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All entrepreneurs interact simultaneously with multiple entrepreneurial contexts throughout their entrepreneurial journey. This conceptual paper has two central aims: (1) it synthesises the current literature on gender and entrepreneurship, and (2) it increases our understanding of how gender norms, contextual embeddedness and (in)equality mechanisms interact within contexts. Illustrative contexts that are discussed include entrepreneurship education, business networks and finance.

This conceptual paper draws upon extant literature to develop its proposed conceptual framework. It provides suggestions for systemic policy interventions as well as pointing to promising paths for future research.

A literature-generated conceptual framework is developed to explain and address the systemic barriers faced by opportunity-driven women as they engage in entrepreneurial contexts. This conceptual framework visualises the interplay between gender norms, contextual embeddedness and inequality mechanisms to explain systemic disparities. An extra dimension is integrated in the framework to account for the power of agency within women and with others, whereby agency, either individually or collectively, may disrupt and subvert the current interplay with inequality mechanisms.

This work advances understanding of the underrepresentation of women entrepreneurs. The paper offers a conceptual framework that provides policymakers with a useful tool to understand how to intervene and increase contextual embeddedness for all entrepreneurs. Additionally, this paper suggests moving beyond “fixing” women entrepreneurs and points towards disrupting systemic disparities to accomplish this contextual embeddedness for all entrepreneurs. By doing so, this research adds to academic knowledge on the construction and reconstruction of gender in the field of entrepreneurship.

This study aims to assess terrorism activities to identify measures required to mitigate the rise of terrorism activities and their metamorphosis into organised criminal activity through the prevention, disruption and dismantling of sources of financing terrorism.

A qualitative methodology was adopted for this study using descriptive synthesis from recent publications and reports of reputable organisations, i.e. relevant grey literature, key informant interview and a focus group discussion. This triangulation approach was used to cross-validate the findings.

The findings revealed that terrorism financing is most likely linked to organised crime for generating revenues and is further used to finance the activities of terrorists.

Terrorists operate from places with little or no presence of governance and, better still, ungovernable spaces for carrying out legitimate businesses, raising internally generated revenues from protection fees, ransoms and taxes. This space further allows domestic collaboration with local criminal gangs to exploit natural mineral resources. If the market for these resources is across borders, international or transnational criminal groups collaborate with terrorists to move the goods and assist with financial services for the generated proceeds.

This study assessed the emerging links between terrorism financing and organised crime in Nigeria.

This study examines how assurors make sense of sustainability assurance (SA) work and how interactions with assurance team members and clients shape assurors’ sensemaking and their actual SA work.

To obtain detailed accounts of how SA work occurs on the ground, this study explores three SA engagements by interviewing the main actors involved, both at the client firms and at their Big Four assurance providers.

Individual assurors’ (i.e. partners and other team members) sensemaking of SA work results in the crafting of their logics of action (LoAs), that is, their meanings about the objectives of SA work and how to conduct it. Without organizational socialization, team members may not arrive at shared meanings and deviate from the team-wide assurance approach. To fulfill their objectives for SA work, assurors may engage in socialization with clients or assume a temporary role. Yet, the role negotiations taking place in the shadows of the scope negotiations determine their default role during the engagement.

Two options are available to help SA statement users gauge the relevance of SA work: either displaying the SA work performed or making it more uniform.

This study theoretically grounds how assurors make sense of SA work and documents how (the lack of) professional socialization, organizational socialization and socialization of frequent interaction partners at the client shape actual SA work. Thereby, it unravels the SA work concealed behind SA statements.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

Interlocking directorates are a common phenomenon across several markets around the world. Yet, the institutional environment and the role of corporate elites in forming board networks promote some developing markets as a unique setting to understand the corporate boards network structure. This study aims to first explore the board directors’ network of all publicly listed companies in Kuwait. This paper then evaluates the effects of exogenous factors and endogenous network structural processes on the likelihood of board interlock.

This study analyzes the interlocks network structures of 167 listed companies in 13 different market sectors in Kuwait relying on hand-collected directors’ data and using four measures of network centrality: betweenness, degree, closeness and eigenvector. The authors predict board interlocks using exponential random graph models (ERGM) and firm-level information from the Bloomberg database.

This study observes that both the firms and directors’ networks consist of 55 components, with the largest component containing about half of the total number of firms/directors. The firm’s network consists of one giant component of 85 firms, including all but one bank. This study shows the importance of endogenous network variables, such as the number of edges, centralization and triangles on the estimation of the factors that promote the board interlocks. Highly centralized firms are less likely to interlock with other firms, while two firms that are interlocked with a common third firm are more likely to interlock.

This paper is the first to analyze in-depth the structure of the directors network of companies in Kuwait. This study illustrates the complex map of interfirm and directors social networks in Kuwait. To the best of the authors’ knowledge, this study is among the first to exploit ERGM in the context of board interlocks to account for potential cross-dependencies and emergent network structures. Managers can identify the director interlock with other firms in the network and take advantage of the connection as a source of external knowledge and influence.

This paper studies the effect of digital financial inclusion in banking on the poor and deprived populations of African emerging economies from 1997 to 2023.

Automated teller machines, mobile payments and mobile money transactions measure digital financial inclusion. Household consumption expenditure proxies poverty reduction. The autoregressive distributed lag analyzes the study.

