4.3 Research Approach
This article belongs to the deductive research approach
because it incorporates the evolution of a theory, which is
later applied to a severe test throughout a list of suggestions
(Saunders et al. 2012). More specifically, the study moves
from general presentation to specific, proofing the belonging
of the research into the deductive approach.
4.4 Research Design and Data Collection
Research design is the “blueprint” for the data collection,
measurement and hence the study of the data (Cooper and
Schindler 2008). This study is read as an exploratory
research. Having set the research design, designing and
sampling the data collection is the next step which was done
based on a secondary data collection method from the RICA
database which is the main reference of all the accounting
information of the Italian agricultural companies.
5 Analysis: Data Base and Analytical
Procedures
With these assumptions, due to the limits highlighted for the
models most frequently used to assess the creditworthiness of
farms, the contribution of this work is to propose an innovative methodological approach capable of overcoming some
current critical issues (Mester 1997). To efficiently evaluate
the creditworthiness of the national agricultural fabric and
provide some recommendations for the implementation of a
rating operating system based on the RICA database, it was
necessary to select only the companies present in the archive
for all the years considered in the research.
Accordingly, the companies of the sub-sample generated
were then divided into three classes, using as a criterion the
total composition of the PLV (Gross Sellable Production) of
each company according to the activity carried out:
1. “Companies specialized in various crops”, activities
attributable for more than 60% to a specific crop;
2. “Companies specialized in breeding”, activities attributable for more than 60% to breeding;
3. “Non-specialized companies”, diversified companies,
that did not show any prevailing activity (60% higher
than the total of the PLV).
The analysis showed that some companies, over time,
have changed the type of activity carried out (e.g., companies specialized in breeding in 2004, which have become
nonspecialized in 2005). This condition has therefore
determined, with the same number of companies included in
the sample, a variation in the time of distribution of the same
in the three different classes.
Aware of the description of the individual variables
contained in the RICA dataset, the authors proceeded to
select the variables that are considered to be less “alterable”
in the preparation of the financial statements; for instance the
cash item, which very often becomes the financial container
to which imputation of activities of a different nature from a
financial point of view. Consequently, this choice must be
associated with the need to obtain results that are not very
sensitive to “strategic” accounting behavior.
Resulting from the above explained setting, the variables
selected and used for the current study are the following:
• Sup_TOT, total area available for the company;
• Cap_FOND_TOT, total land capital;
• Inv_FOND_NEW, new land investments;
• Cap_ESE_PROP, working capital in ownership.
With these variables, indicators were constructed,
instrumental to the proposed formalization for the assessment of creditworthiness.
The variable A and the indicator designated by B, C, and
D have been identified as follows:
A: Sup_TOT. This variable, expressed in ha, highly
reflects the level company’s equity, since the farms that have
more surfaces available, should ensure greater strength.
• Assigned score 1 for values between 0 and 10;
• assigned score 2 for values between 10.01 and 30;
• assigned score 3 for values greater than 30.
The increasing score assigned is proportional to the area
available to farms to carry out their business, an implicit
guarantee of company soundness.
B: Cap_FOND_TOT/Sup_TOT. Ratio between land and
land available, expressed in thousands of euros: companies
that have made structural investments over time.
• Assigned score 1 for values between 0 and 15,000;
• assigned score 2 for values between 15.001 and 30.000;
• assigned score 3 for values greater than 30,000.
The usefulness of this index must be identified in the fact
that the structural investment capacity appears directly
related to the financial solidity of the company where it can
rely on the investment of the resources generated by
self-financing operations and by the belief of the entrepreneurs in equipping the company with its own assets.
C: Inv_FOND_NEW/Cap_FOND_TOT. Relationship
between investments and landed capital: companies that
Measurement of Financial and Asset …
113
This article belongs to the deductive research approach
because it incorporates the evolution of a theory, which is
later applied to a severe test throughout a list of suggestions
(Saunders et al. 2012). More specifically, the study moves
from general presentation to specific, proofing the belonging
of the research into the deductive approach.
4.4 Research Design and Data Collection
Research design is the “blueprint” for the data collection,
measurement and hence the study of the data (Cooper and
Schindler 2008). This study is read as an exploratory
research. Having set the research design, designing and
sampling the data collection is the next step which was done
based on a secondary data collection method from the RICA
database which is the main reference of all the accounting
information of the Italian agricultural companies.
5 Analysis: Data Base and Analytical
Procedures
With these assumptions, due to the limits highlighted for the
models most frequently used to assess the creditworthiness of
farms, the contribution of this work is to propose an innovative methodological approach capable of overcoming some
current critical issues (Mester 1997). To efficiently evaluate
the creditworthiness of the national agricultural fabric and
provide some recommendations for the implementation of a
rating operating system based on the RICA database, it was
necessary to select only the companies present in the archive
for all the years considered in the research.
Accordingly, the companies of the sub-sample generated
were then divided into three classes, using as a criterion the
total composition of the PLV (Gross Sellable Production) of
each company according to the activity carried out:
1. “Companies specialized in various crops”, activities
attributable for more than 60% to a specific crop;
2. “Companies specialized in breeding”, activities attributable for more than 60% to breeding;
3. “Non-specialized companies”, diversified companies,
that did not show any prevailing activity (60% higher
than the total of the PLV).
The analysis showed that some companies, over time,
have changed the type of activity carried out (e.g., companies specialized in breeding in 2004, which have become
nonspecialized in 2005). This condition has therefore
determined, with the same number of companies included in
the sample, a variation in the time of distribution of the same
in the three different classes.
Aware of the description of the individual variables
contained in the RICA dataset, the authors proceeded to
select the variables that are considered to be less “alterable”
in the preparation of the financial statements; for instance the
cash item, which very often becomes the financial container
to which imputation of activities of a different nature from a
financial point of view. Consequently, this choice must be
associated with the need to obtain results that are not very
sensitive to “strategic” accounting behavior.
Resulting from the above explained setting, the variables
selected and used for the current study are the following:
• Sup_TOT, total area available for the company;
• Cap_FOND_TOT, total land capital;
• Inv_FOND_NEW, new land investments;
• Cap_ESE_PROP, working capital in ownership.
With these variables, indicators were constructed,
instrumental to the proposed formalization for the assessment of creditworthiness.
The variable A and the indicator designated by B, C, and
D have been identified as follows:
A: Sup_TOT. This variable, expressed in ha, highly
reflects the level company’s equity, since the farms that have
more surfaces available, should ensure greater strength.
• Assigned score 1 for values between 0 and 10;
• assigned score 2 for values between 10.01 and 30;
• assigned score 3 for values greater than 30.
The increasing score assigned is proportional to the area
available to farms to carry out their business, an implicit
guarantee of company soundness.
B: Cap_FOND_TOT/Sup_TOT. Ratio between land and
land available, expressed in thousands of euros: companies
that have made structural investments over time.
• Assigned score 1 for values between 0 and 15,000;
• assigned score 2 for values between 15.001 and 30.000;
• assigned score 3 for values greater than 30,000.
The usefulness of this index must be identified in the fact
that the structural investment capacity appears directly
related to the financial solidity of the company where it can
rely on the investment of the resources generated by
self-financing operations and by the belief of the entrepreneurs in equipping the company with its own assets.
C: Inv_FOND_NEW/Cap_FOND_TOT. Relationship
between investments and landed capital: companies that
Measurement of Financial and Asset …
113