Effective solid waste management is critical to public health and environmental sustainability. However, the municipal solid waste circular supply chain (MSWCSC) often suffers from challenges related to trust, transparency, and traceability. To address these issues, this study develops and analyzes mathematical models across three scenarios: (i) MSWCSC without blockchain (Scenario I), (ii) MSWCSC with blockchain integration (Scenario II), and (iii) MSWCSC with blockchain and the removal of the transfer station (Scenario III). The models incorporate key stakeholder interactions and are evaluated using numerical simulations under realistic conditions. Findings indicate that Scenario II enhances overall supply chain profitability, particularly for recyclers, scrap dealers, and the government, although waste collectors experience reduced margins. Blockchain implementation leads to improved economic outcomes, reduced environmental and social costs, and greater supply chain sustainability. Scenario III yields the highest profitability by eliminating the transfer station, thereby reducing handling and logistics costs. Sensitivity analysis confirms that increased waste segregation significantly boosts value creation across the supply chain. The study contributes to the literature on circular economy and blockchain in waste management and offers actionable insights for policymakers and industry stakeholders seeking to improve MSW systems through digital innovation.

Purpose-This paper aims to explore how organizational business policies can be refined to enhance multigenerational social enterprises (SEs), addressing age-based discrimination, power imbalances and divergent ethical priorities while promoting participatory governance and social innovation. This novel conceptual approach is intended for organizations that are aligned to the social enterprises. Design/methodology/approach-The study adopts the human-centered design (HCD) to analyze the existing organizational business policies and refine them to support the requirements of the multigenerational workforce. HCD considers policy refinement by adopting technological adaptability, organizational culture and environmental sustainability principles to ensure that the refined policies align with the needs of Generation X, Millennials and Generation Z. Findings-Key insights include: participatory governance reduces intergenerational conflict by promoting inclusivity in policy design; ethical frameworks ensure transparency and fairness across age groups; and social innovation is enhanced when SEs leverage generational diversity. Research limitations/implications-The reserach paper is conceptual, lacking empirical validation. It focuses on generational cohorts (Gen X, Millennials, Gen Z) without addressing intersectional factors like gender or culture. The HCD framework's iterative nature may pose implementation challenges for resource-constrained. The findings guide SEs in designing inclusive policies through participatory governance (e.g. 30% higher retention) and ethical frameworks (20% fewer conflicts). Social innovation thrives when generational strengths intersect (45% more solutions). Future research should test the HCD model empirically and explore technology's role in bridging generational gaps. Policymakers can leverage these insights to support SE sustainability. Originality/value-This study contributes a novel HCD-based framework for refining business policies in SEs, integrating ethics, inclusivity and innovation. It offers actionable guidance for leaders managing multigenerational teams in mission-driven organizations.

Efficient analysis of accident data is necessary for managing industrial safety. Since this data is frequently categorical, the main analysis factors that we need to use are frequency and causality. We can develop risk assessments and mitigation strategies from these analyses to prevent future accidents. Heterogeneous data might conceal important correlations between variables, making traditional methods of causality analysis difficult to use. To address this, we have used Latent Class Clustering (LCC) to transform such data into a more homogeneous form, minimizing the reliance on expert opinions. Correspondence Analysis (CA) is then employed to visualize latent classes in a 2D space, aiding in categorization for causal analysis. We have found the direction and strength of cause-effect links by using Bayesian Networks (BN), although it necessitates a balance between algorithmic learning and expert knowledge. Finally, we have extracted safety rules from the homogeneous categorical safety data from a steel factory using CA and t-Distributed Stochastic Neighbor Embedding (t-SNE) in this work, which offers important insights for safety management.

