Δευτέρα, 3 Σεπτεμβρίου 2007

THE PROFILE OF THE SUCCESSFUL GREEK ENTERPRISE AFTER THE YEAR 1992

KOSTAS RONDOS, EFSTRATIOS PAPANIS

The aim of this paper is to determine the profile of the Greek industrial enterprise that has been favored the most from the participation of the country in the program of the E.U. Single Internal Market. Logistic Regression models have been applied on data from a specific sample survey. The general conclusion of the research is that factors such as participation in wider enterprising forms, efforts to approach an optimum enterprise size , selection of suitable areas for business other European countries conduct, and the gaining of comparative advantage in specific sectors, are those by which the success of the Greek industrial enterprise will be judged, in the framework of the Single Internal Market.

Key Phrases:
Greek Industrial Enterprise, Single Internal Market, Participation in group enterprise, Location of company, Size of company, Competitive advantage.






1. INTRODUCTION

The development of industries in Greece has been the focus of extensive research, not only due to the importance it has for the country’s advancement, but also because the particular field has organizational characteristics, which differentiate it from the typical European industry. The small size of Greek companies, as well as their usual location in urban areas has led to district and national developmental problems. These problems are further intensified due to organizational, stuctural and institutional characteristics of Greek economy, such as the overdependence on public sector, high cost of financing, and specific restrictions imposed to several business sectors (Acs, Z. & Audretch, D., 1990, European commission, 1996A).
The study of Greek industrial enterprises business development becomes interesting in light of their participation in Single Internal Market (SIM), a European Union institution founded in 1992. Will Greek companies manage to overcome the aforementioned problems and gain competitive advantage over other E.E.C countries? Which are the alternatives, what procedures will be followed and which choices will be made for that purpose? Greek and international bibliography has provided an adequate amount of research regarding the effects of S.I.M. on Greek economy as a whole (Economou, G., 1992). Additionally, a large body of research exists focusing on the effects of the E.E.C on both Greece (Politis, T., 1992; Delis, K., 1996b), and on other E.U. countries (European Commission, 1996b). Yet, those analyses were conducted between years 1988-92, prior to the establishment of the Single Internal Market, when the issue of the S.I.M. formation was of vast interest. Nowadays the Single Internal Market exists for a sufficient time period that enables researchers to draw safe conclusions. Currently there is a shift of interest on specifying the particular industrial characteristics, related to successful business conduct within the S.I.M. Some of the issues under focus are: Which groups of companies will be more positively influenced by the full abolition of boarders across E.U. counties? What effects will the free movement of individuals, capital, products and services have on those companies? Small or large- size organizations will be more positively affected after 1992? Which business sectors and regions will benefit more? Does the participation in wider enterprise groups offer a competitive advantage over small independent companies?
The aim of the present study is to quantitatively examine those questions, and detect the opportunities that are offered to Greek companies due to their participation in the SIM. The model developed will be used for predicting which particular business characteristics of Greek companies are most likely to be positively related with successful business within S.I.M.
The next section of the article will focus on the data and the research methodology, followed by a description of model application. Research results will be presented in the last article section.



2. STATISTICAL DATA AND METHODOLOGY

The data presented in the first part of the analysis come from a 1995’s research of Greek industrial enterprises. The purpose of the study was to examine the effectiveness and financial consequences of the newly introduced S.I.M.’s legislation and rules for business conduct. A total of 411 industrial enterprises, employing more that 20 individuals each and included in sectors 15-36 of the revised international classification (NACE 1), were selected. The general sample fraction was 0,166.
A stratified systematic sampling procedure depending on company size, was followed. Large-scale organizations, employing more than 20 individuals, were selected for study, since it was predicted that these companies would provide a more accurate account of the SIM economic consequences.
These raw data were analyzed with the Logistic Regression Method, in order to detect the variables (characteristics) which were positively related to the beneficial effects of SIM on Greek industrial enterprises. Independent variables were either quantitative or categorical. During secondary analysis, the explanatory power of the model was higher. The number and the sigh of b factors for each category of sub sample independent variables, created via Logistic Regression method, provided us with the opportunity to locate which values of the independent variable are related or not with particular characteristics of the dependent variable. Generally, as the positive value of the b factor in the Logistic Regression increases, the possibility of finding a specific characteristic related to the independent variable, increases.
Conversely, as the value of b factor decreases, this possibility also decreases (Knapp, M. et al., 1982).
The aforementioned analysis can also be achieved by odds ratio. Its value is higher than one for each variable with positive b factor, and smaller than one for variables with negative b factors. Additionally, the value of the odds ratio increases as the positive value of b factor increases while when the negative value of b factor decreases the value of the odds ratio also decreases.
The application of the logistic regression was based on the maximum likelihood approach. The statistical significance of regression equation factors is detected using the wald statistic, which is equal to the squared value of the t statistic. There is enough evidence that, in the case of the Logistic Regression, the use of the wald statistic in estimating statistical significance should be preferred.
In order to include in the model a certain category of independent variables, the criterion for statistical significance was set at 20%. As supported by Harissis, K. (1986), variables holding as much as 25% statistical significance may be included in models, if previous research has provided evidence for their importance. The total explanatory function of the model created can be evaluated using the likelihood ratio test statistic. When the appropriate model has been developed, the prediction that a particular unit of the sample holding various characteristics will also have the characteristic related to the dependent variable. The equation form that is used to detect this possibility is :

