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Aim of the present study is to describe our robot-assisted simple prostatectomy technique and to report 6-month surgical and clinical outcomes.Eighty men were consecutively submitted to robot-assisted simple prostatectomy in our institution from October 2019 to October 2020. All procedures were performed by the same surgical team. Diagnostic preoperative assessment was based on history, symptoms evaluation by International Prostate Symptom Score, digital rectal examination, flowmetry with post-void residual volume measurement by abdominal ultrasound, prostatic volume estimation by MRI, PSA dosage. Under combined general and subarachnoid anesthesia, surgery was performed via a transperitoneal approach using a Da Vinci Si system in the four-arm configuration. Operative time, blood loss, hospital stay, catheterization time, intraoperative and postoperative complications were assessed. Clinical postoperative 6 months of evaluation was based on physical examination, flowmetry with post-void residual volume measurement, PSA dosage, and International Prostate Symptom Score.Mean operative time was 105.29 min, mean hospital staying 5.4 days. Blood loss was low in all cases and only 2 patients received transfusion. Catheterization time was 7 days. According to the Clavien-Dindo classification, only minor (grade I and grade II) intraoperative and postoperative complications were observed. At 6-month postoperative assessment, a statistically significant increase of urinary flow indexes and bladder capacity was recorded, as a significant reduction of urinary residual volume. No patient experienced stress urinary incontinence and the mean postoperative IPSS score was significantly reduced to 4.3. Two patients were diagnosed with incidental prostatic cancer at the histopathological examination.Data collected in our experience on a large cohort confirm efficacy and safety of Robot-Assisted Simple Prostatectomy. This procedure allows both short operative time and hospital staying, with low incidence of perioperative complications. This surgical technique can therefore be considered a valid alternative to other procedures for the surgical treatment of large volume prostates. Randomized prospective and comparative studies are warranted in the future to assess if different RASP techniques provide similar surgical and functional outcomes.

PurposeThe 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.Design/methodology/approachThe 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).FindingsThe 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.Research limitations/implicationsThe authors provide insights for advancing the academic discussion on process coordination principles in time-constrained, volatile and highly demanding scenarios.Practical implicationsThe 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.Originality/valueThe 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.

PurposeIn times of crisis, innovation management and specifically Research and Development (R&D) investments are critical to temper company losses and stimulate higher revenues. Environmental policies, for their potential to stimulate environmental innovations and efficient management of resources, may hold a magnifying role in this relationship. By relying on the distinction between regulatory policies and institutional incentives, this paper argues about the moderating role of environmental policies between a firm's R&D expenses and its performance.Design/methodology/approachHypotheses are tested on data collected from a sample of small and medium-sized Chinese enterprises after the 2008 financial crisis.FindingsFindings reveal positive moderating effects of both regulatory pressures and institutional incentives, with a more significant effect of government support. The highest impact is reached when both these types of policies are present.Originality/valueThe theoretical and methodological relevance of this distinction, the importance of an appropriate mix of environmental policies in policymaking and their resilience building role in stimulating environmental innovations in the aftermath of crises are discussed.

Engineering management has been historically focused on integrating scientific, engineering, and management know-how to contribute effectively to the functioning of organizations and industries. Today, an array of disruptive socio-technological transformations is bringing new challenges for education and research institutions engaged to advance the knowledge needed to design more performing socio-technical systems. Framed within the larger industrial engineering domain, management engineering has emerged as a new perspective to integrate technological and managerial knowledge, although a shared understanding of the field is yet to be introduced. In particular, the meaning and building blocks of management engineering can be discussed in the light of the evolving nature of engineering management. This article presents an extensive work of search and analysis of academic and practitioner evidence about management engineering, which allowed to derive a taxonomy of 467 concepts and 32 aggregating topics. The proposed framework can support academic discussion on the relevance of integrating management and engineering knowledge in the current socio-technical scenario. For practitioners, the identification of management engineering topics can be used to design global education initiatives as well as competence development and professional certification processes.

PurposeFactors such as stakeholder requirements, emergence of exponential technologies, global business operations and pressures for sustainability increase the complexity of modern projects. This article aims to contribute by developing a systems view of project elements and relations among the same.Design/methodology/approachThe article is based on a design science process and uses business management, project management and the system dynamics literature to support the construction of a conceptual framework, which is preliminary validated using practitioner feedback.FindingsThe project management model includes: (a) a systems view of a project in terms of 5 subsystems, 9 components and 27 dimensions; (b) a flow diagram showing 11 key relations among project attributes and (c) a discussion of ten system archetypes which can be identified in the management of a project.Research limitations/implicationsWhereas the application of systems thinking in project management has been mostly addressed to build system dynamics tools, this article advances the discussion by providing a framework useful to support theory development and contextual knowledge construction in project management activities.Practical implicationsThe article provides (project) managers with an articulated project management model and insights to address the complexity of modern project activities.Originality/valueThe main value of the research stays in the integrative nature of the presented framework, along with its twofold focus on components and flows (dynamic view).

