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Digital transformation, which significantly impacts our personal, social, and economic spheres of life, is regarded by many as the most significant development of recent decades. In an industrial context, based on a systematic literature review of 262 papers selected from the ProQuest database, using the methodology of David and Han, this paper discusses Industry 4.0 technologies as the key drivers and/or enablers of digital transformation for business practices, models, processes, and routines in the current digital age. After carrying out a systematic literature review considering key Industry 4.0 technologies, we discuss the individual and collective ways in which competitiveness in contemporary organizations and institutions is enhanced. Specifically, we discuss how these technologies contribute as antecedents, drivers, and enablers of environmental and social sustainability, corporate growth and diversification, reshoring, mass customization, B2B cooperation, supply chain integration, Lean Six Sigma, quality of governance, innovations, and knowledge related to dealing with challenges arising from global pandemics such as COVID-19. A few challenges related to the effective adoption and implementation of Industry 4.0 are also highlighted, along with some suggestions to overcome them.
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This book explores how digital technologies have proved to be a useful and necessary tool to help ensure that local and regional governments on the frontline of the emergency can continue to provide essential public services during the COVID-19 crisis. Indeed, as the demand for digital technologies grows, local and regional governments are increasingly committed to improving the lives of their citizens under the principles of privacy, freedom of expression and democracy. The Digital Revolution began between the late 1950s and 1970s and represents the evolution of technology from the mechanical and analog to the digital. The advent of digital technology has also changed how humans communicate today using computers, smartphones and the internet. Further, the digital revolution has made a tremendous wealth of information accessible to virtually everyone. In turn, the book focuses on key challenges for local and regional governments concerning digital technologies during this crisis, e.g. the balance between privacy and security, the digital divide, and accessibility. Privacy is a challenge in the mitigation of COVID-19, as governments rely on digital technologies like contact-tracking apps and big data to help trace peoples patterns and movements. While these methods are controversial and may infringe on rights to privacy, they also appear to be effective measures for rapidly controlling and limiting the spread of the virus. Next, the book discusses the 10 technology trends that can help build a resilient society, as well as their effects on how we do business, how we work, how we produce goods, how we learn, how we seek medical services and how we entertain ourselves. Lastly, the book addresses a range of diversified technologies, e.g. Online Shopping and Robot Deliveries, Digital and Contactless Payments, Remote Work, Distance Learning, Telehealth, Online Entertainment, Supply Chain 4.0, 3D Printing, Robotics and Drones, 5G, and Information and Communications Technology (ICT). © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
ABSTRACT
Big data analytics (BDA), as a new innovation tool, played an important role in helping businesses to survive and thrive during great crises and mega disruptions like COVID-19 by transitioning to and scaling e-commerce. Accordingly, the main purpose of the current research was to have a meaningful comprehensive overview of BDA and innovation in e-commerce research published in journals indexed by the Scopus database. In order to describe, explore, and analyze the evolution of publication (co-citation, co-authorship, bibliographical coupling, etc.), the bibliometric method has been utilized to analyze 541 documents from the international Scopus database by using different programs such as VOSviewer and Rstudio. The results of this paper show that many researchers in the e-commerce area focused on and applied data analytical solutions to fight the COVID-19 disease and establish preventive actions against it in various innovative manners. In addition, BDA and innovation in e-commerce is an interdisciplinary research field that could be explored from different perspectives and approaches, such as technology, business, commerce, finance, sociology, and economics. Moreover, the research findings are considered an invitation to those data analysts and innovators to contribute more to the body of the literature through high-impact industry-oriented research which can improve the adoption process of big data analytics and innovation in organizations. Finally, this study proposes future research agenda and guidelines suggested to be explored further.
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In today's digital world, information is growing along with the expansion of Internet usage worldwide. As a consequence, bulk of data is generated constantly which is known to be "Big Data". One of the most evolving technologies in twenty-first century is Big Data analytics, it is promising field for extracting knowledge from very large datasets and enhancing benefits while lowering costs. Due to the enormous success of big data analytics, the healthcare sector is increasingly shifting toward adopting these approaches to diagnose diseases. Due to the recent boom in medical big data and the development of computational methods, researchers and practitioners have gained the ability to mine and visualize medical big data on a larger scale. Thus, with the aid of integration of big data analytics in healthcare sectors, precise medical data analysis is now feasible with early sickness detection, health status monitoring, patient treatment, and community services is now achievable. With all these improvements, a deadly disease COVID is considered in this comprehensive review with the intention of offering remedies utilizing big data analytics. The use of big data applications is vital to managing pandemic conditions, such as predicting outbreaks of COVID-19 and identifying cases and patterns of spread of COVID-19. Research is still being done on leveraging big data analytics to forecast COVID-19. But precise and early identification of COVID disease is still lacking due to the volume of medical records like dissimilar medical imaging modalities. Meanwhile, Digital imaging has now become essential to COVID diagnosis, but the main challenge is the storage of massive volumes of data. Taking these limitations into account, a comprehensive analysis is presented in the systematic literature review (SLR) to provide a deeper understanding of big data in the field of COVID-19.
