In this paper we introduce a linguistic multi-criteria decision-aiding model to support college students with the internship job market application. It considers a fuzzy ordered weighted averaging (FOWA) operator in the matching to capture the inherent uncertainty and vague nature of personnel selection processes. The decision model is integrated in a software tool able to capture data from university student resume and internship databases. The application assesses position characteristics implicitly by means of linguistic descriptions according to each student's preferences. The software tool is enabled with the ability to propose positions according to student preferences. The system selects a reduced list of alternatives from the set of job offers, helping students to decide on which positions to focus their applications.
Manufacturing is undergoing a deep model change due to the convergence of several forces: (a) the simultaneous emergence of new disruptive technologies, (b) the accelerated substitution of men by machines, (c) the restructuring of global competition, with the consolidation of some global manufacturing clusters, and (d) a new market dynamic dominated by a growing consumer power. Much has been said about how industries adapt to these forces (a widely known example is the so-called “industry 4.0” paradigm). Scholarly literature states that, in moments of accelerated technological change and industry effervescence, new technology-based firms play a critical role in reshaping the markets and reconfiguring competition. Yet, little has been said in the literature about the features of new manufacturing start-ups. Our aim is to explore the origins of the new technological firms that are emerging in the manufacturing industry. To do so, we have created a database of 184 manufacturing start-ups, incepted since 2013, and which have attracted some US$ 2.4 billion in total funding; of these firms, we have analyzed a set of 291 founders’ profiles, looking for their backgrounds and previous experiences. Our findings suggest that promising new manufacturing technology-based firms are created mainly by teams formed by experienced managers and experts with solid scientific or technological backgrounds.
The creation and development of new technology-based firms (NTBFs) is at the core of national prosperity, and constitutes a key activity of the innovation policies of advanced and developing economies. Many of these companies are founded by postgraduates that hold a doctorate (PhDs). Governments foster the creation of NTBFs by PhDs to take advantage of the stock of knowledge which exists in universities and research centers. It is assumed that founders with a high level of specialization and knowledge, such as PhDs, will bring strong competitive advantages to the companies they found. The literature in this regard, however, is scarce and inconclusive. We have studied the role of PhDs in founding teams of NTBFs in a specific kind of entrepreneurial process: corporate venturing. Our conclusions suggest that companies with PhDs are significantly more attractive to corporate venture capital. Corporate venturing has a higher propensity to invest in NTBFs with PhDs in the founding teams, and these companies concentrate a higher number of corporate investors, in a kind of accumulative effect.
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