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This study aims to examine the nonlinear threshold effect of female board gender diversity (FBGD) on debt financing (DF) and equity financing (EF) decisions arguing that the effect of FBGD varies/changes depending on the numerical strength of the women on the board.

This study uses seemingly unrelated simultaneous panel equation modeling of 19 listed firms on the Ghana Stock Exchange (GSE) between 2010 and 2021. Although natural logs of equity and debts are used to proxy financing decisions, FBGD is measured as a percentage of total female board members to total board members.

This study reveals a nonlinear inverted U-shape effect of FBGD on EF and DF options. Although this result implies that the positive effects transit to negative effects when FBGD reaches numerical thresholds 34.20% and 35.11%, respectively, it also suggests that the risk averse nature of women on EF and DF usage becomes more visible and intense when the percentage of women on board increases above the mentioned thresholds, respectively. Clearly, the effect gender diversity on DF and EF depends on the numerical strength of the women on a board.

These results suggest that corporate entities and managers must be careful in the formation and implementation of gender diversity policies as gender diversity policies can influence/change debt and EF decisions. In addition, the thresholds show that a smaller number of women on board is required to lower EF compared to debt and this highlights risk-aversion nature women toward riskier financing decision. Also, the nonlinear inverted U-shape nexus from FBGD to EF and DF confirms the inverted U curve theory implying that the numerical strength of females on boards is critical for financing decisions.

This study contributes to the “gender diversity-financing decision” literature by simultaneously conceptualizing and modellng debt and EF structures and providing an emerging economy perspective on how gender diversity nonlinearly affects financing decisions.

The purpose of this study is to explore the impact of knowledge management (KM) practices (knowledge creation, knowledge storage, knowledge transfer and knowledge application) and demographic and occupational factors on team learning (TL) in public enterprises in Jordan.

A convenient random sample of 389 employees working in 52 various functional teams in the Jordanian public enterprises completed a self-administrated questionnaire. Descriptive statistics, confirmatory factor analysis and hierarchical regression analysis were used to analyze the data and test the proposed hypotheses.

The results of this study showed that KM practices explained an additional 53% of the variance in TL above the 9% variance explained by the demographic and occupational factors (i.e. gender, work experience, age, education, occupational position, team size and participation in training on KM and TL). Notably, in the absence of the effects of KM dimensions, work experience, age, team size and “participation in training on KM and TL” were significant predictors of TL. However, after including the effects of KM dimensions in the regression analysis, only the participation in training variable, along with the KM dimensions, remained significant predictors of TL.

This study contributes to public enterprise administration by highlighting the importance of KM practices in nurturing a healthy TL climate that can ultimately enhance job performance and organizational success.

To the best of the authors’ knowledge, this study is one of the few studies in the Arab world that examines real functional teams to understand the role of KM in enhancing the practice of TL.

The paper aims at investigating the association between dimensions of livelihood capital access including financial, human, physical, natural, social, cultural, institutional capital access and sustainable livelihood outcomes – improved well-being. The study aims at establishing whether livelihood capital access enhances the livelihoods of national park adjacent communities in Uganda.

The study employed a quantitative approach and cross-sectional research design. A theoretically driven model was tested on data from 364 respondents from Uganda Community Tourism Association (UCOTA). Structural Equation Modelling (SEM) was used to test relationships between the study variables.

The results indicate that among the seven distinct forms of capital access, financial, human, physical and social capital are the most crucial. Accessing financial, human, physical and social capital has been shown to significantly enhance the livelihoods of communities residing adjacent to national parks.

This study adds to the body of sustainable livelihoods literature by highlighting how access to livelihood capital impacts the sustainable livelihoods. The research suggests prioritizing the improvement of access to financial, human, physical and social resources, with a particular emphasis on integrating livelihood capital access into livelihood and tourism policies.

