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A Business Intelligence Model for SMEs Based on Tacit Knowledge M. Sadok
A Business Intelligence Model for SMEs Based on Tacit
Knowledge
M. Sadok, H. Lesca
To cite this version:
M. Sadok, H. Lesca. A Business Intelligence Model for SMEs Based on Tacit Knowledge. cahier
de recherche n2009-12 E5. 2009, 9p.
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A business intelligence model for SMEs based on tacit
knowledge.
SADOK Moufida
LESCA Humbert
CAHIER DE RECHERCHE n°2009-12 E5
Communications of the IBIMA, Volume 7, number 20, 2009
A Business Intelligence Model for SMEs Based on Tacit Knowledge
Moufida SADOK, Institute of Technology in Communications, Tunisia, [email protected]
Humbert LESCA, Laboratory CERAG UMR 5820 CNRS, France, [email protected]
Abstract
This paper proposes a specific model of business
intelligence in relation with SMEs practices,
culture and competitive environment. This model is
based on the mobilization of corporate tacit
knowledge and informal information, aiming at
interpreting anticipatory environmental
information and assist strategic decision making.
An empirical survey assessing the existing business
intelligence practices in 20 French SMEs has
identified seven necessary acceptance conditions of
a business intelligence project as well as a
managerial tool allowing tacit knowledge
traceability.
Keywords: business intelligence, tacit knowledge,
SMEs, sense-making
1. Introduction
In highly competitive markets, and because of a
complex and accelerated evolution of the economic
and technological context, firms need to focus on
proactively managing their business intelligence
process to ensure survival and to respond
efficiently to turbulent environmental changes
(Hambrick, 1982; Jain, 1984; Montgomery and
Weinberg, 1998; Ebrahimi, 2000; May et al., 2000;
Choo, 2002). Business intelligence is the process of
gathering and interpreting pertinent information
about external environment, the knowledge of
which can assist strategic decisions, and generate
or sustain long-term competitive advantages (Gilad
and Gilad, 1988; Fuld, 1995; Thomas et al., 1993).
Many other benefits can be derived from using
business intelligence (e.g. see Lönnqvist and
Pirttimäki, 2006). Business intelligence requires
also a sense-making process and constitutes a
privileged means of knowledge creation in the
enterprise (huber, 1991; Choo, 1996) in relation
with its environment. In this regard, Herschel and
Jones (2005) contend the importance of integration
of knowledge management and business
intelligence in order to improve decision making
and firm performance. The study of Heinrichs and
Lim (2005) to evaluate the impact of competitive
intelligence tool implementation on knowledge
creation and strategic use of information shows that
knowledge workers can produce greater
competitive advantage for the organization when
they are assisted by efficient competitive tools.
In fact, knowledge is a strategically important
resource (Wernerfelt, 1984; Stewart, 1997; Solow,
1997; Malone, 2002). It can be explicit, easily
formulated and transferred to others (Johannesses
et al., 2001), or tacit (Polanyi, 1964), that is,
difficult to express, formalize, or share (Lubit,
2001) because it is deeply rooted in practice and
experience and transmitted by apprenticeship and
training (Fleck, 1996). In an environment
characterized by uncertainty and complexity, tacit
knowledge helps to create sustainable competitive
advantage for companies (Howells, 1996; Nonaka
and Takeuchi, 1995). In this setting, it is crucial to
integrate disparate global sources of knowledge
available within the organizations (Desouza and
Awazu, 2006).
In the case of a large company, efforts can be
planned to formalize knowledge that is required to
interpret and exploit environmental information. In
SMEs, such efforts are unrealistic: almost all
mobilized knowledge is tacit. The selection and
interpretation of environmental information require
human competences and knowledge. And yet many
SMEs would like to optimize their business
intelligence process at lower cost. Because of their
specific features, it seems clear that SMEs have
distinctive needs in developing knowledge
management practices (Sparrow, 2001). In addition,
small firm’s adaptation and competitiveness depend
on its knowledge, detection and interpretation of the
trends in its environment (Beal, 2000; Raymond,
2003).
Thus, the main purpose of this paper is to propose an
original model aiming at helping SMEs to develop
their environmental intelligence by means of
business intelligence process, while remaining close
to their current practices and culture in order to
facilitate the acceptance of the associated
organizational changes. It seeks to produce
«actionable knowledge» (as formulated by Argyris,
1996), in order to improve the management of useful
tacit knowledge for the interpretation and use of
business intelligence information.
Our contributions in this paper are three-fold. First,
we propose a business intelligence model, supported
by a managerial tool and in relation with SMEs
practices and culture, through the identification of
the necessary acceptance conditions of business
intelligence project. Second, despite the fact that
researchers have studied intelligence activities based
on explicit organizational knowledge, only a few
have discussed the role played by tacit knowledge in
intelligence management settings. Third, an
empirical survey of 20 French SMEs which involved
personnel interviews was conducted in order to
assess the existing business intelligence practices and
to build a model of business intelligence based on the
mobilization of the corporate tacit kowledge and
informal information.
The rest of the paper is organized as follows. The
first part is a presentation of business intelligence
activities. The second part focuses on the crucial step
in business intelligence related to collective sense-
making and highlights the specific role of tacit
knowledge during this phase. The third part deals
with the research methodology, while the fourth
reports the results and discusses the study’s
findings and their implications. In the fifth part, a
specific business intelligence model is proposed.
2. Business intelligence process
Business intelligence is a collective process
through which the enterprise is actively seeking
relevant and timely environmental information
referred to as “weak signals” (Ansoff, 1975), to
grasp business opportunities, increase anticipative
capacity, and reduce uncertainty (Blanco et al.,
2003). It is also an iterative learning and an
adaptive process that helps companies reduce
business risk and cope with unstable and
unpredictable external events. Lesca (2003)
proposed a model referred to as VAS-IC (a French
formulation of Anticipative Strategic
Environmental Scanning-Collective Intelligence)
with a core process of collective sense-making. The
main steps of VAS-IC are described in figure 1
below.
The targeting step aims to identify the
environmental actors and themes to be monitored
and therefore to optimize the costs and time
dedicated to the environmental scanning activity.
The target definition must be made in a dynamic
way according to the operational users’ needs. The
targting phase aims also to specify the information
needs and sources. Two types of information
sources can be distinguished:
- Informal: through contacts with customers,
competitors, suppliers, distributors; involvements
in scientific symposia and professional lounges;
abroad missions and contacts with experts, etc.
- Formal: available in the scientific and technical
publications; data bases; enterprises publications;
patents and copyright registrations, etc.
Fig 1. VAS-IC phases
Several contributions have shown that personal
sources have a richer content, allowing weak signals
detection (Ansoff, 1975; Daft and Lengel, 1986) as
well as a better comprehension and interpretation of
the problems in a situation of highly perceived
strategic uncertainty (Daft et al., 1988).
The tracking step refers to the proactive information
acquisition about key environmental actors or events
and aims to identify environmental trackers or
gatekeepers (MacDonald, 1995) which are of two
types:
- “Sedentary” trackers who work in their offices with
documentary sources and databases.
- “Nomad” trackers who move to reach for external
sources. It is the case of the salesperson, for example.
