Background: This study explores sourcing risk in supply chains by identifying key risk categories, trends, and management strategies. It responds to increased vulnerabilities exposed by recent global disruptions such as the COVID-19 pandemic and geopolitical conflicts. Methods: The research applies a Systematic Literature Network Analyses (SLNA) combined with textual analysis to examine 687 peer-reviewed publications over the past three decades using the PRISMA protocol. Citation network analysis, keyword co-occurrence mapping, and main path analysis were conducted to map intellectual developments. Additionally, textual analysis using the Semantic Brand Score (SBS) approach revealed thematic relevance, novelty, and impact. Results: A shift exists from foundational supplier optimization models to resilience-building/strengthening, ethical sourcing, and technology-enabled strategies. Responsible sourcing and modern slavery were found to be the most innovative and underexplored areas. Research on sector-specific challenges, particularly for small and medium-sized enterprises, remains limited. Conclusions: Sourcing risk has become a systemic challenge requiring resilience, ethics, and data-driven coordination across supply networks.

Industry 4.0 marks a significant change in the business world, brought about by technologies like automation, IoT, AI, smart factories, and cyber-physical systems. Industry 5.0, on the other hand, prioritizes worker well-being and emphasizes human-centric approaches in manufacturing. While these revolutions offer improved productivity, sustainability, and resilience, integrating them presents challenges, particularly in securing a skilled workforce adept in Artificial Intelligence (AI). Higher education institutions (HEIs) are critical in addressing this need by updating their curricula and enhancing their infrastructure. The boundaries between industries are becoming less distinct due to digital technologies, which means that education needs to adjust to changing requirements. The use of AI tools in education has become a driving force in transforming learning experiences, fostering creativity, and getting people ready for the era of digitalization. This paper explores how Learning Factories (LFs) can serve as a means for AI education, focusing on existing white- and blue-collar workers who primarily benefit from tools like LFs for reskilling and upskilling, as well as prospective workers such as university graduates who can acquire the necessary skills through these factories before entering the workforce.

GoogLeNet is a pre-trained Convolutional Neural Network (CNN) that allows transfer learning and has achieved high recognition rates in image classification tasks. A Recurrence Plot (RP) is an imaging method that depicts the recurrence of the state space system using coloured points and lines in 2D images. This work contributes to facilitating time series feature extraction by proposing a method that applies the GoogLeNet to time series images obtained with RP. The developed method is tested using simulated time series and selected time series from the M3 competition dataset. The results shows that the transfer learning approach allowed the extraction of business time series features by means of a GoogLeNet fine-tuned using 100 simulated time series. The combination of GoogLeNet and RPs outperforms the alternative and easier combination of GoogLeNet and plots of the time series and support the convenience of the RP transformation step. This application of deep learning techniques to business time series imaging offers opportunity for further developments.

Within business forecasting, the choice of the forecasting model is crucial. In fact, not all models can be functional to all time series and their applicability is based on the features of the considered time series, such as trend, seasonality and randomness. The identification of features is, then, a fundamental activity to achieve the desired forecasting performance. On the other hand, time series features identification is known to be a time-consuming activity that requires the intervention of the decision maker. Hence, performing such a step of the forecasting process in an automated and reliable way would considerably improve the whole process. To this aim, this work proposes a method that combines the Recurrence Plot (RP) and the Convolutional Neural Networks (CNNs) to perform typical business time series features identification. RP is a visual tool that represents recurrence of the state space system by means of oriented structures in 2D images. CNNs are popular deep learning models that have achieved high recognition rates performing image classification tasks. Machine learning algorithms have been already successfully applied to the business forecasting process, limited to forecasting model selection step, while the CNN have been successfully applied in different fields, such as medicine, distinguishing ischemic disease in electroencephalogram images, not yet to business time series features identification. This work applies the GoogLeNet CNN to the RPs that represent simulated time series and measures the obtained classification accuracy by means of confusion matrix.

