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Sri Lanka is a lower-middle-income country with a high poverty rate, particularly in urban areas. The urban poverty rate increased to 5.3% in 2020 and the population fell below the poverty line. The contribution of small and medium enterprises (SMEs) to alleviate urban poverty has now been recognized as a key indicator. More than 50% of SMEs have contributed in diverse ways to the growth and development of Sri Lanka. Therefore, this chapter aims to examine the impact of SMEs on urban poverty in Sri Lanka. The main objectives of this study are to identify urban poverty, understand the importance and propensity of individuals to establish SMEs, identify the impact of SMEs to reduce urban poverty and find solutions to reduce poverty and enhance growth in Sri Lanka. This study was based on quantitative methodology by selecting a study sample in the Bamunakotuwa Divisional Secretariat Division of Kurunegala District in Sri Lanka. Probability sampling techniques like cluster and simple random sampling methods were used with survey and questionnaire methods for data collection. Correlation analysis and multiple regression were used for analysing data using SPSS software. The key findings in this research indicate that SMEs have a significant impact on reducing urban poverty and they help to generate growth in Sri Lanka.

This investigation aims to compare the usefulness and the potential contributions of Artificial Neural Networks (ANNs) in the marketing field, particularly, when compared to traditional modelling based on Structural Equations. It uses neural network modelling and structural equation modelling (SEM) to evaluate loyalty in the bank industry in Brazil. Based on a data collection of 229 bank customers (micro, small, and medium companies) from the Northeast of Brazil, the key objective of this study is to investigate the main drivers of customer loyalty in this industry. Neural networks highlight the role of the relationship quality on customer loyalty. The technique SEM confirmed six of the seven hypotheses of the proposed model. The findings highlighted the point that micro, small, and medium companies’ loyalty to their main bank is strongly influenced by affective commitment. Comparing the results achieved from both methodologies, some similarities can be found. Relationship quality is a second order construct that includes satisfaction and affective commitment as its key components, both of which are highlighted on the structural model. The strongest impact in this model is in the relation between satisfaction and affective commitment. This result suggests that, for this marketing problem, ANN and SEM seem to be complementary statistical tools, bringing complementary conclusions.

This investigation aims to compare the usefulness and the potential contributions of Artificial Neural Networks (ANNs) in the marketing field, particularly, when compared to traditional modelling based on Structural Equations. It uses neural network modelling and structural equation modelling (SEM) to evaluate loyalty in the bank industry in Brazil. Based on a data collection of 229 bank customers (micro, small, and medium companies) from the Northeast of Brazil, the key objective of this study is to investigate the main drivers of customer loyalty in this industry. Neural networks highlight the role of the relationship quality on customer loyalty. The technique SEM confirmed six of the seven hypotheses of the proposed model. The findings highlighted the point that micro, small, and medium companies' loyalty to their main bank is strongly influenced by affective commitment. Comparing the results achieved from both methodologies, some similarities can be found. Relationship quality is a second order construct that includes satisfaction and affective commitment as its key components, both of which are highlighted on the structural model. The strongest impact in this model is in the relation between satisfaction and affective commitment. This result suggests that, for this marketing problem, ANN and SEM seem to be complementary statistical tools, bringing complementary conclusions.