Fuzzy cognitive map-based approach to evaluate EDI performance: a test of causal model

Expert Systems with Applications 27 (2004) 287–299 www.elsevier.com/locate/eswa

Fuzzy cognitive map-based approach to evaluate EDI performance: a test of causal model Sangjae Leea,*, Byung Gon Kimb, Kidong Leec a

Department of E-business, College of Business Administration, Sejong University, 98 Kunja-dong, Kwangjin-gu, Seoul 143-747, South Korea b Department of Digital Business Administration, Namseoul University, Cheonan 330-707, South Korea c College of Business, University of Incheon, 177 Dohwa-dong, Nam-ku, Incheon 402-749, South Korea

Abstract This paper proposes the usage of fuzzy cognitive map (FCM) for the evaluation of electronic data interchange (EDI) performance. Although there has been a stream of research on the performance of EDI systems during the last decades, possible interrelationships among those individual factors of EDI performance have been largely ignored and thereby were not adequately examined. The main task of evaluation of EDI performance demands consideration of the complex causal relationship among EDI performance factors. It is difficult even for experts in organizational behavior to cognitively predict the causal effect of one factor on the others. A FCM is used to describe the inference process for the evaluation of EDI performance. Initially, structural equation models are used for identifying relevant relationships among the factors and indicating their direction and strength. This study collected data from the 202 companies, which already experienced EDI systems implementation. The derived five factors include reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency, competitive advantage. Findings indicate that a causal model of EDI performance adequately capture each of the proposed variables. The cognitive map provides preliminary insights into the causal model of EDI performance. q 2004 Elsevier Ltd. All rights reserved. Keywords: Fuzzy cognitive map; Electronic data interchange; Electronic data interchange performance; Casual model

1. Introduction Electronic data interchange (EDI) is an on-line transaction system that facilitates computer-to-computer communication of standardized business documents and transactions across organizational boundaries (Aggarwal & Rezaee, 1996). Its value includes such benefits of reduced paperwork, elimination of data entry overheads, improved accuracy, timely information receipt, accelerated cash flow, and reduced inventories (Hansen & Hill, 1989). EDI is changing rapidly from traditional EDI to Internetbased EDI due to rapid development of Internet-based information technologies (Attaran, 1999; Mehrtens, Cragg, & Mills, 2001). The Internet-based EDI systems bring such results as increased precision in transactional information between/among businesses, increased information transfer speed through simplified work processes, promoted * Corresponding author. Tel.: þ 82-234-083980; fax: þ 82-234-083310. E-mail addresses: [email protected] (S. Lee), [email protected] (B.G. Kim), [email protected] (K. Lee). 0957-4174/$ - see front matter q 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.eswa.2004.02.003

productivity and work efficiency and so on (Chan & Swatman, 2000). Many researchers identified that intermediary function or electronic market enabled by Internet-based EDI systems, as a networking technology enhancing efficiency of organization and managerial performance, give many advantages (Chircu & Kaufman, 2000). It is needed for domestic companies to strategically utilize EDI systems and increase external competitiveness by adopting EDI systems in order to actively respond to global environment and unlimited competition. Researchers tried to identify major factors affecting successful implementation of IOIS and EDI systems (Ramamurthy, Premkumar, & Crum, 1999). The utilization and implementation of EDI systems in the organizations can be considered to the adoption of innovation, which has many potential advantages to the companies adopting, as we can see in many other Information Technology (IT) adoption and implementation (Riggins & Mukhopadhyay, 1999). This research considers the adoption of EDI systems to be a form of innovation, and intends to identify factors facilitating the implementation of EDI systems, and to

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derive theoretical background of this study by reviewing previous research on implementation of information systems. Many studies deal with the intranet or extranet or other technologies linked with interorganizational information systems like EDI (Nagi & Wat, 2002; Riggins & Rhee, 1999; Sherer & Swatman, 1999; Watson & McKeown, 1999). They focused on the analysis of telecommunicationsbased system that played a role in supporting business strategy. Based on organizational innovation and IS implementation theory, they investigated the factors influencing adoption and implementation of Internet-based information intermediary system. The major objectives of these studies are to identify the success factors that explain or predict the successful implementation of Internet-based information intermediary system, and to evaluate the impact of the system on competitive advantage of the organization. The purpose of these studies was to identify success factors of system implementation within an organization and the benefits organizational could obtain by using the technologies. The success factors include the organizational support, the implementation process, the control procedures, compatibility, organizational size, functional differentiation, training, MIS support, vendor support, customer influence and the level of system integration in the firm. The level of success also depends upon the level of imposition of the systems by partners. Further, they attempt to identify the factors which explain or predict. Fuzzy cognitive map (FCM) is useful in modeling complex systems (Stylios & Groumpos, 2001) that involve lots of parameters. Example systems include Web-mining systems (Lee, Kim, Chung, & Kwon, 2002), supervisory control systems (Stylios & Groumpos, 2000). The study describes the development and testing of sets of items to capture constructs of EDI performance. This study adopts a causal structure of EDI performance model where variables of EDI performance are causally interrelated. Sets of items to measure variables of EDI performance are assessed. The structural model is tested using data collected from firms adopting EDI. To analyze interrelationships among the factors, this study adopts what-if simulation analysis using a FCM approach where input is operational performance and output is strategic performance. This study illustrates the reason that FCM approach is adopted and delineates a questionnaire survey method and associated measurements.

