the role of
social marketer-generated content in
influencing UGC sentiment and attitude. The
authors actually suggest that there is a positive
relationship between “the richness of
information embedded in both user-and
marketer-generated content and firm sales
performance”. Finally, Ferrara et al. (2014)
provide a classification of the available UGC
extraction tools in two main categories, namely
the enterprise and social web data extraction
(WDE) tools, through a structured literature
review. In a natural language processing (NLP)
context, Dey et al. (2011) discuss a series of
methodologies that can be followed in order to
“obtain competitive intelligence from different
types of web resources, including social media,
using a wide array of text mining techniques”.
As they explain, social media do not only
provide valuable competition insights but also
constitute an open forum where customers
express their opinions about different brands’
offerings. Sleem-Amer et al. (2012) introduce
‘DoXa’, a semantic search engine for the French
language, with NLP capabilities and social BI
application. Centering their work on two
separate business cases, the authors explain
how ‘DoXa’ can be applied to discover “hidden
patterns in social media data, using rich
linguistic resources”. Lastly, Bjurstrom and
Plachkinova (2015) propose a controlled
natural language that does not require
advanced technical skills and can be directly
compiled into executable code, for automated
social media data extraction.
4.5 Sentiment Analysis and Opinion

Mining
Sentiment Analysis and Opinion Mining
techniques are two research areas that
attracted the academic interest from the early
stages of introduction of social media as a
powerful leverage for BI systems. Upon the rise
of Web 2.0 and the increasing popularity of
social network sites (SNS), Castellanos et al.

31
(2011), in collaboration with HP and its BI
software solutions, introduced ‘LCI’, a
prototype sentiment analysis platform able to
extract sentiment from textual data in real-
time. The platform’s interface consisted of
multiple chart and visualization options that
dynamically changed as soon as new data was
ingested, exploiting state-of-the art sentiment
analysis algorithms. A year later, Yang and
Shih (2012) proposed a rule-based sentiment
analysis (R-SA) technique “to automatically
discover interesting and effective rules capable
of extracting product features or opinion
sentences for a specific product feature”. That
way, they offered a means of effective and real-
time analysis of the tremendous volume of
data, hidden in social media applications,
regarding customer reviews about business
offerings. In the same direction, Liu and Yang
(2012) developed a buyer behavior prediction
technique, using dynamic social network
analysis and behavior pattern mining
algorithms on e-commerce purchases and viral
marketing applications. Qazi et al. (2014)
focused on the suggestive type of customer
reviews, found on online review forums, by
combining machine learning techniques and
sentiment analysis. Their findings suggested
that sentiment analysis “achieves maximum
performance when employing additional
preprocessing in the form of negation handling
and target masking, combined with sentiment
lexicons”. Later, Colombo et al. (2015)
compared two novel methodologies for
sentiment analysis with cross-industry
application, by using secondary unstructured
textual data from Twitter, Yelp and Cars.com,
while Kim and Jeong (2015) applied their
opinion mining methodology in online reviews
about the oldest instant noodle snack in Korea.
Finally, Nithya and Maheswari (2016)
implemented a scoring system technique to
identify the most promising features of a
product offering, consisted of two rating
attributes, namely the ‘sentiment score’ and the
‘feature score’. Their technique provides
managers with valuable insights regarding
future demand, brand promotion and product
penetration.
4.6 User Profiling and Personalized

marketing tools
SBI tools and techniques for customer-centric
marketing applications constitute another
popular research field in academic circles.
Personalized advertising messages, based on
intelligent user profiling, is top priority for the