1
P = ----------------- (1)
1+ e-(Σb)

Where b are the regression factors.


The usefulness of the method should be noted at this point, since it simultaneously takes into consideration various characteristics in trying to detect the possibility of existence or not of the characteristic related to the dependent variable. Generally, international bibliography provides ample evidence of the advantages the particular method has over multiple linear regression models or over the use of x2 tests (Knapp, M. et al., 1981, 1982; Goldberg, A., 1964; Nerlove & Press, 1973; Cox, 1970).

3. MODEL APPLICATION

The pseudo variable “Effect of SIM program over Greek industrial enterprise” was used as dependent variable Yi. When the company stated that the program had a positive effect on it, value 1 was assigned while value0 (zero) was given to companies for which the program had no positive effect. Explanatory factors related to the positive effect of the SIM program included the business sector that the company belonged to (SECTOR), the region in which the company was located in (REGION), the size of the enterprise, in terms of number of employees, (SIZE), and the participation or not of the company in a group of enterprises (PART). The subcategories of each categorical variable can be seen in Table 2.
The selection of the particular variables as explanatory in the present model application was made in light of specific basic characteristics of Greek economy. Of great importance is the sector segregation of Greek industrial enterprises. The sectors, in which Greece, as a Mediterranean country, has competitive advantage, are different from those of Northern or Middle European countries. Additionally, the size of the company is an important factor related to its survival in a competitive single Market environment, while entering a group enterprise has become a basic goal for companies that want to maintain their competitive advantage. Finally, the region in which an enterprise is located in is an important parameter since many rural districts in Greece are industrially underdeveloped.

4. BASIC RESEARCH FINDINGS

The Single European Market program has a positive effect in companies according to the 50,4% of Greek entrepreneurs (N.S.S.G., 1997). For the European Union as a whole, the corresponding percent is 33%, which is lower in relation to Greece (Eurostat, 1996). Interestingly enough, Greek industrial companies differentiate themselves not only by their general view of the SIM program effectiveness, but also with their views about the effectiveness of single measures adapted by S.I.M.
(Insert Table 1 about here)
The data of Table 1 pinpoint to the importance Greek entrepreneurs pay in the elimination of bureaucracy and of delays on the exporting of Greek products, two of the most firm and difficult problems faced by the export Greek sector.
The values of categorical explanatory variables used in the model, as well as their frequencies can be seen in Table 2.
(Insert Table 2 about here)

According to the Logistic Regression application results, the model has a strong explanatory function, with likelihood ratio test statistic being X2 = 55,055, df= 22, p=0,0001.
The statistical significance of separate explanatory factors, as well as the odd analysis (eb ) results can be seen in Table 3.
The business sector that the company belongs to (SECTOR), and the size of the enterprise (SIZE) are significantly related with the course of the Greek enterprise within S.I.M (wald stat.= 26,58, p=0,03 and wald stat.= 9,49, p=0,05 respectively). The effect of the region in which the company is located in (REGION) and the company’s participation in a group enterprise (PART) remains unclear (wald stat.= 4,30, p=0,11 and wald stat.= 2,29, p=0,12 respectively). Even though it is not clear whether these two variables significantly affect the course of companies in the S.I.M., they are not excluded from the model since, as was mentioned in the previous section, it is safer to utilize four instead of two independent variables in order to increase the predictive function of the model. The constant is not statistically significant (Table 3).
When the values of the independent variables were varied and tested in the model, the results remained generally stable. Special emphasis was placed in the manipulation of the SECTOR variable, since the number of observations was adequate in the original model. As a result, the number of companies in some cases was small, yet greater than 5 (for example, sector 20 included 9 companies). During a testing of the model, in which minimum importance sectors 31 and 34 were excluded and small size sectors 20, 21 and 22 were merged, the model was significantly improved as far as the statistical significance of b factors. Indeed, for SECTOR wald stat. = 19,12, df=11, p=0,058, for variable SIZE wald stat.= 8,88, df=4, p=0,064, for variable REGION wald stat.= 2,76, df=1, p= 0,097. The constant is also markedly improved (wald stat.= 2,37, df=1, p=0,12).
As mentioned in section 2 of this paper, the value and sign of b for the levels of each factor give us the opportunity to detect the industry categories for each variable, which are more likely to be positively affected by the participation of Greece in S.I.M :

