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Social interaction in comment sections has become a key factor for backers' decision making in crowdfunding platforms. However, current research on the two-way social interaction in crowdfunding is insufficient, and there exist inconsistent conclusions. This study focuses on the social interaction between creators and backers and explores its influence on the successful exit of crowdfunding projects.

The extended Cox model is used for the empirical analysis of 1,988 crowdfunding projects on the Modian (www.modian.com) platform, a crowdfunding platform for cultural and creative projects in China. The two-way social interaction is reflected in comment quantity and sentiment, as well as reply rate.

Results reveal an inverted U-shaped relationship between comment quantity/sentiment and the successful exit of crowdfunding projects. This relationship is strengthened by high reply rate.

This study focuses on comment quantity and sentiment. The inverted U-shaped results reconcile previous conclusions. Replies from creators are regarded as a separate factor, and their moderating role is explained. The study research proves the importance of social interaction in crowdfunding platforms and provides suggestions for backers, creators and platform managers.

In the corporate world, there is no certainty of survival. This research aims to identify firm-level factors that increase or decrease a firm's probability of exit and survival.

The study examines 153 listed textile sector firms in Pakistan over a 10-year period from 2009 to 2018, comprising 1,413 observations. The semi-parametric Cox regression model is used to process the results.

The study finds that larger and exporting firms are more likely to survive, while those with a high ratio of fixed assets to total assets, high expenditure on advertising and variable costs are less likely to survive. The relationship between age and firm survival is inconclusive.

Adaptability to the external environment provides a competitive advantage that is crucial for textile firms to reduce their chances of exit. The research is valuable for strategic managers and policymakers to identify focus areas to prevent firm exit.

This study supports the active learning theory, which suggests that new entrants in the textile sector of Pakistan should focus on becoming active market players, increasing efficiency and reducing variable costs to survive.

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.

The purpose of the study is to mainstream the disaster risk reduction (DRR) and disaster resilience measures into the GreenSL® building rating system in Sri Lanka to demonstrate a more holistic approach in achieving sustainability with resilience.

First, a comprehensive literature review was carried out on green building practices and rating tools, natural hazards and DRR and global and local frameworks. Next, criteria were developed incorporating the disaster resilience aspects through eight expert committee meetings and included the rating tool in three levels. Finally, 11 green-certified buildings from the Green Building Council of Sri Lanka were reevaluated using the new disaster resilient GREENSL® rating system for built environment as a validation for the new tool and to analyze the new certification levels.

All the green-certified buildings in hazard-prone areas dropped their certification by one level while the buildings in nonhazard-prone areas remained in the certification after being evaluated by the new DRR-incorporated rating system, which recommends the use of new tool for the buildings in hazard-prone areas. But due to the rapid changes in climate and the unpredictable trends of natural hazards, the use of new DRR incorporated GREENSL® rating system for built environment for the certification of buildings is highly endorsed.

This is the first research study based on the GREENSL rating system. And it is the first effort to incorporate DRR aspects to any rating tool in Sri Lanka. The projects that were evaluated for the validation of the new tool are the actual projects which obtained green building certification. Also, it is apparent that this study has gone some way towards enhancing the understanding of blending the sustainability and resilience in rating systems for built environment.

This study aims to examine the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries.

This study uses semi-annual panel data covering the period 2000–2020 among 152 developing and middle-income countries and Cox proportional hazard model for the analysis.

Estimates indicate that effective operations of environmental institutions, investment in research and development, subsidizing the production of renewable energy, government investment in producing renewable energy and investment in renewable energy production made by the private sector will contribute immensely toward achieving sustainability of renewable energy production.

This study recommends that governments should rationalize their expenditures to mobilize enough resources for investment in renewable energy production. Again, operations of environmental institutions should be enhanced through giving their managers’ performance contracts and licensing its employees. Enabling environment should be created for private sector to increase their investment in renewable energy production.

Empirical studies have been carried out exploring measures to deal with climate change. Nonetheless, the appropriate measures needed toward achieving sustainability of renewable energy production among developing and middle-income countries have not been explored in existing empirical studies. Hence, this study fills the gap in existing empirical studies.

The main purpose of this study is to evaluate the extent of dropout students and identify the relationship between risk factors of dropout and the survival time of students.

