scholarly journals Mapping of the Emergence of Society 5.0: A Bibliometric Analysis

Organizacija ◽  
2021 ◽  
Vol 54 (4) ◽  
pp. 293-305
Author(s):  
Vasja Roblek ◽  
Maja Meško ◽  
Iztok Podbregar
Keyword(s):  
Big Data ◽  
Bibliometric Analysis ◽  
Industry 4.0 ◽  
Research Question ◽  
Future Research ◽  
Physical Systems ◽  
Digital Service ◽  
Current Characteristics ◽  
Further Development ◽  
Insight Into

Abstract Background and purpose: The study aims to answer a research question: With which essential cornerstones technological innovations the transformation from Society 4.0 and Industry 4.0 to Society 5.0 and Industry 5.0 is enabled? The study is important for practitioners and researchers to understand the meaning of Society 5.0 and to familiarise themselves with the drivers that will help shape Society 5.0 policies and play an important role in its further development. Therefore, the authors conducted a quantitative bibliometric study that provides insights into the importance of the topic and incorporates current characteristics and future research trends. Methodology: The study used algorithmic co-occurrence of keywords to gain a different insight into the evolution of Society 5.0. Thirty-six selected articles from the Web of Science database were analysed with the bibliometric analysis and overlay visualisation. Results: The co-occurrence analysis shows that terms artificial intelligence, cyber-physical systems, big data, Industry 4.0, Industry 5.0, open innovation, Society 5.0, super-smart society have been widely used in researches in the last three years. Conclusion: The study presents a bibliometric analysis to analyse the current and future development drivers of a Society 5.0. According to the results, the transition from Society 4.0 to Society 5.0 can be achieved by implementing knowledge and technologies in the IoT, robotics, and Big Data to transform society into a smart society (Society 5.0). In particular, the concept would enable the adaptation of services and industrial activities to individuals’ real needs. Furthermore, these technologies allow advanced digital service platforms that will eventually be integrated into all areas of life.

scholarly journals Sustainable Supply Chain in the Era of Industry 4.0 and Big Data: A Systematic Analysis of Literature and Research

2020 ◽  
Vol 12 (10) ◽  
pp. 4108 ◽  
Author(s):  
Ricardo Chalmeta ◽  
Nestor J. Santos-deLeón
Keyword(s):  
Big Data ◽  
Supply Chain ◽  
Industry 4.0 ◽  
Future Research ◽  
Sustainable Supply Chain ◽  
Research Directions ◽  
Systematic Analysis ◽  
Different Types ◽  
Future Research Directions ◽  
Insight Into

Supply chain sustainability (SCS) in the age of Industry 4.0 and Big Data is a growing area of research. However, there are no systematic and extensive studies that classify the different types of research and examine the general trends in this area of research. This paper reviews the literature on sustainability, Big Data, Industry 4.0 and supply chain management published since 2009 and provides a thorough insight into the field by using bibliometric and network analysis techniques. A total of 87 articles published in the past 10 years were evaluated and the top contributing authors, countries, and key research topics were identified. Furthermore, the most influential works based on citations and PageRank were obtained and compared. Finally, six research categories were proposed in which scholars could be encouraged to expand Big Data and Industry 4.0 research on SCS. This paper contributes to the literature on SCS in the age of Industry 4.0 by discussing the challenges facing current research but also, more importantly, by identifying and proposing these six research categories and future research directions.

Bibliometric analysis and critical review of the research on big data in the construction industry

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yusheng Lu ◽  
Jiantong Zhang
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Bibliometric Analysis ◽  
Critical Review ◽  
Rapid Development ◽  
Heterogeneous Data ◽  
The Body ◽  
Future Research ◽  
Temporal And Spatial Distribution ◽  
Content Type

PurposeThe digital revolution and the use of big data (BD) in particular has important applications in the construction industry. In construction, massive amounts of heterogeneous data need to be analyzed to improve onsite efficiency. This article presents a systematic review and identifies future research directions, presenting valuable conclusions derived from rigorous bibliometric tools. The results of this study may provide guidelines for construction engineering and global policymaking to change the current low-efficiency of construction sites.Design/methodology/approachThis study identifies research trends from 1,253 peer-reviewed papers, using general statistics, keyword co-occurrence analysis, critical review, and qualitative-bibliometric techniques in two rounds of search.FindingsThe number of studies in this area rapidly increased from 2012 to 2020. A significant number of publications originated in the UK, China, the US, and Australia, and the smallest number from one of these countries is more than twice the largest number in the remaining countries. Keyword co-occurrence is divided into three clusters: BD application scenarios, emerging technology in BD, and BD management. Currently developing approaches in BD analytics include machine learning, data mining, and heuristic-optimization algorithms such as graph convolutional, recurrent neural networks and natural language processes (NLP). Studies have focused on safety management, energy reduction, and cost prediction. Blockchain integrated with BD is a promising means of managing construction contracts.Research limitations/implicationsThe study of BD is in a stage of rapid development, and this bibliometric analysis is only a part of the necessary practical analysis.Practical implicationsNational policies, temporal and spatial distribution, BD flow are interpreted, and the results of this may provide guidelines for policymakers. Overall, this work may develop the body of knowledge, producing a reference point and identifying future development.Originality/valueTo our knowledge, this is the first bibliometric review of BD in the construction industry. This study can also benefit construction practitioners by providing them a focused perspective of BD for emerging practices in the construction industry.

