The Application of Fuzzy Cognitive Map in Soft System Methodology

Syst Pract Action Res (2011) 24:325–354 DOI 10.1007/s11213-011-9190-z ORIGINAL PAPER

The Application of Fuzzy Cognitive Map in Soft System Methodology Payam Hanafizadeh • Rojin Aliehyaei

Published online: 26 January 2011 Ó Springer Science+Business Media, LLC 2011

Abstract Facing the issues of structural complexity, on which stakeholders have different views, has increasingly led to the use of Soft Systems Methodology (SSM) in solving managerial problems. Moreover, the weaknesses of this methodology in considering all point of views and ensuring the effectiveness of the proposed changes have provided the motivation for applying Fuzzy Cognitive Map (FCM) in SSM. Using FCM as a modeling tool makes it possible to combine the views of different experts and form group FCM (GFCM). GFCM has the potential to be applied as a useful decision support tool in the stage of offering recommendations and changes. The methodology proposed in this article is applied to ticketing system of Raja passenger train company. This system, influenced by various policies and views, is analyzed with the recommended methodology and then the solutions for developing the system are suggested in a prioritized manner. Keywords Soft system methodology Fuzzy cognitive map Root definition Action research

Introduction One of the most important challenges of organizations, nowadays, is making decisions to effectively solve soft problems, and managers encounter many conflicts in dealing with such issues (Montazemi and Conrath 1986). In such a situation, the use of SSM as a framework for solving ill-structured problems is increasingly growing among analysts (Brocklesby 1995; Ingram 2000; Rose 2002; Shapiro and Shapiro 2003; Mirijamdotter and Bergvall-Ka˚rebirn 2006). P. Hanafizadeh (&) R. Aliehyaei (&) Department of Industrial Management, Allameh Tabataba’i University, P.O. Box 14155-6476, Tehran, Iran e-mail: [email protected] URL: www.hanafizadeh.com R. Aliehyaei e-mail: [email protected]

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Syst Pract Action Res (2011) 24:325–354

Peter Checkland’ SSM, suitable for dealing with highly complex situations, is the lesson learned from many action research projects (Wilson 1993). Checkland believes that the traditional engineering approaches are appropriate for structured issues where the problem situation is well described (hard problems). However, with large and ill-structured problems about whose definition divergent views exist (soft problems), the role of traditional engineering approach is weakened and that of soft systems approach is emphasized (Checkland 1978). This methodology is a 7-stage process using the conceptual model to show relevant activities in human activity system. SSM begins when crisis happens in the current circumstances of a particular company and forces it to search for a solution. It is then the time for root definitions and models to be formed based on a distinct ‘worldview’, i.e., that view of the world which enables an analyst to attribute meaning to what is observed. These models are compared with what is understood in reality which results in clarification of necessary actions for reaching the desirable situation (Wilson 1993; Mirijamdotter and Bergvall-Ka˚rebirn 2006). While the importance of this methodology has been realized in recent decades, the limitations of its use have also been taken into consideration by many researchers (Mingers 1984; Flood and Jackson 1991; Lane and Olivia 1998; Jackson 2003; Rodr’iguez-Ulloa and Paucar-Caceres 2005; Yinghong 2007). In its fourth stage, this methodology has no precise modeling tool as well as a definite technique to compare recommended solutions in the real world. Furthermore, the root definitions of relevant systems and the models obtained from them are meaningful only under special worldview and analysts propose the final recommendations based on the selected view. Besides, the effectiveness of system thinking in this methodology increasingly depends upon the knowledge and experience of the participants (Checkland and Scholes 1990; Lane and Oliva 1998; Avison and Fitzgerald 2003). Another limitation of this methodology is that it does not revise the consistency and contrast among various solutions in practice. For example, imposing some changes simultaneously may cause conflicting results (Lane and Oliva 1998; Rodr’iguez-Ulloa and Paucar-Caceres 2005; Yinghong 2007). Thus, utilizing a new model as an aiding tool in SSM and creating combined methodologies including some methods of similar or different thinking paradigms have became one of the relatively new debates in system thinking (Mingers 1984; Munro and Mingers 2002). For instance, Soft System Dynamics Methodology (SSDM) is a result of an action research project conducted by Rodr’iguez-Ulloa with an integrated framework of 10 steps in 1999. In the proposed methodology, SSM and systems dynamics have been combined to create a synergetic tool for solving soft problems (Rodr’iguez-Ulloa and Paucar-Caceres 2005). The FCM, as a model showing the mind map of decision makers and representing causal relations among various factors of the issue, attracts the attention of some researchers to have it combined with the SSM (Hjortsø et al. 2005; Siau and Tan 2005). In this study, FCM is combined with SSM so that in addition to defining the issue more precisely, the analysis of different concepts and their effects on the goals of the system can be carried out. Considering the definition of methodology, the authors assert that the phrase ‘‘combined methodology’’ has been employed in the sense that soft systems methodology is used from the viewpoint of systems development, and FCM is used from the modeling perspective. Generally, FCM is considered as a modeling tool embedded in SSM in this study. Regarding the constraints of SSM, especially lack of a precise modeling tool and regarding the capabilities of FCM in modeling stockholders’ perceptions, applying FCM in SSM is proposed. FCM is used as a tool to represent various viewpoints of system

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beneficiaries through a visualized model enabling them to communicate with each other on system requirements. In addition, the efficiency of models in SSM is highly affected by the perceptions, knowledge, and expertise of their developers (Fiol and Huff 1992; Siau and Tan 2005). Using combined techniques to aggregate FCMs in this methodology facilitates the application of group knowledge and reduction of the probability of individual’s errors (Kosko 1986; Khan and Quaddus 2004). The graphic structure of FCM allows systematically indicating causal relations, particularly backward or forward chaining by which the analyst can determine the strength of a concept’s influence on the goals of system (Kosko 1986). Furthermore, causal relations among concepts clarify the contrast and consistency of various solutions. Thus, it can be concluded that using this model in the final stage of SSM can facilitate choosing comprehensive solution to improve the current condition of problems. Table 1 summarizes the Table 1 How FCM can address SSM’s limitation Limitations of soft systems methodology

How FCM can address these limitations

SSM does not possess an accurate tool for changing root definitions into model.

Fuzzy cognitive map helps to produce a chain of concepts which define the problem in an integrated form and represents the perceptions of stakeholders. Also, it is used as an empowering tool to increase the effectiveness of their decision (Siau and Tan 2005; Hjortsø et al. 2005; Lin and Wu 2008). Fuzzy cognitive map can attract interviewees’ attentions and activate their minds in order to structure the problem (Fiol and Huff 1992; Siau and Tan 2005) When the manger faces a massive amount of information, cognitive map can magnify the preferences and key factors (Siau and Tan 2005) Fuzzy cognitive map specifies the information defects, weak reasoning, and helps to find out the areas needing information gathering (Siau and Tan, 2005)

Meaningful definitions and models proposed under a particular view (Lane and Oliva 1998; Rodr’iguez-Ulloa and Paucar-Caceres 2005)

Fuzzy cognitive map can show the expert’s knowledge in group decision making (it has the potential to be used as a group tool) (Khan and Quaddus 2004)

The effectiveness of systems thinking in SSM depends on individual’s knowledge, wisdom, expertise, and attitude (Yinghong 2007)

Fuzzy cognitive map makes it possible to combine the experts’ views.So, it can give more accurate results and reduce the ambiguity (Kosko 1987, 1992; Banini and Bearman 1998; Kardaras and Karakostas 1999; Khan and Quaddus 2004; Hossain and Brooks 2008)

Not revising the coordination and conflicts between the proposed changes (Checkland and Scholes 1990; Lane and Oliva 1998)

Fuzzy cognitive map provides the feasibility for static and dynamic analysis of different scenarios (Khan et al. 2001; Irani et al. 2002)

Lack of precise and normative tools for ensuring the effectiveness of changes and conformity among them (Avison and Fitzgerald 2003) Not prioritizing and offering optimum solution for improving system (Flood and Jackson, 1991; Lane and Oliva 1998)

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limitations of SSM and advantages of using FCM for improving the recommended methodology. This article is organized in seven sections; after the introduction, the second section briefly addresses SSM methodology and the stages where FCM is applied in it. The third section reviews literature on FCM and the analytical and combinational techniques of this model. In the forth section, the improved stages in SSM are presented, followed by a case study on the application of the proposed framework in section five. Finally, discussion and conclusions about the results of applying methodology in the real world are presented and suggestions for further studies are summarized.