(Insert Table 3 around here)



Total of sample industries

There is a small possibility for the total of the examined company cases to be positively affected, since the value of the constant b factor is hardly higher than zero (b0= 0,107).Constant b factor is not statistically significant, therefore its value is not examined in light of the regression model (Table 3). Nevertheless, its value remains positive and high enough (0,27) in the alternative model discussed earlier, while its statistical significance is accepted at the level set at the methodology section (p=0,12<0,20).

Sector of business activity (SECTOR)

Enterprises which operate in food and drink industry (15), manufacture of thread, textile and weaving products (17), manufacture of wood and wood-made products (20), chemical substance and product industry (24), and steel and metal industry (28), have increased possibilities to be positively affected by the S.I.M. program. On the contrary, cloth and fur manufacture (18), manufactures of rubber and plastic products (25) and manufacture of car vehicles, towing and semi-towing vehicles and their parts-equipment (34), have the lower possibility of being positively affected by the program, especially the sector of car vehicles, towing and semi-towing vehicles and their parts-equipment manufacture (b=-1,33). Independently of a positive or negative direction, b factors of sectors 19, 21, 22, 26,27,29 and 31 are not statistically significant, and therefore cannot be evaluated (Table 3).
The results are in accordance with the established Greek business advantage in traditional and trade or handicraft sectors, such as food and drink, wood industry or manufacture of particular chemical and metal products. On the other hand, the competitive advantage of other advanced E.U. countries in heavy industry sectors, prevents Greek enterprises from succeeding in these areas.

Company size (SIZE)

Large-scale enterprises, with more than 200 employees, have an increased possibility of being positively affected from S.I.M. operation, as compared to those employing 20-199 individuals. This difference is significant and systematic within the different size categories, when examined in light of the b factor sign. Yet, enterprises employing 50-199 individuals do not statistically differ from those with 500-999 employees.
Size is important for a company’s financial survival, especially when there is excess competition in the business environment (Ghatak, S., 1998). Small-to-medium size companies try to maintain an optimum company size in order to achieve scale economy. It must be noted that company size is an important problem that Greece enterprises face, since most of them employ less than 10 individuals.


Company location (REGION)

Companies operating in Macedonia have an increased possibility of success within the European framework over those located in Attica or in other Greek geographical regions. One explanation is the developmental opportunities and industrialization potential existing in the rural district area. A large percent of industries (44%) is located within the region of Attica, making the area satiated for industrial operation. In Macedonia, an industrially developed district, 25% of Greek enterprises are located. Macedonia’s geographical location, utilizing both European and Mediterranean elements, as well as the existence of urban centers such as Thessalonica, make the particular area one of the most attractive locations for Greek industrial operation.

Participation in wider enterprise groups (PART)

Companies participating in wider industrial groups feel more confident and have greater effectiveness when operating in a competitive European environment (b= 0,185). Participation in wider business forms and cooperation with other enterprises in general, is a survival mean for companies operating under excess competition. Additionally, choices such as franchising or venture capital forms, allow companies to achieve optimum employee size, overcome financing problems and approach easier national and international markets. This finding is related to other countries as well (for Polland see Ghatak et.al., 2001).
Odds ratio-presented in the last column of Table 3- can also be used in the previous analysis. When this criterion is used, possibility for positive S.I.M. effect is high in cases in which odds ratio is high, such as in sectors 15,17,20,24,28 and 34, in Macedonia region, in companies employing 200-499 employees and in companies participating in wider business forms (Table 3).
Equation (1) and regression factors contribute to the prediction of potential success of a company with specific business characteristics. Prediction is based on the second model-which is statistically significant- and the constant factor. At this point, predictions will be made for a) the total of sample industrial enterprises, and b) enterprises that belong in the sector of food and drink production and are occupying 20-50 employees, are located in Macedonia, and participate in an enterprise group. In the first case the possibility (P1) that a sample company has in succeeding within S.I.M., is 0,443 or 44,3%, and the corresponding estimated constant b factor is b0 =0,2694. For a company with the characteristics described in case b, the possibility (P2) is 0,66 or 66% respectively. The corresponding b values for constant factor, region (Macedonia), size (20-50 employees), participation in industrial enterprise, and sector (Food and drink industry), are 0.2694, 0.3879, 0.6379, 0.21103, and 0.4308 respectively. In the same way, the probability of success within the S.I.M. market can be calculated for any combination of company characteristics.


