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Introduction: Why companies pay dividends and the determinants of dividend policy are considered an unresolved dividend puzzle. To reach a consensus over the puzzle, researchers must investigate the factors affecting dividend policy by incorporating all the determinants into a single research effort.

Purpose: We examine the dividend policy determinants of Sri Lankan firms, explicitly focusing on the banking, finance, and insurance (BFI) sectors.

Methodology: This study uses the quantitative approach applying the Generalized Method of Moments (GMM) system to examine the dividend policy determinants by obtaining secondary data from 51 listed BFI organisations in Sri Lanka.

Findings: The analysis disclosed that the variables of changes in revenues, firm size, liquidity, corporate tax, business risk, and profitability have a positive relationship with dividend yield, whereas investment opportunities, leverage, change in revenues, corporate tax, and firm size impact positively on the propensity to pay dividends in BFI organisations in Sri Lanka. Our findings opine that managers in the BFI industries should prioritise changing their dividend policies by paying close attention to factors, such as dividend yield, changes in revenue, firm size, liquidity, corporate tax ratio, business risk, and profitability because the dividend policy is critical to retaining current investors and luring new ones.

This chapter provides an overview of the explanations and factors affecting dividend policy. This study employs a systematic literature review approach to review a large sample of studies related to the dividend puzzle. Although the analysis reveals mixed evidence involving the theories and determinants of dividend policy, some determinants appear in numerous studies. However, no consensus exists on an optimal dividend to resolve the dividend puzzle, and the authors propose a model to deal with the same. When examining dividend policy, researchers should consider the firm, market, behavior, and other determinants. When making significant dividend or stock decisions, managers and shareholders should also contemplate the factors, interactions, inadequacies, and consequences. Future researchers should strive to take a more comprehensive view when resolving the dividend puzzle. This study provides a current and complete picture of dividend policy's available theories and empirical determinants. Its significant contribution is identifying some of the more consistently essential determinants of dividend policy while proposing a holistic model to address the prevailing dividend dilemma.

This study aims to determine the impact of health-related views on mobile payment adoption in Sri Lanka from a broader viewpoint. The scale used to quantify each construct was based on earlier research, with modest alterations to fit the pandemic situation. First, an online survey was administered to undergraduates using convenience sampling to acquire appropriate replies. Eliminating incomplete and unusable questionnaires, 266 responses were gathered with an 88.7% response rate. Finally, after removing incomplete and ineffective questionnaires, 243 responses were selected for the analysis. Health consciousness, perceived ease of use, and usefulness have a significant positive relationship between attitude and behavioural intention to mobile payments. Moreover, the attitude has a significant positive relationship with mobile payment usage. As the health consciousness increases the usefulness and intention to use mobile payments, bank managers can focus on this new customer segment. Accordingly, they can use their promotional campaigns to highlight the importance of shifting towards m-payments during the pandemic times. This is the first study that investigates the role of health-related perceptions on the mobile payment adoption in Sri Lanka to the best of the authors’ knowledge.

Purpose: This chapter examines the effect of COVID-19 on the stock market volatility (SMV) in the Colombo Stock Exchange (CSE), Sri Lanka.

Need for the Study: The study is necessary to understand investor behaviour, market efficiency, and risk management strategies during a global crisis.

Methodology: Utilising daily All Share Price Index (ASPI) data from 2 January 2018 to 31 August 2021, the data are divided into subsamples corresponding to the pre-pandemic period, the pandemic period, and distinct waves of the pandemic. The impact of the pandemic is investigated using the Mann–Whitney U test, the Kruskal–Wallis test, and the Exponential Generalised Autoregressive Conditional Heteroscedasticity (EGARCH) model.

Findings: The pandemic considerably affected CSE – the Mann–Whitney U test produced different market returns during the pre-COVID and COVID eras. The Kruskal–Wallis test improved performance during COVID-19 but did not continue to do so across COVID-19 waves. The EGARCH model detected increased volatility and risk during the first wave, but the second and third waves outperformed the first. COVID-19 had a minimal overall effect on CSE market results. GARCH and Autoregressive Conditional Heteroskedasticity (ARCH) models identified long-term variance memory and volatility clustering. The News Impact Curve (NIC) showed that negative news had a more significant impact on market return volatility than positive news, even if the asymmetric term was not statistically significant.

Practical Implications: This study offers significant insight into how Sri Lanka’s SMV is affected by COVID-19. The findings help create efficient mitigation strategies to mitigate the negative consequences of future events.

The goal of this study is to look into the connection between Sri Lanka’s fiscal deficit and inflation. Sri Lanka is currently experiencing one of its worst inflation crises in its history, necessitating an investigation into how fiscal deficit affects inflation, as it has been experiencing an ever-increasing fiscal deficit for the last four decades. The quantitative methodology is employed in this study using annual data from 1977 to 2019 following the ARDL technique in the analysis. The findings showed that both in the long run and the near term, Sri Lanka’s fiscal deficit had a positive and significant link with inflation. The policymakers should increase the revenue through the taxes in order to bridge the fiscal deficit. As a developing country, it cannot afford to continue with the ever-increasing fiscal deficit which has become a burden to country. Also, it is the responsibility of each government to think carefully to reduce its massive expenditure which has become a common feature in the country for the last four decades. Cutting down government expenditure can improve the economic growth and well-being of the citizens too. The government should therefore concentrate on short-term investment programmes that will benefit the country while doing the same in the long run.

