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Building information modelling (BIM) research studies are highly contextual as the contexts provide lenses for interpreting the results. However, there has been a growing decontextualization in extant studies especially between the small and medium-sized enterprises (SMEs) and large firms; and between developed and developing countries. Albeit these contexts are all in the same construction industry, they often react differently to the same conditions. Thus, this study aims to evaluate the perceptions of firms in varying contexts of size and location on the perceived barriers to the implementation of BIM in the architecture, engineering and construction (AEC) industry.

The perceptions of 228 firms gleaned from 26 countries across the 6 continents were collated via an international empirical questionnaire survey. The data was analysed using the mean score, rank agreement analysis, Mann-Whitney U test and factor analysis.

The findings revealed the major factors impending BIM implementation in each of the contexts and a comparative analysis emphasized the difference in their perceptions. The findings underscore that there is a general digital divide as regard BIM implementation between the SMEs and large firms, and a deepening divide between the developed and developing countries.

The study has provided empirical evidence for the BIM divide in the AEC industry, which would influence the promulgation of BIM policy and transferability of best practices across varying contexts of both firm size and country level.

This study aims to analyze the effect of female top management and female dominant owner on whether firms experience obstacles to obtaining external finance in 136 medium- and low-income countries during 2006–2019. The analysis controls for the role of corporate governance and other firm-specific characteristics, as well as for the impact of national institutions.

The analysis elucidates the economic and non-economic factors driving female corporate leadership. Further, in order to capture the causal effect, the analysis uses univariate tests, multivariate regression analysis, disaggregation testing, sensitivity and endogeneity analysis to confirm the quality of the estimates. The analysis controls for various additional country-level factors.

The results show that female top management and female ownership are broadly significant determinants of firms' access to external finance, especially in relatively larger and more developed countries. The role of controlling shareholders is significant and mediates the gender effect. The latter appears more pronounced in smaller and medium-size firms, operating in the manufacturing and services sectors as well as in the countries with higher levels of development. This also varies with the countries' macroeconomic conditions and institutions governing gender development and equality as well as institutional governance effectiveness.

The results suggest that firms wishing to improve the firms' access to external finance should consider the role of gender in both top management and corporate ownership coupled with the effect of the specific characteristics of firms and the conditioning role of national institutions.

The study examines the gender effects of top management and dominant ownership for the external financing decisions of firms in low- and middle-income countries, which are underresearched. These gender effects are mitigated in various ways by the specific characteristics of firms and especially on national institutions.

This study employs Stein Rokkan's methodological approach to analyse state formation in the Greater Middle East. It develops a conceptual framework distinguishing colonial, populist and democratic pacts, suitable for analysis of state formation and nation-building through to the present period. The framework relies on historical institutionalism. The methodology, however, is Rokkan's. The initial conceptual analysis also specifies differences between European and the Middle Eastern state formation processes. It is followed by a brief and selective discussion of historical preconditions. Next, the method of plotting singular cases into conceptual-typological maps is applied to 20 cases in the Greater Middle East (including Afghanistan, Iran and Turkey). For reasons of space, the empirical analysis is limited to the colonial period (1870s to the end of World War 1). Three typologies are combined into one conceptual-typological map of this period. The vertical left-hand axis provides a composite typology that clarifies cultural-territorial preconditions. The horizontal axis specifies transformations of the region's agrarian class structures since the mid-19th century reforms. The right-hand vertical axis provides a four-layered typology of processes of external intervention. A final section presents selected comparative case reconstructions. To the authors' knowledge, this is the first time such a Rokkan-style conceptual-typological map has been constructed for a non-European region.

The paper investigates if the process that led to the birth of the Euro Area had a significant impact in homogenizing the capital structure decisions of European firms since the first introduction of the common currency.

A large sample of firms was constructed, and a Tobit-censored regression model was utilized to investigate the determinants of firms' observed capital structures. The Black–Scholes–Merton model was used to infer market values of assets, as well as the volatility of those values, from the observed market values of equity and the corresponding volatility. The existing differences in national tax rules were considered for estimating firm-specific marginal tax rates.

