Empowering sustainable manufacturing: Unleashing digital innovation in spool fabrication industries.

In industrial landscapes, spool fabrication industries play a crucial role in the successful completion of numerous industrial projects by providing prefabricated modules. However, the implementation of digitalized sustainable practices in spool fabrication industries is progressing slowly and is still in its embryonic stage due to several challenges. To implement digitalized sustainable manufacturing (SM), digital technologies such as Internet of Things, Cloud computing, Big data analytics, Cyber-physical systems, Augmented reality, Virtual reality, and Machine learning are required in the context of sustainability. The scope of the present study entails prioritization of the enablers that promote the implementation of digitalized sustainable practices in spool fabrication industries using the Improved Fuzzy Stepwise Weight Assessment Ratio Analysis (IMF-SWARA) method integrated with Triangular Fuzzy Bonferroni Mean (TFBM). The enablers are identified through a systematic literature review and are validated by a team of seven experts through a questionnaire survey. Then the finally identified enablers are analyzed by the IMF-SWARA and TFBM integrated approach. The results indicate that the most significant enablers are management support, leadership, governmental policies and regulations to implement digitalized SM. The study provides a comprehensive analysis of digital SM enablers in the spool fabrication industry and offers guidelines for the transformation of conventional systems into digitalized SM practices.

Does the purchase intention of green consumers align with their zero-waste buying behaviour? An empirical study on a proactive approach towards embracing waste-free consumption.

In recent years, emerging retail markets in cosmetics have transformed into green markets, as consumers demand more eco-friendly products. However, in scholarly literature, limited studies are available where researchers discuss green consumers' purchase intentions towards eco-friendly products and their relationship with zero-waste buying behaviour. This study is conducted to address the existing gap in current literature. An empirical investigation is carried out, focusing on individuals who use cosmetic products and are active followers of zero-waste beauty influencers on social media. Responses are gathered for data analysis with the help of partial least squares structural equation modelling (PLS-SEM). The results indicate that eco-friendly packaging, pro-environmental belief, and para-social interaction positively impact the altruistic motivation and purchase intention of consumers. Ultimately, these factors collectively contribute to the purchase of zero-waste cosmetic products. The findings of this study can provide valuable insights for policymakers and brand managers in the field of herbal cosmetic formulations. These insights can help in understanding the fundamental reasons that drive consumers to buy zero-waste cosmetic items, potentially leading to a decrease in ecological impact. Additionally, the study contributes to the theory of planned behaviour (TPB) by investigating consumers' purchase intention towards eco-friendly products and their zero-waste buying behaviour.

Are we really addressing the roadblocks to adoption of renewable and sustainable energy technologies? Total interpretive structural modeling approach.

Urban areas serve as a vital contribution to the global structural change towards renewable and sustainable energy technologies which also influence climate change. The aim of this paper is to identify the adoption roadblocks to renewable and sustainable urban energy technologies. This research has three parts: a mini-systematic literature study was conducted to identify the most prevalent roadblocks. Using total interpretive structural modeling (ISM), the relationships between the roadblocks and the source of causation were then examined. The roadblocks are classified based on their dependence and driving powers using MICMAC analysis in the third part of this research. The principal results and major conclusions demonstrate that all roadblocks are necessary for renewable and sustainable urban energy technologies. The roadblocks at level I are insufficient infrastructure, lack of coordination among authorities, lack of quality and reliable data and information, and competition with non-renewable technologies; roadblocks in level II are lack of skilled and trained personnel, limited public participation, awareness, and consumer interest, and lack of standardized technology; roadblock in level III is high initial investment cost; and lastly, roadblocks in level IV are lack of subsidies and financial support programs and absence of coherent related policies. Furthermore, as a result of the MICMAC analysis, none of the aforementioned roadblocks are classified as autonomous variables, implying that they are all required. The dependent roadblocks to renewable and sustainable energy technologies are defined as lack of coordination among authorities, lack of information, and competition with non-renewable technologies. Moreover, linkage roadblocks have high dependence and driving powers which are insufficient infrastructure, limited awareness and consumer interest, and lack of standardized technology. Lastly, high initial investment costs, lack of subsidies and financial support programs, absence of coherent related policies, and lack of skilled and trained personnel are the driving roadblocks with high driving power however not dependent.

Psychological well-being of young adults during COVID-19 pandemic: Lesson learned and future research agenda.

