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The purpose of this paper is to review existing research on burnout in the audit profession using the job demands-resources theory (JD-R) with attention to the post-COVID-19 era.

Consistent with prior studies, this paper adopts a systematic review methodology, incorporating a comprehensive synthesis of diverse archival materials. Using relevant keywords, the authors systematically retrieve papers on burnout from reputable databases, such as Google Scholar and Web of Science. Following rigorous selection criteria, the authors identified and analysed 43 academic and practitioner papers. Through this process, the authors contextualise the findings within the JD-R theory framework, which offers valuable insights into the interplay between job characteristics and burnout. Additionally, the authors explore the gender perspective, specifically examining the impact of work-home conflict on the burnout levels of female individuals. This dual focus enhances the understanding of burnout dynamics, considering both theoretical underpinnings and gender-specific experiences in the workplace.

The review reveals that lower-ranked accounting professionals face a greater risk of burnout compared to their higher-ranked counterparts. Additionally, female professionals tend to experience heightened levels of burnout, primarily attributed to work–home conflict, as they often shoulder more domestic and familial responsibilities than their male counterparts. Flexible working arrangements have been shown to mitigate burnout among auditors. However, the transition to remote work during the pandemic yielded mixed outcomes, with professionals exhibiting increased susceptibility to burnout symptoms in some cases.

The study provides new insights into the relevance of flexible work arrangements in the accounting profession in the post-COVID-19 era. The paper also makes suggestions for further research on burnout within the context of the accounting profession.

The dynamic of the business environment has escalated the competition and uncertainty, which is challenging business survivability, particularly for small and medium enterprises (SMEs). SMEs attract researchers due to their unique characteristics that have limited resources but great flexibility and adaptability. Furthermore, Collaborative Networks (CNs) have been proposed by business scholars as a critical strategy to gain resilience and antifragility. However, the concept of antifragility and its relation with CNs is still vague in the SME sector. Therefore, this study aims to develop a complete understanding regarding: (1) the emerging knowledge that is critical in explaining antifragility in the business sector based on co-citation and thematic analysis; (2) the relation between resilience and antifragility in emerging business research; (3) the relation between CNs and antifragility in emerging business research and (4) a framework of antifragility in the SME context.

Bibliographic Analysis and Systematic Literature Review are performed to reach the research objectives. We use co-citation and thematic analysis to identify the map of emerging knowledge and the related concepts, which are the fundamentals of antifragility. Furthermore, we use a systematic literature review to determine the relation of antifragility, resilience and CNs in the SME context.

Antifragility is a higher level of survivability compared to resilience. Antifragile SMEs could gain an advantage from the uncertain business environment. However, both in resilience and antifragility, SMEs should become active learners. Furthermore, CNs are proposed as the gateway for SMEs to manage their resource limitations. The conceptual framework of Antifragile SMEs is presented as the theoretical contribution of this manuscript.

This article explains the knowledge structure of antifragility in the business sector, particularly among SMEs. Based on bibliometric data, we describe critical characteristics or mental states entrepreneurs should have when facing uncertainty. Furthermore, we propose a conceptual framework for antifragile SMEs where active learning and positive psychology are the pillars, and CNs are critical ingredients of antifragility in SMEs.

The study presented healthcare service quality, lean thinking and Six Sigma to enhance patient satisfaction. Moreover, the notion of machine learning is combined with lean service quality to bring about the fundamental benefits of predicting patient waiting time and non-value-added activities to enhance patient satisfaction.

The study applied the define, measure, analyze, improve and control (DMAIC) method. In the define phase, patient expectation and perception were collected to measure service quality gaps, whereas in the measure phase, quality function deployment (QFD) was employed to measure the high-weighted score from the patient's voice. The root causes of the high weighted score were identified using a cause-and-effect diagram in the analysis phase.

The study employed a random forest, neural network and support vector machine to predict the healthcare patient waiting time to enhance patient satisfaction. Performance comparison metrics such as root-mean-square error (RMSE), mean absolute error (MAE) and R2 were accessed to identify the predictive model accuracy. From the three models, the prediction performance accuracy of the support vector machine model is better than that of the neural network and random forest models to predict the actual data.

Lean service quality improvement using DMAIC, QFD and machine learning techniques can be generalized to predict patient waiting times. This study provides better realistic insights into patient expectations by announcing waiting times to enable data-driven service quality deliveries.

Prior studies lack lean service quality, Six Sigma and waiting time prediction to reduce healthcare waste. This study proposes lean service quality improvement through lean Six Sigma (LSS), i.e. DMAIC and machine learning techniques, along with QFD and cause-and-effect diagram.