PurposeBlockchain technology (BCT) is a novel and disruptive innovation that can transform procurement processes in various industries, including the food sector. BCT can offer benefits such as transparency, traceability, security, and efficiency for procuring food products, which are essential for ensuring food security and quality. However, BCT faces several challenges that hinder its adoption and implementation in the food procurement domain, potentially impacting food security. Thus, this present work aims to identify the challenges of adopting blockchain technology in food Procurement 4.0 to support food security.Design/methodology/approachEleven key challenges were identified through a literature search and are validated by domain experts. These challenges are modeled using the fuzzy-Decision-Making Trial and Evaluation of Laboratory (fuzzy-DEMATEL) technique. The fuzzy-DEMATEL technique allows us to explore the causal behavior of challenges that help further build strategic action.FindingsThe findings reveal that lack of understanding and awareness (BTCP4) and immature legal acceptance/ regulatory policies (BTCP2) are the most prominent barriers. The insights obtained from this work could be utilized by government agencies, policymakers and key industry players to prepare strategies to eliminate the challenges impeding the successful adoption of BCT in Procurement 4.0, thus ensuring food security for all.Research limitations/implicationsThe limitations of this research are listed here, and they can be taken up in future research. This research employed a fuzzy-DEMATEL technique contingent upon experts' subjective judgments and preferences, which may result in biases and inaccuracies in data collection and analysis. The number of experts is also a serious concern for the present study. Future studies may apply an integrated modeling approach with more experts to realize the results better. Additionally, the readiness assessment for adopting BCT could be done in a contextual setting to understand challenges better.Originality/valueTo the best of the authors' knowledge, the present work is the first of its kind to navigate blockchain challenges in Procurement 4.0 to ensure food security.

The modern food supply chain (FSC) faces pressing challenges, including food fraud, safety and security issues, food waste and sustainability concerns. Simultaneously, consumers are becoming increasingly interested in understanding the origins and pathways of their food. To tackle these challenges, digitisation and traceable FSCs are vital. However, numerous obstacles hinder the widespread adoption of traceability in digital FSCs. This study identifies and explores interconnections between barriers to traceability adoption in digital FSCs through an integrated DELPHI and Fuzzy DEMATEL approach. The findings highlight that Education and Training Gaps (B13), Data Integration Challenges (B14), Data Silos (B10), Environmental Sustainability Concerns (B16) and Short-Term Focus (B17) are among the most influential barriers, impacting several other challenges. To mitigate these barriers, the study proposes a nine-pillar framework. The insights derived from this research can support government agencies, policymakers and agro-food industries in advancing traceability adoption across digital FSCs.

Several studies have explored the antecedents of automobile purchase intention (API) of customers across different geographical regions of the world. These have, however, come up with contradictory findings about the polarities of effects of some antecedents, as well as about some of their differences across countries. We were motivated by these contradictions and differences to carry out a meta-analysis on API. In the study, we considered articles from peer-reviewed databases such as Google Scholar, Web of Science, Scopus, ScienceDirect, and Wiley. Our study and analysis of the full texts of 136 articles yielded 64 antecedents. The specific polarity of effect (positive or negative) on API is established for 59 of these antecedents, whereas the same remained undetermined for the rest five. We identified these as research gaps, the areas yet to be convincingly explored. Furthermore, based on the absence of publication bias and the 95% CI of impact not crossing the line of null effect, we enlisted 20 antecedents spread across the different stages of the automobile purchase decision process, which we propose to add to the theory of consumer behavior as the automobile consumer behavior (TACB). Our study can help managers to better understand consumer behavior in terms of the discovered antecedents, especially those having a higher impact at a particular stage of the process, and can result in better business decisions.

