implementing logistic regression analysis to identify...

Implementing Logistic Regression Analysis to Identify Incentives for Agricultural Cooperative Unions to adopt Quality Assurance Systems

Implementing Logistic Regression Analysis to Identify Incentives for Agricultural Cooperative Unions to adopt Quality Assurance Systems

Achilleas Kontogeorgos*, Panagiota Sergaki1, Euthimios Migdakos, & Anastasios Semos1

Abstract

The purpose of this paper is to examine the factors that determine the decision made by agricultural cooperative unions and businesses to implement a Quality Assurance System (QAS) such as ISO 9001 and HACCP. A questionnaire was distributed to 122 second-degree agricultural cooperative unions and businesses throughout Greece. A total of 88 cooperatives responded to the survey, across a range of locations, representing a response rate of approximately 70 per cent. Results reveal that 43 have implemented a QAS. The findings are reported with logistic regression analysis to profile the criteria that affect the incentives for quality assurance systems adoption. However, a significant percentage of cooperatives reported that they either do not know what the QAS are or that they know of them but they have not implement one. The findings derived from the logistic regression analysis generally suggest that there is a whole range of criteria such as size, perceptions concerning QAS and the cooperatives’ activities that affect the degree of a coop’s involvement in a QAS. The research explores the incentives for implementing a QAS, which can significantly aid a coop to draw up a development strategy. However, the research was conducted only on second decree agricultural cooperative unions. Therefore, it may not adequately illustrate the incentives of investor owned firms with similar characteristics. This is the first effort that has been made to detect the situation of the Greek cooperative unions and businesses with respect to quality assurance systems and it provides a potential base to carry out further research on the incentives that can lead other small and medium size firms to adopt a quality assurance system.

JEL codes: Q130, C250

Key Words: Agricultural Cooperative Unions, Logistic Regression Analysis, Quality Assurance Systems

1. Introduction

In the existing literature there are a lot of studies examining, on the one hand, the relationship between ISO 9001 and its effect on company performance (either operational and business performance or financial performance), or its relationship with the TQM implementation (for more details about ISO papers see: Van der Wiele et al., 2005). On the other hand, the majority of papers examining the HACCP implementation basically focuses on the advantages generated by the HACCP adoption such as the minimization of hazards that can take place in foods (i.e.. microbiological, chemical or natural hazards; e.g. Henson et al., 1999, Maldonado et al., 2005, Eves & Dervisi, 2005). An important number of studies have generally examined, how effective a food safety system is (e.g. Van Der Spiegel et al., 2005, Zugarramurdi et al., 2007, Cormier et al., 2007). Consequently, there have only been a few studies with the exception of Herath et al., (2007) who investigated the incentives for the Canadian food processing sector to adopt HACCP and food safety controls, which have exclusively examined the relationship between the incentives to adopt a QAS and the firms’ characteristics.

The adoption of a quality system can play an important role in a firm’s performance, profits and costs. Nevertheless, specific impacts differ from one firm to another depending on their characteristics and the activities in which they are engaged (e.g. Holleran et al., 1999; Henson and Holt, 2000). Thus, the importance of different incentives is likely to vary among the firms with respect to their adoption of quality systems. The main objective of this paper is to illustrate whether there is a relationship between firm characteristics and the trend to adopt quality systems in the context of the Greek agricultural cooperative unions.

It is assumed that food companies, such as the Greek agricultural cooperative unions, strive to maintain or improve both food safety and quality attributes and such efforts are closely interrelated and are most likely managed as a whole. If there are such synergistic effects between food safety assurance systems and quality management systems, it is assumed that firms are more likely to adopt a broader array of quality systems to improve both food safety and quality attributes. Thus, public interventions that are exclusively promoting food safety improvements, basically through HACCP adoption, should recognize such synergies and perhaps broaden the scope of intervention to facilitate these effects (Herath et al., 2007).

Henson and Holt (2000), suggested that it is not possible to generalize the impacts of a set of incentives on the level and/or type of food safety controls that are adopted by particular firms, since they have different characteristics and objectives that vary according to the type of product manufactured and the environment in which they operate. The incentive variation of individual firms is probably a result of the relationship between the firms’ characteristics and their propensity to adopt quality systems. Indeed, there is evidence from other industries of a relationship between firm characteristics and adoption of certain business practices; for example, Shavell, (1987) suggests that a firm’s incentives to supply safe products will be affected by its size, organization, and structure of its market.

