In den letzten Monaten sind zahlreiche Nutzer/innen von X (ehemals Twitter) zu Bluesky gewechselt oder nutzen inzwischen beide Plattformen parallel. So meldeten sich bspw. Politiker/innen wie die Bundesaußenministerin Annalena Baerbock oder Marie Agnes Strack- Zimmermann sowie diverse...

In today's world, the increasing significance of social media as a news outlet has brought its benefits and drawbacks for brands and companies. One such drawback is individuals sharing fake news about brands and organizations. While existing research offers essential insights into sharing...

We study how privacy regulations impact information transmission. A receiver faces a decision whose payoff depends on an unknown state. A platform sends a message to her given a sensitive and a nonsensitive signal. Privacy loss occurs when the receiver updates about the former. When privacy is...

What are the dynamic effects of fact-checking on the behavior of those who circulate misinformation and on the spread of false news? In this paper, we provide causal evidence on these questions, building on a unique partnership with the Agence France Presse (AFP), the world’s largest...

This study uses Baidu News data and introduces a novel proxy for the rate of information flow to examine its relationship with return volatility in Chinese commodity futures and to test two competing hypotheses. We examine the contemporaneous relationships using correlation coefficient analysis,...

Artificial intelligence (AI) is diffusing rapidly in the workplace, yet aggregate productivity gains remain limited. This paper examines the dual diffusion of AI - through both formal, employer-led and informal, employee-initiated adoption - as potential explanation. Using a representative...

Information often shapes behavior regardless of its quality: unreliable claims wield influence, while reliable ones are neglected. We propose that this occurs in part because word-of-mouth transmission tends to preserve claims while dropping information about their reliability. We conduct...

This study investigates the impact of outsiders' demand for more information (or transparency) on corporate social responsibility (CSR) initiatives. Drawing on a dataset of U.S. companies from 2010 to 2023, CSR performance is measured using ASSET4 ratings, while CSR disclosure levels are...

Information often shapes behavior regardless of its quality: unreliable claims wield influence, while reliable ones are neglected. We propose that this occurs in part because word-of-mouth transmission tends to preserve claims while dropping information about their reliability. We conduct...

This briefing summarises the evaluation of hundreds of data deliveries received from reporting countries for 13 Eionet core data flows. It shows progress against agreed reporting criteria, allowing countries to identify and prioritise the resources they need for regular reporting procedures.

Little is known about the extent and drivers of information flow within couples and whether spouses hold aligned expectations about the same outcomes. To provide new evidence, we conduct an online survey of 2,200 middle-aged married couples in the U.S. Our focus is on expectations about Social...

In this paper, we investigate the classical Axelrod model of cultural dissemination under an adaptive network framework. Unlike the original model, we place agents on a complex network, where they cut connections with any agent that does not share at least one cultural trait. This rewiring...

We present a simple model where, before competing in prices, firms announce which prices they intend to choose. Deviating from these announcements involves a cost. We show that sharing pricing intentions results in prices being set above their competitive levels. All equilibria result in prices...

We study equilibrium behavior in incomplete‐information games under two information constraints: seeds and spillovers. The former restricts which agents can initially receive information. The latter specifies how this information spills over to other agents. Our main result characterizes the...

Federated Learning (FL) is an innovative technique that allows multiple firms to collaborate in training machine learning models while preserving data privacy. This is especially important in industries where data is sensitive or subject to regulations like the General Data Protection Regulation...

Policymakers face a dilemma in determining the optimal approach to regulating highly concentrated markets, especially in the context of digital government initiatives in healthcare. The current study investigates whether vendor market concentration facilitates or impedes efficiency in health...