The investigation intends to contribute to the growing interest among academics and practitioners worldwide by advancing knowledge of how manufacturers can improve their 3P (planet, profit, and people) performance through systemic mechanisms. The objectives of this research are to (1) assess the effect of lean six sigma practices and green culture on developing circular manufacturing capabilities among manufacturers and (2) assess how such developed circular manufacturing capabilities translate into better 3P performance in the backdrop of Industry 4.0 technologies. In pursuit of the stated objectives, the research team conducted a survey of 232 manufacturers and analyzed the collected data using the structural equation modeling approach. The assessment demonstrates that the enactment of lean six sigma practices and the adoption of a green culture have a significant and positive influence on the development of circular manufacturing capabilities. This, in turn, leads to improvements in environmental, economic, and social performance (i.e., 3P performance). Further, it has been found that the utilization of Industry 4.0 technologies by manufacturers has a significant moderating effect on the impact of their circular manufacturing capabilities on their 3P performance. The results have substantial implications for administrators looking to streamline their adoption of Industry 4.0 technologies and lean six sigma practices and cultivate a green culture to enhance their competitive advantage and boost their reputation.

PurposeThis study investigates the mediation effect of financial well-being on the relationship between financial literacy, personal financial management behaviour and investment decision-making behaviour among women entrepreneurs in India.Design/methodology/approachA sample of 396 rural women entrepreneurs from four rural-dominated districts in Bihar, specifically Gaya, Muzaffarpur, Nalanda and Madhubani, was randomly selected for the study. The data collected underwent analysis using the partial least square structural equation modeling (PLS-SEM).FindingsThe study found a positive significant relationship between financial literacy and financial well-being in enhancing investment behaviour and personal financial decisions of self-employed rural women in Bihar. This underlines the importance of financial literacy and well-being in addressing rural women's investment decision-making and personal financial management behaviour.Practical implicationsAcknowledging the critical importance of financial literacy and overall well-being, policymakers can develop focused financial literacy interventions and education initiatives to improve rural women entrepreneurs' knowledge and financial skills in Bihar. The study further contributes substantially to the existing body of research, especially the Theory of Planned Behavior.Originality/valueUnderstanding rural women entrepreneurs' financial literacy, investment decision-making behaviour, personal financial management behaviour and financial well-being is novel since it propels modern society's economic and social advancements in the emancipation of women.Peer reviewThe peer review history for this article is available at https://publons.com/publon/10.1108/IJSE-05-2024-0434.

While a lot of hype surrounds novel technologies such as big data and cognitive analytics, their adoption in organisations and value appropriation is often a concern. Recent research has noted that cognitive analytics can help leverage value from big data, while a different strand of research emphasises the need for organisations to develop capabilities around emerging technologies such as cognitive analytics. Focusing on cognitive computing-enabled analytics, this paper uses the resource-based view (RBV) and the resource-orchestration theory to conceptualise a cognitive analytics capability and identifies its underlying resources. This paper combines insights from extant literature and in-depth interview data collected from 44 domain experts across six organisations, analysed using thematic analysis. The analysis reveals four categories of tangible resources, four categories of human resources, and three categories of intangible resources underlying a cognitive analytics capability. Further, resource management actions in terms of structuring the resource portfolio, bundling and leveraging of resources are identified. This paper engages with the big data productivity paradox and contributes to the literature on business value of IT, resource-based view, cognitive analytics, and IT capabilities. By unpacking the resources and resource management actions underlying cognitive analytics capability, this paper has prescriptive value for managerial practice.

Manufacturing entities in emerging economies face significant obstacles implementing circular economy principles due to resource-constrained environments. Arguably, manufacturers need customer green pressure to propel the circular economy, alongside frugal innovative strategies. There is a dearth of research on digital platform (DP) capabilities, customer green pressure, and frugal innovative strategies with circular economy. Drawing on dynamic capabilities theory, 889 manufacturers were surveyed in the emerging economy of India. Customer green pressure moderation was assessed on the links between frugal innovative strategies and circular economy, DP integration capabilities and frugal innovation, and DP reconfiguration capabilities and frugal innovative strategies. Structural equation modelling revealed that DP integration and reconfiguration capabilities positively influence frugal innovation strategies enabling circular economy performance. Customer green pressure positively moderated DP reconfiguration and frugal innovative strategies. Actionable insights are provided to prioritize DP capability development to satisfy customer green pressure and promote frugal innovative strategies, ultimately facilitating the circular economy.