The affirmation of open innovation and collaborative systems is enabling unprecedented opportunities to create business value while facilitating multistakeholder conversation on sustainability issues. In particular, platform-based models are emerging as organization archetypes able to facilitate cooperative dynamics among industrial actors, policy makers, academicians, scientists, and citizens. In this article, we use interdisciplinary business management and collaborative innovation literature to build the conceptual framework of a multisided platform as a collaboration environment gathering actors willing to define responses to sustainable development challenges. We present five dimensions or “genes,” that is, the focus and strategic intent or orientation of the platform (what), the participating sides, actors and groups (who), the actions, flows and coordination mechanisms (how), and the value drivers, benefits and externalities (why), and the rules regulating the affiliation and interaction processes (governance). We also present and discuss 30 subtopics or management items that are associated with the five dimensions defined. We then apply the conceptual model to analyze a case in the climate change endeavor and to show how competitive and cooperative dynamics can be virtuously integrated to provide individual- and company-driven responses to a timely socioenvironmental issue. The article provides a new perspective on collaboration to enhance social development, and it offers theoretical and practitioner insights for a broad interdisciplinary audience including scholars, practitioners, business, and platform managers.

PurposeIn recent years, the penetration of digital technologies in the financial industry determined the arising of Fintech, which generated a dynamic and rapid change that business operators and supervisory authorities in the banking industry are struggling to follow it. This is especially due to issues affecting financial intermediaries and customers, and potential risks of stability of the financial system. The aim of this paper is to provide a review of Fintech in the banking industry thus to update the knowledge about technology innovation in the banking sector, identify the major trends in the domain and delineate future research directions.Design/methodology/approachThe study reviews 377 articles indexed on Scopus from 2014 to 2021 that focus on Fintech and the banking industry. The methodology adopted is structured in two steps: the keywords selection and the analysis of the documents extracted. The first step identified “Fintech” and “bank” as keywords to be searched within the title, abstract or keywords of documents indexed on Scopus; whereas the second step combined R and VOSviewer to provide a descriptive analysis of the dataset and the analysis of keywords and occurrences, respectively.FindingsResults achieved in the study allow providing a systemic view of the Fintech in the banking industry, including the emergent phenomenon of digital banking. In particular, it is provided with a general overview and descriptive information on the entire sample of documents analyzed, their authors, the keywords used and the most cited works. Besides, a deepening on the model of digital banking is provided, by delineating the six dimensions of the key effects generated by the digital bank model.Originality/valueTwo main elements of originality characterize this study. The first one is related to the fact that few review studies have been published on Fintech in the banking industry, and the second one concerns the multiple dimensions of the impact of Fintech in the banking sector, which includes customer, company, bank, regulation authority and society.

In the last years, Blind Deconvolution methods demonstrated their effectiveness for the diagnostics of rotating machines through the extraction of impulsive signatures directly from noisy observations. Recently, in this scenario the explicit combination between Blind Deconvolution and cyclostationary theory strongly improved the fault detection ability of this diagnostic tool. This work presents a novel criterion based on the Fourier–Bessel series expansion instead of the common Fourier transform. This idea comes from the comparison between the mathematical nature of the Fourier–Bessel and the Fourier series, based on modulated and constant amplitude sinusoidal functions, respectively. The two criteria are compared through the analysis of both simulated and real vibration signals of faulty bearings. The results highlight the ability of the proposed criterion to detect the fault-related source with a lower number of characteristic cyclic frequency harmonics, strongly reducing the computational time required by the algorithm.

Purpose This paper aims to highlight how big social data (BSD) and analytics exploitation may help destination management organisations (DMOs) to understand tourist behaviours and destination experiences and images. Gathering data from two different sources, Flickr and Twitter, textual and visual contents are used to perform different analytics tasks to generate insights on tourist behaviour and the affective aspects of the destination image. Design/methodology/approach This work adopts a method based on a multimodal approach on BSD and analytics, considering multiple BSD sources, different analytics techniques on heterogeneous data types, to obtain complementary results on the Salento region (Italy) case study. Findings Results show that the generated insights allow DMOs to acquire new knowledge about discovery of unknown clusters of points of interest, identify trends and seasonal patterns of tourist demand, monitor topic and sentiment and identify attractive places. DMOs can exploit insights to address its needs in terms of decision support for the management and development of the destination, the enhancement of destination attractiveness, the shaping of new marketing and communication strategies and the planning of tourist demand within the destination. Originality/value The originality of this work is in the use of BSD and analytics techniques for giving DMOs specific insights on a destination in a deep and wide fashion. Collected data are used with a multimodal analytic approach to build tourist characteristics, images, attitudes and preferred destination attributes, which represent for DMOs a unique mean for problem-solving, decision-making, innovation and prediction.

The strategic relevance of innovation and scientific research has amplified the attention towards the definition of quality in research practice. However, despite the proliferation of evaluation metrics and procedures, there is a need to go beyond bibliometric approaches and to identify, more explicitly, what constitutes good research and which are its driving factors or determinants. This article reviews specialized research policy, science policy and scientometrics literature to extract critical dimensions associated with research quality as presented in a vast although fragmented theory background. A literature-derived framework of research quality attributes is, thus, obtained, which is subject to an expert feedback process, involving scholars and practitioners in the fields of research policy and evaluation. The results are represented by a structured taxonomy of 66 quality attributes providing a systemic definition of research quality. The attributes are aggregated into a three-dimensional framework encompassing research design (ex ante), research process (in-process) and research impact (ex post) perspectives. The main value of the study is to propose a literature-derived and comprehensive inventory of quality attributes and perspectives of evaluation. The findings can support further theoretical developments and research policy discussions on the ultimate drivers of quality and impact of scientific research. The framework can be also useful to design new exercises or procedures of research evaluation based on a multidimensional view of quality.

