Expert Systems
with Applications
Expert Systems with Applications 32 (2007) 687–702
www.elsevier.com/locate/eswa
Intelligent modeling of e-business maturity
George Xirogiannis
b
a,*
, Michael Glykas
b
a
University of Piraeus, Department of Informatics, 80, Karaoli & Dimitriou St., 185 34 Piraeus, Athens, Greece
University of Aegean, Department of Financial and Management Engineering, 31, Fostini Street, 82 100 Chios, Greece
Abstract
E-business has a significant impact on managers and academics. Despite the rhetoric surrounding e-business strategy formulation
mechanisms, which support reasoning of the effect of strategic change activities to the maturity of the e-business models, are still emerging. This paper describes an attempt to build and operate such a reasoning mechanism as a novel supplement to e-business strategy formulation exercises. This new approach proposes the utilization of the fuzzy causal characteristics of Fuzzy Cognitive Maps (FCMs) as
the underlying methodology in order to generate a hierarchical and dynamic network of interconnected maturity indicators. By using
FCMs, this research aims at simulating complex strategic models with imprecise relationships while quantifying the impact of strategic
changes to the overall e-business efficiency. This research establishes generic adaptive domains – maps in order to implement the integration of hierarchical FCMs into e-business strategy formulation activities. Finally, this paper discusses experiments with the proposed
mechanism and comments on its usability.
2006 Elsevier Ltd. All rights reserved.
Keywords: Fuzzy cognitive maps; E-business modeling; Strategy planning; Decision support
1. Introduction
Today, there is an increasing demand for a strategiclevel assessment of e-business capabilities that can be
assembled and analyzed rapidly at low cost and without
significant intrusion into the subject enterprises. The benefits from completing such an exercise are quite straightforward, for instance, identification of significant strengths
and weaknesses, establishment of a rationale for action, a
reference point for measuring future progress, etc.
This paper proposes a novel supplement to strategiclevel maturity assessment methodologies based on fuzzy
cognitive maps (FCMs). This decision aid mechanism proposes a new approach to supplement the current status
analysis and objectives composition phases of typical ebusiness strategy formulation projects, by supporting
‘‘intelligent’’ modeling of e-business maturity and ‘‘intelligent’’ reasoning of the anticipated impact of e-business
*
Corresponding author.
E-mail addresses:
[email protected] (G. Xirogiannis), mglikas@
aegean.gr (M. Glykas).
0957-4174/$ - see front matter 2006 Elsevier Ltd. All rights reserved.
doi:10.1016/j.eswa.2006.01.042
strategic change initiatives. The proposed mechanism utilizes the fuzzy causal characteristics of FCMs as a new
modeling technique to develop a causal representation of
dynamic e-business maturity domains. This research proposes a holistic set of adaptive domains in order to generate
a hierarchical network of interconnected e-business maturity indicators. The proposed mechanism aims at simulating the operational efficiency of complex hierarchical
strategy models with imprecise relationships while quantifying the impact of strategic alignment to the overall
e-business efficiency. Also, this paper proposes an updated
FCM algorithm to model effectively the hierarchical and
distributed nature of e-business maturity.
This application of FCMs in modeling the maturity of
e-business is considered to be novel. Moreover, it is the
belief of this paper that the fuzzy reasoning capabilities
enhance considerably the usefulness of the proposed mechanism while reducing the effort to identify precise maturity
measurements. The proposed model has both theoretical
and practical benefits. Given the demand for effective
strategic positioning of e-business initiatives, such a succinct mechanism of conveying the essential dynamics of
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G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
e-business fundamental principles is believed to be useful
for anyone contemplating or undertaking an e-business
strategy formulation exercise. Primarily, the proposed
model targets the principle beneficiaries and stakeholders
of strategy formulation projects (enterprise top administration, strategic decision makers, internal auditors, etc)
assisting them to reason effectively about the status of
e-business maturity metrics, given the (actual or hypothetical) implementation of a set of strategic changes. Nevertheless, the explanatory nature of the mechanism can
prove to be useful in a wider educational setting.
This paper consists of five sections. Section 2 presents a
short literature overview, Section 3 presents an overview of
the FCM based system, while Section 4 discusses the new
approach to e-business maturity modeling based on FCMs.
Finally, Section 5 concludes this paper and briefly discuses
future research activities.
2. Literature overview
2.1. E-business drivers
E-business offers promise to apply web and other electronic channel technologies to enable fully the integration
of end-to-end processes. It involves both core and support
business aspects, it focuses on information sharing efficiency, not just financial transactions. E-business primary
objective is business improvement through:
• Deployment of new technologies in the value chain.
• Connection of the value chains between enterprises
(B2B) and between enterprises and consumers (B2C)
in order to improve service, exploit alternative distribution/communication channels and support cost
reduction due to the associated value chain optimization.
• Increase of the speed of information processing (mainly
at real-time) and responsiveness by utilizing common
information sources (both external and internal).
E-business has a significant impact on every business
function. Integrated information technology causes a shift
in the value chain of the enterprise. It causes a considerable
deflation of prices due to radical cost reductions, annihilation of profit margins, disintermediation of companies and
industries due to the transparent product/service delivery
to the end customer, increase in cross selling volumes and
so forth. On the other hand, no industry is immune to
intense competition due to chain reactions that affect all
electronic network partners (Palmer, 2002). This may cause
a higher level of uncertainty of future business prospects,
but it is only fair to say that adaptive risk management
may reduce such pitfalls. Also, the current enterprise valuation can be radical altered by this new business environment therefore enterprises must reconsider their core
competencies and strategies to maintain their competitive
advantages.
The new economy associated with e-business has broken
down many of the traditional barriers. The fundamental
shift in focus from optimizing the efficiency of individual
enterprises to optimizing the efficiency of a network of
enterprises for competitive advantage is a considerable
challenge (Chung, Yam, Chan, & Potter, 2005). E-business
activities now operate across an extended network of digitally connected partners to enable demand/capacity/price
optimizations while offering self-service client relationships
at multiple channels with a significant communication
speed.
It is the view of this paper that while e-business solution
providers promise financial prosperity and sales volumes,
case studies clearly indicate that awareness, targeted strategic planning and holistic organizational alignment are the
key success factors for managing business in the digital
age. Understanding the speed and scope of e-business
impacts while generating the essential momentum forms
the basis for setting realistic strategic priorities, mapping
out a go-forward plan while evaluating the critical factors
for e-business success. Effective service/product delivery
through electronic channels requires efficient process control and management of measurable targets, in order to
maintain the necessary range of organizational buy-in, to
manage risk and assure accountability.
