Description
Strategic information is currently considered by proactive companies as a real strategic resource, the same as corporate assets and human resources.
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Perceptions of barriers to Business Intelligence and weak signals
management in Kuwait
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Dr Kamel Rouibah
Department of Quantitative & Information Systems
College of Business Administration
Kuwait University
Abstract
Strategic information is currently considered by proactive companies as a real strategic resource, the
same as corporate assets and human resources. Business intelligence systems created for this
purpose have become a real decision support system to cope with information that informs about
unexpected events and turbulent changes, also referred to as "weak signals", and transform it to
actionable knowledge. Business intelligence is the informational process through which a company
keeps itself aware of opportunities and anticipates threats which occur in its socio-economic
environment. This paper aims to describe: (i) the concepts of weak signal and business intelligence,
(ii) the process that eases weak signal management, and (iii) a number of issues (24) that may
inhibit the appropriation of the concept by practitioners. An instrument was developed and validated
in terms of reliability and validity in a pilot phase. Data collected from 194 Kuwaiti executives
revealed the ten most important issues facing the respondents. These problems are related to six
phases of business intelligence: assessing the need for business intelligence and weak signals,
targeting efforts, tracking weak signals, selection of weak signals, rooting and communicating weak
signals, and processing of collected pieces of information. Results also reveal a significant
relationship between three problems related to the selection phase and availability of R&D activities
in the sampled organizations. This paper discusses these issues as well as their implications for
practitioners
Key-words
Strategic management, business intelligence, environmental scanning, weak signals, anticipatory
information, problems of scanning activities, training, information quality.
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1 Introduction
Studies in the well developed countries have theorized that Early Warning Systems can be quite
successful in helping managers to identify organization's environment strengths and weaknesses.
These systems intend to identify the so-called weak signals at an early stage and try to interpret the
information to ensure a preventive quality assurance. These systems are also able to provide
support to a manager’s need to be aware of external changes which leads to threats or opportunities.
When organizations in less developed countries (LDC) try to adopt such systems, they may face
several issues. We argue in this paper that several managerial and organizational issues must be first
identified and solved before the introduction of these systems into organizations. This paper is a
first step toward understanding the executives' requirements for an early warning system in LDC
before the development of such systems.
By learning from the best practices in Western, developed countries, organizations in LDC are able
to imitate them with positive results. Learning is therefore becoming the most indispensable activity
in the current knowledge-based economy. In the current turbulent environment, in order to compete
and survive, firms must be constantly alert, capable of adapting to fast changes constantly learn,
evolve, and transform themselves rapidly.
An effective strategic response to environmental changes requires a clear perception of events and
trends in organization's external environment. Literature in the West labels these perceptions
through a process named environmental scanning and business intelligence (Rouibah and Ould-Ali
2002)
While several studies investigated business intelligence, environmental scanning and competitive
intelligence in well developed countries, little research has been conducted in LDC. For example, in
the West, the topic was studied in several countries including France (Lesca 1994), the USA (El
Sawy 1995; Beal 2000), Canada (Auster and Choo 1994) and the UK (Xu et al., 2003), Israel
(Fiegenbaum and Latvia (2000), Australia (Hall 2001), Korea (Ghoshal 1988), Greece (Kourteli
2000), and Germany (Voigt and Czaja 2007). However, very few studies were done in LDC
including Nigeria (Sawyerr et al. 2000), Algeria (Rouibah and Bessam 2001), Bulgaria (Elenkov
1997), Tunisia (Chouk-Kamoun and Salles 1998), Thailand (Ngamkroeckjoti and Johri 2000), and
South Africa (Du Toit 2003). While these studies focused on different issues (see Table 1), the
current study aims to investigate the following research question:
(1) What are the major perceived problems encountered by executives when translating
theory of business intelligence into practice?
(2) What is the level of business intelligence awareness of this concept in Kuwait?
This paper focused on the business intelligence perception in Kuwait. This study was conducted
with the potential differences may exist between perceptions in the West and those in LDC in mind.
The remainder of this paper is structured as follows: 1) it discusses the theory of business
intelligence and the problem that may be raised when translating theory into practices; 2) it
describes the used research methodology, following by the description of the study's results, and 3)
it summarizes the results and points to potential implications for practice.
2 Theory and background
2.1 From environmental scanning to business intelligence
Strategic management is defined as the art, science and craft of formulating, implementing and
evaluating cross-functional decisions that will enable an organization to achieve its objectives
(Davis 1989). Strategic management process can be divided into two phases (Wang & Turban
1991): 1) scanning information from the external and internal environment and the interpretation of
such information the outcome of which feeds the strategic decision-making and implementation
phase; and 2) utilizing four basic activities within the strategic decision-making and implementation
phase: strategic formulation, corporate capability planning which attempts to support new
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strategies, real-time strategic response to various surprises in the environment, and implementation
of strategies.
Environmental scanning has its origins in publications on strategic management. It is explicitly
recognized as a starting point and vital phase in the strategic management process (Aguilar 1967).
It refers to the acquisition of information about events, trends and relationships in an organization's
environment, the knowledge which will be of assistance to decision makers (Aguilar 1967). This
concept calls for internal and external diagnosis in order to evaluate strengths and weakness as well
as opportunities and threats in the organization's environment.
External
environment
Internal
environment
Intelligence
Strategic decision making
Strategy Formulation
(planning)
Corporate capability
Planning
Real time
response
Implementation
Phase I: scanning &
Interpretation of information
Phase II: strategic
Decision making
External
environment
Internal
environment
Intelligence
Strategic decision making
Strategy Formulation
(planning)
Corporate capability
Planning
Real time
response
Implementation
Phase I: scanning &
Interpretation of information
Phase II: strategic
Decision making
Figure 1. The relationship between the strategic management process and environmental scanning adapted from Wang
and Turban (1991)
While environmental scanning focuses on all types of information useful to assessment of SWOT
(strengths, weaknesses, opportunities and threats), this paper focuses only one type of information
named as "weak signals", which constitute the core of business intelligence with the purpose of
creating an Early Warning System.
Unlike environmental scanning, business intelligence is an approach that is oriented toward
gathering intelligence from the external environment of an organization.. It is defined as a strategic
approach for systematically targeting, tracking, communicating and transforming relevant weak
signals into actionable information on which strategic decision-making is based (Rouibah and
Ould-Ali 2002).
Business intelligence aims to link an organization to its external environment in a way that will
assure the organization's continued success and make it secure from surprises and unexpected
events. It also tends to help a company exploit information elements (in its environment) to grasp
opportunities and avoid surprises when discontinuities occur.
Business intelligence includes two modes of scanning: reactive (also called ‘problematic search’)
and proactive. Reactive scanning corresponds to an oriented and well defined mode of information
search. The task of collecting information is limited, rational and well organized. The proactive
mode is a continuous search where the scope of scanning is very large, information is little targeted,
information is anticipatory and ambiguous, and tasks of scanning are complex and ill structured.
This is why target information has been termed ‘weak signals’.
Early warning systems are also involved in this process. These systems aim to assist an organization
achieve its competitive advantage through better weak signals management. The emphasis here is
on information systems that enhance strategic decision-making and that support the competitive
strategy of an organization (Wiseman 1988)
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2.2 The nature of Weak Signals and early warning systems
The main intention of Early Warning Systems is to anticipate and to detect hints of possible changes
in a company’s environment to maximize options for reaction. In 1967, AGUILAR published the first
dissertation focussing on this topic from the point of view of business. Even if AGUILAR claims for
a systematic and continuous monitoring of companies´ environment and involving this gained
information in the decision-making process, he offered no way for implementation. Eight years
later, in 1975, ANSOFF developed his concept of weak signals. Based on this concept, ANSOFF
enhances the so-called Strategic Issue Management, which is very similar to the concept of weak
signals. Ansoff was the first author who called senior mangers to keep track of strategic information
that may create discontinuities in the company strategies and to initiate further probing based on
such detection (Walls et al. 1992). He called this kind of information "weak signals" to denote
information that may feed into an unexpected decision or that may lead to opportunities or avoid
unexpected threats (El Sawy 1985). Weak signals were defined as uncertain and fragmented
information about developments and trends. These have not been completely realized, or they have
potential consequences, or are perceived to have a significant impact on organizational
performance, either as threats or opportunities (Rouibah & Ould-Ali).
A weak signal is a piece of information in progress and requires much more effort to be caught.
Weak signals are different than strong signals, in that a strong signal is information that informs
about some thing already happened, or confirms a decision already taken. Consequently, firms have
no response delay. This is the case when date for a request for quote (bidding) is over.
Weak signal management can be rooted in the resource-based theory. Its view of the business posits
that businesses compete on the basis of “unique” corporate resources that are valuable, rare,
difficult to imitate, and non-substitutable by other resources (Conner 1991; Schulze 1992). It
includes tangible, intangible, and personnel-based resources. The resource-based view illustrated
that businesses can differentiate themselves on the basis of weak signal management.
The field of research of Early Warning and Early Warning Systems deals with the identification of
business relevant developments and changes at an early stage. We distinguish a difference between
Early Warning and Early Detection. While the wording of “Early Warning” just signals possible
hazards, “Early Detection“ means that there are also chances, but the company has the "will" or the
motivation to identify possible dangers. However, the pure identification of chances and risks is not
enough; it also is necessary to develop collect, interpret, and proposed appropriate actions/strategies
to face chances and risks.
Management of weak signals is recognized as an important research topic for strategic decision-
making (El Sawy and Pauchant 1988; Martinsons 1994; Freeman 1999). Accordingly, several
experts in the strategic management discipline (e.g. Martinsons 1994, Attaway 1998; Rouibah and
Ould-Ali 2002) built different models to formalize the business intelligence as well as to transform
the weak signal management into feasible processes.
In this paper, we defend the idea that the business intelligence approach should be a continuous and
proactive so that a company may be an exploiter/innovator (Mair et al., 1997). The company should
collect weak signals that are essential for its survival and that have an impact on the company's
performance and informs about events not yet realized. In the strategic management literature,
theorists consider the business intelligence role for a business as a radar for a ship. It aims to inform
managers early enough about interesting opportunities and threats so that they can cope with them
and propose adequate answers. Such a strategy will help to keep a sustainable competitive
advantage.
The particular characteristics of weak signals make them very difficult to manage. According to
Rouibah & Ould Ali (2002) a weak signal is:
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- anticipatory; it informs about changes when they begin to occur,
- uncertain; it concerns events that have not yet been realized,
- ambiguous; its content is usually uncertain or could be deliberately contaminated or
distorted (for example, by a competitor),
- fragmentary; each information element taken alone is insignificant; however, its significance
gradually increases when combined with other weak signs,
- dynamic; it evolves over time,
- cyclical; it has a complex life cycle from growing to declining that varies in its duration and
significance, and
- qualitative; in most cases, numbers are not involved and information may be available in
other forms such as written, verbal, or visual images.