The knowledge memorization step manages the
traceability and capitalization of the corporate
knowledge created during the business intelligence
process. This step requires the construction of a
knowledge base to set up an intelligent and dynamic
storage by creating useful links to structure and
organize the collected knowledge. The knowledge
base provides an important support to the ongoing
business intelligence process when it is assisted by
efficient retrieval mechanisms that are able to
conduct approximate and heuristic search based on
semantic, dependence and hierarchical links.
The diffusion step deals with the dissemination of the
collective sense-making findings to the appropriate
users. In the same way, a potential user can initiate
information requests if he feels the need to have
some information he is capable of designating or
which has been recommended to him by other users.
The phase of diffusion/access also includes the
problem of media appropriation related to the nature
and features of shared information. The media
richness theory (Daft and Lengel, 1986) should be of
particular interest in relation to the efficiency of this
phase.
Knowledge
memorization
Diffusion/
Access
Targeting
Tracking
Collective
Sense-making
Action
In the action step, if the information that is
processed is sufficiently meaningful, it can be
integrated into the decision process to provide
possible operational fields for subsequent actions.
If the output of the interpretation process hasn’t
reached a clear vision, a complementary request for
additional information can be made.
3. Collective sense-making
The core of business intelligence is the collective
sense-making. In literature, sense-making has been
defined in different ways. It is an interpretative
process where people assign meaning to ongoing
events (Gioia and Chittipeddi, 1991). It is the
amplification of weak signals and the search for
contexts within small details fitted together for
sense-making (Weick, 1995). It is considered as a
creative and collective method that can help the
organization to give sense and see possibilities in
the surrounding disorder (Choo, 2001; Ashmos and
Nathan, 2002).
We propose here that collective sense-making
refers to the collective operation during which
knowledge is created from some information that
plays the role of inductive stimuli, and by means of
interactions between individual and collective
memories. It describes the capability of a group to
create significant links between collected data,
which can be inferred iteratively, using the tacit
knowledge of those participants involved in the
collective work sessions, and supported by data
retrieved from structural databases that are updated
during such processes. The result of this operation
can provide an efficient support to the decision
making process by reducing the information
ambiguity and the uncertainty of business
environment.
Fig 2. Collective sense-making process
Therefore, the complexity and diversity of external
events require a dynamic formulation of links and the
establishment of variable parameters depending on
time or context. The sense-making process should
integrate less linear reasoning, especially when
sense-making teams are facing unstructured
situations, a high degree of equivocality, or
incomplete data, which may be encountered by an
organization in managing risks, or building
development strategies. Thus, this phase gains from
using appropriate heuristics in order to reduce
complexity reasoning and converge to a collective
decision making.
The implementation of the collective sense-making
process makes use of efficient tools available in
information technologies. This includes a database
for storing the data collected and all data traces
generated during the reasoning phase, and a
knowledge base that stores all the data that has been
analyzed, and traces all links inferred through the
collective sense-making process.
This definition is illustrated by the conceptual model
provided by figure 2. This model must be adapted
according to contextual factors of contingency,
particularly in the case of SMEs in order to take into
consideration the existing practices in such
companies.
In addition, efficient collective sense-making rests
upon several success conditions. First, it is necessary
to detect, monitor and track early signals that could
help anticipate strategic threats and opportunities. It,
then, requires interpreting these signs to deduce the
appropriate hypothetical anticipations. It finally
requires knowledge, behaviors, aptitudes, and a high
degree of motivation by those who are commissioned
to select and interpret signals and data captured from
the environment.
New
information
Sense and
conclusions useful
to decide and act
Success
conditions
Heuristics
Tacit
knowledge
Anticipation
Plausible hypotheses
generation
Formalized
knowledge
bases
Formalized
data bases
4. Data collection
The data were collected through personal
interviews (two hours each) conducted in 20 SMEs
with the head of the SME, or his direct deputy. The
businesses have between 30 to 300 employees,
international activities and subsidiaries, and
permanent correspondents in the countries with
which they trade. The interviews have been audio-
taped and transcribed, then, written up in individual
case studies. Then the interviews have been
analyzed (Myers and Avison, 2002) by carving out
“verbatims”, that were coded in order to be able to
locate each verbatim anonymously in the corpus.
We regrouped the verbatim while using the
business intelligence model represented in figure 1
as “a posteriori reading grid”. We first present the
results regarding the possible existence of a
formalized system of business intelligence and
knowledge management (in relation globally with
figure 1), and then the results regarding the phases
of the business intelligence process (detailed
phases recovered on figure 1 as well as on figure
2). The data collection is exploratory and aims to
answer the following questions:
1) Is there a global and formalized system of
business intelligence?
2) Are the possibly collected signs and data stored?
3) Are the data sources used, essentially writings,
databases, the Internet (formalized / documentary
sources)?
4) Are the data gathered about the environment
formalized at the end of the process?
5) Are the circulation and the diffusion of
anticipative information systematically organized?
6) Do knowledge bases exist to treat the
environmental signs and data? or, if they don’t, any
formalized knowledge? or some occasionally
explicit knowledge?
7) Does the perceived “time pressure” by SME
leader play a specific role to set up a business
intelligence system?
We try to discover the possible practices of
business intelligence, as they effectively exist in
the visited enterprise, even though these practices
might be very rudimentary and even if the word
“business intelligence” isn’t used in the visited
enterprise: we are interested in the spontaneous
practices (Hamrefors 1998). That is why we ask a
small number of questions during the interview and
we don't use academic jargon while speaking to our
interlocutors. Considering the fact that the visited
SMEs are not supposed to have any experience
concerning business intelligence, we relied on an
open-ended questionnaire.
During the interview we didn't use any
sophisticated vocabulary, nor did we even use the
words business intelligence or knowledge
management. We merely asked if it was important,
for this enterprise “to see things coming in its
environment”, and if it was important for them “to
actively monitor” the possible signs of change. For
every answer, we asked for a concrete example, in
order to be sure that we understood each other well.
5. Empirical results
Results discussion
Whilst, visited enterprises recognize the usefulness
and importance of environmental scanning, most
underline the absence of a structured and global
business intelligence system due to the scarcity of
organizational resources that could be dedicated to
this function. However, we could observe in their
answers existing practices of business intelligence
that are fragmentary, spontaneous and built on the
initiative of some isolated individuals.
In most cases, data acquisition and storage are not
formalized. Certain enterprise members
spontaneously collect field data that remain in an
abstract state, memorized only in the mind of the
individuals who collected them. Consequently, the
collected data are disseminated and not easily
accessible if a person needs to mobilize them quickly
to make a decision or to triangulate with other
sources.
Data are captured in most enterprises with the
assistance of informal field sources. Tracker behavior
and prior knowledge are involved: the tracker
mentally records any information that seems to be
interesting or surprising. He makes a selection on the
basis of prior knowledge on which he relies and that
is activated at that moment. In this way, as soon as it
is captured, anticipative information loses its
cohesion and becomes removed from its context. It
becomes integrated with the tracker knowledge to
enrich and to influence his knowledge. According to
the assimilation / adaptation process, the recently
acquired data and the previous tacit knowledge form
the tracker’s new set of tacit knowledge. If a tracker
shares the new information with a colleague, he
might very likely present it not as such, but coated
with commentaries derived from prior knowledge.
Thus, the colleague in question does not merely
receive the so-called information, but a richer
perspective (in the sense of Daft and Lengel, 1986).