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This book comprehensively covers the topic of COVID-19 and other pandemics and epidemics data analytics using computational modelling. Biomedical and Health Informatics is an emerging field of research at the intersection of information science, computer science, and health care. The new era of pandemics and epidemics bring tremendous opportunities and challenges due to the plentiful and easily available medical data allowing for further analysis. The aim of pandemics and epidemics research is to ensure high-quality, efficient healthcare, better treatment and quality of life by efficiently analyzing the abundant medical, and healthcare data including patient's data, electronic health records (EHRs) and lifestyle. In the past, it was a common requirement to have domain experts for developing models for biomedical or healthcare. However, recent advances in representation learning algorithms allow us to automatically learn the pattern and representation of the given data for the development of such models. Medical Image Mining, a novel research area (due to its large amount of medical images) are increasingly generated and stored digitally. These images are mainly in the form of: computed tomography (CT), X-ray, nuclear medicine imaging (PET, SPECT), magnetic resonance imaging (MRI) and ultrasound. Patients' biomedical images can be digitized using data mining techniques and may help in answering several important and critical questions related to health care. Image mining in medicine can help to uncover new relationships between data and reveal new and useful information that can be helpful for scientists and biomedical practitioners. Assessing COVID-19 and Other Pandemics and Epidemics using Computational Modelling and Data Analysis will play a vital role in improving human life in response to pandemics and epidemics. The state-of-the-art approaches for data mining-based medical and health related applications will be of great value to researchers and practitioners working in biomedical, health informatics, and artificial intelligence. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.
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Concerns about the effects of global warming and predicted rising sea levels are radically changing government policies to lower carbon emissions using sustainable green technologies. The United Kingdom aims to reduce its carbon emissions by 78% by 2035 and achieve net zero by 2050. This is a major driver for energy management and is influencing development of buildings which use autonomous smart technologies to assist in lowering carbon footprints. These Smart Buildings use digital technologies by connecting sensor data with intelligent systems which can be monitored remotely to provide more efficient facilities management. The data harvested and transmitted from the IoT sensors provides a key component for Big Data Analytics using techniques such as Association rule mining for intelligent interpretation which can assist facilities management becoming more agile regarding office space utilization. The shift toward hybrid working particularly instigated by the COVID-19 pandemic and recent energy supply concerns caused by the Ukraine crisis presents facilities management with opportunities to optimize their space, reduce energy consumption, and allow them to identify commercial opportunities for the unused space throughout the building. This chapter discusses the use of association rules for data mining derived from a simulated dataset for an investigative analysis of office workflow patterns for facilities management operations, resource conservation, and sustainability. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Despite great academic interest in global green supply chain management (GSCM) practices, its effectiveness for environmental management systems (EMS) and market competitiveness during COVID-19 remains untapped. Existing literature suggests that a fundamental link between GSCM, EMS, and market competitiveness is missing, as supply management is critical to maintain market competitiveness. To fill this gap in the literature, this study examines whether environmental management systems influence the link between GSCM practice and market competitiveness in China. We also propose the articulating role of big data analytics and artificial intelligence (BDA-AI) and environmental visibility toward these associations in the context of the COVID-19 pandemic. We evaluated the proposed model using regression-based structural equation modeling (SEM) with primary data (n = 330). This result provides empirical evidence of the impact of GSCM on EMS and market competitiveness. Moreover, the results show that the BDA-AI and the environmental visibility enhanced the positive relationship between GSCM-EMS and EMS and market competitiveness in China. Recent research shows that supply chain professionals, policymakers, managers, and researchers are turning to formal EMS, BDA-AI, and environmental visibility to help their organizations achieve the competitiveness that the market indicates they need.
