The impact of negative social environmental events on the market value of supply chain partners

The present work analyzes the impact of negative social / environmental events on the market value of supply chain partners. The study offers a contextualized discussion around important concepts which are largely employed on the Operations Management and Management literature in general. Among them, the developments of the literature around supply chains, supply chain management, corporate social responsibility, sustainable development and sustainable supply chain management are particularly addressed, beyond the links they share with competitive advantage. As for the theoretical bases, the study rests on the Stakeholder Theory, on the discussion of the efficient-market hypothesis and on the discussion of the adjustment of stock prices to new information. In face of such literature review negative social / environmental events are then hypothesized as causing negative impact in the market value of supply chain partners. Through the documental analysis of publicly available information around 15 different cases (i.e. 15 events), 82 supply chain partners were identified. Event studies for seven different event windows were conducted on the variation of the stock price of each supply chain partner, valuing the market reaction to the stock price of a firm due to triggering events occurred in another. The results show that, in general, the market value of supply chain partners was not penalized in response to such announcements. In that sense, the hypothesis derived from the literature review is not confirmed. Beyond that, the study also provides a critical description of the 15 cases, identifying the companies that have originated such events and their supply chain partners involved.

Learning in peer-to-peer markets: evidence from Airbnb

Peer-to-peer markets are highly uncertain environments due to the constant presence of shocks. As a consequence, sellers have to constantly adjust to these shocks. Dynamic Pricing is hard, especially for non-professional sellers. We study it in an accommodation rental marketplace, Airbnb. With scraped data from its website, we: 1) describe pricing patterns consistent with learning; 2) estimate a demand model and use it to simulate a dynamic pricing model. We simulate it under three scenarios: a) with learning; b) without learning; c) with full information. We have found that information is an important feature concerning rental markets. Furthermore, we have found that learning is important for hosts to improve their profits.