Monitoring learners' satisfaction (LS) is a vital action for collecting precious information and design valuable online collaborative learning (CL) experiences. Today's CL platforms allow students for performing many online activities, thus generating a huge mass of data that can be processed to provide insights about the level of satisfaction on contents, services, community interactions, and effort. Big Data is a suitable paradigm for real-time processing of large data sets concerning the LS, in the final aim to provide valuable information that may improve the CL experience. Besides, the adoption of Big Data offers the opportunity to implement a non-intrusive and in-process evaluation strategy of online courses that complements the traditional and time-consuming ways to collect feedback (e.g. questionnaires or surveys). Although the application of Big Data in the CL domain is a recent explored research area with limited applications, it may have an important role in the future of online education. By adopting the design science research methodology, this article describes a novel method and approach to analyse individual students' contributions in online learning activities and assess the level of their satisfaction towards the course. A software artefact is also presented, which leverages Learning Analytics in a Big Data context, with the goal to provide in real-time valuable insights that people and systems can use to intervene properly in the program. The contribution of this paper can be of value for both researchers and practitioners: the former can be interested in the approach and method used for LS assessment; the latter can find of interest the system implemented and how it has been tested in a real online course.

PurposeThe paper aims to propose a knowledge visualization approach and algorithm to support public decision makers to define the inner areas, which represents a strategic topic in the European debate about territorial inequality and development.Design/methodology/approachThe study has been developed by following the design science research, which includes six steps: problem identification and motivation; identification of the objectives for a solution; design and development; demonstration; evaluation; and communication. As for the design and development step, the proposed approach and algorithm ground on association mining to discover hidden relationships existing among municipalities. They have been applied to analyse the 97 municipalities of the Lecce province, and each municipality has been described through 30 multi-domain indicators organized into seven categories, whose data have been collected from institutional datasets, local sources or web-scraping process.FindingsA set of complementary analyses has been generated through the construction of dynamic and interactive knowledge maps that show “similar” municipalities according to the indicators selected.Originality/valueThe approach and algorithm proposed allow discovering similarities existing among distinct municipalities, based on the analysis of a set of multi-domain indicators. The approach may complement or completely substitute the existing ones used to define inner areas, thus overcoming both the methodological limits of the “top-down” line imposed by the central legislator, and the “bottom-up” paradox consisting in the illusion that single (and often small) towns have the economic and cognitive resources necessary to implement effective territorial mapping and development strategies. In such a way, policy makers can be aware on similarities existing among distinct towns and can thus share cognitive and financial resources to define a common plan and a set of practices for territorial development.

Highlights • COVID-19 emergency forced the Italian Universities to reorganize teaching in entrepreneurship education. • Digital technologies can support the development of entrepreneurial mindset. • Building an entrepreneurship education curriculum through distance learning technologies is critical. • Digital technologies can enhance successfully education ‘about’ entrepreneurship. • Digital technologies present some critical issue when used in educating students ‘for’ entrepreneurship. • CLab@Salento offered a favourable environment to experiment the use of digital tools for the creation of entrepreneurial mindset using a blended learning approach., The COVID-19 crisis has forced universities worldwide to seek urgent solutions to reconfigure traditional education programs for distance learning. The transformation process faces a number of complexities deriving from both institutional and contextual factors. It may generate threats and as well as opportunities to enhance the education system and prepare for potential future emergencies. In this article, we adopted a combined research approach to describe the experience of the Contamination Lab of the University of Salento (CLab@Salento), an entrepreneurship education program focused on innovative and technology-based entrepreneurship for university students. Moving from the analysis of the main challenges the pandemic generated for the institution, students and faculty, we illustrate the process of redesigning the entrepreneurial learning program by leveraging digital technologies. We show a new approach to entrepreneurial storytelling, pitching and business planning and development through digital technologies. We also report the outcomes of a student survey to highlight the strengths of the redesigned program and some weaknesses, especially associated with digital technologies’ limitations in education, which represent areas for future improvement. The study contributes at theory level with a new discussion on digital-supported entrepreneurship education. At practitioner level, it offers insights on redesigning traditional university programs to effectively address emergencies.

Purpose This study aims to investigate the Big Social Data (BSD) paradigm, which still lacks a clear and shared definition, and causes a lack of clarity and understanding about its beneficial opportunities for practitioners. In the knowledge management (KM) domain, a clear characterization of the BSD paradigm can lead to more effective and efficient KM strategies, processes and systems that leverage a huge amount of structured and unstructured data sources. Design/methodology/approach The study adopts a systematic literature review (SLR) methodology based on a mixed analysis approach (unsupervised machine learning and human-based) applied to 199 research articles on BSD topics extracted from Scopus and Web of Science. In particular, machine learning processing has been implemented by using topic extraction and hierarchical clustering techniques. Findings The paper provides a threefold contribution: a conceptualization and a consensual definition of the BSD paradigm through the identification of four key conceptual pillars (i.e. sources, properties, technology and value exploitation); a characterization of the taxonomy of BSD data type that extends previous works on this topic; a research agenda for future research studies on BSD and its applications along with a KM perspective. Research limitations/implications The main limits of the research rely on the list of articles considered for the literature review that could be enlarged by considering further sources (in addition to Scopus and Web of Science) and/or further languages (in addition to English) and/or further years (the review considers papers published until 2018). Research implications concern the development of a research agenda organized along with five thematic issues, which can feed future research to deepen the paradigm of BSD and explore linkages with the KM field. Practical implications Practical implications concern the usage of the proposed definition of BSD to purposefully design applications and services based on BSD in knowledge-intensive domains to generate value for citizens, individuals, companies and territories. Originality/value The original contribution concerns the definition of the big data social paradigm built through an SLR the combines machine learning processing and human-based processing. Moreover, the research agenda deriving from the study contributes to investigate the BSD paradigm in the wider domain of KM.

