Ricardo Gimeno

We introduce the Zealot–Preacher model, a generalized diffusion framework on networks with simultaneous heterogeneity at the node and edge levels. Standard diffusion models typically assume homogeneous susceptibility across nodes and homogeneous transmission strength across edges. We relax both assumptions by assigning to each node a latent receptiveness parameter and to each edge a latent conductiveness parameter governing influence propagation. The framework accommodates both binary contagion and continuous consensus dynamics and nests standard homogeneous diffusion as a special case. We characterize the resulting weighted consensus process and show that low-receptiveness nodes disproportionately influence long-run network outcomes. To estimate the model in large networks, we develop a hierarchical latent-variable approach in which receptiveness and conductiveness follow Beta distributions, with hyperparameters estimated by maximum likelihood and individual effects recovered through Bayesian updating. An application to U.S. interlocking director networks shows that diffusion of board gender diversity is constrained primarily by widespread node-level resistance rather than by the absence of conductive transmission channels. Female shared directors exhibit higher average conductiveness than male shared directors, suggesting gendered heterogeneity in diffusion channels.

This study investigates how gendered facial cues shape the visibility of executives personal brands on LinkedIn. Building on signaling theory, we analyze 5575 public executive profiles, extract facial metrics through machine‐learning landmark detection, and model direct and indirect pathways to digital visibility outcomes proxied by follower counts using negative binomial regressions and generalized structural equation modeling (GSEM). Facial trustworthiness is strongly associated with follower counts for both genders, especially for women, while friendliness shows gender‐opposite patterns, and attractiveness is not associated with the number of followers. Career capital (years of experience and number of roles) serves as a key mediator linking facial impressions to digital visibility outcomes. The study shows that trustworthiness can be an effective facial signal in professional digital markets; theorizes career capital as a signal‐conversion mechanism; and shows how gendered role expectations can shape symbolic consumption on LinkedIn.

We apply discrete probability forecasts to the expectations of monetary policy rate changes, both in the United States and in the euro area. By using binomial trees from options theory, forecast distributions are derived from the instantaneous forward yield curve, based on interest rate swaps. We then use a non-randomised discrete probability forecast evaluation that confirms the presence of a systematic upward bias, consistent with the presence of a term premium. Consequently, we propose a bias-correction methodology to increase the accuracy of the density forecasts regarding monetary policy expectations. This research provides pivotal insights into understanding and improving predictive tools in monetary policy forecasting.

Research question/issue: This study examines the influence of group behavior on board gender diversity and identifies obstacles to its diffusion. It investigates whether boards are influenced by group behavior in determining their gender composition and explores barriers that impede the diffusion process.Research findings/insights: Analyzing a network of over 18,000 US‐listed boards observed over 20 years, we find a pattern of mimetic behavior, whereby boards imitate the proportion of women on boards (WoB) among their directly tied boards. We also identify resistant firms with low representation of women on their boards. Our findings suggest that, among those boards, there are heavily male‐dominated boards resisting the appointment of women, even when surrounded by women directors among their tied boards. The diffusion of board gender diversity is slowed down by these resistant boards, hindering the overall progress in increasing gender diversity within the board network.Theoretical/academic implications: This research contributes insights into group behavior and resistance in board gender diversity. Adopting a network theory lens, our study sheds light on interactions between firms and their connected companies in terms of imitation practices. Drawing on social identity theory, we highlight the significance of the resistance to increasing women's representation exhibited by some boards.Practitioner/policy implications: Resistant boards, despite group pressure, slow down the diffusion of board gender diversity within the network, leading to overall stagnation. Understanding the sources of resistance allows an exploration of alternative measures to promote diversity without rigid mandates.

The authors propose a means to capture climate change risk exposure by combining a green factor with typical frameworks used for explaining stock returns.

Network diffusion processes or how information spreads through networks have been widely examined in numerous disciplines such as epidemiology, physics, sociology, politics, or computer science. In this paper, we extend previous developments by considering a generalization of the diffusion by considering the possibility of differences in the speed of diffusion and reduction depending on the forces’ directions. In this situation, the differential speed of diffusion produces deviations from the standard solution around the average of the initial conditions in the network. In fact, this asymmetry gives rise to non-linear dynamics in which, contrary to the symmetric case, the final solution depends on the topology of the graph as well as on the distribution of the initial values. Counter-intuitively, less central nodes in the network are able to exert a higher influence on the final solution. This behavior applies also for different simulated networks such as random, small-world, and scale-free. We show an example of this kind of asymmetric diffusion process in a real case. To do so, we use a network of US Boards of Directors, where boards are the nodes and the directors working for more than one board, are the links. Changes in the proportion of women serving on each board are influenced by the gradient between adjacent boards. We also show that there is an asymmetry: the gradient is reduced at a slower (faster) rhythm if the board has less (more) women than neighboring boards. We are able to quantify the accumulated effect of this asymmetry from 2000 to 2015 in the overall proportion of women on boards, in a 4.7 percentage points (the proportion should have been an 14.61% instead of the observed 9.93% in 2015).

We show the role that board interlocks play in the diffusion of company strategies on the proportion of Women on Boards. We analyze two networks of more than 12,000 American and 6,500 European comp...