The paper is organized as follows. In the next section, a methodological consideration about logistic regression analysis is presented illustrating how a company would decide to adopt a new strategy i.e. the implementation of a quality assurance system. Then, the data collection and description of the empirical variables used in the analysis are outlined. The following part presents the results of the analysis followed by the summary of the results and its discussion.

2. Methodological considerations

According to Karshenas and Stoneman (1993), the expected profit gain by adopting a new technology in a firm in a given industry will depend on the characteristics of the firm (rank effect), the number of other adopters (stock effect), and the firm’s position in the order of adoption among the competitors (order effect). While the stock effects and order effects are important in determining the dynamic diffusion path of technology adoption, rank effect would determine the cross-sectional difference in new technology adoption behaviour among firms (Madlener and Wickart, 2004). The categorization of firms into “adopters” and “non-adopters” is based on the dichotomous outcome of the adoption decision, which characterizes the dependent variable (Y). Thus, a firm is defined as an “adopter” where Yi = 1 or as a “non-adopter” where Yi = 0

In this paper, the binary logistic regression analysis will be used to classify the agricultural cooperatives in adopters and “non-adopters”. Binary logistic regression is most useful in cases where we want to model the event probability for a categorical response variable with two outcomes. Since the probability of an event (QAS adoption or not) must lie between 0 and 1, it is impractical to model probabilities with linear regression techniques, because the linear regression model allows the dependent variable to take values greater than 1 or less than 0. The logistic regression model is a type of generalized linear model that extends the linear regression model by linking the range of real numbers to the range 0-1. In this study, the adoption decision is based on a set of cooperative level incentives, which are related with the cooperative’s specific characteristics. These characteristics would finally determine the cooperatives’ decision of whether or not to adopt a QAS. However, these characteristics could have a multiple or multidimensional effect on a cooperative’s decision. Thus, a given characteristic may be associated with many incentives related to the decision to adopt a QAS.

Using the logistic regression model, the probability of adopting a QAS can be described as:

πi=

i

z

e

-

+

1

1

where: pi is the probability that the ith case will adopt a QAS and zi is the value of the unobserved continuous variable for this ith case.

The model also assumes that Z is linearly related to the predictors (the cooperative’s characteristics). Thus, zi=b0+b1xi1+b2xi2+...+bpxip where xij is the jth predictor for the ith case, bj is the jth coefficient and p is the number of predictors.

Finally, the regression coefficients are estimated through an iterative maximum likelihood method. Table 1 presents the dependent and independent variables that are developed using the information collected in the research stage and Table 3 depicts the analysis used.

Table 1: Variable description

Variables

Description

Range

Mean

Std. Deviation

Dependent Variables

QAS Implementation

At least one HACCP or ISO 9001, implemented = 1*3

1

0,49

0,503

HACCP Implementation

HACCP implementation with certificate or in progress to

certify *1 = 1*3

1

0,43

0,498

ISO 9001 Implementation

ISO 9001 implementation with certificate or in progress to certify*1 = 1*3

1

0,35

0,480

Independent Variables (cooperatives’ characteristics)

Turnover (million €)

Turnover in million € (2006)

76,09

10,82

13,281

Exports (million €)

Export value in million € (2006)

61,49

3,51

10,282

Trademarks

Registered*2 trade marks = 1*3

1

0,41

0,494

Highly-educated personnel

Personnel with a university or a technical school degree

74

11,97

10,976

Commercial strategy to increase market share

Managers’ statement for their commercial strategy = 1*3

1

0,73

0,448

“QAS are just more bureaucracy”

Managers’ statement for the use

of a QAS= 1*3

1

0,72

0,503

“QAS are improvement tools”

Managers’ statement for the use

of a QAS = 1*3

1

0,53

0,454

Number of activities

Different activities and products

10

5,44

1,911

Dairy – cheese products

Dairy subsector = 1*3

1

0,20

0,406

Wine

Wine subsector = 1*3

1

0,14

0,345

Olive oil

Olive oil subsector = 1*3

1

0,33

0,473

Fruit & vegetables

Fresh Fruits and vegetable subsector =1*3

1

0,27

0,448

Sample size: 88 agricultural cooperative unions (2nd degree cooperatives)