Manufacturing's transition to sustainable development depends on integrating green with lean under credible environmental policy and stakeholder engagement. Although benefits are well established, the literature underspecifies implementation barriers and their prioritisation. This study identifies, structures, and prioritises barriers to green-lean adoption using an Indonesia-based, mixed-methods design, producing a decision-oriented roadmap for sustainable production. A structured literature review (SLR) informed a survey of manufacturing managers (n = 132), with exploratory factor analysis (EFA) categorising barriers into five dimensions: managerial orchestration (MO), ecosystem and governance (EG), behaviour and belief (BB), knowledge and know-how (KK) and tools, methodology and technology (TMT). An analytic hierarchy process (AHP) with a diverse expert panel (n = 12) then ranked these dimensions and derived sequenced deployment guidance. MO and EG emerged as the most significant barriers, with salient impediments including inadequate top management support, substantial initial capital requirements, limited government incentives and resistance to organisational change; BB, KK and TMT factors further constrain diffusion via skills gaps and methodological complexity. We propose and validate a theory-linked framework for green-lean integration (GLI) barriers-derived via EFA and prioritised with AHP-anchored in diffusion of innovation (DoI), institutional theory, resource-based view (RBV)/dynamic capabilities and change-management; the process is portable, whereas weights require local reestimation. Bounded by Indonesia's institutional conditions-regulatory stringency and enforcement, energy mix and cost structures, small and medium-sized enterprises (SMEs) prevalence and supplier informality, and hierarchical managerial norms-this study delivers a decision-oriented roadmap for Indonesian manufacturing and a clearly defined process (SLR -> EFA validation -> AHP prioritisation with consistency ratio [CR] control) that can be adapted, via local reweighting and replication, to settings with comparable profiles.

Blockchain technologies are increasingly recognized as a transformative force across industries, offering potential solutions for information management, data security, and operational efficiency improvement. However, integration into the healthcare sector faces significant barriers, ranging from technical challenges to organizational resistance. In this study, a methodology is proposed that examines these critical barriers through a comprehensive analysis of 3265 academic papers and derives actionable solutions from 1566 patents published between 2016 and 2023. This approach bridges the gap between identifying challenges and implementing solutions. Using the Stepwise Weight Assessment Ratio Analysis (SWARA), twelve critical adoption challenges are analyzed, while Top2Vec-based topic modeling identifies innovations that best address the ranked barriers. In addition to this, the proposed 'healthcare ecosphere' knowledge map serves as a comprehensive tool to analyze key stakeholders, their interactions, and the alignment of adoption barriers in solution spaces. The findings show that innovations in blockchain technologies are heavily concentrated in areas such as data security and application functionalities, whereas other critical domains-such as consensus mechanisms, governance, and regulatory frameworks-remain underexplored, pointing to opportunities for growth and development. The mapping of barriers to solutions provides practical guidance for healthcare providers, policymakers, and technologists seeking to implement the blockchain technologies effectively.

Industries undergoing rapid digital transformation are facing a significant global challenge: a widening gap between required and available workforce digital skills. This study identifies key determinants of this gap and develops a comprehensive conceptual framework for mitigation. The research adopted a mixed-methods strategy, integrating expert insights through interviews and a PRISMA-informed systematic review of existing literature. The findings identify critical enablers, including accessible digital infrastructure, targeted investments in skilling, inclusive policies, government-led digital literacy initiatives, industry-aligned curricula, and organizational digital maturity. In contrast, key barriers include limited access to quality educational resources, outdated curricula, high infrastructure costs, and inadequate regulatory support. Further, integrating the Resource-Based View and Technology Acceptance Model, Technology-Organization-Environment frameworks, we propose a novel conceptual model capturing organizational and individual factors influencing skill acquisition. This framework elucidates the complex dynamics driving digital skill gaps and provides actionable guidance for designing inclusive, future-ready upskilling strategies. Our findings offer vital insights for researchers, HR leaders, educators, and policymakers building resilient, digitally competent workforces for Industry 4.0 and beyond.

