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Drawing on the Theory of Planned Behavior, this study aims to explore the role of individual digital competencies, encompassing information and data literacy, communication and collaboration, safety and security, and problem-solving, in shaping cognitive determinants and influencing digital entrepreneurial intentions as well as investigates the moderating effect of performance expectancy of AI solutions on the relationship between digital competencies, cognitive determinants, and digital entrepreneurial intention.

Using a sample of 1326 MBA students in Vietnam with a stratified sampling approach, the second-order PLS-SEM is used to test the formulated hypotheses rigorously.

The study reveals that individual digital competencies, sculpted by information and data literacy, communication and collaboration, safety and security, and problem-solving, significantly impact cognitive determinants (attitude towards digital entrepreneurship, subjective norms, and perceived behavioral control), influencing digital entrepreneurial intentions. Performance expectancy of AI solutions also plays a crucial moderating role, enhancing the relationship between digital competencies and digital entrepreneurial intention.

Some practical implications have been recommended for policymakers, educators, and entrepreneurs.

This research provides original empirical findings, validating the impact of a varied array of digital competencies on entrepreneurial mindsets/cognition and intentions. The introduction of performance expectancy of AI solutions as a moderator introduces a nuanced dimension to comprehending the interaction between technological skills and entrepreneurial intentions.

In modern healthcare environments, collective leadership within nursing teams serves as a fundamental pillar for providing high-quality patient care. The purpose of this study is to identify the factors to improve the collective leadership among the healthcare practitioners.

Using data collected through an online survey of 417 registered nurses in 12 general hospital in Malaysia, the study uses partial least squares structural equation modeling to test the proposed hypotheses.

The result indicate that the collective leadership is directly driven by team shared vision, team commitment and team collaboration. Finding also shows that team shared vision, team commitment and team collaboration has a positive and significant impact on collective leadership. Finally, this study also revealed that, the team collaboration is the most significance factor that affecting the collective leadership among nurses.

This work contributes to a better understanding on collective leadership, ultimately improving team effectiveness and patient care outcomes.

Drawing on mindfulness theory, this study attempts to gain insights into whether leader-mindfulness (LM) influences workplace cynicism (WPC) among non-family employees (NFEs) working in small and medium-sized private family firms. Furthermore, the study leverages the self-determination theory to examine if the above relationship is mediated by the belongingness of the NFEs to the organization and leader–member exchange quality (LMXQ).

A conceptual framework with a set of hypotheses is developed. Using cross-sectional survey data collected from 376 NFEs working in small and medium-sized private family firms in Oman, the hypotheses are tested using structural equation modeling that was analyzed through PLS-SEM.

The results indicate that LM influences WPC both directly and through the mediating effect of LMXQ. The findings also suggest that, while LM influences belongingness, and that belongingness also mediates the relationship between LM and LMXQ. However, belongingness does not mediate the relationship between LM and WPC, either by itself or serially.

The prevalence of WPC among NFEs in family firms hinders the ability of such organizations to retain talented employees that are outside the family circle. Hence, this study presents nuanced insights to those who manage such organizations, as it reveals that leading the family business mindfully can markedly reduce WPC among NFEs, particularly when LM enhances LMXQ.

The study makes four novel contributions. First, this appears to be the first study at the crossroads of the family business and organizational behavior literature to investigate the under-researched topic of WPC among NFEs in family firms. Second, the study provides insights into the relationship between LM and WPC by developing a conceptual framework that draws on mindfulness theory and self-determination theory. Third, it identifies the mediating role of LMXQ in the link between LM and WPC. Finally, it reveals that, although the belongingness of NFEs to their organization is influenced by LM, it does not automatically influence WPC.

This paper aims to review the literature extensively by analysing recent work and providing a guide for models, data sets and research findings.

This paper reviews the literature extensively by analysing recent work and providing a guide for models, data sets and research findings within the context of capital market imperfections. The authors further break down the literature into closer-in-nature categories for reader’s convenience and comprehension. Finally, the authors address gaps in the existing literature and propose government policies that can tone down the potential effect of credit rationing on employment.

This paper provides a map of the literature so as to help future researchers in the relevant literature and give a short insight of what has been explored so far.

This paper is original and is the result of a thorough review of an extensive literature.

Although women-owned small and medium enterprises (SMEs) represent only 21.1% of all SMEs in South Africa, they play a fundamental role in the SME sector in terms of job creation, employment and poverty alleviation that is critical for economic growth. This study aims to explore (FFIs) financing of women-owned SMEs in South Africa from a credit provider perspective (supply-side).

A qualitative research approach positioned in the interpretivistic research paradigm was used to accomplish this study objectives. The five-step process of content analysis proposed by Terre Blanche, Durrheim and Kelly was used to analyse the qualitative data collected from the 16 participants via semi-structured in-depth interviews.

