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Supply chain decarbonisation has become a strategic requirement in the era of a net-zero economy. Despite the significant role of Big Data Analytics (BDA) in decarbonising the supply chain (SC), no prior study has evaluated it systematically. The present study aims to provide a systematic literature review on the applications and outcomes of big data analytics in SC decarbonisation. A total of 69 papers on applying BDA technology for supply chain decarbonisation published between 2016 and 2021 have been selected following the PRISMA protocol. The findings show that the topic is evolving. Studies employed methods such as surveys (30), case studies (11), and conceptual research designs (8). Thematic analysis reveals that 65% of the studies are grounded in resource-advantage theories, organisational theories, and system theories. Studies from India and China (35%) dominate the topic, while most studies have been conducted on the food and manufacturing industries. Further, this study applied the Antecedent-Decision-Outcomes (ADO) framework in BDA-based SC decarbonisation. Antecedents include BDA resources and capabilities, workforce skills, and supplier capabilities. Decisions refer to improving decision-making across the supply chain. Outcomes refer to improving decarbonisation, sustainable growth, and sustainable innovativeness. Future research directions and questions are provided using the Theory-Context-Methodology (TCM) framework. [ABSTRACT FROM AUTHOR]

The Industry 4.0 revolution is changing the manufacturing landscape. A broad set of new technologies emerged (including software and connected equipment) that digitise manufacturing systems. These technologies bring new vitality and opportunities to the manufacturing industry, but they also bring new challenges. This paper focuses on the impact of Industry 4.0 on production planning approaches and software. We first propose a digital twin framework that integrates production planning systems and frontier technologies. The frontier technologies that may impact production planning software are the internet of things, cloud manufacturing, blockchain, and big data analytics. Second, we provide a state-of-the-art on the application of each technology in the production planning, as well as a detailed analysis of the benefit and application status. Finally, this paper discusses the future research and application directions in the production planning. We conclude that Industry 4.0 will lead to the construction of data-driven models for production planning software. These tools will include models built accurately from data, account for uncertainty, and partially actuate the decision autonomously. [ABSTRACT FROM AUTHOR]

Although research has investigated the drivers of pro-environmental intentions, little research has adopted big data to understand the socio-demographic profile of customers who share electronic word-of-mouth (i.e., online reviews) about tourism services' environmental practices (i.e., green eWOM). Our methodology includes a large sample of customers with different demographic characteristics and trip motivations, who shared green eWOM about different product types (496,813 hotel reviews, 129,455 airline reviews, and 22,373 reviews of tourist attractions). This sampling enables us to get an accurate picture of the customer segments who are sharing green eWOM. We used text analytics for data extraction (i.e., environmentally-related semantics), ANOVA tests, and ordinary least squares (OLS) regression for data analysis. Findings reveal that Gen Z Asian male customers traveling for business and staying at hotel chains are more likely to discuss sustainability practices in their reviews. The longitudinal analysis shows a steady increase in green eWOM for hotels and consumers are more often sharing green eWOM for tourist attractions, rather than hotels or airlines. Interestingly, green eWOM is associated with 5-star reviews and country of origin influences green eWOM of various travel and tourism services. [ABSTRACT FROM AUTHOR]

This study proposes that social-mediated leadership communication is a dynamic influence process, involving visible leader communications, follower emotional responses, and subsequent leader actions. These interactions form a feedback loop, where follower reactions influence leaders' strategic communication. We tested this framework by analyzing U.S. political leaders' engagement with the Green New Deal during the 2020 Presidential campaign. Using Facebook posts from congressional politicians and data on follower reactions, we applied longitudinal network models, natural language processing, and multilevel event history models to examine factors behind leaders' decisions to co-share content and discuss specific policy topics. Our findings support the dynamic influence framework, showing that follower reactions, especially those indicating support and boosting message virality, are most likely to prompt shifts in leaders' communication strategies. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has become an integral part of every industry. With the emergence of big data, the industries, and more especially textile and apparel (T&A) industry, are on the brink of relationships with consumers, suppliers, and competitors. They need to handle different scenarios with a multitude of complex correlations and dependencies between them and uncertainties arising from human interaction. It has become imperative for them to manage huge amounts of data for the optimization of decision-making processes. In such circumstances, AI techniques have shown promise in every segment of the T&A value chain, from product discovery to robotic manufacturing. The potential wide-ranging applications of AI in T&A industry have found their ways into design support systems to T&A recommendation systems, intelligent tracking systems, quality control, T&A forecasting, predictive analytics in supply chain management or social networks and T&A e-commerce. The research recourses to the qualitative method in the form of systematic literature review and in-depth interviews from senior management people and industry experts. Findings identify the dimensions of AI to develop dynamic capability along with its potential impact and probable challenges. As such, the findings contribute to relevant literature and offer useful insights for academia and practitioners. [ABSTRACT FROM AUTHOR]