Weak signs are thus subject to perception and interpretation, and multiple meanings are possible.
2.3 Business intelligence process
The assimilation of business intelligence is quite similar to knowledge management in several ways.
Weak signals are tacit knowledge since they represent anticipative pieces of information in the mind
of gatekeepers who believe they might be useful to senior managers. Moreover, each piece of
information is insignificant unless it is interpreted and transformed into actionable information.
Such activity is completed when several viewpoints are combined and opposed.
With regard to weak signal management approach, companies may adopt two different policies.
The first is an offensive strategy via a proactive attitude in that it seizes opportunities by having a
focus on events that have not yet taken place and that are announced by weak signals. The second
policy consists of adopting a defensive or reactive behavior. This is evidence when a company waits
until the best company grasps the opportunity, and then it reacts after tangible benefits are achieved.
Adopting one of the two previous strategies besides the particular characteristics of weak signals
make their management somewhat difficult.
The translation of theories on business intelligence to actionable knowledge useful for decision
making requires the existence of a process model. While several processes were proposed in the
past, this paper emphasizes the model developed by Rouibah and Ould-Ali (2002), see Figure 1.
This process aims to translate weak signals into early warnings that keep management informed of
what's looming ahead in the competitive environment. This process is oriented to ease weak signal
management. This process requires several activities: (i) evaluation of the process relevancy for a
kind company; (ii) the delimitation of scanning areas; (iii) the collection of target information; (iv)
the selection of relevant information; (v) the routing and diffusion of collected information; (vi) the
interpretation of collected pieces of information; and (vii) the integration of processed information
in the decision-making process.
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Action
Targeting
Routing
1
2
4
6
Tracking
2
Processing
interpretation
Selection
3
Evaluation of
Relevancy
0
5
Action
Targeting
Routing
1
2
4
6
Tracking
2
Processing
interpretation
Selection
3
Evaluation of
Relevancy
0
5
Figure 2. Business intelligence process (Rouibah & Ould-Ali 2002)
When executives try to translate Rouibah and Ould-Ali's (2002) model into practice, they may
encounter several obstacles. The following sections will review these problems.
2.4 Obstacles to business intelligence
In trying to review past studies on the subject, we categorized the literature into two groups: studies
based on empirical studies versus studies based on a conceptual problem view. The first category
includes both empirical studies and studies that only mentioned some problems related to business
intelligence, scanning and competitive intelligence, and strategic management. The second category
refers to studies that concentrate on the conceptualization of a specific problem related to weak
signals and then try to propose guideline, methods and methodologies to overcome these problems.
Identified problems were categorized according to Figure 1 that depicts phases of business
intelligences process.
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Activities of business
intelligence process Problems/ obstacles of weak signals management
Support literature –Empirical-
based.
Support literature –Process-
based
1. Inability to perceive potential benefits of weak
signals on the company 's performance
Lesca (1994);
Groom & David (2001);
Rouibah (2003); Voigt & Czaja
(2007)
El Sawy (1985)
Beal (2000); Du Toit (2003)
Wang & Turban, (1991); Lesca
(1994); Rouibah & Lesca (1996)
Ansoff (1975)
2. Difficulty shaping the meaning of several concepts
which refer to "weak signals", "business
intelligence" and managers do not have a clear vision
about their content and usefulness
Lesca (1994); Rouibah (2003)
Voigt & Czaja (2007)
Rouibah & Lesca (1996)
Phase 1: Relevancy of weak
signal management
3. Company culture is not oriented toward usage of
weak signals
Lesca (1994); Rouibah & Bessam
(2001)
Freeman (1999)
N.A
4. Difficulties identifying the company's
requirements in terms of scanning activities
Ghoshal & Westney (1991); Lesca
(1994); Yasai-Ardekani &
Nystrom (1996); Elenkov (1997);
Ngamkroeckjoti & Johri (2000);
Sawyerr et al., (2000); Rouibah &
Bessam (2001); Du Toit (2003);
Xu et al., (2003)
Martinsons (1994); Attaway (1998)
5. Lack of knowledge in on how to start scanning
activities
Auster & Choo (1994); Du Toit
(2003); Lesca (1994); Rouibah
(2003)
Aguilar (1967); Gelb et al., (1991);
Rouibah & Lesca (1996); Attaway
(1998)
6. Uncertainty regarding governmental long-term
policies
Sawyerr et al., (2000); Beal
(2000); Elenkov (1997)
N.A.
7. Lack of qualified gatekeepers to conduct scanning
activities
Lesca (1994) Wang & Turban (2001);
Martinsons (1994); Attaway
(1998); Rouibah & Lesca (1996)
Phase 2: Targeting of weak
signals
8. Problem of legal and ethical practices of scanning
activities
N.A. Gelb et al., (1991)
9. Inadequate management education and training Sawyerr et al., (2000), Rouibah
(2003)
Rouibah & Lesca (1996); Rouibah
(2000);
10.Lacking of motivation and rewards for company
personnel
Lesca (1992); Lesca (1994);
Rouibah (2003)
Wang & Turban (2001);
Martinsons (1994); Attaway
(1998); Rouibah & Lesca (1996)
11. Lack of time allocated to scanning activities N.A. Rouibah & Lesca (1996)
12. Lack of trust and cooperation among members of
the organization
Lesca (1994); Rouibah (2003) Rouibah & Lesca (1996); Attaway
(1998); Wang & Turban (2001)
Phase3; Tracking of weak
signals
13. Lack of support from top management Lesca (1994) Martinsons (1994); Wang &
?
Turban (1991)
14. Frequent changes in the market resulting in a lot
of information and there are no methods to identify
and select relevant information
Lesca (1994) Gelb et al., (1991)
15. Difficulty analyzing the quality of information
sources
Sawyerr et al., (2000); Rouibah
(2003)
N.A
16. Poor quality of collected information N.A Wang & Turban (1991)
17. Lack of data sources where weak signals can be
found
Auster & Choo (1994); Sawyerr et
al., (2000);
Aguilar (1967); Martinsons (1994)
Phase4: Selection of weak
signals
18. Data gathered is not useful since it is used to
confirm only decisions already made and not new
decisions
Lesca (1994) Gelb et al., (1991); Rouibah &
Rouibah (1996)
19. Lack of a formal process to root out & share
collected information
Lesca (1994); Du Toit (2003);
Rouibah & Bessam (2001)
Kourteli (2000); Rouibah & Lesca
(1996); Attaway (1998)
Phase5; Rooting &
communication
20. Employees' resistance to business intelligence
and scanning culture
Rouibah & Bessam (2001);
Rouibah (2003)
Wang & Turban (2001)
21. Inability to analyze collected information and to
generate actionable information
Lesca (1994); Rouibah & Ould-Ali
(2002); Rouibah (2003); Freeman
(1999)
Gelb et al., (1991); Rouibah &
Lesca (1996); Attaway (1998);
Rouibah & Ould-Ali (2002);
Rouibah (2000); El Sawy &
Pauchant (1988)
Phase6: Processing &
interpretation of weak signals
22. Collected data are not often presented in an
adequate format for decision making function
Lesca (1992) Gelb et al., (1991)
Phase 7: Action "relationship
weak signals/ decision
making"
23. No integration of collected information in the
decision making process
Lesca (1992); Groom & David
(2001)
Gelb et al., (1991)
Phase8: Evaluation of current
practices of weak signal
management.
24. Difficulties evaluating current practices of weak
signal management and scanning activities
Ghoshal & Westney (1991); Lesca
(1994); Yasai-Ardekani &
Nystrom (1996); Rouibah &
Bessam (2001); Du Toit (2003)
Aguilar (1967); Martinsons (1994);
Rouibah & Lesca (1996); Chouk-
Kamoun & Salles (1998)
Table 1. Summary of literature review on business intelligence, environmental scanning and competitive intelligence
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In the Arab region, few studies have focused on business intelligence.
Chouk-Kamoun and Salles (1998) studied the application of business intelligence in 24 Tunisian
SMEs. They found that executives don’t perceive the value of weak signals as a factor that
contributes to company performance. Such a result suggests that the majority of small companies
did not perceive benefits from business intelligence.
Rouibah and Bessam (2001) interviewed 12 Algerian SMEs about practices of environmental
scanning. Results showed scanning activities lack efficiency because employees lack the
knowledge to: (i) evaluate current practices, (ii) set up an efficient environmental scanning
process, and (iii) share collected information.
Rouibah (2003) studied the awareness of environmental scanning by executives within 86 Kuwaiti
firms. In particular, the author studied which types of information (control, influence or weak
signals) receive the most attention of executives. Rouibah suggested propagating the culture of
"word to mouth", attention to "weak signals" among employees as well as the culture of
information sharing. In addition, the author examined five problems faced by executives. Results
revealed that the inability to shape the meaning of environmental scanning was the most perceived
problem, followed by the confusion in the exiting of different concepts used to refer to
environmental scanning.
Other researchers studied problems and challenges facing Arab managers in the 19
th
of the 20
th
century, which inhibited the practice of modern management styles (modern development)
(Atiyyah 1993). These include findings that included 1) the turbulent political environment is
unfavorable to the development of modern development; 2) unethical practices of business
activities; 3) managers' concentration on meeting their current needs rather than satisfying long-
term objectives; 4) a shortage of time allocated to planning; 5) the resistance of managers to
changes (existence of doubtful managers who don't believe on trainings effectiveness, and 6)
attendance in training not for the sake to improve their skills but to earn a cash bonus or qualify
for a promotion. He asserted that one of the most critical issues facing Arab managers is
management development and saw a strong link between its success and economic development.
Based on the previous literature review, we can observe the following remarks.
First, past literature has discussed several obstacles, but none of the past studies integrated these
problems in one empirical study nor validated these problems either in the West or in LDC.
Second, many past studies have focused on what companies should do to solve the issues of
business intelligence, rather than to look at what firms actually do. Much prior literature is devoted
to prescriptions or techniques. Moreover, this study also reviewed papers that were published in
Competitive Intelligence Review between 1997 and 2007, and of the 128 published papers only
three have had any solid empirical research content.
Third, most empirical studies on actual practices have tended to focus on the leading firms in well
developed (Western) countries. By contrast, this study looks more at how firms in LDC carry out
business intelligence and thus gives a better guide as to the general level of acceptance and use of
this approach.
Fourth, the study looks at actual business intelligence across a range of different people in the
organization. The instrument-based questionnaire used in the study is completed by people selected
from different management levels (senior, middle, and operational level) and thus gives a broader
organizational and cultural perspective on the role and current perceptions of business intelligence
in firms operating in LDC.