*1 within a 6 months period since the survey according to cooperative managers’ statement

*2 Source: ICAP Business directories, 2006 (www.icap.gr)

*3 Otherwise = 0

3. Data analysis

The examined sample consists of the members of PASEGES – the Greek Federation of Agricultural cooperatives. One hundred and twelve agricultural cooperative unions (second degree cooperatives) and 14 cooperative businesses are the members of PASEGES (Pahellenic federation of agricultural cooperatives unions). A questionnaire was mailed to these agricultural cooperative businesses in order to determine which QAS they implemented and more specifically, to identify their perceptions about the implemented QAS. Finally, a valid response rate of 72,13% was achieved. This rate is quite satisfactory and representative of the entire population of agricultural cooperative unions and businesses.

Finally, the participating cooperatives were classified into 3 groups depending on the level of knowledge and application of the QAS:

1st group: 43 cooperatives (48,9%) that apply at least one QAS.

2nd group: 29 cooperatives (33,0%) that are familiar with the meaning of a QAS, but do not implement one.

3rd group: 16 cooperatives (18,1 %) that do not know the meaning of a QAS and consequently do not implement a QAS.

Table 2 presents the QAS implemented by the cooperatives. It is clear that the most widely spread quality system is ISO 9001 followed by HACCP. Thus, 38 cooperatives implement ISO 9001 and 31 cooperatives implement HACCP principles.

Table 2: The quality assurance systems implemented by the cooperatives

Quality Assurance System

Frequency

ISO 9001

38

ISO 14001

3

HACCP principles*

31

BRC

6

IFS

4

AGRO 2.1 & 2.2 & EUREPGAP

9

* Including the following standards: ELOT 1416, Codex Alimentarious and ISO 22000

The relationship between the implementation of ISO and HACCP must be examined in order to determine whether the implementation of these systems is independent. Table 3 illustrates the relation between the existence of HACCP and ISO in the cooperatives. Firstly, it must be mentioned that 45 cooperatives (51,1%) do not apply any QAS at all, while, from the remaining 43 cooperatives, 26 (29,5%) of them apply both ISO and HACCP. Twelve cooperatives implement only ISO, and 5 cooperatives implement only HACCP. The use of a X2 test can examine if the implementation of ISO 9001 is independent of the HACCP implementation. The Pearson Chi-Square value is 32,295 (with df=1) meaning that these 2 variables (HACCP implementation and ISO implementation) are not independent and the implementation (or not) of a QAS affects the implementation of the other. Upon calculating the index phi (2X2 table, phi value = 0,606, and significance 0,00) it can be concluded that there is a positive and relatively important relationship between HACCP and ISO implementation. This is probably due to the fact that in most cases an external consultancy agency takes over the implementation procedures for both HACCP and ISO 9001 systems. This is a common practice among the Greek food companies especially for HACCP (Semos and Kontogeorgos, 2007).

Table 3: The relationship between HACCP and ISO 9001

Without HACCP

With HACCP

Total

Without ISO 9001

45 (51,1%)

5 (5,7 %)

50 (56,8%)

With ISO 9001

12 (13,6%)

26 (29,5%)

38 (43,2%)

Total

57 (64,8%)

31 (35,2%)

88

4. Results Analysis

The main analysis was conducted in two stages. In the first stage, all the variables, describing the different cooperative characteristics selected for the analysis were used to identify their intervention with the implementation of a QAS (without examining if it is the ISO 9001 or the HACCP system). In the second stage of the analysis, a separate model was estimated for each QAS. Consequently, in order to illustrate each variable’s effect on cooperatives’ decision to implement a QAS, the entry method of variable selection of the logistic regression analysis was used. In this model the dependent variable takes on the value one (Y=1) if the cooperative implements HACCP or ISO 9001. The Statistical Package for Social Sciences (SPSS 14.00 for Windows) was used for the analysis of the results. The analysis results are presented in Table 4. The different types of R2 and the classification table (Table 5), where 4 out of 5 cases are correctly predicted, suggest that the model adequately fits the data.