5. CONCLUDING REMARKS

The factors related to the successful course of Greek industrial enterprises within Single Internal Market, since 1992, constitute a critical issue for the country’s economy. A research conducted during 1995 and employing a large sample of industrial enterprises, showed that 50% of the companies believed that S.I.M. affected them positively, due to obliteration of bureaucracy, minimization of export/ import product delays in the borders and free capital movement across countries. In relation to other European countries’ industrial enterprises, Greek enterprises seem to be more optimistic about the positive effects of S.I.M. (50% as opposed to 33%). The results of the Logistic Regression model provides quantitative evidence of an existing relationship between the company’s business size and sector of operation, and the successful company’s course within the S.I.M. framework It is yet unclear whether the region of operation and the participation in wider enterprise forms have an effect on company success. Additionally, research indicates that enterprises operating in the traditional industrial sector and characterized by employment of more than 200 individuals, Macedonia regional location, and participation in enterprise groups, have higher possibility to be positively affected by the S.I.M. program.
Based on these findings, general guidelines for industrial enterprise action include: a) efforts in maintaining an optimum company size, and participating in wider enterprise forms, b) gradual movement of public and private financial resources into Greek industrial sectors that have a competitive advantage, and c) application of appropriate political interventions focusing on the improvement of industrial effectiveness in industrially satiated urban areas or in less industrially developed Greek districts. A strategy developed along these lines will contribute to the survival and further development of Greek industrial enterprises, within the Single Internal Market’s competitive business environment.



















REFERENCES

Acs Z. and Audretch D., (1990). “The Economics of Small Firms : A European Challenge”. Kluver Academic Publishers.
Delis, Κ., (1990). “Commerce in Single Internal Market”. ΙΟΒΕ, Athens.
N.S.S.G. (1997). “Research on positive and negative consequences of Single Internal Market program, on enterprises”. Athens.
European Commission, (1996A). “Trade, Labour and Capital Flows : The Less Developed Regions”. The Single Market And Regional Impact Subseries VI, Brussels.
European Commission, (1996B). “The 1996 Single Market Review”, Commission Staff Working Paper, Brussels.
Eurostat, (1996). “Statistics in focus- Energy and industry”. no 25, Luxembourg.
Ghatak S. and Jalal U.S, (1999). “Size and Growth of Small Firms : A Methodological Survey”. School of Economics, Kingston University, U.K.
Ghatak S., Rontos K., Vavouras I. and Manolas G., (2001). “ Research on the profile of the Successful Polish Small Enterprise using Logit Analysis”. Journal of the Polish Statistical Association : “Statistics in Transition” Vol.5, No 1, Poland.
Ghatak S., Rontos K., Vavouras I. and Manolas G., (2002). “European Intergration and the Survival of Polish Small Enterprises” in “ Enrerprises in Developing and Transitional Economies” Ed. Katrak H. and Strange R.,p.p 137-156, Palgrave-Macmilan Publishers, G.B.
Goldbergen A., (1964). “Econometric Theory”. Willey, N.Y.
Harissis K., (1986). “ Staff Turnover in the Personal Sosial Servises : A Statisitcal Approach” Unpublished Ph.D Dissertation, University of Kent, U.K.
Knapp M., Harissis K. and Missiakoulis S., (1981). “Labour Turnover”. Management Research News, vol. 4, No 1, England.
Knapp M., Harissis K. and Missiakoulis S., (1982). “Investigating labour turnover and wastage using the logit technique”, Journal of Occupational Psychology, no 55, pp 129-138, G.B.
Nerlove M. and Press J. (1973). “Univariate and Multivariate Loglinear Logistic Models”. R-1306 EDA/NIH, Santa Monica, California.
Economou G., (1992) “Greek Economy and 1992 Perspective”, ΙΟΒΕ, , Athens.
Politis Τ., (1992). “Greek Industry- Estimations, expectations and enterprise strategies for the Single Internal Market ”. ΙΟΒΕ, Athens.
Skovgaard M. “Analytic Statisitcal Models”. Lecture Notes-Monograph Series, Institute of Mathematical Statistics, Hayward, California.
