This case study discusses the concept of big data analytics, which involves managing and analyzing large, complex data to discover patterns and insights. Implementing big data analytics has many benefits, such as improving decision-making processes, enhancing accounting processes and compliance, boosting marketing campaigns, and providing better credit risk assessment procedures. This case study aims for the student to understand the challenges of big data analytics implementation in a Malaysian commercial bank. Students take on the role of a Senior IT Manager who needs to consider solutions to the implementation problems. Although the team proposed using cloud-based services as a solution to these issues, the CEO expressed concerns about security risks and requested other alternative solutions. The team has also been requested to come up with solutions to the data quality issues experienced by the case study bank. [ABSTRACT FROM AUTHOR]

This study delves into the transformative potential of integrating big data analysis and information sharing in innovation and entrepreneurship education. Employing a comprehensive methodology encompassing K-Means Clustering, Decision Trees, Apriori Algorithm, and Neural Networks, the research investigates student engagement patterns, influential factors, collaborative relationships, and predictive modelling within educational settings. The findings reveal significant outcomes, with K-Means achieving a clustering precision of 75%, Decision Trees demonstrating an accuracy of 82%, Apriori Algorithm uncovering frequent itemsets with 68% support, and Neural Networks achieving a notable accuracy of 90%. Drawing insights from a diverse range of literature, including studies on big data management, demand prediction models, ecological approaches to entrepreneurship education, qualitative inquiries into startup strategies, applications of ICTs in education, and the impact of virtual gaming on SMEs' growth, the research provides a robust foundation for understanding innovation and entrepreneurship education. This study contributes to both theoretical understanding and practical implications, guiding educators and policymakers in tailoring interventions and strategies to foster an adaptive and effective educational environment.. [ABSTRACT FROM AUTHOR]

Recognizing the importance of affective fulfillment for contemporary customers, this study aims to investigate how the expressions of affective needs in online reviews impact review usefulness. Drawing on information processing theory, we proposed a framework based on big data analytics and econometrics to explore 469,202 online reviews. Empirical findings unveil that consumers’ expressions of affective needs involve Service, Room, Decoration, Enhancement facility, and Location; both the affective needs presence and intensity regarding the first four attributes positively influence review usefulness. The expressions of functional needs have bigger impacts than that of affective needs regarding Room but smaller than Service, Enhancement facility, and Decoration. Hotel type moderates this relationship: the influence of affective need expressions is amplified in luxury (vs. economy) hotels, indicating that their customers crave more disclosure for affective needs. This study contributes to research on review usefulness and hospitality market segmentation. Practical implications for multiple stakeholders are discussed. [ABSTRACT FROM AUTHOR]

This study introduces the Data-Driven Personalized Learning Model (DDPLM), a sophisticated framework designed to enhance foreign language acquisition through the integration of big data analytics. Implemented within the educational platforms Edmodo and Duolingo, DDPLM utilizes real-time data processing to tailor learning paths and content dynamically to individual learner needs. Our findings indicate significant improvements in language learning efficiency, engagement, and retention. The model's adaptability across different digital environments showcases its potential scalability and effectiveness in various educational contexts. Additionally, the research addresses the critical role of personalized feedback and adaptive challenges in maintaining learner motivation and promoting deeper linguistic comprehension. The outcomes suggest that DDPLM significantly transforms traditional language education, making it more personalized, efficient, and aligned with individual learning preferences. [ABSTRACT FROM AUTHOR]

Anticipating the prognosis of diseases is essential for clinical decision-making. The objective of this research is to develop a predictive model using big data analytics and technology transfer functionalities to harness the potential of technology transfer processes and predict real patient data. Based on a systematic literature review, the Predictive Model for Big Data and Technology Transfer (PMBDTT) was proposed, incorporating big data analysis and technology transfer functionalities. Data are captured in hospitals, processed, and subjected to analysis using supervised learning algorithms to identify patterns related to the prognosis of hospital readmission – when patients can return after receiving treatment. The discovery of new knowledge and, as a final step, technology transfer and decision-making, represent probabilistic measures known as clinical referral rates. After developing the proposed predictive model, it can be applied to real patient data, selected using specific criteria, and its results can be utilised to improve the quality of care and health outcomes for patients. [ABSTRACT FROM AUTHOR]