The information flows orally and step by step
through enterprise meetings. It can be “push”
information, if the information’s possessor takes the
initiative to talk about it, or more probably “pull”
information if someone feels the need to request
information.
The individuals’ isolation, within a SME, is a
counterintuitive finding. Indeed, it goes without
saying that in a small company people know one
another. But this doesn’t mean that information flows
easily. It is common to see a good number of
collaborators who are constantly on work trips and
whom you rarely meet. Organizing a collective work
session within a SME is often a daunting task.
In the studied SMEs, we did not find any trace of
formalized knowledge at the organisational level.
The knowledge mobilized by these enterprises to
interpret and exploit information is completely tacit.
The interpretation is made in a “spontaneous” way,
individually, and without explicit method.
The information tracker spontaneously selects
anticipative data that grabs his attention. He doesn't
especially try to rely on any specific selection
criterion over another. If we ask him why he
selected such data and what use he intends to make
of them, he will probably be unable to fully answer.
Just as a craftsman, he makes, but is unable to give
comprehensive verbal explanation of why and how
he makes, which doesn't mean that he does things
badly. He is guided by his tacit knowledge, by his
know-how, by his acquired experience. He deploys
a managerial knowledge (and creativeness in some
cases) that is essentially tacit or, in any case,
informal, and not engineer's procedural knowledge.
The SME leader doesn't see the need for
knowledge formalisation which is essentially
informal by nature and which constantly evolves
with the new experiences. The formalisation task is
perceived to be sterile work, paralyzing or
expensive. The implementation of a business
intelligence system must be considered then as a
project of optimization for what already exists, a
rapidly- implemented project that produces
conclusive results very quickly: a one month
horizon already seems to be excessively long and
uninteresting. Spontaneously, the SME leader
associates the implementation of a business
intelligence project to an improvement of his
business’ agility. If he perceives the project to be
long (therefore complicated) then he might deduce
that it is bad for his business.
Identifying necessary acceptance conditions of the
business intelligence model
We have found out that the empirical study’s
findings provide seven necessary acceptance
conditions (NAC) of business intelligence model,
as presented in the table below. We propose using
them to help set up an environmental intelligence
system within SME.
Table 1: Necessary acceptance conditions of
business intelligence model by SMEs leaders
These suggestions could be seen in two different
ways either as normative recommendations or as
hypotheses.
We have found out that the normative
recommendations are not appropriate to the needs of
the interviewed leaders. In fact, if the manager of a
business intelligence project satisfies these necessary
conditions of acceptance, then he should succeed in
making the SME leader accept the setting up of an
environmental intelligence system. The validation
criterion of the hypotheses is therefore the
acceptance of the project by the leader. If he is
willing to start the business intelligence project, then
we can say that the hypotheses are validated, at least
in the case under consideration.
6. Toward a business intelligence model based on
tacit knowledge and informal information
A major conclusion of our empirical results can be
expressed in the form of a paradox. The business
owners that we interviewed reject any formalization
of business data or the relevant knowledge, yet they
wish to optimize current practices in order to be
effective and to save time and resources.
We started from this paradox to propose a business
intelligence model practically without any
formalization, investment, and personnel recruitment.
Such a model is based on tacit knowledge
management and on the use of informal data.
Figure 3 illustrates the conceptual model derived
from figure 2 which is interested mainly in the data
and knowledge traceability.
The conceptual model appears to be based on several
actors (enterprise employees) and a process of
collective interpretation of business information.
This process aims to amplify collected informal data,
analyze them, and react properly to the inherent
threat or opportunity. It is composed of three phases.
To have more chance of being accepted by the SME leader, the business intelligence system should satisfy the
following necessary acceptance conditions:
NAC
1
: The proposed environmental intelligence model to the SMEs must be built on the simplest possible
formalisation.
NAC
2
: The proposed environmental intelligence model to the SMEs must avoid the storage of data.
NAC
3
: The proposed environmental intelligence model to the SMEs must essentially build on the use of
relational data sources.
NAC
4
: The proposed environmental intelligence model to the SMEs must essentially build on the exploitation
and interpretation of informal data.
N AC
5
: The proposed environmental intelligence model for SMEs must be organized in such a way as to save
time and reduce data deterioration.
NAC
6
: the proposed environmental intelligence model to the SMEs must avoid the formalisation knowledge
implemented to interpret data.
NAC
7
: The proposed environmental intelligence model to the SMEs must be immediately ‘attractive’ for the
SME leader. It must provide conclusive results very quickly.
Fig 3. The business intelligence model based on tacit knowledge and informal information
Links initialization is considered as an initial phase.
Its objective is to amplify signals and to set up the
linkages capable of providing a clear picture of
potential risk encountered by the enterprise or
business opportunity.
During the second phase, new linkages are inferred
iteratively using existing linkages, tacit knowledge
of business intelligence actors. The third phase
aims to check whether the iterative process has
reached a clear knowledge of the risk encountered
or the opportunity, and a global assessment of other
potentially related decisions. At the end of this
phase, a reactive process can be triggered to
propose, if needed, the appropriate actions to
reduce risk or to take advantage of a business
opportunity and to anticipate business decisions.
Thus, we can build a binary matrix M, presented in
figure 4 that provides an operational tool and
allows identifying, at all times, the trace of useful
data or knowledge within or outside the enterprise,
provided that it is up-to-date.
Fig 4. Matrix “Who knows What”
Therefore, let M be the aforementioned matrix and
m
ij
its generic component, m
ij
is defined as follows:
m
ij
= 1 if actor j can process topic I, has got the
knowledge needed to process or help process the
item i.
m
ij
= 0 elsewhere
In addition, a row in M gives the list of actors who
are able to handle a given topic, while a column
describes the capability of a given actor to process
the list of topics.
Topics include but are not limited to the
identification of potentials customers, looking for
information about competitors or suppliers,
investigation of new markets or products.
The list of topics can be fixed by companies’ leaders
according to the business needs of the enterprise.
This list is neither definitive nor static and can evolve
in relation with the targeting activity objects.
The size of this matrix should be kept as small as
possible, and sufficiently large to keep the control of
all knowledge needed. Indeed, in SMEs of about a
hundred people, the number of names to write down
in the top horizontal margin hardly exceeds the
twenties.
However, the number of lines (where the names of
themes or those of outside actors to the enterprise are
registered) can reach several dozens (vertical margin
on the left of the table).
The construction and the up-to-date maintenance of
this management tool, the informal information and
the tacit knowledge shouldn’t take the person
charged to do this work more than half a day per
week.
The information that is useful for updating of matrix
M has mainly two sources:
- Personnel management service (or other services)
supposed to be informed of the travelling of the
enterprise members: visits of customers, of
colleagues, trade shows, etc. This information
permits to inform the table on “who is to contact the
Informal
information
Collective interpretation of
information
Tacit knowledge
Who? Where? When?
Tacit knowledge
Who? Where? When?
Tacit knowledge
Who? Where? When?
Tacit knowledge
Who? Where? When?
Estimation
Risk/opportunity
Anticipation
Actor
j
Topic
i
m
ij
other”, and “who is susceptible to detain some
information on what”.
- Contacts (face to face, or by telephone, or
messaging, etc.) between the person who cares for
the table updating and his colleagues who move
outside of the enterprise.
