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This paper explores Lean Six Sigma principles and the DMAIC (define, measure, analyze, improve, control) methodology to propose a new Lean Six Sigma 4.0 (LSS 4.0) framework for employee occupational exams and address the real-world issue of high-variability exams that may arise.

This study uses mixed methods, combining qualitative and quantitative data collection. A detailed case study assesses the impact of LSS interventions on the exam management process and tests the applicability of the proposed LSS 4.0 framework for employee occupational exams.

The results reveal that changing the health service supplier in the explored organization caused a substantial raise in occupational exams, leading to increased costs. By using syntactic interoperability, lean, six sigma and DMAIC approaches, improvements were identified, addressing process deviations and information requirements. Implementing corrective actions improved the exam process, reducing the number of exams and associated expenses.

It is important to acknowledge certain limitations, such as the specific context of the case study and the exclusion of certain exam categories.

The practical implications of this research are substantial, providing organizations with valuable managerial insights into improving efficiency, reducing costs and ensuring regulatory compliance while managing occupational exams.

This study fills a research gap by applying LSS 4.0 to occupational exam management, offering a practical framework for organizations. It contributes to the existing knowledge base by addressing a relatively novel context and providing a detailed roadmap for process optimization.

This study investigates Rokkan's research programme in the light of the differences between case- and variables-based methodologies. Three phases of the research process are distinguished. Studying the way Rokkan actually proceeded in the research within his Europe project, we find that he follows the protocols of case-methodologies such as grounded theory. In the second phase of the research process, however, he constructs variables-based models as tools for his macro-historical comparisons. To get to variables from the sensitizing concepts coded in the first phase, Rokkan defines his variables as close to cases as possible: variables as nominal level typologies, types as variable values. He thus faces two interrelated dilemmas. First, a philosophy of science dissonance: he legitimates his research only with reference to a variable-methodology, while his research is thoroughly case based. Second, a paradox of double coding: using variable-based models in the second phase, the status of the knowledge available in the first phase memos is degraded. Rokkan cannot decide between the two main solutions to these dilemmas: The first solution is to discard his heterogeneous data, instead working only with homogeneous data that opens up to more consistently variables-oriented research. The second solution is to replace the notion of variables/variable values with typology/types, thereby returning to cases, pursuing comparative case reconstructions in the third phase of research. The study concludes in favour of the second solution.

The purpose of this study was to examine the lived experiences of working mothers during the first year of the COVID-19 pandemic and assess their perceptions of the types of training opportunities that would help advance their careers as they navigated pandemic-related challenges.

In study 1, 53 participants responded to an online survey that included open-ended questions regarding the impact COVID-19 has had on their careers and desires they have for training to ameliorate these concerns. For study 2, 10 participants completed an interview that included open-ended questions regarding workplace changes and professional development/training opportunities since the pandemic.

Thematic analysis showed mothers facing several setbacks in their careers, including delays, loss of hours and wages, childcare stressors and strained or lost relationships with colleagues and supervisors. A small number of participants also reported some surprisingly positive experiences, such as a push toward career innovation. Participants reported little to no exposure to career development opportunities and expressed a desire for training for leadership and interpersonal connection in the workplace.

Although quantitative data have been helpful in identifying and quantifying growing gender gaps in work during the pandemic, the qualitative analyses the authors used highlight how recent difficulties encountered by working mothers risk a growing gender gap in work and career mobility as women in the leadership pipeline struggle with challenges. Further, the findings suggest ways that women in management can support the working mothers on their staff, including by suggesting ways additional training may address some concerns.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This study aims to analyse the structure of the Indian vaccine supply chain (SC) during the Covid-19 crisis and explore the underlying challenges at each stage in the network. It also brings out the difference in performance of various constituent states.

This study relied on both primary and secondary data for the analyses. For the primary data, the study gathered experts’ opinions to validate the authors’ inferences. For the secondary data, it relies on government data provided in websites.

Based on the quartile analysis and cluster analysis of the secondary data, the authors find that the constituent states responded differently during the first and second waves. This was due to the differences in SC characteristics attributed to varied demographics and administrative efficiency.

This paper’s analyses is primarily limited to secondary information and inferences are based on them. The study has important implications for implementing the large-scale vaccination drives by government and constituent states for better coordination and last-mile delivery.

The contribution is unique in studying the performance of constituent states using statistical techniques, with secondary data from authentic sources. It is also unique in combining this observation with validation from experts.

Conflict and violence are the main drivers of globally escalating humanitarian needs. Local grassroots initiatives are pivotal in distributing humanitarian supplies in the acute response phase until more established humanitarian aid organizations can enter. Nevertheless, scant research exists regarding the role of grassroots associations in providing humanitarian assistance during a military conflict. The purpose of this paper is to understand the role of grassroots associations and identify important themes for effective operations.

This paper adopts a case-study approach of three Ukrainian grassroots associations that began operating in the immediate days of the full-scale invasion of Ukraine. The findings are based on analyzing primary sources, including interviews with Ukrainian volunteers, and are supported by secondary sources.

