PurposeThe present study aims to evaluate and rank Critical Success Factors (CSFs) necessary for the adoption of Industry 5.0 (I5.0) in the manufacturing sector using a decision-making approach. Specifically, it investigates how human-centric, environmentally sustainable and digitally integrated enablers, such as system interoperability, real-time communication, upskilling, renewable energy integration and circular product design can be prioritized to support effective implementation strategies.Design/methodology/approachThe model is structured to help decision-makers recognize the critical CSFs even in the context of uncertainty and vagueness. This study adopted the Trapezoidal intuitionistic fuzzy technique for order preference by similarity to the ideal solution (TraInFuTOPSIS) method as a solution methodology to assess and rank 18 critical success factors (CSFs) based on six criteria. Six criteria include operational flexibility (C1), human-centric integration (C2), customization (C3), operational efficiency (C4), innovativeness (C5) and sustainability (C6). A panel of experts assigned linguistic expressions for the criteria weights and factor ratings.FindingsThe findings of the study revealed Supply chain modularity (CSF6), Sustainable thinking (CSF3) and Employee technical assistance (CSF13) as top-priority success factors, which highlighted the need for flexibility, environmental consciousness and human-machine cooperation to adopt I5.0 with a sustainable focus. Therefore, these CSFs are essential for establishing sustainable and robust pathways for I5.0 in manufacturing.Practical implicationsThe study delivers a structured methodology to recognize and prioritize CSFs that can help industry practitioners and managers identify which factors are most important to successfully implement I5.0 and industry sustainability goals.Originality/valueThe originality of the study is the introduction of trapezoidal intuitionistic fuzzy numbers to the decision-making strategy in the critical evaluation of success factors in I5.0 adoption. The study is a systematic decision-making model for organizations aiming at adopting I5.0, ensuring sustainability.

Achieving gender equality is crucial both as a fundamental human right and as a cornerstone of sustainable development. This study presents a decision-making framework to support stakeholders, particularly policymakers, in achieving Sustainable Development Goal 5 (SDG 5) by identifying and addressing the barriers that perpetuate discrimination against women. The framework enables informed policy and intervention strategies by rank-ordering potential barriers and analysing causal relationships among them. Employing a multi-criteria decision-making (MCDM) approach, the framework integrates Fermatean fuzzy sets to manage uncertainty, the logarithmic percentage change-driven objective weighting (LOPCOW) method for weighting expert opinions, and the decision-making trial and evaluation laboratory (DEMATEL) method for prioritization and causal analysis of the barriers, effectively capturing multi-attitudinal perspectives on the issue. Key findings highlight that prevalence of traditional viewpoints and lack of awareness regarding women's rights and policies are primary barriers, with causal impact analysis indicating that factors lack of awareness regarding women's rights and policies, economic inequality and lack of access to education and training stem from the causes prevalence of traditional viewpoints, lack of effective laws and policy interventions, explicit and implicit biases, and lack of male engagement in gender equality. Sensitivity analysis confirms the decision framework's robustness and helps determine the barriers' final rank ordering. The framework proposed not only advances decision-making in gender studies but also offers a readily applicable tool for policymakers focused on gender equality, making a valuable contribution to research on discrimination reduction strategies.

PurposeThe trend of outsourcing heavy equipment maintenance, including belt conveyor installations, is on the rise. Owners and operators of such systems, on the other hand, face a difficult problem in finding a competent contractor to whom these maintenance services might be outsourced. This study proposes a framework for selecting a suitable contractor for the outsourcing of maintenance of belt conveyor installations.Design/methodology/approachThrough a review of the literature, field visits and expert opinion, this article defines the selection criteria for contractors performing outsourced maintenance tasks on belt conveyor installations. To pick the best contractor among options, a hybrid structural methodology incorporating AHP and PROMETHEE-II is used.FindingsThe article has identified seven criteria that affect the selection of maintenance contractors for belt conveyor systems, including financial health, organizational strength, service track record, maintenance service quality, personal skills, tools and equipment support and health, safety, and environment. Each criterion is built upon several sub-criteria. AHP methodology has been used to rank the selection criteria and the methodology of PROMETHEE-II has been used for selecting the best contractor among alternatives.Practical implicationsA comprehensive list of selection criteria for outsourced maintenance activities of belt conveyor installations has been established. Following that, a framework for selecting the best contractor among alternatives is created. This will assist the organizations that own and operate these installations in making informed decisions about the hiring of suitable maintenance contractors.Originality/valueUsing the input of various stakeholders, this research considerably contributes to the literature on outsourcing maintenance of heavy machinery installations such as belt conveyor systems. Such a study will lead to the formulation of guidelines for the selection of maintenance contractors.