7. Conclusion
This paper has focused on outlining the importance
of tacit knowledge for the business intelligence
process in SMEs. Our interviews with 20 French
SMEs leaders indicate that the acceptance of
business intelligence models depends on a number
of necessary conditions, including mainly the lack
of formalization of storage and interpretation of
collected information and the optimization of time
and resources allocated to business intelligence
activity.
The empirical results also reveal that the
exploitation of informal and anticipative data
necessary for business intelligence is hardly
possible without tacit knowledge mobilisation of
many enterprise members. Consequently, the
optimisation of business intelligence practices must
be based on the tacit knowledge traceability in the
enterprise in order to be reactive face to
environmental changes. We believe this to be a
valuable insight that can make of the tacit
knowledge management an operational reality.
Due to the exploratory nature of this study, future
research on larger samples would help in gaining
better perspective on business intelligence practices
and address the questions of the knowledge
management, particularly tacit knowledge, within
SMEs.
8. References
Ansoff, I. “Managing strategic surprise by response
to weak signals”, California Management Review
(18:2), 1975, pp. 21-33.
Argyris, C. “Actionable knowledge: Intent versus
actuality”, The Journal of Applied Behavioral
Science (32:4), 1996, pp. 390-406.
Ashmos, D.P. and Nathan, M.L. “Team sense-
making: a mental model for navigating uncharted
territories”, Journal of Managerial Issues (14:2),
2002, pp. 198-217.
Beal, R.M. “Competing effectively: environmental
scanning, competitive strategy, and organizational
performance in small manufacturing firms”,
Journal of Small Business Management, January
2000, pp. 27-47.
Blanco, S., Caron-Fasan, M-L. and Lesca, H.
“Developing Capabilities to Create Collective
Intelligence Within Organizations”, The Journal of
Competitive Intelligence and Management (1:1),
2003, pp. 80-92.
Choo, Ch.W. “The Knowing Organization: How
Organizations Use Information to Construct
Meaning, Create Knowledge and Make Decisions”,
International Journal of Information Management
(16:5), 1996, pp. 329-340.
Choo, Ch.W. “The knowing organization as learning
organization” (43:4/5), Education & Training, 2001,
pp. 197-205.
Choo, Ch.W. Information Management for The
Intelligent Organization: the art of scanning
environment, Information Today, Inc. Medford, NJ,
2002.
Daft, R.L. and Lengel, R. “Organization information
requirements, media richness and structural design”,
Management Science (52:5), 1986, pp. 554-571.
Daft, R.L., Sormunen, J. and Parks, D. “Chief
executive scanning, environmental, characteristics,
and company performance: an empirical study”,
Strategic Management Journal 9, 1988, pp. 123-139.
Desouza, K. and Awazu, Y. “Integrating local
knowledge strategies”, Knowledge Management
Review (9:4), Sep/Oct 2006, pp.20-23.
Ebrahimi, B.P. “Perceived Strategic Uncertainty and
Environmental Scanning Behavior of Hong Kong
Chinese Executives”, Journal of Business Research
49, pp. 67-77.
Fleck, J. “Informal information flow and the nature
of expertise in financial services International”,
Journal of Technology Management 11(1-2), 1996,
pp. 104-128.
Fuld, L.M. The new competitor intelligence, John
Wiley & Sons Inc. Chapter 1: Understanding
Intelligence, 1995, 23-43.
Gilad, B. and Gilad, T. The Business Intelligence
System: A New Tool for Competitive Advantage,
New York: AMACOM, 1988.
Gioia, D.A. and Chittipeddi, K. “Sense-making and
Sense giving in Strategic Change Initiation”,
Strategic Management Journal (12:6), 1991, pp. 433-
448.
Hamrefors, S. “Spontaneous environmental scanning,
part two: Empirical findings and implications for the
organizing of competitive intelligence”, Competitive
Intelligence Review (9:4), 1998, pp. 73-83.
Hambrick, D.C. “Environmental scanning and
organizational strategy”, Strategic Management
Journal (3:2), 1982, pp. 159-174.
Heinrichs, J. and Lim, J-S. “Model for organizational
knowledge creation and strategic use of
information”, Journal of the American Society for
Information Science and Technology (56:6), April
2005, pp. 620-629.
Herschel, R. and Jones, N., “Knowledge
management and business intelligence: the
importance of integration”, Journal of Knowledge
Management (9:4), 2005, pp. 45-55.
Howells, J. “Tacit knowledge, innovation and
technology transfer”, Technology Analysis &
Strategic Management (8:2), 1996, pp. 91-105.
Huber, G. “Organizational Learning: the contributing
process and the literatures”, Organization Science
(2:1), 1991, pp. 88-115.
Jain, S.C. “Environmental scanning in U.S.
Corporations”, Long Range Planning (17:2), 1984,
pp. 117-128.
Johannessen, J.A., Olaisen, J. and Olsen, B.
“Mismanagement of tacit knowledge: the importance
of tacit knowledge, the danger of information
technology, and what to do about it”, International
Journal of Information Management 21, 2001, pp.
3-20.
Lesca, H. Veille stratégique, la méthode
L.E.SCAnning, Ed. Ems, Management et Société,
2003, p.190.
Lönnqvist, A. and Pirttimäki, V. “The
measurement of business intelligence”, Information
Systems Management (23:1), Winter 2006, pp. 32-
40.
Lubit, R. “Tacit knowledge and knowledge
management: The keys to sustainable competitive
advantage”, Organizational Dynamics (29:4),
2001, pp. 164-178.
MacDonald, S. “Learning to change: an
information perspective on learning in the
organization”, Organization Science (6:5), 1995,
pp. 557-568.
Malone, D. “Knowledge management a model for
organizational learning”, International Journal of
Accounting Information Systems 3, 2002, pp. 111-
123.
May, R.C., Stewart, J.R. and Sweo, R.
“Environmental scanning behavior in a transitional
economy: evidence from Russia”, Academy of
Management Journal (43:3), 2000, pp. 403-427.
Montgomery, D.B. and Weinberg, C.B. “Toward
Strategic Intelligence System: the quality of
strategic planning depends on the quality of
information gathering”, Marketing Management,
1998, pp. 44-52.
Myers, M.D. and Avison, D. Qualitative research
in information systems, Sage Publications, 2002.
Nonaka, I. and Takeuchi, H. The knowledge
creating company, Oxford University Press, New-
York, 1995.
Polanyi, M. Personal knowledge: toward a post-
critical philosophy, Harper and Rw, New York,
1964.
Raymond, L. “Globalization, the knowledge
economy, and competitiveness: a business
intelligence framework for the development of
SMES”, Journal of American Academy of Business
(3:1/2), Sep 2003, pp. 260-269.
Solow, R.M. Learning from learning by doing:
Lessons for economic growth, Stanford, CA:
Stanford University Press, 1997.
Sparrow, J. “Knowledge management in small
firms”, Knowledge and Process Management (8:1),
2001, pp. 3-16.
Stewart, T.A. Intellectual capital: the new wealth
of organizations, London: Doubleday, 1997.
Thomas, J.B., Clark, S.M. and Gioia, D.A.
“Strategic sensmaking and organizational
performance linkages among scanning,
interpretation, action, and outcomes”, Academy of
Management Journal (36:2), 1993, pp. 239-270.
Weick, K.E. Sensemaking in organizations,
London: Sage Publications, 1995.
Wernerfelt, B. “A resource-based view of the
firm”, Strategic Management Journal 5, 1984, pp.