This study examines the impact of financial services employees’ engagement on the attitude toward artificial intelligence (AI) in Sri Lanka. The model developed in this study comprises three constructs to demonstrate employee engagement (EE), namely, psychological state engagement (PSE), trait engagement (TE), and cognitive engagement (CE). Further, the model tests the moderating effect of age and gender on the relationship between EE and attitude toward AI. The authors conducted this research through a survey of 200 employees at Sri Lankan financial service firms. The findings indicate that EE substantially impacts attitudes regarding AI. Additionally, PSE, CE, and attribute engagement substantially affect how people feel about AI. Further, age has a moderating effect on the association between EE and attitude toward AI among financial services personnel. Based on the findings and discussion, the researcher recommends AI’s most significant importance in promoting a high level of engagement. The results would be helpful for both academic researchers and practitioners, especially HR managers, to understand the status of AI in Sri Lanka and formulate strategies to expedite EE.

Purpose: This study investigates internal/own shock in the domestic market and three external volatility spillovers from India, the UK, and the USA to the Sri Lanka stock market.

Need for the Study: The external market’s internal/own shocks and volatility spillovers influence portfolio choices in domestic stock market returns. Hence, it is required to investigate the internal shock in the domestic market and the external volatility spillovers from other countries.

Methodology: This study employs a quantitative method using ARMA(1,1)-GARCH(1,1) model. All Share Price Index (ASPI) is the proxy for the Colombo Stock Exchange (CSE) stock return. It uses daily time-series data from 1^st April 2010 to 21^st June 2023.

Findings: The findings revealed that internal/own and external shocks substantially impact the stock price volatility in CSE. Significant volatility clusters and persistence with extended memory in ASPI confirm internal/own shock in the market. Furthermore, CSE receives significant volatility shock from the USA, confirming external shock. This study’s findings highlight the importance of considering internal and external shocks in portfolio decision-making.

Practical Implications: Understanding the influence of internal shocks helps investors manage their portfolios and adapt to market volatility. Recognising significant volatility spillovers from external markets, especially the USA, informs diversification strategies. From a policy standpoint, the study emphasises the need for robust regulations and risk management measures to address shocks in domestic and global markets. This study adds value to the literature by assessing the sources of volatility shocks in the CSE, employing the ARMA-GARCH, a sophisticated econometrics model, to capture stock returns volatility, enhancing understanding of the CSE’s volatility dynamics.

This study investigates the motivation among customers to be more loyal to online food delivery applications (OFDA) services even after the COVID-19 epidemic by using perceived service quality aspects in Sri Lanka. The data were gathered by physically distributing a self-administrated questionnaire to clients in Sri Lanka who continue to use OFDA services on platform to customer (P2C) service delivery platforms to buy food despite the COVID-19 outbreak. Multiple regression is employed to analyse 287 effective observations, and the data revealed the significant positive effect of interaction, environment, outcome, and food qualities on customer loyalty to OFDA services. In fact, there is no impact from the delivery quality on customer loyalty to OFDA services due to outsourced food delivery. The findings suggest regular improvements in attributes such as interaction, environment, outcome, and food qualities in this hyper-competitive business environment. Further, this study sets substantial facts for the interested parties to establish an exemplary delivery system and other technological advancements to have a sustainable competitive advantage and solid customer base in the long run.

Purpose: This chapter aims to unveil the challenges of adopting and using banking chatbots in India and identify the challenges of Chat Generative Pre-trained Transformer (ChatGPT) for future banking.

Need for the study: Unveiling the challenges of chatbots and ChatGPT in the banking industry in India is crucial to understand the limitations and areas of improvement to enhance customer experience, ensure data security, and maintain regulatory compliance.

Methodology: The researchers conducted a narrative review systematically summarising and analysing existing literature on chatbots and ChatGPT, providing a comprehensive overview of the challenges faced in the industry.

Findings: The authors identify perceived risk, platform quality, connectivity and infrastructure, data privacy and security, user education and acceptance, existing legacy systems, and regulatory guidelines as the challenges of adopting chatbots. Additionally, the findings reveal that the challenges posed by ChatGPT in future banking include the potential reduction of traditional banking jobs, linguistic diversity, data privacy and security, ethical considerations and bias mitigation, explainability and accountability, integration with existing banking systems, and user trust and acceptance. However, implementing these new technologies also presents opportunities for individuals with unique human skills, such as critical thinking, empathy, and creativity, which are difficult to replace with technology.

Practical implications: By minimising the challenges of ChatGPT and chatbots, the banking industry could achieve improved customer service, cost efficiency, automation of routine tasks, and 24/7 availability, leading to enhanced customer satisfaction and operational efficiency in the banking industry. Additionally, these artificial intelligence (AI) tools enable data-driven insights, personalised experiences, scalability, and efficient handling of large customer volumes, contributing to better decision-making and enhanced customer engagement.