2. Theoretical framework 2.1. EDI performance EDI performance is measured in terms of the major benefits realized from EDI such as lower inventory cost, greater accuracy in information, quick response time, reduced operations cost, and reduced paperwork. In EDI,

the positive effect of the implementation on performance has been consistently proven. Integration of the information collected through EDI with internal IS applications has been asserted to be a critical factor for system effectiveness and efficiency (Iacovou, Benbasat, & Dexter, 1995; Premkumar, Ramamurthy, & Nilakanta, 1994). EDI performance includes increased processing speed, operational efficiency, improved information quality, competitive advantage, accuracy in output information, system reliability, convenience in system use, system accessibility, improvement in corporate image, improvement in customer service, increased cooperation, increased sales, decreased inventory and transaction cost, reduction of data reentry, personnel reduction, decreased paper work, and improvement in data administration system use (Bakos, 1991; Banerjee & Golhar, 1994; Benjamin, De Long, & Scott-Morton, 1988; Bergeron, Buteau, & Raymond, 1991; Bergeron & Raymond, 1992; Emmelhainz, 1990; Farthoomand and Drury, 1996; Gifkins & Hitchcock, 1988; Hansen & Hill, 1989; Ives, Olson, & Baroudi, 1983; Miller, 1989; Mukhopadhyay, Kekre, & Kalathur, 1995; Kimberley, 1991; O’Callaghan, Kaufmann, & Konsynski, 1992; Premkumar & Ramamurthy, 1995; Ragsdale & Gillbert, 1990; Sokol, 1989). Others doubt the EDI benefits such as cost reduction and operational efficiency (Eckerson, 1990; Venkatraman & Zaheer, 1990), especially when EDI adoption is coerced upon by business partners (Bergeron & Raymond, 1992; Grover, 1990; Holland, Lockett, & Blackman, 1992; Monczka & Carter, 1988; Swatman & Swatman, 1991). Scala and McGrath (1993) suggests pros and cons of EDI adoption using Delphi method; the disadvantages of EDI adoption include security, cross-vulnerability, integration risks, less auditability, possible legal issues. Dearing (1990) and Iacovou et al. (1995) suggest direct, indirect, and strategic effects, and Hoogeweegen, Streng, and Wagenaar (1998) indicates tangible and intangible effects. Dearing (1990) suggests the direct effects such as cost reduction, work process efficiency, improvement of management performance through communicating transaction data in digital form, and indirect effects such as improvement in relationships with trading partners, increased productivity, and competitive advantage. Emmelhainz (1993) indicates that EDI benefits include short-term benefits such as the reduction of transaction processing time and cost, and long-term benefits such as increased competitive advantage, improved customer relationships. Farthoomand and Drury (1996) investigate the causal structure of factors affecting EDI success, which exhibits multidimensional and hierarchical structure. Confirmatory factor analysis is performed to examine the factor structure and patterns of factors. Hoogeweegen and Wagenaar (1996) suggest layered structure of the benefits of EDI investment; business layer, transaction layer, information layer, and communication layer. Layer 1 exhibits the reduction of transaction processing time, and layer 2 represents

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the improved relation with customers. Layer 3 shows the improved quality of information and operational efficiency and layer 4 indicates the cost reduction and improved competitive advantage. Fearon and Philip (1998, 1999) develops a conceptual framework to assess strategic and operational benefits of EDI. The model consists of EDI implementation method, EDI implementation success, expected benefits, and realized benefits. Operational benefits are lower in expected benefits and realized benefits. Strategic benefits are higher in expected benefits and realized benefits. When EDI is incrementally implemented, effectiveness and risks are low. When EDI is radically implemented, benefits and risks are high. 2.2. Fuzzy cognitive map The CM is able to represent causal relationships among the factors describing a given object and/or problem. CMs can be generalized into FCMs by fuzzifying edge values or causality values. A cognitive map is composed of nodes that represent the factors most relevant to the decision environment and arrows indicating different causal relationships among factors. The causal relationships, however, can be indicated by weighted directed connections. The concern of a CM is to see whether the state of one element is perceived to have an influence on the state of the other. Positive causal links (denoted as ‘ þ ’ in CM) should be regarded as excitatory relationships while negative causal links (denoted as ‘ 2 ’ in CM) as inhibitory relationships between nodes (Zhang, Chen, & Bezdek, 1989). A CM is composed of nodes, signed directed arrows, and causality coefficient. Nodes represent causal concepts, and signed directed arrows causal relations between two concepts. Causality coefficient means ‘ þ ’ and ‘ 2 ’. The causality coefficient can be fuzzified into a real value between 2 1 and þ 1 (Lee, Courtney, & O’Keefe, 1992). The simple CM with causality coefficient ‘ þ ’ and ‘ 2 ’ is sufficient for replicating human cognition because decision-makers typically do not use a more complicated set of relationships. If specific nodes are stimulated, the resulting activities can resonate through other nodes on the map until equilibrium is reached. The nodes transmit activities to others in the network along positively or negatively weighted connections. In turn, the activities of each recipient node increase or decrease, and are then transmitted throughout the network. Cognitive map can yield insights into indirect effects among nodes. Such indirect effects can be understood only after the entire map is displayed. CMs have been found especially useful in solving problems where many decision variables and uncontrollable variables are causally interrelated with each other. CMs can help decision makers analyze hidden causal relationships which might contribute to more relevant and meaningful solutions (Eden & Jones, 1980; Klein & Cooper, 1982; Montazemi & Conrath, 1986; Park & Kim, 1995;

