Assessing “Transaction Climate” Influencing the Adoption of Innovative ICT and E-business in the Greek Agri-food Sector Sophia Zioupou1, Zacharoula Andreopoulou2, Basil Manos3, Fedra Kiomourtzi4 1 Department of Agricultural Economics, School of Agriculture Aristotle University of Thessaloniki, Greece Ε-mail: szioupou@agro.auth.gr 2 Laboratory of Forest Informatics, School of Forestry and Natural Environment Aristotle University of Thessaloniki, Greece Ε-mail: randreop@for.auth.gr 3 Department of Agricultural Economics, School of Agriculture Aristotle University of Thessaloniki, Greece Ε-mail: manosb@agro.auth.gr 4 Laboratory of Agricultural Informatics, School of Agriculture Aristotle University of Thessaloniki, Greece Ε-mail: fkiomour@agro.auth.gr Abstract. Recently, a large number of innovative ICT systems and network tools facilitate the use of e-business frameworks. Modern organizations through innovative ICT models can confront competition, uncertainty and complexity. Supply chain faces organizations as a chain of interrelated entities, and provides a complete aspect of their prospects. A survey has been contacted to test the impact of the factor “transaction climate” on agri-food firms in Greece. A total of 20 variables was initially proposed to determine the factor “transaction climate” related to the four organizations that companies deal with, customers, suppliers, carriers and 3rd Party logistics provider companies, while for each one of the four were investigated separately 5 features: Commitment, Reliability, Firm’s Satisfaction, Satisfactory Information Exchange and Long-lasting Relationships. Finally, through factor analysis, were expressed all 10 of the original 20 variables that describe the “transaction climate” in an agri-food firm, as linear combinations of the fewer and derived 2 component constructs/factors, leading firms in agri-food sector in Greece to adopt innovative IT and web-based technologies aiming to enhance e-business, supply chain management, organizational productivity, flexibility and competitiveness. Each factor is described with 5 questions of the questionnaire that load highly in each factor. The 2-factor model has to be further confirmed in a second sample. Keywords: factor analysis, innovative ICT, ICT adoption, supply chain, agri- food sector. _________________________________ Copyright ©by the paper’s authors. Copying permitted only for private and academic purposes. In: M. Salampasis, A. Matopoulos (eds.): Proceedings of the International Conference on Information and Communication Technologies for Sustainable Agri-production and Environment (HAICTA 2011), Skiathos, 8-11 September, 2011. 913 1 Introduction Information and Communication Technologies (ICTs) have been highly recognized lately in all aspects of human endeavors, primarily as a result of the improvements in the effectiveness and efficiency in business (Andreopoulou et al, 2008). Innovative ICT components influence organizational structure, firm strategy, information exchange, operational procedures, buyer and supplier relationships and bargaining power (Zioupou et al., 2010). E-business applications are changing relationships in the business world, linking businesses, consumers, building new models and communities. Companies and individuals have become more familiar to do business as and when they like, therefore conventional companies in every area of interest are increasingly searching for internet-enabling their products and services (Krueger & Swatman, 2004). Traditional manufacturing and service environments have been transformed into more physically distributed enterprise environments, which include supply chains, e- commerce and virtual enterprises (Gunasekaran & Ngai, 2007). Enterprises are now required to come up against multiple and significant challenges, arising from market globalization, increasing competitiveness among businesses and a constantly changing business environment. The concept of supply chain is an area of growing interest, both for the scientific community and for businesses worldwide. This growing interest accounts for the existence of a multitude of definitions and approaches, by many authors and from different points of view. Supply chain is defined as “Product life cycle processes comprising physical, information, financial, and knowledge flows whose purpose is to satisfy end-user requirements with physical products and services from multiple, linked suppliers” in “Handbook of Supply Chain Management” (Ayers, 2006) According to APICS Dictionary (Blackstone, 2008), supply chain is the global network used to deliver products and services from raw materials to end customers through an engineered flow of information, physical distribution, and cash. In addition, the definition of supply chain management includes design, planning, execution, control, and monitoring of supply chain activities with the objective of creating net value, building a competitive infrastructure, leveraging worldwide logistics, synchronizing supply with demand, and measuring performance globally. According to Robinson and Malhotra (2005), management of the supply chain poses challenges such as the building