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This study aims to examine the degree to which a selection of home country factors affects the proclivity of firms to internationalise. The study also proposes and tests a conceptual model that fuses institutional and resource-based theories to improve our understanding of firm internationalisation.

The study uses cross-sectional, national-level secondary data from the 2018 Global Entrepreneurship Development Institute and World Economic Forum data sets on global entrepreneurship and competitiveness indices for 137 countries. The data is analysed using correlation and hierarchical regression analysis to test the hypotheses.

The results indicate that national income, institutions, trade openness and availability of risk capital positively influenced firm internationalisation, while home-country networking had an inverse effect. However, home country infrastructure had no statistically significant effect on firm internationalisation.

The findings highlight the importance of considering home country attributes in understanding the internationalisation of firms.

This study contributes to the body of knowledge by providing empirical evidence of the role of local factors on the internationalisation of entrepreneurial ventures. It also tests a novel conceptual model that integrates institutional and resource-based theories to explain the nuances of the internationalisation of business ventures globally.

This paper explores international trade of the Chinese manufacturing industries through the lenses of network analysis (NA) to visualise the world trade network of the Chinese economy, describe its topology and better explain the international organisation of Chinese manufacturing industries.

The authors built a dataset of 40,550 Chinese companies and their 107,026 subsidiaries in 118 countries from Orbis-BVD and used a NA to investigate the connection between China and other countries. In particular, the authors studied the connections between Chinese companies and their subsidiaries in order to build a network of Chinese industries.

The authors found that the network of Chinese companies is ramified but not wide and it can be divided into two clusters. Moreover, the relations between China and other peripheral countries are strongly mediated by a few leading locations (e.g. Hong Kong and the USA).

This paper contributes to the literature in several ways. First, the authors provide empirical evidence on the magnitude and ramifications of Chinese enterprises in the world. The existing studies generally focus on applying NA to sectoral insights (Mao and Yang, 2012; Shaikh et al., 2016; Zheng et al., 2016; Wanzenbö ck, 2018; Krichene et al., 2019), whereas in this work the authors take a comprehensive view of the entire Chinese manufacturing system. Second, this paper complements the existing literature identifying the difference between cluster levels in Chinese manufacturing (Wu and Jiang, 2011) by proposing a cluster centralisation method to analyse the international network of Chinese firms rather than just the national network. Finally, the results also shed light on the international trade relationship between China, Hong Kong and the USA.

For policymakers and participants of financial markets, predictions of trading volumes of financial indices are important issues. This study aims to address such a prediction problem based on the CSI300 nearby futures by using high-frequency data recorded each minute from the launch date of the futures to roughly two years after constituent stocks of the futures all becoming shortable, a time period witnessing significantly increased trading activities.

In order to answer questions as follows, this study adopts the neural network for modeling the irregular trading volume series of the CSI300 nearby futures: are the research able to utilize the lags of the trading volume series to make predictions; if this is the case, how far can the predictions go and how accurate can the predictions be; can this research use predictive information from trading volumes of the CSI300 spot and first distant futures for improving prediction accuracy and what is the corresponding magnitude; how sophisticated is the model; and how robust are its predictions?

The results of this study show that a simple neural network model could be constructed with 10 hidden neurons to robustly predict the trading volume of the CSI300 nearby futures using 1–20 min ahead trading volume data. The model leads to the root mean square error of about 955 contracts. Utilizing additional predictive information from trading volumes of the CSI300 spot and first distant futures could further benefit prediction accuracy and the magnitude of improvements is about 1–2%. This benefit is particularly significant when the trading volume of the CSI300 nearby futures is close to be zero. Another benefit, at the cost of the model becoming slightly more sophisticated with more hidden neurons, is that predictions could be generated through 1–30 min ahead trading volume data.

The results of this study could be used for multiple purposes, including designing financial index trading systems and platforms, monitoring systematic financial risks and building financial index price forecasting.

Using overlapped portfolio data on public equity funds in Korea, the authors construct several types of fund-stock weighted bipartite networks and measure fund network centrality. The authors also examine the relationship between network centrality and fund investment performance. The authors' results are three-fold. First, the authors find that the fund centrality of the network in which funds and stocks are connected based on the most active investing behavior positively affects the fund performance. Second, the funds with a high centrality level based on the same network generate higher returns by holding stocks with high value uncertainty. Third, the authors find that fund centrality is not associated with herd behavior. Based on these results, the authors argue that fund centrality is a proxy of information advantage and skill of fund managers. The authors' paper shows that network analysis could be a new way to identify funds with better performance and measure the skill and information advantage to construct an optimal portfolio.

This paper aims to clarify how organizations manage their participation in networks to share and jointly create knowledge but also risk unwanted knowledge spillovers at the same time. As formal governance, trust and observation are less applicable in informal networks, the authors need to understand how members address the need to protect knowledge by informal practices. The study aims to investigate how the application of knowledge protection practices affects knowledge sharing in networks. The insights are relevant for organizational and network management to control knowledge risks but harvest the benefits of network engagement.

The authors opted for an exploratory study based on 60 semi-structured interviews with members of 10 networks. In two rounds, network managers, representatives and members of the networks were interviewed. The second round of interviews was used to validate the intermediate findings. The data were complemented by documentary analysis, including network descriptions.

Through analyzing and building on the theory of psychological contracts, two informal practices of knowledge protection were found in networks of organizations: exclude crucial topics and share on selected topics and exclude details and share a selected level of detail. The authors explored how these two practices are enacted in networks of organizations with psychological contracts.