PurposeThis study examines the effect of digital technology adoption on the performance of dairy cooperatives. It assesses the advantages and effects of digitalisation on intermediary variables, which subsequently influence outcome variables such as financial, operational, and firm-level employee performance.Design/methodology/approachThe research employed a quantitative, explanatory design, collecting data using a questionnaire survey. A structured questionnaire was administered to 996 senior officials of dairy cooperative societies of South India. SmartPLS was used for structural equation modelling. An importance-performance matrix analysis (IPMA) of path modelling was conducted to evaluate the effect of exogenous variables.FindingsThe study's findings indicate that digitalisation is a crucial factor influencing organisational performance, encompassing financial, operational, and employee performance. Digitalisation directly influences the integration of digital infrastructure, human resources, business processes, marketing and sales, external stakeholders, supply chain and logistics, and strategic decision-making.Research limitations/implicationsThis study evaluates a theoretically relevant, refined conceptualisation of digitalisation performance effects. It also illustrates an improvement in the digitalisation performance network, emphasising the specific relationships between digitalisation and organisational performance outcomes, thereby making several significant contributions to the field. The primary contribution of this research is the unified model, which incorporates operational and managerial practice dimensions along with various performance outcomes-financial, operational, and firm-level employee performances. This study demonstrates the interplay of factors in digitalisation that contribute to the enhanced performance of dairy cooperatives. We extend the findings of Abou-Foul et al. (2021), Alma et al. (2018), Alzadjali and Elbanna (2020), Caldera et al. (2021), Hughes et al. (2006) and Redoli et al. (2008) by illustrating that enhanced financial performance in dairy cooperatives is predominantly achieved through the proficient digitalisation of operational and managerial practices, with optimal results observed in the areas of ISCLM and IPI. Unlike previous studies in various industries by Bhatt et al. (2019), Lopez-Nicolas and Soto-Acosta (2010), and Mohd Salleh et al. (2017), which do not examine the extent of adoption, the present study has extensively taken into account the level and degree of digitalisation.Practical implicationsDigitalisation is of utmost importance in financial, operational, and employee performance, emphasising its pivotal role in the success of contemporary organisations (Martinez, 2019). The research emphasises the significance of supply chain and logistics management (ISCLM) for optimising operational performance (Zhang et al., 2022), whereas marketing and sales integration (IMS) and human resources integration (IHR) need improvement. These data may inform strategic decision-making by emphasising high-performance sectors and rectifying underperforming regions, therefore enhancing operational efficiency and staff performance. The study emphasises the necessity of prioritising digital transformation initiatives, leveraging the predictive capabilities of digitalisation for strategic decision-making, and perpetually enhancing performance through targeted management efforts in areas characterised by high impact and high performance (Bresciani et al., 2021). Digitalisation exerts diverse impacts on intermediary variables, including ISCLM and IPI. Managers ought to prioritise investments in digital tools that directly influence these factors, such as ISCLM and IPI, due to their substantial effect sizes.Social implicationsThe study underscores the importance of digital adoption in the dairy cooperative industry, highlighting its practical benefits such as improved operational efficiency, strategic decision-making, and workforce development. Digital tools can optimise supply chains, inventory management, and finance procedures, enhancing overall performance. Managers can use predictive analytics to make informed business decisions and mitigate risks. Training programs and capacity-building activities are crucial for enhancing digital literacy and technical skills among employees. Digitalisation can improve dairy farmers' livelihoods by increasing market access, real-time pricing data, and financial inclusion. Digital marketing and sales channels can reach a wider consumer demographic, enhancing product availability and quality assurance. The research emphasises the need to close the digital divide in rural regions, emphasising education and infrastructure for equitable access to technology. The research highlights the significance of socioeconomic inclusion in capacity-building initiatives and digital literacy education, alongside ethical issues such as data protection and equitable access to digital resources. The implementation techniques may include public-private collaborations and incentives for adoption via metrics of performance associated with technology utilisation.Originality/valueThis research is the first empirical study on the effects of digitalisation on the dairy cooperative sector in India, although prior studies have addressed digitalisation in other industries. It examines the impact of digitalisation on the perceived organisational performance of dairy cooperatives and the degree of variation within the sector. The model's predictive capability facilitates managerial recommendations, highlighting the importance of predictive analytics in the dairy sector.

