Abstract
We study price personalization in a two period duopoly with vertically differentiated products. In the second period, a firm not only knows the purchase history of all customers, as in standard Behavior Based Price Discrimination models, but it also collects detailed information on its old customers, using it to engage in price personalization. The analysis reveals that there exists a natural market for each firm, defined as the set of customers that cannot be poached by the rival in the second period. The equilibrium is unique, except when firms are ex-ante almost identical. In equilibrium, only the firm with the largest natural market poaches customers from the rival. This firm has highest profits but not necessarily the largest market share. Aggregate profits are lower than under uniform pricing. All consumers gain, total welfare is higher herein than under uniform pricing if firms' natural markets are sufficiently asymmetric. The low quality firm chooses the minimal quality level and a quality differential arises, though the exact choice for the high quality depends upon the cost specification.

Abstract
A durable good monopolist faces a continuum of heterogeneous customers who make purchase decisions by comparing present and expected price-quality offers. The monopolist designs a sequence of price-quality menus to segment the market. We consider the Markov perfect equilibrium (MPE) of a game where the monopolist is unable to commit to future price-quality menus. We obtain the novel results that: (a) under certain conditions, the monopolist covers the whole market in the first period (even when a static Mussa-Rosen monopolist would not cover the whole market), because this is a strategic means to convince customers that lower prices would not be offered in future periods and that (b) this can happen only under the stage-wise Stackelberg leadership assumption (whereby consumers base their expectations on the value of the state variable at the end of the period). Conditions under which MPE necessarily involves sequentially trading are also derived.

Abstract
Entrepreneurial Ecosystems (EEs) have attracted the attention of academics, practitioners, and policymakers, that attempt to unlock 'a winning recipe' considering the different EEs pillars in order to ignite entrepreneurship at large. Therefore, understanding the degree of influence of each pillar on Entrepreneurial Initiative (EI) is helpful in framing more effective policies towards entrepreneurship. This study aims to bring a new facet to entrepreneurship research, specifically on decomposing the transformation of EEs and the influence of EEs pillars on EI. The transformation of EEs is shown by a balanced panel approach based on the Global Entrepreneurship Monitor (GEM) dataset over 8 years (2010-2017), comprising 18 countries. The study has several implications for entrepreneurship theory and practice as well as public policy since discusses three main issues, mainly supported by empirical results. First, the results show an unbalanced influence of EEs pillars on EI. Second, results also show the ineffectiveness of institutions in encouraging the desire to act entrepreneurially. Third, entrepreneurship needs to be part of the acculturation process evidencing the importance of collective normative. Therefore, providing the instruments and structures is not enough to encourage individuals to start an entrepreneurial journey. Generally, the results reveal that contextual determinants are significant in fostering entrepreneurial propensity to start a business. But the impact of the nine pillars is not equalized, revealing a fragmented influence with funding measures, R&D transfer, and cultural and social norms discouraging entrepreneurial initiative. Overall, the study contributes to the understanding of a multidimensional perspective on EEs and points future policy directions to overcome the lack of entrepreneurship and amend flawed entrepreneurship policies.

Abstract
The main goal of this article is to appraise the existence of different patterns of the Entrepreneurial Ecosystems, to identify its relationship with Entrepreneurial Initiative, and recommend entrepreneurship policies that may influence the growth of entrepreneurial action. Without evidence on entrepreneurial ecosystems landscape and what determinants stimulate entrepreneurship in a given environment, policies could become flawed and miss the target. To address research purposes, the analysis was performed using data extracted from the Global Entrepreneurship Monitor (GEM) Database carried out between 2010 and 2016. To ensure a longitudinal perspective, it was used a balanced panel approach followed by Logistic Regression estimations. The article offers a novel and systematic approach, the Entrepreneurial Ecosystem Taxonomy, to overcome a disaggregated perspective on entrepreneurial ecosystems, between individual and context levels. Empirical findings capture four different country profiles, based on two measures: Entrepreneurial ecosystems and entrepreneurial initiative. The results allow to compare the four groups and appraise significant disparities around entrepreneurship determinants, namely, the education factor. While education is commonly recognized as a positive influence on entrepreneurship, the results suggest a contradictory effect. The existence of differentiated profiles and its determinants points outs the importance of developing specific entrepreneurship policy packages attending group specificities.

Abstract
Entrepreneurial universities are a significant element of entrepreneurial ecosystems and aspire to foster entrepreneurial initiative through their "third mission". However, while entrepreneurial ecosystems are scrutinized using a contextual approach to detect differences and similarities and how they affect entrepreneurship, little is known about how entrepreneurial universities impact entrepreneurial initiatives in general, considering multiple environments. Drawing on entrepreneurial university and entrepreneurial ecosystem theories, a conceptual framework is proposed that aims to explain the effect of the entrepreneurial university on an entrepreneurial initiative through its three "missions", using an entrepreneurial ecosystem taxonomy. Based on individual data from the Global Entrepreneurship Monitor, this entrepreneurial initiative analyzed 18 European countries in 2017. The results do not generally support the importance of entrepreneurial universities to entrepreneurial initiative. The relevance of entrepreneurial universities increases in more fragile entrepreneurial ecosystems since individuals need support over multiple dimensions. Conversely, the entrepreneurial universities that are embedded in stronger entrepreneurial ecosystems lose relevance and negatively affect the entrepreneurial initiative. Therefore, the value of entrepreneurial universities is reduced when individuals receive greater support from other dimensions. The variations across both groups suggest that the concept of entrepreneurial universities is not a contemporaneous phenomenon; however, their effect is progressively revealed by the maturity of each university's mission. This perspective substantially changes the understanding of entrepreneurial universities as a thwartwise strategy, suggesting that the universities' impact is expanded as their missions gradually evolve. Overall, the study contributes to an understanding of the implications for universities that blindly follow entrepreneurship, neglecting the exogenous environment, namely, the entrepreneurial ecosystem and individual drive.

Abstract
After the Cold War, the defense industries found themselves at a crossroads. However, it seems that they are gaining new momentum, as new technologies such as robotics and artificial intelligence are enabling the development of autonomous, highly innovative and disruptive intelligent systems. Despite this new impetus, there are still doubts about where to invest limited financial resources to boost high-tech defense industries. In order to shed some light on the topic, we decided to conduct a systematic literature review by using the PRISMA protocol and content analysis. The results indicate that autonomous intelligent systems are being developed by the defense industry and categorized into three different modes-fully autonomous operations, partially autonomous operations, and smart autonomous decision-making. In addition, it is also important to note that, at a strategic level of war, there is limited room for automation given the need for human intervention. However, at the tactical level of war, there is a high probability of growth in industrial defense, since, at this level, structured decisions and complex analytical-cognitive tasks are carried out. In the light of carrying out those decisions and tasks, robotics and artificial intelligence can make a contribution far superior to that of human beings.