of trust and cooperation between the chain’s parts, the ability to recognize optimum practices to help align and accomplish its processes, and successfully implement information technologies that will lead to efficiency and quality throughout the supply chain. In recent decades, the acknowledgment of supply chain as a key area for business success has been a great change and challenge for the businesses’ operation. In many cases, firms’ ability to compete was linked to their ability to interact with others. Over the years, many authors have found an increased necessity for cooperation, 914 recognizing the establishment of lasting partnerships with suppliers, at different levels of the supply chain, as a mean of creating a more efficient and responsive supply chain. The partnership includes organizations and companies working together on a level beyond simple commercial relations. In terms of supply chain collaboration means, members of the chain involved in coordination activities exceed the limits of own business (Bowersox, 1990). Nowadays, organizations have realized that real improvement cannot come from individual business practices anymore, but from cooperative action between organizations. Thereby, organizations can expand their boundaries as individual firms. Changes in the organizational characteristics of firms involve changes at a functional level. The relationship among businesses has been transformed from simple transactional relationships, to cooperation characterized by smooth communication and extensive sharing of information. The employ of network technology within e-business results into the elimination of the required cost and time for the transactions, yet the gap between the production site and the final users of the products can be bridged using the Internet. Moreover, Pan, Gunasekaran & McGaughey in their recent paper explore the impact of company size on an important financial consideration affecting the decision to adopt e-business in international trade, and they assert that firm size will influence the choice of payment method in global e-commerce (Pan et al., 2006). Moreover, the role of managers has changed from managing physical assets and people to managing knowledge assets in digital enterprise environments and it has become critical to look into the management function and the role of managers in the so-called 'digital enterprise' environment (Gunasekaran & Ngai, 2007). The agri-food sector is an key-sector, which includes organizations from both the food industry (processing plants, manufacturers, wholesalers, retailers, catering companies, etc.) and the agricultural sector (farmers, producer groups, cooperatives, suppliers of agricultural raw materials, etc.). Vorst (2001) distinguishes two main types of supply chain in the agri-food sector: 1) Supply chain for fresh agricultural products, such as fresh fruit, vegetables and flowers 2) Supply chain for processed foods, such as deserts, canned goods and more. However, the distinction cannot be absolute, since in many cases, a supply chain may be a subtotal of another one, and partners may differ from a supply chain to another. Certain particular characteristics of agri-food sector however, prevent the extensive employ of ICT. The resistance in the change, the attachment in tradition, the lack of familiarity with the technology, the different nature of the rural products and transactions are some of the issues that differentiate and prevent the integration of the new practices of electronic business in the rural sector (Andreopoulou et al., 2008). 1.1 Theoretical model The theoretical model proposed by Patterson et al. (2003), quotes seven factors important for the adoption of innovative ICT by organizations. 915 The 1st factor is “organization’s size”, for many researchers have concluded that firms’ size influences their decision to adopt Innovative ICT. However, different opinions have been expressed as far as the direction of this relationship is concerned. Theoretically, larger organizations have the financial and technology resources to invest in new technologies and absorb the associated risks (Grover & Goslar, 1993). However, there is evidence that smaller firms are more flexible, and consequently more likely to adopt new information technologies (Patterson et al., 2003). Table 1. Variables used to measure “transaction climate” Variable Question V1 Commitment between customers and firm V2 Reliable customers V3 Firm’s satisfaction with customers V4 Satisfactory information exchange with customers V5 Long-lasting relationships with customers V6 Commitment between suppliers and firm V7 Reliable suppliers V8 Firm’s satisfaction with suppliers V9 Satisfactory information exchange with suppliers V10 Long-lasting relationships with suppliers V11 Commitment between carriers and firm V12 Reliable carriers V13 Firm’s satisfaction with carriers V14 Satisfactory information exchange with carriers V15 Long-lasting relationships with carriers V16 Commitment between 3rd party logistics providers and firm V17 Reliable 3rd party logistics providers V18 Firm’s satisfaction with 3rd party logistics providers V19 Satisfactory information