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The rapid growth of digitalization is being used for the betterment of the banking and financial services sector and many other industries. Digital banking (DB) is transforming traditional banking activities into a digital environment. The benefits and conveniences that DB bring to consumers and financial institutions (FIs) have led FIs to adopt various DB innovations. However, to determine whether the demand for DB is at a healthy level, it is necessary to evaluate how DB innovations are accepted among consumers. This chapter is a “viewpoint” of the author that reviews the background of DB in Sri Lanka (SL) and evaluates the success of its diffusion.

The status of the DB diffusion in SL is discussed under DB ecosystem, and DB customer adoption. The DB ecosystem is discussed through the topics of the country’s digital infrastructure (DI), technological know-how within the banks, technology adoption of the market vendors, and consumer’s digital literacy. Then, the consumer use of the DB services is evaluated using the transactions that happened through DB systems against paper-based payments. Statistics presented by Central Bank of Sri Lanka (CBSL) are used as secondary data for the study.

According to the findings of this report, consumer DB adaption is still in its infancy compared to the development of the country’s DB ecosystem. Considering the causes that drives consumer innovation decisions, this chapter highlights the need for industry practitioners to revisit their DB marketing strategies based on consumers’ culture and innovativeness. To that end, further studies are necessary on how individuals’ culture influences DB adoption.

Under-reporting of occupational accidents is a common problem in many countries. This is mainly because of the shortfalls in accident reporting and recording systems. Construction industry being a hazardous industry, the rate of accidents is higher compared with other industries and apparently a high rate of under-reporting. The purpose of this paper is to investigate the rate of under-reporting, significant reasons for under-reporting and identify the shortcomings in the existing accident reporting system in Sri Lanka in aiming to recommend efficient mechanisms for occupational accident recording and reporting to construction industry.

Both secondary and primary data were tapped to gather required data. The secondary data were extracted from the records available in year 2014-2015 at the office of the commissioner for workmen’s compensation and the industrial safety division of the Department of Labor (DoL) to analyze the rate of under-reporting. The primary data were obtained through expert interviews to explore the gaps in reporting system and to identify mechanisms to reduce under-reporting.

The findings revealed 80 per cent of construction accidents are under-reported. Eight gaps in the current accident recording and reporting system and key recommendations at organizational and national level for its improvements were identified.

The findings provide an insight of occupational safety and health (OSH) practices in construction industry and it can be used as an eye opening flash for safety law-makers and practitioners to revisit the existing regulations and practices.

The time series forecasting is an essential methodology which can be used for analysing time series data in order to extract meaningful statistics based on the information obtained from past and present. These modelling approaches are particularly complicated when the available resources are limited as well as anomalous. The purpose of this paper is to propose a new hybrid forecasting approach based on unbiased GM(1,1) and artificial neural network (UBGM_BPNN) to forecast time series patterns to predict future behaviours. The empirical investigation was conducted by using daily share prices in Colombo Stock Exchange, Sri Lanka.

The methodology of this study is running under three main phases as follows. In the first phase, traditional grey operational mechanisms, namely, GM(1,1), unbiased GM(1,1) and nonlinear grey Bernoulli model, are used. In the second phase, the new proposed hybrid approach, namely, UBGM_BPNN was implemented successfully for forecasting short-term predictions under high volatility. In the last stage, to pick out the most suitable model for forecasting with a limited number of observations, three model-accuracy standards were employed. They are mean absolute deviation, mean absolute percentage error and root-mean-square error.

The empirical results disclosed that the UNBG_BPNN model gives the minimum error accuracies in both training and testing stages. Furthermore, results indicated that UNBG_BPNN affords the best simulation result than other selected models.

The authors strongly believe that this study will provide significant contributions to domestic and international policy makers as well as government to open up a new direction to develop investments in the future.

The new proposed UBGM_BPNN hybrid forecasting methodology is better to handle incomplete, noisy, and uncertain data in both model building and ex post testing stages.

This study examines and compares different specifications of the fiscal policy rule in the fiscal sustainability analysis of Nigeria.