Psychological and mental well-being has become a topic of concern worldwide after the hit of the COVID-19 pandemic. It has triggered enormous global health care vulnerabilities and resulted in full and partial lockdowns to prevent the new case. This research study provides a comprehensive overview of the published international scientific studies on the effect of COVID-19 on the psychological well-being of young adults. This study aims to review the top-cited authors, documents, journals, productive countries, the most used keywords, and trending themes in this area. Articles related to psychological well-being during the COVID-19 pandemic from January 2020 to December 2022 were extracted from the Scopus database with specified keywords. In total, 482 documents were retrieved as original articles and bibliometric analysis, thematic analysis and content analysis are performed and analyzed. The results show that the United States has contributed the largest publications followed by the United Kingdom and Italy. Through the cluster analysis, it is found that many articles have been published and considered the psychological and mental impact of COVID-19. Young adults from both developed and developing countries are majorly affected by the COVID-19 pandemic. The pandemic prioritizes the importance of global psychological well-being and health care. This study focused on different aspects, such as stress, resilience, and the mental health of young adults. The research findings of this study put forth the urgent need to provide preventive policies and intervention procedures to address the psychological health of young adults and proposed a conceptual framework.

Blockchain technology for sustainable supply chains: a network cluster analysis and future research propositions.

Achieving the sustainable goals of the United Nations requires improving supply chain sustainability (SSC). Blockchain technology (BCT) has attracted attention on a global level with the ability to transform supply chain management and sustainability efforts. Recognizing this, this study investigates how BCT plays a role in SSC. The current study looks into the importance of BCT in order to move supply networks towards sustainability by performing bibliometric analysis, and network cluster analysis. Through the literature review, the current literature was analyzed and future research directions were concluded. We begin our study by selecting 297 papers on the relevant subjects by applying various filters to the Web of Science (WoS) database. Influential individuals, journals, and organizations in this field were identified using bibliometric analysis. A network analysis was performed to identify influential co-author, and keywords, and for page rank, and cluster analysis. The network analysis was revealed ten distinct study clusters, and ten propositions were suggested from the analysis of these clusters. Additionally, a conceptual framework for the research was proposed, which can advise managers, practitioners, and researcher communities on the key trends and topics in this emerging research domain. Furthermore, to guide research scholars in this field, 33 future research directions were suggested.

Hope-hype of green innovation, corporate governance index, and impact on firm financial performance: a comparative study of Southeast Asian countries.

The current production and conception have impacted the environmental hazards. Green innovation (GI) is the ideal solution for sustainable production, consumption, and ecological conservation. The objective of the study is to compare comprehensive green innovation (green product, process, service, and organization) impact on firm financial performance in Malaysia and Indonesia, along with the first study to measure the moderation role of the corporate governance index. This study has addressed the gap by developing the green innovation and corporate governance index. Collected panel data from the top 188 publicly listed firms for 3 years and analyzed it using the general least square method. The empirical evidence demonstrates that the green innovation practice is better in Malaysia, and the outcome also shows that the significance level is higher in Indonesia. This study also provides empirical evidence that board composition has a positive moderation relationship betwixt GI and business performance in Malaysia but is insignificant in Indonesia. This comparative study provides new insights to the policymakers and practitioners of both countries to monitor and manage green innovation practices.

Are emerging technologies unlocking the potential of sustainable practices in the context of a net-zero economy? An analysis of driving forces.

Increasing globalization and climate change have significantly affected business activities. Government and other stakeholders are creating pressure to have a sustainable business model for efficient resource utilization and minimizing negative environmental impact. Many organizations have started focusing on sustainable and cleaner production through the adoption of net-zero economy (NZE) practices. Certain technological advancements are required to put these concepts into practice. Firms have begun to adopt digital technologies (such as big data analytics, artificial intelligence, and internet of things), and have been widely used in practice to achieve NZE. Is digitalization unlocking the potential of sustainable practices in the context of a net-zero economy? This question is still unanswered; therefore, this study aims to identify and analyze the drivers of digitalization that ensure sustainable practices to achieve net-zero economy. Through an extensive literature review and experts' opinions, a list of drivers was identified. An empirical investigation was conducted to validate the identified drivers and further understand the influencing relationship among the drivers, Pythagorean fuzzy decision-making trial and evaluation laboratory (PF-DEMATEL) was employed. The findings of the study show that "high degree of automation," "enhancing the flexibility in the manufacturing process," and "real-time sensing capability" are the main influencer drivers among all cause group forces. The present study can be a source for industrial practitioners and academia that can provide significant guidance on how the adoption of digitalization can unlock the potential to achieve CE, which can lead us toward net-zero.

