工业4.0:商业和经济学视角下制造业的未来——文献计量学综述

IF 2.9 Q2 BUSINESS Competitiveness Review Pub Date : 2022-09-27 DOI:10.1108/cr-07-2022-0091
Josélia Elvira Teixeira, Ana Teresa Tavares-Lehamann
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引用次数: 0

摘要

目的本文旨在从商业和经济学的角度对工业4.0以及制造业数字化的趋势和影响的文献进行全面、系统的回顾。设计/方法/方法使用文献计量方法,可以严格提取相关文献,从而建立一个有目的的同行评审出版物数据库,描述该领域的最新技术。对文献的关键特征和趋势进行了定量分析,并使用VOSViewer软件作为绘图工具以图形方式呈现结果。确定了相关的专题组,并确定了未来有希望的研究途径。发现这篇文献是最新的——90%的出版物来自2019年和2020年,这一领域的领先期刊是《制造技术管理杂志》。确定的关键集群将数字化转型与价值链联系起来;工业4.0技术和商业模式之间存在密切联系;与可持续性的紧密联系也是显而易见的;毫不奇怪,工业4.0对人力资源管理的影响是极其相关的。原创性/价值据作者所知,这是最全面的系统综述,将工业4.0与商业和经济学文献联系起来,与经济学/商业和技术领域最新的最先进研究相结合。这一贡献可能对有兴趣了解该领域的关键趋势和机遇的研究人员非常有帮助。
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Industry 4.0: the future of manufacturing from the perspective of business and economics – a bibliometric literature review
Purpose This paper aims to provide a comprehensive, systematic review of the literature on Industry 4.0 – and on the trends and implications of the digitalization of manufacturing – from the perspective of business and economics. Design/methodology/approach A bibliometric methodology was used, allowing to extract rigorously the relevant literature, leading to a purposefully constructed database of peer-reviewed publications depicting the state of the art in this area. A quantitative analysis of the key characteristics of – and trends emanating from – the literature was performed, and results were presented in a graphic way using the VOSViewer software as a mapping tool. Relevant thematic clusters were identified, and promising future research avenues were identified. Findings This literature is extremely recent – 90% of the publications are from 2019 and 2020, and the leading journal publishing in this area is the Journal of Manufacturing Technology Management. Key clusters identified relate digital transformation to value chains; a close link is observed between Industry 4.0 technologies and business models; a strong connection to sustainability is also clear; and the implications of Industry 4.0 for human resources management are, not surprisingly, extremely relevant. Originality/value This is, to the best of the authors’ knowledge, the most comprehensive systematic review linking specifically Industry 4.0 to the business and economics literature, bridging with recent, state-of-the-art research in the economics/business and technological fields. This contribution may be very helpful to researchers interested in understanding the key trends and opportunities offered by this area.
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来源期刊
CiteScore
6.60
自引率
17.20%
发文量
50
期刊介绍: The following list indicates the key issues in the Competitiveness Review. We invite papers on these and related topics. Special issues of the Review will collect papers on specific topics selected by the editors. Definition/conceptual framework of competitiveness Competitiveness diagnostics and rankings Competitiveness and economic outcomes Specific dimensions of competitiveness Competitiveness and endowments Competitiveness and economic development Location and business strategy International business and the role of MNCs Innovation and innovative capacity Clusters and cluster initiatives Institutions for competitiveness Public policy (e.g., innovation, cluster development, regional development) The Competitiveness Review aims to publish high quality papers directed at scholars, government institutions, businesses and practitioners. It appears in collaboration with key academic and professional groups in the field of competitiveness analysis and policy, including the Microeconomics of Competitiveness (MOC) network and The Competitiveness Institute (TCI) practitioner network for competitiveness, clusters and innovation.
期刊最新文献
A TOPSIS analysis of regional competitiveness at European level Evaluating Uganda’s preferential market access; trade intensity, comparative advantage and the potential for trade Nexus between corporate governance and FinTech disclosure: a comparative study between conventional and Islamic banks Mapping the intellectual landscape of financial inclusion and sustainable development: a bibliometric analysis Comparative computer simulation and empirical analysis of MIDAS and artificial neural network-UMIDAS models for short- and long-term US GDP forecasting
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