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Fifth, earlier studies treated the business environment as a single entity (except Xu et al., 2003),
while this study is based on a multi-industry approach.
For the purpose of this study, the author has summarized 24 issues that are well grounded in past
literature (see Table 1). The current research is an attempt to test their validity in the Kuwaiti
business environment.
3 Research methodology
Since our interest focuses centers on clarifying the problems associated with business intelligence
from a Kuwaiti executives’ point of view, the survey method, based on a questionnaire, was chosen
as being most appropriate. The designed questionnaire includes three sections. The first one records
data related to company profile (size, annual total revenue, number of total employees, number of
employees involved in scanning activities). The second section highlights information related to
respondents (department, position, and time respondent has been with the company). The third
section includes major obstacles Kuwaiti executives may encounter.
3.1 Sample and procedure
In an attempt to test the validity of the 24 problems (see Table 1), this study targets Kuwaiti
executive as a unit for analysis.
We followed a procedure involving three steps to collect the data and to approach the companies.
First, we have chosen to contact companies through personal visits, using personal networks, the
telephone and, in some cases, e-mail to solicit their participation. Second, the executive is identified
and an appointment was made to visit him. During the appointment, the executive was delivered a
letter that describes the research objectives and the questionnaire. Third, each participant was
briefed on the purpose, content, and procedure for completing the questionnaire and provided with a
copy of the instrument. A letter was also attached to the questionnaire that explains the main
objective of the study and includes definition of key concepts (such as business intelligence, weak
signals, etc.). Fourth, another appointment was made to pick up the questionnaire.
The instrument was written in Arabic and a translated copy into English was also made available. It
was also checked by two faculty members at the College of Business Administration. The
instrument required respondents to provide information based on their perceptions of what was
currently in practice in their organization. Of the 250 questionnaires distributed, 194 were retuned,
giving a response rate of 77.6%.
The following table provides the demographic data about the respondents.
Position of respondents Frequencies Percentage
Operational level 15 7.73
Middle Management 113 58.24
Strategic level 66 34.01
Total 194 100
Time respondent has been with the company Frequencies Percentage
Less than 1 year 11 5.68
1-5 years 72 37.11
6-10 years 52 26.8
11-15 years 26 13.4
More than 15 years 33 17.01
Total 194 100
Table 2. Demographic data and Profile of respondents
34.7 % of participants in this study are free businesses (i.e. owned by a single family) followed by
sales and marketing department (26.8%), while people belonging to the information system
department are not involved in scanning activities. In addition, the majority of respondents belong
to management level followed by the strategic level which depicts the importance of scanning
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activities in Kuwait. 37.1% of respondents have been with their company between 1 and 5 years.
26.8% between 6 and 10 years. The profile of the respondent provides credibility and validity of the
business intelligence practices in Kuwait.
3.2 Profile of companies
Table 3 provides the profile of companies involved in the survey.
Nature of company Frequencies Percentage
Public 34 17.65
Private 160 82.47
Total 194 100
Age of company Frequencies Percentage
Less than 5 years 25 13.22
5.1 -10 years 21 10.35
10.1-20 years 17 8.62
More than 20 years 131 67.81
Total 194 100
Annual turnover (in million $) Frequencies Percentage
Less than 1.6 17 8.88
1.6-3.3 14 7.4
3.3-16.5 45 22.96
16.5-33 70 36.29
33-108.9 24 12.59
109-165 7 2.96
Above 165 17 8.88
Total 194 100
Number of employees in the organization Frequencies Percentage
Less than 100 59 30.41
101-499 40 20.61
More than 500 95 48.96
Total 194 100
Existence of R&D activities Frequencies %
No 62 31.9
Yes 132 68.1
Total 194 100
Percentage of employees involved in scanning Frequencies Percentage
1-5 % 100 51.54
6-20 % 60 30.9
More than 20 % 34 17.57
Total 194 100
Table 3. Characteristics of the companies and practices of scanning
Most of companies involved in scanning are in the finance, banking, and insurance sector (25.3%)
followed by trading and retailers (16%), and by construction (15.3%). The majority of companies
belong to the private sector (82.47%), while a small percentage (17.65%) belong to the public
sector. 67.81 % of the companies have been in existence more than 20 years, while 13.22% have
been in existence for less than 5 years. More than half of respondents (59.25%) work in companies
with an annual turnover comprised between $3.3. millions and $33 millions. 51.04 % of companies
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are small and medium companies (SME), while 48.96% are large firms. Among the sampled
companies, 68.1% have research & development (R&D) activities and 82.5% have less than one
quarter of their employees (?20%) involved in scanning activities.
3.3 Measures
Each of the 24 obstacles to the efficient practice of business intelligence, identified from the past
literature (see table 1), was measured using a five-point scale: strongly agreed (1), agreed (2),
neither agreed nor disagree (3), disagree (4), and strongly disagree (5). The respondents were asked
to indicate on a scale whether each of the factors had been an obstacle to efforts to collect, receive
and disseminate the information from the environment.
3.4 Reliability and validity of the instrument
Before data analysis, content validity and reliability of constructs were examined. To ensure content
validity, a thorough examination was made of the relevant literature. Before questionnaire
distribution, and to further reduce the possibility of non-random errors, a pilot study was conducted
to examine the questionnaire for validity (measuring what is intended), completeness (including all
relevant variable items), and readability/understandability. A pilot phase was conducted with 20
executives and the authors. This was done in order to test the applicability of such instrument. The
results of the pilot study suggested several changes to the questions. These changes were
incorporated in the instrument.
Data analysis & results
3.5 Obstacle to business intelligence
Table 4 provides the survey percentages responses. It provides the analysis of obstacles executives
are facing (1 strongly agree to 5 strongly disagree)
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Problem of scanning
[1]
Strongly
Agree (%)
[2] Agree
(%)
Total [1]
+ [2]
[3] Neither
Agree Nor
Disagree
(%)
[4]
Disagree
(%)
[5] Strongly
Disagree
(%)
1. Difficulty shaping the meaning of several concepts which refer to "weak signals", "business
intelligence" and managers do not have a clear vision about their content and usefulness 28 42.5 70.50 19.4 9.5 0.5
2. Difficulties identifying the company's requirements in terms of scanning activities 17.6 48.4 66.00 19.7 12.8 1.6
3. Lack of knowledge in how to start scanning activities 25.7 40.1 65.80 20.9 12.3 1.1
4. Inadequate management education and training 25.1 40.1 65.20 16.6 12.3 5.9
5. Difficulty analyzing the quality of information sources 19.7 44.1 63.80 14.4 18.6 3.2
6. Frequent changes in the market resulting in a lot of information and there are no methods to
identify and select relevant information 16.6 42.8 59.40 20.3 18.2 2.1
7. Lack of data sources where weak signals can be found 24.2 34.2 58.40 15.3 23.2 3.2
8. Poor quality of collected information 17.2 37.1 54.30 21.5 21.5 2.7
9. Lack of a formal process to root out & share information 16 36.9 52.90 26.2 18.2 2.7
10. Collected data are not often presented in an effective format for planning function 14 38.2 52.20 21 23.7 3.2
11. Employees' resistance to business intelligence and scanning culture 11.5 37.7 49.20 26.8 17.5 6.6
12. Difficulties evaluating current practices of weak signal management and scanning activities 9.8 38.3 48.10 27.9 21.3 2.7
13. Lack of support from top management 19.3 28.3 47.60 21.4 22.5 8.6
14. No integration of collected information in the decision making process 14 32.8 46.80 19.4 26.9 7
15. Uncertainty regarding governmental long-term policies 16.8 29.6 46.40 35.2 15.6 2.8
16. Inability to perceive potential benefits of weak signals on the company 's performance 10.3 35.9 46.20 17.9 30.4 5.4
17. Lack of motivation and rewards 20.2 25.5 45.70 16 26.1 12.2
18. Lack of trust and cooperation among members of the organization 22.7 22.2 44.90 11.9 29.2 14
19. Inability to analyze information and to generate actionable information 15.2 28.8 44.00 25 26.1 4.9
20. Problem of legal and ethical practices of scanning activities 14.3 28.6 42.90 16.2 30.8 10
21. Lack of time allocated to scanning activities 5.6 36.1 41.70 32.2 22.2 3.9
22. Company culture is not oriented toward usage of weak signals 15.6 25.3 40.90 22.6 26.3 10.2
23. Data gathered is not useful since it is used to confirm only decisions already made and not new
ones 9.7 28.6 38.30 24.9 31.4 5.4
24. Lack of qualified gatekeepers to conduct scanning activities 15.9 21.7 37.60 16.4 32.8 13.2
Table 4. Means, standard deviation, and internal reliability of obstacles facing executives to perform business intelligence
??
The above table highlights the most critical problems with "Strongly Agree" and "Agree"
perceptions. It can be seen that that among the 24 problems, only 10 scored more than 50%. Such a
result means that the total score of those respondents who "Strongly Agree" and "Agree" is bigger
than those who were "Neither Agree & Nor Disagree", "Disagree" and "Strongly Disagree".
These ten problems belong to the six phases of business intelligence.
Among the three hypothesized problems related to the first phase of business intelligence, results
revealed that only one is valid and ranked the most crucial problem (70.5%). It is related to the
relevancy of weak signals (phase 1). Respondents have difficulties shaping the meaning of several
concepts which refer to "weak signals", "business intelligence" and managers have no clear vision
about their content and usefulness. This issue is supported by previous studies (Lesca 1994;
Rouibah 2003; Voigt & Czaja 2007). Assessing of the relevancy and value of the business
intelligence approach and weak signal is a prerequisite and first step toward efficient practice.
Business intelligence may best work for company X and not for company Y.
With regard to the second phase "targeting of weak signals”, two problems out of five are valid.
Results revealed that respondents perceived difficulties identifying the company's requirements in
terms of scanning activities, i.e. how to delimit the scanning areas, (66%), and lack of knowledge in
how to start scanning activities (65.8%). Scanning all areas of the company's environment is
complex to achieve. It needs a careful narrowing and creation of targets. Several empirical studies
support the second problem (Ghoshal & Westney 1991); Lesca 1994; Yasai-Ardekani & Nystrom
1996; Elenkov 1997; Ngamkroeckjoti & Johri 2000; Sawyerr et al., 2000; Rouibah & Bessam 2001;
Du Toit 2003; and Xu et al., 2003), and the third problem (Auster & Choo 1994; Du Toit 2003;
Lesca 1994; Rouibah 2003). Other studies did propose some approaches in how to facilitate
companies in carrying out targeting activities (see Martinsons 1994; Rouibah & Lesca 1996; and
Attaway 1998).