PurposeThe agility of the global logistics and supply chain (SC) environment, characterized by metaverse adoption, redefines business models and supports current business operations. For the successful integration of technology into current operations, it is essential to understand the managerial challenges associated with adopting and scaling the metaverse.Design/methodology/approachThis study employs a detailed literature review and a case study method, utilizing elite interviews with industry experts to explore real-time managerial issues in metaverse adoption.FindingsThe literature findings were coherently classified into themes. The three themes were idiosyncratic arrangements, intra-organizational and contextual specificity. Grounded on dynamic capabilities theory, these themes were conceptualized as managerial challenges and actions at each stage - accelerate, enable and mobilize. Further, the case-based investigation validated the framework.Research limitations/implicationsThe framework may extend the researcher's interest to test it in different contexts and strengthen its interdisciplinary nature.Practical implicationsThe study can aid managers with specific capabilities while enhancing their understanding of metaverse adoption in the logistics and SC industry in volatile conditions.Originality/valueThe managerial aspects of metaverse adoption have not been explored in the core of any research study. Most metaverse adoption studies highlight operational and organizational challenges but fail to highlight managerial challenges. This would be a one-of-a-kind study to address managerial challenges and propose capabilities-driven business solutions.

Humanitarian supply chains (HSCs) have undergone significant changes over the years, shifting from traditional systems to more intelligent and, eventually, AI-enabled operations. With technological advancements accelerating across sectors, humanitarian organizations have also begun adopting artificial intelligence (AI) to enhance their workflows, improve efficiency, and reduce losses. While much of the existing research has focused on the benefits of AI in business and logistics, there is still limited understanding of its potential downsides-particularly within humanitarian settings. This study addresses that gap by exploring how AI may negatively affect HSC activities, both at the individual (micro) and organizational (macro) levels. To guide our analysis, we draw on the Belief-Action-Outcome (BAO) framework, which helps connect personal and institutional beliefs to actions and resulting outcomes. Humanitarian supply chains operate in complex environments where technology use intersects with human behavior, organizational culture, and social values. To better understand these dynamics, we conducted qualitative interviews with professionals working in humanitarian organizations. These insights allowed us to identify and map various challenges-what we refer to as the dark side of AI-onto specific functions within HSC operations. Our findings not only highlight areas of concern but also contribute to the broader application of the BAO model in the humanitarian field.

To achieve carbon neutrality, it is crucial that private commercial and noncommercial firms also do their bit to adopt and implement green projects (GPs). Nonetheless, there is a lack of scholarly understanding regarding the elements that influence the implementation of GP by private firms, as well as the barriers to GP adoption and strategies to overcome them. This study seeks to fill the existing knowledge vacuum by examining open-ended essays to provide useful perspectives into the experiences and viewpoints of organizations that have implemented GP. The qualitative data collected were analyzed to determine four unique dimensions, including a variety of elements connected to GP implementation. (a) The study identifies four enablers that push firms to go for GP and three critical areas during the evaluation. (b) The study further identifies four major stumbling blocks of resource management, project viability, external issues, and integration challenges that affect GP and three risk management strategies to overcome these. Our study adds essential theoretical and practical perspectives to the ongoing debate about implementing GP in a comprehensive and inclusive manner.

Firms increasingly pursue operational circularity to achieve sustainable growth amid volatile and uncertain business conditions. Although embracing a design for circularity strategy is recognized as important for enhancing operational outcomes, the mechanisms by which boundary-spanning search influences circularity performance are not well explored. This study, rooted in the resource-based view and dynamic capability view theories, investigates the effects of both proactive and reactive boundary-spanning search on the circularity performance of firms, considering the mediating role of design for circularity and the moderating influence of artificial intelligence-powered analytics. Survey data from 196 manufacturing firms in India indicate that proactive boundary-spanning search directly enhances circularity performance, and both forms of boundary-spanning search positively affect design for circularity. Notably, design for circularity mediates the relationship between proactive search and performance. Artificial intelligence-powered analytics capabilities further strengthen the positive impact of proactive search on circularity outcomes. Conversely, several relationships were found to be nonsignificant: Reactive boundary-spanning search showed no direct effect on circularity performance, and design for circularity did not significantly mediate the effects of reactive search. Additionally, artificial intelligence-powered analytics did not moderate the impact of reactive search nor did firm age, size, or sector significantly affect circularity outcomes. These findings illuminate the context-dependent nature of implementing circular strategies and underscore that only certain combinations of search behaviors and analytics capabilities drive operational benefits. The results highlight the strategic value of artificial intelligence technologies and design for circularity in fostering sustainable manufacturing practices.

