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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.

The paper aims to tackle the classification of Remote Sensing Images (RSIs), which presents a significant challenge for computer algorithms due to the inherent characteristics of clustered ground objects and noisy backgrounds. Recent research typically leverages larger volume models to achieve advanced performance. However, the operating environments of remote sensing commonly cannot provide unconstrained computational and storage resources. It requires lightweight algorithms with exceptional generalization capabilities.

This study introduces an efficient knowledge distillation (KD) method to build a lightweight yet precise convolutional neural network (CNN) classifier. This method also aims to substantially decrease the training time expenses commonly linked with traditional KD techniques. This approach entails extensive alterations to both the model training framework and the distillation process, each tailored to the unique characteristics of RSIs. In particular, this study establishes a robust ensemble teacher by independently training two CNN models using a customized, efficient training algorithm. Following this, this study modifies a KD loss function to mitigate the suppression of non-target category predictions, which are essential for capturing the inter- and intra-similarity of RSIs.

This study validated the student model, termed KD-enhanced network (KDE-Net), obtained through the KD process on three benchmark RSI data sets. The KDE-Net surpasses 42 other state-of-the-art methods in the literature published from 2020 to 2023. Compared to the top-ranked method’s performance on the challenging NWPU45 data set, KDE-Net demonstrated a noticeable 0.4% increase in overall accuracy with a significant 88% reduction in parameters. Meanwhile, this study’s reformed KD framework significantly enhances the knowledge transfer speed by at least three times.

This study illustrates that the logit-based KD technique can effectively develop lightweight CNN classifiers for RSI classification without substantial sacrifices in computation and storage costs. Compared to neural architecture search or other methods aiming to provide lightweight solutions, this study’s KDE-Net, based on the inherent characteristics of RSIs, is currently more efficient in constructing accurate yet lightweight classifiers for RSI classification.

In the present study, using a novel fractional logit model, the link between R&D (Research & Development) investment and shareholder value-based CEO (Chief Executive Officer) compensation has been examined within the non-financial sector in the Euro area economies using a firm-level dataset for 2002–2019.

The fractional logit model is utilized to examine the effects of corporate payment on R&D investment. The fractional logit model can be considered the empirical approach that takes into account R&D non-performer firms to avoid reducing the sample size. The fractional logit model is superior to the censored or truncated models, like Tobit, since the fractional logit model is useful to address the econometric limitations that are found in the censored and truncated models in the non-linear models.

The findings obtained in this study showed a significant and negative effect of short-term aim-based CEO payment on R&D expenditures in the Euro area economies using firm-level data. These findings are robust to different robustness checks and modeling alternatives.

To the author's knowledge, there is no study that examines the effects of short-term shareholder value maximization-based CEO compensation on R&D in the European context in the literature.

This study examines the existence of a causal relationship between the capital structure at the creation of the small and medium-sized enterprise (SME) and its viability after 3 years.

The empirical strategy consists of proceeding in two stages: first, the use of the Logit model to regress the studied variable reflecting the state of an SME of being in default or not, on the variables likely to significantly explain its default risk. Second, the authors investigate the existence of a relationship between the capital structure at the time of SME creation and viability. The obtained results are analyzed to confirm the initial hypothesis.

The results obtained indicate that the Logit model performs well in terms of discriminating and classifying SMEs. These findings are consistent with previous studies and support their conclusions regarding the model's strong classification capability. Furthermore, the model demonstrates a noteworthy classification rate of 90% for capital SMEs, specifically joint-stock companies (SpA). Out of the 10 observed SMEs, 8 nonfailing SMEs were still operational three years after the observation period, resulting in a survival rate of 80%.

The results allow bankers to better understand the main determinants of SME default risk and demonstrate the existence of a causal relationship between the capital structure of an SME and its viability. This study is conducted in the construction, public works, and hydraulics sector (second largest sector in Algeria after the services sector). In future works, the authors try to extend the results of this study to other sectors of activity.

The richness of the established Logit model is to consider both financial and non-financial and qualitative variables. Although the qualitative variables are not statistically significant in the results obtained, the authors used the “Legal form” variable to demonstrate the existence of a causal relationship between the capital structure of an SME and its viability.

More hotels are beginning to embrace green practices given increasing awareness of sustainable development. The purpose of this study is to examine factors contributing to hotels’ participation in TripAdvisor’s GreenLeaders program.