2.2. Relevant research in business modeling
2.2.1. Modeling traditional business activities
Enterprises usually employ modeling techniques to drive
re-design activities and communicate the impact of internal
change. Such modeling techniques may loosely fit within
the area of decision support systems (Carlsson & Turban,
2002; Shim et al., 2002; Sprague & Watson, 1986). Several
modeling approaches can be brought to bear on the task of
supplementing business modeling activities. In particular
the field of knowledge-based systems (Harmon & King,
1985; Metaxiotis, Psarras, & Samouilidis, 2003) could fulfill the desire for more accurate predictive business modeling tools. The research presented by Lin, Yang, and Pai
(2002) proposed generic structures with no formal reasoning capabilities to model traditional business processes,
which could represent a business process in various concerns and multiple layers of abstraction.
The research presented by Burgess (1998) modeled business process models with system dynamics to support the
feasibility stage of business process re-engineering (BPR).
Similarly, research (Burgess, 1998) modeled the interaction
between competitive capabilities of quality and cost during
total quality management (TQM) initiatives (Burgess,
1996). This model did not decompose hierarchical relationships, nor did it allow the connection of the sub-models.
Finally, the model required formal definition of causal relationships (e.g. functions), which posed a significant overhead in supplementing the business modeling exercise.
The research presented by Crowe, Fong, Bauman, and
Zayas-Castro (2002) reported the development of a tool
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
to quantitatively estimate the potential risk level of a process change effort based on simple arithmetic approximations. The utilization of uncertainty was also suggested
by Jones and Ryan (2002) which proposed a contingency
model of quality management practices, whereby quality
management orientation, process choice, and environmental uncertainty were the contextualizing variables.
Other research attempts built on theories like non-linearity. The research presented by Murray, Priesmeyer,
Sharp, Jensen, and Jensen (2000) revealed that nonlinear
science offered a practical new frame of reference for business modeling initiatives (e.g. health care settings).
Research (Kwahk & Kim, 1999) offered a two-phase
cognitive modeling (called TCM), to help enterprises identify potential organizational conflicts. It proposed a number of informal/ambiguous techniques to generate and
validate the organizational cognitive maps, like interviews,
observation, group discussions, questionnaires, document
analysis, and so forth. Causal values were generated
according to the pairwise comparison technique with no
fuzzy definitions allowed.
Each of the aforementioned tools and techniques offers
distinct advantages in modeling business architectures.
However, all of them focus in modeling traditional business
activities and offer limited functionality in modeling process – technology integration. They tend to visualize the
enterprise as an isolated entity, while e-business practices
build on horizontal interconnections between networks of
coupling value chains. Therefore, it is only fair to say that
since most contemporary e-business principles depart from
traditional business practices, contemporary e-business
modeling tools should also build on contemporary modeling approaches.
2.2.2. Modeling contemporary e-business activities
While information and communication technology
(ICT) in the form of e-business is advocated as an enabler
by allowing to be shared by all business stakeholders in the
value chain, there is little analytical or quantifiable evidence that it will actually improve the overall performance
of the enterprise in delivering customer wants. It is usually
proposed that passing information to all entities in the
value chain may improve performance, but still no formal
reasoning evidence has been provided to support this argument. The impact of the e-business enabled value chain on
strategic decisions, materials/component suppliers, distribution channel operations, etc., however, is less well understood and exploited. For established enterprises, change is
the key challenge, as argued by Jackson and Harris (2003)
and Phan (2003). Such enterprises must rethink fundamental aspects of their strategy, which may lead to a radical
overhaul of existing ways of doing business, with company
structure and culture becoming much more customerfocused.
Research (Hooft & Stegwee, 2001) discussed a method
for the development of an e-business strategic framework.
However it focused on qualitative analysis based on the
689
SWOT framework without identifying any causal relationships among value chain drivers.
The experimental research presented in Bharati and
Chaudhury (2003) endeavored to understand factors that
affect decision-making satisfaction in web-based decision
support systems. Using a structural equation modeling
approach, the analysis revealed that information quality
and system quality influenced e-business decision-making.
While the underlying model built on structured relationships, no formal automated reasoning was present.
Research (Long & Schoenberg, 2002) presented similar
empirical analyses to discuss whether e-business requires
different leadership characteristics.
Research (Disney, Naim, & Potter, 2005) investigated
how e-business affects the supply chain dynamics of an
enterprise in an attempt to establish e-business enabled supply chain models for quantifying the impact of ICTs. It concluded that only robust models could enable considerable
quantitative insights into the impact of e-business on supply
chain dynamic behavior prior to their implementation.
Research (Koh & Kim, 2005) modeled a virtual community activity framework, integrating community knowledge
sharing into business activities in the form of an e-business
model. This proposition attempted to model business activities relationships by limiting itself to statistical analysis of
raw electronic interactions, thus presenting limited research
portability to other business cases.
Research (Duffy, 2001) attempted to formalize a blueprint of maturity modeling. This model utilized maturity
level indicators for each key success driver (KSD) category
to estimate the overall e-business maturity of the enterprise. Despite the fact that KSDs were well defined, maturity indicators were loosely related to the holistic business
performance indicators, which could approximate the traditional performance measurement exercise of the enterprise. Moreover, there was no concrete mechanism that
could implement the proposed underlying construct, which
questioned its practical added value.
Software agents (autonomous or semi autonomous)
capable of modeling routine, tedious, and recurrent timeconsuming e-business activities were proposed by Albrecht,
Dean, and Hansen (2003). The implementation of this reasoning aid used situation calculus as the underlying methodology. However, it is fair to say that the proposed tool
could impose significant startup and initialization overheads. Also, it focused on agents utilizing large amounts
of pre-existing concrete knowledge. This prerequisite could
compromise the precision of the results in the case of
imprecise or incomplete knowledge availability.
Finally, Mahajan and Venkatesh (2000) presented a
comprehensive analysis of several contemporary marketing
modeling techniques for e-business. Most of the techniques
discussed followed a statistical/stochastic approach to estimate the impact of e-business initiatives to the overall business performance with limited (if any at all) ‘‘intelligent’’
reasoning capabilities and limited identification of causal
relationships among e-business performance concepts.