scholarly journals Digital Twins in Pharmaceutical and Biopharmaceutical Manufacturing: A Literature Review

Processes ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1088
Author(s):  
Yingjie Chen ◽  
Ou Yang ◽  
Chaitanya Sampat ◽  
Pooja Bhalode ◽  
Rohit Ramachandran ◽  
...  
Keyword(s):  
Pharmaceutical Industry ◽  
Literature Review ◽  
Industry 4.0 ◽  
Process Analytical Technology ◽  
Manufacturing Sector ◽  
Current Status ◽  
Future Research ◽  
Physical Systems ◽  
Digital Twins ◽  
Biopharmaceutical Manufacturing

The development and application of emerging technologies of Industry 4.0 enable the realization of digital twins (DT), which facilitates the transformation of the manufacturing sector to a more agile and intelligent one. DTs are virtual constructs of physical systems that mirror the behavior and dynamics of such physical systems. A fully developed DT consists of physical components, virtual components, and information communications between the two. Integrated DTs are being applied in various processes and product industries. Although the pharmaceutical industry has evolved recently to adopt Quality-by-Design (QbD) initiatives and is undergoing a paradigm shift of digitalization to embrace Industry 4.0, there has not been a full DT application in pharmaceutical manufacturing. Therefore, there is a critical need to examine the progress of the pharmaceutical industry towards implementing DT solutions. The aim of this narrative literature review is to give an overview of the current status of DT development and its application in pharmaceutical and biopharmaceutical manufacturing. State-of-the-art Process Analytical Technology (PAT) developments, process modeling approaches, and data integration studies are reviewed. Challenges and opportunities for future research in this field are also discussed.

scholarly journals Early survey with bibliometric analysis on machine learning approaches in controlling coronavirus

2020 ◽  
Author(s):  
Haruna Chiroma ◽  
Absalom E. Ezugwu ◽  
Fatsuma Jauro ◽  
Mohammed A. Al-Garadi ◽  
Idris N. Abdullahi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ct Scan ◽  
Bibliometric Analysis ◽  
Survey Methodology ◽  
Vaccine Development ◽  
Diagnostic Tools ◽  
Future Research ◽  
X Rays ◽  
New Perspective ◽  
Further Development

AbstractBackground and ObjectiveThe COVID-19 pandemic has caused severe mortality across the globe with the USA as the current epicenter, although the initial outbreak was in Wuhan, China. Many studies successfully applied machine learning to fight the COVID-19 pandemic from a different perspective. To the best of the authors’ knowledge, no comprehensive survey with bibliometric analysis has been conducted on the adoption of machine learning for fighting COVID-19. Therefore, the main goal of this study is to bridge this gap by carrying out an in-depth survey with bibliometric analysis on the adoption of machine-learning-based technologies to fight the COVID-19 pandemic from a different perspective, including an extensive systematic literature review and a bibliometric analysis.MethodsA literature survey methodology is applied to retrieve data from academic databases, and a bibliometric technique is subsequently employed to analyze the accessed records. Moreover, the concise summary, sources of COVID-19 datasets, taxonomy, synthesis, and analysis are presented. The convolutional neural network (CNN) is found mainly utilized in developing COVID-19 diagnosis and prognosis tools, mostly from chest X-ray and chest computed tomography (CT) scan images. Similarly, a bibliometric analysis of machine-learning-based COVID-19-related publications in Scopus and Web of Science citation indexes is performed. Finally, a new perspective is proposed to solve the challenges identified as directions for future research. We believe that the survey with bibliometric analysis can help researchers easily detect areas that require further development and identify potential collaborators.ResultsThe findings in this study reveal that machine-learning-based COVID-19 diagnostic tools received the most considerable attention from researchers. Specifically, the analyses of the results show that energy and resources are more dispensed toward COVID-19 automated diagnostic tools, while COVID-19 drugs and vaccine development remain grossly underexploited. Moreover, the machine-learning-based algorithm predominantly utilized by researchers in developing the diagnostic tool is CNN mainly from X-rays and CT scan images.ConclusionsThe challenges hindering practical work on the application of machine-learning-based technologies to fight COVID-19 and a new perspective to solve the identified problems are presented in this study. We believe that the presented survey with bibliometric analysis can help researchers determine areas that need further development and identify potential collaborators at author, country, and institutional levels to advance research in the focused area of machine learning application for disease control.