Based on a sample of 48,064 hotels from 328 destinations in 29 countries, the authors leverage a multi-level logit model to examine antecedents of GreenLeaders participation. A multi-level ordered logit model is then estimated to uncover factors influencing the ranking of this participation.

Empirical results indicate that hotels with a larger size, a higher class, a better online reputation, greater reliance on business travelers, fewer neighboring hotels and a more long-term-oriented culture are more apt to join the program. Online reputation factors, hotel size and the number of neighboring hotels explain GreenLeaders hotels’ rankings. A series of robustness checks reinforces the results.

The results shed light on green program design and promotion. These findings can help hotel practitioners identify ideal target markets and better use their organizational resources to establish green programs. Several strategies can be implemented to promote hotels’ commitment to sustainability and to encourage guests’ awareness of and involvement in green practices.

This study enriches knowledge of sustainable hospitality and tourism. The findings of this study also address corporate social responsibility by analyzing factors that can promote and inhibit GreenLeaders program participation. Further, as a complement to hotel- and location-specific factors, the authors scrutinize the effects of cultural features in shaping green strategies.

This paper examines household and community characteristics that influence the entry of rural households into non-farm entrepreneurship and investigates the various factors that influence the market exit of non-farm enterprises (NFEs).

The authors use data from three rounds (2011/12, 2013/14 and 2015/16) of the World Bank’s Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA). The authors employ panel logit and multilevel logit models to examine the probability of opening one or more enterprises and the enterprise exit rates.

Results indicate that the likelihood of starting a NFE is positively associated with primary education attainment, access to credit, experiencing idiosyncratic shocks and availability of formal financial institutions. Age, higher education attainment and rising farm input prices constrain entry into non-farm entrepreneurship. The enterprise exit rate is negatively associated with small-town residence, wealth, access to tar/gravel roads and cellphone communication.

Policymakers and administrators should strive to address the challenges that communities face in transportation, communication and financial services. Policies aimed at stabilizing prices and increasing access to mobile communication, primary education and road infrastructure could help expand the rural non-farm sector.

Previous studies primarily examined the determinants of participation in NFEs at a given time using cross-sectional data. The current study uses panel data to study the dynamics of NFE ownership by investigating households’ decisions to enter into or exit from the sector.

The peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-09-2022-0611

Micro, small and medium-sized enterprises (MSMEs) are the backbone of economic development for every economy. They contribute to local economic development through household wealth creation, employment generation and poverty reduction. Despite this pivotal role, MSMEs lack access to finance, and scholarship on the enabling role of financial inclusion on micro, small and medium-sized enterprises' performance is scant. The authors contribute to closing the knowledge gap by examining the enabling effect of financial inclusion on MSMEs using the FinScope MSME 2017 survey for the Kingdom of Eswatini. This paper aims to discuss the aforementioned objective.

The study used the re-centered influence function regression framework to estimate unconditional quantile regressions and the generalized ordered logit model to analyze the data.

The findings from the unconditional quantile regression revealed that small changes in access to bank accounts, saving for business, formal saving, stokvel and informal saving at the 50th and 75th percentiles have a positive and statistically significant effect on microenterprises' annual turnover profit. Conversely, small changes in formal insurance have a mixed effect on annual turnover profit. At the 10th and 25th percentiles, a small increment in insurance reduces annual turnover profit but increases microenterprise annual turnover profit at the 75th percentile. Meanwhile, the evidence from the generalized ordered logit model showed that financial inclusion reduces the likelihood of microenterprises being classified as least developed and increased the chances of microenterprises falling into emerging and developed business categories.

This study makes use of a cross-sectional survey dataset, as a result, it does not infer causal relationships over the long term, but rather an association between the independent and dependent variables.

Overall, formal and informal financial inclusion enhances the annual turnover profit for microenterprises, particularly at the 50th and 75th percentiles in the Kingdom of Eswatini. The authors recommend a specialized institution such as a micro, small and medium-sized partial credit guarantee scheme to improve the quality and affordability of credit for microenterprises, and a mix of financial and non-financial supports depending on the development stage to boost a sustainable microenterprises' sector.

The study uses two advanced cross-sectional techniques, the recentered influence function framework and the generalized ordered logit model to analyze the data. The paper is original and contributes to the discussion of the role of financial inclusion in enabling microenterprises' success in Africa, using the FinScope 2017 survey of microenterprises in Eswatini as a case study.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-10-2020-0689.

This paper aims to study the default risk of small and medium-sized enterprises in the construction sector.

