The study aims to explain the relationship between the effectiveness of a business and its management through the analysis of working capital. The findings prove the complementary relationship. The analysis of working capital will always have a significant impact on the effectiveness of business management. The main objective of any corporation is to be effective in business, which can be achieved by analyzing the working capital. The result shows that analysis of working capital based on factors like operational efficiency, the company’s earnings and profitability, cash management, corporate receivable management, and corporate inventory management creates room for improvement and effectiveness in business management. Firms might enhance finances for business expansion by lowering their working capital requirements. It has also been revealed that there is a considerable difference in industries across time. It was observed that there is a high association between working capital efficiency and firm profitability. A highly efficient corporation is less vulnerable to liquidity risk and is also self-sufficient in terms of external finance. Numerous studies have been done to regulate the true rapport between working capital investments and their impact on financial presentation. It demonstrates that effective investment in working capital management may boost profitability and business value. The relationship between accounting and finance was explained by measuring working capital management in demand to illustrate the status of profitability. It was suggested that accountants take a more professional approach to updating their accounting and finance skills in their organization through effective working capital management.

This study investigates the buying styles of young consumers, especially the millennials—Gen Y, and Gen Z whose idiosyncrasies and consumption peculiarities are quite different from the older generations. Besides Sproles and Kendall’s eight (8) consumer-style inventory dimensions, this study presents new dimensions and develops six constructs that define young consumers’ decision-style inventory in a developing market. The study population consisted of all younger consumers—Gen Y, and Gen Z in Lagos State, Nigeria. One hundred and twenty-five (125) respondents were selected randomly across all 20 Local Governments in Lagos State, Nigeria. Factor analyses through varimax rotation, latent root criterion (eigenvalue = 1), screen plot test and the percentage of variance were conducted to determine the significant factors to retain among the variables. The findings clearly showed that newly developed CSI constructs in this study (sexiness, trendiness, global branding, smartness, socialisation and entertainment) were strong and significant among young consumers’ decision-making styles. The six (6) constructs developed showed that the younger consumers’ consumption styles are evolving, becoming sophisticated and relatively dynamic, hence the reliance on Sproles and Kendall’s dimensions to measure the younger consumers’ consumption decision styles will be inadequate in business/behaviour strategy development. The dimensions of entertainment, sexy, social, trendy, smartness and global branding variables are mostly underpinned and dominate considerations in purchase decision styles and behaviours among young consumers.

Accurate demand forecasting is key for companies to optimize inventory management and satisfy customer demand efficiently. This paper aims to Investigate on the application of generative AI models in demand forecasting. Two models were used: Long Short-Term Memory (LSTM) networks and Variational Autoencoder (VAE), and results were compared to select the optimal model in terms of performance and forecasting accuracy. The difference of actual and predicted demand values also ascertain LSTM’s ability to identify latent features and basic trends in the data. Further, some of the research works were focused on computational efficiency and scalability of the proposed methods for providing the guidelines to the companies for the implementation of the complicated techniques in demand forecasting. Based on these results, LSTM networks have a promising application in enhancing the demand forecasting and consequently helpful for the decision-making process regarding inventory control and other resource allocation.