Research on climate literacy and decarbonization has grown exponentially in recent years, aligning with sustainable consumer behavior. With the rise in global concerns about climate change and its negative environmental impact, it is critical to understand how climate literacy can foster decarbonization and sustainable consumption practices among individuals and businesses. This study aims to give a comprehensive review of the knowledge published over the past two decades in the field of decarbonization and climate literacy using the lens of sustainable consumption. Utilizing bibliometric analysis and the TCCM (Theory-Context-Characteristics-Methodology) framework, we performed a hybrid literature review of 658 relevant publications released between 2004 and 2025. Our results provide insights into the publication trends, dominant theories, contexts, and methodologies adopted in the extant literature. The review identifies major research clusters related to the roles of different stakeholders in decarbonization, climate adaptation measures, and climate resilience through education. Significant research gaps and potential future research directions are also outlined to advance theory development and provide marketing implications for the industry to align with sustainable consumer preferences.

PurposeThe study aims to investigate the association between climate change financial disclosure and financial performance, considering the moderating effect of industry sensitivity on developing nations.Design/methodology/approachThe study analyzes a panel data set of 93 non-financial companies from developing countries listed in the Fortune Global 500 from 2018 to 2022. The authors have used system generalized method of moments model followed by two-stage least square model and fixed effects model to test the hypotheses. Three cultural dimensions and a sub-sample analysis have been included to check the robustness of the results.FindingsThe findings indicated that climate change financial disclosure negatively affects financial performance, supporting the propositions of neoclassical theory of corporate social responsibility. Also, climate sensitivity negatively moderates the relationship between climate change disclosure and market performance. The results are robust to alternative estimation techniques, country differences and sectors.Originality/valueTo the best of the authors' knowledge, this is a novel attempt to examine the impact of climate change disclosure on financial performance in a cross-country context using the task force on climate-related financial disclosure (TCFD) framework. It also contributes to the existing literature by incorporating climate-sensitive sectors as moderating variables. The study recommends a mandatory framework of law to protect the environment.

Purpose - This study examines the impact of corporate political strategies (CPS) on mergers and acquisitions (M&A) outcomes, emphasizing their evolution, trends, and managerial influences. It investigates how CPS drives firm expansion, technological acquisition, and revenue growth while assessing top management's role in selecting and implementing these strategies. This understanding is vital for refining corporate strategies that balance market and nonmarket forces. Design/methodology/approach - This study employs a systematic literature review methodology using the Context-Intervention-Mechanism-Outcome (CIMO) framework. By synthesizing findings from academic sources, the review maps CPS's evolution, managerial influences, and effectiveness in shaping M&A outcomes across global markets. Findings - The review finds that CPS positively impacts M&A outcomes by easing regulatory approvals and improving post-acquisition performance. However, its effectiveness depends on factors like legal frameworks and ownership structures. Risks include prolonged deal reviews and over-reliance on political connections, leading to suboptimal decisions and reputational damage. Managerial behaviors significantly influence CPS success, and strategies have become more adaptive. Future trends point to increasing use of predictive technologies to manage risks in M&A. Originality/value - This study systematically reviews the evolving role of CPS in M&A transactions, incorporating managerial behavior and institutional context. Integrating the CIMO framework and examining the interplay between CPS and managerial traits, it offers a novel perspective on how firms strategize within political environments during M&A.

This study investigates Cross-Border Electricity Trade (CBET) in South Asia, centering on India and its neighbours-Bangladesh, Bhutan, Nepal, and Sri Lanka-to harness opportunities for regional cooperation, energy security, and renewable energy (RE) integration. A comprehensive literature review identifies core research themes, underscoring CBET's advantages in bolstering energy security and sustainability, while confronting challenges such as market asymmetries, policy inconsistencies, and RE intermittency. Leveraging Nepal and Bhutan's hydropower abundance alongside India's solar and wind expansion, yet constrained by technical hurdles, the analysis employs theories of comparative advantage, trade diversion, and reciprocal load smoothing to advocate for synchronized policies and infrastructure enhancements. Utilizing LEAP modelling in a pioneering CBET context, results indicate that intensified CBET optimizes RE deployment, curbing 100-year global warming potential (GWP) by over 30 % relative to the Baseline Scenario, fulfilling escalating demand, and mitigating intermittency via interconnected grids. These outcomes resonate with SDGs 7 and 13, positioning India as a pivotal hub and furnishing policymakers with strategic guidance for sustainable regional energy frameworks.