contemporary business world, striving to
survive in a highly competitive and globalized
environment. Ranjan et al. (2014), using an
association rules mining (ARM) algorithm,
exploit social media data to locate tie-strength
networks and active friends, in order to be used
as a basis for targeted and relevant advertising
campaigns. Yang and Chen (2014) introduce a
novel profile expansion mechanism which
enhances the effectiveness of personalized
recommendation systems in social
bookmarking sites to assist companies in
developing “effective service offerings that are
better tailored to their customers’ needs”
(Gronroos, 2008). In a more targeted approach
about accurate profiling of social media users,
Liu et al. (2014; 2015) develop ‘HYDRA’, a
solution framework to identify linkages across
multiple social networks and discover
correlations between different user profiles.
The authors argue that ‘HYDRA’ can be a
profitable addition to existing BI solutions, as
it was successfully implemented in a ten-
million data base and correctly identified real
user linkage, across seven dominant social
network platforms, outperforming “existing
state-of-the art algorithms by at least 20%
under different settings”. Finally, Yang and
Chang (2015) highlight the knowledge gained
from social tagging system (also known as
folksonomy) as an invaluable asset for
enhancement and upgrade of existing BI
applications. On that account they employ
Delicious, an established social bookmarking
service, to construct “a statistical-based
thesaurus, which is then applied to support
personalized document clustering”. Their
empirical study indicated that such services
improve the overall quality of SBI systems, and
promote their efficiency in handling targeted
marketing applications.

4.6.1 BI applications in prevalent
social media

A fairly important percentage of existing
academic literature on BI focuses on specific
social media use-cases and their potential
applicability on corresponding systems. Tools
and techniques able to extract value added
data from popular social network platforms,
such as Twitter and Facebook blogs or websites
containing customer review content, are among
the most preferable research subjects.
4.7 Twitter
Rui and Whinston (2011) argue that Twitter
hides a huge business potential as a base

32
platform for BI applications, given its valuable
structural information and the tremendous
volume of data flows that are produced by its
users in real-time. Within this context, they
introduced a Twitter-based BI system for
revenue forecasting, which was successfully
implemented in movie box office revenue
prediction, achieving remarkable results.
Seebach et al. (2012) focused on the corporate
reputation management area and how firms
can use social media intelligence in order to
handle reputation threats timely and
effectively. By using sentiment and manual
content analysis techniques on Twitter,
regarding posts about a large American bank,
they showed “how social media might impact
corporate reputation and what organizations
can do to prepare themselves”. Lee et al. (2013)
used Twitter as a real-time event detection
system for crisis management and BI
applications. Their proposed framework is able
to detect emerging events from social network
streams and “accurately extract ontology
entities associated with specific events for
decision supporting applications”. O’Leary
(2015) highlights ‘Twitter Mining’ as an
invaluable asset for the majority of Fortune
100 companies. In his paper he reviews some of
the most prevailing BI applications of Twitter
data extraction on a prediction, discovery and
informative basis. In the same year, Arora et
al. (2015) applied sentiment analysis tools in
order to investigate whether tweets provide a
sufficient ground to gain useful insights on
competitive brands, using the smart-phone
industry. Their results showed that although
Twitter data is rich regarding costumer
sentiments, the exposure of different brands
varies significantly making their comparison a
rather ambiguous task. In a similar approach,
Chilhare et al (2016) designed a marketing-
driven competition analysis tool to “recognize
specific areas in which businesses are leading
and lagging”. In their paper, they propose a
methodology combining NLP techniques and
sentiment benchmarks, in order to analyze and
structure multilingual Twitter data for
competitive FMCG companies into meaningful
business insights. Sijtsma et al. (2016)
introduced ‘Tweet-viz’, an interactive tool to
assist companies in actionable information
extraction from unstructured textual Twitter
data. In their paper, they prove that Twitter
can provide BI systems, customer preferences,
demographics and location data. Completing
the Twitter BI academic cycle, Piccialli and
Jung (2016) summarize the business-

generated tweet content in three categories;
namely informative, advertising or a hybrid of
the two. According to their estimations, the
hybrid approach increases customer
engagement and promotes UGC activity with
brand related content.