The findings reveal that FFIs are willing to finance women-owned businesses provided they can contribute a reasonable percentage of the equity capital and a first-class collateral. Lack of equity, business experiences and first-class collateral are the most serious challenges faced by FFIs when considering lending to women-owned SMEs.

This study investigated the financing of women-owned SMEs in South Africa from a supply-side perspective, compared to other studies that used quantitative methodology. This study findings provide insights into how FFIs perceive financing women-owned SMEs, women-owned SMEs credit approval rate, the factors that influence the willingness of FFIs to provide credit to women-owned SMEs and the challenges experienced by FFIs in financing women-owned SMEs.

This paper addresses the under-explored collateral damage of economic sanctions, shedding light on the disproportionate harm endured by the most vulnerable segments of societies, which at the same time lack political influence to effect the ruling government into change. The primary objective is to review the literature on humanitarian repercussions associated with sanctions, concluding if they really are a comparably human way of international interference.

Employing a systematic literature review, adhering to the PRISMA approach, and incorporating key term definitions and clear selection criteria, this review analyses 52 studies sourced from Scopus and EconBiz.

The surveyed literature reveals profound adverse impacts of sanctions on health, economic well-being, inequality, and education. Critical gaps in the literature such as disproportional focus on extreme cases like Iran and Iraq, scarce literature on effects on education and inequality, and predominantly inadequate control groups are identified, limiting the generalizability of existing findings.

This paper is the first systematic and replicable review of the literature on the effects of sanctions using a capabilistic approach to define poverty. Highlighting gaps in the current research landscape underscores the limited generalizability of reviewed results. Providing a well-structured summary of existing literature, this work serves as a foundation for future research.

Innovation in financing processes, enabled by the advent of new technologies, has supported the development of alternative finance funding tools. In this context, the study analyses the growing importance of alternative finance instruments (such as equity crowdfunding, peer-to-peer (P2P) lending, venture capital, and others) in addressing the small and medioum enterprises' (SMEs) financing needs beyond traditional bank and market-based funding channels. By providing more flexible terms and faster approval times, these instruments are gradually reshaping the traditional bank-firm relationship.

To comprehensively understand this innovation shift in funding processes, the study employs a novel approach that merges three MCDA methods: Spherical Fuzzy Entropy, ARAS and TOPSIS. These methodologies allow for handling ambiguity and subjectivity in financial decision-making processes, examining the effects of multiple criteria, including interest rate, flexibility, accessibility, support, riskiness, and approval time, on the appeal of various financial alternatives.

The study’s results have significant theoretical and practical implications, supporting SMEs in carefully evaluate financing alternatives and enables banks to better identify the main “competitors” according to the “financial need” of the firm. Moreover, the rise of alternative finance, notably P2P lending, indicates a shift towards more efficient capital access, suggesting banks must innovate their funding channels to remain competitive, especially in offering flexible solutions for restructuring and high-risk scenarios.

The study advises top management that SMEs prefer traditional loans for their reliability and accessibility, necessitating banks to enhance transparency, innovate, and adopt digital solutions to meet evolving financing needs and improve customer satisfaction.

The study introduces a novel integration of Spherical Fuzzy TOPSIS, Entropy, and ARAS methodologies to face the complexities of financial decision-making for SME financing, addressing ambiguity and multiple criteria like interest rates, flexibility, and riskiness. It emphasizes the importance of traditional loans, the rising significance of alternative financing such as P2P lending, and the necessity for banks to innovate, thereby enriching the literature on bank-firm relationships and SME funding strategies.

The design of effective policies that increase access to agricultural credit should consider understanding credit constraint farmers’ groups and their response to changes in the credit conditions. To contribute to this understanding, this study surveyed farmers from Chile and classified them into five credit constraint categories discussed in credit literature. In addition, these farmers indicated how they would react to a series of hypothetical conditions related to changing interest rates, loan maturity and grace periods. Their responses were employed to measure credit demand scores (i.e. relative elasticities). Regression tests evaluated how different types of farmers reacted to changing credit conditions.

Farmers from Chile were surveyed using a mix of random and convenience sampling. Surveyed farmers were classified into five credit constraint categories proposed by previous research. Farmers rated their demand for credit on a five-point Likert-type scale for hypothetical changes in interest rates, loan maturities and grace periods. Their responses were employed to measure credit demand scores or relative credit elasticities. The study evaluated credit elasticity as a function of farmers’ credit constraint and some control variables using several regressions, including OLS, ordered probit and hierarchical regression.

The study identified 44% unconstrained nonborrowing farmers, 23% unconstrained borrowers, 14% quantity-constrained, 16% risk-constrained and 3% transaction cost-constrained farmers. Unconstrained borrowers and quantity-constrained farmers responded most to changing interest rates and loan maturity conditions. In addition, unconstrained nonborrowers and risk-constrained farmers were statistically less sensitive to changes in credit conditions than unconstrained borrowers. This finding is significant because, as discussed, unconstrained nonborrowers represent 44% of our sample. Furthermore, risk-constrained farmers were the least sensitive to changes in interest rates and loan maturity across all other credit categories.