exchange with 3rd party logistics providers V20 Long-lasting relationships with 3rd party logistics providers The 2nd factor is “organizational structure” that occupies the majority of researchers, regarding the adoption of innovative ICT. The approach of Bowersox and Daugherty (1995) reasons that organizations which have adopted a more transparent, flatter and more decentralized structure are expected to adopt more innovative technologies in order to improve both internal and external communication and coordination. Past performance is the 3rd factor that seems to have an impact on organizations decision to adopt or not innovative ICT. Companies that have been successful in their past performance are expected to be more reluctant to change their strategies by adopting new and possibly risky new technologies (Clemons et al., 1996). Therefore, less successful companies are more likely to adopt innovative ICT in order to improve their performance. The 4th factor mentioned is the “integration of supply chain management strategy into the overall strategy of the organization”. According to Bowersox and Daugherty 916 (1995), the successful implementation of ICT depends on its consistency with the overall corporate strategy, and this consistency can lead to the overall firm’s success. An inter-organizational factor that can lead an organization to adopt supply chain technology is “enacted power by supply chain partners” (Premkumar et al., 1997) or by the industry. For example, a supply chain partner can either encourage or coerce the company to adopt some particular technology. This kind of pressure is usually exerted in order to improve information flow and communication between organizations. However, such a pressure can sometimes lead to extensive organization risk or business loss. Furthermore, Reekers and Smithson (1994) claim that the initiating firm obtains more benefits than the follower (Table 1). Past research reveals another important factor, as many researchers focus on the “transaction climate” between partners, which represents the relationships and social elements between organizations (Patterson et al., 2003). Thus, a favorable “transaction climate” combined with enduring and trusting relationships between organizations (customers, suppliers, carriers and 3PL provider companies), is assumed to be a factor encouraging companies adopt innovative information technologies (Konsynski & McFarlan, 1990) (Table 1). The last factor researched in the present study is “environmental uncertainty”. Previous research has shown that environmental uncertainty is positively related to a greater need for innovation (Ettlie, 1983), and the consequent adoption of new information technologies. According to Ahmad and Schroeder (2001), an uncertain environment requires more frequent exchange of information between business partners so that activities can be prioritized as changes occur and delivery expectations met, and demands faster and more accurate decisions. In a recent research (Zioupou et al., 2010) it was identified that the critical factors, relating to the adoption of ICT, are company’s size and the integration of the supply chain strategy pursued by the company into the overall corporate strategy. As far as the first factor is concerned, larger companies seem more likely to adopt new information technologies in their supply chain management. Regarding the second factor, the consistency of the supply chain management strategy with the overall corporate strategy appears to be a prerequisite for the successful implementation of new information technologies. This paper through factor analysis, tested a 20-variable model regarding the “transaction climate” leading firms in the agri-food sector in Greece to adopt innovative IT and web-based technologies aiming to enhance e-business, supply chain management, organizational productivity, flexibility and competitiveness. Finally, a model will be confirmed having an acceptable fit, including correlated factors. 2 Methodology The survey was conducted by sending questionnaires to businesses, via e-mail. Furthermore, a structured questionnaire, including questions representing both the independent and the dependent variables, was used to collect the necessary data in order to investigate the relationships among the variables. Businesses included in the survey’s sample were agri-food sector, located in Greece that manages their supply chain activities using innovative ICT. These firms were identified by the companies, 917 which provide those information technologies. A prerequisite for participation in this research was the adoption of any information technology for the management of supply chain activities. The innovative ICT proposed by the existing literature and included in the present study are listed below: 1) Supply Chain Management (SCM) 2) Customer Relationship Management (CRM) 3) Enterprise Resource Planning (ERP) 4) Warehouse Management Systems (WMS) 5) Manufacturing Execution Systems (MES) 6) Transportation Management Systems (TMS) 7) Bar Coding Technology 8) Radio Frequency (RF) Identification systems 9) Geo-coded Tracking Systems 10)Electronic Commerce Technologies