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.

The growing importance of Big Data in the food industry enables businesses to leverage information to gain a competitive advantage. This paper provides a systematic literature review (SLR) to provide an insight into the use of state-of-art of Big Data applications in the food industry. The SLR relies on available literature that provides the context, theoretical construct and identifies gaps. Based on the findings, we suggest recommendations, identify limitations and suggest policy implications and future directions. Using search databases were examined and 38 relevant studies were identified for retrospective analysis. The review shows that Big Data supports the food industry in ways that enable using Artificial Intelligence to manage restaurants and mobile based applications in supporting consumers with restaurant selection. This SLR open new avenues for future research in the importance of Big Data in the food industry, which will surely help researchers/practitioners in effective utilization of Big DataBig Data.

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.