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|id=Vol-1152/paper81
|storemode=property
|title=Assessing "Transaction Climate" Influencing the Adoption of Innovative ICT and e-Business In the Greek Agri-food Sector
|pdfUrl=https://ceur-ws.org/Vol-1152/paper81.pdf
|volume=Vol-1152
|dblpUrl=https://dblp.org/rec/conf/haicta/ZioupouAMK11
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==Assessing "Transaction Climate" Influencing the Adoption of Innovative ICT and e-Business In the Greek Agri-food Sector==
https://ceur-ws.org/Vol-1152/paper81.pdf
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,
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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.
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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
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(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.
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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
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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.
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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.
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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.
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