Although the deployment of diverse brand capabilities is essential for business-to-business (B2B) firms to adapt to changing industrial market demands, the understanding of how brand ambidexterity (BA) influences brand performance (BP) remains limited. This study aims to investigate the importance of BA in relation to BP, analyzing the mediating effect of buyer dependence (BD) and the moderating effect of the supplier’s degree of digital use (DDU) within B2B firms.

Drawing on resource dependence theory (RDT) and the literature on BA and BD, the authors developed a theoretical research model. The authors surveyed 261 pairs of firms, including suppliers and their buyers in China. After the data were collected, the authors used partial least squares structural equation modeling to analyze the data.

The findings of the study indicate that BA significantly and positively influences BP in B2B firms. Furthermore, both buyer relationship value dependence (RVD) and switching cost dependence (SCD) mediate the relationship between supplier’s BA and BP. In addition, the supplier’s DDU positively moderates the relationship between BA and buyer RVD; however, its effect on SCD is not significant.

To the best of the authors’ knowledge, this study is among the first to reveal the underlying mechanism by which BA is leveraged by B2B firms to enhance BP. In addition, by adopting a dual supplier-buyer perspective, the findings provide valuable insights for managers seeking to understand the influence of BA on interorganizational relationships.

This study aims to assess the impact of information and communication technologies (ICT) and financial inclusion on economic growth. This study also examines whether ICT can be a determinant of financial inclusion. Moreover, this study provides new evidence concerning whether ICT can reduce the financial inclusion gap.

This study uses the country-level data over the period 2005–2019 and estimate using the dynamic and the static panel model.

The results show that the ICT and financial inclusion interaction variable substantially and positively impacts economic growth for only certain interaction variables. ICT is an essential determinant of financial inclusion and reduces some gaps.

This study contributes to the literature by considering whether ICT and financial inclusion impact economic growth in high- and low-income countries. The other contribution of this study is that ICT represents a determinant in promoting financial inclusion. The final contribution of this study is providing new evidence concerning whether ICT can reduce the financial inclusion gap so that financial access can increase, financial inclusion can develop and simultaneously encourage economic growth.

In innovation management, the complexity inherited in the supply network may be necessary for success. This study aims to holistically examine innovation complexities and system attractors within a hierarchically nested supply network and explore how they dynamically interact and influence adaptive innovation processes.

Taking a complexity theory perspective, we employed a methodological bricolage approach using a single case study with multiple embedded units of analysis – namely, a supply network encompassing 36 firms. We drew upon primary data obtained from 42 interviewees and rich secondary data, and we employed a temporal exponential random graph model to examine the micro-foundations of the evolution of the sampled supply network over a decade.

This study presents a comprehensive overview of the innovation complexities—relational, temporal, dynamic, operational and structural – and how they manifest within a supply network. It also identifies three systemic attractors – point, periodic and strange – and elucidates their relationships with the complexities and their impact on innovative supply network dynamics. The resulting conceptual framework and working propositions provide a detailed perspective on the complex interplay between balanced order and chaos and the potentially unbalanced innovation states within a supply network.

This research offers an in-depth perspective on the innovation complexities and dynamic attractors within a supply network from a holistic, multilevel perspective. It advances complexity theory and deepens the understanding of supply networks as complex adaptive systems.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

This study aims to determine how Basel III capital requirements affect the stability of Islamic banks globally during the global financial crisis and the COVID-19 pandemic.

The secondary data for all Islamic banks worldwide from 2004 to 2021 is obtained from the FitchConnect database. The main technique was a two-step generalized method of moment (GMM) system, and the data were tested using pooled ordinary least squares, fixed effects and difference GMM models for robustness checks.

Regression results support the moral hazard hypothesis based on evidence that both the total capital ratio and the Tier 1 capital ratio have a statistically significant positive impact on the stability of Islamic banks globally. Furthermore, neither the global financial crisis of 2008–2009 nor COVID-19 (2020–2021) significantly impacted the stability of Islamic banks worldwide. The results are robust across alternative measures of stability, capital buffers, dummy variables and estimation techniques. According to the descriptive statistics, the number of Islamic banks that disclose their regulatory capital ratios to the public has increased over the study period, and the mean of total capital and Tier 1 ratios are considerably greater than what is required by Basel II and Basel III.