It was found that, despite the currency union and the institutional harmonization process, certain factors still play a different role. In particular, the impact of profitability is consistent with the pecking order view in some countries, and with the trade-off theory in others. Assets risk, measured as the annualized volatility of the market enterprise value, is the best predictor of observed leverage ratios. The sector of activity is significant in determining leverage decisions even when assets' risk is taken into account. Despite the monetary union and the increased financial and institutional integration in the Euro Area, the country of origin still plays a significant role in capital structure decisions, suggesting that other country-level factors may affect firms' financing behaviour.

The paper indicates that, despite the long harmonization process of institutions, regulations and public budget required to join the Euro, firms' financing decisions are still affected by country-specific factors once the common currency is introduced. Therefore, new entrant countries in the Euro area should not expect their companies to immediately conform with those located in other countries within the common currency area.

This article investigated the impact of the currency change from national currencies to the Euro on the determinants of capital structure choices. It was shown that, despite the long harmonization process that led to the birth of the Euro Area, national factors still affect firms' financing decisions. This provides guidance for policymakers in countries that are planning to join the Euro about the impact this will have on firms' financing decisions in the entrant country.

This chapter provides an empirical assessment of the effects of infrastructure provision on structural change and aggregate productivity using industrylevel data for a set of developed and developing countries over 1995–2010. A distinctive feature of the empirical strategy followed is that it allows the measurement of the resource reallocation directly attributable to infrastructure provision. To achieve this, a two-level top-down decomposition of aggregate productivity that combines and extends several strands of the literature is proposed. The empirical application reveals significant production losses attributable to misallocation of inputs across firms, especially among African countries. Also, the results show that infrastructure provision has stimulated aggregate total factor productivity growth through both within and between industry productivity gains.

The purpose of this paper is to provide an early assessment of India’s South-South cooperation for trade and technology (SSTT) with East Africa, focusing on Ethiopia, Rwanda, Kenya, Uganda and Tanzania. It aims to analyse the role of SSTT in providing support to targeted sectors.

The paper examines SSTT, focusing on India and East Africa over a specific period (2000–2016) of its emergence, and extends the public sponsorship literature in international business (IB) to better understand the relationship between SSTT and value addition – applying to a particular case study of SSTT interventions in spices.

The paper highlights SSTT as a pathway to support value addition in global value chains (GVCs). Trade between India and East African countries has grown, with three developments over the period of analysis in particular: shifting trade patterns, growing share of intermediate goods trade and differences in GVC insertion. However, East African exports are largely of lower value. Capacity building to support processing capability and thriving markets can encourage greater value addition. Preliminary findings suggest early gains at the margins, as SSTT interventions have been focusing on capacity boosting with buffering and bridging mechanisms for increased volume of trade. Moving up the value chain however requires that specific value-enhancing activities continue to be targeted, building on regional capacities. Our high-level case study for spices suggests that activities are starting to have a positive effect; however, more focus is needed to specifically target value creation before export and in particular higher levels of processing.

While findings are preliminary, policy implications emerge to guide SSTT interventions. There is capacity for building higher value-added supply chains as is evident among East African countries that trade with each other – future SSTT programmes could tap into this and help build capacity in these higher-value value chains. Future SSTT programmes can take a comprehensive approach by aiming at interventions at key points of the value chain, and especially at points that facilitate higher value addition than initial processing. An example is that Ethiopia and Rwanda are likely to benefit from an expanded spice industry, but the next phase should be towards building processing for value-addition components of the value chain, such as through trade policies, incentivising exporters to add value to items before export. From a development perspective, more analysis needs to be done on the value chain itself – for instance, trade facilitation measures to help processers engage in value chains and to access investments for increasing value add activities. (iv), Future research should examine more closely the development impacts of SSTT, namely, the connection between increased trade, local job creation and sustained innovation, as it is these tangible benefits that will help countries in the Global South realise the benefits of increased trade.

The paper underlines how the SSTT approach can contribute to the critical IB and GVCs literature using a theoretical grounded approach from public sponsorship theory, and with a unique lens of development cooperation between countries in the global south and its emerging impact on development outcomes in these countries.

While the Olympic Games are always under the spotlight, the Paralympic Games are somehow ignored. This paper aims to invite the general public to think about the para-athletes and the differential treatments they received.