Therefore, the questionnaire included questions referring to the size of the organization, past performance of the company, the organizational structure, the external environment, the relations between partners of the supply chain, and finally questions about the adoption of new information technologies by the companies. The majority of questions were formulated using a 5-point Likert scale, where the respondents were requested to indicate the degree of implementation of information technologies or agreement with the given statements. There were also some open- ended questions, where the respondents were free to formulate their own answers. The research is based on the theoretical model proposed by Patterson et al. (2003), and attempts to empirically apply this model in the case of Greek agri-food sector. Past research reveals another important factor, as many researchers focus on the “transaction climate” between partners, which represents the relationships and social elements between organizations (Patterson et al., 2003). To investigate the 5th factor in Patterson’s model «transaction climate» affecting companies in deciding whether to adopt new ICTs in their supply chain management, a total of 20 factors were proposed to determine the variable. These 20 factors were related to the four organizations that companies deal with. These are customers, suppliers, carriers and 3PL provider companies. For each one of the four organizations, were investigated separately, the following factors: - Commitment, - Reliability - Firm’s satisfaction - Satisfactory information exchange - Long-lasting relationships They resulted in 20 variables, named “x”, x=1,..,20, presented in Table 1. Sample companies had to define whether they were satisfied by the proposed factors, describing relations with their partners. To do so, a graded Likert scale was used, where 1 represents 'not at all ', 2 'very little', 3 'somewhat', 4 'a significant amount' and 5 represents 'to a great extent'. Further, through factor analysis, it was tested the 20-factor model regarding the “transaction climate” leading firms in the agri-food sector in Greece to adopt 918 innovative IT and web-based technologies, aiming to reduce the number of factors describing the variable «transaction climate». Factor analysis is a technique, which seeks a simpler structure for a complex set of multivariate 20 variables. The emphasis of factor analysis is to explain the co- correlation (or covariance) of the original 20 variables. The intent of factor analysis is to express all p of the original 20 variables as linear combinations of the fewer, derived F factors. Initially, the correlation matrix was estimated using PASW Statistics, aiming to check inter-correlations between variables and exclude variables that represent questions from the test. If questions measure the same underlying dimension then it is expected to correlate with each other. Variables that do not correlate with any other variables should be considered excluded before running the analysis. The opposite problem is when variables correlate too highly. It is important to check for variables that are highly correlated (multicollinearity) or perfectly correlated (singularity). A KMO and Bartlett's Test was measured. Then, the Kaiser rule was used to decide the number of extracted factors in parallel with a Scree plot. The first i factors are chosen where with eigenvalues (l) greater than the average eigenvalue (for factors analysed by the R matrix, the average l is 1) in parallel with using a Scree test for the scree plot of eigenvalues of R such that if the graph drops sharply to a shallower- slope line, we choose i as the number of eigenvalues before the shallow-slope line; Communality, or achieved communality, (h) was also estimated that is that part of the variance which is accounted for by that factor. It is the common variance of the common factors. Rotation was estimated using Varimax method. 3 Results The companies representing agri-food sector are in their majority public limited companies (SA), while some of them state their activities and are Industrial and Commercial Limited Companies. The majority of firms are located in the Prefecture of Attica (12 companies), while five of them are located in the Prefecture of Thessaloniki, and the last 5 are located in other prefectures of Greece (Figure 1). 23% Attica Thessaloniki 54% Other prefectures 23% Figure 1. Geographical location of sample firms The following table summarizes the characteristics of the companies that participated in the sample. 919 Table 2. Summary of the companies’ characteristics Number Average Minimum Maximum Average Legal Form of Turnover Turnover Turnover Number of Firms (in €) (in €) (in €) Employees SA 14 146,750,842 2,557,874 714,100,000 504 Industrial and 8 119,636,803 10,975,338 630,232,000 622 Commercial SA The number of the companies’ employees was the main criterion for measuring the firms’ size, which is hypothesized to be one of the most important factors affecting the adoption of innovative information technologies. The classification of companies regarding the number of employees is presented in Figure 2. It should also be mentioned that businesses’ turnover varies widely, as can be seen in the Table 2. 