SecureVision is an advanced and trustworthy deepfake detection system created to tackle the growing threat of 'deepfake' movies that tamper with media, undermine public trust, and jeopardize cybersecurity. We present a novel approach that combines big data analytics with state-of-the-art deep learning algorithms to detect altered information in both audio and visual domains. One of SecureVision's primary innovations is the use of multi-modal analysis, which improves detection capabilities by concurrently analyzing many media forms and strengthening resistance against advanced deepfake techniques. The system's efficacy is further enhanced by its capacity to manage large datasets and integrate self-supervised learning, which guarantees its flexibility in the ever-changing field of digital deception. In the end, this study helps to protect digital integrity by providing a proactive, scalable, and efficient defense against the ubiquitous threat of deepfakes, thereby establishing a new benchmark for privacy and security measures in the digital era. [ABSTRACT FROM AUTHOR]

The target-oriented robust optimization (TORO) approach converts the original objectives to system targets and instead maximizes an uncertainty budget or robustness index. Machine learning techniques are used to tighten uncertainty sets which also reduce the pessimism of robust solutions. However, existing approaches operate only on a one-way sequential flow, where the data estimation and optimization modules implement their tasks independently. This overlooks the potential of capturing feedback within the solution framework to update forecasts and decisions based on the recent realizations of the uncertainty and system outcomes. This research proposes a novel closed-loop data-driven TORO framework, leveraging on the power of machine learning and feedback systems for optimization under uncertainty. The framework provides an array of solutions for various risk appetites and supports efficient decision-making as computational tractability is retained. A hypothetical case study is solved on a combined inventory and routing problem to demonstrate its applicability and features. [ABSTRACT FROM AUTHOR]

Copyright of GeSec: Revista de Gestao e Secretariado is the property of Sindicato das Secretarias e Secretarios do Estado de Sao Paulo (SINSESP) 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Escalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement. Highlights: Data analytics as a powerful tool The study underscored that data analytics is a crucial tool for addressing environmental challenges, offering transformative benefits at all stages of environmental decision-making. Enhanced environmental management The study also revealed that advancements in big data and machine learning empower earlier detection of environmental concerns, facilitate accurate prediction, and foster proactive management strategies. Multifaceted approach for success The major implication of the study is that maximising the potential of data analytics requires a multifaceted approach that addresses data quality, human and ethical considerations, interoperability, stakeholder engagement, and cultural factors. [ABSTRACT FROM AUTHOR]

In the rapidly evolving telecommunications industry, organizations in Bangladesh are facing the challenge of improving their performance to stay competitive. However, there is limited research on how big data analytics (BDA) impacts organizational performance (OP) in this context. Therefore, this study examines the impact of BDA on OP in Bangladesh's telecommunications industry, with green innovation (GI) and knowledge management (KM) as mediating variables, and big data analytics technical capabilities (BDATCs) as a moderating variable. We collected data from 384 management-level employees across five major telecom companies in Bangladesh using a structured survey questionnaire. Our analysis employed partial least squares structural equation modeling (PLS-SEM) with Smart-PLS 4.0 software. The findings indicate that BDA positively influences OP, and both GI and KM significantly mediate this relationship. However, while BDATCs enhance the BDA–OP relationship, they do not significantly moderate the BDA–GI link. These results underscore the importance of integrating BDA with KM and GI to boost organizational performance. Telecom companies should invest in advanced data analytics, foster a culture of sustainability, and enhance knowledge management practices to achieve superior performance. This study contributes to the Resource-Based View (RBV) theory by demonstrating the strategic role of BDA, GI, and KM in a developing economy context. Future research should expand this investigation across different sectors and consider longitudinal approaches to capture the dynamic nature of BDA's impact on organizational performance. [ABSTRACT FROM AUTHOR]

The explosion of data volume in the digital age has completely changed the corporate and industrial environments. In-depth analysis of large datasets to support strategic decision-making and innovation is the main focus of this paper's exploration of big data management engineering. A thorough examination of the basic elements and approaches necessary for efficient big data use—data collecting, storage, processing, analysis, and visualization—is given in this paper. With real-life case studies from several sectors to complement our exploration of cutting-edge methods in big data management, we present useful applications and results. This document lists the difficulties in handling big data, such as guaranteeing scalability, governance, and data quality. It also describes possible future study paths to deal with these issues and promote ongoing creativity. The results stress the need to combine cutting-edge technology with industry standards to improve decision-making based on data. Through an analysis of approaches such as machine learning, real-time data processing, and predictive analytics, this paper offers insightful information to companies hoping to use big data as a strategic advantage. Lastly, this paper presents real-life use cases in different sectors and discusses future trends such as the utilization of big data by emerging technologies. [ABSTRACT FROM AUTHOR]

