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The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

With the continuous increase of users, resources and tags, social tagging systems gradually present the characteristics of “big data” such as large number, fast growth, complexity and unreliable quality, which greatly increases the complexity of recommendation. The contradiction between the efficiency and effectiveness of recommendation service in social tagging is increasingly becoming prominent. The purpose of this study is to incorporate topic optimization into collaborative filtering to enhance both the effectiveness and the efficiency of personalized recommendations for social tagging.

Combining the idea of optimization before service, this paper presents an approach that incorporates topic optimization into collaborative recommendations for social tagging. In the proposed approach, the recommendation process is divided into two phases of offline topic optimization and online recommendation service to achieve high-quality and efficient personalized recommendation services. In the offline phase, the tags' topic model is constructed and then used to optimize the latent preference of users and the latent affiliation of resources on topics.

Experimental evaluation shows that the proposed approach improves both precision and recall of recommendations, as well as enhances the efficiency of online recommendations compared with the three baseline approaches. The proposed topic optimization–incorporated collaborative recommendation approach can achieve the improvement of both effectiveness and efficiency for the recommendation in social tagging.

With the support of the proposed approach, personalized recommendation in social tagging with high quality and efficiency can be achieved.

The study research aims to examine the effects of supply chain management practices on risk mitigating (RM) by studying the correlation between social ties, institutional support, interagency cooperation and external flexibility in public healthcare sectors. Moreover, this research examines the impact of RM on supplier trust, and also the authors examine the relationship among supplier trust and logistics performance.

The authors used a structural equation model (SEM) based on 539 quantitative data from Ethiopian healthcare organizations. The model included control variables like company size and position to understand better how employees perceive risk mitigation.

The study's findings indicate that interagency collaboration and external flexibility positively affects RM. On the other hand, RM positively impacts supplier trust and also the supplier trust has a positive effects on performance dimensions. This study also shows that RM has not positively impacting institutional support and social ties.

The study investigation may help the pharmaceutical industry, healthcare service SC agencies and other stakeholders better understand the effects of supply chain management practices on RM and obtain information on progress made thus far.

This research helps managers and their organizations to manage the risk associated with their organizations. Though, this study focuses on Ethiopian healthcare SCM. The authors expect the findings might apply to other countries organizations with comparable demographic or SCM features.

The building sector is vital to a nation’s economy, as it has a major influence on economic activity and growth, job creations and the advancement of infrastructure. Intricate challenges that are inherent in crises such as the COVID-19 outbreak lead to material scarcities, project delays, labor shortages, escalated expenses, funding challenges, regulatory obstacles and dwindling investment funds, all of which culminate in costs that are in excess of those budgeted. While numerous studies have explored the ramifications of COVID-19 on project budgets, there is little, if any, data available on forecasting the magnitude of this impact.

This investigation seeks to bridge this knowledge deficiency by devising a predictive tool grounded in an ordinal logistic regression method. An online survey was designed and disseminated to gauge the views of construction field experts about the diverse contributors to excessive costs during the viral outbreak, and a predictive tool, crafted from the survey participants’ feedback.

Findings showed that smaller-scale enterprises and contractor-centric establishments faced greater adversities than medium-to-large ones and consultancy-or-owner-type entities.

The insights from this research shed light on the amplified risk of higher project costs amid health crises or analogous events, underlining the imperative need for fortified risk management approaches to bolster project outcomes. By factoring in demographics, this research offers policymakers a refined lens through which to customize interventions and promote balanced and enduring advancement in the construction industry.

This research aims to examine the impact of religiosity and other control variables on Muslims’ environmental preservation and economic growth choices in 33 nations.

The study uses data from the World Values Survey (Waves 4–7) with a large sample size of 30,242 individuals. Logistic regression analysis is used to analyze the data, and the robustness principle is applied using the marginal effect of interaction variables method to select a viable model.

This study reveals that different aspects of religiosity – cognitive, affective and behavioral – positively impact the tendency of Muslims in 33 countries to prioritize environmental protection over economic progress. However, these influences vary significantly, as seen through odds ratios. In essence, the degree of religious devotion in these nations affects individuals’ leaning toward environmental preservation. This impact is further shaped by other factors such as politics, governance, economic development, environmental measures and legal frameworks.

The practical implication of this study is the development of an alternative theory that explains the conditions and categories under which religious beliefs and attitudes can influence the preferences of Muslims concerning environmental issues and economic growth.

This study fills a void in the body of literature by examining the nonlinear relationship between religiosity and individual Muslim preferences for environmental preservation and economic growth. It offers a framework for comprehending religion’s impact on Muslims’ redistributive individual preferences in these fields.

The purpose is to examine the relationship between entrepreneurs’ exit strategies and modes of entry. The topic of exit strategies in the context of approaching retirement warrants further attention.

We apply logistic regression to analyse 1,192 responses to an online survey of firms with entrepreneurs aged over 55.

Family successors are more likely to choose family succession and buyers to choose to sell, but the association between founding and exit mode cannot be confirmed. Firm size is also significant. Our findings suggest that entry and exit via a business transfer are linked. Entrepreneurs might be influenced by their form of entry when choosing their exit strategy.