We develop a comprehensive business-data-science alignment model for deriving business value from data-science initiatives, thus assisting business organizations in transforming digitally. With a purposive sample of 13 data science leaders employed as C-level executives, we conducted semistructured interviews to unfold their data-science journeys. Our study extends the findings from recent literature on the alignment between business and information technology to data-science initiatives. The alignment dimensions and their components are extracted through the interview transcripts using a general inductive approach. We propose a business-data-science alignment model as a dynamic and iterative process in which the data-science initiatives are shaped while aligning with intellectual, operational, social, cultural, and societal dimensions to derive business value. The five most relevant components represent each of these alignment dimensions. The study emphasizes the groundwork to be done before investing in data-science endeavors by capturing the findings from the literature and the wisdom of practitioners. With the proposed data-science alignment framework, our in achieving business value. (c) 2024 Kelley School of Business, Indiana University. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

Deep learning (DL) methods have opened unprecedented opportunities in the textile industry by providing critical insights into fibre-yarn-fabric defect detection, quality control, quality management, process automation and optimisation. The present work aims to summarise the literature related to DL applications in the textile industry and highlight future research agenda. This review combines bibliometric and network analyses of the relevant research papers. Content analysis was also done to elucidate various DL algorithms used at different stages of textile manufacturing and quality management. Convolutional neural network (CNN), deep neural network (DNN) and You Only Look Once (YOLO) are the most widely used DL techniques for fibre classification, fabric defect detection, garment classification, body shape reconstruction, etc. Some practical difficulties related to the applications of DL include nature of the data, size and scope of the data, selection of appropriate models, process uncertainty, etc. This research makes threefold contributions. First, mapping of different DL algorithms used in the textile industry; second, classifying the applications of DL based on the stages of textile manufacturing and quality management; and finally, identifying the challenges and themes for future research directions. This review provides a comprehensive reference for industries and academia on DL applications in the textile industry.

Individuals are motivated to switch to alternatives when their existing retailers do not meet their expectations. The study intends to examine the factors affecting the decision of consumers walking out of physical stores and opting for metaverse shopping through the PPM (Push, Pull, Mooring) framework. The data is gathered using a structured online questionnaire through Google Forms and SMART-PLS software is used to evaluate the results. The study revealed that expectation disconfirmation and alternative attractiveness both significantly impacted switching intention. Buyers' intention to switch was positively influenced by social influence, content creation, and tranquility. Remarkably, switching costs had no impact on the customer's switching intention. This paper contributes to research as it is the first of its kind to explore the switching intention from offline to metaverse shopping using the PPM framework. The results provide valuable insights to help retail brand owners, understand and identify the key attractions for consumers.

PurposeThis study aims to attempt to understand how various stakeholders perceive crisis management and recovery, underscoring the pivotal role of digital communication in rebuilding trust in the industry after a crisis. In addition, the study explores the viability of the Health Belief Model (HBM) as a valuable framework for managing crises within the tourism sector.Design/methodology/approachData from a primary survey of users from the tourism sector were analyzed to assess the relationships among the various constructs within the context of tourism services. Structural equation modelling (SEM) was used for data analysis. Subsequently, the results extended the existing model formulation.FindingsThe results of the data analysis indicated that the two main predictors of restoration were perceived usefulness and facilitators. Furthermore, the results affirm that fear of crisis also predicted restoration with an effect size of -0.056. This study contributes to and extends the HBM, providing an integrated perspective for formulating recovery strategies in the tourism and hospitality industry. This research underscores the importance of self-efficacy, showing that confidence in safely using tourism services significantly boosts motivation.Social implicationsThis study's findings can assist industry practitioners and policymakers in developing robust crisis communication and recovery strategies. These strategies are crucial for rebuilding trust and confidence within the industry after a crisis.Originality/valueThe proposed model offers a significant data-driven perspective. This approach enhances our understanding of how these sectors respond to and recover from crises, potentially leading to more effective crisis management and recovery.

