Antecedents of e-loyalty and electronic word-of-mouth

O objetivo principal da dissertação foi o de examinar a relação entre lealdade (e-loyalty) e o boca-a-boca (eWOM) no contexto do varejo eletrônico. Como objetivo secundário, foi feito uma verificação da significância das variáveis preditoras de e-loyalty. Essa pesquisa foi focada em um tipo de produto/serviço: compra de livros através da internet. Duzentos e quarenta e dois questionários online foram respondidos por um público representativo da geração Y (millennials), e rresidentes em diferentes localidades no Brasil e nos Estados Unidos. A análise de dados foi efetuada pela aplicação do método PLS-SEM sobre um modelo de pesquisa cuidadosamente formulado com base em resultados empíricos prévios. Enquanto que a relação entre e-loyalty e eWOM foi classificada como fraca, um ambiente de boca-a-boca online de alta qualidade representou uma variável preditora significativa para o sentimento de e-loyalty. Todas as variáveis preditoras foram classificadas como significativas nesse estudo, sendo que comprometimento tem o efeito mais forte sobre a variável e-loyalty.

Cherry picking with synthetic controls

The synthetic control (SC) method has been recently proposed as an alternative method to estimate treatment e ects in comparative case studies. Abadie et al. [2010] and Abadie et al. [2015] argue that one of the advantages of the SC method is that it imposes a data-driven process to select the comparison units, providing more transparency and less discretionary power to the researcher. However, an important limitation of the SC method is that it does not provide clear guidance on the choice of predictor variables used to estimate the SC weights. We show that such lack of speci c guidances provides signi cant opportunities for the researcher to search for speci cations with statistically signi cant results, undermining one of the main advantages of the method. Considering six alternative speci cations commonly used in SC applications, we calculate in Monte Carlo simulations the probability of nding a statistically signi cant result at 5% in at least one speci cation. We nd that this probability can be as high as 13% (23% for a 10% signi cance test) when there are 12 pre-intervention periods and decay slowly with the number of pre-intervention periods. With 230 pre-intervention periods, this probability is still around 10% (18% for a 10% signi cance test). We show that the speci cation that uses the average pre-treatment outcome values to estimate the weights performed particularly bad in our simulations. However, the speci cation-searching problem remains relevant even when we do not consider this speci cation. We also show that this speci cation-searching problem is relevant in simulations with real datasets looking at placebo interventions in the Current Population Survey (CPS). In order to mitigate this problem, we propose a criterion to select among SC di erent speci cations based on the prediction error of each speci cations in placebo estimations

The Olympic impact on hosting candidate countries

In this paper, we analyze the impact of hosting the Summer Olympics on macroeconomic aggregates such as GDP, consumption, government consumption and investments per capita. The data is in panel structure and includes the period of ten years before and ten years after the event containing the Olympic Summer Games between 1960 and 1996. The sample countries comprise only candidates to host the games. This sampling strategy allows us to estimate the average treatment effect consistently, because it is assumed that these countries are comparable to each other, including those that ultimately hosted the games. The impact of hosting the Olympic games is measured by Fixed Effect and First Difference regressions. Moreover, we do a structural break test developed by Andrews (1993) to identify if hosting the Olympic Games creates anticipation effects for demand changes that stimulate current GDP, consumption, government consumption and investments. The results indicate a positive effect of the Summer Olympics in all variables of interest. However, the distribution in time and anticipation of these effects is unclear in the tests, changing significantly depending on the model and the significance level used.