171-180.
doc_316162545.pdf
A Business Intelligence Model for SMEs Based on Tacit Knowledge M. Sadok
A Business Intelligence Model for SMEs Based on Tacit
Knowledge
M. Sadok, H. Lesca
To cite this version:
M. Sadok, H. Lesca. A Business Intelligence Model for SMEs Based on Tacit Knowledge. cahier
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A business intelligence model for SMEs based on tacit
knowledge.
SADOK Moufida
LESCA Humbert
CAHIER DE RECHERCHE n°2009-12 E5
Communications of the IBIMA, Volume 7, number 20, 2009
A Business Intelligence Model for SMEs Based on Tacit Knowledge
Moufida SADOK, Institute of Technology in Communications, Tunisia, [email protected]
Humbert LESCA, Laboratory CERAG UMR 5820 CNRS, France, [email protected]
Abstract
This paper proposes a specific model of business
intelligence in relation with SMEs practices,
culture and competitive environment. This model is
based on the mobilization of corporate tacit
knowledge and informal information, aiming at
interpreting anticipatory environmental
information and assist strategic decision making.
An empirical survey assessing the existing business
intelligence practices in 20 French SMEs has
identified seven necessary acceptance conditions of
a business intelligence project as well as a
managerial tool allowing tacit knowledge
traceability.
Keywords: business intelligence, tacit knowledge,
SMEs, sense-making
1. Introduction
In highly competitive markets, and because of a
complex and accelerated evolution of the economic
and technological context, firms need to focus on
proactively managing their business intelligence
process to ensure survival and to respond
efficiently to turbulent environmental changes
(Hambrick, 1982; Jain, 1984; Montgomery and
Weinberg, 1998; Ebrahimi, 2000; May et al., 2000;
Choo, 2002). Business intelligence is the process of
gathering and interpreting pertinent information
about external environment, the knowledge of
which can assist strategic decisions, and generate
or sustain long-term competitive advantages (Gilad
and Gilad, 1988; Fuld, 1995; Thomas et al., 1993).
Many other benefits can be derived from using
business intelligence (e.g. see Lönnqvist and
Pirttimäki, 2006). Business intelligence requires
also a sense-making process and constitutes a
privileged means of knowledge creation in the
enterprise (huber, 1991; Choo, 1996) in relation
with its environment. In this regard, Herschel and
Jones (2005) contend the importance of integration
of knowledge management and business
intelligence in order to improve decision making
and firm performance. The study of Heinrichs and
Lim (2005) to evaluate the impact of competitive
intelligence tool implementation on knowledge
creation and strategic use of information shows that
knowledge workers can produce greater
competitive advantage for the organization when
they are assisted by efficient competitive tools.
In fact, knowledge is a strategically important
resource (Wernerfelt, 1984; Stewart, 1997; Solow,
1997; Malone, 2002). It can be explicit, easily
formulated and transferred to others (Johannesses
et al., 2001), or tacit (Polanyi, 1964), that is,
difficult to express, formalize, or share (Lubit,
2001) because it is deeply rooted in practice and
experience and transmitted by apprenticeship and
training (Fleck, 1996). In an environment
characterized by uncertainty and complexity, tacit
knowledge helps to create sustainable competitive
advantage for companies (Howells, 1996; Nonaka
and Takeuchi, 1995). In this setting, it is crucial to
integrate disparate global sources of knowledge
available within the organizations (Desouza and
Awazu, 2006).
In the case of a large company, efforts can be
planned to formalize knowledge that is required to
interpret and exploit environmental information. In
SMEs, such efforts are unrealistic: almost all
mobilized knowledge is tacit. The selection and
interpretation of environmental information require
human competences and knowledge. And yet many
SMEs would like to optimize their business
intelligence process at lower cost. Because of their
specific features, it seems clear that SMEs have
distinctive needs in developing knowledge
management practices (Sparrow, 2001). In addition,
small firm’s adaptation and competitiveness depend
on its knowledge, detection and interpretation of the
trends in its environment (Beal, 2000; Raymond,
2003).
Thus, the main purpose of this paper is to propose an
original model aiming at helping SMEs to develop
their environmental intelligence by means of
business intelligence process, while remaining close
to their current practices and culture in order to
facilitate the acceptance of the associated
organizational changes. It seeks to produce
«actionable knowledge» (as formulated by Argyris,
1996), in order to improve the management of useful
tacit knowledge for the interpretation and use of
business intelligence information.
Our contributions in this paper are three-fold. First,
we propose a business intelligence model, supported
by a managerial tool and in relation with SMEs
practices and culture, through the identification of
the necessary acceptance conditions of business
intelligence project. Second, despite the fact that
researchers have studied intelligence activities based
on explicit organizational knowledge, only a few
have discussed the role played by tacit knowledge in
intelligence management settings. Third, an
empirical survey of 20 French SMEs which involved
personnel interviews was conducted in order to
assess the existing business intelligence practices and
to build a model of business intelligence based on the
mobilization of the corporate tacit kowledge and
informal information.
The rest of the paper is organized as follows. The
first part is a presentation of business intelligence
activities. The second part focuses on the crucial step
in business intelligence related to collective sense-
making and highlights the specific role of tacit
knowledge during this phase. The third part deals
with the research methodology, while the fourth
reports the results and discusses the study’s
findings and their implications. In the fifth part, a
specific business intelligence model is proposed.
2. Business intelligence process
Business intelligence is a collective process
through which the enterprise is actively seeking
relevant and timely environmental information
referred to as “weak signals” (Ansoff, 1975), to
grasp business opportunities, increase anticipative
capacity, and reduce uncertainty (Blanco et al.,
2003). It is also an iterative learning and an
adaptive process that helps companies reduce
business risk and cope with unstable and
unpredictable external events. Lesca (2003)
proposed a model referred to as VAS-IC (a French
formulation of Anticipative Strategic
Environmental Scanning-Collective Intelligence)
with a core process of collective sense-making. The
main steps of VAS-IC are described in figure 1
below.
The targeting step aims to identify the
environmental actors and themes to be monitored
and therefore to optimize the costs and time
dedicated to the environmental scanning activity.
The target definition must be made in a dynamic
way according to the operational users’ needs. The
targting phase aims also to specify the information
needs and sources. Two types of information
sources can be distinguished:
- Informal: through contacts with customers,
competitors, suppliers, distributors; involvements
in scientific symposia and professional lounges;
abroad missions and contacts with experts, etc.
- Formal: available in the scientific and technical
publications; data bases; enterprises publications;
patents and copyright registrations, etc.
Fig 1. VAS-IC phases
Several contributions have shown that personal
sources have a richer content, allowing weak signals
detection (Ansoff, 1975; Daft and Lengel, 1986) as
well as a better comprehension and interpretation of
the problems in a situation of highly perceived
strategic uncertainty (Daft et al., 1988).
The tracking step refers to the proactive information
acquisition about key environmental actors or events
and aims to identify environmental trackers or
gatekeepers (MacDonald, 1995) which are of two
types:
- “Sedentary” trackers who work in their offices with
documentary sources and databases.
- “Nomad” trackers who move to reach for external
sources. It is the case of the salesperson, for example.