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Lee & Kim, 1997). For instance, causation in static and dynamic processes is represented through M-labeled digraph (Burns & Winstead, 1985). Ramaprasad and Poon (1985) suggested MIND (Mapping an Interpreting Influence Diagrams) to support the complex business policy and planning problem using influence diagram (or cognitive map). Cognitive map has been utilized for information requirement analysis (Montazemi & Conrath, 1986); a pattern of effective factors to evaluate the performance of subordinates by insurance claim managers is represented using a cognitive map. An inference engine for causal and diagnostic reasoning has been developed (Kim & Pearl, 1987) based on Pearl’s (1986) causal network formalism. Binary matrices to describe rule-based knowledge were proposed (Looney & Alfize, 1987). Eden and Ackermann (1989) proposed SODA (Strategic Options Development and Analysis) that is designed to encourage organizational members to actively define their own strategy. An inference in semantic network was suggested using binary matrices and matrix multiplication (Burns, Winstead, & Haworth, 1989). Cognitive map also has been used to represent graphtheoretic behavior (Zhang & Chen, 1988) to investigate electrical circuits (Styblinski & Meyer, 1988), to describe plant control (Gotoh, Murakami, Yamaguchi, & Yamanaka, 1989). Lee et al. (1992) developed COCOMAP (Collective Cognitive Modeling) to support group cognitive processes and organizational learning through cognitive modeling. Recently, cognitive map has been used for decision analysis (Zhang et al., 1989), a distributed decision process model in the Internet domain (Zhang, Wang, & King, 1994), wayfinding process (Chen & Stanney, 1999), a stock investment analysis problem (Lee & Kim, 1997), business process redesign (Kwahk & Kim, 1999), knowledge management (Noh, Lee, Kim, Lee, & Kim, 2000), bosphorus crossing problem (Ulengin, Topcu, & Sahin, 2001) diagnosis of language impairment (Georgopoulos, Malandraki, & Stylios, 2002), and design of agents (Miao, Goh, Miao, & Yang, 2002), the design of electronic commerce web sites (Lee & Lee, 2003). The first way of using a CM is that irrelevant factors can be detected as those which have no effect on the target decision outcome. By analyzing the connectivity between factors depicted in a CM, one can easily determine whether a particular factor is relevant to a given decision or not. Fig. 1 shows an example with causal links between six factors. Suppose that one is concerned with the state of F because it will affect a target decision to be made. Fig. 1 shows us that both A and D influence F directly, and C has an indirect influence on F through D: On the other hand, neither B nor E influences F indirectly or directly. Both B and E are therefore ‘irrelevant’ in light of the outcome F: This example demonstrates that such a difficult decision making problem can be simplified by means of a CM. The second way of using a CM is to evaluate the performance of causal knowledge embedded in a CM regardless of its possibility in producing the expected

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Fig. 1. Illustrative cognitive map.

output. Assume that one finds that the value of F is relatively high, while the values of A and D are relatively low. The CM suggests, however, that low A and low D would lead to a low F: Thus, one knows that this is not the expected case. The reason may be attributed to other unknown factors, which might be affecting F in this particular situation. Also some factors, which were not originally perceived to be relevant may be influencing F: Then our effort should focus on identifying such factors implicitly affecting F so that the performance of causal knowledge represented by a CM may not degrade seriously under turbulent decision making situations. The third way of using a CM is to support ‘what-if’ analysis. As revealed in previous studies, CMs must be further improved to deal with uncertainty and vagueness about the decision environments so that they may be used as a knowledge engineering tool to extract causal knowledge from factors representing environments (Kosko, 1986; Taber, 1991). For this purpose, a CM is organized as a matrix, which contains some specific inputs (or stimulus vectors), and producing outputs (or consequence vectors). The ‘what-if’ analysis can be easily performed on this matrix representation. 2.3. Need for cognitive map in evaluation of EDI performance Cognitive map approach is appropriate for representing this sort of context-sensitive judgments. In many cases, knowledge about a specific domain is uncertain as well as fuzzy, because most knowledge is expressed in different causal relationships between concepts or variables. Experts describe their understanding of the relationships among the defined key factors in order to build a cognitive map. As an AI approach, the cognitive map technique can compensate for the lack of rule-based mechanisms in traditional expert systems by providing the higher level of abstraction needed for the design of websites. Cognitive map is commonly considered best for problems where experts have diverse opinions about a correct answer. For example, designers of websites may have different opinions from each other when they rely on past experience rather than explicit rules of

