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The aim of this paper is to develop the conceptual design of an information and communication technologies (ICT) platform supporting the inbound open innovation (OI) process within the technological developments business unit of Leonardo Defence Systems.

After a preliminary phase concerning the context analysis, methodology includes three main steps: conceptualization of functions; preliminary design; and conceptual design of the system/SW architecture. In each of these phases, the authors tried to merge evidence from the scientific literature with empirical insight emerging from the field.

Results report the conceptual design proposal for an integrated ICT platform supporting the OI. It includes the conceptualization of main functions, the preliminary design deriving from use cases and the proposal for the overall system architecture and data model.

The research focuses only on the conceptual design phase; at this stage, the platform has not been still implemented or tested. Also, generalizability concerns may arise from the single-application context.

The outcoming conceptual design can be useful for firms that open their boundaries to external partners, as well as for software developers which could draw on it. Firms approaching similar OI challenges can re-contextualize the platform to their own setting.

Originality of this research relies on the attempt to show how ICT can support firms in their OI processes and, secondly, to support firms aiming to create a positive environment that encourages people at leveraging existing external technological opportunities and sources of knowledge. In so doing, a systematic design approach to the definition of the conceptual proposal is also pursued.

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.

This manuscript discusses the main findings gathered through a systematic literature review aimed at crystallizing the state of art about evidence-based management (EBMgt) in healthcare. The purpose of this paper is to narrow the main gaps in current understanding about the linkage between sources of evidence, categories of analysis and kinds of managerial decisions/management practices that different groups of decision-makers put in place. In fact, although EBMgt in healthcare has emerging as a fashionable research topic, little is still known about its actual implementation.

Using the Scopus database as main source of evidence, the authors carried out a systematic literature review on EBMgt in healthcare. Inclusion and exclusion criteria have been crystallized and applied. Only empirical journal articles and past reviews have been included to consider only well-mature and robust studies. A theoretical framework based on a “process” perspective has been designed on these building blocks: inputs (sources of evidence), processes/tools (analyses on the sources of evidence), outcomes (the kind of the decision) and target users (decision-makers).

Applying inclusion/exclusion criteria, 30 past studies were selected. Of them, ten studies were past literature reviews conducted between 2009 and 2014. Their main focus was discussing the previous definitions for EBMgt in healthcare, the main sources of evidence and their acceptance in hospitals. The remaining studies (n=20, 67 percent) were empirical; among them, the largest part (n=14, 70 percent) was informed by quantitative methodologies. The sources of evidence for EBMgt are: published studies, real world evidence and experts’ opinions. Evidence is analyzed through: literature reviews, data analysis of empirical studies, workshops with experts. Main kinds of decisions are: performance assessment of organization units, staff performance assessment, change management, organizational knowledge transfer and strategic planning.

This study offers original insights on EBMgt in healthcare by adding to what we know from previous studies a “process” perspective that connects sources of evidence, types of analysis, kinds of decisions and groups of decision-makers. The main findings are useful for academia as they consolidate what we know about EBMgt in healthcare and pave avenues for further research to consolidate this emerging discipline. They are also useful for practitioners, as hospital managers, who might be interested to design and implement EBMgt initiatives to improve hospital performance.

In the past decade, in the space industry, many initiatives intended at offering open access to big data from space multiplied. Therefore, firms started adopting business models (BMs) which lever on digital technologies (e.g. cloud computing, high-performance computing and artificial intelligence), to seize these opportunities. Within this scenario, this article aims at answering the following research question: which digital technologies do impact which components the BM is made of?

An exploratory multiple case study approach was used. Three cases operating in the space industry that lever on digital technologies to implement their business were analyzed. Despite concerns regarding reliability and validity, multiple case studies allow greater understanding of causality, and show superiority respect to quantitative studies for theory building.

Big data, system integration (artificial intelligence, high-performance computing) and cloud computing seem to be pivotal in the space industry. It emerges that digital technologies involve all the different areas and components of the BM.

This paper sheds light on the impact that digital technologies have on the different BM components. It is only understanding which technologies can support the value proposition, which technologies make the infrastructural part able to support this proposition, which technologies may be helpful for delivering and communicating this value to customers and which technologies may help firms to appropriate the value that it is possible to seize the impact of digital technologies on BM.

The purpose of this study is twofold: exploring new queue-based variables enabled by process mining and evaluating their impact on the accuracy of waiting time prediction. Such queue-based predictors that capture the current state of the emergency department (ED) may lead to a significant improvement in the accuracy of the prediction models.

Alongside the traditional variables influencing ED waiting time, the authors developed new queue-based predictors exploiting process mining. Process mining techniques allowed the authors to discover the actual patient-flow and derive information about the crowding level of the activities. The proposed predictors were evaluated using linear and nonlinear learning techniques. The authors used real data from an ED.

