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Organisations are increasingly adopting and adapting to technological advancements to stay relevant in the era of intense competition. Simultaneously, employee mental well-being has become a prominent global concern affecting people across various demographics. With this in mind, the present study explores the influence of human resource (HR) analytics, mental health organisational evidence-based management (OEBM) and organisational mental health support on the mental well-being of employees. Additionally, the study examines the moderating effects of manager and peer support on the association between organisational mental health support and the mental well-being of employees.

Data were collected from 418 employees in India and structural equation modelling was performed to analyse the data.

The study found significant positive associations between HR analytics with mental health OEBM, organisational mental health support and mental well-being. Mental health OEBM was also found to be positively related to organisational mental health support and mental well-being. The moderating roles of manager and team support were also found to be significant in the associations between organisational mental health support and well-being.

The study showed that HR analytics is a valuable source of mental health data. This data can facilitate the development of evidence-based management (EBM) strategies to promote the mental well-being of employees.

Organizations widely adopt knowledge management (KM) to develop and promote technologies and improve business effectiveness. Analytics can aid in KM, further augmenting company performance and decision-making. There has been significant research in the domain of analytics in KM in the past decade. Therefore, this paper aims to examine the current body of literature on the adoption of analytics in KM by offering prominent themes and laying out a research path for future research endeavors in the field of KM analytics.

A comprehensive analysis was conducted on a collection of 123 articles sourced from the Scopus database. The research has used a Latent Dirichlet Allocation methodology for topic modeling and content analysis to discover prominent themes in the literature.

The KM analytics literature is categorized into three clusters of research – KM analytics for optimizing business processes, KM analytics in the industrial context and KM analytics and social media.

Systematizing the literature on KM and analytics has received very minimal attention. The KM analytics view has been examined using complementary topic modeling techniques, including machine-based algorithms, to enable a more reliable, systematic, thorough and objective mapping of this developing field of research.

This study's objective is to give a thorough overview of the literature in the field of “quiet quitting.” This paper presents literature review on quiet quitting, analyzing its conceptual framework, antecedents, outcomes, and potential strategies for addressing this pervasive issue.

Using the keywords “quiet quitting” and “silent quitting” from the Scopus database, a systematic review of the literature was conducted, applying the Antecedents-Behaviour-Consequences (ABC) approach, popularized by B.F Skinner, to get a conceptual clarity of this topic. There were just twenty articles discovered in the search until October 15, 2023. This review was undertaken after full-text screening of relevant studies, which are duly cited. The literature review also revealed some themes that are discussed in the study.

The study identifies relevant “quiet quitting” definitions as well as potential individual and organizational-level causes using a descriptive and thematic review of the literature. Additionally, critical insights and research gaps that could be used for empirical studies are identified.

This study provides as a roadmap for organizations to proactively recognize and address concerns, resulting in enhanced workforce well-being and productivity.

The study could spur further research and serve as a resource for scholars interested in the concept of “quiet quitting,” as well as benefiting human resource managers who prefer evidence-based management.

A systematic investigation of postgraduate leadership programs for health and/or human services offered by Australian higher education institutions was undertaken.

Quantitative analysis identified the core characteristics of the programs. A thematic analysis of the course learning outcomes was conducted and six major themes of disciplinary leadership and management knowledge; research and analytical skills; professional practice; communication and collaboration; creativity and innovation; and system knowledge are shared in this study.

The authors conclude that Australian universities have taken an evidence-based approach to leadership education.

More work might need to be undertaken to ensure leadership theories are incorporated into learning outcomes.

The rising number of food recalls has raised concerns about complexity, globalization and weak governance in the food supply chain. This paper aims to investigate the recall of plant-based products with data from the US Food and Drug Administration.

Introducing the structural topic modeling method allowed us to test theories on recall in the context of sustainable food consumption, enhancing the understanding of food recall processes. This approach helps identify latent topics of product recalls and their interwoven relationships with various stakeholders.

The results answer a standing research call for empirical investigation in a nascent food industry to identify stakeholders’ engagements for food safety crisis management for corporate social responsibility practices. This finding provides novel insights on managing threats to food safety at an industry level to extend existing antecedents and consequences of product recall at a micro level.

For practitioners, this empirical finding may provide insights into stakeholder management and develop evidence-based strategies to prevent threats to food safety. For public policymakers, this analysis may help identify patterns of recalls and assist guidelines and alarm systems (e.g. EU’s Rapid Alert System for Food and Feed) on threats in the food supply chain.

Two detected clusters, such as opportunisms of market actors in the plant-based food system and food culture, from the analysis help understand corporate social responsibility and food safety in the plant-based food industry.

Decision-making competence (DMC) of entrepreneurs and managers is a longstanding topic in this increasingly globalized world. These figures operate in conditions not within their own control, and good levels of DMC are often considered to be desirable for the flourishing of business and society. This paper reports an empirical investigation on the DMC of entrepreneurs and managers, in an attempt to inform about their tendencies to incur in risky and costly choices.