Among the five hypothesized problems of the third phase "Tracking of weak signals", only one is
perceived to be valid (scored 65.2%). Inadequate management education & training is ranked the
fourth problem. Once business intelligence is viewed as important for the company, and once the
targets are identified, tasks for gatekeepers need to be identified. These consist of tracking and
collecting weak signals from a number of information sources (formal vs. informal). Previous
studies offered support for this result (Sawyerr et al., 2000; and Rouibah 2003) while other studies
proposed a framework on how to help achieve tracking of weak signals (Rouibah & Lesca 1996;
Rouibah 2000).
Four among five hypothesized problems related to the forth phase of business intelligence,
"Selection of weak signals" is valid. Respondents perceived: Difficulty analyzing the quality of
information sources (63.8%); frequent change in the market resulting in a lot of information and
there is a lack of methods to identify and select relevant information (59.4%), lack of data sources
where weak signals can be found (58.4%), and poor quality of collected information (52.9%). The
selection of weak signals is a complex task. First, it is hard to analyze the quality of the information
source where a relevant piece information can be found. Nowadays, managers are overwhelmed by
the quantity of information they receive. They lack methods that help them to select the crucial
weak signals. Moreover, not all information sources are reliable, and not all collected pieces of
information are weak signals or reliable. Several studies confirmed these problems: difficulty to
analyze the quality of information sources (Sawyerr et al., 2000; Rouibah 2003); lack of methods to
identify and select relevant information (Lesca 1994); lack of data sources where weak signals can
be found (Auster & Choo 1994; Sawyerr et al., 2000); and poor of quality of collected information
(Sawyerr et al., 2000; Rouibah 2003).
??
Among the two hypothesized problem of the fifth phase "rooting & communication of weak
signals", only one was found to be valid. Lack of formal process to root out and share collected
pieces of information (52.9%) is ranked the 9
th
issue. It is not enough to collect and select weak
signals if they are not rooted from the outside to inside of the business. They also need to be
communicated to those who may interpret and use them. Without a formal process, weak signals
will be lost. Such a problem has been highlighted by a few empirical studies (Lesca 1994; Rouibah
& Bessam 2001; Du Toit 2003).
With regard to the sixth phase "Processing & interpretation of weak signals", among the two
hypothesized problems, only one is valid. Collected data are not often presented in an effective
format for planning function (52.2%). Weak signals are pieces of information that might be
informative about events in progress. These pieces of information need to be pulled together in
order to transform them into actionable information. Thus, they require processing and
interpretation. It is worthwhile to mention that not all weak signals will be transformed into strong
signals. This is acknowledged in past literature (Lesca 1994; Rouibah & Ould-Ali 2002; Rouibah
2003; Freeman 1999), and some frameworks were proposed to help achieve this task (El Sawy &
Pauchant 1988; Gelb et al., 1991; Rouibah & Lesca 1996; Attaway 1998; Rouibah 2000; Rouibah &
Ould-Ali 2002).
Finally, none of the issues related to the seventh phase " action: relationship weak signals/ decision
making" and eighth phase "evaluation of current practices of weak signals" were perceived to be
effective issues by respondents. Such results may be explained in that the awareness of business
intelligence level is not high.
3.6 Difference in perceived problems
In order to test whether there are significant differences in the 10 most perceived problems, we first
assessed the normality of all these issues. For this purpose, we examined normality using the
Kolmogorov-Smirnov test (Nunnally 1978). The null hypothesis that was tested was that the ten
problems are normally distributed. The P-value [0.00] was found to be less than alpha [0.05],
indicating that the variables are not normal distributed. Therefore, the Mann Whitney test was
selected to test whether there are significant differences among the ten perceived problems based on
the following seven variables.
1. Type of company (private, public, joint venture, and mixed companies).
2. Age of companies (?5; 5-10; 10-20;? 20).
3. Annual turnover ( ?1.6; 1.6-3.3; 3.3-16.5; 16.5-33; 33-108.9; 109-165; and > 165).
4. Number of employees (?100; 101-500; and > 500).
5. Percentage of employees involved in scanning (1-5 %; 6-20 %; and more than 20 %).
6. Existence of R&D activities (Yes vs. No).
7. Management level (operational, management, and strategic)
The Mann Whitney test shows no statistical significance for the first five variables (type of
company, age, annual turnover, number of employees, and percentage of employees involved in
scanning) and perceived problems. Table 4 only reports the results for the correlation between the
10 most perceived problems and the existence of R&D activities.
Note: ** Significant at p<0.05 and *Significant at p<0.10
Perceived problems
related to environmental
scanning
Presence of R&D Mean rank P-Value
Yes 89.59 1- Difficulty shaping the
meaning of several
concepts which refer to
"weak signals", "business
intelligence" and managers
do not have a clear vision
about their content and
No 90.88
0.870
??
usefulness
Yes 88.77 2- Difficulties identifying
the company's requirements
in terms of scanning
activities
No 95.73
0.371
3- Lack of knowledge of
how to start scanning
activities
Yes 94.16
No 82.99
0.117
Yes 95.19 4- Inadequate management
education and training No 80.38
0.063
Yes 96.79 5- Difficulty analyzing the
quality of information
sources
No 79.03
0.024**
Yes 94.36 6- Frequent changes in the
market resulting in a lot of
information and there are a
lack of methods to identify
and select relevant
information
No 82.58
0.133
Yes 94.13 7-Lack of data sources
where weak signals can be
found
No 82
0.013*
Yes 90 8- Poor quality of collected
information No 88
0.024**
Yes 94.07 9- Lack of formal process
to root out & share
collected pieces of
information
No 81.52
0.145
Yes 94.5 10- Collected data are not
often presented in an
effective format for
planning function
No 81
0.799
Table 5. Differences in the perceived problems based on presence of R&D
The above table reveals statistical significance differences between three problems (difficulty to
analyze quality of collected information, lack of data sources where weak signals can be found, and
poor quality of collected information) and the availability of R&D scanning activities. Thus,
business intelligence is positively related to R&D, meaning that gathering weak signals, assessing
their quality and the quality of information source is very important for R&D. This new result was
not previously reported. The three problems are related to the selection phase of the business
intelligence process and encountered by scanners when there are R&D activities.
Note: ** Significant at p<0.05 and *Significant at p<0.10
Perceived problems
related to environmental
scanning
Management level Mean rank Significance (P)
Strategic 96.04
Management 91.77
1- Difficulty shaping the
meaning of several
concepts which refer to
"weak signals", "business
intelligence" and manager
Operational 94.92
0.86
Strategic 92.06
Management 97.29
2- Difficulties identifying
the company's requirements
in terms of scanning
activities
Operational 86.71
0.63
Strategic 94.98
Management 93.02
3- Lack of knowledge of
how to start scanning
activities Operational 96.13
0.95
Strategic 98.57
Management 86.21
4- Inadequate management
education and training
Operational 92.67
0.61
??
Strategic 102.9
Management 86.65
5. Difficulty analyzing the
quality of information
sources
Operational 110.42
0.049**
Strategic 102.35
Management 87.33
6- Frequent changes in
market resulting in a lot of
pieces of information and
there are few methods to
identify and select relevant
information
Operational 103.18
0.13
Strategic 102.02
Management 90.82
7- Lack of data sources
where weak signals can be
found
Operational 100.50
0.38
Strategic 105.43
Management 93.61
8- Poor quality of collected
information
Operational 84.91
0.49**
Strategic 98.90
Management 116.06
9- Lack of formal process
to root out & share
collected pieces of
information
Operational 87.34
0.06
Strategic 101.10
Management 88.53
10- Collected data are not
often presented in an
effective format for
planning function
Operational 95.53
0.31
Table 6. Differences in the most perceived problems based on respondents' management level
Table 6 reveals significant statistical significance difference between two problems (difficulty to
analyze the quality of information sources and poor quality of collected information) and the
respondents' management level. These two problems are related to the selection phase of the
business intelligence process and means that differences exist only for weak signals selection
among the three management levels.
CONLCLUSION AND IMPLICATIONS
This study examines the perceived problems related to weak signals and business intelligence by a
sample of Kuwaiti executives and has several contributions. First, this study filled in gaps in the
literature about practices of business intelligence in a developing, Arab country and highlights the
perception of this approach in Kuwait. Second, this study shows that the most perceived obstacle to
performing effective weak signal management are related to six phases of the business intelligence
process (assessment of relevancy, targeting, tracking, selecting, rooting & communicating, and
processing). Surprisingly, respondents in our study did perceive problems related to other phases
"action: relationship weak signals/ decision making" and "evaluation of current practices of weak
signals". The three most perceived problems are related to the assimilation of the concept "business
intelligence" and "weak signals" and how scanning activities could be initiated. Finally, results
reveal business intelligence is positively related to R&D and offer support to selecting critical
information.
A number of limitations of the study are recognized. First, we do not claim that this research is
other than exploratory since the sample size was small and this limits the ability to generalize the
result beyond this sample. Second, we obtained data from a single informant and a convenient
sample and that may introduce respondent bias and limit the generalisibility of the results. However,
as companies compete in a turbulent environment, the results and their implications are not
industry-specific.
From a practical view, this study highlights the readiness of Kuwaiti companies to assimilate the
concepts of weak signals and business intelligence. It suggests that approach is worthy to be
propagated in Kuwait, as the world-wide economy is becoming more dynamic. Businesses are not
any more secure from discontinuities, which call for business intelligence to track and interpret
??
weak signals. This approach requires senior managers to propagate the culture of seeing and hearing
in their company. Senior managers are also responsible for initiating specific training in order to
increase awareness and assimilation of weak signals and business intelligence concepts.
Specifically, training should be first directed toward the assessment and significance of the
environmental scanning. Training programs should also directed toward providing help for
companies to assimilate how delimiting the scanning efforts and how to start these activities. Such
programs will benefits the companies in term of optimizing scanning efforts, and allow achieving
competitive advantage by early warning signals management. These training programs could be
easily designed by available resources in Kuwait University, rather.
The business intelligence process is a complex and ill structured approach, which includes several
complex tasks. The input to this process is pieces of information, termed here as weak signals.
These pieces are available outside the organization. These pieces of information need employees'
motivation since they will not come on their own to the company. Top management support,
involvement, and will throughout all the phases are necessary to make this process successful.
Two interesting and future research directions are suggested. First, the study suggests continuing to
study the relationship between business intelligence and R&D, in order to clarify the needed pieces
of information. Second, while this study investigated the perceptions of about the 24 issues,
additional research could be directed toward understanding how executives perceive how
improvements of business intelligence could be achieved.
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doc_596423252.pdf
Strategic information is currently considered by proactive companies as a real strategic resource, the same as corporate assets and human resources.