Climate change is a pressing issue globally, and countries are implementing zero-carbon measures (ZCMs) to combat it. The ranking of such measures is essential, as it facilitates policymakers in preparing action plans for sustainability. As a variant of the hesitant fuzzy set (HFS), the probabilistic hesitant fuzzy set (PHFS) is utilized, which considers both hesitancy during rating and the associated confidence level for each value. Logarithmic percentage change-driven objective weighting (LOPCOW) and simple weighted sum product (WISP) are cuttingedge weighting and ranking methods for multi-criteria decision-making (MCDM), respectively. This study aims to rank ZCMs to achieve SDG13 via a novel integrated LOPCOW-WISP multi-criteria method with PHFSs. So far, there has been no work on developing LOPCOW and WISP methods under PHFSs, and no PHFS-driven research has evaluated ZCMs. Regarding the findings, sustainable agriculture is the foremost measure, followed by research and innovation and decarbonizing industry. Sensitivity and comparison analyses are further conducted to realize the method's robustness. The findings can also help to shape the practical and future end-use vision for energy resources, allowing for significant advantages while incurring zero-carbon emissions costs. Policymakers can readily use this framework in logical decision-making processes. Furthermore, the study proposes an ideal measurement method that prioritizes technological, agricultural, and research and development factors, along with concerns like energy efficiency, to achieve the goal of zero-carbon emissions.

PurposeAgricultural supply chains (ASCs) play a significant role in improving farmers' incomes and responsively meeting customers' growing needs. Even when customers are ready to pay a premium, ASCs do not always make the most of the situation by facilitating efficient and effective deliveries of fresh produce. This leads to a decrease in farmers' sustainable income. Consequently, this study aims to find and evaluate agri-supply chain management strategies (ASCMSs) that can help farmers increase their incomes in a sustainable way so they can meet their customers' growing demands.Design/methodology/approachThis study adopts a hybrid methodology for analyzing the ASCMSs. It incorporates the fuzzy analytic hierarchy process (FAHP), performance value analysis (PVA), total interpretive structure modeling (TISM) and Fuzzy Matriced' Impacts Croises Multiplication Appliquee a un Classement (FMICMAC). The integrated methodology is used to prioritize ASCMSs, categorize them and establish structural relationships through multidimensional models.FindingsThis research has generated a driver dependence diagram and structured digraph of ASCMSs. It serves to illustrate which ASCMSs are critical and should be prioritized in order to augment farmers' incomes. Agricultural Supply Chain (ASC) practitioners and policymakers can improve farmers' incomes by providing Cold Chain Infrastructure (CCI), Supply Chain Coordination (SCC), Material Handling and Maintenance (MHM) in an effective manner. The driver-dependent diagram supports this, showing that while the Reduced Obsolescence at Stakeholders (ROS) has a stronger dependence power, the CCI, SCC and MHM have a stronger driving power in increasing farmers' incomes.Research limitations/implicationsImplementing these strategies can lead to food security, a reduction in rural poverty and community development. Although the study is pertinent to developing and rural economies, any other business can use the suggested integrated framework. This study provides guidelines for policymakers and practitioners to enhance agri-supply chain effectiveness for improving farmers' sustainable income.Originality/valueThe study's particular focus has been on determining and evaluating the strategies that can more effectively increase farmers' earnings for the benefit of society and agribusinesses. Higher farmer earnings led to sustained development in rural areas of agrarian economies.

