Social entrepreneurs: making sense of tensions through the application of alternative strategies of hybrid organizations

Through the use of qualitative analysis, this paper examines the diverse tensions that social entrepreneurs have to deal with in their daily business activity. By using paradox theory and the hybrid organization model as a framework for analysis, we have found three principle causes of tension among social entrepreneurs: social vs economic sustainability; work vs family life; and resistance to change vs innovation. The results show the way in which social entrepreneurs in hybrid organizations resolve these conflicting tensions, usually through a selective coupling strategy, which is eventually complemented with alternative approaches such as compromising or decoupling. Social entrepreneurs tend to focus on one aspect of the tension and deal with it individually, which makes it more manageable. Change and innovation are the triggers for using strategies other than selective coupling, such as compromising or decoupling. When the level of tension rises further, compromising is then used. Decoupling is the last option chosen by social entrepreneurs, and is used only in cases where resolution is not possible with the other two strategies mentioned.

     

Platform Price Parity Clauses and Consumer Obfuscation*

Several antitrust authorities have investigated platform price parity clauses around the world. I analyze the impact of these clauses when platforms design a search environment for sellers and buyers to interact. In a model where platforms choose the unit search cost faced by consumers, I show when platforms can profitably obfuscate consumers through high search costs. Then, I show that price parity clauses, when exogenously given, can increase or reduce obfuscation, prices, and consumer surplus. Finally, when price parity clauses are endogenous, they are only observed in equilibrium if they hurt consumers.     

Neural networks for estimating Macro Asset Pricing model in football clubs

The recent crisis caused by COVID-19 directly affected consumption habits and the stability sof financial markets. In particular, the football industry has been hit hard by this pandemic and therefore has more volatile stock prices. Given this new scenario, further research is needed to accurately estimate the value of the shares of football clubs. In this paper, we estimate an asset pricing model in football clubs with different compositions of risk nature using non-linear techniques of artificial neural networks. Usually, asset pricing models have been estimated with linear methods such as ordinary least squares. Our results show a precision higher than 90% for all the estimated models, which far exceeds those shown by linear methods in the previous literature. We find that the residual represents about 40% of the variance of the price-dividend ratio. Long-term risks follow in importance, and above all, the habit component and its behaviour in the face of changes. The importance of the residual component exists due to a low correlation between the asset price and consumer behaviour, but to a much lesser extent than that shown in previous studies. The estimation carried out with artificial neural networks, both the Deep Learning methods and especially the Quantum Neural Network, opens up new possibilities to estimate more efficiently the pricing of financial assets in the football industry.