Despite researchers having averred that big data analytics (BDA) transforms firms' ways of doing business, knowledge about operationalizing these technologies in organizations to achieve strategic objectives is lacking. Moreover, organizations' great appetite for big data and limited empirical proof of whether BDA impacts organizations' transformational capacity poses a need for further empirical investigation. Therefore, this study explores the association between big data analytics management capabilities (BDAMC) and innovation performance via dynamic capabilities (DC), by applying the PLS-SEM technique to analyzing the feedback of 149 firms. Consequently, we ground our arguments on dynamic capability and social capital theory rather than a resource-based view that does not provide suitable explanations for the deployment of resources to adapt to change. Accordingly, we advance this research stream by finding that BDAMC significantly enhances innovation performance through DC. We also extend the literature by disclosing how BDAMC strengthens DC via strategic alignment and social capital. [ABSTRACT FROM AUTHOR]

The food supply chain (FSC) is becoming more sustainable as companies aim to meet demand with lower waste and emissions. Big data analytics (BDA) can help achieve sustainability goals by extracting meaningful information from past data to help create sustainable strategies. However, in the sustainability literature, BDA's role in enabling sustainable FSC innovations is not explored. Thus, this study investigates how data‐driven analytics might improve FSC innovation by adopting creative tactics in every triple bottom line (TBL) component – green, corporate social responsibility (CSR), and financial – to gain a competitive edge. A resource‐based view (RBV) perspective was used to evaluate the links between supply chain (SC) innovation capabilities and competitive advantage (CA) in FSC innovation and sustainability. Indian food processing enterprises were surveyed using a questionnaire to collect data from 200 respondents. Adopting a structural equation modelling (SEM) approach, six hypotheses were evaluated for significance on the surveyed data using AMOS V.20. Since both goodness and badness fit indices were above cut‐off values, the measurement model was robustly evaluated and found to fit the survey data well. Structural model findings supported all study hypotheses. The results indicate that BDA strongly impacts food supply chain TBL and FSC innovation. Data‐driven innovative TBL methods were shown to boost FSC competitiveness. With the growing demand for value‐added innovation in FSC sustainable development, this study uniquely contributes to the current literature by linking BDA and TBL practice innovation to FSC CA. [ABSTRACT FROM AUTHOR]

This study aims to analyse housing prices in Taiwan in the post-epidemic era, identify the crucial factors influencing them, and develop a suitable method for analysing and forecasting them. This study collects relevant data such as Taiwan's housing price index data from 2002 to 2020 to identify the crucial factors affecting Taiwan's housing prices; this is achieved by constructing a regression model, forecasting Taiwan's housing prices through a constructed long short-term memory (LSTM) model that employs big data analytics, and verifying the efficiency of the proposed models through R-square and root mean square error values. The results indicate that the top 10 factors affecting Taiwan's housing prices are mostly related to mortgage interest rates, suggesting that in Taiwan, the effect on housing prices in the post-epidemic era may be non-significant. This study collects data on Taiwan's housing price for the period from the first quarter of 2002 to the fourth quarter of 2020 to construct an LSTM for forecasting Taiwan's housing prices. The results indicate that the proposed LSTM exhibits good fitness, indicating that the model is suitable for analysing and forecasting housing prices. Given that analysing and forecasting quantity is also crucial in housing market analyses and that this study focuses only on predicting housing prices, future research should explore the simultaneous prediction and analysis of both price and quantity. [ABSTRACT FROM AUTHOR]

Data Pension Explorer (DAPE) introduces an innovative approach to delve into the intricacies of commercial pension insurance adoption within rural communities in China, particularly amid the challenges posed by an aging population. Leveraging advanced Deep Long Short-Term Memory (LSTM) techniques within the domain of big data analysis, this study pioneers the analysis of extensive datasets. It systematically unravels intricate patterns, correlations, and pivotal determinants shaping the landscape of pension insurance adoption in rural areas. Going beyond conventional analyses, this research provides a nuanced understanding of the multifaceted factors influencing both the sustainability and uptake of pension insurance. The culmination of these efforts yields valuable insights that extend beyond the theoretical realm, directly informing strategic decision-making processes. These insights prove instrumental in designing and implementing policies tailored to address the unique challenges faced by rural communities in China. Thus, DAPE not only navigates the complexities of population aging but also serves as a guiding force in fostering widespread adoption and sustainability of pension insurance within rural landscapes in the Chinese context. [ABSTRACT FROM AUTHOR]