?
Perceptions of barriers to Business Intelligence and weak signals
management in Kuwait
=,¡:\! _¡ -ì,«\! =!_\={! _,,=. ¡ -,¡,.!_.={! -=s,\! _,,=. ¸,¡!_=
Dr Kamel Rouibah
Department of Quantitative & Information Systems
College of Business Administration
Kuwait University
Abstract
Strategic information is currently considered by proactive companies as a real strategic resource, the
same as corporate assets and human resources. Business intelligence systems created for this
purpose have become a real decision support system to cope with information that informs about
unexpected events and turbulent changes, also referred to as "weak signals", and transform it to
actionable knowledge. Business intelligence is the informational process through which a company
keeps itself aware of opportunities and anticipates threats which occur in its socio-economic
environment. This paper aims to describe: (i) the concepts of weak signal and business intelligence,
(ii) the process that eases weak signal management, and (iii) a number of issues (24) that may
inhibit the appropriation of the concept by practitioners. An instrument was developed and validated
in terms of reliability and validity in a pilot phase. Data collected from 194 Kuwaiti executives
revealed the ten most important issues facing the respondents. These problems are related to six
phases of business intelligence: assessing the need for business intelligence and weak signals,
targeting efforts, tracking weak signals, selection of weak signals, rooting and communicating weak
signals, and processing of collected pieces of information. Results also reveal a significant
relationship between three problems related to the selection phase and availability of R&D activities
in the sampled organizations. This paper discusses these issues as well as their implications for
practitioners
Key-words
Strategic management, business intelligence, environmental scanning, weak signals, anticipatory
information, problems of scanning activities, training, information quality.
?
1 Introduction
Studies in the well developed countries have theorized that Early Warning Systems can be quite
successful in helping managers to identify organization's environment strengths and weaknesses.
These systems intend to identify the so-called weak signals at an early stage and try to interpret the
information to ensure a preventive quality assurance. These systems are also able to provide
support to a manager’s need to be aware of external changes which leads to threats or opportunities.
When organizations in less developed countries (LDC) try to adopt such systems, they may face
several issues. We argue in this paper that several managerial and organizational issues must be first
identified and solved before the introduction of these systems into organizations. This paper is a
first step toward understanding the executives' requirements for an early warning system in LDC
before the development of such systems.
By learning from the best practices in Western, developed countries, organizations in LDC are able
to imitate them with positive results. Learning is therefore becoming the most indispensable activity
in the current knowledge-based economy. In the current turbulent environment, in order to compete
and survive, firms must be constantly alert, capable of adapting to fast changes constantly learn,
evolve, and transform themselves rapidly.
An effective strategic response to environmental changes requires a clear perception of events and
trends in organization's external environment. Literature in the West labels these perceptions
through a process named environmental scanning and business intelligence (Rouibah and Ould-Ali
2002)
While several studies investigated business intelligence, environmental scanning and competitive
intelligence in well developed countries, little research has been conducted in LDC. For example, in
the West, the topic was studied in several countries including France (Lesca 1994), the USA (El
Sawy 1995; Beal 2000), Canada (Auster and Choo 1994) and the UK (Xu et al., 2003), Israel
(Fiegenbaum and Latvia (2000), Australia (Hall 2001), Korea (Ghoshal 1988), Greece (Kourteli
2000), and Germany (Voigt and Czaja 2007). However, very few studies were done in LDC
including Nigeria (Sawyerr et al. 2000), Algeria (Rouibah and Bessam 2001), Bulgaria (Elenkov
1997), Tunisia (Chouk-Kamoun and Salles 1998), Thailand (Ngamkroeckjoti and Johri 2000), and
South Africa (Du Toit 2003). While these studies focused on different issues (see Table 1), the
current study aims to investigate the following research question:
(1) What are the major perceived problems encountered by executives when translating
theory of business intelligence into practice?
(2) What is the level of business intelligence awareness of this concept in Kuwait?
This paper focused on the business intelligence perception in Kuwait. This study was conducted
with the potential differences may exist between perceptions in the West and those in LDC in mind.
The remainder of this paper is structured as follows: 1) it discusses the theory of business
intelligence and the problem that may be raised when translating theory into practices; 2) it
describes the used research methodology, following by the description of the study's results, and 3)
it summarizes the results and points to potential implications for practice.
2 Theory and background
2.1 From environmental scanning to business intelligence
Strategic management is defined as the art, science and craft of formulating, implementing and
evaluating cross-functional decisions that will enable an organization to achieve its objectives
(Davis 1989). Strategic management process can be divided into two phases (Wang & Turban
1991): 1) scanning information from the external and internal environment and the interpretation of
such information the outcome of which feeds the strategic decision-making and implementation
phase; and 2) utilizing four basic activities within the strategic decision-making and implementation
phase: strategic formulation, corporate capability planning which attempts to support new
?
strategies, real-time strategic response to various surprises in the environment, and implementation
of strategies.
Environmental scanning has its origins in publications on strategic management. It is explicitly
recognized as a starting point and vital phase in the strategic management process (Aguilar 1967).
It refers to the acquisition of information about events, trends and relationships in an organization's
environment, the knowledge which will be of assistance to decision makers (Aguilar 1967). This
concept calls for internal and external diagnosis in order to evaluate strengths and weakness as well
as opportunities and threats in the organization's environment.
External
environment
Internal
environment
Intelligence
Strategic decision making
Strategy Formulation
(planning)
Corporate capability
Planning
Real time
response
Implementation
Phase I: scanning &
Interpretation of information
Phase II: strategic
Decision making
External
environment
Internal
environment
Intelligence
Strategic decision making
Strategy Formulation
(planning)
Corporate capability
Planning
Real time
response
Implementation
Phase I: scanning &
Interpretation of information
Phase II: strategic
Decision making
Figure 1. The relationship between the strategic management process and environmental scanning adapted from Wang
and Turban (1991)
While environmental scanning focuses on all types of information useful to assessment of SWOT
(strengths, weaknesses, opportunities and threats), this paper focuses only one type of information
named as "weak signals", which constitute the core of business intelligence with the purpose of
creating an Early Warning System.
Unlike environmental scanning, business intelligence is an approach that is oriented toward
gathering intelligence from the external environment of an organization.. It is defined as a strategic
approach for systematically targeting, tracking, communicating and transforming relevant weak
signals into actionable information on which strategic decision-making is based (Rouibah and
Ould-Ali 2002).
Business intelligence aims to link an organization to its external environment in a way that will
assure the organization's continued success and make it secure from surprises and unexpected
events. It also tends to help a company exploit information elements (in its environment) to grasp
opportunities and avoid surprises when discontinuities occur.
Business intelligence includes two modes of scanning: reactive (also called ‘problematic search’)
and proactive. Reactive scanning corresponds to an oriented and well defined mode of information
search. The task of collecting information is limited, rational and well organized. The proactive
mode is a continuous search where the scope of scanning is very large, information is little targeted,
information is anticipatory and ambiguous, and tasks of scanning are complex and ill structured.
This is why target information has been termed ‘weak signals’.
Early warning systems are also involved in this process. These systems aim to assist an organization
achieve its competitive advantage through better weak signals management. The emphasis here is
on information systems that enhance strategic decision-making and that support the competitive
strategy of an organization (Wiseman 1988)
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2.2 The nature of Weak Signals and early warning systems
The main intention of Early Warning Systems is to anticipate and to detect hints of possible changes
in a company’s environment to maximize options for reaction. In 1967, AGUILAR published the first
dissertation focussing on this topic from the point of view of business. Even if AGUILAR claims for
a systematic and continuous monitoring of companies´ environment and involving this gained
information in the decision-making process, he offered no way for implementation. Eight years
later, in 1975, ANSOFF developed his concept of weak signals. Based on this concept, ANSOFF
enhances the so-called Strategic Issue Management, which is very similar to the concept of weak
signals. Ansoff was the first author who called senior mangers to keep track of strategic information
that may create discontinuities in the company strategies and to initiate further probing based on
such detection (Walls et al. 1992). He called this kind of information "weak signals" to denote
information that may feed into an unexpected decision or that may lead to opportunities or avoid
unexpected threats (El Sawy 1985). Weak signals were defined as uncertain and fragmented
information about developments and trends. These have not been completely realized, or they have
potential consequences, or are perceived to have a significant impact on organizational
performance, either as threats or opportunities (Rouibah & Ould-Ali).
A weak signal is a piece of information in progress and requires much more effort to be caught.
Weak signals are different than strong signals, in that a strong signal is information that informs
about some thing already happened, or confirms a decision already taken. Consequently, firms have
no response delay. This is the case when date for a request for quote (bidding) is over.
Weak signal management can be rooted in the resource-based theory. Its view of the business posits
that businesses compete on the basis of “unique” corporate resources that are valuable, rare,
difficult to imitate, and non-substitutable by other resources (Conner 1991; Schulze 1992). It
includes tangible, intangible, and personnel-based resources. The resource-based view illustrated
that businesses can differentiate themselves on the basis of weak signal management.
The field of research of Early Warning and Early Warning Systems deals with the identification of
business relevant developments and changes at an early stage. We distinguish a difference between
Early Warning and Early Detection. While the wording of “Early Warning” just signals possible
hazards, “Early Detection“ means that there are also chances, but the company has the "will" or the
motivation to identify possible dangers. However, the pure identification of chances and risks is not
enough; it also is necessary to develop collect, interpret, and proposed appropriate actions/strategies
to face chances and risks.
Management of weak signals is recognized as an important research topic for strategic decision-
making (El Sawy and Pauchant 1988; Martinsons 1994; Freeman 1999). Accordingly, several
experts in the strategic management discipline (e.g. Martinsons 1994, Attaway 1998; Rouibah and
Ould-Ali 2002) built different models to formalize the business intelligence as well as to transform
the weak signal management into feasible processes.
In this paper, we defend the idea that the business intelligence approach should be a continuous and
proactive so that a company may be an exploiter/innovator (Mair et al., 1997). The company should
collect weak signals that are essential for its survival and that have an impact on the company's
performance and informs about events not yet realized. In the strategic management literature,
theorists consider the business intelligence role for a business as a radar for a ship. It aims to inform
managers early enough about interesting opportunities and threats so that they can cope with them
and propose adequate answers. Such a strategy will help to keep a sustainable competitive
advantage.
The particular characteristics of weak signals make them very difficult to manage. According to
Rouibah & Ould Ali (2002) a weak signal is:
?
