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More than 40% of Indian households still rely on unclean cooking fuels, exposing particularly women and children to adverse health effects. This paper explores the factors determining a household’s cooking fuel choice in India.

The study used the fifth round of the National Family Health Survey (2019-21) dataset. A chi-square test and a logistic regression were used for empirical analysis. Cooking fuel was categorized as “clean” and “unclean” cooking fuel.

The result shows that around 47% of the households are still using unclean cooking fuel in India. Households with a better-educated or female head, those with smaller family sizes, those with a higher level of wealth and those located in urban areas are more likely to use clean cooking fuel. Similarly, households headed by younger individuals and Muslim households are linked to more usage of clean cooking fuels. However, Scheduled Tribe (ST) and Other Backward Classes (OBC) households and those headed by older individuals are less likely to use clean cooking fuels.

The cross-sectional nature of data enables us to draw only associations between the variables and not causal relationships between them. The findings of this study have important implications for household energy policies in India and other developing nations. There is a need for policymakers to seriously consider socioeconomic factors in addressing issues and challenges associated with household energy consumption.

The study extends the empirical literature on determinants of household cooking fuel choice using the latest round of National Family Health Survey data from India.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2023-0988

Concerned with the rising social, economic and technological disruption in the world, the impact of the technological disruption had a significant impact on the future of work and it has been tremendously increased in past five years. Further, with the rising uncertainties and COVID-19 in the picture, the trends suggested by earlier literature might not hold. The purpose of this paper is to understand the evolution of technology in the workplace in the past five years, how does it stand during COVID-19, will the trend continue in light of disruption caused by COVID-19, the impact of COVID-19 on the future of work.

The paper uses bibliometric techniques to identify the conceptual and intellectual structure of the studies. The programming language named after authors Robert and Ross (R) software and Biblioshiny were used to identify the structures and the themes underlying those structures, which further helped in forecasting the trends of the studies.

The paper shows the drastic evolution of the studies in past few years and different technologies implemented at the workplace in the same period. It further identified the influential papers, authors, journals in the area with an emphasis on the various collaboration network among authors and countries. It also paints a picture of the impact of COVID-19 on the future of work. The paper finally concluded with future directions for the emerging trends and themes in the area in aftermath of COVID-19.

This paper takes the microscopic view of the studies carried out in the past five years as during the past five years, the studies related to this topic have grown tremendously and accordingly many trends have been identified but with the COVID-19 pandemic in the picture, the trend is likely to get accelerated. This paper takes this view and identifies the trends in the future by identifying the themes based on periods and at different levels – organizational, managerial, individual.

This study aims to reduce carbon emissions and minimize waste in the event of disruptions in a short and fast-food perishable such as fruits, vegetables, packaged food items, etc. supply chain through optimal investment in green and preservation technologies.

This study utilized a Hessian matrix approach to optimize decision variables with an objective to maximize the profit function.

The study demonstrates that investing in both green and preservation technology within a short and fast-food supply chain is highly beneficial for decarbonization and waste reduction and it leads to profit maximization. It has been shown with the help of a numerical experiments with investment in both green and preservation technology that total profit is 3.09% higher than without investment made in either technology.

This study aids the industry in achieving food sustainability by minimizing waste of perishables and also minimizes carbon emissions which is essential for environmental protection. It assists industries in determining the optimal investment in preservation technology to minimize waste and in green technology to reduce emissions, thereby maximizing profits.

The current study formulates an inventory model that helps in decarbonization and waste reduction in food supply chain with the consideration of machine learning, demand disruption, preservation technology investment, screening of purchased items, waste disposal, a double triangular distribution deterioration rate, green technology investment, carbon emissions from various supply chain activities, carbon tax policy and fuel price variation over time for perishable food products in a two-warehouse system.

Participatory wireless sensor networks (PWSN) is an emerging paradigm that leverages existing sensing and communication infrastructures for the sensing task. Various environmental phenomenon – P monitoring applications dealing with noise pollution, road traffic, requiring spatio-temporal data samples of P (to capture its variations and its profile construction) in the region of interest – can be enabled using PWSN. Because of irregular distribution and uncontrollable mobility of people (with mobile phones), and their willingness to participate, complete spatio-temporal (CST) coverage of P may not be ensured. Therefore, unobserved data values must be estimated for CST profile construction of P and presented in this paper.