Impact of Digital Assistant Attributes on Millennials' Purchasing Intentions: A Multi-Group Analysis using PLS-SEM, Artificial Neural Network and fsQCA.

The rising population of millennials, coupled with Digital Assistants (DA) and online purchasing trends among consumers have gained increasing attention by global marketers. The study evaluates the influence of DA attributes on the purchasing intention (PUI) of millennials. A combined approach of PLS-SEM, Artificial Neural Network (ANN) and Fuzzy-set Qualitative Comparative Analysis (fsQCA) is used to predict the PUI of 345 millennials. Also, multi-group analysis is employed to uncover the influence of gender on the relationship between PUI and DA attributes. The findings suggest that DA attributes may amplify purchasing intention among millennials, especially through perceived interactivity and anthropomorphism. Further, the moderating role of gender was found significant on the inter-relationship of perceived interactivity and PUI. This research is a pioneer study in the area of artificial intelligence, conversational commerce, DA and AI-powered chatbots. This study will help marketers and practitioners to predict millennial purchasing intentions. An evaluation of this paper may help them to foster immersive and effective engagement through DA.

Interval-valued intuitionistic fuzzy digraph-matrix approach with PERMAN algorithm for measuring COVID-19 impact on perishable food supply chain.

The outbreak of COVID-19 has prompted a substantial shrinkage in various businesses worldwide, the perishable food sector being one of the worst hits. Henceforth, this manuscript intends to analyse the impact of COVID-19 on perishable food supply chains (PFSCs) of developed and developing countries. For this, the study presents the analysis in two steps. In the first step, the study illuminates the particular factors that frame unique sorts of supply chain (SC) disturbances in PFSC. Secondly, the study proposes a unique interval-valued intuitionistic fuzzy set (IVIFS)-based graph theory and matrix approach (GTMA) to analyse the COVID-19 impact index value. In addition to this, the PERMAN algorithm is used to calculate the permanent function. The study has revealed that developing nations should focus more on their technological and infrastructural factors to improve the condition of PFSC during the pandemic. This study's results can be deployed by decision-makers to forestall the operative and long-haul consequences of COVID-19, or any other disruptions to the PFSC, and make plans to overcome the impact. The significance of this manuscript is that the prominent factors degrading the performance of PFSC amidst the pandemic have been highlighted, with their respective impact on developed and developing nations compared. Moreover, a neoteric comprehensive integration of IVIFS-GTMA technique along with the PERMAN algorithm has been utilised in this manuscript. This particular study is inimitable as it supplements existing literature by providing analytical support to the relationship among various factors impacting the PFSC amidst the pandemic.

Analyzing musculoskeletal risk-severity among small scale casting workers using ergonomic assessment tools: A statistical approach.

BACKGROUND: Work-related musculoskeletal disorders (WMSDs) are leading cause of injuries among economically backward workers employed under small scale metal casting units especially in developing countries. In India, most casting unit's falls under small and medium enterprises having inadequacy of advanced technological equipment's due to several economic constraints and rely intensively on manual labour. Foundry work is very much prone to WMSDs involving much physical interaction of workers with their jobs which includes several risk factors. OBJECTIVE: The study objectives were to analyse the musculoskeletal risk prevalence among small scale casting workers using ergonomic assessment tools and statistical approach. METHODS: In present study, WMSDs risk prevalence has been examined using Rapid Entire Body Assessment (REBA) and virtual ergonomics. Further, risk evaluations were analysed using Mann-Whitney U test and Taguchi L25 orthogonal array. RESULTS: Results revealed manual handling task as being most vulnerable followed by the fettling section. Statistically significant differences were observed (p-value < 0.05) among all the work-sections except lift-lower task and molding section (p = 0.361; p > 0.05) for left side region; and lift-lower task and fettling section (p = 0.230; p > 0.05) for the right side region, where differences were not statistically significant. ANOVA results indicated that workstation height followed by population percentile and object weight were dominant factors significantly affecting the response parameter i.e. L4-L5 spine compression (p-value < 0.01); however workstation width (p-value > 0.05) had no significant effect. CONCLUSION: The present study may guide foundry industrialists in analysing the mismatch between the workers' job profile and redesigning existing workstation layouts in small scale foundries based on minimizing the WMSDs risk severity associated with the work tasks.

Circular dairy supply chain management through Internet of Things-enabled technologies.