- anticipatory; it informs about changes when they begin to occur,
- uncertain; it concerns events that have not yet been realized,
- ambiguous; its content is usually uncertain or could be deliberately contaminated or
distorted (for example, by a competitor),
- fragmentary; each information element taken alone is insignificant; however, its significance
gradually increases when combined with other weak signs,
- dynamic; it evolves over time,
- cyclical; it has a complex life cycle from growing to declining that varies in its duration and
significance, and
- qualitative; in most cases, numbers are not involved and information may be available in
other forms such as written, verbal, or visual images.
Weak signs are thus subject to perception and interpretation, and multiple meanings are possible.
2.3 Business intelligence process
The assimilation of business intelligence is quite similar to knowledge management in several ways.
Weak signals are tacit knowledge since they represent anticipative pieces of information in the mind
of gatekeepers who believe they might be useful to senior managers. Moreover, each piece of
information is insignificant unless it is interpreted and transformed into actionable information.
Such activity is completed when several viewpoints are combined and opposed.
With regard to weak signal management approach, companies may adopt two different policies.
The first is an offensive strategy via a proactive attitude in that it seizes opportunities by having a
focus on events that have not yet taken place and that are announced by weak signals. The second
policy consists of adopting a defensive or reactive behavior. This is evidence when a company waits
until the best company grasps the opportunity, and then it reacts after tangible benefits are achieved.
Adopting one of the two previous strategies besides the particular characteristics of weak signals
make their management somewhat difficult.
The translation of theories on business intelligence to actionable knowledge useful for decision
making requires the existence of a process model. While several processes were proposed in the
past, this paper emphasizes the model developed by Rouibah and Ould-Ali (2002), see Figure 1.
This process aims to translate weak signals into early warnings that keep management informed of
what's looming ahead in the competitive environment. This process is oriented to ease weak signal
management. This process requires several activities: (i) evaluation of the process relevancy for a
kind company; (ii) the delimitation of scanning areas; (iii) the collection of target information; (iv)
the selection of relevant information; (v) the routing and diffusion of collected information; (vi) the
interpretation of collected pieces of information; and (vii) the integration of processed information
in the decision-making process.
?
Action
Targeting
Routing
1
2
4
6
Tracking
2
Processing
interpretation
Selection
3
Evaluation of
Relevancy
0
5
Action
Targeting
Routing
1
2
4
6
Tracking
2
Processing
interpretation
Selection
3
Evaluation of
Relevancy
0
5
Figure 2. Business intelligence process (Rouibah & Ould-Ali 2002)
When executives try to translate Rouibah and Ould-Ali's (2002) model into practice, they may
encounter several obstacles. The following sections will review these problems.
2.4 Obstacles to business intelligence
In trying to review past studies on the subject, we categorized the literature into two groups: studies
based on empirical studies versus studies based on a conceptual problem view. The first category
includes both empirical studies and studies that only mentioned some problems related to business
intelligence, scanning and competitive intelligence, and strategic management. The second category
refers to studies that concentrate on the conceptualization of a specific problem related to weak
signals and then try to propose guideline, methods and methodologies to overcome these problems.
Identified problems were categorized according to Figure 1 that depicts phases of business
intelligences process.
?
Activities of business
intelligence process Problems/ obstacles of weak signals management
Support literature –Empirical-
based.
Support literature –Process-
based
1. Inability to perceive potential benefits of weak
signals on the company 's performance
Lesca (1994);
Groom & David (2001);
Rouibah (2003); Voigt & Czaja
(2007)
El Sawy (1985)
Beal (2000); Du Toit (2003)
Wang & Turban, (1991); Lesca
(1994); Rouibah & Lesca (1996)
Ansoff (1975)
2. Difficulty shaping the meaning of several concepts
which refer to "weak signals", "business
intelligence" and managers do not have a clear vision
about their content and usefulness
Lesca (1994); Rouibah (2003)
Voigt & Czaja (2007)
Rouibah & Lesca (1996)
Phase 1: Relevancy of weak
signal management
3. Company culture is not oriented toward usage of
weak signals
Lesca (1994); Rouibah & Bessam
(2001)
Freeman (1999)
N.A
4. Difficulties identifying the company's
requirements in terms of scanning activities
Ghoshal & Westney (1991); Lesca
(1994); Yasai-Ardekani &
Nystrom (1996); Elenkov (1997);
Ngamkroeckjoti & Johri (2000);
Sawyerr et al., (2000); Rouibah &
Bessam (2001); Du Toit (2003);
Xu et al., (2003)
Martinsons (1994); Attaway (1998)
5. Lack of knowledge in on how to start scanning
activities
Auster & Choo (1994); Du Toit
(2003); Lesca (1994); Rouibah
(2003)
Aguilar (1967); Gelb et al., (1991);
Rouibah & Lesca (1996); Attaway
(1998)
6. Uncertainty regarding governmental long-term
policies
Sawyerr et al., (2000); Beal
(2000); Elenkov (1997)
N.A.
7. Lack of qualified gatekeepers to conduct scanning
activities
Lesca (1994) Wang & Turban (2001);
Martinsons (1994); Attaway
(1998); Rouibah & Lesca (1996)
Phase 2: Targeting of weak
signals
8. Problem of legal and ethical practices of scanning
activities
N.A. Gelb et al., (1991)
9. Inadequate management education and training Sawyerr et al., (2000), Rouibah
(2003)
Rouibah & Lesca (1996); Rouibah
(2000);
10.Lacking of motivation and rewards for company
personnel
Lesca (1992); Lesca (1994);
Rouibah (2003)
Wang & Turban (2001);
Martinsons (1994); Attaway
(1998); Rouibah & Lesca (1996)
11. Lack of time allocated to scanning activities N.A. Rouibah & Lesca (1996)
12. Lack of trust and cooperation among members of
the organization
Lesca (1994); Rouibah (2003) Rouibah & Lesca (1996); Attaway
(1998); Wang & Turban (2001)
Phase3; Tracking of weak
signals
13. Lack of support from top management Lesca (1994) Martinsons (1994); Wang &
?
Turban (1991)
14. Frequent changes in the market resulting in a lot
of information and there are no methods to identify
and select relevant information
Lesca (1994) Gelb et al., (1991)
15. Difficulty analyzing the quality of information
sources
Sawyerr et al., (2000); Rouibah
(2003)
N.A
16. Poor quality of collected information N.A Wang & Turban (1991)
17. Lack of data sources where weak signals can be
found
Auster & Choo (1994); Sawyerr et
al., (2000);
Aguilar (1967); Martinsons (1994)
Phase4: Selection of weak
signals
18. Data gathered is not useful since it is used to
confirm only decisions already made and not new
decisions
Lesca (1994) Gelb et al., (1991); Rouibah &
Rouibah (1996)
19. Lack of a formal process to root out & share
collected information
Lesca (1994); Du Toit (2003);
Rouibah & Bessam (2001)
Kourteli (2000); Rouibah & Lesca
(1996); Attaway (1998)
Phase5; Rooting &
communication
20. Employees' resistance to business intelligence
and scanning culture
Rouibah & Bessam (2001);
Rouibah (2003)
Wang & Turban (2001)
21. Inability to analyze collected information and to
generate actionable information
Lesca (1994); Rouibah & Ould-Ali
(2002); Rouibah (2003); Freeman
(1999)
Gelb et al., (1991); Rouibah &
Lesca (1996); Attaway (1998);
Rouibah & Ould-Ali (2002);
Rouibah (2000); El Sawy &
Pauchant (1988)
Phase6: Processing &
interpretation of weak signals
22. Collected data are not often presented in an
adequate format for decision making function
Lesca (1992) Gelb et al., (1991)
Phase 7: Action "relationship
weak signals/ decision
making"
23. No integration of collected information in the
decision making process
Lesca (1992); Groom & David
(2001)
Gelb et al., (1991)
Phase8: Evaluation of current
practices of weak signal
management.
24. Difficulties evaluating current practices of weak
signal management and scanning activities
Ghoshal & Westney (1991); Lesca
(1994); Yasai-Ardekani &
Nystrom (1996); Rouibah &
Bessam (2001); Du Toit (2003)
Aguilar (1967); Martinsons (1994);
Rouibah & Lesca (1996); Chouk-
Kamoun & Salles (1998)
Table 1. Summary of literature review on business intelligence, environmental scanning and competitive intelligence
?
In the Arab region, few studies have focused on business intelligence.
Chouk-Kamoun and Salles (1998) studied the application of business intelligence in 24 Tunisian
SMEs. They found that executives don’t perceive the value of weak signals as a factor that
contributes to company performance. Such a result suggests that the majority of small companies
did not perceive benefits from business intelligence.
Rouibah and Bessam (2001) interviewed 12 Algerian SMEs about practices of environmental
scanning. Results showed scanning activities lack efficiency because employees lack the
knowledge to: (i) evaluate current practices, (ii) set up an efficient environmental scanning
process, and (iii) share collected information.
Rouibah (2003) studied the awareness of environmental scanning by executives within 86 Kuwaiti
firms. In particular, the author studied which types of information (control, influence or weak
signals) receive the most attention of executives. Rouibah suggested propagating the culture of
"word to mouth", attention to "weak signals" among employees as well as the culture of
information sharing. In addition, the author examined five problems faced by executives. Results
revealed that the inability to shape the meaning of environmental scanning was the most perceived
problem, followed by the confusion in the exiting of different concepts used to refer to
environmental scanning.
Other researchers studied problems and challenges facing Arab managers in the 19
th
of the 20
th
century, which inhibited the practice of modern management styles (modern development)
(Atiyyah 1993). These include findings that included 1) the turbulent political environment is
unfavorable to the development of modern development; 2) unethical practices of business
activities; 3) managers' concentration on meeting their current needs rather than satisfying long-
term objectives; 4) a shortage of time allocated to planning; 5) the resistance of managers to
changes (existence of doubtful managers who don't believe on trainings effectiveness, and 6)
attendance in training not for the sake to improve their skills but to earn a cash bonus or qualify
for a promotion. He asserted that one of the most critical issues facing Arab managers is
management development and saw a strong link between its success and economic development.
Based on the previous literature review, we can observe the following remarks.
First, past literature has discussed several obstacles, but none of the past studies integrated these
problems in one empirical study nor validated these problems either in the West or in LDC.
Second, many past studies have focused on what companies should do to solve the issues of
business intelligence, rather than to look at what firms actually do. Much prior literature is devoted
to prescriptions or techniques. Moreover, this study also reviewed papers that were published in
Competitive Intelligence Review between 1997 and 2007, and of the 128 published papers only
three have had any solid empirical research content.
Third, most empirical studies on actual practices have tended to focus on the leading firms in well
developed (Western) countries. By contrast, this study looks more at how firms in LDC carry out
business intelligence and thus gives a better guide as to the general level of acceptance and use of
this approach.