Employers are increasingly including information about their corporate social responsibility (CSR) effort (the CSR action) and its impact (results of the CSR action) in recruitment messages to attract job seekers. However, in contemporary times, when all employers are using these CSR claims to differentiate themselves in the labor market, evaluating the effectiveness of CSR messages in attracting job seekers becomes important. In this research, we suggest that to leverage the potential of CSR messages in attracting job seekers, it is pertinent to pay attention to the nuances of CSR language used in displaying CSR information. Drawing on the literature on language, communication, and organizational attraction, we claim that using personal compared to impersonal language in communicating CSR effort increases organizational attractiveness. Further, we also claim that the language of CSR effort (personal vs. impersonal) interacts with CSR impact's goal framing (positive vs. negative) to influence job seekers' attraction towards the organization, such that it is highest in the personal + negative condition. Using a pilot study, one preliminary study, two experimental studies, and a follow-up study, we also demonstrate that job seekers' anticipated warmth and meaningfulness mediate the above interaction effect. This research provides important theoretical and practical insights for attracting high-quality applicants by effectively communicating CSR initiatives.

Individuals find it difficult to cope with the emotional pain of loss and trauma in their life based on intensity of the events. In this context, mindful based interventions play an important role in mitigating the loss and trauma. With this, the present collection Mindfulness for mitigating loss and trauma aims to investigate the role of mindfulness to mitigate the loss and trauma among individuals. The twelve papers in the collection discuss the study findings on how mindfulness through various contexts, variables and methodologies play a significant role in mitigating loss and trauma. The collection segregated the papers under the broad categories of mindfulness for mitigating loss and trauma among students and young population, mindfulness for mitigating loss and trauma among parents, and mindfulness interventions for larger good. The collection found out the role of mindfulness in mitigating loss and trauma among various groups with support of mindfulness interventions and practices.

PurposeThis study integrates the conservation of resources theory and sustainable career theory to explore career sustainability for spouses of Indian bureaucrats. Two research questions are investigated: (1) What competing demands arise between spouses' career aspirations and their expected role as partners of bureaucrats? (2) What coping strategies do spouses use to navigate the competing demands arising from the severely constrained agency in their work lives?Design/methodology/approachGrounded in a qualitative inquiry, this study draws on semi-structured interviews with 26 spouses of Indian bureaucrats, applying thematic analysis to systematically map their career sustainability challenges and coping strategies.FindingsSpouses face competing demands, including the struggle for personal identity vs their partner's high-status role, career ambitions vs the constraints of bureaucratic life, and parenthood expectations vs the professional growth conundrum. Limited opportunities to build independent networks and persistent resource depletion further endanger their career sustainability. In response, spouses adopt active strategies to acquire resources and passive strategies to conserve them.Originality/valueThis study advances theoretical understanding by demonstrating how external factors, particularly a spouse's bureaucratic career, contribute to personal resource depletion, negatively affecting happiness, health and productivity indicators of career sustainability. Practically, our findings provide actionable insights for HRM professionals, employers (of both bureaucrats and their spouses) and policymakers in India.

Appropriate selection of solar panels is significant to facilitate solar power generation. With the help of this situation, it can be possible to minimize many problems, such as high maintenance and repair costs and lack of efficiency. However, there are very few studies in literature that determine the most importance of these factors. Accordingly, this study makes an evaluation related to the prioritization of solar panels to facilitate solar power generation. For this purpose, a new model has been established by combining different techniques. First, the weights of experts are determined through statistical variance method and selected criteria are weighted via q-ROF-CRITIC methodology. After that, solar panel types are ranked by using q-ROF-CRADIS method. The main contribution of this study is that a comprehensive analysis can be performed to generate an appropriate priority to select the viable solar panels with a novel framework. Regarding the methodological contribution, the use of CRADIS technique in the ranking of alternatives enhances the resemblance to human-driven decision making. Similarly, the biggest advantage of CRITIC is that the interactions among criteria are considered, and the proposed ranking algorithm enables choice-based ranking of solar panels that provides a sense of personalization during prioritization. The findings indicate that temperature coefficient is the most important factor to select the appropriate solar panel types. Similarly, availability also plays a key role in this context. On the other hand, polycrystalline silicon solar panel and thin film solar panel are found appropriate for the investment within the solar energy market.