Table 4: Logistic regression analysis for the cooperatives characteristics that affect a QAS implementation

Variables (coops characteristics)

B

S.E.

Statistic

Wald

Wald

Sig.

Exp(B)


0,163

0,071

5,239

0,022**

1,177

Exports (million €)

-0,121

0,075

2,589

0,108

0,886

Trademarks *1

2,945

0,976

9,104

0,003*

19,003

Highly - educated personnel

0,074

0,045

2,784

0,095*

1,077

Commercial strategy to increase market share*1

2,573

1,04

6,122

0,013**

13,103

“QAS are just more bureaucracy”*1

-2,317

1,015

5,208

0,022**

0,099

“QAS are improvement tools”*1

2,139

1,16

3,4

0,065***

8,494


-0,709

0,284

6,255

0,012**

0,492

Dairy – cheese products*1

2,802

0,989

8,024

0,005*

16,485

Wine*1

-0,535

0,986

0,295

0,587

0,585

Olive oil*1

0,673

0,782

0,74

0,390

1,959

Fruit & vegetables*1

0,931

0,826

1,269

0,260

2,537

Constant term

-2,709

1,737

2,432

0,119

0,067

R2 = 0,812 (Hosmer & Lemeshow), 0.462 (Cox & Snell), 0,616, (Nagelkerke)

Significance: * p<0.01, ** p<0.05, *** p<0.1

*1 Dummy variable: 0= No, 1 = Yes

Table 5: Classification table (α)

Observed

Predicted

Percentage Correct

Without a QAS

With a QAS

Without a QAS

34

7

82,9%

With a QAS

8

33

80,5%

Total

81,7%

α: The cut value is 0,5

In order to identify both cases, where the estimated model has small adaptation and cases that have an enormous effect in the model, the examination of residuals is required in the logistic regression analysis. Field (2005) provides analytical directions for a residual analysis of such models. However, the residual analysis of this model indicated that there is no need for special treatment over data in order to face extreme values or effects of specific cases in the total adaptation of the model.

Field (2005) also has proposed the examination of multicollinearity by investigating the paired cross-correlations using the process of partial correlation provided by SPSS. Despite, the existence of multicollinearity, this does not affect the values of the factors participating in the model but only their significance (Garson, 2008). The examination of partial cross-correlations and verification of the Pearson statistic showed that there is no statistically important cross-correlation between the variables participating in the model except for the case of exports and turnover where there is a partial correlation (Pearson correlation = 0,8, sig. at p<0.01). This relatively high value of correlation is explained due to fact that exports were calculated as a turnover percentage. Since the exports variable is not significant for the estimated model and there is no other highly correlated pair of variables in the model, there is no need to apply any adjusting treatment.

Having examined whether the estimated model is statistically acceptable we can proceed to the interpretation of the results. The statistically significant variables (Table 4) that influence cooperatives’ incentives and their decision to implement a QAS, are presented below:

Turnover. The turnover represents the size of the cooperatives and has as was expected, a positive relationship with the decision to implement a QAS (positive sign), moreover is statistically significant at level p = 0,022. Zaibet and Bredahl (1997), report distinct incentives to adopt ISO 9000 in the food processing sector according to firm size. According to Herath et al. (2007), the presence of economies of size in food safety and quality assurance technologies will clearly act as an incentive for larger firms to adopt such systems. Additionally, size can affect the transaction costs related to quality systems of a company. Caswell et al. (1998), and Holleran et al. (1999), discussed the importance of lowering transaction costs through adoption of food safety and quality assurance meta-systems such as ISO 9000 and HACCP.

Highly – educated personnel. This characteristic of cooperatives and, more generally, of the investor - owned firms can be used both as a measurement of companies’ size and as an indicator for a company’s learning ability (for example handling new methodologies and techniques of quality assurance that require special knowledge i.e. statistics, chemistry etc). This variable is statistically significant at level p = 0,095 and has a positive sign.

