Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach
IF 6.7 1区 计算机科学Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
{"title":"Bridging realities into organizations through innovation and productivity: Exploring the intersection of artificial intelligence, internet of things, and big data analytics in the metaverse environment using a multi-method approach","authors":"Ashutosh Samadhiya , Rohit Agrawal , Anil Kumar , Sunil Luthra","doi":"10.1016/j.dss.2024.114290","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":55181,"journal":{"name":"Decision Support Systems","volume":"185 ","pages":"Article 114290"},"PeriodicalIF":6.7000,"publicationDate":"2024-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0167923624001234/pdfft?md5=358c5d38e7c9ef28ff47eabad293513e&pid=1-s2.0-S0167923624001234-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Decision Support Systems","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0167923624001234","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0
Abstract
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.
期刊介绍:
The common thread of articles published in Decision Support Systems is their relevance to theoretical and technical issues in the support of enhanced decision making. The areas addressed may include foundations, functionality, interfaces, implementation, impacts, and evaluation of decision support systems (DSSs).