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Employee burnout is increasingly coming under attention due to its negative impact on employee well-being and organisational effectiveness. This study, a systematic review, aims to evaluate the role of servant leadership and its mediators in preventing and mitigating against burnout experiences in organisations.

A preferred reporting items for systematic review and meta-analyses (PRISMA) was conducted using three databases, Academic search Complete, Embase and Scopus, in addition to bibliography searches. Articles were included if they reported on primary data, in English from inception to 2023. The mixed methods critical appraisal tool was used to assess the quality of articles, and a narrative synthesis was used to report results.

The search strategy yielded 4,045 articles, of which (N = 17), with total sample size of (N = 10,444) are included. Findings suggest that servant leadership is predictive of burnout, and that several mediators impact this relationship. Most studies were conducted in health care (n = 8) and banking (n = 3), and while the quality of the studies was mostly high (64%), the methods used were mainly descriptive and cross-sectional, which limits the extent to which causality can be inferred. A theory of change is provided based on the findings from this review and integrated with the extant literature on servant leadership theory, and can be used by organisations to support the policy, training and practice of servant leadership to reduce burnout.

Servant leadership is predictive of burnout; however, further research needs to be undertaken in this important emerging area.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.