Trademarks. The presence of registered trademarks indicates the type of commercial activities for the cooperatives and their marketing strategy. Moreover, it affects the way companies consider reputation matters. Reputation and reputation-related gains vary widely across firms. Branded products typically receive price premiums over generic products, while branding generates repeat purchases and customer loyalty. However, branded products can bring about devastating losses to firms in the event of an outbreak of food-related illness, while similar risks do not apply to indistinguishable generic products (Ollinger et al., 2004). The existence of registered trademarks in the agricultural cooperatives is considered to positively affect their decision to adopt a QAS. Thus, a QAS can be used in a protective way for the cooperatives’ reputation. The variable in the estimated model is statistically significant at level p = 0,003 and has, as it was expected, a positive sign.

Commercial strategy to increase market share. This variable examines the cooperatives’ strategy with respect to their market share. Terziovski et al. (1997), claim that one of the greatest advantages of a certified QAS is its capability of “keeping the customers’ doors open". Consequently, it is quite reasonable for food companies and agricultural cooperatives that are interested in expanding their activities and increasing their market share to seek adoption and certification with a QAS. This variable in the estimated model is statistically significant at the p = 0,013 level and has, as it was expected, a positive sign.

Bureaucracy generated by the QAS. Considering the QAS as a bureaucratic procedure that has nothing to do with the real companies’ procedures is negatively associated with companies’ incentives to adopt a QAS. More specifically, it is believed that the HACCP system introduces many record-keeping procedures that sometimes obstruct production. Ward (2001), Motarjemi and Kaferstein (1999) and Engel, (1998) noted that documentation is often perceived as complicated and unnecessary. Moreover, Sanders, (1994) argues that small organisations often complain that ISO 9000 just results in increased paper work and cannot give them any internal benefits, such as the reduction of scrap or increased productivity. Poksinska et al. (2006), reported that the companies examined in their study expressed a relatively high level of disappointment with the amount of paperwork required by ISO 9001. This variable is statistically important in the estimated model at level p = 0,05 and has a negative sign as was expected.

Improvements generated by the QAS. Contrary to the previous variable, this one examines whether a QAS is considered by the agricultural cooperatives as a tool or a mechanism, which can be used to lead an improvement process. The existence of such perceptions in a company facilitates the improvement process. Consequently, a QAS can be used to improve the production process, to reduce defective production, to increase productivity and consequently to reduce the cost of production. As was anticipated, this variable is statistically significant at the p = 0,065 level with a positive sign.

Number of activities. This variable is used to investigate the relationship between the number of different activities and products with which a cooperative deals and the probability to implement a QAS. It is assumed that the more activities a cooperative deals with, the more difficult and expensive it is for a cooperative to implement a QAS. This variable is statistically significant at the p = 0,012 level and has a negative affect over QAS implementation.

Cheese and dairy products. This is the only variable referring to specific cooperative products and activities which is statistically significant in the estimated model. Dairy products have been identified as “high risk” commodities with respect to food safety and the enhancement of quality control is likely to be a greater priority for companies and cooperatives engaged in their production. The variable in the estimated model is statistically significant at the p = 005 level and, as expected, it has a positive sign.

As far as the non-significant variables are concerned it must be mentioned that the variables referring to cooperatives’ activities are not significant (i.e. olive oil, wine and fruits and vegetables) Also, the variable measuring the cooperatives’ exports are also non-significant and has a negative sign, contrary to our expectation. However, a company’s decision to adopt both HACCP (for example, Herath et al., 2007) and ISO (for example, Anderson et al., 1999) is positively affected by the company’s export orientation, a possible explanation as to why cooperatives with a high volume of exports appeared not to be interested in QAS implementation is probably related to minimal pressure from their existing customers or most probably, is due to the fact that there are a lot of cooperatives whose exports are comprised of unprocessed products such as tobacco leaves.

4.1. Incentives to implement ISO 9001

The results derived from the logistic regression analysis results regarding the cooperatives’ characteristics that affect their incentives and their decision to adopt the ISO 9001 system are presented in Table 6. The backward variable selection method was used in the model estimation in order to take into account any suppressor effects that may be present among the variables according to Field’s (2005) suggestion. In addition, the forward selection method verified the results since both methods selected the same variables.

Table 6: Logistic regression analysis for the cooperative characteristics that affect ISO 9001 implementation

Variables

B

S.E

Statistic

Wald

Wald Sig.