This paper examines big data analytics implications on the central banking financial system's technological progress. A digital technological progress framework and model is established to analyze the economy's aggregate supply via covering the monetary policy, big data analytics, pollutants emissions as independent variables and the economy's aggregate demand as a moderating variable in a modified extensive growth theory framework and model to compute the productivity indicators and the total factor productivity (TFP) as the central banking technological progress that combined the mentioned variables qualities contribution. Besides, data analytics positive and negative externalities that include data analytics shortcomings as unpriced undesirable output in the form of cybersecurity and pollutants' emissions among other proxies are internalized in the framework and the model to integrate the digital technology innovation with digital technology shortcomings and climate change. This revised extensive theory framework and model is a remarkable technique comprehensive of the technological progress matters and sustainable economic development and is considered one of the most important sustainable development and long-run economic growth proportions in the central banking financial system functions to manage the economy's aggregate supply and demand that unnoticed by previous studies. [ABSTRACT FROM AUTHOR]

This paper presents and delves into the architecture and intricacies of SeDaSOMA, a sophisticated framework supporting Serendipitous, Data-as-a-Service-oriented, Open big data Management and Analytics. SeDaSOMA meticulously addresses the multifaceted challenges inherent in open big data management and analytics. SeDaSOMA stands as a testament to the evolving landscape of big data management and analytics, embodying a commitment to harnessing advanced functionalities through a synthesis of innovative research findings and cutting-edge tools. In the context of this framework, the paper not only elucidates its structural components but also underscores its pivotal role in facilitating the seamless integration, processing, and analysis of massive and diverse datasets. By providing a comprehensive overview of SeDaSOMA, this paper contributes to the ongoing discourse within the field of big data management and analytics, shedding light on the intricate interplay between technological innovation and practical application. Moreover, as a complement to the discussion on SeDaSOMA, the paper offers a critical exploration of the emerging topics in the realm of big data research. By delineating current state-of-the-art methodologies and forecasting future research trajectories, this overview enriches the scholarly dialogue surrounding the evolving landscape of big data management and analytics, offering insights into the broader implications and potential advancements in the field. [ABSTRACT FROM AUTHOR]

Purpose: The advantages of applying big data analytics for organizations to boost innovation performance are enormous. By collecting and analysing substantial amounts of data, firms can discern what works for their customer needs and update existing products while innovating new ones. Notwithstanding the evidence about the effects of big data analytics, the link between big data analytics and innovation performance is still underestimated. Especially in today's fast-changing and complicated environments, companies cannot simply take big data analytics as one innovative technical tool without fully understanding how to deploy it effectively. Design/methodology/approach: This study tries to investigate this relationship by building on the knowledge absorptive capacity perspective. The authors conceptualized effective use of big data analytics tools as one general absorptive capacity rather than a simple technical element or skill. Specifically, effectively utilize big data analytics tools can provide values and insights for new product innovation performance in a turbulent environment. Using online survey data from 108 managers, the authors assessed their hypotheses by applying the structural equation modelling method. Findings: The authors found that big data analytics capacity, which can be conceptualized as one absorptive capacity, can positively influence product innovation performance. The authors also found that environmental turbulence has strong moderation effects on these two main relationships. Originality/value: These results establish big data analytics can be regarded as one absorptive capacity, which can positively boost an organization's innovation performance. [ABSTRACT FROM AUTHOR]

This review examines which Industry 4.0 (I4.0) technologies are suitable for improving Lean Six Sigma (LSS) tasks and the benefits of integrating these technologies into improvement projects. Also, it explores existing integration frameworks and discusses their relevance. A quantitative analysis of 692 papers and an in-depth analysis of 41 papers revealed that 'Analyze' is by far the best-supported DMAICs phase through techniques, such as Data Mining, Machine Learning, Big Data Analytics, Internet of Things, and Process Mining. This paper also proposes a DMAIC 4.0 framework based on multiple technologies. The mapping of I4.0 related techniques to DMAIC phases and tools is a novelty compared to previous studies regarding the diversity of digital technologies applied. LSS practitioners facing the challenges of increasing complexity and data volumes can benefit from understanding how I4.0 technology can support their DMAIC projects and which of the suggested approaches they can adopt for their context. [ABSTRACT FROM AUTHOR]