Solar is seen as a potential clean energy solution as world leaders shift energy sources to meet sustainability goals and country demand. Site selection for solar farms is a crucial and complex decision problem involving multiple attributes. Previous studies on site selection (i) could not model natural language preferences; (ii) did not effectively capture expert hesitation; (iii) were prone to the subjective biases of experts; (iv) involved indeterminate weighting of hybrid attributes; and (v) lacked personalized ranking of farms. Motivated by these gaps, this study proposes an integrated decision-making approach using a double-hierarchy hesitant fuzzy context. Firstly, experts' weights are methodically derived via a regret/rejoice measure. Secondly, attributes' weights are determined via an evidence-based rank sum strategy. Thirdly, an algorithm to rank solar farms is formulated with a combinative distance-based assessment (CODAS) and the Copeland scheme. Lastly, a case example of sites from India exemplifies the approach's usefulness. Sensitivity analysis and comparison reveal the pros and cons of the developed approach. Results infer that Vellore and Ramanathapuram are two potential locations where a solar farm can be installed, and annual solar irradiation, technology, distance from river, sunshine hours, and land cover are the top five attributes that aid in solar farm location selection.

Background:Cyberbullying continues to take its toll on social media users, as dark traited individuals (narcissism, psychopathy, machiavellianism) continue to leverage the evolving modes of technology-driven social engagement for the exploitation of others.Purpose:In a similar direction, this study theoretically discusses and meta-analytically examines the strength and nature of the relationship between the Dark Triad and cyberbullying.Method:The study synthesises the relationship between dark triad and cyberbullying spread across close to two decades, from 2005 to 2023, and tests the moderating effect of age, gender, and cyberbullying scale reliability.Result:It was observed that all dark traits had a significant positive relationship with cyberbullying, with psychopathy being the strongest predictor, followed by Machiavellianism and narcissism. Further, the estimates of psychopathy and narcissism displayed low to moderate heterogeneity, which was primarily explained by age and gender.Conclusion:We discuss three probable competing explanations for the dark triad - cyberbullying relationship, namely, low empathy, low agreeableness, and low honesty-humility and advance three important research questions geared towards classification of an ever-expanding repertoire of anti-social online behaviours, rationalisation of definitions and measures used to measure cyberbullying, and distinguishing the effect of the dark core and dark traits effects on cyberbullying.

Blockchain technology (BCT) is emerging as a disruptive innovation in increasingly globalized business ventures. It is believed to offer important benefits to international entrepreneurs, such as reducing transaction cost and risk of fraud, creating better transactional trust, and avoiding exploitation by business partners. However, BCT can bring changes to the business world only if it gets adopted by industries across all spectrums. International entrepreneurs are amongst the critical groups of adopters of BCT. Based on multiple theoretical perspectives, this article identifies and categorizes key barriers to adopting BCT by international entrepreneurs. A survey of BCT field experts was conducted to validate 15 key barriers to BCT adoption. Furthermore, based on pairwise comparison data collection with three groups of international entrepreneurs from India and U.K., a decision-making methodology was adopted to identify the relative importance and the causal relationships among the key barriers. This study develops a framework of strategic solutions to address barriers of BCT adoption by international entrepreneurs. This study offers stakeholders of BCT to better understand the patterns of BCT adoption by international entrepreneurs and will support the development of more targeted and multifaceted BCT adoption strategies.

Big data and computational technologies are increasingly important worldwide in asset and investment management. Many investment management firms are adopting these data science (DS) methods and technologies to improve performance across all investment processes. A good question is whether we can make better decisions in developing quantitative strategies. Therefore, the main objective of this research was to develop a multicriteria assessment framework and scoring decision support system to evaluate quantitative investment strategies that apply machine learning (ML) and DS techniques in their research and development. Subject matter experts will assess all framework perspectives from a systematic literature review to approve their reliability. The perspectives consist of economic and financial foundations, data perspective, features perspective, modeling perspective, and performance perspective. The research methodology applied was the hierarchical decision model (HDM) to provide a 360 degrees view of the quantitative investment strategy and improve and generalize the concept to other asset classes and regions. This study accomplished a rigorous integration of an extensive literature review connecting DS, ML, and investment decision-making in developing quantitative investment strategies. As a result, the major contribution of this study is the comprehensive examination, which included identifying and quantifying perspectives and criteria. The results, while limited indicated significant gaps in strategies examined and therefore generated critical knowledge to improve ML/DS-driven investment strategies, which are valuable for financial companies and policymakers.