Exp(B)


0,062

0,026

5,683

0,017**

1,064

Trade Marks *1

1,378

0,565

5,959

0,015**

3,968


2,528

0,873

8,391

0,004*

12,528


1,142

0,654

3,052

0,081***

3,133

Constant term

-3,789

1,003

14,262

0,000

0,023

R2 = 0,632 (Hosmer& Lemeshow), 0.313 (Cox & Snell), 0,419, (Nagelkerke)

Significance: * p<0.01, ** p<0.05, *** p<0.1,

*1 Dummy variable, 0= No, 1 = Yes

The classification table (Table 7) and the different types of R2 suggest that the estimated model adequately fits the data. Thus, the model correctly predicts more than 3 out 4 cases. At this point, it should be mentioned that the cut value used in the classification table is 0,48. This value is based on the examination of the ROC curve (Receiver Operating Curve) between the observed and the predicted values for ISO implementation. The examination of the residuals indicated that there is no need for special treatment of the data in order to face values or cases with an extreme effect in the model.

To sum up, it must be mentioned that a cooperative’s incentives and decision to adopt the ISO 9001 quality system depends on the cooperative size (turnover), the existence of trademarks and whether the cooperative is interested in increasing its market share. Finally, if the cooperative produces dairy and cheese products, then it is positively motivated to implement ISO 9001.

Table 7:Classification table for ISO 9001 implementation (α)

Observed

Predicted

Percentage Correct

Without ISO 9001

With ISO 9001

Without ISO 9001

35

12

74.5%

With ISO 9001

7

30

81.1%

Total

77,4%


4.2. Incentives to implement HACCP

The logistic regression analysis results for the cooperatives’ characteristics that affect their incentives and finally their decision to adopt the HACCP system are presented in Table 8. The backward variable selection method was used in the model estimation in order to take into account any suppressor effects. Nevertheless, the forward selection method verified the results as both methods selected the same variables.

Table 8: Logistic regression analysis for the cooperative characteristics that affect HACCP implementation

Variables

B

S.E

Statistic

Wald

Wald Sig.

Exp (B)

Trade Marks *1

1,119

0,574

3,795

0,051***

3,061

Highly-educated personnel

0,058

0,028

4,257

0,039**

1,059


1,662

0,751

4,896

0,027**

5,270

“QAS are just more bureaucracy”*1

-1,204

0,581

4,289

0,038**

0,300


-0,401

0,181

4,912

0,027**

0,669


2,060

0,741

7,724

0,005*

7,844

Constant term

-0,651

1,039

0,393

0,531

0,522

R2 = 0,609 (Hosmer& Lemeshow), 0.266 (Cox & Snell), 0,363, (Nagelkerke)

Significance: * p<0.01, ** p<0.05, *** p<0.1

*1 Dummy variable, 0= No, 1 = Yes

The classification table (Table 9) and the different types of R2 suggest that the estimated model adequately fits the data. Thus, 3 out 4 cases are correctly predicted by the model. At this point, it should be mentioned that the cut value used in the classification table is 0,38, based on the examination of a ROC curve between the observed and the predicted values for HACCP implementation. The examination of residuals indicated that there is no need for special treatment over data in order to face extreme values or cases with an extreme effect in the model.

To sum up it should be mentioned that the cooperatives’ incentives and finally their decision to adopt the HACCP system influenced by the existence of trademarks and whether the cooperative is interested in increasing its market share. Moreover, the number of highly –educated personnel affect positively their decision to adopt HACCP. Also, the number of the cooperative activities negatively affects their decision towards HACCP adoption. Furthermore, if the cooperative managers consider the HACCP system only as paperwork, then they will not proceed to adopt HACCP. Finally, if the cooperative produces dairy and cheese products then it is positively motivated to implement HACCP.

