Cacciolatti, Luca and Fearne, Andrew (2011) Empirical Evidence for a Relationship between Business Growth and the Use of Structured Marketing Information Amongst Food and Drink SME's. Working paper. University of Kent, Canterbury, Canterbury (KAR id:29315)
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Official URL: http://www.kent.ac.uk/kbs/research/working-papers.... |
Abstract
SMEs present peculiar characteristics that make their marketing distinctive from
larger companies. We suggest the lack of resources in SMEs is a barrier to effective
marketing and therefore to business growth. SMEs marketing decision-making is
affected by whether the SME manages to acquire, analyse and utilise formalised
marketing information. This paper analyses the relationship between business
growth potential and the use of marketing information in food and drink SMES. The
analysis was conducted using multivariate data analysis techniques, specifically PCA
and binary logistic regression, on a sample of approximately 300 food and drink
SMEs. The logistic regression was significant for both a model (R2=0.18) using the
predictors direct effect on growth probability and a model (R2=0.30) using
interaction terms. The hypothesised relationships on business growth probability
and the use of information have been tested and significant effects have been
identified on the interaction amongst the predictors of growth (23% of correlations
were significant).
Use of formalised marketing information was found to play an important role in
generating SME growth in food and drink SMEs. However, SME characteristics
played an important role in the way information was used and this affected
business growth. Better use of information by SMEs focused their marketing
activities. Therefore owner-mangers should be trained to make the best use out of
formalised marketing information.
Item Type: | Reports and Papers (Working paper) |
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Additional information: | Working Paper No. 243 |
Uncontrolled keywords: | entrepreneurial marketing, food and drink SMEs, marketing information, logistic regression, multivariate data analysis |
Subjects: |
H Social Sciences H Social Sciences > H Social Sciences (General) |
Divisions: | Divisions > Kent Business School - Division > Kent Business School (do not use) |
Depositing User: | Catherine Norman |
Date Deposited: | 23 Apr 2012 14:50 UTC |
Last Modified: | 16 Nov 2021 10:07 UTC |
Resource URI: | https://kar.kent.ac.uk/id/eprint/29315 (The current URI for this page, for reference purposes) |
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