Counter to intuition that the protection of knowledge can be strengthened only at the expense of knowledge sharing and vice versa, networks benefitted from more focused and increased knowledge sharing while reducing the risk of losing competitive knowledge by performing these knowledge protection practices.

Illegal wildlife trade (IWT) is a transnational organized crime that generates billions in criminal proceeds each year. Yet, it is not regarded by many countries as a serious crime. There is also no general consensus on its recognition as a predicate offence for money laundering. In this regard, banks are misused in different ways to facilitate financial flows linked to IWT. This paper aims to illustrate the importance of the banking sector in combating money laundering relating to IWT. It also aims to demonstrate the need for a general recognition of IWT as a predicate offence for money laundering.

This study investigates the implementation of money laundering controls by banks in the illegal-wildlife-trade context. As background to this investigation, it provides an overview of IWT, which is followed by an exploration of some of the general characteristics of the banking sector, before discussing the relevant Financial Action Task Force (FATF) recommendations.

This study finds that the banking sector is well-placed to combat money laundering relating to the IWT and is, by virtue of its international nature and strong focus on compliance, able to be effective in preventing the use of the proceeds of IWT as well as in identifying broader trafficking networks. Moreover, the banking sector is well-equipped to develop appropriate platforms to facilitate the swift, easy and effective sharing of financial intelligence between banks at the local, regional and especially international level.

This study draws on publicly available information on financial flows relating to IWT. Little data and research are available on the financial flows and consequently the money laundering techniques used in cases suspected of IWT.

There has been little scholarly research on the relationship between money laundering and the IWT as well as the financial flows of IWT in general. This study highlights some of the money laundering techniques used in relation to IWT by drawing on the works of various international organizations, including the FATF.

The review examines the existing literature on blockchain-based small and medium enterprise (SME) finance and highlights its trend, themes, opportunities and challenges. Based on these factors, the authors create a framework for the existing literature on blockchain-based SME financing and lay down future research paths.

The review follows a systematic approach. It includes 53 articles encompassing multiple dimensions of blockchain-based SME finance, including peer-to-peer lending platforms, supply chain finance (SCF), decentralized lending protocols and tokenization of assets. The review critically evaluates these approaches' theoretical underpinnings, empirical evidence and practical implementations.

The review demonstrates that blockchain-based SME finance holds significant promise in addressing the credit gap by leveraging blockchain technology's decentralized and transparent nature. Benefits identified include reduced information asymmetry, improved access to financing, enhanced credit assessment processes and increased financial inclusion. However, the literature acknowledges several challenges and limitations, such as regulatory uncertainties, scalability issues, operational complexities and potential security risks.

The article contributes to the growing knowledge of blockchain-based SME finance by synthesizing and evaluating the existing literature. It also provides a framework for the existing literature in the area and future research paths. The study offers insights for researchers, policymakers and practitioners seeking to understand the potential of blockchain technology in filling the SME credit gap and fostering economic development through improved access to finance for SMEs.

To be able to continuously provide affordable services to consumers, managers of critical infrastructure (CI) maintenance supply networks have to balance investments in resilience with costs. At the same time, CI providers need to consider factors that influence resilience such as the geographical spread or the location of the network. This study aims to contextualize supply chain resilience knowledge by exploring how maintenance resource configurations impact resilience and costs in CI supply networks.

An in-depth longitudinal single case study of a representative CI provider that has centralized its maintenance supply network is used. Data were collected before and after the change to evaluate the effect of the changes on the maintenance supply network.

This study shows that in this specific CI maintenance context, structural resource choices such as the quantity or location of spare parts and tools, the creation and exploitation of tacit knowledge and staff motivation impact both resilience and costs due to geographical spread, network location and other network properties.

This study extends general supply chain resilience knowledge to a new setting (i.e. CI) and shows how existing insights apply in this context. More specifically, it is shown that even in engineered supply networks there is a need to consider the effect of human agency on resilience as the creation and exploitation of tacit knowledge are of immense importance in managing the network. In addition, the relationship between normal accidents theory and high reliability theory (HRT) is revisited as findings indicate that HRT is also important after a disruption has taken place.

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.

This study aims to examine foreign direct investment (FDI) factors and develops a rational framework for FDI inflow in Western European countries such as France, Germany, the Netherlands, Switzerland, Belgium and Austria.

Data for this study were collected from the World development indicators (WDI) database from 1995 to 2018. Factors such as economic growth, pollution, trade, domestic capital investment, gross value-added and the financial stability of the country that influence FDI decisions were selected through empirical literature. A framework was developed using interpretable machine learning (IML), decision trees and three-stage least squares simultaneous equation methods for FDI inflow in Western Europe.

The findings of this study show that there is a difference between the most important and trusted factors for FDI inflow. Additionally, this study shows that machine learning (ML) models can perform better than conventional linear regression models.

This research has several limitations. Ideally, classification accuracies should be higher, and the current scope of this research is limited to examining the performance of FDI determinants within Western Europe.

Through this framework, the national government can understand how investors make their capital allocation decisions in their country. The framework developed in this study can help policymakers better understand the rationality of FDI inflows.

An IML framework has not been developed in prior studies to analyze FDI inflows. Additionally, the author demonstrates the applicability of the IML framework for estimating FDI inflows in Western Europe.