Table 9: Classification table for HACCP implementation (α)

Observed

Predicted

Percentage Correct

Without HACCP

With HACCP

Without HACCP

42

10

78,4%

With HACCP

8

22

73,3%

Total

76,5%


5. Discussion and conclusions

To conclude, in this paper the logistic regression analysis has been used in order to identify the characteristics of the Greek agricultural cooperatives that affect their incentives and finally their decision to implement a QAS such as ISO 9001 and HACCP. Three different models have been estimated: one for ISO 9001, one for the HACCP and one for both systems. Initially, it seems that these two quality systems cannot be directly compared with each other since HACCP refers more to technical aspects of production (food safety) and ISO 9001 is related more with administrative subjects. However, it is considered that HACCP also contributes to the organisation of production. More specifically, that it can change the organisation and administration techniques used by a food company. Consequently, the comparison is possible as long as these two systems are considered to be an innovative strategy implemented by the cooperatives in order to improve their products’ characteristics and quality and even more to improve their administration system. For many companies, both ISO and HACCP are considered as their first step to total quality management, although, the Greek reality has shown that this effort towards TQM adoption is not particularly continued after a quality certificate such as ISO 9001 (Gotsamani et al., 2006).

In addition, HACCP and ISO 9001 implementation in agricultural cooperatives suggest that there is a positive and relatively important relation between the implementation of these two systems. It seems, that there are synergistic effects between food safety assurance systems and quality management systems, suggesting that firms are adopting a broader array of quality systems to improve both food safety and quality attributes. Nevertheless, we should keep in mind that in most cases an external consultancy agency takes over the implementation procedures for both HACCP and ISO 9001 systems, which is a common practice among the Greek food companies. In any case, this a matter that requires further examination in two dimensions, first to examine the above relationship in investor owned food companies and second to investigate whether a synergistic effect between these 2 systems really does exist.

With regard to the characteristics that affect cooperative incentives to adopt quality systems, the following characteristics affect both ISO and HACCP implementation. Thus, registered trademarks, efforts to increase their market share and dealing with dairy products and cheese, lead cooperatives to adopt a QAS. It must be mentioned at this point that trademarks lead companies and consequently cooperatives to pay greater attention in their reputation and name. Their protection is more secure with such a QAS. On the one hand, dairy products and cheese are considered as high-risk products with respect to food safety and the other such products offer great market opportunities (i.e. Feta cheese).

In addition, a strict tendency concerning food safety is observed among big retailers in Europe, in an attempt not only to differentiate themselves, but also to offer to their customers and consumers quality and safe food products (i.e. the food safety systems created by the large European retailers such as IFS in Germany and France, BRC in UK and GLOBALGAP in many European countries). Consequently, it is logical to accept Terziovski’s (1997) statement that a QAS certificate can keep the customers’ doors open. Moreover, in many cases the customers’ requirements determine the adoption of a QAS to a such a degree that studies have been carried out suggesting that firms should not proceed in a QAS adoption unless there is huge pressure by their customers to do so (Martinez-costa and Martinez-lorente, 2007).

Besides the above-described characteristics that affect both ISO 9001 and HACCP adoption there are some more characteristics that affect only ISO or HACCP adoption. More specifically, for the ISO 9001 implementation, the characteristic that appears to positively affect its application is the cooperatives’ size (measured as turnover in this paper). In general, there are more available resources (both economic and human) in larger cooperatives and firms that can be used for the development and operation of a QAS such as ISO. On the other hand, the adoption of HACCP system is affected by somewhat different variables than ISO. Thus, the number of employees having a higher education seems to positively affect the HACCP adoption. This is probably due to the fact that HACCP systems require more specialised and technical knowledge. Moreover, the number of different activities and products which a cooperative deals with, and the managers’ perception that a HACCP system only generates paperwork and bureaucracy negatively affect the adoption of HACCP.

Potentially, the results of this study can be used to interpret the incentives of small and medium investor owned firms of the food sector to adopt a QAS. However, it would be more preferable, to conduct a separate study for such firms and then a comparison could be made between the incentives of cooperatives and investor owned firms to adopt a QAS. This study would identify whether there are any differences between these two types of firms regarding both HACCP and ISO 9001 adoption

References

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� Department of Agricultural Economics and Policy, Faculty of Agriculture, Aristotle University of Thessaloniki.

� Department of Business Administration of Food and Agricultural Products, Agrinio, University of Ioannina.

( Correspondence author: e-mail � HYPERLINK "mailto:[email protected]" ��[email protected]�, phone: +30 697 2001338

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