Subject(s)
COVID-19 , Conservation of Natural Resources , Humans , Artificial Intelligence , Pandemics , Efficiency, OrganizationalABSTRACT
This paper focuses the attention on a real-life case study represented by the design, the development and the practice of OLAP tools over big COVID-19 data in Canada. The OLAP tools developed in this context are further enriched by machine learning procedures that magnify the mining effect. The contribution presented in this paper also embeds an implicit methodology for OLAP over big COVID-19 data. Experimental analysis on the target case study is also provided.
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The rapid growth of the Internet and Technology produced a massive amount of data that resulted a phenomenon called Big Data. To process such a complex kind of massive amount of data, an advanced approach and tool is needed that is able to quickly produce results. This approach to analyzing massive amount of data is known as Big Data Analytics. Big data analytics is widely used in various sectors, not to mention the health sector. In the healthcare sector, recently there has been a study that is often carried out in dealing with crisis situations, namely research on implementing big data analytics to provide technological solutions to help deal with pandemics. In this article, we analyze and visualize the data collected from Indonesia. The data analyzed starts from the first case of COVID-19 in Indonesia to present. The proposed solution is to classify the regional case data into a group that can represent the situation of the area. As a result, it is determined based on the data that there are three groups consisting of areas with low risk, moderate risk, and high risk. In addition, this article proposes combining big data analytics technology with cloud technology to facilitate the dissemination of information to citizens to increase awareness about the spread of the COVID-19 virus.
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Big Data and analytics have become essential factors in managing the COVID-19 pandemic. As no company can escape the effects of the pandemic, mature Big Data and analytics practices are essential for successful decision-making insights and keeping pace with a changing and unpredictable marketplace. The ability to be successful in Big Data projects is related to the organization's maturity level. The maturity model is a tool that could be applied to assess the maturity level across specific key dimensions, where the maturity levels indicate an organization's current capabilities and the desirable state. Big Data maturity models (BDMMs) are a new trend with limited publications published as white papers and web materials by practitioners. While most of the related literature might not have covered all of the existing BDMMs, this systematic literature review (SLR) aims to contribute to the body of knowledge and address the limitations in the existing literature about the existing BDMMs, assessment dimensions, and tools. The SLR strategy in this paper was conducted based on guidelines to perform SLR in software engineering by answering three research questions: (1) What are the existing maturity assessment models for Big Data? (2) What are the assessment dimensions for Big Data maturity models? and (3) What are the assessment tools for Big Data maturity models? This SLR covers the available BDMMs written in English and developed by academics and practitioners (2007-2022). By applying a descriptive qualitative content analysis method for the reviewed publications, this SLR identified 15 BDMMs (10 BDMMs by practitioners and 5 BDMMs by academics). Additionally, this paper presents the limitations of existing BDMMs. The findings of this paper could be used as a grounded reference for assessing the maturity of Big Data. Moreover, this paper will provide managers with critical insights to select the BDMM that fits within their organization to support their data-driven decisions. Future work will investigate the Big Data maturity assessment dimensions towards developing a new Big Data maturity model.
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Situational awareness tries to grasp the important events and circumstances in the physical world through sensing, communication, and reasoning. Tracking the evolution of changing situations is an essential part of this awareness and is crucial for providing appropriate resources and help during disasters. Social media, particularly Twitter, is playing an increasing role in this process in recent years. However, extracting intelligence from the available data involves several challenges, including (a) filtering out large amounts of irrelevant data, (b) fusion of heterogeneous data generated by the social media and other sources, and (c) working with partially geo-tagged social media data in order to deduce the needs of the affected people. Spatio-temporal analysis of the data plays a key role in understanding the situation, but is available only sparsely because only a small fraction of people post relevant text and of those very few enable location tracking. In this paper, we provide a comprehensive survey on data analytics to assess situational awareness from social media big data.
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Big data analytics capability (BDAC) is the key resource for competitive advantage in the drastically changing market. Although some studies have investigated the impacts on firm performance, there is limited under-standing of how firms enhance their BDAC. This study draws on organisational culture and investigates the effects of responsive and proactive market orientations on BDAC and firm performance. The results show that both responsive and proactive market orientations increase BDAC. Further, BDAC fully mediates the relationship between these two market orientations and firm performance. Our findings suggest that BDAC researchers should focus on market orientations that enhance BDAC.