Three cognitive biases associated with operational strategies and individual characteristics of entrepreneurs and managers, namely under/overconfidence (UOC, i.e. self-confidence in taking decisions), resistance to sunk costs (RSC, i.e. propensity to take cost investments) and consistency in risk perception (CRP, i.e. how well individuals understand probability rules) were considered . Cognitive biases measures were used in a cross-sectional study on a sample of n = 639 entrepreneurs and n = 512 managers. Data collected via online survey were analyzed using descriptive statistics and inferential statistics to determine differences among entrepreneurs and managers DMC.

Analyses reveal that entrepreneurs exhibit higher levels of UOC compared to managers with a marked presence of UOC among entrepreneurs at younger ages. Conversely, performance regarding RSC improves with higher education levels while age and RSC are positively correlated only for managers, regardless of education. Lastly, entrepreneurs and managers resulted as not being affected by CRP. This study discusses these results to provide initial insights for further avenues of research and practice.

The study offers an innovative, evidence-based viewpoint on how entrepreneurs and managers deal with risky and costly decisions. It offers an initial understanding of the role of UOC, RSC and CRP, that is specific cognitive biases associated with operational strategies and individual characteristics, in the DMC of these working figures. The study forwards avenues of scrutiny of quick-witted entrepreneurs and systematic managers.

The paper aims to propose and validate a process-based model to enhance managerial effectiveness among micro, small and medium enterprises (MSMEs). It has been observed that business uncertainties and inadequate financial resources that MSME entrepreneurs and managers face require them to constantly engage in strong self-awareness and self-regulating behavior to enhance the efficacy in their roles and, henceforth, their role performance effectiveness.

The approach for data collection was based on the clustering of MSMEs belonging to the clusters machine tool, pump manufacturing, foundry, textile and auto-component clusters in India. The respondents to the study were MSME entrepreneurs and managers who oversee and manage multiple functions like operations, quality, marketing, sales, supply chain management, procurement, personnel and administration and general administration.

The self-efficacy of entrepreneurial managers of MSMEs is observed to play an integral role in enhancing the efficacy of their roles, thus highlighting the use of a process-based perspective while dealing with constant resource constraints and excessive dynamism in their business contexts. The ability to handle multiple tasks effectively and resilience to manage challenges enhances their role-making process, which is significant in achieving and sustaining goal-oriented behavior among MSME entrepreneurs and managers.

This paper would serve as an effective model for entrepreneurs and managers to enhance their efficacy in the individual and interdependent role context, which would help achieve their individual and organizational goals. The model emphasizes a process-based perspective that thrusts the need to relate to the organizational context, enhancing individual confidence for goal-related behavior and fulfilling their role-related expectations.

This paper presents a model of enhancing managerial effectiveness that discusses self-efficacy as antecedent behavior. Here, personal and environmental factors aid cognition to one’s capability to construct reality, self-regulate, encode information and engage in effective managerial action.

This paper explores the evident issue of organizational resistance to change. The study delves deep into the underlying resistance factors in organizations using a qualitative approach.

The qualitative study employs a grounded theory coding pattern and network analysis to explore the underlying themes. This approach gives a panoramic view of resistance to change by comprehending the themes of existing literature and verbatims collected from the respondents.

The results of the comprehensive interviews and analyses suggest a significant change in the definition of resistance to change. The network analysis led to the identification of subthemes, antecedents, and consequences of resistance to change. The study highlights the importance of innovation, personnel training, mistrust, and the role of motivation.

The insights gained from this practitioner-oriented study help navigate organizations to identify the issues related to resistance to change. The study also helps trace the issue of over-specialization, which promotes competency traps in organizations. The role of leaders in managing resistance to change is evident, which practitioners can consider for the more significant benefit of organizations.

The study proposes a nuanced understanding of resistance to change by using a qualitative approach to comprehend the phenomena from the perspective of leaders and employees. Using a coding approach for theme generation and network analysis for visualization ascertains the methodical rigor and comprehensive understanding alongside generating practitioner-oriented recommendations.

The paper aims to carry out a comprehensive literature mapping to synthesise and descriptively analyse the research trends of biodiversity accounting, providing implications for managers and policymakers, whilst also outlining a future agenda for scholars.

A bibliometric analysis is carried out by adopting the Preferred Reporting Items for Systematic Review and Meta-Analyses protocol for searching and selecting the scientific contributions to be analysed. Citation analysis is used to map a current research front and a bibliographic coupling is conducted to detect the connection networks in current literature.

Biodiversity accounting is articulated in five thematic clusters (sub-areas), such as “Natural resource management”, “Biodiversity economic evaluation”, “Natural capital accounting”, “Biodiversity accountability” and “Biodiversity disclosure and reporting”. Critical insights emerge from the content analysis of these sub-areas.

The analysis of the thematic evolution of the biodiversity accounting literature provides useful insights to inform both practice and research and infer implications for managers, policymakers and scholars by outlining three main areas of intervention, i.e. adjusting evaluation tools, integrating ecological knowledge and establishing corporate social legitimacy.

Currently, the level of biodiversity reporting is pitifully low. Therefore, organisations should properly manage biodiversity by integrating diverse and sometimes competing forms of knowledge for the stable and resilient flow of ecosystem services for future generations.

This paper not only updates and enriches the current state of the art but also identifies five thematic areas of the biodiversity accounting literature for theoretical and practical considerations.

This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.