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This paper aims to investigate the process performances in Emergency Departments (EDs) with a novel data-driven approach, permitting to discover the entire patient-flow, deploy the performances in term of time and resources on the activities and flows and identify process deviations and critical bottlenecks. Moreover, the use of this methodology in real time might dynamically provide a picture of the current situation inside the ED in term of waiting times, crowding, resources, etc., supporting the management of patient demand and resources in real time.

The proposed methodology exploits the process-mining techniques. Starting from the event data inside the hospital information systems, it permits automatically to extract the patient-flows, to evaluate the process performances, to detect process exceptions and to identify the deviations between the expected and the actual results.

The application of the proposed method to a real ED revealed being valuable to discover the actual patient-flow, measure the performances of each activity with respect to the predefined targets and compare different operating situations.

Starting from the results provided by this system, hospital managers may explore the root causes of deviations, identify areas for improvements and hypothesize improvement actions. Finally, process-mining outputs may provide useful information for creating simulation models to test and compare alternative ED operational scenarios.

This study responds to the need of novel approaches for monitoring and evaluating processes performances in the EDs. The novelty of this data-driven approach is the opportunity to timely connect performances, patient-flows and activities.

The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being.

Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools.

Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker.

The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance.

The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.

Information security awareness (ISA) mainly refers to those aspects that need to be addressed to effectively respond to information security challenges. This research used focus groups to empirically investigate the main ISA dimensions that emerge from the Italian public health-care sector. This study aims to identify the most critical dimension of ISA and to evaluate the diffusion and maturity of information security policies (ISPs) of health-care infrastructure and training programs.

This research adopted a qualitative research design and focus groups as a research methodology. Data analysis was conducted using the NVIVO 14 software package and followed the principles of thematic analysis.

The focus group results highlighted that health-care personnel find it difficult to comply with the main ISA dimensions, a situation that leads to risky behaviors. Password management, data storage and transfer and instant messaging applications emerged as the most critical of the main ISA dimensions in the context of this research. It also transpired that ISPs are not all-encompassing as they mainly focus on privacy problems but neglect security concerns. Finally, training programs are not fully implemented in the investigated context, thus undermining their positive enhancing role for ISA.

The public health-care sector emerged as a critical yet still under-investigated context. The need for an in-depth investigation of organizational sciences approaches to overcoming information security challenges is also recommended in several prior research studies.

The purpose of this paper is to identify the factors influencing the growth of healthcare virtual communities of practice (VCoPs) through a seven-year longitudinal study conducted using metrics from social-network and semantic analysis. By studying online communication along the three dimensions of social interactions (connectivity, interactivity and language use), the authors aim to provide VCoP managers with valuable insights to improve the success of their communities.

Communications over a period of seven years (April 2008 to April 2015) and between 14,000 members of 16 different healthcare VCoPs coexisting on the same web platform were analysed. Multilevel regression models were used to reveal the main determinants of community growth over time. Independent variables were derived from social network and semantic analysis measures.

Results show that structural and content-based variables predict the growth of the community. Progressively, more people will join a community if its structure is more centralised, leaders are more dynamic (they rotate more) and the language used in the posts is less complex.

The available data set included one Web platform and a limited number of control variables. To consolidate the findings of the present study, the experiment should be replicated on other healthcare VCoPs.

The study provides useful recommendations for setting up and nurturing the growth of professional communities, considering, at the same time, the interaction patterns among the community members, the dynamic evolution of these interactions and the use of language. New analytical tools are presented, together with the use of innovative interaction metrics, that can significantly influence community growth, such as rotating leadership.

Using a dynamic capabilities approach, the present study aims to identify and assess the effects of organizational determinants on capabilities underlying Industry 4.0 design principles, such as integration, virtualization, real-time, automation and servitization.

PLS-SEM enables a two-stage hierarchical latent variable reflective-formative model which was used for assessing the effect of organizational determinants on Industry 4.0 design principles. Five hundred six manufacturing companies constitute the effective sample, representing a population of manufacturing companies in an industrialized country.

The findings reveal that Industry 4.0 design principles extensively depend on digitalization resource availability. At the same time, companies that possess digitalization and change management capabilities tend to devote more resources to digitalization. Finally, the paper reveals that networking and partnership capability is the critical enabler for change management and digitalization capabilities.

The paper provides empirical evidence that the successful development of Industry 4.0 design principles and their underlying integration, virtualization, real-time, automation and servitization capabilities are resource dependent, requiring significant upfront investment and continuous resource allocation. Further, the study implies that companies with networking and partnership, change management and digitalization capabilities tend to allocate more resources for Industry 4.0 transformation.

Exclusively focusing on empirical research that reported applied insights into determinants of Industry 4.0 design principles, the study offers unique implications for promoting Industry 4.0 digital transformation among manufacturing companies.

This study investigates the relationships between team dynamics and performance in healthcare operations. Specifically, it explores, through wearable sensors, how team coordination mechanisms can influence the likelihood of surgical glitches during routine surgery.

Breast surgeries of a large Italian university hospital were monitored using Sociometric Badges – wearable sensors developed at MIT Media Lab – for collecting objective and systematic measures of individual and group behaviors in real time. Data retrieved were used to analyze team coordination mechanisms, as it evolved in the real settings, and finally to test the research hypotheses.