Among the participating countries, many of them were unable to win a single Olympic or Paralympic medal. When the dependent variable is left-censored, ordinary least squares regression is asymptotically biased downwards. In the literature, researchers typically employ the maximum likelihood Tobit model to take care of the censoring problem. However, some researchers argue that the Hurdle model has an advantage over the Tobit model in identifying the determinants of winning Olympic medals. Following their wisdom, this paper employs both the Tobit and Hurdle models in analysis.

The empirical evidence gathered in this paper suggests that population size, host status and average years of schooling are the big three socio-economic determinants when it comes to winning medals at the Paralympic Games and Olympic Games. The findings support the hypothesis that sports talent is randomly distributed and a large country has a higher chance to have talented athletes or para-athletes winning the Olympic medals. The strong host advantage also showed up in the following Paralympics but was not so strong at the next Olympics.

This paper not only examines the relationship between various social, economic and political factors in determining the success of a nation in the Paralympic Games but also attempts to identify possible non-traditional determinants.

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.

There is an increasing interest in the international management literature in cultural differences between in-country regions. Yet, the regions of any country may be merely political products and not necessarily cultural units. The goal of this article is to propose clear empirical criteria for deciding if a set of entities, such as a country's administrative regions, can be legitimate units of cross-cultural analysis and to test these criteria in an empirical study.

The authors review the literature on what constitutes a unit of cross-cultural analysis and propose empirical criteria. For instance, the regions of a given country are meaningful units of cross-cultural analysis if one can replicate (an) established dimension(s) of culture at the regional level, including some of the dimension(s)' antecedents and predictive properties. The authors apply this test in the context of the Russian Federation (RF), using an RF database (18,768 respondents from 60 administrative regions) with items borrowed from the World Values Survey.

The RF regions pass the authors’ test. At the regional level, the selected items yield an individualism-collectivism (IDV-COLL) dimension that is similar to its nation-level counterpart in the revised Minkov-Hofstede model in terms of concept and antecedents (wealth differences and geographic latitude) and outcomes that are relevant in business (innovation rates and quality of governance). The authors also find other patterns that confirm the properties of RF regions as meaningful units of cultural analysis.

The authors’ criteria and the test based on them are suitable for large countries, with significant geo-climatic variety and ethnic diversity, but may be inapplicable in small countries with less diversity. It is questionable if the latter countries contain enough cultural variation to justify a cross-cultural analysis of their sub-national regions.

The authors’ criteria can be used in future research in any large country to decide if its regions justify a cross-cultural analysis in the field of management and business or any other field.

Cultural differences within a country are important as they may inform political and management decisions. Yet, to demonstrate that those differences are real, and not imaginary, one needs a methodology like the authors’.

The study contributes to the discussion of the meaningfulness of in-country regions as cultural units for cross-cultural analysis in international business by focusing on the RF.

The authors extend the World Bank's Logistics Performance Index (LPI) for 30 additional countries and 13 additional years. The authors develop an inexpensive method for extending survey data when frequent, universal surveys are unavailable. The authors identify groups of country characteristics that influence LPI scores.

Using data from the World Development Indicators—the broadest global dataset of country socioeconomic features—the authors test machine learning algorithms for their ability to predict the LPI. The authors examine importance scores to identify factors that influence LPI scores.

The best performing algorithm produces predictions on unseen data that account for nearly 90% of observed variation, and are accurate to within 6%. It performs twice as well as an OLS model with per capita income as the only predictor. Explanatory factors are business environment, economic structure, finance, environment, human development, and institutional quality.

Machine learning offers a simple, inexpensive way of extending the coverage of survey data. This dataset provides a richer picture of logistics performance around the world. The factors the authors identify as predicting higher LPI scores can help policymakers and practitioners target interventions.

This paper is one of the first applications of machine learning to extend coverage of an index based on an international survey. The authors use the new data to provide the most wide-ranging analysis of logistics performance across countries and over time. The output is an important resource for policymakers tracking performance, and researchers particularly in smaller and lower income countries. The authors also examine a wider range of explanatory factors for LPI scores than previous work.