The results indicate that automated teller machines, mobile money transactions and financial deepening positively affect poverty reduction, while mobile payments negatively affect poverty reduction. Digital financial inclusion decreases poverty via increased investment and empowerment.

Digital financial products and services should be expanded to all population segments in the economies. The governments should improve the quality and quantity of institutions that guarantee the operation of digital financial activities through the enforcement of law and order. The quality and quantity of mobile money transactions and financial deepening should be increased. The costs and charges involved in using automated teller machines and mobile payments should be regulated to relieve the burden on the population. The government should facilitate access to digital financial services via power supply, transport and telecommunication networks. Banks and telecommunications service providers should improve the payment system network to ensure cost-effective, convenient and secure financial service delivery. The digital infrastructure and financial services markets should be enhanced to fully capture the gains of financial inclusion and reduce poverty.

A literature review provides studies with conflicting findings on the effect of digital financial inclusion on poverty reduction. This study supports that digital financial inclusion decreases poverty.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.

The study uses the Diebold-Yilmaz spillover and connectedness measures in a generalized VAR framework. The author calculates the net transmitters or receivers of shocks between two assets and visualizes their strength using a network analysis tool.

The study found low systemic risks across all assets and countries. However, we found higher systemic risks in the forex market than in the stock and bond markets, and in South Africa than in other countries. The dynamic analysis found time-varying connectedness return shocks, which increased during the peak periods of the first and second waves of the pandemic. We found both gold and oil as net receivers of shocks. Overall, over half of all assets were net receivers, and others were net transmitters of return shocks. The network connectedness plot shows high net pairwise connectedness from Morocco to South Africa stock market.

The study has implications for policymakers to develop the capacities of local investors and markets to limit portfolio outflows during a crisis.

Previous studies have analyzed spillovers across asset classes in a single country or a single asset across countries. This paper contributes to the literature on network connectedness across assets and countries.

The purpose of this paper is to analyze the existing literature in the domain of financial inclusion and emphasizing forthcoming trends. It examines recent literature while assessing the geographical distribution, identifying well-known authors, publications, journals and keyword occurrences.

Based on a scientific search technique, bibliometric analysis in the field of financial inclusion was carried out on a sample of 2,125 Scopus documents for the years 2004–2022. A VOS viewer was used in the study as a tool for performance evaluation and analysis of the science mapping.

The bibliometric analysis illustrates that India and the USA are dominating in financial inclusion field with significant contributions. The most well-known authors were Ghosh, S. and Munene, J.C. and International Journal of Social Economics was considered as the best journal. Finally, six prominent clusters were identified through keyword analysis. The major themes revolve around digitalization, economic development, demographic and geographic factors and financial literacy.

The research helps in providing information for formulating financial inclusion policies for RBI and Government of India. A comprehensive literature assessment is useful for future scholars to develop a solid conceptual framework. This research would help practitioners to formulate strategies for rural population to enhance their earnings, investments and money.

This study can supply data to describe the framework of earlier financial inclusion studies and provides potential directions for future research.

Previous research suggests the crucial role of parents in developing social behaviors of their children. However, less evidence is available on the role of parents in shaping responsible financial management behavior among children for their later life. This study bridges this gap by investigating the role of financial parenting in improving well-being among young Saudi people. Particularly, this study examines the role of financial parenting, childhood financial socialization and childhood financial experiences in developing responsible financial self-efficacy and financial coping behaviors to determine financial well-being among young adults in Saudi Arabia.

This study uses a two-step mixed-method approach comprising analyses of symmetric (net effects) and asymmetric (combinatory effects) modelling to test the proposed model. A symmetrical analysis examines the role of financial parenting factors that are sufficient for improving financial well-being among Saudis. An asymmetrical analysis is used to explore that a set of combinations of financial parenting conditions lead to high performance of financial well-being. Data have been collected from 350 students enrolled in undergraduate and postgraduate programs in Saudi Arabia.

According to asymmetric modeling (i.e. fsQCA) analysis, parents and practitioners can combine financial parenting, childhood financial socialization and childhood financial experiences along with financial self-efficacy and financial coping behaviors in a way that satisfied the conditions (i.e. causal antecedent conditions) leading to high financial well-being. Importantly, the condition of high financial well-being is not mirror opposite of causal antecedent conditions of low financial well-being.

This study contributes to the current knowledge by applying both symmetrical and asymmetrical modelling to indicate a high level of financial well-being. Besides, there is sparse empirical evidence available in the context of Saudi Arabia on how financial parenting, socialization and financial experiences in childhood improve children's financial well-being in their later life.

According to asymmetric modeling (i.e. fsQCA) analysis, parents and practitioners can combine financial parenting, childhood financial socialization and childhood financial experiences along with financial self-efficacy and financial coping behaviors in a way that satisfied the conditions (i.e. causal antecedent conditions) leading to high financial well-being. Importantly, the condition of high financial well-being is not mirror opposite of causal antecedent conditions of low financial well-being. The parents and practitioners must be cautious to regulate the condition in which the combination of the antecedents is not in line with the causal recipes of financial well-being negation.

This study deepens the current knowledge by employing both symmetrical and asymmetrical analysis for testing structural and configurational models indicating the high performance of financial well-being . The study proposes and tests an integrated model to bring new contributions to prior literature. This study also attempts to propose valuable research directions for future researchers interested in the topic.