Although exponential technologies promise to bring unprecedented value at the socio-economic and policy levels, the social acceptability and preparedness for the technological “singularity” should be carefully considered. In particular, whereas digital innovation is able to drive an extraordinary development of entrepreneurial ventures, a number of challenging issues and the ongoing pandemic crisis have increased the need to investigate how technological breakthrough and human capital can be effectively combined in order to build resilient socio-technical and entrepreneurial ecosystems. This paper offers a synopsis of the major investigation areas and a reflection on the themes associated with the emergence of a digital society and the affirmation of digital entrepreneurship ecosystems. The research process follows a systematic literature review and a conceptual development approach aimed to introduce both the concept and a model of the digital society “incubator”. The proposed model identifies the actors, values, flows, and processes that are required to support the construction of a resilient entrepreneurial ecosystem. In this perspective, the study proposes a new focus by hybridizing and integrating both entrepreneurial and technology-related dimensions into a single unifying model. The study also lays the groundwork for further studies aimed at identifying the environmental and institutional factors required to support a smooth and effective transition towards a resilient entrepreneurial and technology-driven society.

Digital transformation is driving the emergence of a new breed of organisations able to scale quickly, innovate throughout their processes, and lead industries and markets. In particular, the increasing adoption of cognitive technologies, like artificial intelligence, advanced analytics, high-performing computing and cyber-physical systems, allow organisations to create new value based on increased reactivity and resilience, situational awareness, agility and operational excellence. Although the idea of cognitive enterprise is rapidly emerging in the practitioner discussion, a theoretical framing of the concept is still not present in literature. We conduct a systematic review of extant contributions and we extract elements related to two key dimensions represented by the cognitive technological infrastructure (i.e. technologies and data) and the cognitive organizational architecture (i.e. processes and capabilities). We use the outcomes of the review work to define: (a) the pillars of the cognitive enterprise; (b) a framework of cognitive maturity stages; and (c) the enhanced value creation capabilities of the cognitive enterprise. The article provides a literature-derived definition of the cognitive enterprise and guidelines to support managers in the cognitive transformation of their organisations.

Purpose Differently from traditional approaches that rely on the analysis of single dimensions of the tourism phenomenon, this study aims to experiment a systemic approach based on structured and unstructured data sources to elaborate a composite index to measure the tourist competitiveness of marginal areas, with the final aim to design and plan proper socio-economic development strategies. Design/methodology/approach The methodology adopted to carry out the study follows a four-step process and relies on indicators that are both relevant and accessible. The first step concerns the analysis of the literature about the existing approaches to calculate a tourism index. The second step concerns the definition of the indicators and the collection of data by using both structured and unstructured sources. The third step focuses on the population of the data set. Finally, the fourth step aims at calculating the tourism index through a composite-based methodology and using it for a pilot application in a Southern Italy province. Findings The study calculates a synthetic tourism index for each of the 97 municipalities of the Province of Lecce (a city located in the southeast of Italy). The proposed index combines administrative, institutional and open data sources to derive a single indicator for each municipality, thus supporting decision-makers in understanding the complex reality and competitiveness level of territories in the tourism industry. Originality/value The main elements of originality of the study are the breadth and typology of data sources considered to calculate the composite indicator of tourism competitiveness (both structured and unstructured); and the use of weighting and aggregation procedures in the methodological issues.

The diffusion of Massive Open Online Courses (MOOCs) is significantly changing the way people learn and update their knowledge and competencies. Although the benefits characterizing MOOCs, which leverage on free and open access to know-how and digitized materials, there are some challenges which call for improving and enhancing the existing methods and approaches for MOOCs design. By combining theory and practice, this paper presents a process of MOOCs design based on a double-loop phase of evaluation. Specifically, the paper provides evidences on how to take advantage of the learners’ and teachers’ feedback to redesign or rethink the course’s architecture, and especially the storyboard and blueprint. A pilot application of the proposed approach has been made to design a course dealing with entrepreneurship domain, and in particular with crowdfunding. The results of the application are presented to validate the approach and provide teachers and course’s designers with some recommendations.

Purpose The generation of new ventures within established companies, also known as corporate entrepreneurship (CE), is a process influenced by a set of individual and organizational factors. This paper aims to focus on creativity and human resource management enablers of CE, with the purpose to define an integrative framework and draw a set of related research propositions. Design/methodology/approach The paper relies on a multidisciplinary literature review in the fields of CE, creativity and organizational innovation. Findings The effectiveness of CE depends on a set of individual factors, distinguished into professional and psychological characteristics, and organizational factors, which include the system of values of the organization and the management practices applied in the same. Research limitations/implications From a theoretical point of view, the paper develops an integrative framework of conditions that impact on CE and outlines a set of propositions and alternative research methods to test. Practical/implications From a practitioner perspective, the study provides managers with a comprehensive set of factors enabling CE by leveraging the creativity of individuals and make it flourish through consistent human resource management practices. Originality/value The value of the paper stays in the integration of individual-related and organizational-related determinants of entrepreneurial performance.

The industrial progress has reached a level in which it is necessary to understand the behavior of mechanical components and to monitor their conditions without disassembling them. Nowadays, a suitable methodology is based on vibrational analysis usually performed through acceleration signals measured directly on the system to be tested. However, in the last years, the industrial scenario has deeply changed due to the need for time reduction, in particular, for the control operations at the end of the productive line. The genuine methods based on acceleration measurements, for example, through piezoelectric accelerometers, came into conflict with the industrial need as the sensors used for the quality control have to be easily and fastly mounted and unmounted. A valid alternative is represented by the exploitation of laser triangulation sensors that are able to measure the dynamic displacement in a contactless way, strongly reducing the (un)mounting time. The target of this paper is to highlight pros and cons of the contactless displacement analysis through laser triangulation sensors with respect to the contact one through genuine accelerometers by means of a comparison between the results obtained both for experimental modal analysis and vibrational diagnostics of rotating machines.