In this paper, the estimation of these missing data samples both in spatial and temporal dimension is being discussed, and the paper shows that non-parametric technique – Kernel Regression – provides better estimation compared to parametric regression techniques in PWSN context for spatial estimation. Furthermore, the preliminary results for estimation in temporal dimension have been provided. The deterministic and stochastic approaches toward estimation in the context of PWSN have also been discussed.

For the task of spatial profile reconstruction, it is shown that non-parametric estimation technique (kernel regression) gives a better estimation of the unobserved data points. In case of temporal estimation, few preliminary techniques have been studied and have shown that further investigations are required to find out best estimation technique(s) which may approximate the missing observations (temporally) with considerably less error.

This study addresses the environmental informatics issues related to deterministic and stochastic approaches using PWSN.

One of the most important components of healthcare is the timely delivery of pharmaceutical products, such as life-saving medicines. However, disruptions like COVID-19 bring new challenges and risks to the pharmaceutical supply chain (PSC) and healthcare organizations that impact their operational performance. This study focuses on mitigating risks in India's generic medicine supply chain (GMSC) as a result of various disruptions, which can assist policymakers develop appropriate plans and strategies to build resilience in the Jan Aushadhi Scheme (JAS) of micro, small and medium enterprises (MSMEs) in order to improve their overall performance.

Risk-causing vulnerabilities and resilience capabilities are identified from the literature review and expert's opinions. Following that, the vulnerabilities are classified into cause-and-effect vulnerabilities, and supply chain resilient capabilities (SCRCs) are measured using a hybrid fuzzy DEMATEL and best worst method (FDEMATEL-BMW) framework.

The outcome of the study reveals that transportation breakdown, loss of human resources and loss of suppliers are the potential risk-causing vulnerabilities that lead to vulnerabilities like shortages of medicines, loss of in-hand stock qualities and loss of sales/revenue. In addition, the analysis suggests that the sustainability of an organization with maximum weightage is the critical factor for building resilience in GMSC followed by flexibility, agility and visibility.

The integration of resilience into Jan Aushadhi GMSC can help in managing disruptions efficiently and effectively to mitigate risk and optimize MSMEs overall performance.

To the best of the authors’ knowledge, this work will be the first of its kind to model resilience in GMSC of MSMEs using a hybrid framework.

The purpose of this paper is to develop a decision support system (DSS) for multi grade fuzzy leanness assessment (MGFLA) (DSS‐MGFLA).

The assessment of leanness gains vital importance. Due to the drawbacks associated with conventional approaches, fuzzy methods are gaining importance. In this context, multi‐grade fuzzy method has been used for leanness assessment. Since the computation is time consuming and error‐prone, a computerized DSS known as DSS‐MGFLA has been developed.

DSS‐MGFLA enables the accurate evaluation of leanness. Besides assessing leanness, DSS also enables the identification of improvement areas. The DSS has been validated in an Indian relays manufacturing organization. Based on the validation, its practical feasibility has been ensured.

DSS‐MGFLA has been validated in a single manufacturing organization for its working feasibility. But the findings could be extended to similar manufacturing organizations.

The idea of developing DSS for leanness assessment is novel, original and unique contribution of authors.

Background: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare.

This paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger.

The system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50.

Several novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2.

This paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.

This study aims to investigate the implementation of lean human resource management (HRM) practices in manufacturing small- and medium-sized enterprises (SMEs) and explore how various factors interact to influence their successful adoption. By exploring the interplay among these factors, the research seeks to identify key drivers affecting the adoption of lean HRM in manufacturing SMEs. Ultimately, the research intends to provide insights that can guide organisations, practitioners and policymakers in effectively implementing lean HRM practices to enhance operational efficiency, workforce engagement and competitiveness within the manufacturing SME sector.

The study combined total interpretive structural modelling (TISM) and Matrice d'Impacts Croisés Multiplication Appliquée à un Classement (MICMAC) analysis. TISM helped in understanding the hierarchical relationship among different factors influencing lean HRM implementation, whereas MICMAC analysis provided insights into the level of influence and dependence of each factor on others.