This study aims to investigate the impact of profitability, life cycle, liquidity, growth opportunities, leverage, size of the firm, age of the firm, and previous year's dividend on the dividend decisions of the firms listed on the S&P 500. The two-step system GMM dynamic panel model has been applied to the data set over 11 years from 2010-2021 to ascertain the influencing of variables on the dividend decisions of the firms. The study results indicate a positive and significant effect of the dividend per share of the previous year along with the firm's profitability, and size on the dividend per share of the firm. It indicates good corporate governance practices that surge the shareholder value. The analysis findings may have several implications for investors, corporate decision-makers, portfolio managers, and policymakers who need to understand the complex factors that influence dividend payout policy. Corporate managers, policymakers, and regulators can lure significant conclusions from the study's outcomes. The study reinforces the previous studies and sheds new insights on the impact of firm-level variables on dividend decisions of firms and helps the firms devise appropriate strategies to boost the firm's growth, which is a value driver of financial development in the USA.

PurposeThe advancement of artificial intelligence (AI) and robotics helps firms achieve seamless production, distribution and service delivery. This study uses a sample of developed and developing countries to examine the impact of robots on AI-related employment.Design/methodology/approachThe present study underlies cross-country evidence using a sample of 28 countries between 2016 and 2022. The source data are captured from the Artificial Intelligence Index Report, Statista, World Intellectual Property Organization, World Development Indicators and World Governance Indicators. We employed panel data techniques for analysis purposes.FindingsThis study unravels the impact of robot use on AI employment in developed and emerging economies. The dynamic panel threshold regression models support the contention that the effects of robots on AI employment are more complex than they are made to be. The impact varies below and above the threshold of country-specific variables such as internet penetration, innovation parameters, gross domestic product (GDP) per capita and labor force quality.Originality/valueThis study offers new perspectives on robot and AI-related employment by utilizing a sample of developed and developing countries. It considers the inclusion of country-specific variables. The study provided insights into the economic value creation by labor that would be shaped by the threshold of technological infrastructure, economic conditions and governance standards of countries, thereby contributing to the employment relations literature.

Studies have examined consumers' intentions and behavior regarding the purchase of recycled products, considering factors such as price, availability, perceived benefits, environmental concerns, subjective norms, and self-image. However, the impact of consumers' concerns for Sustainable Development Goals (SDGs) on their purchase behavior remains largely unexplored. This study investigates how concerns for economic (SDGs 2 and 8), environmental (SDGs 13, 14, and 15), and social (SDGs 1 and 4) SDG factors influence consumers' attitudes, intentions, and behaviors towards recycled products. The study also examines the mediating role of consumer attitudes and intentions in the relationship between consumers' concerns about SDG factors and their purchasing behavior. Using the Theory of Planned Behavior (TPB) and Structural Equation Modeling (SEM), the study examines its hypotheses with data from 318 Indian consumers collected via an online quantitative survey. The findings indicate that Indian consumers concerned with SDGs 1, 2, 8, 13, 14, and 15 are more likely to purchase recycled products, with attitudes and intentions partially mediating these relationships. Among them, consumers' concerns for SDGs 1 and 14 are found to be the strongest impacting factors. However, consumers' purchase behavior for recycled products is not substantially impacted by consumers' concerns for quality education (SDG 4), due to the limitation that the link between consumers' concerns about quality education and the purchase of recycled products is difficult to interpret. Based on these findings, the study offers strategies and framework to managers for enhancing the sales and productivity of recycled products through SDG initiatives.