The focus of this paper is to present an exploratory study on the level of awareness regarding green marketing and its managerial implications, among selected, South African Manufacturing Small, Medium and Micro Enterprises (SMMEs), in the province of KwaZulu–Natal (KZN). The concept of green marketing and thought provoking managerial implications are still an issue of concern in the South African manufacturing sector. The study aimed to explore the awareness levels about green marketing by selected South African manufacturing SMMEs in KZN, and the resulting managerial implications. Primary data was collected from 84 manufacturing SMMEs. This research was quantitative in nature and a questionnaire was used to collect data from SMMEs owners/managers in KZN. Findings of the research indicate that SMMEs in the study are aware of green marketing and its managerial implications. It further reveals that SMMEs’ owners/managers indicate that the South African Environmental Act and Consumer Protection Act are additional factors that influence their businesses operations. The paper will benefit SMMEs owners/managers, SMMEs marketing managers, and affiliated stakeholders, by introducing a new understanding of green marketing and how to cope with the demand of new green marketing strategies. Most work on the Green Zone has concentrated on green products, with little emphasis on green marketing and its implications. The findings are limited by the study’s exploratory, quantitative nature and small sample. Generalisation should be done with care and further research, with a large sample and consideration of other provinces, is therefore recommended.

Credit Decisions are extremely vital for any type of financial institution because it can stimulate huge financial losses generated from defaulters. A number of banks use judgmental decisions, means credit analysts go through every application separately and other banks use credit scoring system or combination of both. Credit scoring system uses many types of statistical models. But recently, professionals started looking for alternative algorithms that can provide better accuracy regarding classification. Neural network can be a suitable alternative. It is apparent from the classification outcomes of this study that neural network gives slightly better results than discriminant analysis and logistic regression. It should be noted that it is not possible to draw a general conclusion that neural network holds better predictive ability than logistic regression and discriminant analysis, because this study covers only one dataset. Moreover, it is comprehensible that a “Bad Accepted” generates much higher costs than a “Good Rejected” and neural network acquires less amount of “Bad Accepted” than discriminant analysis and logistic regression. So, neural network achieves less cost of misclassification for the dataset used in this study. Furthermore, in the final section of this study, an optimization algorithm (Genetic Algorithm) is proposed in order to obtain better classification accuracy through the configurations of the neural network architecture. On the contrary, it is vital to note that the success of any predictive model largely depends on the predictor variables that are selected to use as the model inputs. But it is important to consider some points regarding predictor variables selection, for example, some specific variables are prohibited in some countries, variables all together should provide the highest predictive strength and variables may be judged through statistical analysis etc. This study also covers those concepts about input variables selection standards.