scholarly journals Industry 4.0, Disaster Risk Management and Infrastructure Resilience: A Systematic Review and Bibliometric Analysis

Buildings ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 411
Author(s):  
Mahyar Habibi Rad ◽  
Mohammad Mojtahedi ◽  
Michael J. Ostwald
Keyword(s):  
Risk Management ◽  
Literature Review ◽  
Bibliometric Analysis ◽  
Systematic Literature Review ◽  
Industry 4.0 ◽  
Disaster Risk ◽  
Disaster Risk Management ◽  
Future Research ◽  
Research Activity ◽  
Resilient Infrastructure

The fourth industrial era, known as ‘Industry 4.0’ (I4.0), aided and abetted by the digital revolution, has attracted increasing attention among scholars and practitioners in the last decade. The adoption of I4.0 principles in Disaster Risk Management (DRM) research and associated industry practices is particularly notable, although its origins, impacts and potential are not well understood. In response to this knowledge gap, this paper conducts a systematic literature review and bibliometric analysis of the application and contribution of I4.0 in DRM. The systematic literature review identified 144 relevant articles and then employed descriptive and content analysis of a focused set of 70 articles published between 2011 and 2021. The results of this review trace the growing trend for adoption of I4.0 tools and techniques in disaster management, and in parallel their influence in resilient infrastructure and digital construction fields. The results are used to identify six dominant clusters of research activity: big data analytics, Internet of Things, prefabrication and modularization, robotics and cyber-physical systems. The research in each cluster is then mapped to the priorities of the Sendai framework for DRR, highlighting the ways it can support this international agenda. Finally, this paper identifies gaps within the literature and discusses possible future research directions for the combination of I4.0 and DRM.

Framing Sustainable Practices

2016 ◽  
Vol 1 (2) ◽  
pp. 21-39 ◽  
Author(s):  
Jeffrey Gauthier ◽  
Chris Meyer ◽  
David Cohen
Keyword(s):  
Ethical Decision ◽  
Middle Managers ◽  
Ethical Decision Making ◽  
Middle Management ◽  
Research Question ◽  
Future Research ◽  
Sustainable Practices ◽  
Resource Based View ◽  
The Relationship ◽  
Insight Into

This paper develops and clarifies social intrapreneurship theory by examining the “how” of effective intrapreneurial championing. More specifically, the authors consider the following research question: How does the manner in which middle managers frame sustainable practices influence successful championing outcomes? The authors integrate the natural-resource-based view of the firm with research on middle management championing behaviors and issue-contingent models of ethical decision making to propose a model of sustainability championing for social intrapreneurs. To that end, propositions are developed concerning the relationship between the types of sustainable practice championed, how the argument for a given practice is framed, and successful championing outcomes. This paper contributes to a growing body of literature on social intrapreneurship, providing insight into how intrapreneurial championing can be more effective and building a foundation for future research.

Framing Sustainable Practices

2019 ◽  
pp. 1510-1526
Author(s):  
Jeffrey Gauthier ◽  
Chris Meyer ◽  
David Cohen
Keyword(s):  
Decision Making ◽  
Middle Managers ◽  
Ethical Decision Making ◽  
Research Question ◽  
Future Research ◽  
Sustainable Practices ◽  
Resource Based View ◽  
Growing Body ◽  
The Relationship ◽  
Insight Into

This paper develops and clarifies social intrapreneurship theory by examining the “how” of effective intrapreneurial championing. More specifically, the authors consider the following research question: How does the manner in which middle managers frame sustainable practices influence successful championing outcomes? The authors integrate the natural-resource-based view of the firm with research on middle management championing behaviors and issue-contingent models of ethical decision making to propose a model of sustainability championing for social intrapreneurs. To that end, propositions are developed concerning the relationship between the types of sustainable practice championed, how the argument for a given practice is framed, and successful championing outcomes. This paper contributes to a growing body of literature on social intrapreneurship, providing insight into how intrapreneurial championing can be more effective and building a foundation for future research.

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