evaluating and designing websites. The designer may use subjective or non-deterministic judgments to define interrelationships among system attributes. This happens due to cognitive limitations of the designer. Computerized support for assessing the value of relevant factors would greatly contribute toward successfully evaluating EDI performance. Without investigating such causality among the factors clearly, the evaluation of EDI performance cannot be effective in a changing business environment. Basic argument of this study is that success of evaluation of EDI performance depends on how a variety of EDI performance factors are causally interrelated with each other to produce strategic performance of EDI. Complexities of the causal relationships among the factors, however, make the adjustment process for evaluation of EDI performance hard to be completely computerized. Managers lack ability of analyzing all the relevant factors at the same time. Usually, they tend to evaluate the factors individually or two or three factors simultaneously at best.

3. Methods 3.1. Measurement Based on the literature on EDI performance, 18 items are suggested for EDI performance (Emmelhainz, 1988, 1993; Farthoomand and Drury, 1996; Hansen & Hill, 1989; Hinge, 1988; Scala & McGrath, 1993; Sokol, 1989; Teo, Tan, Woo, & Wei, 1995). The items include the measurements suggested by Emmelhainz (1988, 1993), Scala and McGrath (1993), Premkumar and Ramamurthy (1995), McGowan (1994), and Bergeron and Raymond (1997), encompassing almost all the items in EDI literature. The pretest of the items is performed by 20 experts in EDI from industry and academic institutions to examine the wording, understandability, and content validity of items (Table 1). 3.2. Data collection EDI service has begun in 1992 in Korea. The data is collected from the subscribers to KTNET which is the largest EDI service provider in Korea. The major industries using EDI include trade, retailing, banking, manufacturing, transportation, logistics, etc. The number of years that responding companies have used EDI service is larger than one year. The questionnaire is sent to 520 companies by mail, fax, and e-mail and the number of responding companies reaches 217. Fifteen responses are excluded due to missing responses. The final sample includes responses from 202 companies. The companies from various industries are included as represented in Table 2. The demographic characteristics of the responding companies are suggested in Table 3. The proportion of large, medium, and small companies is 0.27, 0.34, and 0.38. The number of respondents belong to IT department (13.9%) is smaller

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Table 1 Measurement items Items

Sources

Accuracy improvement

Iacovou et al. (1995), Premkumar and Ramamurthy (1995), Scala and McGrath (1993), Ragsdale and Gillbert (1990) Emmelhainz (1988, 1993), Premkumar and Ramamurthy (1995) Bergeron and Raymond (1997), Scala and McGrath (1993), O’Callaghan et al. (1992) Bergeron and Raymond (1997), Kimberley (1991), Scala and McGrath (1993) Gifkins and Hitchcock (1988), Kimberley (1991), Premkumar and Ramamurthy (1995) Kimberley (1991), Sokol (1989) Premkumar and Ramamurthy (1995), Ragsdale and Gillbert (1990), Scala and McGrath (1993), Sokol (1989) Bergeron and Raymond (1997), Farthoomand and Drury (1996), Hansen and Hill (1989), Scala and McGrath (1993) Emmelhainz (1990), Kimberley (1991) McGowan (1994) Banerjee and Golhar (1994), Iacovou et al. (1995), Scala and McGrath (1993), Ragsdale and Gillbert (1990) Ragsdale and Gillbert (1990) Banerjee and Golhar (1994), Emmelhainz (1988, 1993), Premkumar and Ramamurthy (1995) Banerjee and Golhar (1994), Hansen and Hill (1989), Iacovou et al. (1995), O’Callaghan et al. (1992), Premkumar and Ramamurthy (1995), Sokol (1989) Banerjee and Golhar (1994), Bergeron and Raymond (1997), Premkumar and Ramamurthy (1995), Scala and McGrath (1993) O’Callaghan et al. (1992), Scala and McGrath (1993) Banerjee and Golhar (1994), Emmelhainz (1990) Dearing (1990), McGowan (1994)

Reduction of response time Reduction of labor cost Decreased processing cost Decreased transaction cost Increased transmission speed Decreased input cost Decreased manual labor cost Decreased processing time Simplified transaction process Improved transaction relationship Rapid transaction control process time reduction in business process Strategic advantage Improved customer service Decreased inventory cost Increased productivity Increased sales Increased market share

than that of user department (86.1%) indicating that the perception of EDI users is more reflected than EDI developers.

