Fernanda Strozzi

Purpose – This study aims to explore the interaction between social media and firms’ absorptive capacity (ACAP), which involves acquiring, assimilating, transforming and exploiting external knowledge. While existing research often examines these topics separately, this paper bridges the gap by analyzing their interconnectedness to provide a holistic understanding. Design/methodology/approach – A data-driven approach combining bibliographic coupling and authors’ keyword network analysis was used to mitigate the subjectivity of traditional literature reviews. A total of 50 peer-reviewed journal and conference articles from Scopus were analyzed using VOSviewer to identify thematic research streams (RSs). Findings – The analysis identifies five RSs: the impact of social media and ACAP on innovation performance; their effect on performance in a multi-level landscape across different analytical units (individual, team, organization); the role of social media in specific contexts, including internal and external collaborations; online communities; and the new product development process. A key finding is the internal inconsistency in the literature regarding the relationship between ACAP and social media constructs. To address this, two alternative research frameworks are proposed, offering theoretical foundations for future studies. Originality/value – To the best of the authors’ knowledge, this paper is the first to comprehensively address the interaction between social media and ACAP by resolving theoretical inconsistencies, clarifying variable roles and proposing frameworks to guide future research. It also offers actionable insights for R&D and innovation managers to strategically leverage social media for acquiring and exploiting external knowledge, thereby enhancing competitive advantage.

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.

Purpose: over 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/approach: a 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. Findings: starting 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 implications: from 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/value: this study is a first attempt to unveil the evolution of knowledge in the field of sustainable innovation by utilizing bibliometric tools.

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: 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.

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.

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.

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.

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.