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G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
To summarize, it is the belief of this paper that there is
no other tool that integrates FCM simulation into e-business maturity change exercises with the functionality and
characteristics of the proposed mechanism (presentation
will follow in Section 3). TCM for example may supplement business modeling by drawing FCMs with allowed
node values of 0 and 1 and no dynamic simulation capabilities. Frameworks like MIND, SODA, and COCOMAP
(all compared in Kwahk & Kim (1999)) provided methodologies and guidance that allowed the user to perform
FCM analysis by identifying node conflicts in multiple
maps, loops, cycles etc. However, nodes in different maps
could not be linked dynamically to create map hierarchies.
Also, e-business tools either tend to be case specific (e.g.
Albrecht et al., 2003; Disney et al., 2005; Koh & Kim,
2005, etc.), or offer limited practical value (e.g. Duffy,
2001; Hooft & Stegwee, 2001; Mahajan & Venkatesh,
2000).
Accurate predictive models may already exist in e-business consultancies. Through their experiences they are
likely to have built up databases that could underpin more
detailed approaches such as case based reasoning. Unfortunately, the existence and internal features of these models
are more likely to remain confidential, given their commercial sensitivity.
2.3. FCMs as a modeling technique
Fuzzy Cognitive Maps (Kosko, 1986) is a modeling
methodology for complex decision systems, which originated from the combination of Fuzzy Logic (Zadeh,
1965) and Neural Networks. An FCM describes the behavior of a system in terms of concepts; each concept represents an entity, a state, a variable, or a characteristic of
the system (Dickerson & Kosko, 1997).
FCM nodes are named by concepts forming the set of
concepts C = {C1, C2, . . . , Cn}. Arcs (Cj, Ci) are oriented
and represent causal links between concepts; that is how
concept Cj causes concept Ci. Arcs are elements of the set
A = {(Cj, Ci)ji} [ C · C. Weights of arcs are associated
with a weight value matrix wn·n, where each element of
the matrix wji 2 [1, . . . , 1] [ R such that if (Cj, Ci) 62 A
then wji = 0 else excitation (respectively inhibition) causal
link from concept Cj to concept Ci gives wji > 0 (respectively wji < 0). The proposed methodology framework
assumes that [1, . . . , 1] is a fuzzy bipolar interval, bipolarity being used to represent a positive or negative
relationship.
In practice, the graphical illustration of an FCM is a
signed graph with feedback, consisting of nodes and
weight
weighted interconnections (e.g. ! ). Signed and weighted
arcs (elements of the set A) connect various nodes (elements of the set C) representing the causal relationships
that exist among concepts. This graphical representation
(e.g. Fig. 1) illustrates different aspects in the behavior of
the system, showing its dynamics (Kosko, 1986) and allowing systematic causal propagation (e.g. forward and back-
W41
e-customer
satisfaction
W46
W34
W13
e-sales
volumes
W12
W63
product
price
W35
W23
company
profitability
product
defects
W56
W52
internal
cost
Fig. 1. Simple FCM.
ward chaining). Positive or negative sign and fuzzy weights
model the expert knowledge of the causal relationships
(Kosko, 1991). Concept Cj causally increases Ci if the
weight value wji > 0 and causally decreases Ci if wji < 0.
When wji = 0, concept Cj has no causal effect on Ci. The
sign of wji indicates whether the relationship between conW ji
W ji
cepts is positive ðC j ! C i Þ or negative ðC j ! C i Þ, while
the value of wji indicates how strongly concept Cj influences
concept Ci. The forward or backward direction of causality
indicates whether concept Cj causes concept Ci or vice
versa.
Simple variations of FCMs mostly used in business decision-making applications may take trivalent weight values
[1, 0, 1]. This paper allows FMCs to utilize fuzzy word
weights like strong, medium, or weak, each of these words
being a fuzzy set to provide complicated FCMs. In contrast, Kwahk and Kim (1999) adopted only a simple
relative weight representation in the interval [1, . . . , 1].
To this extend, Kwahk and Kim (1999) offered reduced
functionality since it does not allow fuzzy weight definitions.
Generally speaking FCM concept activations take their
value in an activation value set V = {0, 1} or {1, 0, 1} if
in crisp mode or [d, 1] with d = 0 or 1 if in fuzzy mode.
The proposed methodology framework assumes fuzzy
mode with d = 1. At step t 2 N, each concept Cj is associated with an inner activation value atj 2 V , and an external
activation value etaj 2 R. FCM is a dynamic system. Initialization is a0j ¼ 0. The dynamic obeys a general recurrent
relation atþ1 ¼ f ðgðeta ; wT at ÞÞ; 8t P 0, involving weight
matrix product with inner activation, fuzzy logical operators (g) between this result and external forced activation
and finally normalization (f). However, this paper assumes
no external activation (hence no fuzzy logical operators),
resulting to the following typical formula for calculating
the values of concepts of FCM:
!
n
X
tþ1
t
ai ¼ f
wji aj
ð1Þ
j¼1;j6¼i
aitþ1
where
is the value of concept Ci at step t + 1, atj the
value of the interconnected concept Cj at step t, wji is the
weighted arc from Cj to Ci and f : R ! V is a threshold
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
function, which normalizes activations. Two threshold
functions are usually used. The unipolar sigmoid function
where k > 0 determines the steepness of the continuous
function f ðxÞ ¼ 1þe1kx . When concepts can be negative
(d < 0), function f(x) = tanh(x) is used.
To understand better the analogy between the sign of
the weight and the positive/negative relationship, it may
be necessary to revisit the characteristics of fuzzy relation
(Kaufmann, 1975; Lee, Kim, Chung, & Kwon, 2002). A
fuzzy relation from a set A to a set B or (A, B) represents
its degree of membership in the unit interval [0, 1]. Generally speaking, sets A and B can be fuzzy sets. The corresponding fuzzy membership function is lf : A · B !
[0, 1]. Therefore, lf(x, y) is interpreted as the ‘‘strength’’
of the fuzzy membership of the fuzzy relation (x, y) where
x 2 A and y 2 B. Then this fuzzy relation concept can be
lf
denoted equivalently as x ! y and applied to interpret the
causality value of FCM, since wji (the causality value of
the arc from nodes Cj to Ci) in a certain FCM is interpreted
as the degree of fuzzy relationship between two nodes Cj
and Ci. Hence, wji in FCMs is the fuzzy membership value
w
lf(Cj, Ci) and can be denoted as C j j;i ! C i .