4.8 Facebook
According to scholars, Facebook, apart from
being the most dominant social network on a
global basis, contains such a high volume of
UGC data that it could also turn to an
alternative customer relationship
management (CRM) platform, replacing
traditional in-house corporate software.
Bygstad and Presthus (2013), conducting a
case study on two Scandinavian airliners’
pages on Facebook during the ash crisis in
April 2010, showed that CRM through the
platform proved more effective in terms of
dynamic interaction and customer
engagement. Milolidakis et al. (2014), aiming
to provide a generic framework for social media
data extraction and transformation into
meaningful business insights, used Facebook
fan pages of three Greek communication
service providers as their case study. According
to their findings, Facebook includes data
capturing of all the standard BI indicators, and
moreover provides additional user sentiment
information through artifacts features, such as
the “like” button, that can turn to intelligent
business statistics through visual excavation
tools.
4.9 Blogs and Micro-Blogs
Banerjee and Agarwal (2012) used a nature-
inspired theory to model collective user
behavior from blog-originated data in order to
explore its application on BI systems. Based on
swarm intelligence, “where the goal is to
accurately model and predict the future
behavior of a large population after observing
their interactions during a training phase”,
they concluded in promising results about blog
value in trend prediction applications.
Meanwhile, Kulkarni et al. (2013) draw
attention on the importance of social media
brand propagation enablers. On that basis,
they study the degree of customer engagement
through blog contents and the corresponding
analytics for BI systems. Obradović et al.
(2013), in the context of the ‘Social Media
Miner’ project combined textual analysis
methods with a blog processing technique to
“aggregate blog articles of a specific domain
from multiple search services, analyze the social

33
authorities of articles and blogs and monitor
the attention they receive over time”, in order to
provide a highly automated BI tool. Lastly,
Jingjing et al. (2013), using as a reference the
Chinese micro-blogging platform ‘Sina Weibo’,
conduct a social influence analysis to discover
“information retrieval, recommendations and
businesses intelligence opportunities”.
According to their findings, their proposed
framework can overcome difficulties related to
volume and complexity found on micro-
blogging platforms and can find numerous
applications in BI systems.

4.10 Amazon.com
Social media, based on principles and
technologies deriving from the user-centered
Web 2.0, constitute by definition an open
platform where users can express their
sentiments, share their knowledge and build a
social environment. Within this context,
consumers exchange their opinions towards
different brands, share their experiences
through word-of-mouth (WOM) and provide
their own reviews. The importance of social
activity related to brand offerings and the
added-value of publicly available customer
reviews has naturally attracted the interest of
the business and academic world. Zhang and
Chen (2012) studied the business impact of
social media and UGC in sales and marketing,
by applying text mining techniques and a set of
innovative metrics focusing on customer
reviews on two popular e-commerce websites,
namely BN.com (Barnes&Noble) and
Amazon.com. According to their findings, user-
generated reviews have serious effects on
product sales and should be consistently
processed by BI systems, through carefully
selected measures. Similarly, Ngo-Ye and
Sinha (2012) argue that customer-generated
reviews in e-marketplaces “are playing an
increasingly important role in disseminating
information, facilitating trust, and promoting
commerce”. On that account, they developed an
Amazon.com based tool to automatically
identify the most important reviews and
provide meaningful customer feedback.
Finally, Zhang et al. (2013) in an attempt to
further explain how WOM is affecting product
sales, they combined network analysis with
textual sentiment mining techniques to build
product-comparison networks consisted of
customer reviews. Their empirical study on
Amazon.com suggests that it is imperative for
firms to understand and manipulate the WOM
process taking place in social media, in order to

survive in the increasingly competitive online
landscape.

4.10.1 Industry-specific SBI
applications

The integration of social media analytics in
BI systems is a need soon realized both by
organisations and academia. SBI tools and
techniques are not limited in a specific area but
have rather a cross-industry application, a fact
that is clearly reflected in the existing
literature. Heijnen et al. (2013) argue that the
potential of social medial data is invaluable for
multiple facets of BI systems, yet it is “largely
unused by companies, and it remains unclear
what data can be useful for which industry
sectors”. Their findings indicate that key
performance indicators typically differ between
industry sectors and therefore SBI metrics
should accordingly adapt to their
corresponding needs. The need to approach the
matter from industry-specific perspectives led
to a series of academic publications focusing on
distinct sectors: education, automotive,
pharmaceutical, cosmetics, tourism, fashion,
government and politics.
4.11 Education
Moedeen and Jeerooburkhan (2016) focus on
the higher education sector to explore how
“social media strategies can be aligned with
business strategies to help universities gain a
competitive edge”. Using the Facebook page of
a university as their case study, they argue
that higher education organizations pay
attention solely to advertising and reputation
management aspects, while neglecting other
business objectives that could be met through
a holistic SBI application.