The knowledge memorization step manages the
traceability and capitalization of the corporate
knowledge created during the business intelligence
process. This step requires the construction of a
knowledge base to set up an intelligent and dynamic
storage by creating useful links to structure and
organize the collected knowledge. The knowledge
base provides an important support to the ongoing
business intelligence process when it is assisted by
efficient retrieval mechanisms that are able to
conduct approximate and heuristic search based on
semantic, dependence and hierarchical links.
The diffusion step deals with the dissemination of the
collective sense-making findings to the appropriate
users. In the same way, a potential user can initiate
information requests if he feels the need to have
some information he is capable of designating or
which has been recommended to him by other users.
The phase of diffusion/access also includes the
problem of media appropriation related to the nature
and features of shared information. The media
richness theory (Daft and Lengel, 1986) should be of
particular interest in relation to the efficiency of this
phase.
Knowledge
memorization
Diffusion/
Access
Targeting
Tracking
Collective
Sense-making
Action
In the action step, if the information that is
processed is sufficiently meaningful, it can be
integrated into the decision process to provide
possible operational fields for subsequent actions.
If the output of the interpretation process hasn’t
reached a clear vision, a complementary request for
additional information can be made.
3. Collective sense-making
The core of business intelligence is the collective
sense-making. In literature, sense-making has been
defined in different ways. It is an interpretative
process where people assign meaning to ongoing
events (Gioia and Chittipeddi, 1991). It is the
amplification of weak signals and the search for
contexts within small details fitted together for
sense-making (Weick, 1995). It is considered as a
creative and collective method that can help the
organization to give sense and see possibilities in
the surrounding disorder (Choo, 2001; Ashmos and
Nathan, 2002).
We propose here that collective sense-making
refers to the collective operation during which
knowledge is created from some information that
plays the role of inductive stimuli, and by means of
interactions between individual and collective
memories. It describes the capability of a group to
create significant links between collected data,
which can be inferred iteratively, using the tacit
knowledge of those participants involved in the
collective work sessions, and supported by data
retrieved from structural databases that are updated
during such processes. The result of this operation
can provide an efficient support to the decision
making process by reducing the information
ambiguity and the uncertainty of business
environment.
Fig 2. Collective sense-making process
Therefore, the complexity and diversity of external
events require a dynamic formulation of links and the
establishment of variable parameters depending on
time or context. The sense-making process should
integrate less linear reasoning, especially when
sense-making teams are facing unstructured
situations, a high degree of equivocality, or
incomplete data, which may be encountered by an
organization in managing risks, or building
development strategies. Thus, this phase gains from
using appropriate heuristics in order to reduce
complexity reasoning and converge to a collective
decision making.
The implementation of the collective sense-making
process makes use of efficient tools available in
information technologies. This includes a database
for storing the data collected and all data traces
generated during the reasoning phase, and a
knowledge base that stores all the data that has been
analyzed, and traces all links inferred through the
collective sense-making process.
This definition is illustrated by the conceptual model
provided by figure 2. This model must be adapted
according to contextual factors of contingency,
particularly in the case of SMEs in order to take into
consideration the existing practices in such
companies.
In addition, efficient collective sense-making rests
upon several success conditions. First, it is necessary
to detect, monitor and track early signals that could
help anticipate strategic threats and opportunities. It,
then, requires interpreting these signs to deduce the
appropriate hypothetical anticipations. It finally
requires knowledge, behaviors, aptitudes, and a high
degree of motivation by those who are commissioned
to select and interpret signals and data captured from
the environment.
New
information
Sense and
conclusions useful
to decide and act
Success
conditions
Heuristics
Tacit
knowledge
Anticipation
Plausible hypotheses
generation
Formalized
knowledge
bases
Formalized
data bases
4. Data collection
The data were collected through personal
interviews (two hours each) conducted in 20 SMEs
with the head of the SME, or his direct deputy. The
businesses have between 30 to 300 employees,
international activities and subsidiaries, and
permanent correspondents in the countries with
which they trade. The interviews have been audio-
taped and transcribed, then, written up in individual
case studies. Then the interviews have been
analyzed (Myers and Avison, 2002) by carving out
“verbatims”, that were coded in order to be able to
locate each verbatim anonymously in the corpus.
We regrouped the verbatim while using the
business intelligence model represented in figure 1
as “a posteriori reading grid”. We first present the
results regarding the possible existence of a
formalized system of business intelligence and
knowledge management (in relation globally with
figure 1), and then the results regarding the phases
of the business intelligence process (detailed
phases recovered on figure 1 as well as on figure
2). The data collection is exploratory and aims to
answer the following questions:
1) Is there a global and formalized system of
business intelligence?
2) Are the possibly collected signs and data stored?
3) Are the data sources used, essentially writings,
databases, the Internet (formalized / documentary
sources)?
4) Are the data gathered about the environment
formalized at the end of the process?
5) Are the circulation and the diffusion of
anticipative information systematically organized?
6) Do knowledge bases exist to treat the
environmental signs and data? or, if they don’t, any
formalized knowledge? or some occasionally
explicit knowledge?
7) Does the perceived “time pressure” by SME
leader play a specific role to set up a business
intelligence system?
We try to discover the possible practices of
business intelligence, as they effectively exist in
the visited enterprise, even though these practices
might be very rudimentary and even if the word
“business intelligence” isn’t used in the visited
enterprise: we are interested in the spontaneous
practices (Hamrefors 1998). That is why we ask a
small number of questions during the interview and
we don't use academic jargon while speaking to our
interlocutors. Considering the fact that the visited
SMEs are not supposed to have any experience
concerning business intelligence, we relied on an
open-ended questionnaire.
During the interview we didn't use any
sophisticated vocabulary, nor did we even use the
words business intelligence or knowledge
management. We merely asked if it was important,
for this enterprise “to see things coming in its
environment”, and if it was important for them “to
actively monitor” the possible signs of change. For
every answer, we asked for a concrete example, in
order to be sure that we understood each other well.
5. Empirical results
Results discussion
Whilst, visited enterprises recognize the usefulness
and importance of environmental scanning, most
underline the absence of a structured and global
business intelligence system due to the scarcity of
organizational resources that could be dedicated to
this function. However, we could observe in their
answers existing practices of business intelligence
that are fragmentary, spontaneous and built on the
initiative of some isolated individuals.
In most cases, data acquisition and storage are not
formalized. Certain enterprise members
spontaneously collect field data that remain in an
abstract state, memorized only in the mind of the
individuals who collected them. Consequently, the
collected data are disseminated and not easily
accessible if a person needs to mobilize them quickly
to make a decision or to triangulate with other
sources.
Data are captured in most enterprises with the
assistance of informal field sources. Tracker behavior
and prior knowledge are involved: the tracker
mentally records any information that seems to be
interesting or surprising. He makes a selection on the
basis of prior knowledge on which he relies and that
is activated at that moment. In this way, as soon as it
is captured, anticipative information loses its
cohesion and becomes removed from its context. It
becomes integrated with the tracker knowledge to
enrich and to influence his knowledge. According to
the assimilation / adaptation process, the recently
acquired data and the previous tacit knowledge form
the tracker’s new set of tacit knowledge. If a tracker
shares the new information with a colleague, he
might very likely present it not as such, but coated
with commentaries derived from prior knowledge.
Thus, the colleague in question does not merely
receive the so-called information, but a richer
perspective (in the sense of Daft and Lengel, 1986).
The information flows orally and step by step
through enterprise meetings. It can be “push”
information, if the information’s possessor takes the
initiative to talk about it, or more probably “pull”
information if someone feels the need to request
information.