Purpose-This study aims to explore the growing trend towards subtle and inconspicuous forms of luxury consumption in emerging markets and addresses two questions related to this new phenomenon. First, how does inconspicuous consumption (IC) relate to the consumption of personal luxury goods? Second, what are the antecedents that motivate IC? Design/methodology/approach-The study uses a qualitative interview methodology using an interpretive paradigm. Data are collected through face-to-face semi-structured interviews with 17 regular luxury consumers in India. Findings-Findings revealed that while traditional luxury refers to expensive and exclusive products that have a strong brand value; in recent times, mature luxury users redefine luxury around IC and associate luxury consumption with positive emotions and life enrichment. This study grouped the motivational antecedents that drive IC luxury consumption into three categories-consumer, brand and environmental factors. Originality/value-The study contributes to the emerging phenomenon of inconspicuous luxury, which is growing in terms of consumer preferences but inadequately researched. Specifically, to the best of the authors' knowledge, this work is among the first to investigate salient factors impacting IC and mapping them with different goal frames (motives) of Goal Frame Theory. Furthermore, a model is developed that can be used by brand managers to recalibrate and refocus on their strategies to attract this evolving segment.

This study aimed to explore the influence of virtual reality and closed-loop marketing as data-driven strategies for optimizing real estate sector performance. It focused on identifying the factors that influenced the level of awareness of these technologies in the real estate sector. This study explored the perspectives of key stakeholders, namely, developers and agents, in both the commercial and residential sectors. Four latent variables and measures were identified through an extensive literature review. Based on this, a survey instrument was developed, distributed online, and 209 responses were received, including from developers and real estate agents. Exploratory factor analysis was used to validate the latent variables, and covariance-based structural equation modelling was used to measure their influence on the level of awareness. Information accessibility (beta = 0.626) strongly influenced awareness of VR and CLM. Other factors, such as interactive experience quality (beta = 0.322), social influence (beta = 0.291), and system integration complexity (beta = 0.207), had moderate influences. This study contributes to the body of knowledge by providing a validated scale and latent variables that real estate practitioners can use to develop change management strategies.

Prior researchers have extensively explored the factors influencing online impulse buying phenomenon. However, there is a dearth of studies that comprehensively categorize its determinants into impulse buying tendency, social stimuli, platform stimuli, and marketing stimuli. We integrate the findings of 145 prior empirical studies and perform the meta-analysis. We also undertake meta-analytic structural equation modeling to examine the mediating effect of utilitarian and hedonic values. Further, moderator analysis is done using country-level and method-level variables.

PurposeThis paper develops a systematic framework for benchmarking and improving customer satisfaction in hotel services, guiding decision-makers on operational enhancements and effective value-added service deployment.Design/methodology/approachThis research integrates the analytic hierarchy process, gap analysis and integer programming to map customer preferences, identify service deficiencies and create mathematical models for optimizing service improvements, focusing on cost minimization.FindingsThe findings affirm that food quality and service delivery are eminent indicators of guest satisfaction, demonstrating the framework's efficacy in prioritizing interventions and optimizing resource allocation for operational enhancement.Research limitations/implicationsThe study's empirical application is limited to a single entity (a hotel), which restricts immediate generalizability. Future research should explore and validate the model's applicability in multi-site or cross-country settings to enhance its broader practical relevance and theoretical robustness.Practical implicationsThe framework provides hotel managers with a transparent, data-driven decision-support tool. This facilitates prioritized problem identification and cost-optimized resource allocation, fostering continuous improvement and sustainable competitive advantage.Originality/valueThis study provides a novel, data-driven decision-support tool for hotel managers, bridging theory and practice by enabling prioritized problem identification and cost-optimized resource allocation, while suggesting future multi-site validation for broader applicability.

Increasing efforts in emission reduction and innovative practices in the digital economy allow manufacturers to meet Sustainable Development Goals (SDGs) while remaining competitive in the market. However, high investments in energy-efficient technology and the digital economy may impede success. This study considers how Energy Vehicle (EV) firms may cooperate and compete to maximize their profits simultaneously. The manufacturer endows emission curtailment technology to meet the SDGs, while the distributor invests in digital economy efforts to grow the market. Four different EV supply chain structures (i.e., Decentralized and integrated models under competition or cooperation) are considered in this study. The model is solved analytically following a game theoretical approach. Next, the analytical model has been verified and illustrated using multiple simulation runs. This study is aimed at helping EV companies better grasp optimal pricing, greening efforts, and digital investment plans under various market scenarios.