Grassroots associations have local contacts and a contextual understanding of population needs and can respond more rapidly and effectively than large intergovernmental agencies. Four critical themes regarding the operations of grassroots associations emerged: information management, inventory management, coordination and performance measurement. Grassroots humanitarian response operations during conflict are challenged by personal security risks, the unpredictability of unsolicited supplies, emerging volunteer roles, dynamic transportation routes and shifting demands.

Grassroots responses are central to humanitarian responses during the acute phase of a military conflict. By examining the operations of grassroots associations in the early months of the 2022 war in Ukraine, the authors provide a unique perspective on humanitarian logistics. Nonetheless, more inclusive models of humanitarian responses are needed to harness the capacities and resilience of grassroots operations in practice.

This paper aims to propose a new method to derive custom dynamic cyber risk metrics based on the well-known Goal, Question, Metric (GQM) approach. A framework that complements it and makes it much easier to use has been proposed too. Both, the method and the framework, have been validated within two challenging application domains: continuous risk assessment within a smart farm and risk-based adaptive security to reconfigure a Web application firewall.

The authors have identified a problem and provided motivation. They have developed their theory and engineered a new method and a framework to complement it. They have demonstrated the proposed method and framework work, validating them in two real use cases.

The GQM method, often applied within the software quality field, is a good basis for proposing a method to define new tailored cyber risk metrics that meet the requirements of current application domains. A comprehensive framework that formalises possible goals and questions translated to potential measurements can greatly facilitate the use of this method.

The proposed method enables the application of the GQM approach to cyber risk measurement. The proposed framework allows new cyber risk metrics to be inferred by choosing between suggested goals and questions and measuring the relevant elements of probability and impact. The authors’ approach demonstrates to be generic and flexible enough to allow very different organisations with heterogeneous requirements to derive tailored metrics useful for their particular risk management processes.

Emotional labour (EL) is a complex phenomenon that has received increasing attention in recent years due to its impact on employee’s well-being and job satisfaction. For a comprehensive understanding of the evolving field of EL, it is important to extract different research trends, new developments and research directions in this domain. The study aims to reveal 13 prominent research topics based on the topic modelling analysis.

Using latent Dirichlet allocation (LDA) method, topic modelling is done on 1,462 journal research papers published between 1999 and 2023, extracted from the Scopus database using the keyword “EL”.

The analysis identifies several emerging trends in EL research, including emotional regulation training and job redesign. Similarly, the topics like EL strategies, cultural differences and EL, EL in hospitality, organizational support and EL, EL and gender and psychological well-being of nursing workers are popular research topics in this domain.

The findings provide valuable insights into the current state of EL research and can provide a direction for future research as well as assist organizations to design practices aimed at improving working conditions for employees in various industries.

Topic modelling on emotional labor is done. The paper identifies specific topics or clusters related to emotional labor, quantifies these topics using topic modeling, adds empirical rigor, and allows for comparisons across different contexts.

This research presents and evaluates a method for assessing the entrepreneurial mindset (EM) of students in higher education.

The research considers EM a multi-variable psychological construct, which can be broken down into several conceptual sub-categories. Using data from a master course in entrepreneurship, the authors show how these categories can be applied to analyze students’ written reflections to identify linguistic markers of EM.

The research reports three main findings: analyzing student reflections is an appropriate method to explore the state and development of students’ EM; the theoretically-derived EM categories can be nuanced and extended with insight from contextualized empirical insights; and student reflections reveal counter-EM categories that represent challenges in the educator’s endeavor to foster students’ EM.

The commitment of resources to researching EM requires the dedication of efforts to develop methods for assessing the state and development of students’ EM. The framework can be applied to enhance the theoretical rigor and methodological transparency of studies of EM in entrepreneurship education.

The framework can be of value to educators who currently struggle to assess if and how their educational design fosters EM attributes.

This inquiry contributes to the critical research discussion about how to operationalize EM in entrepreneurship education studies. The operationalization of a psychological concept such as EM is highly important because a research focus cannot be maintained on something that cannot be studied in a meaningful way.

Solid, liquid and e-waste pose serious health hazards, environmental pollution and contribute to climate change. To address these issues of solid waste management (SWM), amidst many policy decisions, the Government of India roped in several institutions, including self-help groups (SHGs), into the Swachh Bharat Movement (Clean India Mission). This study aims to illustrate the significant contributions of SHG’s in tackling SWM, particularly the plastic waste menace in India, while fostering socio-economic values and sustainable development goals (SDGs).

Using a from-the-field approach, qualitative data were collected from 30 members of three SHGs to understand their significant contributions in mitigating plastic waste.

This research identifies three major themes: economic value creation, social value creation and SDGs via collection and reduction of plastic waste landfills. Furthermore, several related subthemes are identified.

This study offers pragmatic solutions to deal with plastic waste at personal, community, institutional and governmental levels. Moreover, it recommends engaging SHGs to promote sustainable waste management practices such as segregating wastes at source, regulating plastic bag usage, advocating behavioural change towards waste generation and protecting the environment.

The authors consider a proven case of SHG’s contribution to protect the environment and emphasize the need to involve more such groups in waste management practices.