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The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies' social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers' work and family, and (e) users' action. Besides, the Jaccard coefficient co-occurrence cluster analysis revealed: (a) the most mentioned collocations were ‘construction safety week', ‘safety first', and ‘construction team', (b) the largest clusters were ‘safety training', ‘occupational health and safety administration', and ‘health and safety environment', (c) the most active users were ‘Parallel Consultancy Ltd.', ‘Pike Consulting Group', and ‘Global Training Canada', and (d) positive sentiment accounted for an overwhelming figure of 85%. The findings inform the industry on public perceptions that help create awareness and develop preventative measures for increased health and safety and decreased incidents.
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Following the COVID-19's outbreak in China, wildlife-related issues such as wildlife management and conservation made headlines around the world due to the potential zoonotic nature of the newly discovered coronavirus. In our study, we examined the dynamic interaction of the news agenda and public agenda concerning wildlife-related issues on social media after COVID-19's outbreak. Using big data analytics, we automatically extracted the agendas' attributes and networks from 110,549 social media posts made from January 1 to April 8, 2020, and investigated the effect of second- and third-level dynamic agenda setting in a time series analysis. Our findings suggest that the agenda-setting effect of wildlife-related issues on social media was not a single-step, unidirectional action but a reciprocal, dynamic interaction constantly constructed by news outlets and the general public.
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Supply chain sustainability (SCS) has gone beyond the sustainability-performance approach, towards the increasing adoption of the sustainability-practice approach. The use of digital technologies in this approach can enhance resilience and human rights, particularly in the context of the green and digital twin transition post-COVID-19 pandemic. To enrich the sustainability-practice approach, this paper aims to produce a roadmapping taxonomy, based on knowledge mapping of a dataset collected in late December 2022 from the Web of Science Core Collection. As the knowledge map reveals the dimensions of resilience, human rights, and digital technologies, the proposed taxonomy highlights the importance of dynamic capabilities in facing supply chain disruptions, especially their ripple effects, along with the corresponding digital technologies to enhance human social dynamics in facing such disruptions. The proposed taxonomy provides a knowledge-based framework for professionals and researchers to enhance their understanding of supply chain resilience in designing and implementing digital solutions. The proposed roadmapping taxonomy features a people- and community-centric perspective and several managerial insights, contributing to the wider discussions on the green and digital transformation of the supply chain, by shaping actions and interactions in networked, digitized, and datafied forms to enhance supply chain sustainability. Copyright © 2023 Pan, Liao and Zhang.
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The coronavirus disease 2019 (COVID-19) pandemic enabled many governments around the globe to test and apply big data-based tracing technologies and various big data-driven tools to curb and monitor the spread of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. The regular procedures of data and privacy protection were partially sacrificed to fight the pandemic. For the public health safety, all incidents of COVID-19 are considered of being hazardous, uncertain, and sudden. If a government can continuously and efficiently collect big data from various sources and apply suitable and efficient analytical methods, it might instantly respond to the public health threats by executing optimal decisions to slow the spread of the pandemic and for a fast return to normality. A specific framework is presented as a multidimensional recommendation for the efficient utilization of big data analytical technologies to control and prevent pandemics and epidemics. The recommendations and challenges with regard to employing big data for combatting COVID-19 are being discussed along with the background information. © 2023 Elsevier Inc. All rights reserved.
