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Small Business Economics

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01-04-2015

What drives environmental practices of SMEs?

Authors: Brigitte Hoogendoorn, Daniela Guerra, Peter van der Zwan

Published in: Small Business Economics | Issue 4/2015

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Abstract

The objective of this paper is to develop a better understanding of what drives small and medium-sized enterprises (SMEs) to engage in environmental practices, and whether the drivers differ across types of practices. Two types of environmental practices are distinguished: practices related to production processes (greening processes) and practices related to products and services (greening product and service offerings). Despite a growing literature on socially responsible behavior of large firms, the role of SMEs remains underexposed. This neglect is remarkable given the substantial impact of SMEs on the economy and the natural environment. By using unique data for almost 8,000 SMEs across 12 sectors in 36 countries, we study the influence of firm characteristics on SMEs’ environmental behavior. Our results suggest that different characteristics have dissimilar influences on both types of environmental practices such as the type of customers served. Stringent environmental legislation encourages firms to actively take on environmental activities, but only in case of green products and services. Moreover, the dominant idea that small firms are reluctant to invest in environmental practices is clearly more nuanced: firm size matters most for engagement in greening processes. Finally, SMEs active in tangible sectors and that receive financial support are more involved in either type of environmental practices.

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Enterprises with 250 employees at most are considered SMEs (European Commission 2012a).

Specifically, information about the stringency of environmental legislation is not available for Albania, and there are too few Montenegrin observations to draw reliable conclusions on basis of the regressions in Sect. 5.

The exact source from which the sample is selected is not revealed by TNS because of confidentiality issues.

An overview of the numbers of interviews in each country and names of the TNS institutes that are responsible for data collection is provided on page 4 of the following document of the European Commission: http://ec.europa.eu/public_opinion/flash/fl_342_anx_en.pdf.

SMEs are first asked the following question: “What actions is your company undertaking to be more resource efficient?” One or more alternatives can be selected from the following list of resource efficient activities: saving water, saving energy, using predominantly renewable energy, saving materials, minimizing waste, selling crap material, and recycling. Actions not listed may also be answered. If an SME undertakes at least one resource efficiency action a follow-up question is asked about the fraction of last year’s turnover the SME has invested to be more resource efficient. The following answer possibilities are read out by the interviewer: 1–5; 6–10; 11–30; 31–50; 51–75; and 76–100 %. These intervals have been transformed into 1–10, 11–50, and 51–100 % by the authors.

The relevant question in the questionnaire is “Does your company offer green products or services?” If this question is answered affirmatively, a second question is asked about the percentage of turnover that the green products or services represent (“How much do these green products or services represent in your turnover of the latest available fiscal year?”). The intervals in the questionnaire have been transformed into 1–10, 11–50, and 51–100 % by the authors.

This means that the lowest category of the dependent variable is excluded in Model 3 in Tables 4 and 5. As a result, for greening processes, a binary logit regression is performed in Model 3 in Table 4. For greening product and service offerings, an ordered logit regression with three categories is performed in Model 3 in Table 5.

The external support variable is not included in Model 4.

An ordered logit model with J categories implies J–1 binary logit regressions: category 1 is contrasted with categories 2, …, J; categories 1 and 2 are contrasted with categories 3, …, J, and so on. The parallel regression assumption states that the coefficients of a variable are similar for each implied binary logit regression.

Interestingly stringency of environmental legislation has a significant positive coefficient for the first binary logit regression and a significant negative coefficient for the second binary logit regression. When performing the Wald tests, the EMS variable was excluded from the calculations. This is because the Brant test can only be computed if all independent variables are retained in each implied binary logit model. This is problematic for EMS because it shows very little variation for the lowest category of the dependent variable. Note that the Brant test does not have to be calculated for Model 3 in Table 4 because a binary logit model does not impose the parallel regression assumption.

The corresponding regression results are not shown here but are available from the authors upon request.

For example, by means of social desirability scales that are sometimes included in questionnaires (Nederhof 1985). The Marlowe-Crowne Social Desirability Scale (Crowne and Marlowe 1960) is a well-known and often-used example.

It is known that ordered logit models allow for reporting heterogeneity across several dimensions such as the country dimension which is known as “index shifting” (Lindeboom and Van Doorslaer 2004). The threshold parameters that are estimated in the context of the ordered logit model are made country specific by means of the inclusion of country dummies. Hence, differential response behaviors across countries are controlled for.

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Title: What drives environmental practices of SMEs?
Authors: Brigitte Hoogendoorn
Daniela Guerra
Peter van der Zwan
Publication date: 01-04-2015
Publisher: Springer US
Published in: Small Business Economics / Issue 4/2015
Print ISSN: 0921-898X
Electronic ISSN: 1573-0913
DOI: https://doi.org/10.1007/s11187-014-9618-9

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