This paper proposes a tool for the generation of synthetic mechanical signatures of faulted gears and bearings in non-stationary conditions. Synthetic signals are commonly used to provide early validation of vibration-based diagnostics techniques. In the absence of a standard tool, each researcher builds his/her own model keeping into account the characteristic features expected in a faulted condition. This approach implies two main consequences. The first one is that the complexity of simulated signals strictly depends on the researcher skills that could lead to excessively simple models that compromises the validation process. The second one is that there is not a widely accepted algorithm, or tool, for the generation of synthetic signals. This lack hinders simpler and consistent comparisons among signal processing techniques. Hence, the main goal of this paper is to supply to the scientific community a standard tool for the generation of synthetic mechanical signatures. The tool is provided under Creative Commons license and it allows to simulate different possible situations: from the basic case of a single bearing fault to the case of a multi-stage gearbox having either a localized fault and a distributed bearing fault. Among several features, the proposed tool could take also into account variable speed profiles of the input shaft, cyclostationary contributions and effects of linear time-invariant systems.

In the last decades, academic and managerial literatures have paid great attention by about the notion of the business model (BM). Accordingly, a wide range of literature was created to identify and acknowledge the capacity to create and renew organizational BM as one of the fundamental sources of competitiveness and value creation. However, until now, the notion and application of business model innovation (BMI) research has mostly been developed in relation to large companies and organizations. Very limited studies have explored what and how context-specific characteristics and knowledge asset management may have effects on BM development and organizations’ sustainability. This latter aspect is particularly true for technological start-ups. Until now, research and managerial practice have been not aligned with national and international policies’ great emphasis on technological start-ups, as well as about the role of the BMI to support organizational performance improvement and stimulate entrepreneurship. From this perspective, technological start-ups represent an interesting basis for analyzing business model mapping and BMI along a knowledge-based perspective. The creation, survival, development, and market success of such organizations are mainly based on the identification, acquisition, and exploitation of their knowledge-based assets. Therefore, it can be relevant to study and analyze how such knowledge assets significantly drive organizational performance and value creation by leveraging innovative technology breakthroughs, highly qualified and distinctive competencies, and implicit knowledge embedded into valuable networks of relationships.

Big Data has been heralded as a key agent of the third industrial revolution, and currently represents a promising area for value creation and frontier research. The potential to extract actionable insights from Big Data has gained increasing attention of both academics and practitioners operating in several industrial sectors. However, the adoption of Big Data solutions does not always generate effective value for the adopters. Therefore, the gap existing between the potential of value creation embedded in the Big Data paradigm and the current limited exploitation of this value represents an area of investigation that this paper aims to explore. In particular, this study aims at investigating the following research question: “Which are the multiple value directions that the Big Data paradigm can generate for organizations?”. In this vein, the article presents the result of a systematic literature review aimed at defining a framework that identifies the possible directions of value creation for an organization that may decide to implement the Big Data paradigm. By adopting a systematic literature review, the framework leverages and extends a previous contribution of Wamba et al. (2015) on the same topic. A further element of originality is related to the introduction of a further phase in the analysis, consisting in the application of text mining algorithms to analyse the selected resources, in the final aim to highlight possible terms and concepts remained hidden or latent in the human-led analysis.

The paper focuses on virtual brand community (VBC) as new management tool for open innovation. Existing research on the intersection between brand communities and innovation management shows how the knowledge of the brand communities’ members and their engagement in product-related discussions represent a relevant source of innovation for the companies. However, how do companies implement open innovation through the VBC and how do they implement purposeful practices represent an unexplored research area. By adopting a value-oriented perspective of open innovation, this study interprets the VBC as a co-creation space where undertaking opportunity identification and resource mobilization for value creation and value capture. The paper presents a single case study of a multinational company operating in the semiconductor industry that has recently implemented a VBC as a tool for peer-to-peer support and knowledge sharing. Results show the features of this VBC and the set of practices designed to realize the open innovation strategies. By adopting an open innovation perspective, the article presents a model of VBC as a collaborative space for innovation. For practitioners, the article provides evidences and insights about the use of VBC to support the implementation of open innovation strategies.

Digital technologies have nowadays a significant impact on how new business ventures are imagined and created. The arising technology paradigm is leveraging the potential of collaboration and collective intelligence to design and launch more robust and sustainable entrepreneurial initiatives. However, although the topic of digital entrepreneurship is relevant and timely, there is a limited literature discussion on the real impact of digital technologies and collaboration on the entrepreneurial process. Further research is needed to describe the nature and characteristics of the entrepreneurial ecosystem enabled by the new socio-technical paradigm. Based on extant literature, this article proposes a definition of digital entrepreneurship ecosystem by highlighting the integrated digital-output and digital-environment perspectives. A collective intelligence approach is then adopted to define a descriptive framework and identify the distinguishing genes of a digital entrepreneurship ecosystem. Four dimensions associated to digital actors (who), digital activities (what), digital motivations (why), and digital organization (how) are defined and discussed. The framework was also applied to describe 9 real cases of companies and initiatives, which are analyzed as digital entrepreneurship ecosystems along the four key dimensions presented. The article ends with a discussion about the results and a research agenda for future studies.

