Crisis communication: organization's reactions to unexpected events

A reputação é considerada o ativo mais importante das empresas. Ela permite o estabelecimento de relações comerciais e garante um bom funcionamento da organização. Quando um evento inesperado surge, a reputação pode ser ameaçada. Os gerentes, líderes da organização, têm então que demonstrar reatividade e capacidade em responder as necessidades dos stakeholders, e capacidade de detectar e consertar as falhas dentro da organização através de um processo de aprendizagem, para evitar conseqüências negativas que poderiam danificar a reputação e impactar o desenvolvimento operacional da empresa. Através da comunicação de crise, observamos que depois da queda do avião AF 447, a companhia Air France adotou diferentes posturas adaptadas ao pedido dos stakeholders e ao grau de ameaça sofrido. Logo depois do acidente, a empresa decidiu adotar a estratégia do reconhecimento, assumindo uma responsabilidade simbólica e comunicando prioritariamente para as famílias das vitimas e para a mídia. Nas seguintes semanas ela utilizou a estratégia do silêncio que consiste em não comunicar diretamente a mídia. Finalmente, ela usou a estratégia do “bode expiatório” quando ela foi sujeita a ataques diretos. As reações da empresa somadas ao avanço das investigações judiciais revelaram falhas organizacionais “históricas” dentro da própria empresa, como por exemplo, a falta de comunicação entre pilotos e gerentes ou uma falha de sensibilidade técnica e operacional da parte dos gerentes. Apesar de problemas interno e externo, a Air France demonstrou que uma comunicação de crise bem gerenciada limita os impactos financeiros e de reputação. As conseqüências negativas sofridas pela companhia Air France foram limitadas.

Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyze extremal events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.

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.

Stochastic discount factor bounds and rare events: a review

We aim to provide a review of the stochastic discount factor bounds usually applied to diagnose asset pricing models. In particular, we mainly discuss the bounds used to analyze the disaster model of Barro (2006). Our attention is focused in this disaster model since the stochastic discount factor bounds that are applied to study the performance of disaster models usually consider the approach of Barro (2006). We first present the entropy bounds that provide a diagnosis of the analyzed disaster model which are the methods of Almeida and Garcia (2012, 2016); Ghosh et al. (2016). Then, we discuss how their results according to the disaster model are related to each other and also present the findings of other methodologies that are similar to these bounds but provide different evidence about the performance of the framework developed by Barro (2006).