Revision of SSM for Using FCM Figure 1 indicates a general view of the methodology, and the stages in which FCM has been employed are shown in grey. As it can be seen in Fig. 1, SSM involves seven stages. The first and second stages are stages of finding out about the situation, the output of which is a rich picture depicting real world concerns. The rich picture includes symbols and words that show an image of the concerns and expectations present in the system and create a basis for forming root definitions (RD) based on the worldviews in the system. ‘‘W’’ is in fact the short form for worldview (i.e., Weltanschauung). Worldviews (Ws) indicate the different perceptions of different individuals from the same event. The aim of the third stage in the methodology is to derive root definitions (RDs) from the rich picture. RDs incorporate the point of views which make the activities and performance of the system meaningful. Producing several root definitions help to avoid any hoped-for-utopian analysis (Wilson 1993). At this stage, CATWOE analysis can be used for accurate formulation of RDs. This method is utilized as a checklist for ensuring the

Fig. 1 A general view of the methodology

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completeness of the root definitions. Two important components of this method are worldview and transformation process, both of which must exist in root definitions. Transformation process shows the basic core activities of the system including interconnected set of actions necessary to transform some input(s) into some output(s). At the next stage, i.e., the fourth stage of SSM, the selected root definition(s) are changed into a model. Then, at the fifth stage, the model obtained in the systemic world is compared to the real world and finally, at stages 6 and 7, activities are suggested for making changes in the real world and improving the present system (Checkland and Scholes 1990). In Fig. 1, the stages of applying FCM in SSM are shown in grey. First, FCM is used as a modeling tool, and combinational FCM techniques are utilized for collecting the views of experts and covering various viewpoints in stage 4 of SSM. Then, static analysis of FCM is employed for improving the system and measuring the effect of each change on the goals of system.

Fuzzy Cognitive Map FCMs are the extension of cognitive maps (Axelrod 1976) used for representing causal reasoning. Cognitive maps are collections of nodes connected by edges or links. The nodes represent variable concepts from a domain and the edges represent causal relationships. A positive edge from Ci (node i) to Cj (node j) means Cj increases as Ci increases and Cj decreases as Ci decreases. A negative edge from Ci to Cj means Cj increases as Ci decreases and Cj decreases as Ci increases (Kosko 1986). A FCM is a cognitive map, except that a numerical value is associated with causal links for showing the degree of relationship between two concepts. The directed edges take values form the interval [-1, 1]. Fuzzy linguistic terms such as {very weak, weak, medium, strong, very strong} can be used instead of numerical values (Kosko 1986). An example of FCM is shown in Fig. 2. AFCM can be described by a connection matrix such as F. The matrix F is defined by F ¼ e ij ; where eij is the weighting value of the directed edge from Ci to Cj. The matrices associated with a FCM are always square matrices with diagonal entries as zero.

Fig. 2 An example of FCM (Khan and Quaddus 2004)

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The element in the ith row and jth column of matrix F, eij , would represent the weighting value of the link directed out of node Ci into node Cj. FCMs have been extended through time. Introducing indices such as centrality and ¨ zesmi and O ¨ zesmi 2004) as well as static and dynamic hierarchal degree (kosko 1986; O analysis to forecast the system behaviour (Vasantha and Ram Kishore 1999; Meghabghab 2002; Lee and Chung 2006), incorporating time factor in FCM (Park and Kim 1995), applying combined technique of FCM (Kosko 1988; Taber 1991; Hossain and Brooks 2008) and using FCM in group decisions (Khan and Quaddus 2004) are but some of the advances made in the field of FCM. FCMs are used in various areas of application such as information retrieval, (Montazemi and Conrath 1986), medical research (Vasantha and Ram Kishore 1999), information systems development (Kardaras and Karakostas 1999), software engineering (Hossain and Brooks 2008), web design (Lee and Chung 2006), and forecasting staff behaviour on web (Meghabghab 2002), as well. Static Analysis of FCM A FCM can be applied for a static analysis of the domain to (a) discover the relative importance of concepts in model; and (b) obtain the indirect and total causal effect between two concepts (Khan and Quaddus 2004). The application of each of these analyses is discussed in the next section. Centrality Degree The centrality of concept Ci, C(Ci), is an index for determining the relative importance of this concept. As it is observed in Eq. 1, C(Ci), is obtained from sum of idðCi Þ and odðCi Þ CðCi Þ ¼ odðCi Þ þ idðCi Þ

ð1Þ

id(Ci) is sum of absolute weighting values of causal links constituting all paths connecting node Cj to Ci, where, i 6¼ j, (i.e., the column sum of the absolute values of node Ci in the connection matrix), and od(Ci) is sum of absolute weighting values of causal links constituting all paths connecting node Ci to all nodes Cj, where, i 6¼ j, (i.e., the row sum of the absolute weighting values of node Ci in the connection matrix). High centrality degree of a concept not only shows the number of repetitions of the given concept, but also its importance in the entire model (Kosko 1986). The Indirect and Total Causal Effect Between Two Concepts In order to find out the total causal effect from concept Ci to conceptCj, the indirect effects over all the paths must be obtained. A causal path from concept Ci to concept Ci Ci ! CKr2 ! ! CKrz ! Cj can be denoted with ordered indices as ðKr1 ; Kr2 ; . . .; Krz So the indirect effect of concept Ci on concept Cj over path r, Ir Ci ; Cj , is obtained by Eq. 2. Ir ðCi ; Cj Þ ¼ minf e Cp ; Cpþ1 : ðp; p þ 1Þ 2 ðl; kr1 ; kr2 ; . . .; krz ; jÞ ð2Þ According to Eq. 2, the indirect effect of concept Ci on concept Cj through path r is defined by the minimum operator. The e(Cp, Cp?1) is the weighting value of causal relations between node p and node p ? 1.