The Kaplan–Meier estimator (KM), also known as the product-limit technique, is a nonparametric model function that is commonly used in estimating survival function events (Kaplan and Meier, 1958). The survival function's Kaplan–Meier estimators are used to estimate and graph survival probabilities as a function of time, as well as explanatory data analysis (EDA) for the survival data, including the median survival time, and compare for two or more of the survival events. In addition, Cox proportional hazards model is employed for modelling purpose.

Results of the Kaplan–Meier curves show that male students have lower survival rates than female, researchers have found that there is a difference between the survival times of the student's school types, results show students from English-based schools are higher than Arabic-based schools as suggested by the survival curve. Similarly, there is a difference between the survival times of students aging equal or greater than 25 and students aging less than 25 and survival function estimates of dropout according to high school grade marks has huge difference. These results were confirmed using log rank test as age, school type and marks were statistically significantly different while gender is not statistically significant.

There is no study of this kind from the Somalia context about the student's dropout. Subsequent to the outbreak of civil war in 1988 and the collapse of the central government in 1991, all public social services in Somalia including education centers were severely disrupted.

The statistical methods discussed in the previous section will be applied on a real dataset obtained from different offices of the university; most of the data were extracted from faculty of economics office and admission and record office. The data set comprised of 70 students from SIMAD university, consists of full-time faculty of economics students who enrolled at the university in the academic year of 2017–2018 until two years of diploma, students either complete 24 months of diploma or leave the university and that is the event of interest.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This study examines the appropriate measures that need to be intensified among African countries to achieve sustainable environment to mitigate climate change.

The study employs panel data covering the period 2000 to 2020 among 54 African countries and Cox proportional hazard model for the analysis.

Estimates indicate that the practice of carbon farming, the development of rooftop gardens, renewable energy production and consumption contribute positively toward achieving sustainable environment, while governance adversely affects this objective of achieving sustainable environment.

The study recommends that governments should enforce the constant practice of carbon farming among these countries through passing laws to enforce its application among farmers and allocate 2% of ministry of agriculture's budget toward financing carbon farming for poor farmers.

Empirical studies have been carried out exploring measures to deal with climate change. Nonetheless, the appropriate measures of achieving sustainable environment to mitigate climate change have less been explored in literature on Africa. Hence, this study fills the gap in existing empirical studies.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-04-2023-0290.

The purpose of the study is applying and comparing models that predict optimal time for new product exit based on its demand pattern and survivability. This is to decide whether or not to continue investing in new product development (NPD).

The study investigates the optimal time for new product exit within the hi-tech sector by applying three models: the dynamic learning demand model (DLDM), the generalized Bass model (GBM) and the hazard model (HM). Further, for inter- and intra-model comparison, the authors conducted a simulation, considering Weiner and exponential price functions to enhance generalizability.

While higher price volatility signifies an unstable technology, greater investment into research and development (R&D) and marketing results in higher product adoption rates. Imitators have a more prominent role than innovators in determining the longevity of hi-tech products.

The study conducts a comparison of three different models considering time-varying parameters. There are four scenarios, considering variations in advertising intensity and content, word-of-mouth (WOM) effect, price volatility effect and sunk cost effect.

This research considers the question of whether unemployment insurance benefit and labour-market activation measures induce the likelihood of re-employment and whether this effect differs for natives and immigrants.

Statistical processing was carried out on the European Union Statistics on Income and Living Conditions cross-sectional data for Finland for the period 2004 to 2016. Propensity score matching analysis was undertaken to investigate whether a treatment effect (unemployment insurance benefit) was a predictor of success in increasing re-employment rates, when controlling for participation in labour-market policy measures, subsidized employment and personal background characteristics.

We find that the probability of re-employment for recipients of unemployment benefits is half that of non-recipients of benefits. Due to the influence of subsidized employment, subsequent employment income decreases for recipients of unemployment benefits and especially for immigrants. Finally, we find that due to the influence of subsidized employment, time spent as a full-time employee decreases for recipients of unemployment benefits and especially for immigrants.

Although our results indicate that benefit determination has a marked impact on re-employment probabilities, unobserved variables turn to play a significant role in selection of labour-market activation measures. In this respect, we find the treatment assignment to activation policy measures depends on influence of unobserved variables and this effect is more important for the re-employment rates of natives than it is for immigrants.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2019-0668.