Fourth, the study looks at actual business intelligence across a range of different people in the
organization. The instrument-based questionnaire used in the study is completed by people selected
from different management levels (senior, middle, and operational level) and thus gives a broader
organizational and cultural perspective on the role and current perceptions of business intelligence
in firms operating in LDC.
??
Fifth, earlier studies treated the business environment as a single entity (except Xu et al., 2003),
while this study is based on a multi-industry approach.
For the purpose of this study, the author has summarized 24 issues that are well grounded in past
literature (see Table 1). The current research is an attempt to test their validity in the Kuwaiti
business environment.
3 Research methodology
Since our interest focuses centers on clarifying the problems associated with business intelligence
from a Kuwaiti executives’ point of view, the survey method, based on a questionnaire, was chosen
as being most appropriate. The designed questionnaire includes three sections. The first one records
data related to company profile (size, annual total revenue, number of total employees, number of
employees involved in scanning activities). The second section highlights information related to
respondents (department, position, and time respondent has been with the company). The third
section includes major obstacles Kuwaiti executives may encounter.
3.1 Sample and procedure
In an attempt to test the validity of the 24 problems (see Table 1), this study targets Kuwaiti
executive as a unit for analysis.
We followed a procedure involving three steps to collect the data and to approach the companies.
First, we have chosen to contact companies through personal visits, using personal networks, the
telephone and, in some cases, e-mail to solicit their participation. Second, the executive is identified
and an appointment was made to visit him. During the appointment, the executive was delivered a
letter that describes the research objectives and the questionnaire. Third, each participant was
briefed on the purpose, content, and procedure for completing the questionnaire and provided with a
copy of the instrument. A letter was also attached to the questionnaire that explains the main
objective of the study and includes definition of key concepts (such as business intelligence, weak
signals, etc.). Fourth, another appointment was made to pick up the questionnaire.
The instrument was written in Arabic and a translated copy into English was also made available. It
was also checked by two faculty members at the College of Business Administration. The
instrument required respondents to provide information based on their perceptions of what was
currently in practice in their organization. Of the 250 questionnaires distributed, 194 were retuned,
giving a response rate of 77.6%.
The following table provides the demographic data about the respondents.
Position of respondents Frequencies Percentage
Operational level 15 7.73
Middle Management 113 58.24
Strategic level 66 34.01
Total 194 100
Time respondent has been with the company Frequencies Percentage
Less than 1 year 11 5.68
1-5 years 72 37.11
6-10 years 52 26.8
11-15 years 26 13.4
More than 15 years 33 17.01
Total 194 100
Table 2. Demographic data and Profile of respondents
34.7 % of participants in this study are free businesses (i.e. owned by a single family) followed by
sales and marketing department (26.8%), while people belonging to the information system
department are not involved in scanning activities. In addition, the majority of respondents belong
to management level followed by the strategic level which depicts the importance of scanning
??
activities in Kuwait. 37.1% of respondents have been with their company between 1 and 5 years.
26.8% between 6 and 10 years. The profile of the respondent provides credibility and validity of the
business intelligence practices in Kuwait.
3.2 Profile of companies
Table 3 provides the profile of companies involved in the survey.
Nature of company Frequencies Percentage
Public 34 17.65
Private 160 82.47
Total 194 100
Age of company Frequencies Percentage
Less than 5 years 25 13.22
5.1 -10 years 21 10.35
10.1-20 years 17 8.62
More than 20 years 131 67.81
Total 194 100
Annual turnover (in million $) Frequencies Percentage
Less than 1.6 17 8.88
1.6-3.3 14 7.4
3.3-16.5 45 22.96
16.5-33 70 36.29
33-108.9 24 12.59
109-165 7 2.96
Above 165 17 8.88
Total 194 100
Number of employees in the organization Frequencies Percentage
Less than 100 59 30.41
101-499 40 20.61
More than 500 95 48.96
Total 194 100
Existence of R&D activities Frequencies %
No 62 31.9
Yes 132 68.1
Total 194 100
Percentage of employees involved in scanning Frequencies Percentage
1-5 % 100 51.54
6-20 % 60 30.9
More than 20 % 34 17.57
Total 194 100
Table 3. Characteristics of the companies and practices of scanning
Most of companies involved in scanning are in the finance, banking, and insurance sector (25.3%)
followed by trading and retailers (16%), and by construction (15.3%). The majority of companies
belong to the private sector (82.47%), while a small percentage (17.65%) belong to the public
sector. 67.81 % of the companies have been in existence more than 20 years, while 13.22% have
been in existence for less than 5 years. More than half of respondents (59.25%) work in companies
with an annual turnover comprised between $3.3. millions and $33 millions. 51.04 % of companies
??
are small and medium companies (SME), while 48.96% are large firms. Among the sampled
companies, 68.1% have research & development (R&D) activities and 82.5% have less than one
quarter of their employees (?20%) involved in scanning activities.
3.3 Measures
Each of the 24 obstacles to the efficient practice of business intelligence, identified from the past
literature (see table 1), was measured using a five-point scale: strongly agreed (1), agreed (2),
neither agreed nor disagree (3), disagree (4), and strongly disagree (5). The respondents were asked
to indicate on a scale whether each of the factors had been an obstacle to efforts to collect, receive
and disseminate the information from the environment.
3.4 Reliability and validity of the instrument
Before data analysis, content validity and reliability of constructs were examined. To ensure content
validity, a thorough examination was made of the relevant literature. Before questionnaire
distribution, and to further reduce the possibility of non-random errors, a pilot study was conducted
to examine the questionnaire for validity (measuring what is intended), completeness (including all
relevant variable items), and readability/understandability. A pilot phase was conducted with 20
executives and the authors. This was done in order to test the applicability of such instrument. The
results of the pilot study suggested several changes to the questions. These changes were
incorporated in the instrument.
Data analysis & results
3.5 Obstacle to business intelligence
Table 4 provides the survey percentages responses. It provides the analysis of obstacles executives
are facing (1 strongly agree to 5 strongly disagree)
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Problem of scanning
[1]
Strongly
Agree (%)
[2] Agree
(%)
Total [1]
+ [2]
[3] Neither
Agree Nor
Disagree
(%)
[4]
Disagree
(%)
[5] Strongly
Disagree
(%)
1. Difficulty shaping the meaning of several concepts which refer to "weak signals", "business
intelligence" and managers do not have a clear vision about their content and usefulness 28 42.5 70.50 19.4 9.5 0.5
2. Difficulties identifying the company's requirements in terms of scanning activities 17.6 48.4 66.00 19.7 12.8 1.6
3. Lack of knowledge in how to start scanning activities 25.7 40.1 65.80 20.9 12.3 1.1
4. Inadequate management education and training 25.1 40.1 65.20 16.6 12.3 5.9
5. Difficulty analyzing the quality of information sources 19.7 44.1 63.80 14.4 18.6 3.2
6. Frequent changes in the market resulting in a lot of information and there are no methods to
identify and select relevant information 16.6 42.8 59.40 20.3 18.2 2.1
7. Lack of data sources where weak signals can be found 24.2 34.2 58.40 15.3 23.2 3.2
8. Poor quality of collected information 17.2 37.1 54.30 21.5 21.5 2.7
9. Lack of a formal process to root out & share information 16 36.9 52.90 26.2 18.2 2.7
10. Collected data are not often presented in an effective format for planning function 14 38.2 52.20 21 23.7 3.2
11. Employees' resistance to business intelligence and scanning culture 11.5 37.7 49.20 26.8 17.5 6.6
12. Difficulties evaluating current practices of weak signal management and scanning activities 9.8 38.3 48.10 27.9 21.3 2.7
13. Lack of support from top management 19.3 28.3 47.60 21.4 22.5 8.6
14. No integration of collected information in the decision making process 14 32.8 46.80 19.4 26.9 7
15. Uncertainty regarding governmental long-term policies 16.8 29.6 46.40 35.2 15.6 2.8
16. Inability to perceive potential benefits of weak signals on the company 's performance 10.3 35.9 46.20 17.9 30.4 5.4
17. Lack of motivation and rewards 20.2 25.5 45.70 16 26.1 12.2
18. Lack of trust and cooperation among members of the organization 22.7 22.2 44.90 11.9 29.2 14
19. Inability to analyze information and to generate actionable information 15.2 28.8 44.00 25 26.1 4.9
20. Problem of legal and ethical practices of scanning activities 14.3 28.6 42.90 16.2 30.8 10
21. Lack of time allocated to scanning activities 5.6 36.1 41.70 32.2 22.2 3.9
22. Company culture is not oriented toward usage of weak signals 15.6 25.3 40.90 22.6 26.3 10.2
23. Data gathered is not useful since it is used to confirm only decisions already made and not new
ones 9.7 28.6 38.30 24.9 31.4 5.4
24. Lack of qualified gatekeepers to conduct scanning activities 15.9 21.7 37.60 16.4 32.8 13.2
Table 4. Means, standard deviation, and internal reliability of obstacles facing executives to perform business intelligence
??
The above table highlights the most critical problems with "Strongly Agree" and "Agree"
perceptions. It can be seen that that among the 24 problems, only 10 scored more than 50%. Such a
result means that the total score of those respondents who "Strongly Agree" and "Agree" is bigger
than those who were "Neither Agree & Nor Disagree", "Disagree" and "Strongly Disagree".
These ten problems belong to the six phases of business intelligence.
Among the three hypothesized problems related to the first phase of business intelligence, results
revealed that only one is valid and ranked the most crucial problem (70.5%). It is related to the
relevancy of weak signals (phase 1). Respondents have difficulties shaping the meaning of several
concepts which refer to "weak signals", "business intelligence" and managers have no clear vision
about their content and usefulness. This issue is supported by previous studies (Lesca 1994;
Rouibah 2003; Voigt & Czaja 2007). Assessing of the relevancy and value of the business
intelligence approach and weak signal is a prerequisite and first step toward efficient practice.
Business intelligence may best work for company X and not for company Y.
With regard to the second phase "targeting of weak signals”, two problems out of five are valid.
Results revealed that respondents perceived difficulties identifying the company's requirements in
terms of scanning activities, i.e. how to delimit the scanning areas, (66%), and lack of knowledge in
how to start scanning activities (65.8%). Scanning all areas of the company's environment is
complex to achieve. It needs a careful narrowing and creation of targets. Several empirical studies
support the second problem (Ghoshal & Westney 1991); Lesca 1994; Yasai-Ardekani & Nystrom
1996; Elenkov 1997; Ngamkroeckjoti & Johri 2000; Sawyerr et al., 2000; Rouibah & Bessam 2001;
Du Toit 2003; and Xu et al., 2003), and the third problem (Auster & Choo 1994; Du Toit 2003;
Lesca 1994; Rouibah 2003). Other studies did propose some approaches in how to facilitate
companies in carrying out targeting activities (see Martinsons 1994; Rouibah & Lesca 1996; and
Attaway 1998).
