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Know your customer (KYC), accounting standards, issuance, clearing, and trade settlement became the major barrier to implement accounting, accountability and assurance process in supply chain finance (SCF). Blockchain technology features have the potential to solve accounting problems. This research focuses on exploring how blockchain technology provides solutions to overcome the barriers of accounting process in SCF. The benefits, opportunities, costs and risks related to blockchain adoption are also explored.

Multi-case study and qualitative methods are used with a framework based on blockchain role to overcome the accounting process barriers. Ten blockchain projects in SCF and 29 interviews of participants as a unit of analysis are considered.

The findings indicate that blockchain technology offers solutions to solve accounting, accountability and assurance problems in SCF. Validity, verification, smart contracts, automation and enduring data on trade transactions potentially solve those barriers. However, it is also necessary to consider costs such as implementation, technology, education and integration costs. Then there are possible risks such as regulatory compliance, operational, code development and scalability risk. This finding reflects the current status of blockchain technology roles in SCF.

This study unveils blockchain's SCF accounting potential, emphasizing multi-case method limitations and future research prospects. Diverse contexts challenge findings' applicability, warranting cross-industry studies for deeper insights. Addressing selection bias and integrating quantitative measures can enhance understanding of blockchain's accounting impact.

Accounting professionals can get an idea of the future direction and impact of blockchain technology on accounting, accountability and assurance processes.

This study provides initial findings on the potential, costs and risks of blockchain that is beneficial for parties involved in SCF, especially for banks and insurance underwriters. In addition, the findings also provide direction for the contribution of blockchain technology to accounting theory in the future.

This study aims to explore the challenges faced by the tourism and hospitality industry following the COVID-19 pandemic and to propose effective strategies for recovery and resilience of this sector.

The study analysed the challenges encountered by the tourism and hospitality industry post-pandemic and identified key strategies for overcoming these challenges. The study utilised the modified Delphi method to finalise the challenges and employed the Best-Worst Method (BWM) to rank these challenges. Additionally, solution strategies are ranked using the Criteria Importance Through Intercriteria Correlation (CRITIC) method.

The study identified significant challenges faced by the tourism and hospitality industry, highlighting the lack of health and hygiene facilities as the foremost concern, followed by increased operational costs. Moreover, it revealed that attracting millennial travellers emerged as the top priority strategy to mitigate the impact of COVID-19 on this industry.

This research contributes to understanding the challenges faced by the tourism and hospitality industry in the wake of the COVID-19 pandemic. It offers valuable insights into practical strategies for recovery. The findings provide beneficial recommendations for policymakers aiming to revive and support these industries.

By analyzing tourist choices in Side and Alanya, well-known destinations for tourists in Türkiye’s thriving urban tourism sector, this study aims to fill a crucial vacuum in the body of knowledge about urban tourism. The study examines the changing dynamics of consumer preferences for advertisements and closely examines the underlying factors that influence these preferences, both pre and post-influential COVID-19 period.

This study clarifies the complex interplay between tourism marketing and prospective tourists’ decision-making processes through a thorough examination. This research greatly improves our understanding of urban tourism marketing strategies by examining the varying effects of advertising channels and comparing the persuasive power of emotional versus numerical advertising messages.

This study’s findings significantly advance our understanding of urban tourism. Examining how visitors react to advertisements in the various urban environments of Side and Alanya offers insightful information on how marketing strategies and visitor preferences correlate. This research also reveals the subtleties of efficient communication techniques, providing a practical basis for improving urban tourism experiences.

Being the first study of its sort, to the best of the authors’ knowledge, this research’s originality is supported by its insights into how advertising, consumer preferences and the urban tourism environment interact. The significant contribution to knowledge highlights the implications for those involved in urban tourism and provides practical advice for improving advertising tactics in the post-COVID-19 age.

This study aims to examine the role of financial inclusion and institutional factors such as corruption and the rule of law (RL) on the credit risk and stability of banks.

The study considers a sample of 61 developing countries and uses very robust estimation techniques that allow controlling for endogeneity, heteroskedasticity and serial correlation, such as instrumental variables method in two-stage least squares (IV-2SLS), instrumental variables generalized method of moments (IV-GMM), as well as system of generalized methods of moments in two stages (Sys-2GMM).