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where, p and p ? 1 are contiguous left to right path indices. Then, according to Eq. 3, T(Ci, Cj) is defined using the maximum operator where m is the number of all indirect effects between Ci and Cj. T Ci ; Cj ¼ max Ir Ci ; Cj : 1 r m ð3Þ If the number of relations with negative edge is zero or even, then the indirect effect of the path is positive. If the number of negative relations in the path is odd, then the indirect effect of the path is negative (Kosko 1986). Combining Fuzzy Cognitive Maps Matrix representation of FCMs makes it possible to combine different FCMs obtained from different experts. For combining FCM, first the augmented FCM matrices are summed according to Eq. 4. Fa is the FCM matrix of expert a and n is the number of experts. Wa is equal to the credibility weight of expert a, and in FCM literature, it is common to use Wa = 1 for all experts (Taber and Siegel 1987). Different versions of a FCM specified to a specific domain will consist of unequal number of concepts, and as a result, their connection matrices will have different sizes. Therefore, the matrixes are augmented, so if there are total n nodes, each augmented matrix Fa has n rows and n columns. Fs ¼

n X

Wa Fa

ð4Þ

a¼1

The next step is to determine the mean of the weighting value of causal links obtained from all experts. For this purpose, according to Eq. 5, all arrays of Fs matrix are divided by the total credibility weight (Tsadiras et al. 2001). So Ft is the combined FCM matrix obtained from all experts. Pn Fs a¼1 Wa Fa P ¼ P ð5Þ Ft ¼ n n W a a¼1 a¼1 Wa Group Fuzzy Cognitive Map (GFCM) A group fuzzy cognitive map (GFCM) can be applied as an aiding tool in group decision making. In group decision, there are individual or sub-groups with different point of views or concerns about the same issue, so FCM obtained from each subgroup may have different structure. In forming GFCM, FCMs obtained from sub-groups and individuals are considered as the main components of GFCM. First, the FCMs are merged in order to obtain the first preliminary structure of GFCM. The group will review all these concepts to identify similar concepts, because some concepts with similar meaning may be given different names by different experts. Then, in order to avoid redundant information, the group should make a decision to keep only one of these concepts and remove others. There are some rules for making a decision about removing or retaining a concept. According to these rules, those nodes with fewer outgoing links are more suitable to remove, as eliminating them will have less impact on other concepts. If the two candidate nodes for eliminating have equal number of outgoing links, then the node with less causal influence on

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other concepts in the GFCM is considered as the first for removal. The causal influence of a node is sum of absolute weighting values associated with its entire outgoing links. By removing redundant concepts, the causal links associated with them will disappear. Thus, it is necessary to analyze it in order to decide whether new causal links should be added to the retained equivalent node in GFCM. For example, suppose that graph a and b in Fig. 3 are merged to form the GFCM in Fig. 4. Assume node C3 in graph a and node C03 in graph b are similar in meaning. As C3 has fewer outgoing links, it is removed from GFCM and C03 is remained. Deleting redundant node ðC3 Þ results in the disappearance of associated causal links. As mentioned previously, its effect on outgoing links of a deleted node needs to be analyzed. By eliminating C3 , the causal link between node C3 and node C5 is removed. The influence of the deleted node C3 on node C5 is not reflected by any pathway from its equivalent node. So the removed link between node (C3) and node C5 is maintained by replacing its old deleted source node (i.e. ðC3 Þ; by equivalent node. The Weighting value of causal relations in GFCM matrix is the mean weight of causal links according to Eq. 4 and 5 (Khan and Quaddus 2004). The preliminary and adjusted GFCM, formed by merging the FCMs in Fig. 3, are shown in Fig. 4.

Fig. 3 Two different versions of FCM specific to the same issue

Fig. 4 The preliminary and adjusted GFCM formed by merging the FCMs in Fig. 3

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Improved Stages in Soft System Methodlogy In the next subsections, the way of applying FCM in SSM is explained and stages in the methodology using FCM or influenced by it are reviewed. As indicated in Fig. 1, the stages that FCM has been applied in SSM are shown in grey. FCM is first utilised in modelling stage, i.e., stage 4, and in the next stages of SSM, analytical techniques of FCM are used for interpreting and decision-making on the way of developing the system. Stage 4: Developing FCM Model In SSM, the analyst can use conceptual modeling or other modelling methods for describing the transformation process of root definitions. As it was mentioned, FCM, due to its advantages and analytical strength in presenting the relationships among relevant concepts, has been utilized in this study. The modelling steps of FCM and its application in SSM are described in next section. Constructing Preliminary Fuzzy Cognitive Map for Each Root Definition At this stage, FCM is used as a tool among analysts and system users in order to help clarifying system requirements from the viewpoints of various stakeholders and beneficiaries. To have a FCM equal to each root definition, first the main concepts relevant to each root definition are recognized by related experts (Step 1 in Fig. 5), and then, a meeting is held with them for final confirmation of these concepts and determining the causal relations among them (Step 2 in Fig. 5). After experts reach a consensus about the structure of FCM, a questionnaire equivalent to each FCM is developed to assign weighting values to causal relations in FCM. These values indicate the nature and strength of the relationships. For determining these values, linguistic fuzzy weights such as ‘little’ or ‘strong’ can be applied instead of numerical values, because they makes it easier for experts to express their opinions (Kosko 1994). The linguistic terms used in the questionnaire are equated to the numerical value in interval [0, 1]. In this way, the FCMs are extracted for all experts in each group (Step 3 in Fig. 5) (Kosko 1986; Banini and Bearman 1998; Kardaras and Karakostas 1999; Khan et al. 2001; Tsadiras et al. 2001). Extraction of Combined FCM Relevant to Each Root Definition After formation of various FCMs in each group, the combinational techniques of FCM are used for summing up the views of experts in each group. For developing the equivalent combined FCM of each root definition, first Fs is calculated through Eq. 4. Fs ¼

n X

Wa Fa

ð4Þ

a¼1

where, Fs is sum of FCM matrices obtained from each group of experts. Fa is the augmented FCM matrix of expert a and n is the number of experts. Wa is also equal to the credibility weight of expert a. The next step is to determine the mean of the

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Fig. 5 The steps of forming model (stage 4 of SSM)

weights of causal links obtained from all experts. To this aim, according to Eq. 2, all arrays of Fs matrix are divided by the total credibility weight (Kosko 1992; Tsadiras et al. 2001). Ft is the combined FCM matrix obtained for each group of experts (Step 4 in Fig. 5). Pn Fs a¼1 Wa Fa ¼ P ð5Þ Ft ¼ Pn n a¼1 Wa a¼1 Wa In this way, by combining FCMs related to various experts and assigning different weights to each expert, it becomes possible to more accurately and completely change the root definition and cover the perceptions and expectations of the whole population of the experts. Forming Group FCM or GFCM As mentioned above, one of the limitations of SSM is forming model based on a particular worldview (W), and analysts cannot consider all views as a whole. At this stage, analysts form a comprehensive model using group FCM techniques and use it as their main reference for analyzing different views in an integrated pattern. In order to form a group FCM, the combined FCM of each sub-group with different worldview (W) is considered as the preliminary data. As there may be different

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interpretations for one concept, the similar concepts in GFCM, named differently by each sub-group, are identified in order to eliminate the possibility of providing redundant information. The decision about retaining or removing a concepts and its links is analyzed according to the rules described in the ‘‘Group Fuzzy Cognitive Map (GFCM)’’ section. Then, according to Eqs. 4 and 5, the average of corresponding elements of the FCM matrix is computed for finding the weighting values of causal relations in the GFCM and equivalent matrix (Khan and Quaddus 2004). This step of forming the model is indicated as step 5 in Fig. 5. Since the views of all experts are collected in the model, the obtained model is considered as a comprehensive tool for analyzing and decision-making in the next stages and is not limited to a specific worldview (W). Stage 5: Comparing the System World with the Real World At this stage of methodology, the real world activities are compared with those of system world using the model obtained from modeling stage (Wilson 1993). Since the number of activities in the real world is large, the most important activities must be considered. To this aim, concepts equivalent to the objectives of the system in GFCM, which usually enjoy high degrees of importance, are first identified. In order to determine these objectives, the centrality degree of concepts in GFCM model can be employed, because high centrality of a node indicates its importance in the whole model (Step 1 in Fig. 6.). Thus, the centrality degrees of all concepts in GFCM are obtained according to Eq. 1.