However, we understand that the fuzzy relation (weight)
between concept nodes is more general than the original
fuzzy relation concept. This is because it can include negative () fuzzy relations. Fuzzy relations mean fuzzy causality; causality can have a negative sign. In FCMs, the
negative fuzzy relation (or causality) between two concepts
is the degree of a relation with a ‘‘negation’’ of a concept
node. For example, if the negation of a concept node Ci
is noted as Ci, then lf(Cj, Ci) = 0.6 means that
lf(Cj, Ci) = 0.6. Conversely, lf(Cj, Ci) = 0.6 means that
lf(Cj, Ci) = 0.6.
691
(Diffenbach, 1982; Ramaprasad & Poon, 1985), information retrieval (Johnson & Briggs, 1994) and distributed
decision process modeling (Zhang, Wang, & King, 1994).
Research like (Lee & Kim, 1997) has successfully applied
FCMs to infer rich implications from stock market analysis
results. Research like (Lee & Kim, 1998) also suggested a
new concept of fuzzy causal relations found in FCMs
and applied it to analyze and predict stock market trends.
The inference power of FCMs has also been adopted to
analyze the competition between two companies, which
are assumed to use differential games mechanisms to set
up their own strategic planning (Lee & Kwon, 1998).
FCMs have been integrated with case-based reasoning
technique to build organizational memory in the field of
knowledge management (Noh, Lee Lee, Kim, Lee, &
Kim, 2000). Recent research adopted FCMs to support
the core activities of highly technical functions like
urban design (Xirogiannis, Stefanou, & Glykas, 2004).
Summarizing, FCMs contribute to the construction of
more intelligent systems, since the more intelligent a system
becomes, the more symbolic and fuzzy representations it
utilizes.
In addition, a few modifications have been proposed.
For example, the research in Silva (1995) proposed new
forms of combined matrices for FCMs, the research in
Hagiwara (1992) extended FCMs by permitting non-linear
and time delay on the arcs, the research in Schneider,
Schnaider, Kandel, and Chew (1995) presented a method
for automatically constructing FCMs. More recently,
Liu and Satur (1999) has carried extensive research on
FCMs investigating inference properties of FCMs, proposed contextual FCMs based on the object-oriented paradigm of decision support and applied contextual FCMs to
geographical information systems (Liu, 2000).
2.4. Applications of fuzzy cognitive maps
3. Maturity modeling using FCMs
Over the last 10 years, a variety of FCMs have been used
for capturing – representing knowledge and intelligent
information in engineering applications, for instance, geographical information systems (Liu & Satur, 1999) and
fault detection (Ndouse & Okuda, 1996; Pelaez & Bowles,
1995). FCMs have been used in modeling the supervision
of distributed systems (Stylios, Georgopoulos, & Groumpos, 1997). FCMs have also been used in operations
research (Craiger, Goodman, Weiss, & Butler, 1996),
web data mining (Hong & Han, 2002; Lee et al., 2002),
as a back end to computer-based models and medical
diagnosis (e.g. Georgopoulos, Malandraki, & Stylios,
2002).
Several research reports applying basic concepts of
FCMs have also been presented in the field of business
(e.g. Xirogiannis & Glykas, 2004a, 2004b) and other social
sciences. Research in Axelrod (1976) and Perusich, 1996
have used FCM for representing tacit knowledge in political and social analysis. FCMs have been successfully
applied to various fields such as decision making in complex war games (Klein & Cooper, 1982), strategic planning
3.1. Overview of maturity modeling
Despite the rhetoric surrounding technology integration
intelligent mechanisms that support (a) holistic assessment
of e-business maturity (b) proactive identification of the
associated risks and opportunities, (c) reasoning on the
impact of the strategic convergence of processes and technologies to the maturity of the business model are still
emerging. Furthermore, contemporary business modeling
techniques focus on an ex-post performance assessment
of traditional operations. To this extend, the new proposition of this paper is two-fold:
• E-business gap assessment:
– The proposed methodology tool aims at providing an
interdisciplinary framework to benchmark current
e-business characteristics (if any) by defining maturity
metrics in reference to a wide variety of objective
characteristics. The tool proposes a two dimensional, though practical, perspective: customer facing
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G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
activities and supply chain activities. In contrast,
other research proposals as presented in Section
2.2.2 (for example Albrecht et al., 2003; Disney
et al., 2005; Koh & Kim, 2005, etc.) follow a single
dimensional approach.
– The proposed methodology framework offers a new
source of tangible strategic requirements for e-business based on a multidimensional analysis and a
holistic viewpoint of the enterprise. Also, it aims at
encompassing both traditional and contemporary
infrastructure solution options (essentially a combination of the characteristics presented in Sections
2.2.1 and 2.2.2), without being either product or solution centric.
• E-business model evolution and impact assessment:
– The proposed tool can be used to quantify the impact
of e-business maturity changes to the efficiency of the
enterprise. This tool should be perceived as a decision
aid to support strategy decisions at an executive level,
rather than a sophisticated process simulator.
The proposed methodology tool offers a holistic
approach to understanding e-business challenges through
a broad coverage of business areas.
3.2. FCM as a supplement to strategic change projects
A typical e-business strategy formulation methodology (Fig. 2) consists of a series of phases and layers of
analysis for setting the strategy roadmap of an enterprise:
Phase 1: Current status analysis/best practices benchmarking.
Phase 2: Vision and strategic positioning.
Best practices
Phase 3: Objectives composition, including critical success factors analysis and selection of strategic
alignment indicators (maturity indicators).
Phase 4: Strategic change planning (action planning).
The proposed mechanism focuses on supplementing a
typical e-business strategy methodology by providing a
holistic strategic alignment evaluation framework based
on e-business maturity indicators. In practice the mechanism supplements the recurring feedback loop between
the current maturity status, the future strategic objectives
and the action plans for improving e-business maturity
(the action plan, in turn, affects the future maturity status).
The proposed mechanism actually generates two maturity
assessment flows, as explained in Section 3.1:
• ‘‘Current status analysis ! Objectives’’ to estimate the
gap between existing (‘‘as-is’’) and future (‘‘to-be’’)
e-business maturity and establish objectives which
should bridge this gap.