This study examines energy efficiency investments in smart manufacturing. For this purpose, a hybrid decision framework is put forward within the double hierarchy linguistic context. The hybrid model encompasses methods for rational determination of decision parameters, viz., experts' weights, criteria weights, and ranks. The research gap in methodically determining experts' weights, along with considering the interdependencies among experts, is addressed by DHHFL-CRITIC, which utilizes correlation and variance measures to overcome this challenge. Likewise, the gap of fluctuations or spikes in preferences owing to extreme scale values is nullified through DHHFL-LOPCOW, which uses log functions to smooth spikes. Finally, the computational complexity gap during rank determination is circumvented by DHHFL-OPARA, which eliminates the normalization procedure, thereby reducing the complexity during rank determination. These methods together form the framework, and the framework analyzes investment options in energy efficiency. The findings of this study have important implications for both theory and practice. In the theoretical sense, a new model is proposed to contribute to the literature. Thanks to the latest model, reliable results can be obtained. In practical terms, a set of criteria affecting energy efficiency in smart manufacturing processes is defined, and the most optimal strategies affecting energy efficiency in these processes are identified. The analysis results with the new model show that the most influential criteria are maintenance and idle time, along with the green building design and energy monitoring system being the key options for investments to support energy efficiency with smart manufacturing.

The course allocation process is critical to the administration and the institute's students. Students should be able to learn the courses that align with their genuine interests to encourage a conducive class environment and better lifelong career opportunities. The course allocation literature accounts primarily for quantitative studies, and no systematic literature review (SLR) has been conducted thus far to capture the intellectual structure of the field. With that as motivation, this paper aims to produce an SLR on course allocation literature supporting Sustainable Development Goal 4, dedicated to quality education. 183 articles are extracted from the beginning to the year 2025. A combination of qualitative and quantitative methods is employed using bibliometric analysis and content analysis to examine the intellectual structure and chronological development in the field of course allocation literature, which is still absent from the existing knowledge base. Most influential and contributing articles, authors, and countries are identified along with decade-wise prominent themes. Findings highlight how course allocation depends on factors such as resources, students' preferences, performance, and career objectives. The current trend shows that AI, optimization methodologies, and equitable allocation algorithms can assist educational institutions in planning courses more effectively, reducing expenses, and enhancing student satisfaction. The study suggests that school administrators or program-level decision-makers should adopt the most appropriate and current methods to guarantee a fair and equal distribution of courses.

PurposeThe present study evaluates the sustainable fiscal policy, specifically applied to environmental taxes and their influence on the ecological footprint of Japan. Additionally, the study takes into account economic growth and energy consumption.Design/methodology/approachUsing the Autoregressive Distributed Lag (ARDL) model, along with Dynamic Ordinary Least Squares (DOLS) as a robustness method within the time frame of 1995-2022, the existence of a cointegration and a long-term and short-term impact of sustainable fiscal policy, energy consumption, and economic growth on ecological footprint are validated.FindingsEmpirical analysis demonstrates the existence of a long-run relationship among sustainable fiscal policy, energy consumption, economic growth, and ecological footprint. The finding of an inverted N-shaped relationship between sustainable fiscal policy and ecological footprint suggests the existence of an Environmental Laffer Curve (ELC). In both the short run and the long run, energy consumption leads to greater ecological footprints. Correspondingly, economic growth is in line with the Environmental Kuznets Curve (EKC), showing that environmental degradation will first increase and then decrease over the long term.Practical implicationsIt is important for policymakers to apply a gradual technique. At the first stage, the imposition of environmental taxes accompanied by investments in green infrastructure, tax reductions, and education programs can instantly lessen the ecological footprint. Eventually, the relaxing of fiscal tightness coupled with the support of private sector innovation and circular economy practices can hold the environmental benefits. Furthermore, energy conservation policies, the spread of renewable energy technologies, carbon pricing, and demand-side management are suggested to reduce the environmental effects associated with energy use. The economic policies should combine the use of regulatory and market-based instruments that are backed by awareness campaigns and international collaboration in order to foster sustainable development.Originality/valueThe study presents novel revelations about the non-linear impacts of Japan's sustainable fiscal policy on its ecological footprint, thus demonstrating the existence of an ELC and also suggesting practical ways to have a better environment and economy.

