Proceedings of the 12th European Conference on Knowledge ...

Kamaladdin Rahmani Youshanloui et al
According to o<strong>the</strong>r researches, done by various researchers, regarding <strong>the</strong> influencing factors for
implementing KM, <strong>the</strong> most important influencing factors <strong>of</strong> KM can be summarized as in <strong>the</strong> table 1:
Table 1: Critical Success Factors
Critical Success Factors
Culture
structure
Strategy & goals
Leadership
Information Technology
Measurement
Human Resource
Financial resource
Learning
Benchmarking
2.3 Fuzzy Cognitive Maps
Researchers
Davenport et al(1998), Lee & Choi(2003), Yeh et al(2006), Skyrme &
Amidon(1997), Hung et al(2005), Buckman(1999), M<strong>of</strong>fett et
al(2003), Hasanali (2002), Liebowitz (1999)
Davenport et al(1998), Lee & Choi(2003), Hung et al(2005),
Kuan(2005)
Yeh et al(2006), Chourides et al(2003), Mathi(2004), Khalifa &
Liu(2003), Liebowitz(1999), Zack(1999)
Davenport et al(1998), Choi(2000), Skyrme & Amidon(1997),
Holsapple & Joshi(2000), Hung et al(2005), Dess & Picken(2000),
M<strong>of</strong>fett et al(2003), Hasanali(2002), Liebowitz(1999)
Lee & Choi(2003), Yeh et al(2006), Choi (2000), Skyrme &
Amidon(1997), Chourides et al(2003), Hung et al(2005), Lee &
Hong(2002), Davenport et al(1998), Hasanali(2002)
Holsapple & Joshi(2000), Mathi(2004), Hung et al(2005),
Kuan(2005), Hasanali(2002)
Lee & Choi(2003), Yeh et al(2006), Holsapple & Joshi(2000),
Chourides et al(2003), Kuan(2005), Davenport et al(1998),
Holsapple & Joshi(2000), Davenport & Volpel(2001)
Choi (2000), Skyrme & Amidon (1997), Hung et al(2005),
Greengard(1998), Cohen & Backer(1999), M<strong>of</strong>fett et al(2003)
Choi (2000), Hung et al(2005), Davis(1996), Drew(1997), Day &
Wendler(1998), M<strong>of</strong>fett et al(2003)
Cognitive maps or <strong>the</strong> causal graphical models were first introduced by Robert Ekslord in 1976 in <strong>the</strong>
field <strong>of</strong> political science (Alizadeh et. al. 2008). In 1986, Kosko, for <strong>the</strong> first time, used fuzzy tools to
map <strong>the</strong>se models and introduced <strong>the</strong> models <strong>of</strong> fuzzy cognitive maps. Based on his definition, FCM
is a directed graphical diagram with such concepts as Rules, events and <strong>the</strong> like, along with such
items as nodes and causal relations among <strong>the</strong>m. This graph or diagram intends to illustrate <strong>the</strong>
causal relationship <strong>of</strong> mentioned concepts in <strong>the</strong> nodes (Kosko 1986).
In <strong>the</strong> original concept <strong>of</strong> cognitive map, only two numeric values can be allocated for <strong>the</strong> arcs,
namely 1 (which represents <strong>the</strong> exertion <strong>of</strong> positive influence <strong>of</strong> on <strong>the</strong> node) and -1 (which indicates
<strong>the</strong> reverse effect between two nodes). But in <strong>the</strong> fuzzy cognitive map, <strong>the</strong> value <strong>of</strong> <strong>the</strong> relation
between two nodes can be any number between 1 and -1 (Pedrycz2010).
Fuzzy cognitive maps are an asset for modeling complex systems. This method is widely used in
complex decision-making problems in <strong>the</strong> areas <strong>of</strong> business, formulating strategic issues, identifying
Management problems; knowledge management, communication management in airlines, etc.
(Rodriguez et. al. 2007). Creating an FCM model requires <strong>the</strong> inputs that have been obtained from <strong>the</strong>
experiences and knowledge <strong>of</strong> experts in <strong>the</strong> intended subject. Therefore, in <strong>the</strong> models <strong>of</strong> Fuzzy
Cognitive Maps, <strong>the</strong> accumulated experience <strong>of</strong> people is integrated with <strong>the</strong> existing knowledge in
<strong>the</strong> field for which <strong>the</strong> model is mapped. Based on this, <strong>the</strong> causal relationship <strong>of</strong> <strong>the</strong> formative factors
<strong>of</strong> <strong>the</strong> system is formed (Kandasamy 2003).
The approach <strong>of</strong> FCM used in this paper has four sections. These sections include: initial matrix <strong>of</strong>
success factors, <strong>the</strong> fuzzified matrix <strong>of</strong> success factors, Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success
factors, and <strong>the</strong> final matrix <strong>of</strong> success factor. Initial matrix <strong>of</strong> success is an NXM matrix in which each
cell represents <strong>the</strong> coefficient or <strong>the</strong> scale that a "j" expert has, based on his experience, assigned to
an "i" factor. In <strong>the</strong>se matrices, "n" represents <strong>the</strong> number <strong>of</strong> factors and "m" is <strong>the</strong> number <strong>of</strong> experts.
Fuzzified matrix <strong>of</strong> success factors has been obtained through <strong>the</strong> conversion <strong>of</strong> numerical vectors <strong>of</strong>
<strong>the</strong> previous step to <strong>the</strong> fuzzy sets in which <strong>the</strong> value <strong>of</strong> each <strong>of</strong> its components is between zero and
one. The Strength <strong>of</strong> Relationships Matrix <strong>of</strong> Success is an NXN matrix that is obtained from <strong>the</strong>
previous ones. The rows and columns <strong>of</strong> <strong>the</strong> matrix illustrate <strong>the</strong> identified factors. Each component <strong>of</strong>
this matrix represents <strong>the</strong> amount <strong>of</strong> influence that "i" exerts on "j," which can be a value between [-
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