BACKGROUND:The crowding of emergency departments (EDs) is one of the major poor-quality factors for patients. Because of this, measuring ED performance in Healthcare Systems is a difficult but an important task needed to enhance quality and efficiency.PURPOSE:(i) Development of a tool to observe and evaluate performance measurement, analysing two critical variables (quality and efficiency), verifying the change in performance due to the implementation of a new organizational model; (ii) the implementation of the tool in two EDs with comparable annual volumes of activity in the Italian context.METHODS:A literature review on ED performance was conducted in order to identify acknowledged performance measurements used in this context that can be used in the development of a tool for the evaluation of EDs' performance. The goal is to have a matrix that is easily understood and that shows a simple relationship between quality and efficiency. This was possible by setting up a method that translates the ED annual performance data (in this case the data related to year 2018) into a graph with benchmarking purposes, also including an actual situation (AS-IS) view as compared to a TO-BE situation (i.e., before and after an organizational change occurred).RESULTS:Two real EDs were compared and their results depicted; they can be easily related with each other to benchmark healthcare organisations. More precisely, a comparison can be used for two main tasks: - identifying different strategic areas and observing the positioning of a health organization at any given moment in time, seeing where it stands among its competitors in a matrix; - knowing how to best allocate available resources and where to divert investment. Results show that the tool depicts the situation of EDs, with a clear indication of how performance increases or decreases in the case of AS-IS and TO-BE evaluation, and also offers a quick understanding of the benchmarked EDs' situations.PRACTICE IMPLICATIONS:The results can be shown on a graph that summarises the performance change for the AS-IS versus TO-BE conditions. This can be a useful tool for the ED and for the hospital decision makers, as it allows for an observation of performance by analysing two critical variables: the quality and the efficiency of the service provided. The former represents customer satisfaction, which in this work is the combination of two factors (i.e., appropriateness of assigning the triage code and patient satisfaction), and the latter represents the ED's efficiency in providing emergency care. The tool also helps the organizational changes to be easily evaluated.

PurposeThe authors analyse the impact of European funding research programmes on the topic of Ambient Assisted Living by considering its status, future context, and the implications for prospective knowledge management.Design/methodology/approachThe authors apply our variation of classical Systematic Literature Review – Systematic Literature Network Analysis, which also includes bibliographic networks – to identify the readership cliques of the associated technological publication outputs.FindingsThe authors’ main conclusion suggests that there was an increase in scientific production on AAL fields just after the start of the two EU funding programmes (2008 and 2014). Three main research directions were identified: activity and vital sign recognition, human-computer interaction and technology acceptance.Originality/valueTo date, previous reviews on Ambient Assisted Livig focus on specific aspects, such as the study of technology. The present review provides a complete overview of Ambient Assisted living technology and it grasps how the European funds have impacted on the development of this technology.

The purpose of this review is to describe the landscape of scientific literature enriched by an author's keyword analysis to develop and test blockchain's capabilities for enhancing supply chain resilience in times of increased risk and uncertainty. This review adopts a dynamic quantitative bibliometric method called systematic literature network analysis (SLNA) to extract and analyze the papers. The procedure consists of two methods: a systematic literature review (SLR) and bibliometric network analysis (BNA). This paper provides an important contribution to the literature in applying blockchain as a key component of cyber supply chain risk management (CSRM), manage and predict disruption risks that lead to resilience and robustness of the supply chain. This systematic review also sheds light on different research areas such as the potential of blockchain for privacy and security challenges, security of smart contracts, monitoring counterfeiting, and traceability database systems to ensure food safety and security.

As competition continues swinging from individual 'company level' to 'supply chain (SC) level' courtesy of diverse and erratic customer behaviour in this globalisation era, survival of such SC is hinged on careful adoption of strategies aligned with organisational goals. Lean and agile are critical strategies because the former ensures efficient use of resources while the latter involves matching supply with demand in turbulent/unpredictable markets. Careful adoption of both strategies will lead to SC performance improvement. This paper aims at advancing their understanding by conducting an intensive scientific literature review to unravel the state of art of lean-agile SC by examining the procedure of knowledge conceiving, transmission, and development from a dynamic viewpoint. To achieve this, a dynamic cum quantitative review technique named 'systematic literature network analysis (SLNA)' is applied. The investigation enabled unravelling research directions/emerging themes in the lean-agile field thereby supporting researchers/newcomers to target specific themes to explore.