The results confirm that financial inclusion and strengthening the RL can significantly contribute to reducing credit risk and improving the financial stability of banks; in contrast, the authors find that weak control of corruption aggravates credit risk. In addition, they found that greater competitiveness in the banking sector increases credit risk.

This study supports the need to promote financial inclusion and strengthen institutional factors to improve the stability of the banking sector, as well as promote general well-being in the economy.

This study contributes to the scarce literature by simultaneously using institutional factors such as corruption and the RL and macroeconomic variables such as economic growth and inflation in the relationship between financial inclusion and the banking sector, as well as considering competitiveness as an explanatory factor for banks’ credit risk and stability.

This study examines the extent to which regulatory policy uncertainty, macroeconomic risk, banking industry innovations, etc. influence variability in financial sector development among emerging economies in sub-Sahara Africa (SSA).

Data for the empirical inquiry were compiled from a sample of 25 economies from the subregion from 2010 to 2020. Empirical estimates examining the relationships noted above were carried out using the two-step system generalized method of moments estimation technique.

Results the empirical estimates suggest that regulatory policy uncertainty and macroeconomic risk adversely influence or constrain financial sector development among the economies examined in the study. Banking industry innovations on the other hand is found to positively influence the development of the financial sector in these economies. Furthermore, moderating empirical analysis suggests that effective governance positively moderates the relationship between banking industry innovations and financial development among economies in the subregion.

This study’s approach to the mechanics of financial development among economies in SSA is designed to offer different perspectives to those found in the existing literature on financial development in three fundamental ways. First, although the verification of the role of banking industry innovations in financial development may not be new, it is important to point out that the approach used in this study is based on an index for innovations with different constituents or principal components in its construction; making the variable significantly different from what has been examined in the literature. In addition, the review of regulatory policy uncertainty and macroeconomic risk (both variables are multifaceted constructs using the principal component analysis procedure) further brings into this study’s analysis, a different approach to examining conditions influencing variability in financial development among developing economies.

The main purpose of this research is to (1) prioritise key determinants of Industry 4.0 (I4.0) readiness assessment and (2) evaluate causal relationships among those determinants and associated sub-criteria based on inputs from industry experts.

The methodology involved two phases: (1) an MCDM approach for determining causal relationships among determinants and (2) empirical validation of findings from the first phase using industry experts' inputs.

It was found that while the choice of I4.0 technologies is important, organisational factors are also critical, as evidenced by the ranking of the “Strategy and Organisation” determinant as the highest rank prominent determinant. Also, the ranking of the sub-criteria within each determinant shows the importance of several organisational influencing and resulting sub-criteria.

This research extends the existing literature on I4.0 by demonstrating the prioritisation of determinants and delineating causal relationships among them and associated sub-criteria as a basis for developing I4.0 adoption guidelines. This research is limited to the specific scope of determinants selected/considered and experts' inputs from the Sri Lankan manufacturing sector. Future studies could consider extending this research into a broader global manufacturing context.

Prioritisation and causal relationships of I4.0 readiness assessment determinants, supported with inputs from functional managers and industry experts, could be used to guide practitioners in developing guidelines for I4.0 adoption in a phased manner.

This research provides a re-evaluation and validation of a selected I4.0 readiness assessment framework from the perspectives of interdependencies and casual relationships among its determinants and sub-criteria, based on inputs from industry experts as a basis for developing guidelines for I4.0 adoption.

This study examined the influence of income and health insurance on the health-seeking behaviour of rural residents, addressing the concerns of endogeneity and heterogeneity bias.

A two-stage residual inclusion was utilised to correct self-selection-based endogeneity problems arising from health insurance membership.

This study provides support for Andersen's behavioural model (ABM). Income and health insurance positively stimulate rural residents' use of modern healthcare services, but the effect of insurance risks a downward bias if treated as exogenous. Further, the effect of health insurance differs between males and females and between adults and the elderly.

This study advances the literature, arguing that, within the ABM framework, enabling (i.e. income and insurance) and predisposing factors (i.e. age and gender) complement each other in explaining rural residents' use of modern health services.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-03-2023-0223

Stock markets are highly sensitive to foreign and domestic events. Stock exchange markets react promptly to news and are known as an indicator of good and bad trading conditions. Terrorist attacks leave adverse effects on the economy and cause stock price volatility and, consequently, stock return volatility. Therefore, this paper aims to analyze the spatial effects of terrorism on stock market returns in the Middle East from 2008 to 2019.

