Tariff is an effective means to protect domestic enterprises and improve the competitiveness of domestic products. This paper constructs a differentiated duopoly model considering endogenous timing to investigate the tariff policy and the impact of product differentiation on the equilibrium results. The conclusions are presented by analyzing the observable two-period delay game as follows. In quantity competition, the welfare-maximizing government sets the tariff level under the home-leading Stackelberg equilibrium, which is contained in the choices of the two firms in the subsequent observable delay game. In price competition, the Bertrand equilibrium is best for the government. However, either of the two Stackelberg equilibria is optimal in observable delay game. It suggests that adjusting the tariff level cannot sufficiently encourage the firms to adopt the welfare-maximizing duopoly determinately. Moreover, increasing product differentiation enhances the home social welfare in home-leading Stackelberg competition but reduces consumer surplus in Bertrand competition.

This study examined the impact of historical volatility and spillover volatility on cryptocurrency (Bitcoin), stock market (Standard & Poor’s 500), and commodity market (Bloomberg Commodity Index). The main objective is to shed light on the interrelationships and dynamics of volatility in these three different asset classes, with data collected daily from January 1, 2019 to April 30, 2025. Vector autoregressive (VAR) and structural vector autoregressive (SVAR) models were adopted for analysis, revealing key findings of: (1) a hierarchical volatility structure with Bitcoin often heading other markets; (2) limited short-term spillovers but significant cross-market connections during economic shocks; and (3) the asymmetric role of commodities as partial equity hedges. This study confirmed the principles of modern portfolio theory, as diversification across the three asset classes could still bring benefits during market turbulence. In particular, the combination of Bitcoin and the volatility index (VIX) could improve the portfolio structure and reduce the risk associated with stock volatility. When including these assets in the model, it is, however, necessary to consider long-term imbalances and geopolitical factors.

The voluntary sector in Malta plays a vital role in supporting communities and delivering essential services. However, delays in financial reporting by voluntary organisations can weaken governance, reduce the usefulness of information, and erode stakeholder trust. This study investigates the financial reporting lag (FRL) among Category 2 and 3 voluntary organisations in Malta from 2018 to 2020 (n = 103), aiming to (i) measure the extent of the lag and (ii) identify the key factors influencing it. A quantitative, hypothesis-driven research design was adopted, employing non-parametric statistical tests and a neural network model to detect both linear and non-linear relationships, marking the first application of neural networks to this topic. Findings reveal that Category 2 organisations consistently exceeded the allowable FRL during the study period, with compliance improving only in 2020 due to extended filing deadlines. Category 3 organisations generally demonstrated better timeliness, except in 2019, when COVID-19 disruptions led to significant delays. Compared with Belgium and the UK, where late filings range between 5% and 24%, Malta’s compliance levels were notably lower, reflecting structural and regulatory challenges. The analysis identified “Year” as the most influential variable, capturing pandemic-related effects, policy changes, and learning curve dynamics. Profitability and equity were also strong predictors, while reliance on donations or grants and liquidity had a moderate impact on the results. The organisation’s category and gearing exerted minimal influence on the model’s predictions. The study provides evidence-based insights to guide regulatory and policy reforms in Malta’s voluntary sector, particularly in light of the recent INPAS, the International Non-Profit Accounting Standard, and ongoing reforms. By integrating neural networks into the analysis of financial reporting timeliness, the research enhances our understanding of the complex factors that shape reporting behaviour. It contributes to strengthening transparency and accountability in Malta’s voluntary sector.

This research investigated the impact of investments in physical banking facilities, specifically the quantity of automated teller machines (ATMs) and branch locations, on the operational effectiveness of commercial banks in 14 Southeast European countries. By employing various analytical techniques such as panel methods (both fixed and random effects), dynamic panel estimation (Arellano-Bond), and population-averaged estimation generalized estimating equation (GEE), it is discovered that on average, an increase in the number of ATMs and branches correlated with a reduction in Bank Net Interest Margin (BankNIMRatio). Specifically, models that account for the overall population indicated that each additional ATM corresponded to a decrease of approximately 0.0945 percentage point in NIM, while each extra branch was linked to a decrease of around 0.1332 percentage point. The results from the Arellano-Bond method lost their statistical significance when dynamic factors were taken into account, implying that some of the observed cross-sectional relationships were influenced by historical performance and persistence. The originality of this study stemmed from (1) its focus on Southeast Europe, a diverse region that is rapidly embracing digital technologies while still maintaining significant traditional branch networks; and (2) its use of multiple complementary econometric techniques to distinguish between immediate and dynamic relationships. The findings suggested important policy considerations, such as emphasizing digital channels in situations where cost-benefit evaluations predicted diminishing returns from additional physical assets, and establishing branches strategically in response to local market dynamics and characteristics of individual banks.

The Golden Triangle consisting of cost, time and quality serves as a fundamental framework for assessing the success of infrastructure projects. Effective risk management is critical for optimising these interconnected dimensions by proactively identifying potential threats implementing risk mitigation strategies and ensuring project control. This study investigates the application of the international standard ISO 31000:2018 in enhancing the Golden Triangle’s dimensions—time management, cost optimization and quality assurance—within the context of large-scale infrastructure projects. A qualitative research methodology was employed incorporating semi-structured interviews, document analysis and site observations to collect comprehensive data. Analytical techniques such as Failure Modes and Effects Analysis (FMEA), Bow-Tie analysis and Fishbone diagrams were utilised to prioritise risks, examine preventive measures and identify underlying causes. A total of forty-three (43) critical risks were identified as having significant impacts on the performance of the Algiers Metro project. The findings revealed that the implementation of a structured risk management approach improved adherence to project timelines, optimised cost control and ensured the delivery of quality outcomes. The integration of ISO 31000:2018 principles in conjunction with tailored analytical tools was found to add considerable value providing practical insights for improving infrastructure project performance. This work underscores the importance of systematic risk management and its role in enhancing the efficiency and success of large infrastructure projects.