As the last production link, the diesel engine assembly process (DEAP) significantly impacts the quality consistency of diesel engine products. Therefore, the quality consistency improvement of DEAP has become an urgent problem for academia and industry. The challenge is how to mine the causal relationship in DEAP and establish a reliable quality prediction model. This paper attempts to describe DEAP using a causal relationship network (CRN) and to provide an effective data-based scheme for improving quality consistency by integrating CRN with support vector regression (SVR). First, a two-step CRN learning method is proposed for describing the DEAP. In the first step, the association relationship network is developed by a hybrid direct association detection method of the maximal information coefficient and network deconvolution, which can accurately measure the data relations. In the second step, the information geometric causal inference is employed to determine the direction of the edges in the association relationship network, thus forming the CRN of DEAP. Then, an integrated CRN-SVR approach is proposed to realize the predictive modeling of the critical quality indicators in DEAP, which integrated SVR into CRN. At the same time, it also provides a feasible idea for the interpretability of existing machine learning techniques. Finally, the proposed approach is tested and verified in a real-world DEAP and the obtained RMSE is only 0.033. The results of this study provide theoretical support and technical guarantee for quality consistency improvement in DEAP. [ABSTRACT FROM AUTHOR]

Before vaccine development during the COVID-19 pandemic, Non-Pharmaceutical Interventions (NPIs) were the only solutions to mitigate COVID-19 infections. Governments continued to use them even after starting vaccine administration. In this research, we review different big data analytics models that assess and optimize the effectiveness of NPIs. These models are categorized into three big data analytics groups: descriptive, which measures the infection rate changes caused by NPIs; predictive, which predicts the future of the pandemic by implementing several NPIs; and data-driven prescriptive, which suggests optimal control policies. We further analyze each method's basic assumptions, limitations, and applicability during different pandemic phases and under different scenarios. This review of COVID-19 NPI evaluation methods will be beneficial for decision-makers to know which model to select for policy-making in possible future pandemics, which are more likely recently due to globalization. Finally, we suggest some future research directions. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims at understanding how automotive firms integrate customer relationship management (CRM) tools and big data analytics (BDA) into their marketing strategies to enhance total quality management (TQM) after the coronavirus disease (COVID-19). Design/methodology/approach: A qualitative methodology based on a multiple-case study was adopted, involving the collection of 18 interviews with eight leading automotive firms and other companies responsible for their marketing and CRM activities. Findings: Results highlight that, through the adoption of CRM technology, automotive firms have developed best practices that positively impact business performance and TQM, thereby strengthening their digital culture. The challenges in the implementation of CRM and BDA are also discussed. Research limitations/implications: The study suffers from limitations related to the findings' generalizability due to the restricted number of firms operating in a single industry involved in the sample. Practical implications: Findings suggest new relational approaches and opportunities for automotive companies deriving from the use of CRM and BDA under an overall customer-oriented approach. Originality/value: This research analyzes how CRM and BDA improve the marketing and TQM processes in the automotive industry, which is undergoing deep transformation in the current context of digital transformation. [ABSTRACT FROM AUTHOR]

This paper presents and delves into the architecture and intricacies of SeDaSOMA, a sophisticated framework supporting Serendipitous, Data-as-a-Service-oriented, Open big data Management and Analytics. SeDaSOMA meticulously addresses the multifaceted challenges inherent in open big data management and analytics. SeDaSOMA stands as a testament to the evolving landscape of big data management and analytics, embodying a commitment to harnessing advanced functionalities through a synthesis of innovative research findings and cutting-edge tools. In the context of this framework, the paper not only elucidates its structural components but also underscores its pivotal role in facilitating the seamless integration, processing, and analysis of massive and diverse datasets. By providing a comprehensive overview of SeDaSOMA, this paper contributes to the ongoing discourse within the field of big data management and analytics, shedding light on the intricate interplay between technological innovation and practical application. Moreover, as a complement to the discussion on SeDaSOMA, the paper offers a critical exploration of the emerging topics in the realm of big data research. By delineating current state-of-the-art methodologies and forecasting future research trajectories, this overview enriches the scholarly dialogue surrounding the evolving landscape of big data management and analytics, offering insights into the broader implications and potential advancements in the field.

Abstract Escalating environmental challenges necessitate paramount decision-making to safeguard ecosystems and resources. However, the burgeoning volume and intricate nature of environmental data often present a formidable challenge in gleaning actionable insights. In this context, integrating data analytics tools within environmental monitoring and management frameworks offers a compelling avenue for progress. These tools facilitate efficient data processing, uncover hidden patterns, and enable predictive modelling, leading to more informed decisions. Despite growing research, a comprehensive understanding of specific data analytics applications, methodologies, and demonstrably effective implementations remains elusive. This systematic review aimed to address this gap. Following PRISMA guidelines, a meticulous search across five databases was conducted using predefined inclusion/exclusion criteria. Rigorous data extraction captured salient study characteristics, methodologies, data analysis techniques, key findings, and acknowledged limitations. The review revealed that data analytics offers a powerful toolkit for environmental management, transforming decision-making across all stages. Big data and advanced techniques enable proactive strategies through earlier issue detection and improved predictive models. However, maximising this potential requires a multifaceted approach, including standardised data collection, data literacy, ethical frameworks, and stakeholder engagement.