The paper proposes an analytical procedure for the generalization of the rattle index in any type of ordinary transmission layouts, single or multiple branch, both in idle and loaded conditions. Its definition is obtained by a recursive analytical formulation of the rotational dynamics of gears. Numerical analytical studies are exploited to examine the effectiveness and reliability of the introduced index. Several geartrain layouts are investigated and results show its capability to instantaneously describe the vibro-impact events related to any gear pair of the driveline. Moreover, the general definition is shown to be a proper indicator of the potential presence of mutual interactions between different gear pairs pertaining to the same driveline. The analysis of the results reveals an excellent agreement with the expected behavior of the outlined parameter.

The gear fault diagnosis on multistage gearboxes by vibration analysis is a challenging task due to the complexity of the vibration signal. The localization of the gear fault occurring in a wheel located in the intermediate shaft can be particularly complex due to the superposition of the vibration signature of the synchronous wheels. Indeed, the gear fault detection is commonly restricted to the identification of the stage containing the faulty gear rather than the faulty gear itself. In this context, the paper advances a methodology which combines the Empirical Mode Decomposition and the Time Synchronous Average in order to separate the vibration signals of the synchronous gears mounted on the same shaft. The physical meaningful modes are selected by means of a criterion based on Pearson’s coefficients and the fault detection is performed by dedicated condition indicators. The proposed method is validated taking into account simulated vibrations signals and real ones.

In modern industry, the wide use of reliable and sophisticated sensors with their connection to internet has introduced the phenomena of Big Data, especially in the field of condition monitoring systems (CMSs) in e-maintenance applications. In particular, in the case of vibration signals, high-performance acquisition systems are required, characterized by anti-aliasing filtering and high uniform sampling rate, in order to properly digitalize the meaningful frequency content of the signals. In this context, the capability of non-uniform random sampling (RS) is assessed in this work. While in different fields, such astronomy, structural and biomedical studies, the RS is a problem to be resolved, due to the unavailability of samples at specific instants (missing data problem), in the field of fault detection & diagnosis (FDD), RS can be a chosen sampling method thanks to its advantages: anti-aliasing property and low average sampling rate. Therefore, this paper focuses on studying the anti-aliasing property of the random sampled data, verifying the criterion proposed in literature for establish the Nyquist frequency, and analyzing its sensitivity to the sampling parameters. This study is carried out using simulated signals and computing the spectral window, giving the Nyquist frequency for different random sampling parameters; moreover, a spectral analysis method, the Schuster periodogram, is used to verify when the spectrum is actually free of alias. The results show that the Nyquist frequency depends on the numerical accuracy of the randomly generated time instants.

Established companies operating within highly competitive environments, with strong regulatory issues, new competitors, technological changes and market saturation, have a continuous pressure to pursue innovation and improve their competitive positioning. Corporate Entrepreneurship (CE) and Open Innovation (OI) revealed as very effective and valuable strategies to achieve this goal. This paper describes how the Italian National Railways group (here after FSI group - Ferrovie dello Stato Italiane) has undertaken a path to explore new business models by designing and launching an initiative to find new ideas, support them in the development, and promote them in the final aim to launch new products and services. The ideas that have been researched and explored are not those directly related to the core corporate business, but aim to explore new boundaries that include assets available within the organization, leverage the advantages coming from market positionings and find additional resources already existent within the organization as potential sources of additional value. To achieve these goals, the FSI Group has built in 2017 (and developed in 2018) the Innovate Project, which is the first experimentation of CE process within the FSI Group, by adopting an OI approach.

So far, the study of gear contacts in lightly loaded gears by means of vibration analysis has not been sufficiently addressed in the literature. Indeed, the complex nature of the physical phenomena involved makes the vibration analysis extremely challenging. This paper deals with the development and the validation of an approach for the contact pattern assessment in straight bevel gears within a pass/fail decision process. The proposed methodology is based on blending vibration-based condition indicators with classification algorithms in order to discriminate proper contact patterns from improper ones. Specifically, three different classification algorithms have been investigated: the Naive Bayes classifier, the weighted k-Nearest Neighbors classifier and a novel classifier proposed by the authors. The classifier accuracies are evaluated with a MC cross-validation that includes an extended experimental campaign consisting of more than one hundred different straight bevel gear pairs. The results show that the proposed classifier is superior to the other considered classifiers in terms of average accuracy. Finally, this manuscript proposes an original methodology that provides a reliable and quick assessment of the contact pattern in straight bevel gears considering different speeds, gear parameters and surface finish.

Nowadays, huge emphasis is given to research on diagnostic tools in order to prevent and monitor the health status of gears and bearings. However, the link between advanced signal processing techniques and ease of use is still missing in commercial software tools. Actually, softwares that implement advanced signal processing techniques leak in ease user interaction and automated diagnostic procedures. Authors have developed a commercial software tool, called UniVibe, that attempts to fill the gap between high sensitivity in the diagnostics of faults in complex geared transmission and user-friendly interface. This work focuses on the description of the UniVibe core, highlighting its diagnostic capabilities on the basis of a real industrial case. Specifically, the automated procedure that shepherds the user to the successfully fault diagnosis of a complex geared transmission is pointed out.

