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This paper explores whether the Euro-area sovereign credit default swap market is prone to contagion effects. It investigates whether the sharp increase in sovereign CDS spread of a given country is due to a deterioration of the macroeconomic variables or some form of contagion.

For this purpose, the authors use an innovative approach, i.e. spatial econometrics. Although modeling spatial dependence is an attractive challenge, its application in the field of finance remains limited.

The empirical findings show strong evidence of spatial dependence highlighting the presence of pure contagion. Furthermore, evidence of wake-up call contagion-increased sensitivity of investors to fundamentals of neighboring countries and shift contagion-increased sensitivity to common factors are well recorded.

This study aims to study a crucial financial issue that gained increased research interest, i.e. financial contagion. A methodological contribution is made by extending the standard spatial Durbin model (SDM) to analyze and differentiate between several forms of contagion. The results can be used to understand how shocks are spreading through countries.

The purpose of this study is to examine two issues, namely the degree of current account deficit (CAD) sustainability and the degree of capital mobility.

The sample for this study comprises 24 Latin American and Caribbean countries, including three regional agreements: Andean Community, MERCOSUR (Mercado Común del Sur), and SICA (Central American Integration System). This study employs the dynamic common correlated effects mean group (DCCEMG) estimator in a panel data set to investigate the long-run relationship between savings and investment along with short-run dynamics.

The findings indicate that CAD is weakly sustainable in the Latin American and Caribbean region, MERCOSUR, and SICA, while CAD is strongly unsustainable in the Andean Community. The sub-period analysis reveals that CAD has been adversely affected by the 2008 crisis. However, in the post-crisis period, CAD has been slowly decreasing in the Latin American and Caribbean region and Andean Community, whereas CAD has continued increasing in MERCOSUR and SICA. Further, the estimates of error-correction terms and short-run coefficients indicate that the Andean Community and MERCOSUR observe a higher degree of long-run and short-run capital mobility than SICA.

The results carry fundamental implications for policy-making processes aimed at maintaining sustainable CADs.

This study gives an alternative interpretation of the “Feldstein-Horioka” coefficient in terms of CAD sustainability and analyses the saving–investment relationship in light of Chudik and Pesaran (2015).

This study examines the co-integration relationships between Association of Southeast Nations (ASEAN) stock indices as a way to assess the feasibility of policy initiatives to strengthen market integration in ASEAN and identify implications for portfolio investors.

The authors employ threshold co-integration tests and a non-linear autoregressive distributed lag (NARDL) model to study the asymmetric dynamics of ASEAN equity markets. The study’s data cover the 2009–2022 period for seven member states: Cambodia, Indonesia, Malaysia, the Philippines, Singapore, Thailand and Vietnam.

The authors find evidence supporting co-integration relationships; adjustment toward equilibrium is asymmetric in the short run and symmetric in the long run for these countries. While co-movement in ASEAN equity markets seems encouraging for initiatives seeking to foster financial integration in regional economies, the benefits for international portfolio diversification appear to be neutralized.

The issue of stock market integration is important among policymakers, investors and academics. This study examines the level of stock market integration in ASEAN during the 2009–2022 period. For this purpose, advanced co-integration techniques are applied to different frequencies of data (daily, weekly and monthly) for comparison and completeness. The empirical analysis of this study is conducted using the Enders and Siklos (2001) co-integration and threshold adjustment procedure. This advanced co-integration technique is superior compared to other co-integration techniques by permitting asymmetry in the adjustment toward equilibrium.

This study aims to explore the determinants of public debt in selected South Asian Association for Regional Cooperation (SAARC) countries for 19 years, from 2001 to 2019.

Using ordinary fixed and random effect models, the authors examine the role of internal and external factors in determining the composition of public debt. Furthermore, for robustness, they compare the results with two-stage least square (2SLS) regression estimates after considering the problem of endogeneity, overidentification, under-identification and weak instruments.

The findings show that among the selected macroeconomic variables, inflation, exchange rate and broad money have significant negative effects on the debt-GDP ratio. In contrast, military spending, corruption and interest rates appear to positively influence the same as per 2SLS results. From the policymaking perspective, SAARC countries should focus more on reducing military spending and make a concerted effort to augment investments in productive projects. Further, with strong fiscal consolidation and institutional quality, it is important to mitigate the frequent occurrence of corruption conundrums in emerging economies for the development of a transparent economic system.

The study is distinct from previous studies in two ways. First, to the best of the authors’ knowledge, there are no studies focusing on SAARC countries in the context of public debt. Second, the study expands the existing literature on public debt by taking into account both external and internal debts to decipher the within-country and cross-country determinants of debt accumulation. More specifically, this model considers accountability and transparency in the public sector, cross-border security challenges and benefits of globalization by including explanatory variables such as corruption, military expenditure spending and capital inflows.

This study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

With network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

The purpose of this paper is twofold: first, to present a rigorous bibliometric analysis and a systematic literature review of the critical success factors (CSFs) for Building information modelling (BIM)-based digital transformation; second, to identify the relationship between the dimensions in favour of BIM implementation.

