Liquidity management in small firms: a learning perspective

The paper is concerned with the liquidity management in small firms and how this may be best met. There have also been several ‘how to do it’ literature on the subject dealing with advice to small firms with respect to financial management. The limited impact of these studies is the result of weaknesses in the methodologies used and the over reliant on the financial practices of large firms without paying much attention to the practices actually used by small business owners themselves. The core argument in this paper is that to improve practices in dealing with liquidity problems in small firms, advice given and methods recommended should be those grounded in the actual behaviour small business owners. This means understanding the motivations of small business owners and their experiences and not just making assumptions about it.
This paper uses ‘Insider accounts’ as a qualitative and innovative methodology which involves in-depth, semi-structured interviews and direct observation, conducted longitudinally in 8 case study companies. It is a research method which includes detailed accounts from the actors themselves, incorporating the actual motives and behaviour of owner-managers based on the philosophy that the 'objects' studied are in fact 'subjects', who produce accounts of their world. The paper presents results from all 8 case study firms, which suggest that small firms do not necessarily behave in a manner prescribed in the literature. The evidence suggests that small firms in the study did not generally give credit to customers except in very exceptional circumstances. However, where credits were given, the evidence suggests that they were either based on owner-manager past experiences or credit controls were strongly influenced by industry norms, which are shared rules within the industry, and not based on the calculation of costs and benefits of particular causes of action. The paper conceptualises this practice as a learning process utilising closed and open loop learning as an explanatory framework.

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