4. Results 4.1. Factors of EDI performance Exploratory factor analysis is used against the items of 18 items to derive factors of EDI performance.

The items, which have similar factor loading for more than one factor, are excluded. The three commonly employed decision rules for identification of factors— minimum eigen value of 1, simplicity of structure, minimum factor loading of 0.5—were followed (Hair, Tatham, & Grablowsky, 1979). The derived five factors include competitive advantage, reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency. The principal component factor solution with varimax rotation produced a five factor solution that explained 71.11%

Table 2 Characteristics of samples Industry

Size

Number of IT staff members

Class

Number of companies

Percentage

Number of employee

Number of companies

Percentage

Class

Number of companies

Percentage

Trade Fiber Electronics Mechanical manufacturing Beverage Textile Chemical manufacturing Others Total

38 41 42 17 17 17 16 14 202

18.9 20.3 20.8 8.4 8.4 8.4 7.9 6.9 100

Less than 50 51–100 101– 500 501– 1000 1001–5000 Greater than 5001

38 17 68 20 46 13

18.8 8.4 33.7 9.9 22.8 6.4

Less than 5 6 –10 11 –20 21 –50 51 –100 Greater than 101

102 35 19 25 11 10

50.5 17.3 9.4 12.4 5.4 5.0

Total

202

100

Total

202

100

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Table 3 Demographic characteristics of respondents Position

Education

Years of being employed

Level

Number

Percentage

Level

Number

Employee Assistant manager Manager General manager Executive Total

87 66 32 16 1 202

43.1 32.7 15.8 8.0 0.5 100

High school Undergraduate Graduate

44 150 8

Total

202

of the systematic covariance among the scale items for 18 items. The derived five factors include competitive advantage, reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency. The competitive advantage factor includes increased market share, increased sales, increased productivity and strategic advantage. The reduction of processing time factor involves reduction in business process, decreased processing time, reduction of response time. The improved information quality factor includes accuracy improvement, rapid transaction control process time, increased transmission speed. The decreased processing cost factor represents decreased input cost, decreased transaction cost, decreased inventory cost. The improved operational efficiency indicates decreased manual labor cost, reduction of labor cost, improved customer service. The factors of EDI performance represents both the operational performance and strategic performance. The importance of reduction of processing time, improved information quality is greater than that of competitive advantage. This indicates that Korean companies consider EDI as a way to improve operational efficiency and effectiveness rather than strategic weapon to increase strategic advantage.

Percentage 21.8 74.2 4.0

100

Years

Number

1–5 6–10 11–15 16–20

80 94 19 9

Total

202

Percentage 39.6 46.5 9.4 4.5 100

the items measuring a construct cluster together and form a single construct. Discriminant validity refers to the degree to which a concept differs from other concepts. Convergent and discriminant validity can be tested using principal component factor analysis. The principal component factor solution with varimax rotation produced a five-factor solution that explained 71.1% of the systematic covariance. Two items (T4; I4) were dropped out from the original scale items, due to low factor loading. The results of exploratory factor analyses performed with 16 items of EDI performance indicate that they have high convergent validity as they have minimum factor loading of 0.5 (Farthoomand and Drury, 1996). All the items exhibited adequate discriminant validity since no significant cross-loading of items among factors was noticed (Tables 4 and 5). The coefficient alphas of the research variables are indicated in Table 6. All scales exhibited sufficient reliability as they exceeded Nunnally’s (1978) reliability guidelines of 0.7. The factor analysis and reliabilities show strong evidence for the construct validity of the EDI performance scale. Hence, these measures are appropriate operational definitions of the constructs they purport to measure.

4.3. Test of causal EDI performance model 4.2. Validity and reliability of measures Validity and reliability tests were conducted for each variable. Validity is the degree to which an instrument measures a construct under investigation. In this study, both content and construct validity were tested. The content validity is the representativeness or sampling adequacy of the content of a measure, and is concerned with the representativeness of the content of the measure for the universe of the property being measured. This study adapts the measures used by previous studies and pretests them by practitioners and experts to enhance the content validity of the instrument. Construct validity was assessed using convergent and discriminant validity. Convergent validity tests to see if all

LISREL (LInear Structural Relations) modeling is used to investigate the validity. The LISREL package uses confirmatory factor analysis to generate factor loading. This best describes the specified relationships between measures and constructs. Confirmatory factor analysis is used to test a priori theoretical structures against the data rather than derive an empirical factor structure that cannot be interpreted from a theoretical perspective. The structural equation modeling as implemented in LISREL (Joreskog & Sorbom, 1991) has been employed to test the measurement models. Anderson and Gerbing (1988) propose that convergent validity could be investigated from the measurement model by finding whether the estimated coefficients of each construct are significant. The significant

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Table 4 Results of factor analysis Factors

