MARKETING CHARACTERISTICS OF THE HUNGARIAN SMEs WORKING IN
THE FOOD PROCESSING INDUSTRY
Zsolt Polereczki, György Kövér, Tibor Bareith, Zoltán Szakály
Kaposvár University, Faculty of Economic Sciences, Marketing and Trade Department
International Symposium on Business and Social Sciences
Toshi Center Hotel, Tokyo, Japan March 15-17, 2013
The structure of the presentation
• Antecedents of the research
• Aims of the research
• Methodology
• Main results
• Consequences
Antecedents of the research
• Basic idea based on the models explaining the consumers’
behaviour
General models
Consumers’
behaviour
e.g.: Engel- Blackwell-Miniard
(1987); Howard, Sheth (1969)
Product group related models
e.g.: Shepherd, R.
(1990); Pilgrim, F.
J. (1957); Grunert, Brunso, Bisp (1993)
Market orientation
Verhees, Meulenberg (2004)
Desphande, Farley, Webster (1993); Kohli, Jaworsky (1990); Ruekert (1992);
Kohli, Jaworsky (1990);
Narver, Slater (1990);
Shapiro (1988)
Is it possible to set up a branch related
model relating to SMEs?
Aims of the research
Model creation
3rd step
International expansion of the research
2nd step
In 2010 we investigated 250 agricultural and food industrial SMEs with internationally used standard questions – external and internal factors, market orientation, market efficiency
1st step
Nationwide survey carried out in 2009 with 100 dairy and meat industrial SMEs - general
entrepreneurial practice and opinion about marketing
Cultural focus Managerial focus
MKTOR - Narver, Slater (1990)
MARKOR - Kohli, Jaworsky (1990) Customer orientation Intelligence generation
Competitor orientation Intelligence dissemination
Interfunctional coordination
Responsiveness
Testing MARKOR and MKTOR among food processing SMEs:
1. Can the three-three factors in the two scales be considered one dimension?
2. Does each factor have enough discriminating ability, that is do we have the right to separate the variables constituting the factors into three-three factors?
Methodology
• The composition of the sample
Number of employees
Composition
Head %
0-9 people 136 71,2
10-49 people 42 21,9
50-300 people 13 6,9
Total 191 100
The data were analyzed with the structural equation modelling (SEM)
method – Amos 7 (SPSS)
Results
• Acceptable one-dimensional models
Nomination of factor
Number of variables in the
original model
Number of kept variables
Chi2
Degree of freedom
p
MARKOR
Intelligence generation 10 6 10,430 7 0,166
Intelligence dissemination 8 4 4,790 2 0,930
Responsiveness 14 9 37,049 26 0,074
MKTOR
Consumer orientation 8 4 3,542 2 0,170
Competitor orientation 5 5 - 0 -
Interfunctional coordination
4 4 0,248 1 0,618
• Summary of the discriminating ability investigation between the factor pairs
Factor pairs Chi2 Degree of
freedom p MARKOR
Intelligence generation
Intelligence dissemination 35,649 3 8,88E-08 Intelligence generation
Responsiveness 141,02 1 1,59E-32
Intelligence dissemination
Responsiveness 66,98 1 2,74E-16
MKTOR Consumer orientation
Interfunctional coordination 84,592 1 3,67E-20
The original model can partly be used in the case of SMEs working in the food
industry.
• MARKOR scale, Intelligence generation factor
– The food industrial SMEs collect secondary information through
basically informal channels - enterprises consider this little information satisfactory, they are highly convinced that they react to real
consumer demands (Responsiveness factor).
• MARKOR scale, Intelligence dissemination factor
– One of the weakest elements of market orientation is the effective information flow.
• MKTOR scale, Customer orientation factor
– Enterprises already feel commitment to customer orientation, however, it does not appeal in real activities.
Consequences and implications
• It is possible to decrease the one- dimensional variables in a way that the refusal of the one-factor model cannot be justified in case of 5 of the examined six factors.
The original model can not be used in the case of SMEs working in the food
industry.
Is it possible to set up a branch related model relating to SMEs?
Yes! But new variables should be inserted in the
model to increase its explanation ability.
THANK YOU FOR YOUR ATTENTION!
polereczki.zsolt@ke.hu