• ‘‘Action plans ! Objectives’’ to estimate the evolution
of e-business, assess its maturity and align objectives
to meet any deviations.
During the third phase of this typical strategy formulation exercise, the top management of the enterprise sets the
overall performance targets (strategic maturity). These targets are exemplified further to action plan performance
metrics (tactical maturity) and then to operational performance indicators (operational maturity). All such metrics
present inherent relationships. In practice, strategic maturity metrics must cascade to tactical maturity metrics to
allow the middle management to comprehend inherent
relations among the different managerial levels of the enterprise. Similarly, tactical maturity metrics must propagate
Synthesis &
diagnosis
Current status
analysis
Objective 1
Objective 2
…
Objective k
Action plan 2
…
Action plan n
Critical success
factors
Action plan 1
Fig. 2. Overview of business strategy formulation.
Strategic alignment performance evaluation
Vision & strategic
positioning
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G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
up the overall strategic maturity metrics. However,
relationships between maturity metrics at the same
managerial level or even relationships between metrics of
different managerial levels with no apparent relationships are not always clear and well defined. Thus reasoning of the chained impact of maturity metrics to the
efficiency of the overall e-business model is not always
feasible.
To resolve this issue, this paper proposes the utilization
of maturity indicators (Fig. 3) to develop the FCMs and
reason about the impact of strategic changes to the desired
(‘‘to-be’’) e-business models. The proposed mechanism utilizes FCMs to interpret:
The proposed mechanism supports reasoning about the
overall or partial e-business strategy implementation using
maturity indicators from the e-business philosophy. In contrast to Kwahk and Kim (1999), the proposed mechanism
builds on hierarchical metrics interrelationships identified
and utilized by the e-business strategy formulation methodology. The proposed approach does not perform or guide
the implementation of any stage of the strategy formulation methodology. Also, the approach does not perform
or guide the estimation of the absolute value of any of
the maturity metrics and/or the overall e-business performance. It only allows the stakeholders to reason about
the qualitative state of e-business maturity metrics using
fuzzy linguistic variables like high-neutral-low cost, highneutral-low impact of IT infrastructure to cost, etc.
• e-business maturity metrics as concepts (graphically represented as nodes),
• decision weights as relationship weights (graphically
represented as arrowhead lines),
• decision variables as maturity concept values,
• hierarchical decomposition (top-down decomposition)
of maturity metrics to maturity indicators and constituent sub-metrics as a hierarchy of FCMs. This interpretation allows the stakeholders to reason about lower level
FCMs first (constituent indicators) before they reason
about higher-level e-business maturity metrics (affected
metrics).
3.3. Maturity domains
The proposed methodology tool (in contrast to other
techniques discussed in Section 2.2.2) builds a hierarchy
of domains and indicators to model e-business maturity
(Fig. 4). The tool’s strength is its ability to integrate key
areas (domains) of expertise available within the enterprise
essential to e-business operations. The proposed methodology tool proposes a holistic view to address e-business
maturity in seven major business domains, namely:
B u s i nes s s t rategy formulation hierarc h y
F C M s hierarc h ies
C1
Vision & strategic
positioning
Objective 1
Objective 2
…
Objective k
Action plan 1
Action plan 2
…
Action plan n
C1.1
C1.2
C1.3
maturity metrics
Fig. 3. Inherent relationships between e-business strategy and FCM hierarchies.
FCM
hierarchy
MIs
Organizational
competencies
Strategy
Domains
MI
MII
…
MII
MII
MII
…
……
MII
Tax & Legal
MII
MII
…
Fig. 4. Hierarchical definition of e-business maturity concepts and indicators (M/s).
MII
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G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
• Domain 1 – Overall e-business strategy: It addresses critical strategic issues, such as setting strategic direction,
analyzing competitors, leveraging information technology, etc.
• Domain 2 – Organizational competencies: It addresses
aspects such as whether an organization requires new
skills, new competencies, new ways of working, etc.
• Domain 3 – Channel management: It focuses on the primary e-business processes associated with distribution
channel management, marketing, distribution and logistics management, procurement, and customer
interaction.
• Domain 4 – Performance Management: It addresses
how an organization plans, measures, monitors, and
controls the performance of its e-business capabilities
and functions.
• Domain 5 – Tactical and Support Operations: It covers
issues related to the day-to-day operations, namely content creation, risk management, financial practices, etc.
• Domain 6 – Systems and Technology: It examines
e-business enabling technologies for customer and supply chain support and highlights integrated software
solutions and trends in technology. Also, it addresses
e-business security and privacy.
• Domain 7 – Tax and Legal: It addresses an organization’s e-business tax exposure and liabilities to ensure
that an organization knows its rights, obligations and
potential liabilities.
This paper assumes that coefficients k1 and k2 can be fuzzy
sets.
Coefficient k1 represents the proportion of the contribution of the value of the concept ai at time t in the computation of the value of ai, at time t + 1. In practice, this is
equivalent to assume that wii = k1. The incorporation of
this coefficient results in smoother variation of concept
values during the iterations of the FCM algorithm. Coefficient k2 expresses the ‘‘influence’’ of the interconnected
concepts in the configuration of the value of the concept ai
at time t + 1. It is the proposal of this paper that such a
coefficient should be used to align indirectly causal relationships (essentially, the value of concept Ci) with the centralized/decentralized nature of maturity concept Cj as
well as with the significance of the hierarchical positioning
of concept Cj within the strategic framework of the
enterprise.
Intuitively, the introduction coefficient k2 imposes two
step of analysis for establishing the ‘‘influence’’ of causal
relationships:
Step 1: estimation of the direct influence of a maturity concept Cj to another concept Ci with the weight (wji)
of the relationship. Both Ci and Cj should belong
to the same maturity domain (or level), that is
k2 = 1.
Step 2: approximation of the indirect importance of duplicate causal relationships spanning to different
maturity domains (or levels) using coefficient
k2 < 1.