As expected, the main results show that integrating the set of predictors with queue-based variables significantly improves the accuracy of waiting time prediction. Specifically, mean square error values were reduced by about 22 and 23 per cent by applying linear and nonlinear learning techniques, respectively.

Accurate estimates of waiting time can enable the ED systems to prevent overcrowding e.g. improving the routing of patients in EDs and managing more efficiently the resources. Providing accurate waiting time information also can lead to decreased patients’ dissatisfaction and elopement.

The novelty of the study relies on the attempt to derive queue-based variables reporting the crowding level of the activities within the ED through process mining techniques. Such information is often unavailable or particularly difficult to extract automatically, due to the characteristics of ED processes.

The role of information and communication technologies (ICTs) and digital platforms in enabling connectivity and collaboration among actors is neglected when dealing with outbound open innovation (OI). Moreover, the outbound OI process is not currently defined in a univocal way. Thus, this paper aims to outline the outbound OI phases and to explore role and capabilities of ICTs in supporting it.

Through a literature review approach, the authors specified the outbound OI process. Hence, the authors leveraged the similarities between the knowledge management process phases and the outbound OI phases for developing a conceptual framework that could match the outbound OI phases with acknowledged categories of ICT tools.

Through a process-view, the authors outlined outbound OI as a three-phase process. The authors developed a matrix-shaped framework in which the columns represent the three outbound OI process phases, while the rows are three ICT categories that could be suitable for supporting the outbound OI process.

The framework is designed to guide a deep understanding of how ICTs could support specific phases of the outbound OI process. In so doing, it could be useful for software developers interested in the preliminary design of an ICT platform for outbound OI.

The conceptual framework proposal as follows: specifies a detailed, process-oriented definition of the outbound OI; allows to identify the main ICT categories supporting the phases of the outbound OI process; and provides guidance for further exploration about the role of ICT in outbound OI.

This paper aims to model a decision support system (DSS) that could overcome the oversimplified, subjective, compensatory decision logic of extant purchasing portfolio models (PPMs) by leveraging the firms’ procurement-related knowledge base.

The DSS was developed through a fuzzy-based approach, whose design and application were framed within a case study in a multinational company.

The application of the fuzzy-based DSS to a product class suggests investing in the relationship with two specific suppliers and to loosen the relationship with a third one.

Exploiting the fuzzy set theory and fostering the elicitation of procurement-related knowledge from the decision-makers, the DSS effectively tackles the concerns about the existing PPMs by including strategic-oriented priorities and contextual constraints in the evaluation.

The recommendations in output from the DSS are feasible, more analytical and easy to interpret, enabling knowledge sharing, group decision processes and better decision-making.

To the best of the authors’ knowledge, this manuscript is the first attempt to effectively integrate traditional PPMs with contextual, strategy-related factors to refine the purchasing directions and make them objective.

Open innovation (OI) literature suggests that firms can improve their innovation performance by learning from a large set of actors in the innovation process. However, the extant literature has overlooked the “who” question: which partners should be included in the different phases of the innovation funnel? How should they be selected? This paper aims to offer, while focusing in the early phases of the innovation process, a list of possible criteria for partner evaluation and suggests a structured methodology for their selection.

An empirical test of both the criteria and the methodology is presented with reference to a company operating in the Advanced Underwater Systems sector. The authors propose a peer-based modification of intuitionistic fuzzy (IF) multi-criteria group decision-making with TOPSIS method (peer IF-TOPSIS). IF-TOPSIS allows coping with subjectivity, imprecision and vagueness in group decision-making problem under multiple criteria.

The paper proposes an innovative application of a peer-modified version of IF-TOPSIS to a challenging and complex decision problem – partner selection for OI – which is usually subjected to uncertainty and evaluation from multiple experts.

This work contributes to the extant literature advancing the criteria that could be used in the selection process, avoiding focusing on single specific aspects of the collaboration phases or on specific types of partners (suppliers).

Intellectual property protection mechanisms (IPPMs) include a variety of methods suitable for protecting valuable intangible assets of companies, and it is of great relevance to study how companies use these mechanisms to ensure the appropriability of innovation, in a context in which innovation is increasingly open. Indeed, there is a tension between the aim to share knowledge with external partners and the need to protect valuable know-how. The purpose of this paper is to investigate the relationship among the use of IPPMs, open innovation (OI), and the innovation performance of companies.

The study is based upon a survey conducted on 477 firms from Finland, Italy, Sweden, and UK in 2012.

The study shows that IPPMs have an indirect impact on innovation performance, mediated by the degree of openness. More precisely, IPPMs positively influence the level of openness, which, in turn, positively influences the innovation performance.

The empirical analysis contributes on two issues widely debated in the literature: the impact of IPPMs on innovation performance and the role of IPPMs as enablers or disablers of OI.