The individuals’ isolation, within a SME, is a
counterintuitive finding. Indeed, it goes without
saying that in a small company people know one
another. But this doesn’t mean that information flows
easily. It is common to see a good number of
collaborators who are constantly on work trips and
whom you rarely meet. Organizing a collective work
session within a SME is often a daunting task.
In the studied SMEs, we did not find any trace of
formalized knowledge at the organisational level.
The knowledge mobilized by these enterprises to
interpret and exploit information is completely tacit.
The interpretation is made in a “spontaneous” way,
individually, and without explicit method.
The information tracker spontaneously selects
anticipative data that grabs his attention. He doesn't
especially try to rely on any specific selection
criterion over another. If we ask him why he
selected such data and what use he intends to make
of them, he will probably be unable to fully answer.
Just as a craftsman, he makes, but is unable to give
comprehensive verbal explanation of why and how
he makes, which doesn't mean that he does things
badly. He is guided by his tacit knowledge, by his
know-how, by his acquired experience. He deploys
a managerial knowledge (and creativeness in some
cases) that is essentially tacit or, in any case,
informal, and not engineer's procedural knowledge.
The SME leader doesn't see the need for
knowledge formalisation which is essentially
informal by nature and which constantly evolves
with the new experiences. The formalisation task is
perceived to be sterile work, paralyzing or
expensive. The implementation of a business
intelligence system must be considered then as a
project of optimization for what already exists, a
rapidly- implemented project that produces
conclusive results very quickly: a one month
horizon already seems to be excessively long and
uninteresting. Spontaneously, the SME leader
associates the implementation of a business
intelligence project to an improvement of his
business’ agility. If he perceives the project to be
long (therefore complicated) then he might deduce
that it is bad for his business.
Identifying necessary acceptance conditions of the
business intelligence model
We have found out that the empirical study’s
findings provide seven necessary acceptance
conditions (NAC) of business intelligence model,
as presented in the table below. We propose using
them to help set up an environmental intelligence
system within SME.
Table 1: Necessary acceptance conditions of
business intelligence model by SMEs leaders
These suggestions could be seen in two different
ways either as normative recommendations or as
hypotheses.
We have found out that the normative
recommendations are not appropriate to the needs of
the interviewed leaders. In fact, if the manager of a
business intelligence project satisfies these necessary
conditions of acceptance, then he should succeed in
making the SME leader accept the setting up of an
environmental intelligence system. The validation
criterion of the hypotheses is therefore the
acceptance of the project by the leader. If he is
willing to start the business intelligence project, then
we can say that the hypotheses are validated, at least
in the case under consideration.
6. Toward a business intelligence model based on
tacit knowledge and informal information
A major conclusion of our empirical results can be
expressed in the form of a paradox. The business
owners that we interviewed reject any formalization
of business data or the relevant knowledge, yet they
wish to optimize current practices in order to be
effective and to save time and resources.
We started from this paradox to propose a business
intelligence model practically without any
formalization, investment, and personnel recruitment.
Such a model is based on tacit knowledge
management and on the use of informal data.
Figure 3 illustrates the conceptual model derived
from figure 2 which is interested mainly in the data
and knowledge traceability.
The conceptual model appears to be based on several
actors (enterprise employees) and a process of
collective interpretation of business information.
This process aims to amplify collected informal data,
analyze them, and react properly to the inherent
threat or opportunity. It is composed of three phases.
To have more chance of being accepted by the SME leader, the business intelligence system should satisfy the
following necessary acceptance conditions:
NAC
1
: The proposed environmental intelligence model to the SMEs must be built on the simplest possible
formalisation.
NAC
2
: The proposed environmental intelligence model to the SMEs must avoid the storage of data.
NAC
3
: The proposed environmental intelligence model to the SMEs must essentially build on the use of
relational data sources.
NAC
4
: The proposed environmental intelligence model to the SMEs must essentially build on the exploitation
and interpretation of informal data.
N AC
5
: The proposed environmental intelligence model for SMEs must be organized in such a way as to save
time and reduce data deterioration.
NAC
6
: the proposed environmental intelligence model to the SMEs must avoid the formalisation knowledge
implemented to interpret data.
NAC
7
: The proposed environmental intelligence model to the SMEs must be immediately ‘attractive’ for the
SME leader. It must provide conclusive results very quickly.
Fig 3. The business intelligence model based on tacit knowledge and informal information
Links initialization is considered as an initial phase.
Its objective is to amplify signals and to set up the
linkages capable of providing a clear picture of
potential risk encountered by the enterprise or
business opportunity.
During the second phase, new linkages are inferred
iteratively using existing linkages, tacit knowledge
of business intelligence actors. The third phase
aims to check whether the iterative process has
reached a clear knowledge of the risk encountered
or the opportunity, and a global assessment of other
potentially related decisions. At the end of this
phase, a reactive process can be triggered to
propose, if needed, the appropriate actions to
reduce risk or to take advantage of a business
opportunity and to anticipate business decisions.
Thus, we can build a binary matrix M, presented in
figure 4 that provides an operational tool and
allows identifying, at all times, the trace of useful
data or knowledge within or outside the enterprise,
provided that it is up-to-date.
Fig 4. Matrix “Who knows What”
Therefore, let M be the aforementioned matrix and
m
ij
its generic component, m
ij
is defined as follows:
m
ij
= 1 if actor j can process topic I, has got the
knowledge needed to process or help process the
item i.
m
ij
= 0 elsewhere
In addition, a row in M gives the list of actors who
are able to handle a given topic, while a column
describes the capability of a given actor to process
the list of topics.
Topics include but are not limited to the
identification of potentials customers, looking for
information about competitors or suppliers,
investigation of new markets or products.
The list of topics can be fixed by companies’ leaders
according to the business needs of the enterprise.
This list is neither definitive nor static and can evolve
in relation with the targeting activity objects.
The size of this matrix should be kept as small as
possible, and sufficiently large to keep the control of
all knowledge needed. Indeed, in SMEs of about a
hundred people, the number of names to write down
in the top horizontal margin hardly exceeds the
twenties.
However, the number of lines (where the names of
themes or those of outside actors to the enterprise are
registered) can reach several dozens (vertical margin
on the left of the table).
The construction and the up-to-date maintenance of
this management tool, the informal information and
the tacit knowledge shouldn’t take the person
charged to do this work more than half a day per
week.
The information that is useful for updating of matrix
M has mainly two sources:
- Personnel management service (or other services)
supposed to be informed of the travelling of the
enterprise members: visits of customers, of
colleagues, trade shows, etc. This information
permits to inform the table on “who is to contact the
Informal
information
Collective interpretation of
information
Tacit knowledge
Who? Where? When?
Tacit knowledge
Who? Where? When?
Tacit knowledge
Who? Where? When?
Tacit knowledge
Who? Where? When?
Estimation
Risk/opportunity
Anticipation
Actor
j
Topic
i
m
ij
other”, and “who is susceptible to detain some
information on what”.
- Contacts (face to face, or by telephone, or
messaging, etc.) between the person who cares for
the table updating and his colleagues who move
outside of the enterprise.