Purpose Lean and agile are essential supply chain management (SCM) strategies that enhance companies' performance. Previous studies have reported the capabilities of different SCM strategies to enhance performance; however, the emergence of Industry 4.0 technologies has bred focus on the possibility of attaining more levels of operational performance. Despite being demonstrated helpful at enabling supply chain (SC) strategies, the literature linking Industry 4.0 with SCM strategies is still in its infancy. Thus, this work investigates the degree to which “Industry 4.0 technologies” enable the implementation of lean and agile practices and subsequently assesses the potential performance implications of integrating Industry 4.0 technologies with the SC operations. Design/methodology/approach The work employs an exploratory case study approach using empirical data from selected organisations drawn from an Estonian manufacturing cluster and digital solution providing companies. The data collected via interviews were used to assign numerical scores and subsequently aggregated across the five cases for the research variables of interest. The work is crowned with a model grounded on the cross-case analysis to depict which technologies impact each of the lean and agile practices. Findings The analysis enabled comprehension of the potential impact and level of importance of the main Industry 4.0 technologies on lean and agile practices and ultimately the potential implication on performance. The findings revealed that the technologies have a high impact on the practices. Although the impacts are of varying degrees, the analysis provides means to identify the technologies with the most significant impact on lean and agile SCM and the sets of practices with the greatest likelihood of being enabled by various digital technologies. Practical implications The work presents various lean and agile practices that practitioners can deploy to operations, alongside the technologies that could support the implementation of the practices towards achieving the various performance measures. Also, it provides some guides for the digital solution providing companies towards understanding the SCM practices that can be improved upon by various digital technologies. This enables them to have more saleable proposals for intending companies who might be sceptical about transiting into the digital operation phase. Originality/value This is the first attempt to empirically address the connection between Industry 4.0 technologies and the integrated lean and agile strategies despite literature backing of the complementary nature of the two SCM strategies.

As the world becomes globalised, companies fight for survival by connecting their in-house processes with external suppliers/customers. To remain competitive, companies must integrate innovative capabilities like 'industry-4.0 technologies' with their operation and supply chain (SC) strategies. The integration of various strategies has been investigated with the associated effect on performance; however, studies on how industry 4.0 technologies might support integrated strategies are still incipient. This work investigates the hierarchical relationships of 'industry 4.0 technologies' with lean and agile strategies. Adopting the 'Interpretive Structural Modelling (ISM)' technique to present a model depicting the linkage, the work also classifies the technologies and practices according to their 'driving' and 'dependency' powers. The findings revealed that the technologies have a high affinity to enable the implementation of lean and agile strategies. Among the nine technologies included in the study, 'Cyber-Physical-System', 'Internet-of-Things', 'Cloud-Computing', and 'Big-Data-Analytics' have the highest driving powers, signifying their higher affinity with the practices. Meanwhile, all the practices have a high enough affinity to be influenced by the technologies, except for a few (3/16 of lean and 2/9 of agile) that possess affinities too low to be driven by these technologies. The theoretical and managerial impacts of the research are also emphasised.

The main purpose of this paper is to identify and rank the factors affecting the implementation of blockchain technology as a key component of cyber supply chain risk management (CSCRM). This paper adopted a comprehensive literature review and the opinion of experts with knowledge of blockchain to identify success factors for blockchain adoption in a CSCRM. The seven factors identified were modeled using an “Interpretive Structural Modelling (ISM)” in the structure of a hierarchical model to envision the contextual relationship amongst identified success factors (SFs) for blockchain adoption in a cyber-secure supply chain. Using the MICMAC approach, the influential factors were clustered according to their driving and dependence powers. Findings from the study suggest, “Supporting platforms and tools” and “Interoperability & Standardization” are the two factors at the top level of the hierarchy, implying weak driving power but strongly dependent on the other factors. It is also observed that “Clarity regulatory provisions” and “Organizational culture and top management support” are significant adoption success factors of blockchain in cyber supply chain risk management and were placed at the bottom level of the hierarchy with higher driving power.

PurposeOver the last few decades, more emphasis has been placed on those innovations that can reconcile economic, social and environmental goals in order to achieve a “win-win-win” situation. This paper aims to systematise the scientific literature on Sustainable Innovation as a broad field in order to identify the most relevant scholars and their significant contributions as well as existing lines of research. Finally, future research directions are suggested.Design/methodology/approachA novel methodology, the Systematic Literature Network Analysis, has been applied. By using a dynamic approach to the traditional Systematic Literature Review, the present review investigates the creation, transfer, and development of knowledge throughout the epistemic community of Sustainable Innovation.FindingsStarting from a sample of 1,108 articles, the critical assessment of the results detected five main themes: (1) “the role of Regulation, Market and Technology”; (2) “Eco-Innovation determinants and firm specific factors and the debate between corporate environmental performance and corporate financial performance”; (3) “Green innovation and internal and external drivers”; (4) “The strategic determinants of green (non-green) innovation”; (5) “The interplay between policy, regulations and the green innovation”.Practical implicationsFrom a practitioner's perspective, this study provides an objective view on the current internal, external drivers and strategic determinants of sustainability-oriented innovations and relevant studies that can guide managers in their decision-making processes and enhance sustainable innovation performance.Originality/valueThis study is a first attempt to unveil the evolution of knowledge in the field of sustainable innovation by utilizing bibliometric tools.