Internet of Things-enabled technologies help to collect data and make it understandable, especially in supply chain processes, thus minimizing the problems that may arise in supply chains. It is extremely important to support this process with Internet of Things-enabled technologies, especially in supply chains that are vulnerable to disruptions such as the dairy supply chain. Moreover, dairy supply chains are the type of supply chains where the most waste is generated; evaluating this waste is very beneficial to the circular economy. Therefore, monitoring data in dairy supply chains and using Internet of Things-enabled technologies prevent losses; it is critical to have Internet of Things-enabled circular dairy supply chains in operation. The aim of this study is to determine the success factors of Internet of Things-enabled circular dairy supply chains based on the various stages of these chains; we hope to match each dairy supply chain stage with a success factor of Internet of Things-enabled technology and determine a ranking for these factors. Hence, six success factors of Internet of Things-enabled circular supply chains are weighted for each stage of the chain; Internet of Things-enabled digital technologies are then matched with each stage of the chain, and the success factor is determined. The ranking of factors can then be drawn up through the integration of Step Wise Weight Assessment Ratio Analysis (SWARA) and Technique for Order Preference Similar to Ideal Solution (TOPSIS). The outcome of this study will provide managers and policy makers with insights into Internet of Things-enabled circular dairy supply chains.

Business continuity through customer engagement in sustainable supply chain management: outlining the enablers to manage disruption.

Business continuity in disruptions like the COVID-19 pandemic involves sustainable supply chain management (SSCM) with limited resources and risks for the well-being and prosperity of stakeholders and customers involved with limited environmental effects. The purpose of the paper is to outline enablers in customer engagement that supports SSCM in times of disruption like the COVID-19 pandemic. This research uses an extensive literature review followed by academic and industry practitioners' opinions to identify customer engagement enablers in SSCM for business continuity. Hybrid stepwise weight assessment ratio analysis (SWARA) and rough set numbers rank customer engagement enablers that support SSCM in disruption. The research builds on stakeholder theory and the sustainability framework for economic performance through non-economic aspects. The research concludes that the focus on agility for target customers through collaboration and information sharing in SSCM will support business continuity. It shall support decision-making in the supply chain in uncertainties. Engagement with stakeholders leads to focused execution in response to customer demand through faster communication and crucial information sharing, thus eliminating bottlenecks for business continuity.

Analyzing musculoskeletal risk prevalence among workers in developing countries: an analysis of small-scale cast-iron foundries in India.

Musculoskeletal disorders (MSDs) are the most common health issues around the globe and a major cause of disability among workers. In developing countries, most casting industries fall under Small and Medium Scale Enterprises (SMEs); SMEs lack modern equipment and often intensively rely on manual labor. As processes are mainly labor intensive, workers employed in the metal casting sector are often at risk of MSDs. The main objective of this study is to investigate the exposure of work-related musculoskeletal symptoms prevalent among male workers employed in small scale gray cast-iron foundries of northern India. The techniques used included the Nordic Musculoskeletal Questionnaire (NMQ), anthropometric measures, work postures analysis based on digital human modeling (DHM), Rapid Upper Limb Assessment/RULA and Biomechanics Single Action Analysis/BSAA ergonomic assessment tools. Based on the measured anthropometric variables and work postures attained during various routine tasks, virtual work manikins were created and analyzed using human activity analysis in CATIA V5R20. Subjective assessment revealed that distal upper extremity regions i.e. wrists/hands followed by lower back, neck and shoulders have higher frequency of reported MSDs symptoms. Higher musculoskeletal risk was observed with most of the analyzed work postures, with fettling and Manual Material Handling (MMH) tasks being the most affected. A regression model was developed to predict the L4-L5 spine compression load on the workers' lumbar regions; a coefficient of determination (R2) value of 0.761 indicates an acceptable model fit (R-value = 0.873). Based on the values of the standardized beta coefficients, findings suggested that trunk angle (ß = 0.616), followed by upper arm angle (ß = 0.408), were stronger predictors on the dependent variable (i.e. L4-L5 spine compression) than population percentile (ß = 0.372), object weight (ß = 0.208) and lower arm angle (ß = 0.183). The findings suggest that factors like manual work demands, poor work station structure, repetitive actions and awkward postures held for a longer duration may likely be associated with MSDs risk severity. The present study may guide the foundry industrialists in analyzing the mismatch between the workers' job profiles and redesigning the work station layouts in small scale foundries based on minimizing the risk severity associated with the tasks carried out by staff.

Big Data-Enabled Solutions Framework to Overcoming the Barriers to Circular Economy Initiatives in Healthcare Sector.