Fig. 6 The steps of comparing the system world with the real world (stage 5 of SSM)

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idðCi Þ is the column sum of the absolute values of node Ci in the GFCM matrix, and odðCi Þ is the row sum of the absolute weighting values of node Ci in the GFCM matrix. Then, according to the causal structure of GFCM, the information about those concepts directly affecting system goals (i.e., increasing or decreasing goals of system) is gathered in the comparison table. The table includes the current mechanism of activities equivalent to concepts directly affecting system goals, the criteria for measuring its function, and the recommendation for reaching the optimum situation (Wilson 1993; Platt and Warwick 1995). In Fig. 6, this information is shown as steps 2 and 3. At the end of this stage, a table is obtained which includes suggestions for improving the present situation on the basis of the investigation of causal relations among those concepts directly affecting system goals. Stage 6 and Stage 7: Defining Desirable and Possible Changes and Taking Action As it was mentioned earlier, the output of the previous stage is a set of comparison tables which offer a group of recommendations for improving the situation. The recommended changes should be analyzed carefully, because a particular change may be reasonable to an analyst, but for the stakeholder who has had a particular experience about that, other concerns and policies should be simultaneously taken into account (Wilson 1993). At this stage, the GFCM obtained from the expert groups can also help the analyst. Using this graph, the paths for reaching certain goal, feasibility of solutions, and the indirect and total effect of each concept on system goals can be obtained. In this way, the paths among the concepts obtained from the previous stage and concepts equivalent to the objectives of the system are found (Step 1 in Fig. 7). The indirect and total causal effects between two concepts in GFCM are calculated according to Eq. 4 and 5. Therefore, by calculating the total effect of other concepts on the goals of system, the analyst has a measure to compare the effects of each concept on system goals, and finally, there will be prioritization for changes. This information is summarized in Fig. 7 as step 2.

Fig. 7 The steps of defining and selecting the desired actions

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Case Study In this section, an application of the improved methodology in ticket sales department of RAJA passenger train company is described. RAJA commenced its activities in Iran 13 years ago as a state organization aiming at improving the quality and quantity of facilities for passenger train service. RAJA’s mottos are optimal use of facilities, constant improvement, increasing efficiency, and offering desirable services in order to achieve organizational excellence. In this regard, providing better ticket sales services is one of the key factors in achieving its aims and increasing customer satisfaction. Since ticket sales system has various stakeholders with different expectations and needs, it is important to review all aspects of improving ticket sales system including technical and non-technical issues. Stage 1 and Stage 2: Finding Out About the Situation In the first step, the problem situation is explored and explained whose output is a rich picture (Checkland 1981). For initial identification of the system and exploring the worldviews, the analysts have used open interviews and documents existing in the company. The interviews were conducted with 5 managers and 15 experts in RAJA with more than 5 years of experience in ticket sales activities, as well as 2 managers and 5 personnel of ticket selling agencies. The rich picture of the problem is shown in Fig. 8. The picture contains a symbol of thinking stick figure indicating someone who is expressing his/her particular concerns. The picture of computer and network stands for the concerns of the system managers regarding software and network support of the sales system. The ‘‘money bag’’ symbolizes the concerns about the amount of allocated budget to maintenance and development of the sales system. The ‘‘buildings’’ shown at top of the figure are symbols of the importance of policy-maker organizations and commercial partners in the rail transportation such as the Ministry of Road and Transportation and train operating companies. Also, the logo of ‘‘e-commerce’’ at the right bottom corner of the figure indicates the increasing attention of the company to offering electronic services to customers. Other pictures and explanations show the factors affecting the development of the sales system which will be elaborated later. Stage3. Formulating Root Definitions After determining different views about the ticketing system, the root definitions are formed. By conducting many interviews and gathering the required information in RAJA, three distinctive worldviews are identified which include: privatization, implementing customer-oriented approach, and e-commerce services with IT infrastructure. The proponents of privatization believe that diversity of train services and facilities, and meeting customer demands for trip will be possible through attracting private sector investors. Some others believe that customer-oriented approach and answerability to customers’ questions and complaints via supervising the customer services and information flow are the most important factors for successful ticket selling. Still others believe that applying information technology infrastructures provide rapid access to ticket services and enhance customer satisfaction significantly. On the basis of the three views obtained from the ticketing system, three root definitions were extracted as followings:

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Fig. 8 The rich picture of ticket selling in RAJA passengers’ train company

RD1: A system owned by RAJA passenger train company and train operating partners which attempts to provide the passengers with more access to train ticket in the given path with desirable services. This mission is accomplished through increasing trip offers in various paths by attracting the private sector investment in the rail transportation. The limitations of this system relate to the restricting policies on the part of policy-making organizations in the area of rail transportation and reluctance of private companies for investment in this area due to its high expenses. RD2: A system owned by RAJA passenger train company which attempts to provide the passengers with easy and rapid access to train ticket through the Internet. The limitations of this system relate to the degree of individuals’ capacity to access the Internet and the level of society literacy for using electronic services. RD3: A system owned by RAJA passenger train company which offers the possibility of reservation of ticket and other rail services to the passengers. Accurate and update information regarding the services and facilities of trains and answering the questions and complaints of the customers are among the primary and necessary factors for accomplishing this mission of the system. The limitations of this system relate to the restricting regulations on the part of policy-making organizations in the area of rail transportation.

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In order to define the necessary characteristics of the system and precisely formulate the root definition the CATWOE analysis was applied (Smyth and Checkland 1976). According to the CATWOE analysis for RD1, the system owners are Raja company and train operating partners. The system customers are those requesting travel with train and the system actors are the ticket selling agencies and partners. The input of this system is the request of rail services on the part of the passengers and its output is the ticket for the given path and train. The worldview (W) dominating the system is increasing the rail transportation capacity by exiting from the exclusive market into the competitive market. In RD2, the system owner is Raja company. The system customers are those requesting ticket reservations through the Internet and the main actors of the system are RAJA company and policy-making organizations. In this system, customers’ demand for preparing train ticket is replied. Limitations of society in access to the Internet and people’s literacy level in using electronic services must be considered in offering such services. Finally, according to the CATWOE analysis for RD3, the system owner is Raja company. The system customers are those requesting train tickets. The system actors are the ticket selling agencies and RAJA company. In this system, the possibility of buying train ticket is provided for the customers. Offering timely and correct information, and answering the questions and complaints of the customers, as well as appropriate interaction with the customers are primary requirement of this system. The limitations of this system relate to the policies of policy-making organizations in the area of rail transportation. The results of CATWOE analysis for each root definition are listed in Table 2.

Table 2 The root definitions of ticket selling system of Raja analyzed by CATWOE Analyzed elements of CATWOE

RD1

RD2

RD3

Customers (C)

people who demand rail travel

Customers who purchase ticket

Customers who demand rail travel

Actors (A)

RAJA Co., Train operating RAJA Co., agencies partners, Ministry of Road and Transportation, Islamic Republic of Iran Railway (RAI)

Transform (T)

Providing access to train ticket in the given path and desirable services

Providing access to train Providing access to train ticket through the Internet ticket and answering their questions and complaints

Weltanschauung or Worldview (W)

Changing the exclusive market of railway transportation into a competitive market by privatization

Utilization of information technology services to promote e- commerce services

Developing Customers Relationship Management (CRM)policies

System owner (O)

RAJA Co and Train operating partners

RAJA Co.