This study adopts a two-step approach to combine bibliometric and systematic literature review to explore the research topic of BIM and CSFs. Bibliometric tools such as Biblioshiny in R language and Ucinet software were applied to this study.

Besides identifying the two most influential authors (e.g. Bryde and Antwi-Afari), the key journal for disseminating articles, and the most influential countries in this discourse (e.g. Hong Kong and Australia), the study also identifies four pivotal research themes derived from the co-occurrence analysis of keywords: the fusion of sustainability and technology with BIM; practical application and its integration within construction management; innovation and engineering paradigms; and the advent of emerging technologies (e.g. Blockchain) within developing nations. Additionally, the paper introduces a comprehensive framework for selecting CSFs pertinent to BIM-centred digital transformation as viewed through the lens of dynamic capabilities.

This paper establishes a link between dynamic capabilities theory, CSFs, and BIM dimensions, presenting a multifaceted framework guiding future paths and offering practical insights for managerial and political decision-makers engaged in digital transformation endeavours. The study positions dynamic capabilities as pivotal, aligning digital technologies with continuous business performance, and advocates for a strategic focus on digital transformation.

Official development assistance (ODA) education projects have played a crucial role in improving education and training fields in developing countries, but are often facing several considerable challenges (e.g. long implementation time). Thus, this study aims to identify critical success factors (CSFs) in ODA education projects and investigate the influences of CSFs on ODA project performance measured by 11 nonprofit outcomes (NPOs).

A comprehensive literature review and expert interviews were conducted to compile a list of 35 potential success factors for ODA education projects. Using a survey questionnaire, 143 valid responses were collected from practitioners joining ODA projects in Vietnam. Various statistical methods (e.g. mean score method, Spearman rank correlation test, analysis of variance test, factor analysis and regression analysis) were used to analyze the collected data.

This research identified seven CSFs for ODA education projects in Vietnam: comprehensive project management competency (C1), clarity and compliance in project execution (C2), transparency and committed funding (C3), external context conditions (C4), well-controlled design and project management procedures (C5), preparations in equipment and complexity insight (C6) and punctual site delivery (C7). Furthermore, the results of regression analysis indicated that comprehensive project management competency (C1) and transparency and committed funding (C3) could significantly affect various NPOs.

This study offers significant insights for practitioners (e.g. project managers) to improve ODA projects’ performance and effectiveness in the education and training sector of a developing country context (like Vietnam).

This study aims to elucidate institutional analysis as an effective approach to investigating and designing the multilevel policymaking system of online social networks (OSN) for achieving a participatory model.

The institutional mapping approach has been used to analyze Iran’s OSN multilevel policymaking system. A combination of two matrices, including institutions-institutions and institutions-functions, was used to perform the institutional mapping. Two main steps were taken to draw the mentioned matrices. First, a review of related studies in Iran’s OSN policymaking system was conducted and the policy functions mentioned in these studies were identified and categorized using the meta-synthesis. Second, based on analyzing two policy documents of Iran’s OSN, institutions and their interactions were identified and policy functions were allocated to institutions.

Based on the results, the most important policy functions in the current OSN policymaking system in Iran are support, regulatory, monitoring and evaluation, business environment development, culture building and promotion, organizing licenses and permissions, policymaking and legislation. Also, the results show that there are shortcomings in this system, some of the most important of which are lack of transparency in regulatory, little work in culture building and promotion, neglect of the training of specialized human resources and research and development, slow development of the business environment and neglecting the role of nongovernmental organizations in policymaking.

By examining and analyzing how different institutions operate within a multilevel policymaking system, the policymaking process and its overall effectiveness can be enhanced. This analysis helps identify any inconsistencies, overlaps or conflicts in the roles and policies of these institutions, leading to a better understanding of how a multilevel policymaking system is organized.

Airline scheduling is an extremely complex process. Moreover, disruption in a single flight may damage the entire schedule tremendously. Using an efficient recovery scheduling strategy is vital for a commercial airline. The purpose of this paper is to present an integrated aircraft and crew recovery plans to reduce delay and prevent delay propagation on airline schedule with the minimum cost.

A mixed-integer linear programming model is proposed to formulate an integrated aircraft and crew recovery problem. The main contribution of the model is that recovery model is formulated based on individual flight legs instead of strings. This leads to a more accurate schedule and better solution. Also, some important issues such as crew swapping, reassignment of aircraft to other flights as well as ground and sit time requirements are considered in the model. Benders’ decomposition approach is used to solve the proposed model.

The model performance is also tested by a case including 227 flights, 64 crew, 56 aircraft and 40 different airports from American Airlines data for a 24-h horizon. The solution achieved the minimum cost value in 35 min. The results show that the model has a great performance to recover the entire schedule when disruption happens for random flights and propagation delay is successfully limited.

The authors confirm that this is an original paper and has not been published or under consideration in any other journal.