Purpose: This study examined the influence of Social Capital (SC) on the performance of Small and Medium Enterprises (SMEs) in Wakiso District. Specifically, we identified forms of SC and assessed how they influenced SMEs’ performance in Kitemu Ward. Research methodology: Using qualitative and quantitative methodologies, the study solicited data from 40 participants through interviews and a self-administered questionnaire. Data were edited, cleaned, coded, and entered into MS Excel to generate frequencies and graphs. Content value analysis was used to transcribe the qualitative data and group them into subthemes. Results: The results revealed internal forms of SC, such as friends/peers, trustworthiness, personal saving, family, and social links, while external forms of SC were customers, financial institutions, governments, and companies. Further, SC influences the customer base, leads to high profits, increases sales volume, and enhances creativity and innovation. Limitations: Financial constraints prevented researchers from eliciting data throughout the study area. Furthermore, the small sample size might limit the generalization of the study results to the entire district. In addition, time and data collection biases have implications for this study. Contribution: This study is vital because there are no studies carried out in Kitemu Ward on SC and its influence on SMEs’ performance. The findings can be used by policy and decision-makers to design mechanisms for SMEs’ proprietors to integrate SC into business operations. The literature reveals that SC forms enhance sales, profits, market share, customer base, and accessibility to finance. Novelty: This original study was conducted primarily among women proprietors and managers of SMEs because they are reliable and provide firsthand information as compared to men. Thus, if adopted, the findings are vital for other business proprietors and managers to improve business performance.

Purpose: The main objective of the research was to analyse the effects of conversational artificial intelligence (AI) on bank operational efficiency. The emergency of conversational artificial intelligence (AI) has revolutionised the way business interacts with its customers. Methodology: The study employed a mixed- method approach where interviews and questionnaires were used to collect qualitative and quantitative data. A sample of 92 bank employees was drawn from ten Zimbabwean banks. Findings: Conversational AI has a positive impact on banking operational efficiency. Specifically, conversational AI improves customer services by providing faster and more accurate responses to customer inquiries, reduces operational costs by automating routine tasks and improve workflow efficiency Limitation: The study concentrated on the banking industry of one particular country. Contribution: The study makes a significant contribution in understanding the advantages of adopting conversational artificial intelligence in banking operations.

Purpose: Telecommunications has undergone significant development over time. Telecommunications narrows distance and facilitates access to information. Infrastructure within telecommunications plays a crucial role in the establishment and continuity of communication. Investment involves the allocation of money or capital with the aim of gaining profit. Stocks are one of the instruments in investment; besides gaining profit, investors also face the risk of experiencing losses. Therefore, it is necessary to provide value protection to investors’ stock. The objective of this study is to explore the implementation of option contracts in the Black-Scholes model using Historical Volatility and GARCH Volatility with a collar strategy. Research methodology: The data used in this study consists of the closing prices of TOWR and TBIG stocks from the period to 2011-2022. The research findings conclude that for TOWR stocks under non-crisis conditions with a maturity of three months, the Black-Scholes model with GARCH Volatility performs better than the model with Historical Volatility in every scenario. Meanwhile, for TOWR stocks under crisis and non-crisis conditions with maturities of one and three months, the Black-Scholes model using Historical Volatility is superior to the model using GARCH Volatility in one of the scenarios. Results: In the case of TBIG stocks under crisis conditions with a maturity of three months, the Black-Scholes model with GARCH Volatility outperforms the model with Historical Volatility in all scenarios. However, under the conditions of a 1-month maturity and non-crisis conditions for one and three months in one scenario, the Black-Scholes model using Historical Volatility is superior to the model using GARCH Volatility. Contribution: The contribution is Investment involves the allocation of money or capital with the aim of gaining profit.