Factor1

Factor2

Factor3

Factor4

Factor5

Alpha

Factor 1. Competitive advantage Increased market share ðC1Þ Increased sales ðC2Þ Increased productivity ðC3Þ Strategic advantage ðC4Þ

0.823 0.818 0.799 0.733

0.021 0.040 0.314 0.325

0.262 0.140 0.107 0.047

0.048 0.060 0.166 0.316

0.205 0.313 20.059 20.014

0.8742

Factor 2. Reduction of processing time Reduction in business process ðT1Þ Decreased processing time ðT2Þ Reduction of response time ðT3Þ Simplified transaction process ðT4Þ

0.169 0.115 0.147 0.297

0.785 0.757 0.681 0.481

0.153 0.173 0.363 0.375

0.191 0.351 146 20.174

0.266 0.123 0.201 0.389

0.8345

20.013 0.365 0.344 0.377

0.260 0.113 0.267 0.136

0.783 0.658 0.634 0.443

0.082 0.323 0.215 0.151

0.108 0.065 0.154 0.412

0.7562

Factor 4. Decreased processing cost Decreased input cost ðD1Þ Decreased transaction cost ðD2Þ Decreased inventory cost ðD3Þ

0.196 0.128 0.392

0.309 0.264 0.029

0.187 0.165 0.297

0.765 0.736 0.590

0.195 0.226 0.360

0.7787

Factor 5. Improved operational efficiency Decreased manual labor cost ðO1Þ Reduction of labor cost ðO2Þ Improved customer service ðO3Þ

0.057 0.120 0.446

0.324 0.473 0.135

0.093 0.097 0.332

0.288 0.300 0.206

0.728 0.648 0.538

0.7747

3.491 19.392 19.392

2.772 15.399 34.791

2.351 13.063 47.854

2.113 11.739 59.594

2.073 11.518 71.111

Factor 3. Improved information quality Accuracy improvement ðI1Þ Rapid transaction control process time ðI2Þ Increased transmission speed ðI3Þ Improved transaction relationship ðI4Þ

Eigen value Percent of variance Cumulative variance

coefficient estimates in Table 6 suggest the presence of convergent validity. Farthoomand and Drury (1996) investigate the causal structure of factors affecting EDI success, which exhibits multidimensional and hierarchical structure. Confirmatory factor analysis is performed to examine the factor structure and patterns of factors. Hoogeweegen and Wagenaar (1996) suggest layered structure of the benefits of EDI investment; business layer, transaction layer, information layer, and communication layer. Layer 1 exhibits the reduction of transaction processing time, and layer 2 represents the improved relation with customers. Layer 3 shows the improved quality of information and operational efficiency and layer 4 indicates the cost reduction and improved competitive advantage.

Fearon and Philip (1998, 1999) develops a conceptual framework to assess strategic and operational benefits of EDI. The model consists of EDI implementation method, EDI implementation success, expected benefits, and reaTable 6 Results of confirmatory factor analysis EDI performance factor

Construct

Coefficient

Reduction of processing time

T1 T2 T3 I1 I2 I3 D1 D2 D3 O1 O2 O3 C1 C2 C3 C4

0.62 0.98 0.38 0.97 0.49 0.64 1.00 1.00 0.23 0.17 0.53 0.67 0.59 0.52 0.72 0.17

Improved information quality

Decreased processing cost Table 5 The importance of EDI performance factors

Improved operational efficiency

EDI performance

Mean

Standard deviation Rankings

Competitive advantage Reduction of processing time Improved information quality Decreased processing cost Improved operational efficiency

2.8601 3.6568 3.5660 3.4043 3.4538

0.6933 0.7676 0.7087 0.8014 0.7941

Competitive advantage 5 1 2 4 3

Chi-square ¼ 2.15 (p ¼ 0.34), RMR ¼ 0.025, NFI ¼ 0.98.

GFI ¼ 0.98,

AGFI ¼ 0.98,

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lized benefits. Operational benefits are lower in expected benefits and realized benefits. Strategic benefits are higher in expected benefits and realized benefits. When EDI is incrementally implemented, effectiveness and risks are low, When EDI is radically implemented, benefits and risks are high. Farthoomand and Drury (1996) suggest that interrelationships among EDI benefits take the form of causal structure rather than hierarchical structure. Hoogeweegen and Wagenaar (1996) indicate that operational benefits include automation of tasks, synergy effect, and integration effect while strategic effects include process innovation and competitive advantage. This study proposes that the five EDI performance factors are causally interrelated as suggested in Fig. 2. EDI users obtain the benefit of the reduction of processing time by removing processing inefficiency in the first place. Improved accuracy in data processing leads to improvement of information quality, customer service, and operational efficiency. The improved operational efficiency and decreased processing cost lead to improved competitive advantage (Hoogeweegen & Wagenaar, 1996; Bergeron & Raymond, 1997). The reduction of processing time increases competitive advantage. Structural equation modeling (SEM) analysis was performed and SEM results depicted in Fig. 2 are Chisquare ¼ 122.14, GFI ¼ 0.85, NFI ¼ 0.88, CFI ¼ 0.88, RMR ¼ 0.059. Considering that most of all the fit indices are successfully met, we can judge that the proposed structural model shown in Fig. 2 is statistically proper and valid. EDI performance has been treated as dependent variable of various independent variables such as industry, organizational, and system variables. The results indicate that EDI performance factors are by themselves causally interrelated while the former studies have focused on the relationship between industry, organizational, and system variables and EDI performance factors. The usage of EDI leads to reduction of processing time and this, in turn, contributes to greatly decreased processing cost as the processing time in communication technology is critical factor in determining cost of communication technology.

