通过创新和生产力将现实与组织连接起来:使用多种方法探索元环境中人工智能、物联网和大数据分析的交叉点

IF 6.7 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Decision Support Systems Pub Date : 2024-07-26 DOI:10.1016/j.dss.2024.114290
Ashutosh Samadhiya , Rohit Agrawal , Anil Kumar , Sunil Luthra
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引用次数: 0

摘要

本研究探讨了组织如何通过元宇宙环境效能(MVEE)、人工智能使用(AIU)、物联网使用(IoTU)和大数据分析使用(BDAU)来提高创新力和生产力。本研究收集了游戏、信息技术和娱乐行业的反馈,采用偏最小二乘法结构方程建模、模糊集定性比较分析和人工神经网络等多种方法,研究如何利用这些技术改善商业环境中不同现实之间的联系。人工智能在个性化和决策支持应用中的使用、物联网在实时数据收集中的使用以及 BDAU 在洞察力驱动战略中的使用,共同创造了一个充满活力的 MVEE 生态系统。研究还深入探讨了使用 MVEE 促进创新和生产力的可行性的理论意义。本研究确定了使用人工智能、物联网和 BDA 在创新和生产力方面推动组织绩效的应用。此外,研究还阐述了人工智能、物联网和 BDA 在创建动态元宇宙生态系统中的作用。
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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

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.

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来源期刊
Decision Support Systems
Decision Support Systems 工程技术-计算机:人工智能
CiteScore
14.70
自引率
6.70%
发文量
119
审稿时长
13 months
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).
期刊最新文献
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