Relationships between the genome, transcriptome, and metabolome underlie all evolved phenotypes. However, it has proved difficult to elucidate these relationships because of the high number of variables measured. A recently developed data analytic method for characterizing the transcriptome can simplify interpretation by grouping genes into independently modulated sets (iModulons). Here, we demonstrate how iModulons reveal deep understanding of the effects of causal mutations and metabolic rewiring. We use adaptive laboratory evolution to generate E. coli strains that tolerate high levels of the redox cycling compound paraquat, which produces reactive oxygen species (ROS). We combine resequencing, iModulons, and metabolic models to elucidate six interacting stress-tolerance mechanisms: (1) modification of transport, (2) activation of ROS stress responses, (3) use of ROS-sensitive iron regulation, (4) motility, (5) broad transcriptional reallocation toward growth, and (6) metabolic rewiring to decrease NADH production. This work thus demonstrates the power of iModulon knowledge mapping for evolution analysis.

In recent years, the escalating impact of climate change has brought increasing attention to carbon-neutral strategies as a critical component of global environmental protection efforts. These strategies demand a comprehensive understanding of carbon emissions, which are influenced by a myriad of factors, including external conditions like seasonality and weather, as well as internal dynamics such as production and energy consumption. However, existing approaches often fail to account for these complex, dynamic interactions, resulting in suboptimal outcomes. To address these challenges, we propose an integrated model combining Autoformer, Deep Q-Network (DQN), and Deep Forest. This model is designed to dynamically respond to environmental changes using advanced time-series forecasting, adaptive decision-making, and robust feature extraction. Extensive experiments across multiple datasets reveal that our model significantly enhances carbon capture efficiency and accuracy, outperforming conventional methods. By providing a scalable and intelligent solution for carbon capture and utilization, this research not only supports the advancement of carbon-neutral strategies but also contributes to the broader goals of sustainable development and climate change mitigation.

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica 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 abstract 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 abstract. (Copyright applies to all Abstracts.)

Background: The construction of a robust healthcare information system is fundamental to enhancing countries' capabilities in the surveillance and control of hepatitis B virus (HBV). Making use of China's rapidly expanding primary healthcare system, this innovative approach using big data and machine learning (ML) could help towards the World Health Organization's (WHO) HBV infection elimination goals of reaching 90% diagnosis and treatment rates by 2030. We aimed to develop and validate HBV detection models using routine clinical data to improve the detection of HBV and support the development of effective interventions to mitigate the impact of this disease in China. Methods: Relevant data records extracted from the Family Medicine Clinic of the University of Hong Kong-Shenzhen Hospital's Hospital Information System were structuralized using state-of-the-art Natural Language Processing techniques. Several ML models have been used to develop HBV risk assessment models. The performance of the ML model was then interpreted using the Shapley value (SHAP) and validated using cohort data randomly divided at a ratio of 2:1 using a five-fold cross-validation framework. Results: The patterns of physical complaints of patients with and without HBV infection were identified by processing 158,988 clinic attendance records. After removing cases without any clinical parameters from the derivation sample (n=105,992), 27,392 cases were analysed using six modelling methods. A simplified model for HBV using patients' physical complaints and parameters was developed with good discrimination (AUC = 0.78) and calibration (goodness of fit test p-value >0.05). Conclusions: Suspected case detection models of HBV, showing potential for clinical deployment, have been developed to improve HBV surveillance in primary care setting in China. (Word count: 264) [ABSTRACT FROM AUTHOR]

Purpose: The literature mainly concentrates on the relationships between externally oriented digital transformation (ExtDT), big data analytics capability (BDAC) and business model innovation (BMI) from an intra-organizational perspective. However, it is acknowledged that the external environment shapes the firm's strategy and affects innovation outcomes. Embracing an external environment perspective, the authors aim to fill this gap. The authors develop and test a moderated mediation model linking ExtDT to BMI. Drawing on the dynamic capabilities view, the authors' model posits that the effect of ExtDT on BMI is mediated by BDAC, while environmental hostility (EH) moderates these relationships. Design/methodology/approach: The authors adopt a quantitative approach based on bootstrapped partial least square-path modeling (PLS-PM) to analyze a sample of 200 Italian data-driven SMEs. Findings: The results highlight that ExtDT and BDAC positively affect BMI. The findings also indicate that ExtDT is an antecedent of BMI that is less disruptive than BDAC. The authors also obtain that ExtDT solely does not lead to BDAC. Interestingly, the effect of BDAC on BMI increases when EH moderates the relationship. Originality/value: Analyzing the relationships between ExtDT, BDAC and BMI from an external environment perspective is an underexplored area of research. The authors contribute to this topic by evaluating how EH interacts with ExtDT and BDAC toward BMI. [ABSTRACT FROM AUTHOR]