RAJA Co.

RAJA Co., agencies

Considering people access Limitation resulted from Limitation resulted from System external organization to computer and internet external organization environment (Ministry of Road and society (the percentage of (Ministry of Road and and the people who have access to Transportation, Islamic Transportation, Islamic resulting Republic of Railway) the Internet) Republic of Iran Railway restrictions (E) and train operating partners)

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At this stage, the model of each root definition is formed in order to have a better perception of the issue. Forming the Initial Structure of FCM After exploring different worldviews on the ticket selling system, the relevant experts of each worldview were attributed to distinct groups (i.e., group A, group B, and group C). Expert group A consisted of those experts who believe in privatization. These experts have master’s degree or higher education levels and minimum of 10 years experience in railway transportation and privatization. Group B consisted of specialists with 8 years background in information technology and e-commerce. Group C involved those who believed in implementing customer-oriented approach in Raja; they had graduate degree and minimum of 10 years experience in customer-oriented activities. All experts in 3 groups cooperated in at least 2 projects related to ticket selling. As the numbers of experts with such characteristic were limited, no sampling was conducted and all experts participated in the study. The number of experts from whom the questions were asked was 9 in group A, 6 in group B, and 8 in group C. Using the information gathered from the previous stage, the relevant concepts of each worldview were derived separately, and delivered to related experts (Step 1 in Fig. 5). After finalizing the list of concepts and determining their relationships in different meetings with groups A, B and C, the preliminary FCM of each worldview was formed (Step 2 in Fig. 5). The perceptions and expectations of the experts of each group from the system were made clear by holding meetings with them. The next stage includes assigning weighting values to causal relationships by the experts. Therefore, an equivalent questionnaire for each FCM was prepared. The questions are related to weighting of each causal relation (Step 3 in Fig. 5). A sample of questions related to group C is presented in the Appendix 1. Experts’ response to questions needed to be selected from the alternatives of ‘none, very weak, weak, strong, and very strong (i.e., based on likert spectrum); the linguistic terms were attributed to numerical values in interval [0, 1]. These values are {0, 0. 25, 0. 5, 0. 75, 1} (Taber 1991; Hossain and Brooks 2008). The prepared questionnaires were administered to 5 university professors and experts to estimate their face and content validity and remove possible defects. Then, data of questionnaire were analyzed using SPSS software and Cronbach’s alpha coefficient (a) was computed as a measure of internal consistency in the questions. For the questionnaire of group A, Cronbach’s alpha was 0.713; for group B Cronbach’s alpha was 0.749; and for group C it was 0.718. Since all these values are more than 0.7, it can be argued that all questionnaires have acceptable reliability. Then, the questionnaires of three groups were distributed among the related experts and after completing forms, the connection matrices of FCMs were separately obtained for all experts. Deriving Combined FCM for Each Root Definition At this stage, FCMs of each group of the experts are combined to obtain a combinational FCM as a final model of each RD (Step 4 in Fig. 5). In order to derive the combinational FCM, the combinational matrix of each worldview was calculated using the Eq. 4 and 5, where Fr is the augmented matrix for expert r obtained from the previous stage. As it is common to use Wr = 1 for all experts in the

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literature on FCM (Taber and Siegel 1987), and since there was no remarkable difference among the participants in the study, weight 1 is assigned to all the experts. For instance, the effect of the node ‘‘quality level of software support’’ on ‘‘quality level of services offered by agencies’’, as one item of the combinational matrix FT, is calculated using Eq. 1 and 2. The weights assigned by 8 experts of group C are 0.75, 0.8, 0.25, 1, 0.75, 0.5, 0.75, and 0.75 whose mean is the equivalent to the item of the combinational matrix related to the relationship between these two concepts (Fig. 9). These calculations were done for all causal relations, and the combinational matrices were obtained for groups A, B, C. Combinational FCM graph based on RD1 (experts of group A) is shown in the Appendix 2. Forming the Group FCM (GFCM) In order to form the group FCM, the combined FCM obtained from each group of experts was considered as the preliminary data. At this stage, 3 combined FCMs obtained from the previous step are merged to form the GFCM (Step 5 in Fig. 5). The nodes in GFCM were analyzed and the similar concepts in graphs which may be named differently by each group of experts were found and the decision about removing and retaining concepts was made according to the rule described in ‘‘Group Fuzzy Cognitive Map (GFCM)’’ section. Since there is a large number of concepts and causal relations in the GFCM obtained, by presenting only a part of the nodes and links of the FCM model related to each worldview (W) and the final GFCM obtained from them in Figs. 10, 11, 12, and 13, the way of creating GFCM becomes clear. According to the model obtained from the expert group on privatization in Fig. 10, the concept of ‘‘congruity of the policies of the Ministry of Road and Transportation with development of passenger transportation’’ affects ‘‘allocated budget to RAJA company’’ and finally ‘‘annual budget of sales department for conducting the present activities and projects’’. Also, ‘‘management’s emphasis on improving ticket sale’’ is another concept which influences ‘‘annual budget of sales department for conducting the present activities and projects’’. In Fig. 11, FCM is formed on the basis of RD2 in which the experts have investigated the effects of concepts such as ‘‘the quality of software support’’ on ‘‘quality of services offered by the ticket selling agencies’’. The experts of this group believe that ‘‘allocated budget to sales system’’ and ‘‘management’s emphasis on improving the ticket sales system’’ affect ‘‘quality level of software support’’ and finally, ‘‘quality of services offered by the agencies’’. In the model formed on the basis of implementing customer-oriented view in Fig. 12, the effect of ‘‘quality level of software support’’ on ‘‘quality of services offered by the agencies’’ as the organizations having direct contact with the customers, is investigated. This concept, in turn, is influenced by other concepts such as ‘‘salespersons’ skill level’’.

Fig. 9 Calculation of the weight of causal relationship between two nodes of C20 and C29 in the combinational matrix

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Fig. 10 Part of FCM based on RD1


As it can be seen, the obtained models possess common concepts like ‘‘quality level of software support’’, ‘‘quality level of network support’’, and ‘‘quality level of services offered by the ticket selling agencies’’. In addition, some of these concepts, while having different names, have similar meanings. For instance, ‘‘annual budget of sales department for conducting the present activities and projects’’ in Fig. 11 is the same as ‘‘allocated

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Fig. 13 GFCM obtained from Fig. 10, 11 and 12

budget to sales system’’ in Fig. 10. According to the rules mentioned in the literature on GFCM, since the number of outgoing links of the concept ‘‘allocated budget to sales system’’ in Fig. 11 is more than the number of outgoing links of ‘‘annual budget of sales department for conducting the present activities and projects’’ in Fig. 10, it was decided to

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remove the latter and retain the former in the final GFCM. Since this node does not have any outgoing link, there is no need to add a new link in the final GFCM. At the end, the weights of the causal relations are obtained from the mean of the equivalent causal relations in the present FCMs. In Fig. 13, the final GFCM obtained from Figs. 10, 11, and 12 are presented. For example, the relationship between two concepts of ‘‘quality of software support services of sale system’’ and ‘‘quality level of services offered by the ticket selling agencies’’ is obtained from the mean of experts’ views on the weighting value of the causal relation between these two concepts in groups B and C, i.e., 0.781 and 0.916, which is 0.845. These steps were taken for all nodes and relations in GFCM model.51 concepts were extracted and listed in Appendix 2 and the GFCM obtained in this study is shown in Fig. 14. Stage 5: Comparing the System World with the Real World At this stage, using the GFCM model obtained, the system world is compared with the real world. Since the number of concepts is high in this model and it is not possible to investigate all of them, the concepts with higher importance, known as the system development objectives, are examined (Step1 in Fig. 6). As it was mentioned in ‘‘Stage 5: Comparing the System World with the Real World’’ section, centrality degree is the index showing the importance of concepts in the model. According to Eqs. 1 and 2, C13 and C14 have the highest centrality in FCM. The results of computation of centrality degree for the concepts with the maximum values are summarized in Table 3.