This is methodologically achieved by estimating the baseline constant-parameter and Markov regime switching fiscal models. The asymmetric autoregressive distributed lag fiscal model is also employed to substantiate the differential responses of fiscal authorities to public debt.

The baseline constant-parameter fiscal model provides mixed results of sustainable and unsustainable fiscal policy. The inconclusiveness is adduced to instability in primary fiscal balance–public debt dynamics. This makes it necessary to capture regime switches in the fiscal policy rule. The Markov switching estimations show a protracted fiscal unsustainable regime that is inconsistent with the intertemporal budget constraint (IBC). The no-Ponzi game and debt stabilizing results of the Markov switching fiscal model further revealed that the transversality and debt stability conditions were not satisfied. Additional findings from the asymmetric autoregressive model estimation show that fiscal consolidation responses vary with contraction and expansion in output and spending, coupled with downturns and upturns in public debt dynamics in both the long and short run. These findings thus confirm the presence of asymmetries in the fiscal policy authorities' reactions to public debt. Further, additional evidences show the violation of the IBC which is exacerbated by the deleterious effect of the pro-cyclical fiscal policy response in boom on the improvement of the primary fiscal balance.

This study deviates from the extant literature by accommodating time variation, periodic switches and fiscal policy asymmetries in the fiscal sustainability analysis of Nigeria.

The model and existing practice of the construction supply chain (CSC) in the United Kingdom (UK) and Australia was presented in this chapter. The policies and reports that support the practice of the CSC were examined in both countries. It was discovered from the review of literature that the UK has a more detailed report targeted at improving the CSC than Australia. However, both countries have a common factor affecting their CSC which originates from fragmentation experienced within their supply chain. Construction stakeholders in the UK and Australia believe that collaboration and integration are vital components for improving performance. The majority of the contractors in both countries embrace collaborative working for the sole purpose of risk sharing, access to innovation and response to market efficiency. However, most of the models developed for managing the CSC in the UK are built around building information modelling (BIM). Also, the reviewed studies show that supply chain management practice will be effective following the following principle: shared objectives, trust, reduction in a blame culture, joint working, enhanced communication and information-sharing. Finally, the UK has a more established framework and more CSC models compared to Australia.

This study analyzes the impact of exchange rate volatility (ERV) on inbound tourist arrivals from four ASEAN countries namely Indonesia, the Philippines, Singapore, and Thailand during 1970–2017. Volatility in the exchange rates between the tourist currency and ringgit Malaysia is measured using the Generalized Autoregressive Conditional Heteroskedasticity model. The results from Autoregressive Distributed Lagged models indicate that ERV has no significant impact on tourist arrivals from ASEAN to Malaysia. This implies that tourists from these countries may not be sensitive to ERV when choosing Malaysia as their travel destination. There are two possible explanations for the results. First, Malaysian ringgit has been depreciating against major currencies and regional currencies in recent years, which makes ringgit relatively cheaper than other ASEAN currencies. Second, the empirical results of the study support the argument that ERV has a more serious impact on tourist spending compared to tourist arrivals.

Purpose: The study aims to explore the wider acceptance of blockchain technology and growing faith in this technology among all business domains to mitigate the chances of fraud in various sectors.

Design/Methodology/Approach: The authors focus on studies conducted during 2015–2022 using keywords such as blockchain, fraud detection and financial domain for Systematic Literature Review (SLR). The SLR approach entails two databases, namely, Scopus and IEEE Xplore, to seek relevant articles covering the effectiveness of blockchain technology in controlling financial fraud.

Findings: The findings of the research explored different types of business domains using blockchains in detecting fraud. They examined their effectiveness in other sectors such as insurance, banks, online transactions, real estate, credit card usage, etc.

Practical Implications: The results of this research highlight (1) the real-life applications of blockchain technology to secure the gateway for online transactions; (2) people from diverse backgrounds with different business objectives can strongly rely on blockchains to prevent fraud.

Originality/Value: The SLR conducted in this study assists in the identification of future avenues with practical implications, making researchers aware of the work so far carried out for checking the effectiveness of blockchain; however, it does not ignore the possibility of zero to less effectiveness in some businesses which is yet to be explored.