Table 1.
Percent % of Greek and Ε.Ε industrial enterprises believing that the particular measures included in the S.I.M. program will positively affect their company.

Measure Greek enterprises % Ε.Ε % enterprises
Agreement on common technical standards/models 53,3 31,0
Mutual acceptance of technical standards/models 50,9 32,0
Agreement on typical procedures for product cost. 15,1 23,0
Simplification of procedures for product/service patent. 25,5 13,0
Focus on public agreements 21,2 9,0
Abolishment of custom bureaucracy 88,8 60,0
Release of product transportation from normative arrangements 74,5 43,0
Elimination of boarder delays 83,0 56,0
Change in V.A.T procedures for sales within E.U. 67,4 32,0
Free movement of capital 67,6 23,0
Agreement on double taxation 46,0 17,0


Source: 1. N.S.S.G, “Research on the positive and negative consequences of Single Internal Program, in enterprises”, Athens, 1997.
2. Eurostat, “Statistics in focus-Energy and industry¨”,v.25, Luxembourg,1996.







Table 2.

Categorization of sample enterprises according to the values and levels of explanatory variables.

VARIABLE NUMBER %
SECTOR OF BUSINESS ACTIVITY(SECTOR) 411 100,0
Food and drink industry (15) 70 17,0
Manufacture of thread, textile and weaving products (17) 62 15,1
Cloth and fur manufacture (18) 41 10,0
Leather industry (19) 18 4,4
Manufacture of wood and wood-made products (20) 9 2,2

Production of paper pulp, paper and paper products. (21) 14 3,4
Publishing-printing services-production of sound and visual CD’s, information media (22) 14 3,4
Chemical substance and product industry (24). 35 8,5
Manufacture of rubber and plastic products (25) 16 3,9
Manufacture of other products from non- ore (26) 32 7,8
Production of basic metal ( 27) 18 4,4
Steel and Metal industry (machinery and equipment not included) (28) 16 3,9
Production of machinery and equipment (29) 17 4,1
Manufacture of electronic devices and machinery (31) 15 3,6
Manufacture of car vehicles, towing and semi-towing vehicles and their parts-equipment (34). 16 3,9
Furniture Manufacture. Other Industries (36) * 18 4,4
COMPANY SIZE ( SIZE) ** 411 100,0
20- 49 96 23,4
50-199 149 36,3
200-499 111 27,0
500-999 33 8,0
1000< 22 5,3
REGION OF LOCATION (REGION) 411 100,0
Macedonia 95 23,1
Attica 214 52,1
Rest of Greece 102 24,8
PARTICIPATION IN GROUP ENTERPRISE (PART) 411 100,0
Yes 126 30,7
No 285 69,3
* Layer Manufacture, jewelry, musical instruments, sport products,
games, e.t.c.
** Category of employment size

















Table 3.
Results of Logistic Regression Model (dependent variable :Effect of S.I.M. in Greek Industrial Enterprise
VARIABLE Β S.E Wald df P Exp(B)
SECTOR * 26,576 15 0,03
15 0,476 0,270 3,101 1 0,08 1,610
17 0,377 0,280 1,811 1 0,18 1,458
18 -0,752 0,359 4,402 1 0,03 0,471
19 -0,374 0,495 0,570 1 Nosign 0,688
20 0,894 0,712 1,578 1 0,20 2,448
21 -0,617 0,557 1,228 1 Nosign 0,539
22 -0,363 0,555 0,428 1 Nosign. 0,696
24 0,869 0,386 5,053 1 0,02 2,384
25 -0,710 0,538 1,743 1 0,19 0,492
26 -0,225 0,379 0,005 1 Nosign 0,974
27 0,347 0,476 0,532 1 Nosign 1,415
28 0,760 0,515 2,179 1 0,14 2,138
29 0,026 0,486 0,003 1 Nosign 1,027
31 -0,084 0,516 0,027 1 0,919
34 -1,333 0,640 4,345 1 0,04 0,264
REGION 4,298 2 0,11
Macedonia 0,353 0,182 3,7537 1 0,05 1,423
Attica -0,043 0,158 0,074 1 Nosign 0,958
SIZE 9,489 4 0,05
20-49 -0,639 0,240 7,072 1 0,008 0,528
50-199 -0,173 0,198 0,766 1 Nosign 0,841
200-499 0,309 0,218 2,021 1 0,15 1,363
500-999 0,091 0,329 0,077 1 Nosign 1,096
Participation in group enterprise (PART)
Yes 0,185 0,122 2,294 1 0,13 1,204
(CONSTANT) 0,107 0,165 0,426 1 Nosign

* For industry sector name see Table 2.

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