Ever-changing conditions and emerging new challenges affect the ability of the healthcare sector to survive with the current system, and to maintain its processes effectively. In the healthcare sector, the conservation of the natural resources is being obstructed by insufficient infrastructure for managing residual waste resulting from single-use medical materials, increased energy use, and its environmental burden. In this context, circularity and sustainability concepts have become essential in healthcare to meliorate the sector's negative impacts on the environment. The main aim of this study is to identify the barriers related to circular economy (CE) in the healthcare sector, apply big data analytics in healthcare, and provide solutions to these barriers. The contribution of this research is the detailed examination of the current healthcare literature about CE adaptation, and a proposal for a big data-enabled solutions framework to barriers to circularity, using fuzzy best-worst Method (BWM) and fuzzy VIKOR. Based on the findings, managerial, policy, and theoretical implementations are recommended to support sustainable development initiatives in the healthcare sector.

Modelling Internet of things (IoT)-driven global sustainability in multi-tier agri-food supply chain under natural epidemic outbreaks.

Epidemic outbreak (COVID-19, SARS-CoV-2) is an exceptional scenario of agri-food supply chain (AFSC) risk at the globalised level which is characterised by logistics' network breakdown (ripple effects), demand mismatch (uncertainty), and sustainable issues. Thus, the aim of this research is the modelling of the sustainable based multi-tier system for AFSC, which is managed through the different emerging application of Internet of things (IoT) technology. Different IoT technologies, viz., Blockchain, robotics, Big data analysis, and cloud computing, have developed a competitive AFSC at the global level. Competitive AFSC needs cautious incorporation of multi-tiers suppliers, specifically during dealing with globalised sustainability issues. Firms have been advancing towards their multi suppliers for driving social, environments and economical practices. This paper also studies the interrelationship of 14 enablers and their cause and effect magnitude as contributing to IoT-based food secure model. The methodology used in the paper is interpretative structural modelling (ISM) for establishing interrelationship among the enablers and Fuzzy-Decision-Making Trial and Evaluation Laboratory (F-DEMATEL) to provide the magnitude of the cause-effect strength of the hierarchical framework. This paper also provides some theoretical contribution supported by information processing theory (IPT) and dynamic capability theory (DCT). This paper may guide the organisation's managers in their strategic planning based on enabler's classification into cause and effect groups. This paper may also encourage the mangers for implementing IoT technologies in AFSC.

Now is the time to press the reset button: Helping India's companies to become more resilient and effective in overcoming the impacts of COVID-19, climate changes and other crises.

COVID-19 is the pandemic caused by one of the coronaviruses. This virus was not known before the outbreak in Wuhan, China, in December 2019. By January of 2020 it was declared to be a global human health crisis. The deaths and illnesses caused by the virus caused extensive fear and anxiety among people in all societies. The pandemic slowed economic activities nearly to a halt. The challenges of how companies should respond to the disruptions in their supply chains and how they can build more resilient systems, must be systematically addressed. The authors of this paper highlighted essential factors which can help companies to overcome this crisis and other types of crises, by learning from the approaches taken in India, which has a unique and diverse economic system. The Analytical Hierarchy Process (AHP) technique was used to identify the essential factors which can help companies to improve their resilience so they can recover during and after the COVID-19 pandemic era and potentially in other similar complex crises. The results of the AHP evaluation were prioritized by performing a sensitivity analysis to prioritise the essential factors. The "Role of governance" was found to be the most important factor that can be used to help in rebuilding industries and societies and in helping them to become more resilient to future severe shocks. The results of this research were used to develop recommendations for company managers, practitioners and policy-makers. The authors hope that this advice will help India to become a stronger nation with more resilient companies, which are better prepared to anticipate and to respond to future crises. We hope people in other nations will also benefit from the finding presented in this paper.

Performance evaluation of fuzzy-logic and BP-ANN methods for WEDM of aeronautics super alloy.

The main purpose of this research is to check the relative importance of methods fuzzy-logic and back-propagation neural network to evaluate the performance of wire electric discharge machine (WEDM) of aeronautics super alloy. It has been confirmed that BP-ANN method reveals significant result over the fuzzy logic method for the evaluation of surface roughness and waviness of the WEDM of aeronautic super alloy. On the basis of Taguchi analysis, it has been established that the variable pulse-on, interaction amid the pulse-on and pulse-off time, wire tension and spark-gap voltage have a superlative influence on the surface roughness. The waviness is influenced prominently by pulse-on time, pulse-off time and spark-gap voltage. The thickness of recast layer is minimized up to 9.434 µm.