The role of humor in television advertisement: A study regarding Portuguese consumers’ emotions and reactions

The purpose of this study is to explore the humorous side of television advertisement and its impact on Portuguese consumers’ hearts, minds and wallets. Both qualitative (through in-depth interviews) and quantitative (through an on-line survey and subsequent statistical data analysis) methods were used, guaranteeing a more consistent, strong and valid research. Twenty-five interviews with randomly chosen consumers were conducted face-to-face and three interviews via e-mail with marketers and television advertisers were performed in order to explore profoundly the subject. Moreover, 360 people have answered the on-line survey. Through the analysis of the data collected humor perception was found to be positively correlated with persuasion and intention to purchase the product; intention to share the advert; message comprehension; product liking and development of positive feelings towards the brand and brand credibility variables. The main implication of these findings relies on the fact that humor in advertising is able to boost its effectiveness.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.