Management of technology licensing as a foreign market entry mode:the case of leading Italian pharmaceutical and biotech companies

Technology licensing has been recognized for decades as one of the new market entry modes. Companies often issue licenses in foreign countries in order to enter a new market. This paper aims to unearth how companies manage the technology licensing, purposely used by firms in order to enter new markets. Starting from the perspectives given in the Dunning’s eclectic theory on foreign market entry modes, and by adopting the process view perspective from the technology management literature, and also incorporating the Dynamic Capabilities Framework, this paper tries to explain the managerial aspects of technology licensing as the foreign market entry mode. Although technology licensing as a market entry mode has been previously thoroughly explored, limited attention has been given to the possible ways companies approach in managing technology licensing for the new market entry purpose. In the paper authors rely on the multiple case study research approach in order to reveal the relevant managerial aspects implemented by Italian pharmaceutical and biotech companies that exploit technology licensing for the new market entry purpose. The key findings in this paper indicate two points: (i) companies adopt the process view perspective for managing technology licensing as the foreign market entry mode and (ii) throughout the stages of this process firms tend to develop their dynamic capabilities (sensing, seizing and reconfiguring). These research findings contribute to a deeper understanding of technology licensing as a market entry mode in the Innovation and Technology Management literature, but also in the Internationalization literature, by integrating the elements coming from these two research streams. The managerial implications resulting from this paper may be especially useful for the firms operating in the research intensive industries (like chemical, semi-conductor, biotech, etc.), enabling them to recognize the relevant issues in technology licensing process for the market entry purpose.<br

Migration and public finances in the EU

We provide novel and comprehensive evidence on the net fiscal contributions of natives and migrants to the governmental budgets of EU countries. We account for income taxes and cash benefits, along with indirect taxes and in-kind benefits, which are often missing in standard datasets. We find that on average, migrants were net contributors to public finances over the period of 2014–2018 in the EU and, moreover, that they contribute approximately €1.5 thousand more per capita each year than natives. We also show that this difference is partly due to the selection on characteristics that make migrants net fiscal contributors, such as demographic factors and employment probability

Building an outward-oriented social family legacy: rhetorical history in family business foundations

Scholars have recently paid growing attention to the transfer of family legacies across generations, but existing work has been mainly focused on an inward-oriented, intra-family, perspective. In this article, we seek to understand how family firms engage in rhetorical history to transfer their social family legacy to external stakeholders, what we call “outward-oriented social legacy.” By carrying out a 12-months field study in three Italian family business foundations, our findings unveil three distinctive narrative practices—founder foreshadowing, emplacing the legacy within the broader community, and weaving family history with macro—history—that contribute to transferring outward-oriented social legacies

RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes

We propose a generic framework for gene regulatory network (GRN) inference approached as a feature selection problem. GRNs obtained using Machine Learning techniques are often dense, whereas real GRNs are rather sparse. We use a Tikonov regularization inspired optimal L-curve criterion that utilizes the edge weight distribution for a given target gene to determine the optimal set of TFs associated with it. Our proposed framework allows to incorporate a mechanistic active biding network based on cis-regulatory motif analysis. We evaluate our regularization framework in conjunction with two non-linear ML techniques, namely gradient boosting machines (GBM) and random-forests (GENIE), resulting in a regularized feature selection based method specifically called RGBM and RGENIE respectively. RGBM has been used to identify the main transcription factors that are causally involved as master regulators of the gene expression signature activated in the FGFR3-TACC3-positive glioblastoma. Here, we illustrate that RGBM identifies the main regulators of the molecular subtypes of brain tumors. Our analysis reveals the identity and corresponding biological activities of the master regulators characterizing the difference between G-CIMP-high and G-CIMP-low subtypes and between PA-like and LGm6-GBM, thus providing a clue to the yet undetermined nature of the transcriptional events among these subtypes