3.4. New FCM algorithm
As far as the underlying algorithm is concerned, this
paper extends the basic FCM algorithm (as discussed in
Section 2.3 and also used by Kwahk & Kim (1999)), by
proposing the following updated algorithm:
!
n
X
t
tþ1
t
ai ¼ f k 1 ai þ k 2
wji aj
ð2Þ
j¼1;j6¼i
Consider for example a typical enterprise (Fig. 5) with
several electronic distribution channels (e.g. internet,
mobile phones, video conferencing, etc) operating under
the same e-business strategic framework. Let ‘‘CRM coordination’’ and ‘‘profitability’’ be interrelated maturity concepts. From a theoretical standpoint weight w1 should
appear to be the same for all duplicate ‘‘CRM coordination–profitability’’ relationships across all channels. From
Head Office
Profitability
W1
CRM
coordination
Internet
W2
e-promotion
W1
W2
CRM
coordination
W1
e-promotion
Mobile phones
Fig. 5. Horizontal maturity decomposition.
W2
CRM
coordination
e-promotion
Video conferencing
695
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
a practical standpoint, channels may serve different number
of customers (or even customers with different transaction
volumes). In this example, coefficient k2 models the fact
that CRM coordination in a channel which serves many
customers (or even few customers with large transaction
volumes) is more important to the profitability of the enterprise in comparison to the CRM coordination in channels
which serve few customers (or even customers with very
small transaction volumes), even if the level od CRM coordination is the same for all channels.
Similarly, Fig. 6 presents a generic strategy breakdown
structure, accompanied with a sample concept hierarchy.
Regardless of weight values w1, w2, w3, coefficients
1 = k2,(L1,L1) P k2,(L1,L2) P k2,(L2,L3) model the fact that
affecting maturity concepts at level Li (e.g. concept C5)
are more important in determining the value of affected
maturity concepts at level Li+1 (e.g. concept C3) in comparison to other affecting concepts at level Li1 (e.g. concept
C6). k2,(Li,Lj) stands for the value of coefficient k2 associated
with levels i and j.
3.5. Assigning linguistic variables to FCM weights and
concepts
3.5.1. Expert linguistic variables
In order to define weight value of the association relationships in an adaptive and dynamic manner, the following methodology is proposed. Managers are asked to
describe the interconnection influence of concepts using linguistic notions. Influence of one concept over another, is
interpreted as a linguistic variable in the interval [1, 1].
Its term set T(influence) is: T(influence) = {negatively
very-very high, negatively very high, negatively high, negatively medium, negatively low, negatively very low, negatively very-very low, zero, positively very-very low,
positively very low, positively low, positively medium, positively high, positively very high, positively very-very high}.
This paper proposes a semantic rule M to be defined at
this point. The above-mentioned terms are characterized by
the fuzzy sets whose membership functions l are shown in
Fig. 7.
C1
Level 0
Level 1
C2
W3
C3
C4
W1
k2,(L1,L 2)
C5
Level 2
k2 ≈ k2,(L1,L 2 ) + k2 ,(L 2 ,L3)
W2
k2 ,(L 2,L3)
C6
Level 3
Fig. 6. Top-down maturity decomposition.
µ
µnvvh µnvh µnh µnm
µnl
µnvl µnvvl µz
µpvvl µpvl µpl
µpm
µps µpvs µpvvs
0.1 0.2
0.5
0.65
1
0.5
-1
-0.9
-0.8
-0.65
-0.5 -0.35
-0.2
-0.1
0
0.35
0.8
Fig. 7. Membership functions of linguistic variable influence.
0.9
1
influence
696
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
• M (zero) = the fuzzy set for ‘‘an influence close to 0’’
with membership function lz.
• M(positively very-very low) = the fuzzy set for ‘‘an
influence close to 10%’’ with membership function
lpvvl.
• M(positively very low) = the fuzzy set for ‘‘an influence
close to 20%’’ with membership function lpvl.
• M(positively low) = the fuzzy set for ‘‘an influence close
to 35%’’ with membership function lpl.
• M(positively medium) = the fuzzy set for ‘‘an influence
close to 50%’’ with membership function lpm.
• M(positively high) = the fuzzy set for ‘‘an influence
close to 65%’’ with membership function lph.
• M(positively very high) = the fuzzy set for ‘‘an influence
close to 80%’’ with membership function lpvh.
• M(positively very-very high) = the fuzzy set for ‘‘an
influence close to 90%’’ with membership function lpvvh.
• Similarly for negative values.
The membership functions are not of the same size since
it is desirable to have finer distinction between grades in the
lower and higher end of the influence scale. The suggested
linguistics are integrated using a sum combination method
and then the defuzzification method of center of gravity
(CoG) is used to produce a weight in the interval [1, 1].
This approach has the advantage that experts do not have
to assign numerical causality weights but to describe the
degree of causality among concepts. The same semantic
rule and term set can be used to define the coefficients k1
and k2. A similar methodology can be used to assign values
to concepts.
4. FCM maturity implementation
4.1. FCM hierarchies
This research team uses the Quanta application tool, a
robust visual implementation of FCMs. The implementation of Quanta has been funded by the ESPRIT E.U. programme. The current implementation of the proposed
methodology tool encodes generic maps that can supplement the maturity modeling by storing concepts under different map categories (Fig. 8a), namely:
• Business category: all concepts relating to core e-business activities.
• Social category: all tax and legal related concepts.
• Technical category: all infrastructure related concepts
with emphasis on technology infrastructure.
• Integrated category: essentially all top-most concepts
(e.g. a concept Ci with no backward causality such that
"j: wji = 0), or concepts which may fall under more than
one main categories.
The dynamic nature of the approach allows easy reconfiguration. Further maturity concepts may be added, while
maturity concepts may be decomposed further to comply
with specialized analysis requirements of enterprises. This
categorization is compatible both with the ‘‘process view’’
or the ‘‘organizational view’’ (as adopted by Kwahk &
Kim (1999)) of the enterprise to allow greater flexibility
in modeling dispersed knowledge flows. The hierarchical
decomposition of maturity concepts generates a set of
Fig. 8. Map categories and a sample FCM hierarchy.
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
dynamically interconnected hierarchical maturity maps.
Each map analyzes further the relationships among concepts at the same hierarchical level. Fig. 8b presents such
a sample map hierarchy, which also serves as the FCM
overview.
Currently, the mechanism integrates more than 250 concepts, forming a hierarchy of 20 maps. The Quanta interface allows the user to utilize a sub-set of these concepts
and maps, on demand. The proposed system can portray
the maturity model following either a holistic or a scalable
approach. This is analogous to seeing the e-business strategy of the enterprise either as a single, ‘‘big bang’’ event or
as an ongoing activity of setting successive objectives for
selected operations. The proposed mechanism can accommodate both approaches.
