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An interview with Zeynep Ton, a professor of practice in the operations management group at MIT Sloan School of Management, about er latest book, The Case for Good Jobs: How Great Companies Bring Dignity, Pay & Meaning to Everyone’s Work.

She believes that leaders can either view their employees as a cost to be minimized, invest little in them and operate with high turnover, or they can see them as drivers of profitability and growth—investing heavily in them, designing their work for high productivity and contribution and therefore operating with low turnover.-- “the good jobs strategy.”

The secret sauce of good jobs strategy is four operational choices—focus and simplify, standardize and empower, cross-train and operate with slack—that improve productivity and contribution and make that higher investment possible.

The competitive costs of low people investment are even higher than the poor operational execution costs.

By making the work better and increasing pay, companies can better attract and keep their talent and enforce high standards, which improve execution and service, uplifting revenue. Few have examined this important topic more closely than Zeynep Ton, a professor of practice in the operations management group at MIT Sloan School of Management, best-selling author of The Good Jobs Strategy: How the Smartest Companies Invest in Employees to Lower Costs and Boost Profits.

This study explores the awareness (AWN) levels of good governance amongst Thai credit union cooperatives' (CUCs) members and the factors hindering good governance practice in Thai CUCs.

This study used a survey questionnaire from 629 members of 36 selected CUCs in Thailand. This study analysed the determinants of governance AWN levels of Thai CUCs' members using the ordered probit model. The study also employs OLS estimation to investigate the factors hindering good governance practices.

The study shows that members of different CUC types and sizes have different levels of governance AWN. Members' characteristics, experiences, and perceptions significantly influence CUC members' AWN of governance issues. The findings also suggest that a lack of morality, transparency, participation, responsibility and accountability are key obstacles that hinder good governance practices of Thai CUCs.

This is the first study that attempts to assess the level of AWN amongst Thai CUCs' members in different CUC sizes and types. This is also the first research that identifies the factors that hinder good governance practice in Thai CUCs based on members' evaluations. The study's findings provide important reference and implications for Thai policy makers and CUCs' board of managers to enhance members' AWN and CUCs' governance performance, and thus increase income and living standard of CUCs' members in the long term.

Public realm urban regeneration projects aim to provide facilities for the common good such as improved road systems, public parks, museums and cultural institutions. Driven by political priorities, the expected benefits for society comprise of the proposed regeneration outcomes articulated in a masterplan vision. As a philosophical concept, common good in the context of urban regeneration is explored in this study to understand the expectations for major, long-term regeneration projects and the intended project objectives. In the approach to governance, there should be a relationship between monitoring indicators adopted by the regeneration project as part of the governance framework and their alignment with the common good. These concepts are analysed through a case study of the development and reporting of benchmark indicators established at the start of a major 20-year urban redevelopment in 2010. The monitoring and enhancement concept implemented required indicators to be developed and embedded in the regeneration process to, not only monitor, but also enhance sustainability. The longitudinal case study, at the interim point 10 years since the establishment of these indicators, will evaluate the sustainability of the urban regeneration and evaluate current evidence for the common good. The indicators were developed following the principles of a theme orientated framework in line with the UK and Scottish Government approach at that time. The process of indicator development was iterative, refined and finalised through working closely with local authority, Scottish Enterprise and partnership stakeholders (civic oriented organisations) to capture evidence of progress towards the masterplan vision. Ten years on, conclusions examine whether these indicators could be used a proxy for common good. The conclusion will identify the extent to which we would need to revise indicators to address any gaps to become a more accurate measure of common good.

By merging good countryside and rural capitals frameworks, a model for reimagining the island's development is formulated, which is then applied to the female perspective to provide valuable insights from a group that is often marginalized in rural areas. As Croatian islands are highly tourism-oriented, this study finds it important to explore possibilities for future island development that can provide balanced and vibrant settlements on the islands.

The present paper synthesizes Shucksmith's (2018) model of a good countryside, which serves as a goal, with Gkartzios et al.'s (2022) capitals framework, which is viewed as a means of attaining a good countryside, specifically a good island. The research is delimited to the island of Brac, Croatia. By conducting interviews with female respondents, this study aims to capture the female perspective on envisioning potential futures of “good” island living, a perspective that is frequently underestimated despite its significant contributions to the creation of an ideal locale.