The aim of this study is to investigate the body of literature on digital twins, exploring, in particular, their role in enabling smart industrial systems. This review adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks, and keywords burst detection with the aim of clarifying the main contributions to this research area and highlighting prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, visible within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented, and application areas. The burst detection completes the analysis identifying the trends and most recent research areas characterizing research on the digital twin topic.Decision-making, process design, and life cycle as well as the enabling role in the adoption of the latest industrial paradigms emerge as the prevalent issues addressed by the body of literature on digital twins. In particular, the up-to-date issues of real-time systems and industry 4.0 technologies, closely related to the concept of smart industrial systems, characterize the latest research trajectories identified in the literature on digital twins. In this context, the digital twin can find new opportunities for application in manufacturing, control, and services.

Over the last few years, the increasing level of cyber risks derived from the growing connectedness of Industry 4.0 has led to the emergence of blockchain technology as a major innovation in supply chain cybersecurity. The main purpose of this study is to identify and rank the significant barriers affecting the implementation of blockchain technology as a key component of cyber supply chain risk management (CSCRM). This research relied on the “interpretive structural modeling (ISM)” technique in the structure of a hierarchical model to investigate the contextual relationships of identified challenges for blockchain adoption in CSCRM; it also classifies the influential challenges based on their driving and dependence powers. The results highlight that “cryptocurrency volatility” is the challenge at the top level of the hierarchy, implying weak driving power but it is strongly dependent on the other challenges. “Poor regulatory provisions”, “technology immaturity”, “dependent on input information from external oracles”, “scalability and bandwidth issues”, and “smart contract issues” are significant challenges for the adoption of blockchain in cyber supply chain risk management and are located at the bottom level of the hierarchy with higher driving power. The implications for theory and practice of the research are also highlighted.

Organisational Learning (OL) is essential for the survival of an organisation and has led to a significant amount of conceptual and empirical studies. However, no attempt has yet been made to track the overall evolution of OL literature along with the inter-related concepts of learning organisation and organisational learning orientation. Therefore, the present study attempts to fill this gap and track the interdisciplinary flow of knowledge by applying a structural methodology called Systematic Literature Network Analysis (SLNA). The results reveal four main areas of investigation within the field: i) the fundamentals of OL; ii) OL in relation to managerial and economic variables; iii) management of learning organisation; iv) learning orientation in relation to managerial and economic variables. Furthermore, this review contributes by arranging the findings into a theoretical framework which is termed organisational learning chain. Based on the co-analysis of main themes and key concepts detected, the framework integrates and highlights the factors that influence learning performance in and by organisations. Finally, several further research avenues are discussed, and the benefits of the applied review methodology are highlighted.

BACKGROUND:Change is an ongoing process in any organizations. Over years, healthcare organizations have been exposed to multiple external stimuli to change (eg, ageing population, increasing incidence of chronic diseases, ongoing Sars-Cov-2 pandemic) that pointed out the need to convert the current healthcare organizational model. Nowadays, the topic is extremely relevant, rendering organizational change an urgency. The work is structured on a double level of analysis. In the beginning, the paper collects the overall literature on the topic of organisational change in order to identify, on the basis of the citation network, the main existing theoretical approaches. Secondly, the analysis attempts to isolate the scientific production related to the healthcare context, by analysing the body of literature outside the identified citation network, divided by clusters of related studies.METHODOLOGY:This review adopted a quantitative-based method that employs jointly systematic literature review and bibliographic network analysis. Specifically, the study applied a citation network analysis (CNA) and a co-occurrence keywords analysis. The CNA allowed detecting the most relevant papers published over time, identifying the research streams in literature.RESULTS:The study showed four main findings. Firstly, consistent with past studies, works reviewed pointed out a convergence on the micro-level perspective for change's analysis. Secondly, an organic viewpoint whereby individual, organization and change's outcome contribute to any organizational change's action has been found in its early stage. Thirdly, works reported change combined with innovation's concept, although the structure of the relationship has not been outlined. Fourth, interestingly, contributions have been limited within the healthcare context.CONCLUSION:Human dimension is the primary criticality to be managed to impede failure of the re-organizational path. Individuals are not passive recipients of change: individual change acceptance has been found a key input. Few papers discussed healthcare professionals' behaviour, and those available focused on technology-led changes perspective. In this view, individual acceptance of change within the healthcare context resulted being undeveloped and offers rooms for further analyses.