Wet compression is a strategy adopted to increase the power output of gas turbines, with respect to dry conditions, usually also incrementing the operating range of the compressor. However, stall and surge are two aerodynamic instabilities which depend on many factors, and they are expected to occur even in wet compression at low flow rates. Despite the many studies carried out in the last 80 years, literature does not offer many works concerning these instability phenomena in wet compression. In this paper, an experimental analysis of stall and surge in wet compression conditions is carried out on an axial-centrifugal compressor installed in an existing test rig at the Engineering Department of the University of Ferrara. Some modifications of the test rig were necessary. The intake duct was implemented with a water injection system which, by means of water spray injectors, allows the uniform mixing of air and water before the compressor inlet. The control and data acquisition system of the test bench was updated with new hardware and software to obtain faster data sampling. Transient and steady-state tests were carried out to make a comparison with the experimental results in dry conditions. The analysis was carried out using traditional thermodynamic sensors, by means of both classic post-processing techniques, and cyclostationary analysis. The aim is to i) evaluate the influence of wet compression on the stable performance of the compressor ii) qualitatively identify the characteristics of stall and surge in wet compression by means of sensors which were shown to capture these phenomena well and iii) demonstrate the reliability of cyclostationary analysis in wet compression conditions for stall and surge analysis.

This paper addresses the use of first- and second-order cyclostationary (CS1 and CS2) tools to process the vibration signals picked up from internal combustion (IC) engines during cold tests. This type of analysis is needed in order to detect and diagnose irregular operations for quality control purposes. The effectiveness of indicators such as Mean Instantaneous Power (MIP), Degree of Cyclostationarity ( D C S α ) and Indicator of Cyclostationarity ( I C S n x ) in detecting assembly faults has been tested on real signals concerning three faulty conditions: inverted piston, connecting rod with incorrectly tightened screws, connecting rod without one bearing cap. In the past several authors have mainly used cyclostationary metrics for diagnostics purposes in rolling bearings and gear systems. Moreover, a signal model, qualitatively reproducing the features of actual cold test signals, has been formulated and used in order to preliminarily study the influence of signal parameters on the Indicators of Cyclostationarity. The results indicate that the cyclostationary tools – mainly CS2 tools – are effective in detecting and diagnosing all tested faulty conditions. In particular, indicator I C ⌢ S 2 x is highly sensitive to faults and it is suitable as pass/fail tool in quality control at the end of the engine assembly line. As a further second-order cyclostationary metric, the MIP is effective for detection, as well for fault identification, since it is able to localize regular and fault events within the engine cycle. In addition D C S α effectively characterizes the CS2 periodicities, giving the cyclic order distribution. Since these CS2 tools require a moderate computation cost, they can be considered ready for on-line industrial applications.

In the field of condition monitoring the availability of a real test-bench is not so common. Furthermore, the early validation of a new diagnostic technique on a proper simulated signal is crucial and a fundamental step in order to provide a feedback to the researcher and to increase the chances of getting a positive result in the real case. In this context, the aim of this paper is to detail a step-by-step analytical model of faulted bearing that the reader could freely and immediately use to simulate different faults and different operating conditions. The vision of the project is a set of tools accepted by the community of researchers on condition monitoring, for the preliminary validation of new diagnostics techniques. The tool proposed in this paper is focused on ball bearing, and it is based on the well-known model published by Antoni in 2007. The features available are the following: selection of the location of the fault, stage of the fault, cyclostationarity of the signal, random contributions, deterministic contributions, effects of resonances in the machine and working conditions (stationary and non-stationary). The script is provided for the open-source Octave environment. The output signal is finally analysed to prove the expected features.

International audience; Blind deconvolution algorithms prove to be effective tools for fault identification, being able to extract excitation sources from noisy observations only. In this scenario, the present paper introduces a novel blind deconvolution method based on the generalized Rayleigh quotient and solved by means of an iterative eigenvalue decomposition algorithm. This approach not only is characterized by a weighting matrix that drives the deconvolution process, but can also be easily adapted to arbitrary criteria. Based on this framework, a novel criterion rooted on the maximization of the cyclostationarity of the excitation – as typically encountered with machine faults – is proposed and compared with other blind deconvolution methods existing in the literature. The comparisons involve both synthesized and real vibration signals, taking into account a gear tooth spall and an outer race bearing fault. The results reveal superior capability to recover impulsive cyclostationary sources with respect to other blind deconvolution methods, even in the presence of impulsive noise or under non-constant speed.

Purpose Successful businesses demand high-performing human resource management practices (HRMP) and effective knowledge management capacity (KMC) to enhance the overall organizational performance. Rapid growth of both local and multinational companies operating in knowledge-intensive industries has increased the global competition in the labor market, also for the developing economies. Therefore, attracting valuable human capital, retaining talents and managing effectively knowledge to deliver on the latest technologies and innovative solutions and services are the biggest challenges in the modern IT industry. The purpose of this paper is to examine the influence of HRMP on KMC through a cross-case analysis including four companies operating in Indian IT sector. Based on the existing studies in this field, five key HRMP have been identified (i.e. recruitment and selection, training and development, compensation and reward, employee retention and career development), as well as two key processes supporting the KMC (i.e. knowledge acquisition and knowledge sharing). Design/methodology/approach The paper adopts a qualitative research method based on a multiple case study, and it uses primary and secondary data collected through desk research and field interviews. Findings Results show the existence of HRMP aiming to enhance the individual learning, motivation and retention of employees for knowledge acquisition and knowledge sharing, in the strategic perspective to improve the organizational performance. Research limitations/implications The paper presents some limitations, which may provide scope for future research. First, being the study a qualitative multiple case analysis, a more extensive research is needed to generalize the results and investigate further relationships existing between HRMP and KMC. Moreover, a deep investigation on the organizational performance dimensions (e.g. sales growth, cash turnover, financial goal achievement) is required to verify the existence of possible links. Finally, a further limit consists in considering only knowledge acquisition and knowledge-sharing processes, excluding another crucial knowledge management (KM) process such as knowledge application. As for the research implications, the paper contributes to investigate the role of HRMP in supporting KMC in Indian IT companies, which represents a context of research not so much investigated. Practical implications From a practitioner point of view, the study can be helpful to HR and KM managers for motivating employees to undertake learning processes and enhance their performance, as well as to acquire and share knowledge resources that are useful for the organization to remain innovative and stay competitive. Originality/value As the influence of HRMP on KMC has not been widely studied in the Indian IT industry, the study may open the field for further studies on a deeper investigation of the relationships existing between human resources management (HRM), KM and organizational performance in knowledge-intensive industries in India and, more in general, in developing economies.