The results demonstrate that there is a substantial amount of socio-cultural rural capital that is leveraged to strengthen relatedness and rights as development objectives. However, low levels of economic, built and land-based rural capital pose challenges to achieving repair and re-enchantment, which are crucial for settlements that rely on tourism.

These findings bear immense implications for policymakers and planners, underscoring the imperative to account for the perspectives and needs of diverse social groups, including women, in the design and implementation of development strategies for islands. By doing so, a sustainable and equitable future, rich in tourism potential, can be cultivated on the island.

One of the critical reasons for the nonacceptance of additive manufacturing (AM) processes is the lack of understanding and structured knowledge of design for additive manufacturing (DfAM). This paper aims to assist designers to select the appropriate AM technology for product development or redesign. Using the suggestion provided by the design assist tool, the user’s design alterations depend on their ability to interpret the suggestion into the design without affecting the design’s primary objective.

This research reports the development of a tool that evaluates the efficacy values for all seven major standard AM processes by considering design parameters, benchmark standards within the processes and their material efficacies. In this research, the tool provides analytical and visual approaches to suggestion and assistance. Seventeen design parameters and seven benchmarking standards are used to evaluate the proposed product and design quality value. The full factorial design approach has been used to evaluate the DfAM aspects, design quality and design complexity.

The outcome is evaluated by the product and design quality value, material suit and material-product-design (MPD) value proposed in this work for a comparative assessment of the AM processes for a design. The higher the MPD value, the better the process. The visual aspect of the evaluation uses spider diagrams, which are evaluated analytically to confirm the results’ appropriateness with the proposed methodology.

The data used in the database is assumed to make the study comprehensive. The output aims to help opt for the best process out of the seven AM techniques for better and optimized manufacturing. This, as per the authors’ knowledge, is not available yet.

The coronavirus (COVID-19) pandemic has led to a surge in demand for health-care facilities, medicines, vaccines and other health-care items. The purpose of this study is to investigate different facets of integrated Green Lean Six Sigma and Industry 4.0 approach in the context of COVID-19 for better healthcare management. Integrating Green Lean Six Sigma (GLSS) and Industry 4.0 (I4.0) has the potential to meet the modern demand of health-care units and also leads to improving the quality of inpatient care with better safety, hygiene and real-time diagnoses. A systematic review has been conducted to determine the tools/techniques, challenges, application areas and potential benefits for the adoption of an integrated GLSS-I4.0 approach within health-care facilities from the perspective of COVID management. Further, a conceptual framework of integrated GLSS-I4.0 has been proposed for better COVID management.

To conduct the literature review, the authors used the preferred reporting items for systematic reviews and meta-analysis and covers relevant papers from the arrival of COVID-19. Based on the systematic understanding of the different facets of the integrated GLSS-I4.0 approach and through insights of experts (academicians and health-care personnel), a conceptual framework is proposed to combat COVID-19 for better detection, prevention and cure.

The systematic review presented here provides different avenues to comprehend the different facets of the integrated GLSS-I4.0 approach in different areas of COVID health-care management. In this study, the proposed framework reveals that the Internet of Things, big data and artificial intelligence are the major constituents of I4.0 technologies that lead to better COVID management. Moreover, integration of I4.0 with GLSS aids during different stages of the COVID management, right from diagnosis, manufacture of items and inpatient and outpatient care of the affected person.

This study provides a significant knowledge database to the practitioners by understanding different tools and techniques of an integrated approach for better COVID management. Moreover, the proposed framework aids to grab day-to-day information from the affected people and ensures reduced hospital stay with better space utilization and the creation of a healthy environment around the patient. This inclusive implementation of the proposed framework will enhance knowledge base in medical areas and provides different novel prospects to combat other medical urgencies.

To the best of the authors’ knowledge, this study is the first of its kind to review different facets of the integrated GLSS-I4.0 approach with a view of the COVID health-care perspective and provides a conceptual framework.

The purpose of the paper is to study how technology choice is affected by capital accumulation when there is unemployment and firms engage in oligopolistic competition.