Fig. 14 GFCM obtained by merging all FCMs

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Table 3 The results of computation of centrality degree for the concepts with the maximum values Concepts

idðCi Þ

odðCi Þ

C ðCi Þ ¼ idðCi Þ þ odðCi Þ

C14

Customers’ satisfaction (internet users)

14.102

1.402

15.504

C13

Customers’ satisfaction (Non internet users)

7.800

1.528

9.328

As it can be seen, C13 and C14 have the highest centrality degree and are considered as the main objectives of system development. Thus, using the GFCM model, the concepts having direct influence upon these two concepts are identified and after defining activities equivalent to each concept, the columns of comparative table are filled (Steps 2 and 3 in Fig. 6). An example of these comparisons for concept C29 is represented in Table 4 (Wilson 1993; Platt and Warwick 1995). As it can be observed in GFCM model, node C29 entitled as ‘‘quality of services offered by the agencies’’ is among the important factors which directly influence C13 and C14. The criteria for measuring the performance of activity equivalent to this concept, i.e., services offered by ticket selling agencies, is the customer satisfaction percentage from the services offered by agencies. Then, the relations leading to this concept, i.e., C29, in GFCM model is investigated so that the concepts affecting this specific concept are identified. The causal relations shown in red in Fig. 14 are as followings: 0:667

0:833

0:69

0:656

0:75

0:75

C27 ! C33 ! C17 ! C40 ! C29 C47 ! C46 ! C29 0:719

C45 ! C29 0:495

C39 ! C29 0:875

0:875

0:849

C7 ! C22 ! C20 ! C29 0:639

0:792

0:849

0:639

0:708

0:75

C2 ! C7 ! C20 ! C29 C2 ! C7 ! C21 ! C29

Table 4 Comparison table of concept C29 Quality of services offered by agencies (C29) 1 Activity

Offering ticket selling services to customers

2 Exist or not

Yes

3 Current mechanism

Selling ticket and helping customers to find and reserved ticket for their destination considering price and other concerns. The agencies get their required information by the ticket selling system of RAJA

4 Criteria for measuring performance

The percentage of customers’ satisfaction from offering services

5 Proposed change

Improving the network and software support services, promoting salespersons’ skill. Appropriate information, controlling the distribution of agencies in the city, investigating violations at agencies

The percentage of customer satisfaction from services is 60%

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The chain of causal relations related to concept C29, reveals that the quality of services offered by agencies (C29) is influenced by C20 (quality level of software support), C21 (quality level of network support), C45 (salespersons’ skill level), C39 (availability of information and brochures for customers), C47 (number of agencies in the city), and C40 (investigating agencies’ violation). Also C20 and C21 are influenced by C7 (allocated budget to sales system) and C2 (management ‘emphasis on improving ticket sales system). Consequently, the mentioned concepts must be taken into consideration for offering better services by the agencies. Such tables are created for other concepts which directly affect the Internet and nonInternet customers’ satisfaction. The methods of getting the favorable situation are identified through comparisons and analyses. The list of these concepts is presented in the first column of Table 5. Stages 6 and 7: Defining the Desirable and Possible Changes and Taking Actions As it is mentioned in the 7-stage methodology of Checkland, the conceptual models include a collection of activities named as ‘‘what’’ and the analyst observes a collection of ‘‘hows’’ in the real world to achieve each of these whats. Within the framework offered in this study, the whats are the concepts equivalent to the objectives of the system and the hows are the paths showing the ways of making desirable changes in the system objectives (Step 1 in Fig. 7). Hence, after determining the changes which are effective in the present situation for reaching the desirable situation, the degree of the effect of each change on system objectives is determined and the decisions as to the conduction of the final activities in the real world are made. Using the model, the analyst can calculate the effect of each concept upon the system development objective over all paths (Step 2 in Fig. 7). For example, in order to calculate the indirect effect of C7 (allocated budget to sales system) on C13 (customers’ satisfactionnon Internet users), first the paths between the two concepts are specified and then, the indirect effect from concept C7 to concept C13 is calculated in all paths according to Eq. 2. The results of these calculations are as following: 0:875

0:875

0:542

0:708

0:75

0:807

0:875

0:667

C7 ! C22 ! C20 ! C24 ! C34 ! C13 I1 ðC7 ; C13 Þ ¼ feðC7 ; C22 Þ; eðC22 ; C20 Þ; eðC20 ; C24 Þ; eðC24 ; C34 Þ; eðC34 ; C13 Þg I1 ðC7 ; C13 Þ ¼ minf0:875; 0:875; 0:542; 0:875; 0:667g ¼ 0:542 C7 ! C21 ! C29 ! C13 I2 ðC7 ; C13 Þ ¼ feðC7 ; C21 Þ; eðC21 ; C29 Þ; eðC29 ; C13 Þg I2 ðC7 ; C13 Þ ¼ minf0:708; 0:75; 0:807g ¼ 0:708 Then, the total effect of C7 on C13 is calculated according to Eq. 3. T ðC7 ; C13 Þ ¼ maxfI1 ðC7 ; C13 Þ; I2 ðC7 ; C13 Þg ¼ 0:708 All these computations are performed for the considered concepts and the results are shown in Table 5. The concept C4 has the most effect of the non-Internet customers in comparison to other concepts, and as it can be seen in the columns 2 and 3 of Table 5, the effect of C4 on each of the concepts C13 and C14 are 0.867 and 0.639. In the two final columns of the table, the present situation of activities equivalent to these concepts and solutions for improving them are offered.

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Table 5 The total effect of concepts on customers’ satisfaction Concept

The The Current mechanism effect effect on C13 on C14

Recommendations

1. Congruity of the policies 0.867 of the Ministry of Road and transportation with development of passenger transportation (C4)

0.639

At present, the Ministry of Defining project, contact with the interested parties Road and transportation for continuous relation greatly stresses the with policy-making development of rail organizations transportation, but RAJA Company does not have a definite mechanism for interacting with policymaking organizations

2. Government subsidy regarding rail passengers (C19)

0.855

At present, the ticket price subsidies are determined by the Ministry of Road and Transportation

3. Appropriate Response to 0.75 customer complaints (C49)

0.75

At present, the complaints of Defining CRM project for investigating and the customers are replied controlling via different channels, but communicative channels there is a need for integration and homogenizing communicative channels

4. Quality level of network (C21) support

0.75

0.667

At present, many problems Promotion of the services of exist, the connection of the network support team agencies with the selling system is lost, and many problems arise in offering services to the customer

5. Management ‘s ideas regarding privatization (C6)

0.734

0.734

Conducting privatization At present, 30% of the project for explaining the passenger rail way of delegating transportation sector is delegated to private sector, passenger rail services to private sector but its acceleration requires the integration of policies adapted by the managers