PurposeThe sugar industry is always pressured to be suitable and enhance its operational effectiveness. Digital twin (DT) has the potential to transform this industry. However, the lack of industry-specific key performance indicator (KPI) frameworks makes it challenging to implement them. This study provides a hierarchical KPI model for DT adoption in the sugar sector, interconnecting the United Nations' sustainable development goals (SDGs).Design/methodology/approachIt has two phases: the first is an intense literature review, which identifies 24 relevant KPIs for DT adoption and sets the procedure for expert validation. Second, hierarchical relationships were identified through interpretive structural modeling (ISM) and MICMAC was used to classify KPIs based on driving and reliant power. The developed framework has several dimensions: technology, environmental, strategic, operational, financial and security.FindingsOperational and environmental KPIs relate to important factors; however, environmental, social and governance (ESG) tracking, stakeholder satisfaction, governance, social and environmental factors, and reduction of defects were also critical outcomes. Key drivers came after technological studies, such as integrating old legacy systems, data integration and return on investment, or digital investment. The KPI hierarchy establishes the structural relationship that ensures DT investments align with sustainability goals, including cutting carbon emissions, maximizing resource usage and reducing water consumption.Research limitations/implicationsLikewise, earlier studies, the existing paper is also not free from limitations. We consulted experts from emerging economies like India to develop ISM modeling and collect data. However, the experts' opinions may change from country to country, impacting results. Also, the proposed research is applicable in the sugar sector.Originality/valueDeveloping the DT adoption KPIs in the sugar sector using ISM and MICMAC is a unique contribution of this research. In addition, all KPI frameworks are also interconnected with the United Nations sustainability goals.

This study presents a systematic literature review exploring the role of Digital Twins (DTs) in Circular Supply Chain Management (CSCM). Employing a structured methodology based on the PRISMA protocol and analyzed through the Theory, Context, Characteristics, and Method (TCCM) framework, the review synthesizes 109 peer-reviewed articles published between 2000 and 2024. Using bibliographic coupling, the research identifies six thematic clusters: (1) Integration of DT in Supply Chain; (2) DT and Industry 4.0; (3) Big Data, AI, and Circular Economy; (4) Risk Management and Supply Chain Resilience; (5) Additive Manufacturing and Spare Parts Logistics; and (6) Metaverse and Smart Factory Operations. Additionally, the study categorizes 86 unique theories applied in the DT-CSCM literature, with Resource-Based Theory, Complexity Theory, and Control Theory among the most cited. Key contributions include clarifying the intellectual structure of DT-CSCM research, highlighting emerging research directions, and offering practical recommendations for industry practitioners and policymakers seeking to enhance supply chain sustainability and digital transformation. These deeply meaningful findings considerably improve our comprehension of the rapid pace of technological improvement in DT and its significant implications for the development of resilient, sustainable supply chains.

Connected vehicles present a wide range of applications that have the potential to revolutionize the transportation landscape, offering benefits to drivers, passengers, and transportation systems while enabling innovative business models for automakers. Despite these opportunities, consumer acceptance of Connected Vehicle Technologies (CVT) has progressed at a slower pace than anticipated, creating obstacles for automakers in monetizing the associated data. As a result, it becomes essential to determine the factors influencing consumers' behavioural intention to use CVT, particularly in emerging markets like India. This study addresses this gap by gathering data from various metropolitan cities across India to identify the factors influencing intention to use CVT. Using a multi-method approach incorporating Structural Equation Modelling (SEM) and Fuzzy-Set Qualitative Comparative Analysis (fsQCA), the study reveals that perceived usefulness, perceived ease of use, and brand consciousness serve as core factors driving the intention to use CVT. Meanwhile, perceived enjoyment and social influence function as complementary factors, further supporting this intention. Perceived risks and technology readiness of consumers were not found to influence intention to use CVT. The findings provide valuable insights into consumer behavior concerning advanced transportation technology and offer actionable recommendations to stakeholders to promote the acceptance of CVT.

