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Empirical Evidence for a Relationship between Business Growth and the Use of Structured Marketing Information Amongst Food and Drink SME's

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)


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)
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: (The current URI for this page, for reference purposes)

University of Kent Author Information

Cacciolatti, Luca.

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CReDIT Contributor Roles:

Fearne, Andrew.

Creator's ORCID:
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