Also the current implementation allows easy customization of the function f and easy reconfiguration of the formula Aitþ1 to adapt to the specific characteristics of
individual enterprises, generation of scenarios for the same
skeleton FCM, and automatic loop simulation until a userdefined equilibrium point has been reached. Alternatively,
step-by-step simulation (with graphical output of partial
results) is also available to provide a justification for the
partial results.
The proposed framework exemplifies further e-business
maturity by decomposing maturity domains into their consistent concepts. The following sections exhibit sample
(though typical) skeleton maps, which provide relevance
and research interest to this paper.
4.1.1. E-business maturity
The mechanism proposes four (4) basic maturity maps
each consisting of generic e-business maturity metrics as
follows:
• The Strategy top-level map reasons on the maturity of
issues like strategic direction, e-business information
technology planning, competition analysis, etc.
697
• The Organizational competencies top-level map (Fig. 9a)
summarizes concepts like roles and responsibilities,
e-business change management, alliance management, etc.
• The Performance management top-level map (Fig. 9b)
interconnects concepts like CRM, performance management, supply chain management, etc.
• The Tactical and support operations top-level map reasons on the maturity of issues like content creation,
financial practices, e-business project management, risk
management, etc.
Concepts denoted as ‘‘#’’ expand further to lower level
maps. Similarly ‘‘"’’ denotes bottom-up causal propagation.
4.1.2. Social maturity metrics
The mechanism proposes the tax and legal top-level map
(Fig. 10a) to support reasoning on the maturity of issues
like tax planning, web site shopping, VAT compliance, residence/permanent establishments, tax compliance, transfer
pricing, contracting with customers, contracting with suppliers, regulatory controls, unlawful content, etc.
4.1.3. Infrastructure maturity metrics
The mechanism proposes the Systems and Technology
top-level map (Fig. 10b) to interconnect concepts like infrastructure, capacity planning and management, web quality,
encryption, maintenance, network security, technology
selection, database security and control, etc.
4.1.4. Integrated maturity metrics
The mechanism proposes two basic maturity maps consisting of generic e-business maturity metrics as follows:
• The Channel management top-level map (Fig. 11a) supports reasoning on the maturity of issues like channel
management, customer integration processes, logistics
management, pricing, direct procurement, product
development, targeted promotion.
Fig. 9. (a) Organizational competencies map, (b) performance management concepts.
698
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
Fig. 10. (a) Tax and legal concepts map, (b) systems and technology concepts map.
Fig. 11. (a) Channel management concepts map, (b) high level concepts map.
• The High Level FCM top-level map (Fig. 11b) interconnects all maturity domains to reason on the overall ebusiness maturity of the enterprise.
4.2. Preliminary experiments
Two experiments were conducted by utilizing metrics
from actual (though random) e-business strategic change
exercises in two major financial sector enterprises. For each
experiment, a team of experts was engaged to provide linguistic variables for the causal weights, the concept values
and the coefficients values to let the FCM algorithm reason
about the impact of potential change initiatives, as well as
to provide their independent expert estimates (using similar
linguistic variables) of the impact of the strategic change
choices to specific maturity metrics.
Fig. 12 compares the e-business maturity as estimated
by FCMs and the team of experts respectively for the first
experiment.
The majority of the concepts cascade to several constituent metrics, hence the tool traverses complicated concept
interrelations spreading over different maps and hierarchies. The FCM mechanism calculated the value of affected
concepts based on the initial weight and concept value.
Similarly, Fig. 13 e-business maturity as estimated by the
FCM mechanism and the team of experts respectively for
the second experiment.
4.3. Discussion
Various aspects of the proposed modeling mechanism
are now commented on. As far as the theoretical value
is concerned, the proposed mechanism extends previous
research attempts by (a) introducing a novel supplement
to e-business strategy formulation activities which adapts
better to the characteristics of e-business initiatives, (b)
introducing a holistic framework of e-business maturity
assessment, (c) introducing the notion of interconnected
maturity hierarchies, (d) concentrating on the actual strate-
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
699
1.0
0.8
0.6
0.4
0.2
FCMs
Experts
st
ra
te
gi
c
pa
in
te
rtn
ern
pr
er
a
oc
sh
le
ur
ip
b
iz
em
s
ch
en
t m ang
es
an
ag
em
cr
os
en
s
eb
t
fu
nc
iz
fin
H
tio
an
R
na
ci
M
al
lb
co
cu
us
nt
st
in
co
ro
om
e
nt
ss
l
er
ra
co
ct
co
nt
m
m
ro
an
pl
ai
ag l
nt
em
s
m
en
an
t
ag
IT
em
in
te
en
r
o
eb
t
pe
iz
ra
in
bi
IT
fr a
lit
y
st
st
ra
ru
te
ct
gy
ur
pr
e
&
od
pl
uc
an
td
ni
ch
ev
ng
an
el
o
ne
l m pme
nt
an
ag
em
een
pr
om t
ris
k
ot
na
io
or
m
n
de
ga
ag
l
i
ve
ni
em
za
ry
e
tio
nt
op
n
er
&
at
co
sy
io
m
ns
st
pe
em
ta
s
nc
&
ie
te
s
ch
no
lo
gy
0.0
Fig. 12. E-business maturity – Scenario A.
1.0
0.8
0.6
0.4
0.2
FCMs
Experts
st
ra
te
gi
c
pa
in
te
ertn
rn
pr
er
al
oc
sh
eb
ur
ip
em
iz
s
ch
en
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e
an
ag s
cr
em
os
en
s
fu
eb
t
nc
fin
iz
tio
an
H
R
na
ci
M
al
lb
cu
co
u
st
nt
co sine
om
r
o
nt
ss
l
er
ra
co
ct
co
n
m
m
an trol
pl
ai
ag
nt
em
s
m
en
an
t
IT
ag
in
em
te
e
r
op
nt
eb
er
iz
ab
in
IT
fra
ilit
st
y
st
ra
ru
te
ct
gy
ur
pr
e
&
od
pl
uc
an
td
ni
ch
ev
ng
an
el
op
ne
m
lm
e
an
nt
ag
em
een
pr
ris
om t
k
ot
na
io
or
m
de
ga
ag n
li
ni
em
za very
en
tio
op
t
n
er
&
at
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sy
io
m
n
st
s
pe
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ta
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nc
&
ie
te
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ch
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lo
gy
0.0
Fig. 13. E-business maturity – Scenario B.
gic formulation activity and its impact on the e-business
model, (e) allowing fuzzy definitions in the cognitive maps,
(f) introducing an interpretation mechanism of fuzzy sets,
(g) proposing an updated FCM algorithm to suit better
the e-business maturity domains, (h) allowing dynamic
map decomposition and reconfiguration.