This study gives insights that can guide agribusiness policies to enhance access to credit in developing countries such as Chile. Agricultural credit capital institutions can better target their clientele by identifying farmers’ possible reactions before implementing policy changes to increase access to credit. This study’s credit constraint categorization and the results discussed can guide that identification. For instance, policies directed toward unconstrained borrowing farmers may find positive responses. However, implementing policies targeting the other three groups (unconstrained nonborrowing, risk-constrained and transaction cost-constrained farmers) is more challenging because these farmers are less responsive to changing credit conditions.

This article correlates farmers’ propensity to borrow and credit constraints across five categories of farmers. Prior research using this categorization framework has not identified farmers into the five groups. Furthermore, in addition to interest rate and loan maturity credit demand relative elasticity, this study adds the grace period elasticity, which has not been included in previous studies on agricultural credit.

In the textile dyeing industry, the foam dyeing has been recognized as a significantly sustainable alternative for the cotton fabrics. However, this efficient technology undergoes the many issues related to the foam generation, foam optimization and the required performance of the resultant fabrics. The purpose of this paper is to address these issues through the development and optimization of the novel reactive foam dyeing recipes for the cotton fabrics.

The foam dyeing recipes were generated and optimized using the different stabilizers, foaming agents and three primary colors of reactive dyes. The different recipes were applied onto the cotton fabric using laboratory scale foam coating machine. The performance of the foam coated and padded fabrics was evaluated using different criteria including the shade depth, rubbing fastness, air permeability, washing fastness, perspiration fastness, light fastness and tear strength. Then, a complex decision-making approach, namely, analytic hierarchy process (AHP), was applied for the ranking of the key recipes based on the main criteria.

The newly optimized foam dyeing recipes were found very competitive with the conventional pad dyeing process with respect to the shade-depth and the other performance properties. The optimization of foaming parameters and addition of stabilizers have advanced the foam dyeing process, which would accelerate the implementation of foam dyeing methods in the textile industry. Furthermore, significant water and energy savings would be achieved as compared to the conventional foam dyeing. AHP model offered a comprehensive and rational way to identify the most important recipes amongst the selected recipes.

In this research, novel foam dyeing recipes have been developed for the cotton fabrics through the optimization of the different stabilizers, foaming agents and the three primary colors of reactive dyes. Until now, the exiting literature has not reported the combination of these stabilizers with the different foaming agents and three primary reactive dyes for the improvement of sustainable foam cotton dyeing process.

This paper examines why farmers self-select out of formal credit markets even though they need external funds.

We use probit and Bayesian probit estimators to detect the determinants of self-selection behavior based on a primary dataset of 2,212 rice farmers in Vietnam. After that, we use the multinomial probit (MNP) and Bayesian MNP estimators to reveal the impact of relevant factors on the decision to self-select for farmers belonging to each self-selection category.

The probit and Bayesian probit estimators show that the decision to self-select depends on household head age, income per capita, farm size, whether or not to have relatives or friends working for banks, the number of previous borrowings, risks related to natural disasters, diseases, and rice price, and the number of banks with which the farmer has relationships. The MNP and Bayesian MNP estimators give further insights into the decision of farmers to self-select in that determinants of the self-selection behavior depend on the reasons to self-select. In concrete, farm size and the number of previous borrowings mitigate the self-selection of farmers who did not apply for loans due to having access to other preferred sources of credit. The self-selection of farmers not applying for loans because of unfavorable loan terms is conditional on household head age, farming experience, income, farm size, the number of previous borrowings, natural disaster risk, and the number of banks the farmer has relationships with. Several factors, including education, income, the distance to the nearest bank, whether or not having relatives or friends working for banks, the number of previous borrowings, risks, and the number of banks the farmer has relationships with, affect the self-selection of farmers not applying for loans because of high borrowing costs. The self-selection of farmers not applying for loans because of complex application procedures depends on income and the number of previous borrowings. Finally, the household head’s age, gender, experience, income, farm size, the amount of trade credit granted, the number of previous borrowings, natural disaster risk, and the number of banks the farmer has relationships with are the determinants of the self-selection of farmers not applying for loans because of a fear not being able to repay.

This paper fills the knowledge gap by investigating why farmers self-select out of formal credit markets. It provides evidence of how the farmers’ subjective perceptions of rural credit markets contribute to their self-selection.

This paper shows that demand-side constraints are also vital for farmers’ access to bank credit. Improving credit access via easing supply-side constraints may not increase credit uptake without addressing demand-side factors. Given that finding, it recommends policies to improve access to bank credit for farmers regarding the demand side.