An unbalanced sample of 2,754 Portuguese companies from the construction sector, from 2008 to 2020, is analysed. Companies are classified in default or compliant following an ex-ante criterion. Then, using the stepwise analysis, the most relevant variables are selected, which are later used in the logit model. To verify the robustness of the results, a sample of legally insolvent companies is added (mixed criterion) and the initial sample is split into two subperiods.

Financial variables are the most relevant to predict the pattern for this sample. The main conclusions show that smaller and older companies, more indebted, with more liquidity and with higher EBIT have a higher probability of default. These conclusions are confirmed using a mixed criterion to classify companies as default or compliant and including a macroeconomic dummy.

This work not only contributes to enlarging the literature review but also makes relevant contributions to practice. Companies from the construction sector can understand which indicators must control to avoid financial problems. The government also has relevant information that can help in adapting or creating regulations for recovering or revitalizing companies.

This study proposed an ex-ante criterion that can be used for all types of companies. Most works use a legal or a mixed criterion that does not allow for detecting signs of financial problems in advance. Moreover, the sample used is almost unexplored – SMEs from a sector with great mortality rate.

Ticket sales are an essential source of income for football clubs and federations. Analyzing the determinants of fans' willingness-to-pay for tickets is therefore an important exercise. By knowing the match- and fan-related characteristics that influence how much a fan wants to pay for a ticket, as well as to what extent, football clubs and federations can modify their ticket offering and targeting in order to optimize this revenue stream.

Using a detailed discrete choice experiment, based on McFadden's random utility theory, this paper formulates a Bayesian hierarchical multinomial logit model. Such models are very common in the discrete choice modeling literature. The analysis identifies to what extent match and personal attributes influence fans' willingness-to-pay for games of the Belgian men's and women's football national teams.

The results show that the strength of the opponent, the type of competition, the location of the seats in the stadium, the day and kick-off time of the match and the ticket price exert an influence on the choice of the respondent. Fans are attracted most by competitive games against strong opponents. They prefer to sit along the sideline, and they have clear preferences for specific kick-off days and times. The authors also find substantial variation between socio-demographic groups, defined in terms of factors such as age, gender and family composition.

The authors use the results to estimate the willingness-to-pay for match tickets for different socio-demographic groups. Their findings are useful for football clubs and federations interested in optimizing the prices of their match tickets.

To the best of the authors' knowledge, no stated preference methods, such as discrete choice analysis, have been used to analyze the willingness-to-pay of sports fans. The advantage of discrete choice analysis is that options and variations in tickets that are not yet available in practice can be studied, allowing football organizations to increase revenues from new ticketing instruments.

The outbreak of covid-19 has affected international migration and remittance and has also narrowed down the opportunities for internal labor migrants. The pandemic has also left internal migrants in a threatening situation due to the closure of job opportunities. Taking the migration of labor from the highland toward the sesame production belt into consideration, this study aims to examine the influencing factors of migration to the sesame belt amid covid-19 and ascertain the link between migration and translocal vulnerability.

The study followed a mixed approach that combines quantitative and qualitative methods. However, the quantitative approach tends to dominate due to the nature of the objectives of the study. The study was conducted in the central Gondar zone, using a cross-sectional survey design with a sample size of 150 households collected from January to March, 2021. Both descriptive and econometrics models such as binary logit model have been used.

Based on the study result, we came to understand that migration is part and parcel of the livelihoods of the farm household that accounts for 35% of migration status amid covid-19. Particularly, the study came to conclude that households’ decision to send family members heavily relied on their prior information and fear of transmission of the coronavirus to family members which are statistically significant. As a result, this piece of work can be a good witness for translocal vulnerability where studies are very limited in the area. For this, this study suggests that concerned bodies like social and labor affairs in consultation with the agriculture offices and bureaus at a different level and the investors in the migrant’s destination should facilitate the protection and awareness mechanisms so that the spread of covid-19 can be minimized and thereby both the migrants and the investors can be benefitted from the migrants’ work amid covid-19.

This study tries to connect the current spread of covid-19 with the translocal vulnerability context. Primarily, it empirically argued the translocal vulnerability factor is the main determinant for the farm households to send families’ labor as a livelihood diversification strategy. Very limited studies consider the translocal vulnerability implication of migration; notably to the best of the researchers’ knowledge, studies that linked covid-19 with translocal vulnerability context are scant. On top of that, many studies that link migration with covid-19 tend to be inclined to international migration with very limited attention to internal migration.