Among the five hypothesized problems of the third phase "Tracking of weak signals", only one is
perceived to be valid (scored 65.2%). Inadequate management education & training is ranked the
fourth problem. Once business intelligence is viewed as important for the company, and once the
targets are identified, tasks for gatekeepers need to be identified. These consist of tracking and
collecting weak signals from a number of information sources (formal vs. informal). Previous
studies offered support for this result (Sawyerr et al., 2000; and Rouibah 2003) while other studies
proposed a framework on how to help achieve tracking of weak signals (Rouibah & Lesca 1996;
Rouibah 2000).
Four among five hypothesized problems related to the forth phase of business intelligence,
"Selection of weak signals" is valid. Respondents perceived: Difficulty analyzing the quality of
information sources (63.8%); frequent change in the market resulting in a lot of information and
there is a lack of methods to identify and select relevant information (59.4%), lack of data sources
where weak signals can be found (58.4%), and poor quality of collected information (52.9%). The
selection of weak signals is a complex task. First, it is hard to analyze the quality of the information
source where a relevant piece information can be found. Nowadays, managers are overwhelmed by
the quantity of information they receive. They lack methods that help them to select the crucial
weak signals. Moreover, not all information sources are reliable, and not all collected pieces of
information are weak signals or reliable. Several studies confirmed these problems: difficulty to
analyze the quality of information sources (Sawyerr et al., 2000; Rouibah 2003); lack of methods to
identify and select relevant information (Lesca 1994); lack of data sources where weak signals can
be found (Auster & Choo 1994; Sawyerr et al., 2000); and poor of quality of collected information
(Sawyerr et al., 2000; Rouibah 2003).
??
Among the two hypothesized problem of the fifth phase "rooting & communication of weak
signals", only one was found to be valid. Lack of formal process to root out and share collected
pieces of information (52.9%) is ranked the 9
th
issue. It is not enough to collect and select weak
signals if they are not rooted from the outside to inside of the business. They also need to be
communicated to those who may interpret and use them. Without a formal process, weak signals
will be lost. Such a problem has been highlighted by a few empirical studies (Lesca 1994; Rouibah
& Bessam 2001; Du Toit 2003).
With regard to the sixth phase "Processing & interpretation of weak signals", among the two
hypothesized problems, only one is valid. Collected data are not often presented in an effective
format for planning function (52.2%). Weak signals are pieces of information that might be
informative about events in progress. These pieces of information need to be pulled together in
order to transform them into actionable information. Thus, they require processing and
interpretation. It is worthwhile to mention that not all weak signals will be transformed into strong
signals. This is acknowledged in past literature (Lesca 1994; Rouibah & Ould-Ali 2002; Rouibah
2003; Freeman 1999), and some frameworks were proposed to help achieve this task (El Sawy &
Pauchant 1988; Gelb et al., 1991; Rouibah & Lesca 1996; Attaway 1998; Rouibah 2000; Rouibah &
Ould-Ali 2002).
Finally, none of the issues related to the seventh phase " action: relationship weak signals/ decision
making" and eighth phase "evaluation of current practices of weak signals" were perceived to be
effective issues by respondents. Such results may be explained in that the awareness of business
intelligence level is not high.
3.6 Difference in perceived problems
In order to test whether there are significant differences in the 10 most perceived problems, we first
assessed the normality of all these issues. For this purpose, we examined normality using the
Kolmogorov-Smirnov test (Nunnally 1978). The null hypothesis that was tested was that the ten
problems are normally distributed. The P-value [0.00] was found to be less than alpha [0.05],
indicating that the variables are not normal distributed. Therefore, the Mann Whitney test was
selected to test whether there are significant differences among the ten perceived problems based on
the following seven variables.
1. Type of company (private, public, joint venture, and mixed companies).
2. Age of companies (?5; 5-10; 10-20;? 20).
3. Annual turnover ( ?1.6; 1.6-3.3; 3.3-16.5; 16.5-33; 33-108.9; 109-165; and > 165).
4. Number of employees (?100; 101-500; and > 500).
5. Percentage of employees involved in scanning (1-5 %; 6-20 %; and more than 20 %).
6. Existence of R&D activities (Yes vs. No).
7. Management level (operational, management, and strategic)
The Mann Whitney test shows no statistical significance for the first five variables (type of
company, age, annual turnover, number of employees, and percentage of employees involved in
scanning) and perceived problems. Table 4 only reports the results for the correlation between the
10 most perceived problems and the existence of R&D activities.
Note: ** Significant at p<0.05 and *Significant at p<0.10
Perceived problems
related to environmental
scanning
Presence of R&D Mean rank P-Value
Yes 89.59 1- Difficulty shaping the
meaning of several
concepts which refer to
"weak signals", "business
intelligence" and managers
do not have a clear vision
about their content and
No 90.88
0.870
??
usefulness
Yes 88.77 2- Difficulties identifying
the company's requirements
in terms of scanning
activities
No 95.73
0.371
3- Lack of knowledge of
how to start scanning
activities
Yes 94.16
No 82.99
0.117
Yes 95.19 4- Inadequate management
education and training No 80.38
0.063
Yes 96.79 5- Difficulty analyzing the
quality of information
sources
No 79.03
0.024**
Yes 94.36 6- Frequent changes in the
market resulting in a lot of
information and there are a
lack of methods to identify
and select relevant
information
No 82.58
0.133
Yes 94.13 7-Lack of data sources
where weak signals can be
found
No 82
0.013*
Yes 90 8- Poor quality of collected
information No 88
0.024**
Yes 94.07 9- Lack of formal process
to root out & share
collected pieces of
information
No 81.52
0.145
Yes 94.5 10- Collected data are not
often presented in an
effective format for
planning function
No 81
0.799
Table 5. Differences in the perceived problems based on presence of R&D
The above table reveals statistical significance differences between three problems (difficulty to
analyze quality of collected information, lack of data sources where weak signals can be found, and
poor quality of collected information) and the availability of R&D scanning activities. Thus,
business intelligence is positively related to R&D, meaning that gathering weak signals, assessing
their quality and the quality of information source is very important for R&D. This new result was
not previously reported. The three problems are related to the selection phase of the business
intelligence process and encountered by scanners when there are R&D activities.
Note: ** Significant at p<0.05 and *Significant at p<0.10
Perceived problems
related to environmental
scanning
Management level Mean rank Significance (P)
Strategic 96.04
Management 91.77
1- Difficulty shaping the
meaning of several
concepts which refer to
"weak signals", "business
intelligence" and manager
Operational 94.92
0.86
Strategic 92.06
Management 97.29
2- Difficulties identifying
the company's requirements
in terms of scanning
activities
Operational 86.71
0.63
Strategic 94.98
Management 93.02
3- Lack of knowledge of
how to start scanning
activities Operational 96.13
0.95
Strategic 98.57
Management 86.21
4- Inadequate management
education and training
Operational 92.67
0.61
??
Strategic 102.9
Management 86.65
5. Difficulty analyzing the
quality of information
sources
Operational 110.42
0.049**
Strategic 102.35
Management 87.33
6- Frequent changes in
market resulting in a lot of
pieces of information and
there are few methods to
identify and select relevant
information
Operational 103.18
0.13
Strategic 102.02
Management 90.82
7- Lack of data sources
where weak signals can be
found
Operational 100.50
0.38
Strategic 105.43
Management 93.61
8- Poor quality of collected
information
Operational 84.91
0.49**
Strategic 98.90
Management 116.06
9- Lack of formal process
to root out & share
collected pieces of
information
Operational 87.34
0.06
Strategic 101.10
Management 88.53
10- Collected data are not
often presented in an
effective format for
planning function
Operational 95.53
0.31
Table 6. Differences in the most perceived problems based on respondents' management level
Table 6 reveals significant statistical significance difference between two problems (difficulty to
analyze the quality of information sources and poor quality of collected information) and the
respondents' management level. These two problems are related to the selection phase of the
business intelligence process and means that differences exist only for weak signals selection
among the three management levels.
CONLCLUSION AND IMPLICATIONS
This study examines the perceived problems related to weak signals and business intelligence by a
sample of Kuwaiti executives and has several contributions. First, this study filled in gaps in the
literature about practices of business intelligence in a developing, Arab country and highlights the
perception of this approach in Kuwait. Second, this study shows that the most perceived obstacle to
performing effective weak signal management are related to six phases of the business intelligence
process (assessment of relevancy, targeting, tracking, selecting, rooting & communicating, and
processing). Surprisingly, respondents in our study did perceive problems related to other phases
"action: relationship weak signals/ decision making" and "evaluation of current practices of weak
signals". The three most perceived problems are related to the assimilation of the concept "business
intelligence" and "weak signals" and how scanning activities could be initiated. Finally, results
reveal business intelligence is positively related to R&D and offer support to selecting critical
information.
A number of limitations of the study are recognized. First, we do not claim that this research is
other than exploratory since the sample size was small and this limits the ability to generalize the
result beyond this sample. Second, we obtained data from a single informant and a convenient
sample and that may introduce respondent bias and limit the generalisibility of the results. However,
as companies compete in a turbulent environment, the results and their implications are not
industry-specific.
From a practical view, this study highlights the readiness of Kuwaiti companies to assimilate the
concepts of weak signals and business intelligence. It suggests that approach is worthy to be
propagated in Kuwait, as the world-wide economy is becoming more dynamic. Businesses are not
any more secure from discontinuities, which call for business intelligence to track and interpret
??
weak signals. This approach requires senior managers to propagate the culture of seeing and hearing
in their company. Senior managers are also responsible for initiating specific training in order to
increase awareness and assimilation of weak signals and business intelligence concepts.
Specifically, training should be first directed toward the assessment and significance of the
environmental scanning. Training programs should also directed toward providing help for
companies to assimilate how delimiting the scanning efforts and how to start these activities. Such
programs will benefits the companies in term of optimizing scanning efforts, and allow achieving
competitive advantage by early warning signals management. These training programs could be
easily designed by available resources in Kuwait University, rather.
The business intelligence process is a complex and ill structured approach, which includes several
complex tasks. The input to this process is pieces of information, termed here as weak signals.
These pieces are available outside the organization. These pieces of information need employees'
motivation since they will not come on their own to the company. Top management support,
involvement, and will throughout all the phases are necessary to make this process successful.
Two interesting and future research directions are suggested. First, the study suggests continuing to
study the relationship between business intelligence and R&D, in order to clarify the needed pieces
of information. Second, while this study investigated the perceptions of about the 24 issues,
additional research could be directed toward understanding how executives perceive how
improvements of business intelligence could be achieved.
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