Findings highlight that a relevant portion of glitches in routine surgery is caused by improper team coordination practices. In particular, results show that the likelihood of glitches decreases when practitioners adopt implicit coordination mechanisms rather than explicit ones. In addition, team cohesion appears to be positively related with the surgical performance.

For the first time, direct, objective and real time measurements of team behaviors have enabled an in-depth evaluation of the team coordination mechanisms in surgery and the impact on surgical glitches. From a methodological perspective, this research also represents an early attempt to investigate coordination behaviors in dynamic and complex operating environments using wearable sensor tools.

The purpose of this paper is to provide a brief answer to the following questions: in the arena for State and non-State actors, who and how may guarantee the balance between democracy, State and market? How can the citizens’ economic well-being be prioritized, in terms of national security?

Adapting both the link analysis approach and the order of Pierce’s inference stages, the author illustrates the state of the art, some economic indices and Italy’s need for countermeasures.

Starting from the notion of “social sustainability of the political decisions,” given by the author, the paper highlights the opportunity to rethink the concept of political warfare, in view of a productive fabric characterized by both a high number of small and medium-sized enterprises and the pervasiveness of mafia-type organizations. At the end, the author shares some proposal in the fields of Public Law and Social Marketing and a broader definition of the above-mentioned concept.

This study shows the link between security studies, people’s perception of grey areas and polarization of opinions and wealth, giving the reader a bottom-up input to the comprehension of the contemporary complexity.

Worldwide challenges impose public organizations to rethink their processes and satisfactorily meet citizens’ needs. Process mining (PM) techniques enable organizations to objectively analyse and improve their processes, by providing higher process transparency and efficiency. However, extant literature on PM applications in the public sector reveals there is still limited evidence on the opportunities and challenges perceived from PM introduction in the public sector, and on PM potential to enhance public sector digital transformation: this study aims to fill these gaps.

Based on Business Process Management and digital innovation fields of research, we administered a questionnaire to a sample of Italian civil servants working in different public organizations to investigate their perceptions of PM opportunities and challenges and the extent to which it can support public sector digital transformation. A three-level analysis was conducted to inspect findings with different levels of granularity, and results were analysed both descriptively and quantitatively.

We found a positive attitude towards PM introduction in the public sector, and perceived opportunities and challenges related to both the technical and the social systems. The triangulation between close-ended and open-ended questions suggests that PM could be the missing link between public sector digitalization and digital transformation. These findings can be used by policymakers to develop the best strategies to introduce PM into public organizations and support its adoption, and by researchers to further explore PM role in public sector digital transformation.

Despite PM claiming to push digital transformation, it is not clear if it is also true for public sector organizations. This paper addresses this gap and it is among the first attempts to explore PM from civil servants’ viewpoint to investigate their perceptions of PM opportunities and challenges, as well as the variables that influence these perceptions.

This study aims to provide a methodology and tools to design new organizational processes and artificial intelligence (AI)-based scoring to optimize the resources management in healthcare units.

Process design and process data-driven simulation: the processes are designed by the business process modeling and notation and the unified modeling language standards. Data processing is performed by Correlation matrix analysis and by Fuzzy c-Means data clustering. The matching between the two methods provides the most indicated final corrective actions of the “TO BE” organizational model.

This proposed method, experimentally applied in this work merging the lean management model (LMM), process mining (PM) and AI methods, named process mining organization (PMO) model (Rosa et al., 2023 (b)), is able to improve organizational processes of a hospitalization unit (HU) by developing three propaedeutic phases: (1) analysis of the current state of the processes (“AS IS”) by identifying the critical issues as bottlenecks of processes, (2) AI data processing able to provide additional classified and predicted information allowing the “TO BE” workflow process and (3) implementation of corrective actions suggested by the PMO in order to support strategic decision-making processes in the short, medium and long term by classifying an order of priority about the healthcare procedures/protocols to perform.

The main limitation of the proposed case study is in the limited number of available digital data to process. This aspect reduces the capability to interpret result. In any case, the proposed methodology is a “launch” work to define a new approach to integrate organizational processes including workflow design and AI scoring. Future work will be focused on managerial implications due to use of the discussed method: design and development of new human resource (HR) organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.

Main implications are in healthcare managerial scenarios: design and development of new HR organizational protocols following data analysis to optimize costs and care services and to decrease injury compensation claims.

Care services optimization is addressed on HUs.

The design of HR organizational processes integrates AI-driven data decision-making processes. This case study examines AI-based innovation analytics addressed on resource efficiency.

This study aims to apply Process Mining (PM) techniques to identify the critical elements that primarily affect the trials’ duration and suggest the best practices to enable their more efficient execution, reduce their duration and, consequently, create public value, through a case study conducted in an Italian Civil Court.

Through PM analyses and in-depth discussions with the court staff, we analysed the trials with the longest duration and those belonging to a specific subject matter to identify peculiar features and inefficiencies that prolong the trials’ duration.

Our results highlight how innovative tools like PM can revolutionise the judicial system by providing judges with objective trials data that can support and facilitate the entire trials’ definition. However, many issues, especially related to the little spread data culture and process-oriented approach in courts, are highly present, leading to data inconsistencies and subsequent difficulties in trials’ analysis and interpretation.

Little research has devoted attention to the PM potential to enhance the judiciary. Our study contributes to this strand, yet adopting a different approach: it investigates the trials’ excessive length by focusing on bottlenecks and inefficient activities that slow down trials and identifies activities’ thresholds to monitor the trials’ execution and limit delays.