Industry 5.0 (I5.0) presents opportunities for streamlining processes, reducing waste, improving resource utilization, and enabling real-time monitoring and decision-making in the supply chain. This study proposes a strategy for information-sharing among pharmaceutical supply chain partners, leveraging I5.0 technologies and circular economy strategies like redistribution of unused medicine to reduce waste. This will enable real-time demand data sharing throughout the SC, tracking the expiration date, disposal, and redistribution of returned products. The study identifies 10 drivers for the adoption of I5.0 and the circular pharmaceutical supply chain (CPSC) to reduce medicine waste applying a Technology-Organization-Environment (TOE) framework in consultation with pharmaceutical supply chain professionals, analyzing their relationships using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method. The findings reveal that recognition of the significance of I5.0 and government-supportive policies for circular supply chain and I5.0 are the most influential cause drivers, exerting significant influence on other drivers. Policies such as tax reductions, funding opportunities, and the development of robust digital infrastructure are essential to accelerate adoption. The findings highlight that strengthening these drivers will enhance the adoption of I5.0 and CPSC, thereby reducing medicine waste. This study contributes to the literature in several significant ways by integrating I5.0 and circular supply chain strategies within the pharmaceutical sector to reduce medicine waste, addressing a critical research gap. The proposed framework supports practitioners, policymakers, and industry associations in formulating strategies for adopting I5.0 and CPSC, enabling the reduction of medicine waste and contributing to sustainable development by aligning with environmental goals.

Purpose-The arrival of the Metaverse is expected to revolutionize organizational practices, which substantially impact sustainability in logistics and supply chain. In addition, prescriptive analytics-based methodological improvements might make Metaverse self-sustaining. This study assesses the current reflective discussion about the function of prescriptive analytics in Metaverse. It proposes alternative streams for additional research in this area so that we can understand the relationship between Metaverse, prescriptive analytics, sustainable operations and supply chain. Design/methodology/approach-We use structural topic modeling (STM), a text-mining approach, to critically assess the literature and analyze 161 articles. Findings-Primary and secondary topics were developed using STM findings for comparison. Also, a research framework is created by sketching out the study following the findings of the review. Finally, we conclude with a list of unanswered research issues that might serve as a starting point for future investigations into the role of prescriptive analytics in empowering Metaverse for sustainable operations. Originality/value-This study provides original insights into how prescriptive analytics can drive sustainable operations through Metaverse, offering a roadmap for future empirical research in this emerging area.

Financial technology (fintech), communication, and digital payments have significantly contributed to the evolution of e-commerce. Inspired by the rapidly growing innovations in e-commerce models induced mainly by fintech and digital payment firms, this study examines the time-varying causality and linkages between fintech stocks (KBW Nasdaq Financial Technology Index) and firms in future payments and future communication businesses (S&P Kensho Future Payments Index and S&P Kensho Future Communication Index) using a battery of three robust estimation techniques with daily data from May 15, 2017, to May 7, 2022. To this end, the rolling window wavelet correlation results demonstrate the existence of positive market correlation at short and medium horizons, mainly between fintech and future payment firms, and a variety of causal relationships, with a notable decrease during the COVID-19 crisis due to the decrease in e-commerce. However, Granger causality between the markets was noticed at different market periods and frequencies. Additionally, the influence of phase synchronization and frequency specificity in the causation between analyzed assets also supports the positive relationship among analyzed assets, implying power predictability among them. These findings have managerial, theoretical, and policy implications, especially in terms of the improved implementation of these new technologies in firms' financial structures.