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This paper is aimed at exploring the role of natural language in organizational learning performances, particularly under conditions of high complexity of external environment. To address this issue, the stylized representation of organizational learning proposed in the seminal paper of March of 1991 “Exploration and exploitation in organizational learning” is used as a reference model to build up an agent-based computational laboratory. Through this lab simulative experiments have been settled and performed to verify in which environmental conditions a cognitive system capable of managing vague descriptions of reality is more effective than a formal system which instead provides precise descriptions. Experimental results show that in stable environments the language-based management of organizations does not offer significant advantages with respect to traditional approaches. In turbulent environments, instead, the benefits of natural language are related to the ability of the fuzzy organization to cope effectively with environmental complexity (modelled as turbulence) in the long run.

Overcrowding is a very challenging problem that Emergency Departments (EDs) face every day. Many scholars dealt with it focusing on a single ED and few works analyse the problem in a network perspective. In this paper, we reinforce the idea that managing EDs as a network can be an appropriate solution for mitigating overcrowding. Based on energy smart grid concept, we propose an optimization model of demand side management for reducing the waiting time of the ED network. In order to show the potential of model, its application to a 3 EDS network located in Naples (Italy) is presented.

This article describes how innovations are exploited in Campania (Italy) to improve health outcomes, quality of life, and sustainability of social and healthcare services. Campania's strategy for digitalization of health and care and for healthy aging is based on a person-centered, life-course, “One Health” approach, where demographic change is considered capable of stimulating a growth dynamic linked to the opportunities of combining the “Silver Economy” with local assets and the specific health needs of the population. The end-users (citizens, patients, and professionals) contribute to the co-creation of products and services, being involved in the identification of unmet needs and test-bed activity. The Campania Reference Site of the European Innovation Partnership on Active and Healthy Aging is a flexible regional ecosystem to address the challenge of an aging population with a life-course approach. The good practices, developed in the context of research and innovation projects and innovative procurements by local stakeholders and collaborations with international networks, have been allowing the transfer of innovative solutions, knowledge, and skills to the stakeholders of such a multi-sectoral ecosystem for health.

The significant progress in scientific research and innovation has led to the need for a new paradigm to legitimise the innovation process in society and politics. The European Union, with the Horizon 2020 framework program and Horizon Europe, institutionalises this change by defining the concept of responsible research and innovation (RRI), aiming at greater inclusiveness and sustainability in the research and innovation processes. This paper aimed to present an agent-based model (ABM) to simulate the dynamics between the different actors that cooperate within networks during the innovation process, taking the inclinations toward RRI practices into account. The different types of agent, their characteristics, and the different strategies that they follow have been formulated within the Horizon 2020 project I AM RRI-Webs of Innovation Value Chains (IVCs) of Additive Manufacturing (AM) under consideration of RRI. Besides, some experiments are reported to validate the model, ensuring its rigor and making our model a useful tool for policymakers, assisting them in defining strategic guidelines for disseminating and encouraging RRI best practices and defining the critical factors of the innovative cooperative process.

This paper intends to contribute, theoretically and methodologically, to the debate in organizational design’s research on the need of new approaches and tools able to cope with the increasing complexity of the organizations and of the environments in which they operate. The article presents a set of simulative experiments carried out through an agent-based computational laboratory (Computational Laboratory of Organizational Design-CLOD), to explore the eventual benefits on organizational performances obtained through informal coordination mechanisms based on natural language. Main results of the research show that internal coordination mechanisms based on informal systems, like the natural language, allow organizations to achieve better performances under different external and internal conditions. Particularly, in turbulent environments the coordination through natural language provides the organizations with additional capabilities to adapt to changing external requirements.