The derived five factors include competitive advantage, reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency. Decreased processing cost is not causally related to improved operational efficiency. Operational efficiency can be determined by a number of factors such as change of organizational structure, human resource management, job scheduling, etc. Thus, it is important to consider many organizational factors in improving operational efficiency. Improved information quality is negatively related to decreased processing cost indicating that improvement of information quality demands investment of hardware, database management system, communication system, data warehousing systems, human resources and data management, which increases processing cost in order to increase information accuracy, timeliness, and communication speed. 4.4. Construction of FCM The purpose of cognitive map is to aid the evaluation of EDI performance. It is necessary to devise a systematic way to estimate the causal relationships among EDI performance factors. Methods of determining causal relationships among factors include the use of statements of decision makers (Eden, Jones, & Sims, 1979), questionnaires prepared specifically for this purpose or neural network-based learning (Caudill, 1990). The first and second approaches are based on the assumption that experts in a domain can accurately articulate the weights in causal relationships. Integration of the individual cognitive maps created by experts is needed when there exist multiple maps devised by experts from the same domain with varying degrees of credibility. It is difficult to determine the precise strength of the interrelationships among factors at the outset. The edge weights define the degree to which concepts interact. Experts can assign numbers to the entries of adjacency matrices but it is difficult to gauge their strength. In addition, in cases where each map has less accuracy and reliability, the resulting combined map cannot precisely describe, through algorithms, the actual state of the domain environment. There are methods of combining experts’

Fig. 2. Causal EDI performance model (value in the parenthesis indicates t-value, Chi-square ¼ 122.14, GFI ¼ 0.85, NFI ¼ 0.88, CFI ¼ 0.88, RMR ¼ 0.059).

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295

Table 7 Adjacency matrix ðE1Þ Cause

Reduction of processing time Improved information quality Decreased processing cost Improved operational efficiency

Effect Reduction of processing time

Improved information quality

Decreased processing cost

Improved operational efficiency

0 0 0 0

0 0 0 0

1.15 20.57 0 0

0.0064 0.84 0 0

knowledge in business (Lee & Kim, 1997) or estimation of weights (Kwahk & Kim, 1999), but these methods demand a comparison of opinions from experts. As the number of experts increases, the comparison of their opinions becomes very complex. Hence it is necessary that the knowledge of experts be accurately represented when the cognitive map is first constructed. In this study, modeling with LISREL 8.30 (Linear Structural Relationships) was used to determine the complex causal relationships among factors based on a large number of cases. This approach can validate the significance of causal links. A cognitive map was first built to aid the evaluation of EDI performance by representing how the state of one deign attribute affects that of others. The interrelationships among EDI performance factors are modeled using structural equations. The latent variables in the paths represent factors; the relationships among them can be determined after LISREL estimates the causal relation. The adjacency matrix can be derived from the estimated causal effect as suggested in Fig. 2 (Tables 7 and 8). All estimated effects range from 2 0.57 to 1.15. The highest and lowest causal effects are found in the path from reduction of processing time to decreased processing cost and from improved information to decreased processing cost. 4.5. Example of cognitive map application Since FCM encodes its own causal knowledge in its networked structure in which all concepts are causally connected, its inference is emitted based on a given set of initial conditions and on the underlying dynamics in the FCM. The direction and strength of cause and effect linkages were identified using a number of cases representing the state of EDI performance. However, this result does not show that the cognitive map is of value. It is necessary to assess the impact of positive and negative causalities when stimuli are exerted on one or more elements. The objective of this section is to illustrate the recommendation of status of reduction of processing time, improved information quality, decreased processing cost, and improved operational efficiency that result in the

highest competitive advantage. The adjacency matrix shows that the enhancement of some factors causes an effect on other factors and the competitive advantage. ‘What-if’ questions are answered by entering an input vector that, multiplied by the adjacency matrix produces an ordered list of consequences and diagnoses. The value of each element of the input vector can be 1 or 0 according to whether one element is enhanced or not. A number of hypothetical situations can be provided regarding the status of reduction of processing time, improved information quality, decreased processing cost, and improved operational efficiency. There are 10 combinations of input, depending on whether each state of six factors is activated. As an example, the effect of enhancing improved information quality and improved operational efficiency on all the other factors and the competitive advantage can be tested by setting the second and fourth concept node in an input vector to 1: C1 ¼ ð 0

1

0 1Þ

This results in the output vector: C1 p E1 ¼ ð 0

0 2 0:57 0:84 Þ ¼ C2

C2 p E2 ¼ 0:0141 where E1; E2 and C2 is 4 £ 4, 4 £ 1 and 1 £ 4 matrix. The improved operational efficiency is positively affected by enhancing improved information quality and improved operational efficiency (i.e. the level is 0.84). The level of competitive advantage is 0.0141, showing that enhancing the enhancing improved information quality and improved operational efficiency improves competitive advantage.