4.12 Government and Politics
Bendler et al. (2014) associate static
environmental characteristics with dynamic
user-generated content from social media to
explain and predict criminal activity in
metropolitan areas. By combining traditional
statistics, such as zero-inflated Poisson
regression and geographically weighted
regression with social media data, they provide
a framework that enhances the accuracy of
criminal activity forecasting. Meanwhile,
Chung et al. (2014) developed an approach to
pinpoint opinion leaders in social networking
sites that could be approached by policy
makers to collaborate and “bring about change
in the communities and the general public
welfare”. In a more generic approach, Golfarelli

34
(2014; 2015) studies SBI options in politics.
According to him, processing of user-generated
content through a robust SBI system could
prove invaluable for political entities in order
to align their governmental decisions with
environmental trends and public opinion.
Finally, Beigi et al. (2016) explore crisis and
disaster management through sentiment
analysis and social media visual analytics.
According to them, individual posts in social
media about natural disasters and
emergencies can be used as inputs in
governmental SBI systems “to improve
situational awareness and crisis management
(…) while assisting in locating people who are
in specific need during emergency situations”.

4.13 Automotive
Abrahams et al. (2012) introduce a decision
support technique for vehicle quality
management designed to identify and
prioritise automotive defects, deriving from
reviews in vehicle enthusiasts’ online forums.
They suggest that conventional sentiment
analysis does not suffice to efficiently detect
customer complaints and therefore, BI systems
should incorporate advanced text mining
algorithms specifically designed for social
media applications. Baur et al. (2015) also
focus on the vehicle industry by exploring
Chinese auto forums as a new proactive means
of market research. According to them,
although the increasing popularity of social
media offers a fertile ground for novel
marketing techniques, there is a number of
arising challenges to be confronted, namely the
tremendous volume of posts, their
unstructured format and the wide range of user
languages requiring complex natural language
processing techniques. On that basis, they
propose ‘MarketMiner’ a novel framework for
“search, integration, and analysis of cross-
language user-generated content”, specifically
designed for the competitive automotive sector.
One year later, Baur (2016) examines
alternative applications for ‘MarketMiner’ in
public administrative bodies and commercial
firms. His results indicate that the tool can
significantly improve the processing of multi-
source and multi-language social media
generated content and apply to cross-industry
SBI systems.

4.14 Pharmaceutical
According to Basset et al. (2012) the social
media sphere is a challenging environment for
the pharmaceutical industry, as it is associated

with a number of ethical and legal issues
imposed by governments globally. However,
SBI systems can prove valuable to such an
antagonistic sector mainly for marketing,
customer relationship management and
competition monitoring applications. Bell and
Shirzad (2013a; 2013b) propose a social media
data extraction model to assist pharmaceutical
companies to effectively position themselves in
new marketplaces. According to them, social
media networks offer a channel of
communication for business-to-business
environments and can enable companies to
connect with all the actors of their value chain
(i.e. customers, partners and even competitors)
on a real-time, global basis. Finally, He et al.
(2016) using the three biggest drugstore chains
in US as their case-study, suggest a model for
competitive strategy formulation by applying
quantitative analysis, sentiment analysis and
text-mining techniques in social media UGC
content. Their findings indicate that such tools
can prove invaluable if adopted by existing BI
systems.