In this infinite horizon model, unemployment results from the existence of efficiency wages. Consumers choose saving optimally, and there is capital accumulation. Firms producing intermediate goods engage in oligopolistic competition and choose technologies to maximize profits. A more advanced technology has a higher fixed cost but a lower marginal cost of production.

In the steady state, it is shown that an increase in population size or a decrease in the discount rate leads intermediate good producers to choose more advanced technologies and the wage rate increases. Interestingly, the equilibrium unemployment rate decreases with the size of the population.

In this model, unemployment results from the existence of efficiency wages and firms engage in oligopolistic competition. One difficulty with efficiency wage models is that saving is not allowed. However, in this model, consumers choose saving optimally, and capital accumulation is allowed. With oligopolistic competition, the authors show that an increase in population size or a decrease in the discount rate leads intermediate good producers to choose more advanced technologies and the wage rate increases. The equilibrium unemployment rate decreases with the size of the population.

The purpose of this paper is to reviews some of the learnings, challenges and solutions suggested by the article author regarding the role of implementing emotional intelligence by corporate social responsible (CSR) leaders and offers ideas for future research. The aim is to offer a positive conclusion to the problems and their solutions.

The study investigates the relationship between emotional intelligence and effective CSR leadership. The author evaluates the body of research on the issue and provides a reassuring assessment of the problems and recommendations.

Having emotional intelligence is essential for executives who wish to implement successful CSR initiatives. It allows CEOs to create a culture of social responsibility inside their organizations, highlight the importance of CSR initiatives and strengthen relationships with stakeholders. Key emotional intelligence traits, including self-awareness, self-regulation, motivation, empathy and social skills, are necessary for effective CSR leadership.

The study focuses on the role of emotional intelligence in corporate social responsibility leadership, offering a unique perspective on the subject. It also explores practical solutions and ideas for future research, adding originality and value to the existing body of literature on emotional intelligence and CSR leadership.

Although risk culture is a key determinant for an effective risk management, identifying the risk culture of a firm can be challenging due to the abstract concept of culture. This paper proposes a novel approach that uses unsupervised machine learning techniques to identify significant features needed to assess and differentiate between different forms of risk culture.

To convert the unstructured text in our sample of banks' 10K reports into structured data, a two-dimensional dictionary for text mining is built to capture risk culture characteristics and the bank's attitude towards the risk culture characteristics. A principal component analysis (PCA) reduction technique is applied to extract the significant features that define risk culture, before using a K-means unsupervised learning to cluster the reports into distinct risk culture groups.

The PCA identifies uncertainty, litigious and constraining sentiments among risk culture features to be significant in defining the risk culture of banks. Cluster analysis on the PCA factors proposes three distinct risk culture clusters: good, fair and poor. Consistent with regulatory expectations, a good or fair risk culture in banks is characterized by high profitability ratios, bank stability, lower default risk and good governance.

The relationship between culture and risk management can be difficult to study given that it is hard to measure culture from traditional data sources that are messy and diverse. This study offers a better understanding of risk culture using an unsupervised machine learning approach.

Data quality and data resolution are essential for computer vision tasks like medical image processing, object detection, pattern recognition and so on. Super-resolution is a way to increase the image resolution, and super-resolved images contain more information compared to their low-resolution counterparts. The purpose of this study is analyzing the effects of the super resolution models trained before on object detection for aerial images.

Two different models were trained using the Super-Resolution Generative Adversarial Network (SRGAN) architecture on two aerial image data sets, the xView and the Dataset for Object deTection in Aerial images (DOTA). This study uses these models to increase the resolution of aerial images for improving object detection performance. This study analyzes the effects of the model with the best perceptual index (PI) and the model with the best RMSE on object detection in detail.

Super-resolution increases the object detection quality as expected. But, the super-resolution model with better perceptual quality achieves lower mean average precision results compared to the model with better RMSE. It means that the model with a better PI is more meaningful to human perception but less meaningful to computer vision.

The contributions of the authors to the literature are threefold. First, they do a wide analysis of SRGAN results for aerial image super-resolution on the task of object detection. Second, they compare super-resolution models with best PI and best RMSE to showcase the differences on object detection performance as a downstream task first time in the literature. Finally, they use a transfer learning approach for super-resolution to improve the performance of object detection.