The data were collected from a single European country, limiting generalisation. Future research should incorporate intervening variables not controlled for here, such as, entrepreneurial experience. Future studies should also seek to test the existence of imprinting directly, as it is implied rather than verified here.

If the entry mode has a lasting effect on the entrepreneur as our results suggest, thus influencing the exit strategy selected, entrepreneurs could benefit from greater awareness of the imprinting mechanism. Increasing awareness of imprinted biases could unlock the benefits of exit strategies previously overlooked.

The study is the first to consider sale, family succession and liquidation as exit strategies in relation to the original entry mode of ageing owners. It contributes to the understanding of exit strategies of ageing entrepreneurs and proposes using entrepreneurial learning and imprinting as lenses to clarify the phenomenon.

The primary purpose of this study is to explore the relationship between the unsolicited proposal (USP) and the performance of private participation infrastructure (PPI) projects in developing countries.

The main data set for this study was collected from the World Bank database consisting of 8,951 PPI projects that occurred in developing countries from 1996 to 2020. Hierarchical logistic regression was applied for investigating the effects of USPs on project success. Three moderators, namely, control of corruption, presence of local sponsor and project size were also included in the model to test the impact of their interactions with the USP on the performance of PPI projects. Further, to assess the impact of the effect of USPs, the average marginal effect was calculated. The framework used in this study consists of 18 control variables, three moderators and one noncontrolled independent variable (the USP).

The results of hierarchical logistic regression indicate that USPs have a significant and negative effect on the success of PPI projects occurring in developing countries. The negative effect of a USP weakens with the presence of local sponsors and stronger control of corruption in the host country. However, contrary to the authors’ expectations, the results show that project size does not significantly affect the association between USPs and the success of PPI projects. Moreover, the results of average marginal effects show that the negative impact of USP on the success of PPI projects ranges between 2.4% and 3.8%.

This study quantifies the negative impact of USP on the success of PPI projects in developing countries, which will be helpful for the practitioners to understand the associated risk with USP projects. Furthermore, it also identifies the moderating roles of control of corruption and the presence of local sponsors on the relationship between USP and the success of PPI projects.

The purpose of this paper is to estimate the effect the five different generations and the key financial inclusion indicators of gender, education and location (rural–urban) in exacerbating disparities in financial inclusion in Kenya. This paper considers whether the five generational cohort groups in Kenya differ on the financial inclusion determinants and behaviour as predicted by common generational stereotypes.

The authors applied a multinomial logistic regression approach to nationally representative household survey data from Kenya to estimate the effect that key financial inclusion indicators have on belonging to one of the five generations: Z, Y, X, baby boomers and traditionalists.

The authors found significant links between all tested variables and financial inclusion. The authors found an access gap between Generations X and Y, with the latter being more prone to access and use financial services and products. These differences are compounded by gender and rurality. People in rural locations and women generally were found to have less access to financial services and products, thus causing significant exclusion of a large proportion of the population.

The research has important implications for governments, financial institutions and educational providers, notably on targeted policies and programmes that strategically aim to eliminate disparities and promote greater financial inclusion, denoting the value of such variables as generational differences and gender inclusivity.

This paper deepens the understanding of differences that can divide generations on financial inclusion.

This study empirically identifies household demographic and socioeconomic characteristics as well as restaurant characteristics that affect the probability of households choosing an ethnic restaurant (American, Asian, European, Mexican and other) in the USA.

A multinomial logistic regression model is applied using the data derived from the information from the National Household Food Acquisition and Purchase Survey conducted between April 2012 and January 2013.

The empirical findings suggest that such factors as the unit cost on away-from-home food items (i.e. price), region of residence, primary respondent's ethnicity, race, education level, marital status and employment status as well as such restaurant characteristics as availability of loyalty program and presence of coupons significantly affect the probability of households choosing a particular ethnic restaurant in the USA.

The original dataset employed in this study does not permit the quantification of information associated with size, location, and number of years in operation for the ethnic restaurants considered. Also, the dataset does not permit the classification of the ethnic restaurants included in the “other” category.

To the best of the authors' knowledge, there has been no empirical micro-level analysis associated with determining factors impacting households' choice of ethnic restaurants using a polytomous logistic regression model allowing for a wide range of ethnic restaurants and covering the entire USA, based on an extensive set of household demographic and socioeconomic factors and restaurants characteristics. As such, the current study plugs this research gap, with the empirical findings furnished by this study being of importance to ethnic restaurant operators (owners) in the operators' effort to develop effective marketing strategies.

This study aims to investigate whether the extent of corporate disclosure, proxied by COVID-19-related disclosure, affects the dividend policy of listed firms.

The study uses a multinomial logistic regression model to examine the relation between corporate disclosure and the dividend policy of the 100 largest market-cap firms in Vietnam in 2021. The COVID-19 pandemic, with its unique impact on business operations, serves as the backdrop for this analysis.

The findings indicate that firms with more extensive COVID-19-related disclosure are more inclined to distribute dividends in the form of stocks or cash instead of omitting them.

This research contributes to the understanding of how corporate disclosure practices influence a firm’s financial decisions, particularly in the context of the COVID-19 pandemic. The findings hold implications for corporate financial decision-making during times of macroeconomic shock.