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The study focuses on green competitive advantage from a multi-dimensional perspective, investigating the impact of green marketing tools and company descriptive variables on these dimensions. The data were collected from small, medium and micro enterprises (SMME) from Western Cape/South Africa, an area marked by long-term water consumption restrictions. A qualitative approach was considered for variable tailoring to the SMMEs’ peculiarities, followed by a quantitative study, employing a sample of 237 companies, for testing each competitive advantage dimension against the established green marketing tools and company descriptive variables using logistic regressions. Each competitiveness variable was explained by at least one green marketing tool. Donating money and/or allocating time for environmental purposes explained three dependent variables, while selling biodegradable/recycled/refurbished products had an inverse relationship with two of them. Business type and number of operational years had a significant impact on three dimensions. This study enriches the literature by using green competitive advantage dimensions and not a latent factor, analyzing the impact of company descriptive variables as explanatory variables and prompting green strategies for small and medium businesses. The model could be improved by tests in other geographic areas, including green distribution and price variables and other descriptive factors (turnover, responsible investment and internationalization).

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Marketing practitioners nowadays often have to base important management decisions on information obtained in research surveys conducted in shopping malls. The shopping mall-intercept survey is a very popular method used by research agencies to gather information of almost all kinds. While the advantages of shopping mall-intercept interviewing are considerable, their adoption without recognition of their shortcomings is not prudent. Selection error, resulting in time sampling bias, occurs in shopping mall-intercept surveys as a result of significant differences between the characteristics of persons visiting a shopping mall at different time segments during a month period. In this empirical study, the authors explain the problem of time-based selection error in mall-intercept data, the failure of common research practices to correct it, and develop a specific time-bias reduction technique.