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The COVID-19 pandemic has stimulated a global movement of open innovation initiatives aimed to provide knowledge and tools to support policy decisions and actions in the emergency scenario. The authors describe an open innovation process aimed to build an information coordination system to reduce the infection diffusion within the population.

The authors use coordination theory principles to elaborate a framework of activities, resources and dependencies among information resources and producers in the COVID-19 emergency. The framework was applied to develop a case study aimed at describing a health emergency system implemented by Dyrecta Lab (a research laboratory on computer science) and CITEL (a medical research center).

The authors describe the existence of relevant “flow,” “fit” and “share” dependencies within the activities of infection containment and medical treatment. The authors identify eight key resources and a number of actors involved in those activities, and describe a platform able to gather a multitude of epidemic-related metrics with the purpose to address dependencies and support decision making.

The authors provide insights for advancing the academic discussion on process coordination principles in time-constrained, volatile and highly demanding scenarios.

The value of the authors’ research can be identified for practitioners engaged to develop innovative development projects for public utility. The authors provide a contribution also for first responders and health operators involved in management of the current and future emergencies.

The adoption of process coordination principles is a relatively new and powerful approach to analyze and optimize the processes that characterize the management of emergency scenarios. Besides, the study and application of open innovation in healthcare are partially limited.

The main aim of the study is to empirically investigate the relationship between strategic orientation and a dual conceptualization of performance (i.e. non-economic performance and economic performance) in the research context of corporate museums, which are owned and run by private companies. Furthermore, the study aims to explore the nature of the relationship between the dual performance, shedding light on the relevance of non-economic results for this peculiar category of museums.

The study is based on survey data from 105 Italian corporate museums, which represent almost the entire population in the country (91%). A structural model was estimated using SmartPLS software in order to examine the direct and indirect effects of strategic orientation on corporate museums' non-economic and economic performance.

The findings show that only if corporate museums are able to achieve non-economic performance, creating value for the owning company and the local community, they can also have good results in economic terms. Thus, the non-economic performance acts as a mediator into the relationship between strategic orientation and economic performance.

The current work is a pioneer study for the empirical investigation of performance within corporate museums. The empirical model of the study, based on a dual conceptualization of performance and a mediation analysis, is completely innovative in this research context.

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.