The research revealed that “top management support” emerged as the most independent factor, indicating that strong support from top management is crucial for initiating and sustaining lean HRM practices in manufacturing SMEs. On the other hand, “employee involvement and empowerment” was identified as the most dependent factor, suggesting that fostering a culture of employee engagement and empowerment greatly relies on the successful implementation of lean HRM practices.

While the study provided valuable insights, it has certain limitations. The research was conducted within the specific context of manufacturing SMEs, which might limit the generalizability of the findings to other industries. Expert opinions introduce subjectivity in data collection. Additionally, the study may not cover all critical factors, allowing room for further exploration in future research.

The findings have practical implications for manufacturing SMEs aiming to implement lean HRM practices. Recognising the pivotal role of top management support, organisations should invest in cultivating a strong leadership commitment to lean HRM initiatives. Furthermore, enhancing employee involvement and empowerment can lead to better adoption of lean HRM practices, resulting in improved operational efficiency and overall competitiveness.

This research contributes to the field by offering a comprehensive exploration of the interplay among factors influencing lean HRM implementation. The use of TISM and MICMAC analysis provides a unique perspective on the relationship dynamics between these factors, allowing for a nuanced understanding of their roles in the adoption of lean HRM practices in manufacturing SMEs. The identification of “top management support” as the most independent and “employee involvement and empowerment” as the most dependent factors adds original insights to the existing literature.

The purpose of this paper is to investigate the applicability of lean and green practices to foundry industry in India for improving productivity and eliminating waste, incorporating the sustainability into business performance measures.

The study used survey questionnaire method to collect data against 16 lean and green practices from 71 middle- to senior-level professionals belonging to Indian foundry industry. The survey instrument of lean and green practices was developed based on a number of sources from the literature and formal discussions with academicians and foundry industry professionals. The responses were received on a five-point Likert scale ranging from least applicable to most applicable. Exploratory factor and reliability analyses are conducted to obtain and validate constructs and measure each constructs Cronbach’s α (i.e. a consistency coefficient). The lean and green practices are categorized into the four constructs, namely, workplace organization practices, management practices, inventory control practices, and industrial manufacturing and quality improvement practices. Further, descriptive statistics is employed to find out the relative significance of lean and green practices.

Factor and reliability analyses show that all four constructs are adequate and reliable to illustrate lean and green practices. Descriptive statistics indicates that lean and green practices are applicable for implementation to a certain extent in the foundry industry. Correlation analysis shows that lean practices are positively and moderately interrelated with green practices. Thus, the results present a strong evidence that lean and green practices are moderately applicable for implementation in the foundry industry.

The paper provides insights into the applicability of lean and green practices implementation in the context of a developing country and presents evidence that lean and green practices are moderately applicable in the foundry industry. In addition, this paper is one of the few efforts to promote sustainable development within the industry.

The authors propose a blockchain platform for managing clinical trial data to enhance data validity, integrity, trust and transparency in the pharmaceutical research process. The authors also provide an extensive review of how blockchain technology supports the business processes of clinical trials.

A systematic literature review was conducted to identify the existing applications of blockchain in pharmaceutical process management. A conceptual design for a blockchain infrastructure to address clinical trial challenges is developed by outlining the entire clinical trial value chain and identifying the coordination and communication among its stakeholders. A stakeholder analysis is conducted to ensure that the clinical trial processes satisfy the requirements and preferences of each stakeholder.

The proposed blockchain platform offers a promising solution for enhancing integrity, trust and transparency in the clinical trial process. Additionally, blockchain can help streamline communication and collaboration between stakeholders by enabling multiple parties to access and share data in real time, lowering the possibility of delays or errors in data analysis and reporting.

The proposed blockchain platform can benefit patients by empowering them to have better-controlled access to their data and by allowing researchers to maintain adherence to reporting requirements. Additionally, the platform can benefit granting agencies, researchers and decision-makers by ensuring the integrity of clinical trial data and streamlining communication and collaboration between stakeholders.

This study builds on existing blockchain applications in pharmaceutical process management by developing a blockchain framework that can address clinical trial concerns from an integrated perspective.