Purpose – Artificial intelligence (AI) can enhance human resource resiliency (HRR) by providing the insights and resources needed to adapt to unexpected changes and disruptions. Therefore, the present research attempts to develop a framework for future researchers to gain insights into the actions of AI to enable HRR. Design/methodology/approach – The present study used a systematic literature review, bibliometric analysis, and network analysis followed by content analysis. In doing so, we reviewed the literature to explore the present state of research in AI and HRR. A total of 98 articles were included, extracted from the Scopus database in the selected field of research. Findings – The authors found that AI or AI-associated techniques help deliver various HRR-oriented outcomes, such as enhancing employee competency, performance management and risk management; enhancing leadership competencies and employee well-being measures; and developing effective compensation and reward management. Research limitations/implications – The present research has certain implications, such as increasing the HR team's proficiency, addressing the problem of job loss and how to fix it, improving working conditions and improving decision-making in HR. Originality/value – The present research explores the role of AI in HRR following the COVID-19 pandemic, which has not been explored extensively.

In the era of the fourth industrial revolution (Industry 4.0), companies are beginning to recognize the importance of large amounts of data available to come up with suitable decisions and to aid their strategies through Big Data. This study sheds light on the importance of using Big Data in the fast-food industry and highlights the potential benefits and challenges associated with its use. The study used structured interviews to gather data from fast-food company managers in Mahikeng. Structured interviews allow for the collection of in-depth and detailed data, which provide a comprehensive understanding of the experiences and perspectives of the participants. The study also identified several challenges that need to be addressed, such as data privacy and security concerns, technical challenges, and organizational barriers. One of the key contributions of this study is its focus on the fast-food industry in Mahikeng, which has not received much attention in the literature on Big Data applications. Overall, the study highlights the need for fast-food companies to invest in data analytics capabilities and to develop strategies for leveraging Big Data to improve their operations, customer experience, and competitiveness in the market. By doing so, fast-food companies can gain a competitive advantage and achieve sustainable business success in the era of Industry 4.0.

Abstract With the accelerating process of global industrialization, the automated fabrication industry, as an important part of the industry, is also developing rapidly. Due to the low efficiency and high resource consumption of traditional manufacturing, it is more important to improve the production efficiency of conventional manufacturing and realize the resource-saving development mode. Due to the characteristics of system digital-driven technology, it can promote the intelligent development of the manufacturing industry. Therefore, the deep integration of system digital-driven technology and manufacturing is conducive to the intelligent promotion of manufacturing. Comparing the difference between the traditional manufacturing industry and the intelligent manufacturing industry, the application potential of system digitalization technology in the manufacturing field is analyzed. Based on the analysis of the theoretical basis of the system digital-driven technology and the application of the system digital technology in the main fields, the importance of the technology for the improvement of the accuracy index in the manufacturing process is emphatically analyzed. The maximum increase can be 5.3%, and the minimum increase can be 3.3%. It can be seen that the deep integration of digital-driven technology and manufacturing can not only improve the intelligent level of manufacturing, realize the intensive development of the industry, but also realize the data sharing of the entire industrial chain.

Abstract Simulation in healthcare, empowered by big data analytics and artificial intelligence (AI), has the potential to drive transformative innovations towards enhanced interprofessional collaboration (IPC). This convergence of technologies revolutionizes medical education, offering healthcare professionals (HCPs) an immersive, iterative, and dynamic simulation platform for hands-on learning and deliberate practice. Big data analytics, integrated in modern simulators, creates realistic clinical scenarios which mimics real-world complexities. This optimization of skill acquisition and decision-making with personalized feedback leads to life-long learning. Beyond clinical training, simulation-based AI, virtual reality (VR), and augmented reality (AR) automated tools offer avenues for quality improvement, research and innovation, and team working. Additionally, the integration of VR and AR enhances simulation experience by providing realistic environments for practicing high-risk procedures and personalized learning. IPC, crucial for patient safety and quality care, finds a natural home in simulation-based education, fostering teamwork, communication, and shared decision-making among diverse HCP teams. A thoughtful integration of simulation-based medical education into curricula requires overcoming its barriers such as professional silos and stereo-typing. There is a need for a cautious implantation of technology in clinical training without overly ignoring the real patient-based medical education.