Bankers, regulators and policymakers should benefit from the evidence on capital and risk management in Islamic banking according to Basel Committee on Banking Supervision (BCBS) and Islamic financial services board (IFSB) international standards in various jurisdictions.

This research builds on earlier studies that were both beneficial and instructive by exploring the relationship between BCBS and IFSB capital guidelines and the trustworthiness of Islamic banks in greater depth. This study uses numerous capital ratios, buffers and stability measures to provide an international context for research on Islamic banking. In addition, the database is up-to-date to include information about the COVID-19 pandemic aftereffects in the year 2021. This study also introduces the Basel membership of Islamic banks to provide context for countries still at the Basel II stage or are yet to begin implementing the Basel III international standard.

This study conducts a meta-analysis to investigate the impact of board diversity, independence and size on carbon emission disclosure.

The results of 22 empirical investigations on the association between board qualities and carbon emission disclosure are synthesised using a meta-analysis approach. Inclusion and exclusion criteria are established, and search strategies are devised to locate relevant material. Data extraction entails gathering important information such as the names of the authors, variables and correlation coefficients. Fisher's z-transformation is used to compute and synthesise effect sizes and assumptions, sensitivity testing and subgroup analysis are performed to assess the robustness of the findings.

A substantial association was discovered between board characteristics and carbon emission disclosure. Board independence and gender diversity revealed small to medium-strength positive relationships, whilst board size had a medium-strength positive correlation. The study periods varied from 2011 to 2022, with 2018 having the most studies. However, highly heterogeneous groups were discovered; further subgroup analyses were then carried out to sort out this issue.

Several limitations were recognised due to the limited number of studies and heterogeneity, although subgroup analysis was used to reduce the influence of heterogeneity. To investigate alternate outcomes, more analysis of the heterogeneity level and potential modifications to the model assumptions may be required.

Companies should consider board size, independence and gender diversity when formulating long-term competitive strategies in the climate change movement. These characteristics can aid in bridging information gaps and garnering stakeholder support for carbon-reduction initiatives.

This meta-analysis addresses a gap in the literature by addressing prior studies' conflicting and inconsistent findings on the association between board characteristics and carbon emission disclosure. It employs a rigorous approach and synthesis strategy to provide a thorough and robust understanding of the crucial role of board characteristics in carbon emission disclosure.

The substantial expansion of technology and the efficacy of digital platforms in reaching young audiences have led to enhanced targeting and customization of promotional communications. Notwithstanding the expansion and efficacy of contemporary advertising platforms, scholarly attention has not kept pace with this domain of inquiry. This study aims to assess the antecedents of Google Shopping Ads (GSA) on intention to purchase behavior among the Generation Y and Z cohorts.

The current study used a quantitative approach and snowball sampling technique to gather primary data via a questionnaire and Google Forms, which resulted in the collection of 5,808 questionnaires among the cohort members. A principal component analysis and multigroup confirmatory multigroup structural equation modeling (between Generation Y and Z) were used to assess the research data and model.

The results show positive trust and perceived value associations with intention to purchase, particularly among Generation Y and Z consumers. The findings also show negative irritation, product risk and time risk associations with intention to purchase, especially among the Generation Y cohort, which indicates that young consumers generally do not observe perceived risk due to the usage of GSA.

GSA will continue to grow and become an increasingly important integrated marketing communications tool as the digital landscape develops. It can be concluded that young consumers show a high degree of perceived value and low levels of perceived risk due to the use of GSA. This study, therefore, promotes improved understanding among academics, marketers and businesses of search engine advertising among young cohorts of consumers (Generation Y and Z) in a developing country context.