14% 2 27% 1-100 18% 101-250 32% 251-500 501-1000 >1001 Figure 2. Classification of companies based on their number of employees The geographical scope of the sample companies is not limited only to the prefecture of their location. Twelve of the companies operate globally, seven of them at a national level, while only three companies are limited to a regional level, which includes the prefecture of company’s location and its neighboring prefectures. The wide geographic spread of businesses action also states the existence of fierce competition. 1.1 Results of factor analysis Initially in factor analysis, inter-correlations between variables are checked. At this early stage, we look to eliminate any variables do not correlate to any other variables or that correlate very highly with other variables (R<0.9). Also, multicollinearity can be detected by looking at the determinant of the R-matrix. Using the correlation matrix, the pattern of relationships was checked. In variables V2, V3, V5, V6, V10, V15, V17, V18, V19, V20, which represent questions in the questionnaire (Table 1), the majority of significance values in the correlation matrix, are greater than 0.05, hence a problem can arise due to singularity of data. Moreover, the initial determinant, while running the analysis with all 20 variables is 7.13x10-14 which is less than the necessary value 0.00001. There is need to eliminate questions from the questionnaire, that are attributed to variables. By 920 successive eliminations of the above variables, the determinant is found to be 3.96x 10-5, which is acceptable. However, the questions from the questionnaire attributed to the following variables: V2, V3, V5, V6, V10, V15, V17, V18, V19, V20 are excluded from the questionnaire before running the analysis Further, the KMO Kaiser-Meyer-Olkin test that measures sample adequacy is 0,791 being between 0 and 1. A value close to 1 indicates that patterns of correlations are relatively compact so factor analysis should yield distinct and reliable factors. Kaiser recommends that values between 0.7 and 0.8 are good while values close to 1 are superb. Bartlett’s test of sphericity is a measure that tests the null hypothesis that the correlation matrix is an identity matrix. For these data, Bartlett’s test is highly significant (p<0,001) and therefore factor analysis is appropriate. In Table 3, the total variance explained by factors is presented, thus we can decide on the final factor to be extracted, based on eigenvalues greater than 1, as recommended by Kaiser’s rule. Table 3. Total Variance Explained Initial Eigenvalues Extraction Sums of Rotation Sums of Squared Comp Squared Loadings Loadings onent Total % of Cumulative Total % of Cumulative Total % of Cumulative Variance % Variance % Variance % 1 6,503 65,026 65,026 6,503 65,026 65,026 3,902 39,018 39,018 2 1,080 10,797 75,824 1,080 10,797 75,824 3,681 36,806 75,824 3 ,764 7,645 83,468 4 ,545 5,446 88,915 5 ,374 3,744 92,659 6 ,235 2,347 95,005 7 ,194 1,944 96,950 8 ,150 1,495 98,445 9 ,105 1,049 99,494 10 ,051 ,506 100,000 Extraction Method: Principal Component Analysis. Table 3 lists the eigenvalues associated with each linear component (factor) before extraction, after extraction and after rotation. Factors with eigenvalues greater than 1 are extracted, which results in 2 factors. Rotation optimizes the factor structure and the relative importance of the 2 factors is equalized. Finally, eigenvalues after rotation associated with each factor represent the variance explained by that particular linear component, having excluded other factors also presented in percentage of variance explained. Factor 1 explains 65,026% of total variance explained and the 2 factors explain together 75,824%. Communalities in the extraction column reflect the common variance in the data structure. The amount of variance in each variable that can be explained by the 921 retained factors is represented by the communalities after extraction. According to Kaiser criterion the average of the communalities should be more than 0.7 after extraction when there are less than 30 variables in the analysis and we have a small sample. However, a Scree plot is used to assess the final number of factors (Figure 3). Figure 3. Scree plot It is evident in Figure 3 that the curve begins to tail after 2 factors. Table 5 presents the rotated component matrix, where rotation method was Varimax with Kaiser normalization. It is a matrix of the factor loadings for each variable onto each factor. The variables are listed in the order of size of their factor loadings. 