Intelligent modeling of e-business maturity

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: georgex@unipi.gr (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 688 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. 690 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 692 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 693 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 694 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 t m an g 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 co sy io m n st s pe em ta s nc & ie te s ch no 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. 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