As far as the practical value of the proposed mechanism
is concerned:
• The mechanism does not provide fundamentally different ‘‘diagnosis’’, compared to the expert estimates. It
provides reasonably good approximations of e-business
maturity changes.
• The mechanism tends to under-estimate slightly the
maturity of concepts which have several hierarchical
dependencies. This conservatism, however, does not
reduce the effectiveness of the proposed mechanism. It
simply indicates that when complex maturity factors
are involved, it may be safer to assume a conservative
strategic impact scenario.
• The proposed mechanism provides a uniform behavior
regardless of the degree of maturity gap. The first experiment (see Fig. 12) involved a financial sector enterprise
with limited electronic presence; while the second experiment (see Fig. 13) involved an enterprise with establish
electronic presence seeking to enhance further its alternative delivery channels.
• The justification of the ‘‘diagnosis’’ (essentially the
maturity decomposition) proved helpful in comprehending the sequence of concept interactions (essentially the
maturity roadmap).
• The concept-based approach did not restrict the
interpretation of the estimated e-business maturity.
The fuzzy interpretation of concept and weight values
served as indications rather than precise arithmetic
calculations.
700
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
Having established the theoretical and practical value of
the proposed mechanism, it is useful to discuss also the
added value of incorporating such a mechanism into
e-business strategy formulation exercises. It is the belief
of this paper that the resulting tool provides real value to
the principle beneficiaries and stakeholders of e-business
projects. For example:
• The mechanism eases the complexity of deriving expert
decisions concerning e-business initiatives. Informal
experiments indicated that the time required by experts
to estimate manually the extensive impact of major strategic changes could pose as a considerable overhead. On
the other hand the elapsed time for automated estimations using FCM decision support can be insignificant,
once the map hierarchies have been set up.
• To extend further this syllogism, realistic e-business
strategy formulation projects should involve continuous
argument of change options until an equilibrium solution accepted by all stakeholders has been agreed upon.
Informal discussions with the principle beneficiaries and
stakeholders of the two e-business projects revealed that
the proposed FCM decision support can reduce significantly the maturity estimation overheads, letting the
stakeholders focus on the actual strategic planning
exercise while exploring in depth alternative objectives
and controlling effectively major strategic change
initiatives.
• The proposed mechanism can also assist the maturity
evaluation of the enterprise on a regular basis. FCMs
may serve as a back end to performance scorecards
(Bourne, Mills, Wilcox, Neely, & Platts, 2000; Kaplan
& Norton, 1996, 2001) to provide holistic strategic performance evaluation and management. However a
detailed analysis of this extension falls out of the scope
of this paper.
Senior managers of the two major financial sector enterprises have evaluated the usability of the proposed tool and
have identified a number of benefits that can be achieved
by its utilization as a methodology framework for e-business maturity measurement. Detailed presentation of the
usability evaluation results fall out of the scope of the
paper. However, a summary of major benefits is provided
to improve the autonomy of this paper:
• Shared Goals: Concept-driven simulation pulls individuals together by providing a shared direction and determination of strategic change.
• Shared Culture: All business units feel that their individual contribution is taken under consideration and provide valuable input to the whole strategic change
process.
• Shared Learning: The enterprise realizes a high return
from its commitment to its human resources, establishing a constant stream of improvement within the
enterprise.
• Shared Information: All business units and individuals
have the necessary information needed to set clearly
their individual objectives and priorities, while senior
management can control effectively all aspects of the
strategic change process.
Summarizing, experimental results showed that FCMbased ex ante reasoning of the impact of e-business maturity changes (actual or hypothetical) can be effective and
realistic. This is considered to be a major contribution of
the proposed methodology to strategic change exercises.
5. Conclusion
This paper presented an intelligent supplement to typical
e-business strategy formulation methodologies based on
fuzzy cognitive maps (FCM). This decision aid mechanism
proposed a new domain-based approach to supplementing
the current status analysis and objectives setting phases of
typical e-business strategy formulation projects, by supporting ‘‘intelligent’’ modeling of e-business maturity and
‘‘intelligent’’ reasoning of the anticipated impact of strategic change initiatives. By using FCM, the proposed mechanism drew a causal representation of e-business maturity
principles; it simulated the operational efficiency of complex
strategy models with imprecise relationships and quantified
the impact of strategic change to the e-business model.
Preliminary experimental results indicated that the
mechanism did not provide fundamentally different estimates than expert decisions. It provided reasonably good
estimates of the impact of strategic change initiatives to
the e-business model, while the maintenance effort did
not pose as a prohibitory factor. Moreover, the decomposition of maturity metrics supported reasoning of the performance roadmap and the complex relationships that
affect the overall e-performance.
The proposed mechanism should not be regarded only
as an effective e-business modeling support tool. Its main
purpose is to drive strategic change activities rather than
limit itself to qualitative simulations. Moreover, the proposed mechanism should not be seen as an ‘‘one-off’’ decision aid. It should be a means for setting a course for
continuous strategic alignment (Langbert & Friedman,
2002).
Future research will focus on conducting further real life
experiments to test and promote the usability of the tool,
but also to identify potential pitfalls. Furthermore, future
research will focus on the automatic determination of
appropriate fuzzy sets (e.g. utilizing pattern recognition,
mass assignments, empirical data, etc.) for the representation of linguistic variables to suit each particular e-business
project domain. Finally, further research will focus on
implementing backward map traversal, a form of adbuctive
reasoning (Flach & Kakas, 1998). This feature offers the
functionality of determining the condition(s) Cij that
should hold in order
to infer the desired Cj in the causal
wjk
relationship C ij ! C k . Incorporating integrity constraints
G. Xirogiannis, M. Glykas / Expert Systems with Applications 32 (2007) 687–702
reduces the search space and eliminates combinatory
search explosion. Backward reasoning has been tested
extensively in other applications and its integration in the
proposed methodology may prove beneficiary.
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