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The role of a relatively small cadre of high-tech startup firms in driving innovation and economic growth has been well known and amply celebrated in recent history. At the same time, it is well recognized that, while the overall contribution of startups is crucial, the high-risk and high-reward strategy followed by these startups leads to significant failure rates and a low ratio of successful startups. So, it is curious to notice that literature tends to focus on successful startups and on quantitative studies looking for determinants of success while neglecting the numerous lessons that can be drawn by examining the stories of startups that failed. This paper aims to fill this gap and to contribute to the literature by providing a repeatable and scalable methodology that can be applied to databases of unstructured post-mortem documents deriving startup failure patterns. A further and related contribution is the analysis carried out with this methodology to a large database of 214 startup post-mortem reports. Descriptive statistics show how the lack of a structured Business Development strategy emerges as a key determinant of startup failure in the majority of cases.based on discriminant analysis [5] and multiple discriminant analysis [4], followed by more recent approaches exploiting regression [6][7][8]. Since the 80s, artificial intelligence methods started to be used as well to predict ventures success/failure. Suggested solutions relied on decision trees algorithms [9], artificial neural networks [10], clustering [11] and hybrid genetic algorithms [12]. Approaches based on financial data had the advantage of being potentially applied to a high number of companies, since data could be gathered from their annual reports. Nonetheless, company revenues were frequently consequences of other aspects, such as entrepreneur's ability, company's core competencies, market, etc. In this view, other research works investigated whether such aspects could contribute as well to the success or failure of a venture. For instance, analysis conducted on entrepreneurs examined the influence of their gender and ethnic origin on the likelihood to succeed or fail [13,14]. The work in [15] conducted a logistic regression analysis based on 15 independent variables success versus failure prediction model in Israel, including the management experience, the education, and the age of the owner. The logistic regression analysis was also adopted in [16] to model the relationship between small business mortality rates and the aggregate levels of internal and external risks (e.g., bankruptcy related to interest rates, discontinuance of business or of ownership, etc.). Other researchers focused on entrepreneurial attitudes and linked startup failure to dissonances between corporate goals and the goals of its founders [17]. Others presented failure as the result of entrepreneurs' overconfidence and hubris [18]. In contrast, other researchers argued that, without a reasonable level of positive perception of one's abilities, several successful...

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Abstract:The logistics sector faces substantial challenges in meeting customer demands for higher service quality, speed and flexibility under conditions of continued growth in world trade and worldwide transportation movements, increasing distances and vulnerabilities of the supply chain. Additional challenges relate to the economic and environmental sustainability of logistics operations. While a lot of attention was devoted in the past decades to the operational and technical aspects, the business development phase has been put aside, causing the market failure of several projects. The paper presents the SYNCHRO-modal supply chain eco-NET (SYNCHRO-NET) project, which will demonstrate the effectiveness of slow steaming combined with synchro-modality in reducing the cost and the emissions of international supply chains and improving reliability and sustainability through the optimization of the planning process. Differently from other similar projects, SYNCHRO-NET combines operational aspects with a business perspective and represents a stakeholder-driven approach aimed at developing a close-to-market solution over the timeframe of the project.

In recent years, the concept of a “Smart City” became central in the agenda of researchers, practitioners, and stakeholders. Although the application of information and communication technologies on city management has advanced exponentially, also other components would be needed for building a truly sustainable urban environment. Researchers from different domains debated the definition of a smart city and the conceptual variants. However, a broad view of the smart city field is still missing. This paper attempts to fill this gap by proposing a taxonomic classification of the most 105 outstanding smart city projects in Europe and North America. Collected data are then processed by statistical tools for clearly highlighting the success factors, trends and future paths in which all these projects are moving, along with different aspects (e.g., business model, purpose, industry). We then investigate the European and the North American Smart City concepts, illustrating the key role of mixed public and private partnerships in creating successful projects and the focus on the urban transportation, and freight and last-mile delivery in particular. Moreover, it emerges how the business modeling and the exploitation aspects have still low integration in the projects.