Table 8 Adjacency matrix ðE2Þ Effect

Cause (competitive advantage)

Reduction of processing time Improved information quality Decreased processing cost Improved operational efficiency

0 0 0.55 0.39

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Table 9 Inference results using various input cases Input case

Input#1a Input#2 Input#3 Input#4 Input#5b Input#6 Input#7 Input#8 Input#9 Input#10

Input

Output

Reduction of processing time

Improved information quality

Decreased processing cost

Improved operational efficiency

Decreased processing cost

Improved operational efficiency

Competitive advantage

1 0 0 0 1 1 1 0 0 0

0 1 0 0 1 0 0 1 1 0

0 0 1 0 0 1 0 1 0 1

0 0 0 1 0 0 1 0 1 1

1.1500 20.5700 0 0 0.5800 1.1500 1.1500 20.5700 20.5700 0

0.0064 0.84 0 0 0.8464 0.0064 0.0064 0.8400 0.8400 0

0.6350 0.0141 0 0 0.6491 0.6350 0.6350 0.0141 0.0141 0

a and b represent the case that results in the highest competitive advantage among the cases where the number of input factors that are enhanced is one and two.

It is possible to find the input case that leads to the highest competitive advantage. Table 9 depicts the inference (i.e. multiplication operation) results for the input cases where the number of factors that are enhanced is one and two. If the number of factors that are enhanced becomes higher, this leads to the greater competitive advantage. Thus, it is necessary to compare system outcomes among the cases where the number of factors that are enhanced is equal. When the number of factors that are enhanced is two, the case that result in the highest competitive advantage is input#5. Thus management should focus on the input case and invest their limited organizational resources for EDI performance; management should reduce processing time and improve information quality.

5. Conclusions and implications Cognitive map is fuzzy-graph structure for representing causal reasoning. Their fuzziness allows the representation of hazy degrees of causality between various controllable components. Their graph structure enables systematic causal propagation, in particular, forward and backward chaining. Cognitive map is developed to discover EDI performance factors, i.e. reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency, related to the competitive advantage. The causal reasoning process of managers is inevitably subject to human cognitive limitations and thus likely to be biased. Furthermore, human being’s power of memory is variable and finite. Cognitive map may help management to ease the effect of cognitive limitations through

furnishing information of the desirable direction of evaluation of EDI performance. The extent to which each factor is ‘relevant’ in the light of competitive advantage is determined. This is difficult to determine by direct inquiry or observation. The desirable combination of status of reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency, which might lead to great competitive advantage can be discovered. This is possible only after the complex interactions between EDI performance factors are captured. The structural equation modeling approach is used to derive causal relationships among variables, as it is difficult for managers to predict causal relations among these factors. This approach will enhance the quality of decision making in the evaluation of EDI performance. The results of this study suggest that EDI performance has multifaceted nature, i.e. competitive advantage, reduction of processing time, improved information quality, decreased processing cost, improved operational efficiency. These performance factors are causally inter-related. The benefit of the reduction of processing time is obtained by removing processing inefficiency in the first place. Improved accuracy in data processing leads to improvement of information quality, customer service, and operational efficiency. The improved operational efficiency and decreased processing cost lead to improved competitive advantage. EDI adopting companies should firstly focus on the reduction of processing time and then improvement of information quality in order to decrease processing cost and improve operational efficiency. This will ultimately improve competitive advantage. As Value Added Network (VAN) is still widely used in EDI (Khazanchi & Sutton, 2001), the results of this study can be used to explain the EDI performance of companies

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potentially adopting VAN service for EDI. The results of this study on B2B systems may be used to explain the benefits of supply chain management system (Clark & Stoddard, 1996; Krause, Scannel, & Calantone, 2000), the business process redesign in B2B systems, the trust in interorganizational relations (Nooteboom, Berger, & Noorderhaven, 1997; Weltry & Becerra-Fernandez, 2000). The causal model proposed in this study can be applied to describe the benefits of Internet-based information systems. The results of the study may be used to build causal model of system benefits for systems such as enterprise resource planning, data warehousing systems, and e-business systems. In the future study, longitudinal data analysis may be useful to make more accurate investigation of causal model of EDI performance. This will improve the generalizability of causal EDI performance model. Further, the future study may examine the industry and organizational characteristics affecting the performance of EDI.

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