6. Salesperson’s skill level (C45)

0.719

0.61

At present, many clients complain about the behavior of the salespersons

Holding training courses and periodical tests for sales personnel

7. Budget allocated to sales 0.708 system(C7)

0.71

At present, a low budget is allocated to development of sales system

Allocation of sufficient financial resources for maintenance and promotion of sales system

0.851

Media movements at the time of approving budgets by the related organization (within the framework of relationships with the interested parties)

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Table 5 continued Concept

The Current mechanism The effect effect on C13 on C14

8. Integration level of databases (C24)

0.667

0.875

At present, the sales system Acceleration of launching of the agencies is separate the new sales system which enjoys from the from internet selling necessary integration system which has brought about many problems in offering information to customers and getting report from the system

9.Appropriate design of databases(C27)

0.667

0.75

There is no specific problem In the new project for at present developing sales system, the way of designing data bases must be considered as an important factor in system development

10. Agreement of President 0.639 Deputy Strategic Planning and Control with the estimated expenses of RAJA (C18)

0.639

At present, there is no stable Definition of continuous interaction with the related mechanism for the organization within the interaction with this framework of relation with organization the interested parties

11. level of programming for sales system (C23)

0.583

0.869

The sales software programming language is very old (programs running under DOS operating system) which has brought about many problems for system development

12. Quality level of software 0.542 support (C20)

0.652

At present, the software has Selecting appropriate contractor for supporting many errors and has the sales system software imposed many expenses

13.Appopriate interaction with banking system (C36)

0.75

Appropriate and continuous Lack of appropriate interaction with contracted interaction with banks has banks and identifying created many problems in other opportunities for offering electronic making more contracts payment services and developing internet electronic payment services

0.844

At present, many users, due Changing the design of web pages and offering to lack of familiarity with sufficient instructions for internet, have problems in completing internet completing their internet shopping process shopping process

14. User friendly online booking (C51)

_

15. Number of agencies in the city(C47)

Dual policies regarding 0.719 0.75 increasing the number of 0.656 0.614 agencies in the city

123

Recommendations

Considering an appropriate programming language for the new software which is in line with the other parts of the system

Estimation of the number of agencies in each area so that the distribution of profit among them becomes appropriate


349

As it can be seen in Table 5, suggestions were proposed for promoting the present sale system in 15 areas, and the percentage and way of their effect on the main objectives of the system were determined. This provides the analysts with an appropriate criterion for prioritizing activities. For example, ‘‘congruity of the policies of the Ministry of Road and Transportation with development of passenger transportation’’ has been defined as the most important factor in increasing ‘‘non-Internet customer satisfaction’’ in which issues like appropriate interaction with policy-making organizations or media movements for improvements in decision-making and attention to the rail travel are considered. Among other suggestions offered were defining customer relationship management (CRM) project for analysis and control of customer communicative channels as a comprehensive project, and holding training courses and periodical tests for measuring and promoting the ‘‘skills of salespersons’’. Integrating databases, selecting appropriate programming language, and the amount of government subsidy regarding rail passengers affect Internet-customers’ satisfaction by 0.875, 0.869, and 0.855, respectively. Another significant result was attention of decision making to increase the number of sale agencies in the city that, according to Table 6, has dual effects on customer satisfaction. On the one hand, by increasing the number of agencies, it is easier for the customers to access the agencies and this pleases them, and on the other, increasing the number of agencies results in decreasing the profit of each agency. This can lower the quality of services delivered by the agencies and finally have negative effect on customer satisfaction. This model is considered as an aiding tool for comparing different solutions to reach the optimum situation in the real world. It can measure the impact of different concepts on goals of the system. Thus, it can be claimed that this model helps making effective managerial decisions in the organizational strategic level and makes it possible to measure the effect of various changes on system objectives before taking any action in the real world.

Discussion In this paper, using SSM and FCM model, suggestions were offered for improving the present situation of ticket selling system in RAJA company. Exploring the real world by common techniques of SSM and construction of FCM models on the basis of various worldviews (Ws), provides a great insight into the causes of the crises. For example, using the rich picture obtained in Fig. 8, a comprehensive image from the problem situation comes to the mind. Besides, since SSM provides the analyst with the opportunity to construct separate definitions and models, it becomes possible to investigate various viewpoints regarding the system. In this study, employing FCM model in the modeling stage, the needs and expectations of the various stakeholders were identified and an algorithm was created for modeling the mental perceptions of them in SSM. In the case study of this research, the final GFCM was obtained by combining three distinct worldviews on the development of sales system. In this way, system development was not solely based on the views of a specific group of specific interested parties; rather, all worldviews (Ws) were covered. Another advantage of using FCM model in SSM is the possibility of static analysis for estimation of the effect of changes on a system condition which provides a criterion for

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making changes on the basis of the amount of its effect on system objectives in stages 6 and 7. As it was explained, using these analyses, a list of activities and projects for improving the system was proposed. Of course, using FCM models in SSM has some limitations; finding experts and collecting information from them to construct the initial structure of FCM is a time consuming step. Also computing the weights of the edges between nodes to create group fuzzy cognitive maps for large systems is complex.

Conclusion Checkland methodology gained great achievements in previous decades and has had many applications for solving ill-structured and unstructured problems. However, some limitations of this methodology particularly in the modeling stage have restricted its application. In this study, by applying FCM as a modeling tool in SSM some improvements were achieved. Constructing these models, give a complete picture of different stakeholders’ perceptions about the issue and create a tool for combining the experts’ views so that the analyst is able to consider different attitudes of beneficiaries in the modeling step. Also, at the stage of comparison of the model with the real world, by analyzing causal relations in FCM, the way to achieve the optimum status was formed. At the final stage, through calculating the total effect of concepts on system goals, the normative scales for prioritizing the recommendations were presented. By increasingly using this methodology in the real world and investigation of its results, improvements can be achieved in the application of FCM in SSM. In addition, dynamic analysis of FCM model can be utilized in future studies enabling us to predict the state of the system through time.

Appendix 1 See Table 6.

Table 6 The sample questions of questionnaire for estimating the severity of relations among factors Concept name

1

Availability of ticket printing Machine Affects online customer’s satisfaction

2

The ticket price affects nononline customers’ satisfaction

3

The ticket price affects online customers’ satisfaction

4

The Possibility of refunding E-ticket or canceling trip affects online customers’ satisfaction

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Effect

Effect degree

1

Very strong

2

Strong

Average

Weak

None


351

Appendix 2

.

