This study investigates how organizations may increase innovation and productivity through the Metaverse environment efficacy (MVEE), Artificial intelligence usage (AIU), Internet of Things usage (IoTU), and Big Data Analytics usage (BDAU). The study gathers responses from the gaming, information technology, and entertainment industries, using a multi-method involving Partial Least Squares Structural Equation Modeling, Fuzzy-set Qualitative Comparative Analysis, and Artificial Neural Networks to investigate how these technologies might be used to improve the linking of disparate realities in a business context. The use of AI in personalized and decision-support applications, IoT for real-time data collecting, and BDAU for an insights-driven strategy all combine to create a dynamic MVEE ecosystem. The research also delves into theoretical implications concerning the viability of using the MVEE to boost innovation and productivity. This research identifies the applications of using AI, IoT, and BDA to drive organizational performance in terms of innovation and productivity. Also, the research lays out the role of AI, IoT, and BDA in creating a dynamic metaverse ecosystem.

Metaheuristics have become increasingly popular due to the complex and extensive optimization models that have emerged in the circular supply chain field. This study aims to fill a gap in the literature by conducting a comprehensive analysis of the role of metaheuristics in reverse logistics and circular supply chains. The methodology involves a meticulous review of 77 meticulously selected research articles published until 2023. The findings of our analysis indicate a significant increase in the number of publications in recent times. Through descriptive and content analyses, key themes emerge, including types of metaheuristics used, addressed supply chain issues, and circularity aspects. This study presents a novel approach to analyzing the application of metaheuristics from a circularity perspective. It also introduces a conceptual framework and suggests cluster-based future research directions. The results of the study hold valuable implications for researchers and other stakeholders in terms of understanding and advancement of the field.

Several electronic-engagement-related questions arise at work due to the beginning of a new era of social distancing, lockdowns, quarantining, and sanitization. These terms were not so common before. What challenges do employees face while working from home? Why do they face those challenges? How are they overcoming these challenges? In summary, in a work-from-home setting, what are the issues and solutions in engaging remote workers electronically? To answer these questions, 23 Information Technology (IT) employees in India and four in the United Kingdom were interviewed, and data were analyzed using interpretive phenomenological analysis (IPA). Few Information Technology employees from the United Kingdom were also interviewed to ensure the transferability of the results. Along with a few suggestions, six challenges emerged. These may help employers formulate their electronic engagement strategies for employees in a better manner.

Private, public, profit, and non-profit organizations, and society as a whole currently face a significant reliable information necessity problem. Especially supply chains need trustworthy information to perform their activities successfully. This study aims to propose a framework and identify how reliability of information can be evaluated and measured through the concept of transparency. In this context, dimensions such as; comprehensiveness, regularity, timeliness, content, scope, and user-friendliness are the pillars of the proposed framework. Selected criteria have been used as inputs to develop the information transparency level. The Fuzzy Analytic Network Process (ANP) is used to obtain weights of these inputs, and Data Envelopment Analysis (DEA) is used for the determination of the efficiency ranking for transparency. Results demonstrated that Content, Scope and Comprehensiveness dimensions have 75% impact on the transparency of data. Remaining 25 percent is affected by Timeliness, Regularity and User-friendliness.

The objectives of this study are to mitigate the risk and disturbances to the supply chain, to offer required models for resolving the complex issues that arise, and to maintain the stability of the support system. Also, the uncertain conditions in a supply chain force decision-makers and experts to adopt a fuzzy-based evaluation platform to ensure secure and reliable consequences. The current study proposed a fuzzy neutrosophic decision-making approach for supplier evaluation and selection. The model is composed of a new weight aggregator that uses pairwise comparison, which has not been reported to date. The model uses a Dombi aggregator that is more qualified than other aggregators. The Dombi t-conorms and t-norms have the same properties as those of the general t-conorm and t-norm, which can enhance the flexibility of the information aggregation process via the adjustment of a parameter. A decision-making environment with uncertain conditions and multiple factors is supposed. We applied this approach in a construction company to analyse the suppliers in a resilient supply chain management (RSCM) system using a MABAC (multiattribute border approximation area comparison) tool. The accuracy of the proposed model was examined via sensitivity analysis tests. This study proposes a novel fuzzy-neutrosophic-based approach for resilient supplier selection. The main contributions of this study work are the design, implementation and analysis of a multiattribute evaluation system with respect to fuzzy neutrosophic values. In this evaluation system, a new pairwise comparison is conducted with trapezoidal neutrosophic linguistic variables to determine the importance weights of supplier criteria. Typically, the provision of opinions regarding the qualitative performances of suppliers is a difficult and confusing responsibility for experts and supplier evaluators. Therefore, the proposed approach overcomes this problem by utilizing a pairwise comparison by neutrosophic values and proposes original Dombi aggregation operators for dealing with fuzzy neutrosophic sets.