922 Table 5. Rotated Component Matrix Component 1 2 V16 ,858 V12 ,804 ,378 V7 ,801 ,384 V13 ,765 ,473 V1 ,544 ,494 V8 ,872 V4 ,316 ,809 V9 ,461 ,782 V11 ,608 ,684 V14 ,557 ,655 In Table 6 are presented the questions of the questionnaire that load highly in factors 1 and 2. These two constructs are sub-components of “climate change” in the firm. Table 6. Questions loaded in the 2 extracted factors Factor 1. Is there a strong commitment between firm and the customers? Is there a strong commitment between the firm and 3 rd Party Logistics suppliers?. Is the firm satisfied with the level of cooperation with carriers? Are the carriers reliable? Are the suppliers reliable? Factor 2. The exchange data system between the firm and suppliers is satisfactory?? The exchange data system between the firm and carriers is satisfactory?? The exchange data system between the firm and customers is satisfactory?? Is the firm satisfied with the level of cooperation with suppliers? Is there a strong commitment between firm and carriers? Also, component scores coefficient matrix is presented in Table 7. 923 Table 7. Component Score Coefficient Matrix Component 1 2 V1 ,095 ,064 V4 -,146 ,328 V7 ,272 -,096 V8 -,309 ,464 V9 -,060 ,256 V11 ,055 ,145 V12 ,276 -,100 V13 ,218 -,032 V14 ,039 ,149 V16 ,439 -,315 4 Conclusion Using factor analysis we have expressed all 10 of the original 20 variables that describes the “transaction climate” in an agri-food firm, as linear combinations of the fewer, derived 2 factors. The 2-factor model has to be further confirmed in a second sample. Decisions regarding the adoption of new information technologies in supply chain management in the Greek food and drink industry are not affected by most of the factors investigated by many researchers in the past. Nevertheless, the following can be summarized. Businesses sense the risk of development and its speed. In their effort to move forward to the latest technologies, they make choices and decide to adopt those technologies that would make the company more profitable, or, will help, at least, in this uncertain period, to maintain the existing profit. References 1. Ahmad, S. and Schroeder, R. G. (2001) The impact of electronic data interchange on delivery performance. Production and Operations Management, Vol. 10, No. 1, p. 16-30 2. Andreopoulou, Z., Tsekouropoulos, G., Koutroumanidis, T., Vlachopoulou M. & Manos, B. (2008) Typology for e-business activities in the agricultural sector. International Journal of Business Information Systems, Vol.3, No 3, p.231-251 3. Ayers, J. B. (2006) Handbook of Supply Chain Management. 2nd ed. Boca Raton: Auerbach Publications. 4. Blackstone, J. H. Jr. (2008) Advancing Productivity, Innovation, and Competitive Success (APICS) Dictionary. 12th ed. USA: APICS 5. Bowersox, D. and Daugherty, P. (1995) Logistics paradigms: the impact of information technology. Journal of Business Logistics , Vol. 16, No. 1, p. 65-80 924 6. Bowersox, D. (1990) The strategic benefits of logistics alliances. Harvard Business Review , Vol. 68, No. 4, p. 36-43 7. Clemons, E. K., Croson, D. C. & Weber, B. W. (1996) Market dominance as a precursor of a firm's failure: emerging technologies and the competitive advantage of new entrants. Journal of Management Information Systems, Vol. 13, No. 2, p. 59-75 8. Ettlie, J.C. (1983) Organizational policy and innovation among suppliers to the food processing sector. The Academy of Management Journal, Vol. 26, No. 1, p. 27-44 9. Friedman, M. (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association, Vol. 32, No. 200, p. 675-701 10. Grover, V. and Goslar, M. D. (1993) The initiation, adoption, and implementation of telecommunications technologies in US organizations. Journal of Management Information Systems , Vol. 10, No. 1, p. 141-160 11. Gunasekaran, A. and Nagai, E.W.T. (2007) Managing digital enterprise. International Journal of Business Information Systems, Vol. 2, No.3, p. 266-275 12. Konsynski, B. R. and McFarlan, W. F. (1990) Information partnerships-shared data, shared scale. Harvard Business Review, Vol. 68, No. 5, p. 114–120 13. Krueger, C.C. and Swatman, P.M.C. (2004) Developing e-business models in practice: the case of the regional online newspaper. International Journal of Information Technology and Management, Vol. 3, No.2/3/4, p. 157–172 14. Pan, X., Gunasekaran, A. & Mcgaughey, R.E. (2006) Global e-business: firm size, credibility and desirable modes of payment. International Journal of Business Information Systems, Vol. 1, No.4, p. 426-438 15. Patterson, K. A., Grimm, C. M. & Corsi, T. M. (2003) Adopting new technologies for supply chain management. Transportation Research, Part E, 39, p. 95-121 16. Premkumar, G., Ramamurthy, K. & Crum, M. R. (1997) Determinants of EDI adoption in the transportation industry. European Journal of Information Systems, Vol. 6, No. 2, p. 107–121 17. Reekers, N. and Smithson, S. (1994) EDI in Germany and UK: Strategic and operational use. European Journal of Information Systems, Vol. 6, No. 2, p. 161– 172 18. Robinson, C. J. and Malhotra, M. K. (2005) Defining the concept of supply chain quality management and its relevance to academic and industrial practice. International Journal of Production Economics, 96, p. 315-337 19. Vorst, van der G. A. J., Dijk, S. J. Van & Beulens, A. J. M. (2001) Leagile supply chain design in food industry: an inflexible poultry supply chain with high demand uncertainty. The International Journal on Logistics Management, Vol.12, No. 2, p. 73-85 20. Zioupou, S., Manos, B. & Papanagiotou, P. (2010) Adopting new information technologies in supply chain management in the Greek food industry. 6th HSSS Conference, Mytilene 925