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.
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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
Innovative ICT components influence organizational structure, firm strategy,
information exchange, operational procedures, buyer and supplier relationships and
bargaining power
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
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”
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,
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
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
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
The theoretical model proposed by Patterson et al. (2003), quotes seven factors important for the adoption of innovative ICT by organizations.
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
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
The 4th factor mentioned is the “integration of supply chain management strategy into the overall strategy of the organization”. According to Bowersox and Daugherty (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”
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
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
In a recent research
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
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, 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 openended 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
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 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 cocorrelation (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 shallowerslope 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
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% 23% 54%
Attica Thessaloniki
Other prefectures Legal Form
SA Industrial and 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. 2 32% 14% 27% 18%
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.
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 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.
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 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). It is evident in Figure 3 that the curve begins to tail after 2 factors. V16 V12 V7 V13 V1 V8 V4 V9 V11 V14
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.
Is there a strong commitment between firm and the customers? Is there a strong commitment between the firm and 3rd Party Logistics suppliers?. Is the firm satisfied with the level of cooperation with carriers? Are the carriers reliable? Are the suppliers reliable?
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. V1 V4 V7 V8 V9 V11 V12 V13 V14 V16 1
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.