This paper aims at identifying common patterns that technological startups follow during their Business Model Innovation (BMI) process, trying to understand which are the enabling conditions at strategic and operational level, as well as at individual, organizational level and environmental, which can be associated to the Intellectual Capital (IC) dimensions and that may activate, support and sustain the overall process. The research is carried out by using a semi-structured questionnaire submitted through a web survey to a sample of technological startups located in Italy. The main insights of the research lie in a framework presenting a set of possible trajectories that technological startups can follow to innovate their business model by leveraging the components of their IC. Besides, the analysis has also revealed a set of enabling conditions associated to each trajectory in order to support technological startups in the identification of the most promising trajectory according to their internal conditions.

Wicked problems are complex and multifaceted issues that have no single solution, and are perceived by different stakeholders through contrasting views. Examples in the social context include climate change, poverty, energy production, sanitation, sustainable cities, pollution and homeland security. Extant research has been addressed to support open discussion and collaborative decision making in wicked scenarios, but complexities derive from the difficulty to leverage multiple contributions, coming from both experts and non-experts, through a structured approach. In such view, we present a conceptual framework for the study of wicked problem solving as a complex and multi-stakeholder process. Afterwards, we describe an integrated system of tools and associated operational guidelines aimed to support collective problem analysis and solution design. The main value of the article is to highlight the relevance of collective approaches in the endeavor of wicked problem resolution, and to provide an integrated framework of activities, actors and purposeful tools.

Il volume fornisce un modello di contenuti complementare rispetto agli standard internazionali di conoscenza e certificazione in ambito di project management. Pensato come manuale per gli studenti di economia e management, ingegneria e degli altri ambiti in cui si svolgono corsi sulla gestione di progetti e dell’innovazione, è un testo fondamentale anche per tutti coloro che si vogliono avvicinare al project management o siano interessati a una sistematizzazione delle conoscenze già possedute.

Purpose – Big Data has been heralded as a key agent of the third industrial revolution, and currently represents a promising area for value creation and frontier research. The potential to extract actionable insights from Big Data has gained increasing attention of both academics and practitioners operating in several industrial sectors. However, the adoption of Big Data solutions does not always generate effective value for the adopters. Therefore, the gap existing between the potential of value creation embedded in the Big Data paradigm and the current limited exploitation of this value represents an area of investigation that this paper aims to explore. In particular the present study aims at investigating the following research question: “Which are the multiple value directions that the Big Data paradigm can generate for organizations?”. In this vein, the article presents the result of a systematic literature review aimed at defining a framework that identifies the possible dimensions of value creation for an organization that may decide to adopt the Big Data paradigm. Design/methodology/approach – The research methodology adopted in this study is based on a Systematic Literature Review, and consists in four key steps such as search, selection, analysis, and synthesis. The result of the search process was a list of 481 resources. Then, by adopting the exclusion criteria, a subset of 91 resources was obtained. The analysis allowed to highlight the different types of value created through the adoption of Big Data paradigm within the organizations. Through the synthesis step, 12 value directions were defined and grouped into 5 key value dimensions. Originality/value – This paper introduces and describes a framework highlighting the possible value creation dimensions associated to the adoption of the Big Data paradigm by the organizations. By adopting a systematic literature review, the framework leverages and extends a previous contribution of Wamba et al. (2015) on the same topic. A further element of originality is related to the introduction of a further phase in the analysis, consisting in the application of text mining algorithms to analyse the selected resources, in the final aim to highlight possible terms and concepts remained hidden or latent in the human-led analysis. Practical implications – From a research perspective, the proposed framework aims at providing a twofold contribution: the more general one consists in the development of the debate on how organization can generate value by adopting the Big Data paradigm; the more specific one refers to the extension of the framework previously proposed by Wamba et al. (2015) on the same topic. From a practitioner point of view, the proposed framework may support managers and executives to understand better and define the strategic perspective of innovative projects based on the Big Data paradigm, which can be promoted and sponsored by the organizations.

Corporate entrepreneurship is a process of strategic renewal and development of an existing business through the creation of new products, services, and activities, as well as new competitive postures and independent ventures. The performance of this process, which leverages the creativity and the initiative spirit of employees and managers, thus relies on the capacity of the organization to create favorable conditions for the emergence of such latent entrepreneurial potential. The development of participatory innovation models and collective intelligence offers new insights for conducting research on factors enabling corporate entrepreneurship. In particular, the internal company ‘crowd’ can be investigated with the purpose of studying the conditions under which the corporate entrepreneurship process can be successfully nurtured and conducted. In such view, this article moves from an extended review of corporate entrepreneurship and organizational innovation literature to define the concept of crowdventuring and to present an assessment tool aimed at evaluating the maturity of the crowdventuring process within an organization. The tool, which captures both individual and organization-related factors, is also used for an illustrative application into a multinational IT company. Some implications are drawn at the theory and practitioner levels.