4.15 Tourism
Online social networks, and Web 2.0
applications in general, are rapidly becoming a
significant marketing channel for the tourist
industry which is challenged by new and
emerging business models utilizing social
media and other crowd sourcing and shared
economy applications, such as Airbnb. In this
new and turbulent environment, social BI can
be a source of critical competitive advantage in
a very demanding and customer-service
intensive industry such as tourism. In that
direction, Palacios-Marqués et al. (2015) study
the effect of online social networks on firm
performance and explore ways of adding value
to established market competences. The
authors conduct a large survey in one of the
world’s largest tourist destination, Spain, with
the participation of top managers from 197
four- and five-star hospitality firms. Their
results show that social BI has a significant
positive relationship with firm performance by
enhancing market intelligence and knowledge
management competences, thus leading to the
acquisition of a significant advantage over the
competition. Remaining in the same
geographic territory but penetrating one layer
deeper in the social BI area, Marine-Roig and
Clave (2015) study the usefulness and
applicability of big data analytics for the
industry and specifically for a smart city
tourist destination, Barcelona. The authors

35
study the online image of the city by analyzing
more than a hundred thousand travel blogs
and online travel reviews by people who have
visited the destination in the last decade. By
extracting BI through these large volumes of
user generated content, the authors provide an
efficient decision support tool for industry
executives and city officials to develop and
evaluate competition, marketing, branding and
positioning strategies and policies, which will
enhance the city’s image as a smart tourist
destination.
4.16 Fashion and Luxury
The fashion and luxury products industry has
for many years resisted the adoption of the e-
commerce channel, since they associated
anything digital with malpractices such as
discounting, counterfeiting and brand dilution.
This is not the case anymore and currently
emblematic brands, such as Ferragamo, have
entered the e-market arena, which according to
a report from McKinsey and Altagamma (2015)
has reached €14 billion in 2014, a 50% increase
from 2013. This change has created the need
for managing user-generated content in order
to better understand customer profiles, identify
preferences and determine trends, with the
latter playing a crucial role for product
development of companies in the fashion
industry. In that direction, Petychakis et al.
(2016) turn their research focus on a very
important aspect of social media analysis,
which is the identification of opinion makers
within the social media ecosystem, the
monitoring of their behavior and the extraction
of targeted campaigns utilizing their media
presence. The authors present a platform
providing marketers and product designers
with data analytic services for influencer
identification and trend analysis and evaluate
it in a single case from the fashion industry.
Fourati-Jamoussi (2015) explores the concept
of e-reputation by applying BI practices to
analyze the social media presence of four
companies from the organic cosmetics
industry. The author attempts to compare the
reputation of the participating brands by using
different monitoring tools, conducts user
profiling for each brand and finally proposes
recommendations for enhancing marketing
strategies.

5. CONCLUSIONS
In this paper 83 papers, which were published
in the period from 2006 to 2016 dealing with
SBI concept, management, tools and

applications, were collected. The review of
these papers and the analysis of their content,
presented in the previous chapter, produced
useful information, in order to synthesize a
comprehensive research agenda for SBI
including major directions and identified
shortcomings that seem to shape the future of
research in this area. The core focus of the
research, as expected, seems to be the
unearthing of the currently unused, to its full
potential, value of SBI and put it to good use
for the benefit of businesses and organizations
around the globe. In that direction, academic
literature in the novel research field of SBI is
essentially developed around three main
pillars of research orientation.

The first pillar attempts to provide answers
to the question ‘What is SBI and how can it
help a business or organization”, putting SBI’s
business validation and real-life applications
in the epicenter of research, thus given the title
‘business descriptive’. Papers in this pillar are
attempting to highlight the prevalent
acceptance of social media as a source of
business value and the parallel expanded
usage of BI systems through social media data
for multiple operations within companies, in a
variety of industry-specific applications. In
doing so, they provide mostly definitions,
methods, models and frameworks, which
support a wide range of corporate activities,
spanning but not limited to strategic decision-
making functions, business processes’
optimization, operational efficiency
improvement and revenue management.
Within this pillar, one can identify two discrete
waves of publications that can be organized
together based on their focus and objectives.