Appendix 3 See Table 7. Table 7 List of variable concepts Variable concepts

Description

c1

Congruity of the policies of the of RAI ‘s (Islamic Republic of Iran Railways)with development of passenger transportation

c2

Management’s emphasis on improving ticket sales system

c3

Allocated budget to RAJA Co.

c4

Congruity of the policies of the Ministry of Road and Transportation with development of passenger transportation

c5

Percentage of passenger trains assigned to private sector

c6

Management ‘s ideas regarding privatization

c7

Allocated budget to sales system

c8

Facilities given to private sectors

c9

Passengers rail Capacity to transport passenger

c10

Ticket availability (inventory)

c11

Ticket price

c12

RAJA’s income

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Table 7 continued Variable concepts

Description

c13

Customers’ satisfaction(Non online users)

c14

Customers’ satisfaction(Online users)

c15

Availability and variety of trains and services

c16

Managers’ satisfaction with sales system

c17

Comprehensiveness of reports on sale

c18

Agreement of President Deputy Strategic Planning and Control with the estimated expenses of RAJA

c19

Government subsidy regarding rail passengers

c20

Quality level of software support

c21

Quality level of network support

c22

Quality of hardware maintenance

c23

Level of programming for sales system

c24

Integration level of databases

c25

Sales data Security

c26

Hardware Security

c27

Appropriate design of data bases

c28

Web sites Bandwidth

c29

Quality of services offered by agencies

c30

Processing speed of sales system

c31

Easy access to Raja’s website

c32

Appropriate link with banks

c33

System flexibility in scheduling

c34

Update information about ticket inventory

c35

Customer satisfaction(Internet user) about e-payment

c36

Appropriate interactions with different banking systems

c37

Availability of credit cards

c38

Feasibility of developing sales system

c39

Availability of information and brochures

c40

Investigating agencies violation (failure)

c41

Possibility to receive the print of reserved ticket from agencies in stations

c42

Availability of Ticket Printing Machine

c43

Possibility of refunding E-ticket or canceling trip

c44

Possibility of refunding ticket or canceling trip in agencies

c45

Salespersons’ skill level

c46

Profit of agencies

c47

Number of agencies in city

c48

Number of ticket available online

c49

Appropriate Response to customer complaints

c50

Offering presales services

c51

User-friendly online booking

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References Avison DE, Fitzgerald G (2003) Information systems development: methodology techniques and tools. McGraw-Hill Education, Oxford Axelrod R (1976) Structure of decision. Princeton University Press, Princeton, NJ Banini GA, Bearman RA (1998) Application of FCMs to factors affecting slurry rheology. Int J Min Process 52(4):233–244 Brocklesby J (1995) Using soft systems methodology to identify competence requirements in HRM. Int J Manpow 16(5):70–84 Checkland PB (1978) The origin and nature of hard system thinking. J Appl Syst Anal 5(6):99–109 Checkland P (1981) Systems thinking, systems practice. Wiley, Chichester, pp 163–164 Checkland PB, Scholes J (1990) Soft systems methodology in action. Wiley, Chichester, UK Fiol CM, Huff AS (1992) Maps for managers: where are we? Where do we go from here? J Manag Stud 29(3):267–285 Flood RL, Jackson MC (1991) Creative problem solving: total systems intervention. Wiley, Chichester Hjortsø CN, Christensen SM, Tarp P, Rapid A (2005) Stakeholder and conflict assessment for natural resource, management using cognitive mapping: the case of Damdoi Forest Enterprise, Vietnam. Agric Hum Values 22(2):149–167 Hossain S, Brooks L (2008) FCM modeling educational software adoption. Comput Educ 51(4):1569–1588 Ingram H (2000) Using soft systems to manage hotel. Manag Serv Qual 10(1):6–10 Irani Z, Sharif A, Love PED, Kahraman C (2002) Applying concepts of FCM mapping to model: the IT/IS investment evaluation process. Int J Prod Econ 75(1):199–211 Jackson MC (2003) System thinking: creative holism for managers. Wiley, Chichester Kardaras D, Karakostas B (1999) The use of FCMs to simulate the information systems strategic planning, process. Inf Softw Technol 41(4):197–210 Khan M, Quaddus MA (2004) Group decision support using FCMs for causal reasoning. Group Decis Negot 13(5):463–480 Khan MS, Quaddus MA, Intrapairot A (2001) Application of a FCM for analyzing data warehouse diffusion, applied informatics: artificial intelligence and applications; advances in computer applications In: Proceedings of IASTED international symposia, pp 32–37 Kosko B (1986) FCMs. IntJ Man-Mach Stud 24(1):65–75 Kosko B (1987) Adaptive inference in fuzzy knowledge networks. In: Proceedings IEEE international conference on neural network,San Diego, pp 261–268 Kosko B (1988) Hidden patterns in combined and adaptive knowledge networks. Int J Approx Reason Arch 2:377–393 Kosko B (1992) Neural networks and fuzzy systems: a dynamical systems approach to machine intelligence. Prentice-Hall, New York Kosko B (1994) The new science of fuzzy logic: fuzzy thinking. HarperCollins, London Lane DC, Oliva R (1998) The greater whole: towards a synthesis of the system dynamics and soft system methodology. Eur J Oper Res 107(1998):214–235 Lee K, Chung N (2006) Cognitive map-based web site design: empirical analysis approach. Online Inform Rev 30(2):139–154 Lin C, Wu W (2008) A causal analytical method for group decision making under fuzzy environment. Exp Syst Appl 34(1):205–213 Meghabghab G (2002) Fuzzy cognitive state map vs. Markovian modeling of user’s web behavior, invited paper. Int J Comput Cogn 1(8):51–92 Mingers J (1984) Subjectivism and soft systems methodology—a critique. J Appl Syst Anal 11:85–103 Mirijamdotter A, Bergvall-Ka˚rebirn B (2006) An appreciative critique and refinement of Checkland’s soft systems methodology. In: Strijbos A (ed) Search of an integrative vision of technology—interdisciplinary studies in information systems SaB. Editor Springer, New York, pp 79–102 Montazemi A, Conrath DW (1986) The use of cognitive mapping for information requirements analysis. MIS Quart 10(1):45–58 Munro I, Mingers J (2002) The use of multimethodology in practice-results of a survey of practitioners. J Operat Res Soc 53(4):369–378 ¨ zesmi U, O ¨ zesmi S (2004) Ecological models based on people’s knowledge multi-step FCM modeling O approach. Ecol Model 176(1–2):43–64 Park K, Kim S (1995) Fuzzy cognitive maps considering time relationships. Int J Hum Comput Stud 42(2):157–168 Platt A, Warwick S (1995) Review of soft systems methodology. Ind Manage Data Syst 95(4):19–21

123

354


Rodr0 ıguez-Ulloa RA, Paucar-Caceres A (2005) Soft system dynamics methodology (SSDM): combining soft systems methodology and system dynamics (SD). Syst Pract Act Res J 18(3):303–334 Rose J (2002) Interaction, transformation and information systems—an extended application of soft system methodology. Inf Technol People 15(3):242–268 Shapiro F, Shapiro R (2003) Towards and improved collaboration model for the national healthcare system in England and Wales: a critical and constructive approach using operational research. Logist Inf Manage 16(3/4):246–258 Siau K, Tan X (2005) Improving the quality of conceptual modeling using cognitive mapping techniques. Data Knowl Eng 55(3):343–365 Smyth DS, Checkland PB (1976) Using a systems approach: the structure of root definitions. J Appl Syst Anal 5(1):75–83 Taber WR (1991) Knowledge processing with FCMs. Exp Syst Appl 2:83–87 Taber WR, Siegel MA (1987) Estimation of expert weights using FCMs. In: Proceedings of the 1st IEEE international conference on neural networks, San Diego, pp 319–325 Tsadiras AK, Kouskouvelis I, Margaritis KG (2001) Making political decisions using FCMs: the FYROM crisis. In: Proceedings of the 8th Panhellenic conference on informatics, Cyprus, pp 501–510 Vasantha WB, Ram Kishore M (1999) Symptom disease model in children. Ultra Sci 11(3):318–324 Wilson B (1993) Systems: concepts, methodologies, and applications. Wiley, UK Yinghong Z (2007) Soft systems methodology based on decision making knowledge integration. In: Proceedings of IEEE international conference on the wireless communications, networking and mobile computing conference (WICOM), Shanghai, pp 21–25, Sep 2007

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