This paper uses analytical research design and estimates spatial model. Before estimating the spatial model, the spillover effects were confirmed for the spatial autoregressive model using Moran’s diagnostic test for spatial dependence, Geary’s C test and Akaike statistic.

The results of this study on spatial panel data and based on spatial autoregressive estimator indicated that terrorism and associated neighborhood effects had a negative impact on stock returns in Middle East countries. Also, the corruption index and oil price negatively affected stock market return in these countries, while the democracy index had a positive effect on stock market returns. According to the results, to achieve a high and stable stock market return, it is recommended that high-level consultation is pursued with leaders of involved countries to reduce the devastating effects of terrorist activities, increase political and economic stability, attract stockholders to stock markets and spend corresponding incomes developing the infrastructures in this sector in countries of this region.

Most of the studies have investigated the impact of terrorism on the stock market returns at the national or provincial level. However, the effect of terrorism on the stock market index in the tense region of the Middle East, which is the center of terrorist attacks in the world, has not been dealt with by considering the spatial econometric effects. Thus, to the best of the authors’ knowledge, this research is a first attempt to study the impact of terrorism on the stock market returns in Middle East countries using the spatial econometric approach.

Accelerating the green economy’s transition is a practical means of lowering emissions and conserving energy, and its effects on the greenhouse effect merit careful consideration. Growing environmental deterioration has compelled decision-makers to prioritize sustainability alongside economic growth. Policymakers and the business community are interested in green investment (GRE), but its effects on social and environmental sustainability are still unknown. Based on this, this study aims at looking into the time-frequency interplay between GRE and carbon dioxide emissions and assessing the impacts of economic growth, financial globalization and fossil fuel energy (FUE) usage on this nexus in Vietnam across different time and frequency domains.

The authors employ continuous wavelets, cross wavelet transforms, wavelet coherence, Rua’s wavelet correlation and wavelet-based Granger causality tests to capture how the domestic variance and covariance of two-time series co-vary as well as the co-movement interdependence between two variables in the time-frequency domain.

The results shed new light on the fact that GRE will increase the levels of environmental quality in Vietnam in the short and medium run and there is a bidirectional causality between the two indicators across different time and frequencies. In addition, when the authors observe the effect of economic growth, financial globalization and fossil fuel energy consumption on this interplay, the findings suggest that, in different time and frequencies, any joined positive change in these indicators will move the CO₂ emissions-GRE nexus.

Policymakers and governments can greatly benefit from this topic by utilizing the function of economic institutions in capital control of GRE and CO₂ emissions and modifying the impact of GRE on the greenhouse effect by accelerating the green growth of economic industries.

The current work contributes to the current literature on GRE and CO₂ emissions in several dimensions: (1) considering the sustainable development in Vietnam, by employing a new single-country dataset of GRE index, this paper aims to contribute to the growing body of research on the factors that influence CO₂ emissions, as well as to provide a detailed explanation for the relationship between GRE and CO₂ emissions; (2) localized oscillatory components in the time-domain region have been used to evaluate the interplay between GRE and CO₂ emission in the frequency domain, overcoming the limitations of the fundamental time-series analysis; (3) the mediation role of economic growth, financial globalization and FUE in affecting the GRE-CO₂ relationship is empirically explored in the study.

Motivated by empirical features that characterize cryptocurrency volatility data, the authors develop a forecasting strategy that can account for both model uncertainty and heteroskedasticity of unknown form. The theoretical investigation establishes the asymptotic optimality of the proposed heteroskedastic model averaging heterogeneous autoregressive (H-MAHAR) estimator under mild conditions. The authors additionally examine the convergence rate of the estimated weights of the proposed H-MAHAR estimator. This analysis sheds new light on the asymptotic properties of the least squares model averaging estimator under alternative complicated data generating processes (DGPs). To examine the performance of the H-MAHAR estimator, the authors conduct an out-of-sample forecasting application involving 22 different cryptocurrency assets. The results emphasize the importance of accounting for both model uncertainty and heteroskedasticity in practice.