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Even though the COVID-19 epidemic caused many problems for global supply chains, it also created opportunities that helped get people interested in cleaner production, sustainability, and business practices that are good for the environment again. This study seeks to examine the pandemic's opportunities for enhancing green supply chain management (GSC-management) and Sustainable environmental-performance by exploring the link between the uncertainty-anxiety of the pandemic, GSC-management, and Sustainable environmental-performance and inspecting the moderating effect of novel technology adoption like big-data analysis capabilities (BDA) and blockchain technologies (BCT) on this relationship. A questionnaire of 517 managers of SMEs in Egypt was used to test and analyze hypotheses with the PLS-SEM method. The findings show that the uncertainty-anxiety of the pandemic improves GSC-management significantly. Also, BDA moderates the link between the uncertainty-anxiety of the pandemic and GSC-management. However, BCT does not moderate that direct link. Also, GSC-management positively affects a firm's sustainable environmental-performance. In addition, GSC-management significantly mediates the correlation between the uncertainty-anxiety of the pandemic and sustainable environmental-performance. The study's findings have massive implications for how SMEs will be managed during COVID-19. Also, it contributes to the theory by using the Social cognitive theory to show how the uncertainty-anxiety of the pandemic positively affects the GSC-management practices and how the dynamic capabilities theory explains the innovative technologies moderating effect of such relationship. • Uncertainty-anxiety of the pandemic improves GSC-management significantly. • BDA moderates uncertainty-anxiety and GSC-management relationship. • BDA does not moderate uncertainty-anxiety and GSC-management relationship. • GSC-management improves firms' sustainable environmental-performance. • GSC-management mediates uncertainty-anxiety and environmental-performance. [ FROM AUTHOR] Copyright of Journal of Cleaner Production is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)
ABSTRACT
Vaccine hesitancy ranks among one of the top barriers to controlling the COVID-19 pandemic within the United States. Motivated by such a threat, this quantitative experimental study addressed the problematic lack of understanding about the effect of analytic and communicative strategies for handling pre- and post-marketing adverse reaction data on COVID-19-related vaccine hesitancy among American adults. Five experimental safety-based presentations were developed to consider written descriptions, positive framing, negative framing, myth-debunking, and background fact provisioning. Each presentation style was incorporated into a survey with questions related to demographics, healthcare status, and vaccine hesitancy. SurveyMonkey was used to randomize participants to a single presentation and disseminate the survey to 460 American adults. None of the experimental presentations were associated with a statistically significant increase in vaccine hesitancy which indicated a lack of backfiring effect. Furthermore, COVID-19-related vaccine hesitancy was significantly decreased within the group that contained a summary of adverse events supported by background information about pre- and post-marketing safety surveillance (p<.001). Subgroup-specific influences were also identified with respect to gender, political inclination, highest level of education, and age group. The generated findings may help to inform public health strategies for mitigating COVID-19-related vaccine hesitancy among American adults. Additional value might be gained with potential applications to pharmaceutical marketing campaigns and advancements within the field of big data. Future work is warranted to enhance the participation of minority respondents, incorporate larger sample sizes, consider the research against a different landscape of current events, and/or supplement with qualitative narratives. (PsycInfo Database Record (c) 2022 APA, all rights reserved)
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Determining the perception and sentiment of public's opinion of telemedicine and telecare has benefits to healthcare organizations, physicians and patients. Determining a relationship between opinion and demographic elements will aid in developing ways to close the gap between perception and readiness to implement healthcare technology for patients. The concept of telemedicine becomes more critical due to the onset of pandemics such as COVID-19. In addition, with telemedicine being a viable option to reduce cost and inconvenience for the patient, while delivering care that is effective and efficient, having patient buy in will be a key element. This study aims to identify the perception about telemedicine and telecare based on the posts by Twitter users eighteen months before and after COVID-19 pandemic. We leveraged VADER sentiment analysis model to identify the sentiment of the public using the tweets they posted. Out of approximately 1,073,817 tweets included, 491,695 unique tweets from 10,495 unique users met the inclusion criteria Among all countries, United States dominated the tweet volume. Among all the states in US, it is interesting to note that district of Columbia dominated the tweet volume. Among tweets from top five English speaking countries, interestingly after March 2020, the average sentiment of all countries seems to converge to the same value. Results indicate that before COVID-19 outbreak, people had neutral perception or sentiment towards telemedicine, while after the onset of increased cases and high alert situations, people tend to support Telemedicine and the overall perception started to grow towards the positive side. © 2022 IEEE.
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The world has changed dramatically since the outbreak of COVID-19 pandemic. This has not only affected the humanity, but has also badly damaged the world's socio-economic system. Currently, people are looking for a magical solution to overcome this pandemic. Similarly, scientists across the globe are working to find remedies to overcome this challenge. The role of technologies is not far behind in this situation, which attracts many sectors from government agencies to medical practitioners, and market analysts. This is quite true that in a few months of time, scientists, researchers, and industrialists have come up with some acceptable innovative solutions and harnessing existing technologies to stop the spread of COVID-19. Therefore, it is pertinent to highlight the role of intelligent technologies, which play a pivotal role in curbing this pandemic. In this paper, we devise a taxonomy related to the technologies being used in the current pandemic. We show that the most prominent technologies are artificial intelligence, machine learning, cloud computing, big data analytics, and blockchain. Moreover, we highlight some key open challenges, which technologists might face to control this outbreak. Finally, we conclude that to impede this pandemic, a collective effort is required from different professionals in support of using existing and new technologies. Finally, we conclude that to stop this pandemic, machine learning approaches with integration of cloud computing using high performance computing could provision the pandemic with minimum cost and time. [ FROM AUTHOR] Copyright of Computing is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)