ABSTRACT The aim of this study is to investigate the body of literature on digital twins, exploring, in particular, their role in enabling smart industrial systems. This review adopts a dynamic and quantitative bibliometric method including works citations, keywords co-occurrence networks, and keywords burst detection with the aim of clarifying the main contributions to this research area and highlighting prevalent topics and trends over time. The analysis performed on citations traces the backbone of contributions to the topic, visible within the main path. Keywords co-occurrence networks depict the prevalent issues addressed, tools implemented, and application areas. The burst detection completes the analysis identifying the trends and most recent research areas characterizing research on the digital twin topic. Decision-making, process design, and life cycle as well as the enabling role in the adoption of the latest industrial paradigms emerge as the prevalent issues addressed by the body of literature on digital twins. In particular, the up-to-date issues of real-time systems and industry 4.0 technologies, closely related to the concept of smart industrial systems, characterize the latest research trajectories identified in the literature on digital twins. In this context, the digital twin can find new opportunities for application in manufacturing, control, and services.

The debate about Circular Economy (CE) has been increasingly enriched by academics through a vast array of contributions, based on several theoretical perspectives and emanating from several research domains. However, current research still falls short of providing a holistic and broader view of CE, one that combines existing themes and emerging research trends. Accordingly, based on a Systematic Literature Network Analysis, this paper tackles this gap. First, a Citation Network Analysis is used to unearth the development of the CE literature based on papers’ references, whilst the Main Path is traced to detect the seminal papers in the field through time. Second, to consider the literature in its broader extent, a Keywords Co-Occurrence Network Analysis is conducted based on papers’ keywords, whereby all papers in the dataset, including the non-cited papers, are assessed. Additionally, a Global Citation Score analysis is conducted to uncover the recent breakthrough research, in addition to the Burst Analysis used to detect the dynamic development of CE literature over time. By doing so, the paper explores the development of the CE body of knowledge, reveals its dynamic evolution over time, detects its main theoretical perspectives and research domains, and highlights its emerging topics. Our findings unfold the evidence of eight main trends of research about CE, unearth the path through which the CE concept emerged and has been growing, and concludes with promising avenues for future research.

Abstract The article analyzes the interconnectedness and gaps between two interrelated streams of literature, evolving in different time periods, by employing bibliometric tools. The research on the factors of life insurance demand started in the late 1960s and early 1970s, while more intensive works on the drivers of lapses in life insurance appeared during the 2000s. We map the research fields using our own criteria to create clusters and visualize the flow of knowledge within and between the clusters employing citation network analysis (CNA). We contribute by providing the most comprehensive systematic review that integrates both fields, additionally encapsulating studies on the demand for policy loans. The article detects the most important drivers of life insurance policyholder behavior during his/her lifetime and opens new horizons for future research.

The ongoing COVID-19 epidemic highlights the need for effective tools capable of predicting the onset of infection outbreaks at their early stages. The tracing of confirmed cases and the prediction of the local dynamics of contagion through early indicators are crucial measures to a successful fight against emerging infectious diseases (EID). The proposed framework is model-free and applies Early Warning Detection Systems (EWDS) techniques to detect changes in the territorial spread of infections in the very early stages of onset. This study uses publicly available raw data on the spread of SARS-CoV-2 mainly sourced from the database of the Italian Civil Protection Department. Two distinct EWDS approaches, the Hub-Jones (H&J) and Strozzi-Zaldivar (S&Z), are adapted and applied to the current SARS-CoV-2 outbreak. They promptly generate warning signals and detect the onset of an epidemic at early surveillance stages even if working on the limited daily available, open-source data. Additionally, EWDS S&Z criterion is theoretically validated on the basis of the epidemiological SIR. Discussed EWDS successfully analyze self-accelerating systems, like the SARS-CoV-2 scenario, to precociously identify an epidemic spread through the calculation of onset parameters. This approach can also facilitate early clustering detection, further supporting common fight strategies against the spread of EIDs. Overall, we are presenting an effective tool based on solid scientific and methodological foundations to be used to complement medical actions to contrast the spread of infections such as COVID-19.