PurposeThe primary objective of this research is to discern the (sequential mediating) roles of strategic intent (SI) and strategic human resource management practices (SHRMP) in enabling artificial intelligence adoption in human resources (AIAHR) to foster employee engagement (EENG) and experience (EEXP).Design/methodology/approachData were collected from 208 HR professionals working in various industries in the United States using a quantitative research design. PLS-structural equation modeling was used to test the hypothesized relationships.FindingsThis study's results indicate that strategic intent and strategic HRM practices act as mediating mechanisms to channel the effect of AI adoption in HR on enhancing employee engagement and experience. In addition to individual meditation, the strategic intent and strategic HRM practices sequentially mediate the adoption of AI in HR and employee engagement and employee experiences.Practical implicationsThis study highlights the importance of developing a clear strategic intent for AI adoption, implementing strategic HRM practices that align with the strategic intent and prioritizing employee engagement and experience in AI adoption efforts.Originality/valueThe current research significantly enhances the prevailing literature by proposing a model that integrates the key factors influencing AI adoption in HR and provides empirical evidence of the mediating roles of strategic intent (SI) and strategic HRM practices (SHRMP) in this relationship. This study provides practical guidance for HR practitioners and organizational leaders to facilitate successful AI adoption and integration in HR.

PurposeThis paper combines the key aspects of artefact-based systems. DSS and artificial intelligence in a unified approach to solve a typical financial analysis problem within the design science paradigm.Design/methodology/approachOur proposition is based on the seven design science guidelines. Our proposed method artefact is a financial application catering to the investment domain in particular. We present a detailed analysis of stock data from the Indian market for an extended period of 17 years by deploying state-of-the-art algorithms. The use of computational intelligence involving machine and deep learning helps in automatically identifying winning stocks within a value portfolio, that too, on a forward-looking basis.FindingsOur method artefact depicts superior results by identifying outperforming stocks, differentiated from the weak ones, within the value portfolio.Originality/valueThis has significant implications for the investing community, particularly the Indian investors. Traditional research in value stocks has shown underwhelming performance in differentiating lucrative stocks from the rest.

Ensuring a consistent supply of vaccines from manufacture to distribution, storage, and administration relies on efficient management of the Vaccine Supply Chain (VSC). However, in recent years, vaccine shortages have emerged as a major issue for vaccine producers and child immunization programs, especially in low- and middleincome countries, which hampers VSC overall performance. This study aims to address the ongoing problem of vaccine shortages in India's child immunization program by identifying the main causes and investigating possible solutions to address the shortage issue. To do this, we propose an integrated framework that combines the Analytic Hierarchy Process (AHP) and Complex Proportional Assessment with Grey Theory (COPRAS-G) methodologies. This framework yields 12 potential solutions for the 10 issues causing shortages. We show that demand uncertainty is the primary cause of vaccine shortages and that a better monitoring system is necessary to detect and treat shortages in a timely manner. To validate the stability of the results, we run a Monte Carlo simulation using a uniform probability distribution on the interval [0, 1]. The results of this study will provide valuable insights for policymakers on how to effectively manage the vaccine shortage issue and improve the performance of child immunization programs.

In this study, we examine the impact of global economic indicators on connectedness spillovers among energy, metal, and grain commodity markets. Global economic indicators directly create a transmission channel to commodity markets by reflecting financial and trade fluctuations worldwide. Especially in tail regions, basic commodities such as energy, metal, and grain are affected by economic uncertainties, oil supply-demand, and economic policy uncertainty shocks. In particular, Copper emerges as the main net transmitter of shocks in both short- and long-term periods, while oil and gas prices are net shock receivers in the median and extreme right tail regions. This finding strengthens the narrative of the demand-side mechanism of oil prices. On the other hand, Copper and Nickel are clear shock transmitters in tail regions. These results emphasize the interconnectedness of commodity markets and the importance of short-term dynamics in shaping market behavior during downturns and upturns in market conditions. However, economic policy uncertainty significantly attenuates spillover connectedness across energy, metal, and grain commodity markets during periods of heightened complexity. This indicates that EPU disrupts cross-market linkages, leading to desynchronization in price transmissions. Conversely, the Baltic Dry Index and oil prices amplify spillover effects, reinforcing market integration through global demand channels. This finding strikingly indicates that energy, metal, and grain market interconnectedness increases, driven by global aggregate demand. In this context, our study supports the demand-side transmission narrative and provides evidence that economic policy uncertainty does not create momentum among connectedness spillover among energy, metal, and grain commodity markets.