7. Conclusion
This paper has focused on outlining the importance
of tacit knowledge for the business intelligence
process in SMEs. Our interviews with 20 French
SMEs leaders indicate that the acceptance of
business intelligence models depends on a number
of necessary conditions, including mainly the lack
of formalization of storage and interpretation of
collected information and the optimization of time
and resources allocated to business intelligence
activity.
The empirical results also reveal that the
exploitation of informal and anticipative data
necessary for business intelligence is hardly
possible without tacit knowledge mobilisation of
many enterprise members. Consequently, the
optimisation of business intelligence practices must
be based on the tacit knowledge traceability in the
enterprise in order to be reactive face to
environmental changes. We believe this to be a
valuable insight that can make of the tacit
knowledge management an operational reality.
Due to the exploratory nature of this study, future
research on larger samples would help in gaining
better perspective on business intelligence practices
and address the questions of the knowledge
management, particularly tacit knowledge, within
SMEs.
8. References
Ansoff, I. “Managing strategic surprise by response
to weak signals”, California Management Review
(18:2), 1975, pp. 21-33.
Argyris, C. “Actionable knowledge: Intent versus
actuality”, The Journal of Applied Behavioral
Science (32:4), 1996, pp. 390-406.
Ashmos, D.P. and Nathan, M.L. “Team sense-
making: a mental model for navigating uncharted
territories”, Journal of Managerial Issues (14:2),
2002, pp. 198-217.
Beal, R.M. “Competing effectively: environmental
scanning, competitive strategy, and organizational
performance in small manufacturing firms”,
Journal of Small Business Management, January
2000, pp. 27-47.
Blanco, S., Caron-Fasan, M-L. and Lesca, H.
“Developing Capabilities to Create Collective
Intelligence Within Organizations”, The Journal of
Competitive Intelligence and Management (1:1),
2003, pp. 80-92.
Choo, Ch.W. “The Knowing Organization: How
Organizations Use Information to Construct
Meaning, Create Knowledge and Make Decisions”,
International Journal of Information Management
(16:5), 1996, pp. 329-340.
Choo, Ch.W. “The knowing organization as learning
organization” (43:4/5), Education & Training, 2001,
pp. 197-205.
Choo, Ch.W. Information Management for The
Intelligent Organization: the art of scanning
environment, Information Today, Inc. Medford, NJ,
2002.
Daft, R.L. and Lengel, R. “Organization information
requirements, media richness and structural design”,
Management Science (52:5), 1986, pp. 554-571.
Daft, R.L., Sormunen, J. and Parks, D. “Chief
executive scanning, environmental, characteristics,
and company performance: an empirical study”,
Strategic Management Journal 9, 1988, pp. 123-139.
Desouza, K. and Awazu, Y. “Integrating local
knowledge strategies”, Knowledge Management
Review (9:4), Sep/Oct 2006, pp.20-23.
Ebrahimi, B.P. “Perceived Strategic Uncertainty and
Environmental Scanning Behavior of Hong Kong
Chinese Executives”, Journal of Business Research
49, pp. 67-77.
Fleck, J. “Informal information flow and the nature
of expertise in financial services International”,
Journal of Technology Management 11(1-2), 1996,
pp. 104-128.
Fuld, L.M. The new competitor intelligence, John
Wiley & Sons Inc. Chapter 1: Understanding
Intelligence, 1995, 23-43.
Gilad, B. and Gilad, T. The Business Intelligence
System: A New Tool for Competitive Advantage,
New York: AMACOM, 1988.
Gioia, D.A. and Chittipeddi, K. “Sense-making and
Sense giving in Strategic Change Initiation”,
Strategic Management Journal (12:6), 1991, pp. 433-
448.
Hamrefors, S. “Spontaneous environmental scanning,
part two: Empirical findings and implications for the
organizing of competitive intelligence”, Competitive
Intelligence Review (9:4), 1998, pp. 73-83.
Hambrick, D.C. “Environmental scanning and
organizational strategy”, Strategic Management
Journal (3:2), 1982, pp. 159-174.
Heinrichs, J. and Lim, J-S. “Model for organizational
knowledge creation and strategic use of
information”, Journal of the American Society for
Information Science and Technology (56:6), April
2005, pp. 620-629.
Herschel, R. and Jones, N., “Knowledge
management and business intelligence: the
importance of integration”, Journal of Knowledge
Management (9:4), 2005, pp. 45-55.
Howells, J. “Tacit knowledge, innovation and
technology transfer”, Technology Analysis &
Strategic Management (8:2), 1996, pp. 91-105.
Huber, G. “Organizational Learning: the contributing
process and the literatures”, Organization Science
(2:1), 1991, pp. 88-115.
Jain, S.C. “Environmental scanning in U.S.
Corporations”, Long Range Planning (17:2), 1984,
pp. 117-128.
Johannessen, J.A., Olaisen, J. and Olsen, B.
“Mismanagement of tacit knowledge: the importance
of tacit knowledge, the danger of information
technology, and what to do about it”, International
Journal of Information Management 21, 2001, pp.
3-20.
Lesca, H. Veille stratégique, la méthode
L.E.SCAnning, Ed. Ems, Management et Société,
2003, p.190.
Lönnqvist, A. and Pirttimäki, V. “The
measurement of business intelligence”, Information
Systems Management (23:1), Winter 2006, pp. 32-
40.
Lubit, R. “Tacit knowledge and knowledge
management: The keys to sustainable competitive
advantage”, Organizational Dynamics (29:4),
2001, pp. 164-178.
MacDonald, S. “Learning to change: an
information perspective on learning in the
organization”, Organization Science (6:5), 1995,
pp. 557-568.
Malone, D. “Knowledge management a model for
organizational learning”, International Journal of
Accounting Information Systems 3, 2002, pp. 111-
123.
May, R.C., Stewart, J.R. and Sweo, R.
“Environmental scanning behavior in a transitional
economy: evidence from Russia”, Academy of
Management Journal (43:3), 2000, pp. 403-427.
Montgomery, D.B. and Weinberg, C.B. “Toward
Strategic Intelligence System: the quality of
strategic planning depends on the quality of
information gathering”, Marketing Management,
1998, pp. 44-52.
Myers, M.D. and Avison, D. Qualitative research
in information systems, Sage Publications, 2002.
Nonaka, I. and Takeuchi, H. The knowledge
creating company, Oxford University Press, New-
York, 1995.
Polanyi, M. Personal knowledge: toward a post-
critical philosophy, Harper and Rw, New York,
1964.
Raymond, L. “Globalization, the knowledge
economy, and competitiveness: a business
intelligence framework for the development of
SMES”, Journal of American Academy of Business
(3:1/2), Sep 2003, pp. 260-269.
Solow, R.M. Learning from learning by doing:
Lessons for economic growth, Stanford, CA:
Stanford University Press, 1997.
Sparrow, J. “Knowledge management in small
firms”, Knowledge and Process Management (8:1),
2001, pp. 3-16.
Stewart, T.A. Intellectual capital: the new wealth
of organizations, London: Doubleday, 1997.
Thomas, J.B., Clark, S.M. and Gioia, D.A.
“Strategic sensmaking and organizational
performance linkages among scanning,
interpretation, action, and outcomes”, Academy of
Management Journal (36:2), 1993, pp. 239-270.
Weick, K.E. Sensemaking in organizations,
London: Sage Publications, 1995.
Wernerfelt, B. “A resource-based view of the
firm”, Strategic Management Journal 5, 1984, pp.
171-180.
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