Patients affected by coronary artery obstruction, generally undergo aortocoronary bypass, an open-heart surgery that considerably affect health care expenditure. Since that, the monitoring and government of Aortocoronary bypass performance may be of help in health care management. In this work we compare various machine learning-based classification algorithms, to determine the length of stay for aortocoronary bypass. Data were collected on a group of 116 patients of the "San Giovanni di Dio e Ruggi D'Aragona"University Hospital of Salerno (Italy). Different socio-demographic, clinical, and organizational factors were taken into consideration as input parameters of the model for carrying out the classification analysis. The predictive capability of each of the tested machine learning algorithms was assessed in terms of accuracy and error percentages in the classification and obtained results were compared. Among the adopted algorithms, the Random Forest showed far better performances than the other ones, with an accuracy level of around 97%, thus potentially suggesting the Random Forest as a reliable predictive tool in the determination of the length of hospital stay of healthcare data for patients undergoing coronary artery bypass surgery.

PurposeThe purpose of this paper is to contribute to the debate on exploration–exploitation trade-off in the context of new ventures creation, where, particularly at the empirical level, there is a limited understanding of whether and how this trade-off is achieved and how start-ups performances are affected by the way in which they face the exploration–exploitation dilemma.Design/methodology/approachA qualitative case study approach has been adopted as a methodology to conduct the research. Six Italian innovative start-ups were selected and analyzed through in-depth interviews with founders and data collection to understand whether and how start-ups adopt exploration and exploitation solutions to face critical events in their business lives.FindingsThe most evident result of this study is that start-ups adopt more frequently a temporal separation of exploration and exploitation activities as the preferred mode for balancing learning and innovation tension. They do not seem to exhibit a defined or a common path in the way they realize the temporal separation between exploration and exploitation. Instead, they mostly oscillate. The ambidextrous solution is selected in only a few cases and not consecutively. The pre-entry knowledge profile seems to influence the choice of start-ups at the beginning of their lives.Practical implicationsThis research has implications for the whole start-up’s ecosystem, comprising incubators/accelerators, advisors, intermediaries, venture capitalists, new venture founders and policymakers. For example, by knowing the typology of knowledge and competence gaps start-ups usually aim to fill when they face particular events, intermediaries (such as incubators) could better plan initiatives and strategies supporting new ventures in the process of growth and stabilization. Furthermore, the venture capitalists can benefit from this research, by planning specific interventions for each critical event based on specific resources and competencies gaps and guiding for more promising start-ups.Originality/valueThis paper presents a novel application of entrepreneurial learning approach in the context of new venture creation. To reach this aim, a classification of exploration/exploitation solutions has been developed.

This paper presents a dashboard for assessing the quality of Triage decision-making process in the assignment of the priority code to patients arriving at an emergency department. The quality is assessed through performance indexes aimed at measuring both the nurses’ accuracy and the presence of nurses’ shared behavior in the Triage assignment process. The theoretical perspective of the cognitive heuristic, that is a cognitive shortcut strategy to make decisions, is the framework to design performance indexes of the dashboard. The dashboard has been tested in two Emergency Departments. We gather the data input through the submission to 54 nurses of a questionnaire, simulating 25 patients’ scenarios. The dashboard reveals a disparity between nurses in priority level assignment. This disparity depends on nurses’ capacity in disregarding some information and in considering the right cue. We find the presence of sharing behaviors and inclination to over or under assessment by nurses. This performance analysis, integrated with the nurses’ reasoning in assigning the priority code, can address the management in designing improvement actions at individual level and at the organizational one. In addition, the application to two Emergency Department confirms the influence of organizational aspects in the individual decision process.

The objective of the paper is to present the use of the March model on exploration and exploitation in organisational learning (1991) as a virtual laboratory of organisational design. The virtual laboratory, named CLOD (Computational Laboratory of Organisational Design), will be devoted to exploring the behaviour of organisational learning and evaluation systems under different external and internal conditions. The contribution of the paper would be twofold: firstly, the cellular automata model of March is transposed in an agent-based environment and then extended to be used as a virtual lab supporting organisational design; secondly, a fuzzy version of the model is proposed in order to analyse, through generative experiments, the differences between crisp and fuzzy organisational learning approaches to environmental complexity.