The first identified wave of publications
within the business descriptive pillar deals
mostly with determining the current status
quo of BI in contemporary organizations and
provides means of expanding its reach through
the exploitation of social media. The first step
in this direction is the identification and
validation of social media potential and
functionalities to act as a consistent BI
decision-making support tool through solid
argumentation and empirical tools like
surveying experts in interested business areas.
At the same time, the second wave of
publications attempts to deal with SBI by
exploring the enhanced capabilities that it
gives to traditional BI systems and how these
can be rethought and restructured in order to
be ready to absorb and process the abundance
and large variety of data that social media

36
produce. What is interesting at this point is the
determination of SBI usefulness and
transformative impact on other established
business functions such as marketing and
customer relationship management (CRM).
The introduction of novel marketing and CRM
strategies such as social CRM, VoM and VoC
as a result of information harnessed by SBI
practices is explored in depth by many
publications and specific algorithmic SBI
techniques and tools, e.g. opinion mining,
sentiment mining, are mentioned as playing a
critical role for business success and
competitive advantage. Finally, the main
barriers/shortcomings identified in this pillar
of literature are the following:

– Probably the most important issue

identified is that of data security and
privacy. There are major concerns for all
levels of data usage, i.e. data creators
(users), data suppliers (e.g. Facebook or
Telco companies) and businesses in need of
the data. What makes the situation even
more complicated is the fragmentation of
legislation between continents and nations,
which make compliance a cumbersome and
sensitive task, especially in the case of
companies operating at a global level.

– The second most important issue identified
in this pillar of literature is data
governance by businesses. In other words,
the ability of companies to streamline their
processes and systems in order to provide
more accurate information, achieve
increased visibility and in essence better
analytics. There seems to be a consensus
that much more is needed to be
accomplished in this area.

– Finally, the third prominent issue

identified in this pillar is process
governance. The huge impact of social
media data on current established business
processes and its transformative effect on
every-day operations, coupled with the
need for the use of more advanced
analytical systems, creates the need for
research on business process management
and reevaluation of traditional processes
and their efficient transformation.

The second pillar attempts to reveal ‘under

the hood’ knowledge and answer the question
“How does SBI work”. It sets technical
implementation in the epicenter of research,

thus this pillar is given the title, ‘technical
descriptive’. Papers in this pillar provide
mostly technical information on algorithms,
techniques and tools that are used in order for
SBI to process social media data and produce
meaningful information to be used directly or
passed for further processing by traditional BI
systems. The prevalent techniques, which
seem to dominate the research interest in this
pillar are those dealing with three major issues
of SBI at the technical level: user profiling,
user (customer) voice translation into
actionable information and data visualization.
The main shortcomings identified in this pillar
of literature are the following:

– There is an increased demand for new AI

algorithms for the automation of the user
generated content extraction and
translation procedure. Current algorithmic
efforts in research are many. Still their
validation in actual commercial
environments does not commensurate with
the materialized research. The need for a
switch towards an AI based, data-scientist
agnostic SBI process is evident in the
literature.

– User profiling and the underlying targeted
marketing and personalized recommender
systems are very important issues in the
SBI literature especially for companies that
are forced to enter the paid advertising
arena by increased competition and the
need to sustain profitability. Although
profiling models and algorithms present a
rapid increase in numbers and variety of
approaches there are still several
unexplored areas in profiling that need
intensified research and investments.

– Data visualization has seen many advances
in the last few years with the emergence of
the dashboard logic in data presentation
and display. Although there is a fair
number of social media tools already
providing services like data collection,
aggregation and analysis into key
performance indicators, there is still a
deficiency in visualizations, especially
when it comes to standards and design
principles, thus making the support from
data scientists and supplementary systems
mandatory.

Finally, the third trend attempts to answer

the question “Does SBI work in real life?” Real-

37
life cases of SBI applications in practice are the
focus of research in this pillar, which thus is
given the title “case descriptive”. Two discrete
waves of publications can be identified. The
first focuses on industry-specific applications
and describes how SBI can provide valuable
services for businesses operating in these
industries. In doing so, papers in this pillar
explore successful applications of SBI in real
business cases, highlighting the cross-
industrial nature of SBI and its potential
impact for a variety of industries and
governmental organizations. Specifically, they
provide focus on the impact of SBI in
traditional business models and processes and
its operational fit in order to support industry-
particular requirements. The second wave of
publications includes papers focusing on
specific social media use-cases, with Twitter
and Facebook being the platforms most widely
used as data providers and application test
beds. Tools and techniques able to extract
value added data from popular social network
platforms, blogs or websites containing
customer review content, are among the most
preferable research subjects. The main
shortcomings identified in this pillar of
literature are the following:

– Utilizing SBI to support real-life cases is a

cumbersome task demanding a holistic
approach, including technological and
organizational aspects, leading to a
complex transition requiring high
executive competences supported by a
global strategy. This is not the case dealt
within the publications studied in this
pillar. Empirical evidence provided is
rather fragmented and cases seem isolated
from the business ‘big picture’, while

connection with ‘bottom line’ metrics is
loose.

– There is a, to some point justifiable, strong
focus of SBI research on social networking
giants, like Facebook and Twitter. Still,
there is an abundance of social networking
sites and other emerging social media
business models like Snapchat, Vine and
Reddit for example, for which the
possibility of more open data extraction and
enhanced algorithmic testing could take
place, that are currently not sufficiently
explored.

At this point, a research agenda can be

formed including eight discrete research
directions, each one dealing with the
shortcomings identified in literature and
discussed previously in this section. In Table 4,
a summary of the literature review’s main
findings is presented. The three main pillars’
research offerings are shortly described and
specific publications are assigned to each one of
the pillars in accordance with their number in
the reference list at the end of this assignment.
The eight research directions comprising the
future research agenda for SBI are categorized
per pillar and presented in Table 4.

Finally, it has to be noted that adoption of
this paper’s findings should take into account
the inherent limitations of this study, which
are:

– The big difference between current and

published capabilities of academia,
especially coupled with the fast pace of the
SBI scientific field. The author is certain
that several research efforts providing
innovative approaches and empirical use

Table 4 SBI future research directions.

TITLE (MAIN RESEARCH
OFFERINGS) PUBLICATIONS (by Ref. Number)

FIRST
PILLAR

Business Descriptive
(Definitions, Methods, Models

& Frameworks) RD1: Data Security & Privacy RD2: Data Governance RD3: Process Governance

[116], [201], [19 ], [20 ], [55], [163], [128],
[129], [173], [166], [22], [190], [30], [158],
[9], [169], [120 ].

SECOND
PILLAR

Technical Descriptive
(Algorithms, Techniques and

Tools)

RD4: Improvement /
Development of new AI
Algorithms – SBI Process

Automation RD5: User Profiling RD6: Data Visualization

[210 ], [211], [64], [211], [212], [65],
[110], [52], [28], [179], [114], [204], [202],
[118], [160], [206], [157], [103[, [113],
[112], [205], [96], [101], [39], [25], [156],
[139], [199], [103], [176], [92].

THIRD
PILLAR

Case Descriptive (Empirical
Evidence / Industry Focus &

Social media Focus)

RD7: Holistic SBI
Approaches – Enhanced

Validation
RD8: Extend Research

Coverage in Social Media

[135], [71], [167], [1], [12], [138], [11],
[210], [171], [16], [17], [107], [27], [102],
[80], [209], [91], [142], [131], [18], [37],
[63], [122], [140], [13], [14], [8], [145],

RESEARCH DIRECTIONS

38
cases highlighting novel applications of
techniques do exist, that are either in
development or already finished but yet
unpublished. Unfortunately, the academic
publishing pipeline has a lead time of six to
eighteen months in some cases and work in
progress papers are relatively low in
numbers, thus creating problems to
researchers who conduct a literature
review.

– Furthermore, significant research on SBI is
done by or on behalf of big players in this
area, such as social media platforms, big
advertising companies and global brands.
These studies are based on home-grown
methodologies, use proprietary tools and
perhaps focal datasets and thus never
made public, making the task of the
researcher who conducts the review even
more difficult.

– Finally, the reader, before adopting the
results of this study, has to consider its
methodological limitations, related to the
selection of the academic library, i.e.
Elsevier’s SCOPUS, the inclusion of
specific source types, i.e. peer reviewed
journal papers, conference proceedings and
book chapters and finally the selection of
the search keywords for conducting the
review.

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