Travel bragging, referring to the act of showing off or boasting about travel experiences, is ubiquitous on social media; travel bragging rights (e.g., Instagrammability) have become an important factor in travel decision-making in the social media era. Despite these developments, research on travel bragging remains scarce. This dissertation presents two studies. The first aimed to explore travel bragging via qualitative approaches (i.e., in-depth interviews, means-end analysis, and photo elicitation) to determine how consumers (both braggers and the audience) defined and perceived travel bragging and travel bragging rights. This study provided a systematic conceptualization of travel bragging, including a definition of the term, how to distinguish travel bragging from travel experience sharing, motivations behind travel bragging, the influence of travel bragging on travel braggers and their audience, and coping strategies consumers used to mitigate the negative impacts of travel bragging. This conceptualization of travel bragging highlighted perception gaps between travel braggers and the audience in identifying travel bragging, motivations behind this behavior, and the audience’s emotional reactions to it. The first study also provided a conceptual framework of travel bragging rights, which includes seven dimensions related to destination attributes: difference, similarity, scarcity, functionality, symbolism, hedonism, and consequentiality. Results show that, as a travel motivation, seeking travel bragging rights exerts a stronger influence on young generations and active social media content generators. The second study sought to investigate travel bragging in an online travel review context using an experimental design. Drawing on attribution theory and the emotional contagion effect, this study attempted to identify whether two visual cues in user-generated photos (pictorial self-prominence and selfies) could affect the persuasion effect of online reviews through perceived dubious motivations and positive emotions. Pictorial self-prominence (i.e., the degree to which the image of oneself is noticeable from user-generated photos) is a new concept introduced in this study. It was manipulated in two ways: the ratio of the area of one’s image to a whole self-portrait, and the ratio of the number of photos including the reviewer’s image to the total number of photos uploaded per online review. Results show that pictorial self-prominence has a negative impact on review helpfulness through perceived dubious motivations; however, this effect did not extend to visit intention to the tourist attraction mentioned in the online review. The findings also show that pictorial self-prominence (manipulated by number ratio) and selfie had a significant interaction effect on review helpfulness, such that when more photos contained the reviewer’s image, online reviews with selfies were perceived as more helpful than those with non-selfies. Follow-up analysis revealed that this interaction effect was mediated by perceived authenticity and perceived source expertise. Theoretically, this dissertation presents a systematic and comprehensive conceptualization of travel bragging, including travel bragging behavior and travel bragging rights. This conceptualization provides an update to consumers’ opinions about travel bragging and travel bragging rights in the social media era. This work also contributes to the word-of-mouth literature by uncovering the influences of travel bragging and underlying mechanisms. In addition, this dissertation reveals the influences of pictorial self-prominence and selfies on review helpfulness, highlighting the importance of visual cues in online word of mouth. Managerially, findings regarding travel bragging and travel bragging rights offer important implications for destination marketing organizations and associated social media marketers. The dissertation also outlines a series of tactful self-presentation strategies for individuals who enjoy bragging about or sharing travel experiences on social media while avoiding being perceived negatively. In addition, findings from the first study call for attention from policy makers to the negative psychological effects of travel bragging on travel braggers and the audience. The study on pictorial self-prominence and selfies provides important implications for destination marketers’ visual marketing strategies. / Tourism and Sport

The purpose of this study is to determine the effect of three improvement methods on nonparametric kernel regression estimators. The improvement methods are applied to the Nadaraya-Watson estimator with crossvalidation bandwidth selection, the Nadaraya-Watson estimator with plug-in bandwidth selection, the local linear estimator with plug-in bandwidth selection and a bias corrected nonparametric estimator proposed by Yao (2012). The di erent resulting regression estimates are evaluated by minimising a global discrepancy measure, i.e. the mean integrated squared error (MISE). In the machine learning context various improvement methods, in terms of the precision and accuracy of an estimator, exist. The rst two improvement methods introduced in this study are bootstrapped based. Bagging is an acronym for bootstrap aggregating and was introduced by Breiman (1996a) from a machine learning viewpoint and by Swanepoel (1988, 1990) in a functional context. Bagging is primarily a variance reduction tool, i.e. bagging is implemented to reduce the variance of an estimator and in this way improve the precision of the estimation process. Bagging is performed by drawing repetitive bootstrap samples from the original sample and generating multiple versions of an estimator. These replicates of the estimator are then used to obtain an aggregated estimator. Bragging stands for bootstrap robust aggregating. A robust estimator is obtained by using the sample median over the B bootstrap estimates instead of the sample mean as in bagging. The third improvement method aims to reduce the bias component of the estimator and is referred to as boosting. Boosting is a general method for improving the accuracy of any given learning algorithm. The method starts of with a sensible estimator and improves iteratively, based on its performance on a training dataset. Results and conclusions verifying existing literature are provided, as well as new results for the new methods. / MSc (Statistics), North-West University, Potchefstroom Campus, 2015

The purpose of this study is to determine the effect of three improvement methods on nonparametric kernel regression estimators. The improvement methods are applied to the Nadaraya-Watson estimator with crossvalidation bandwidth selection, the Nadaraya-Watson estimator with plug-in bandwidth selection, the local linear estimator with plug-in bandwidth selection and a bias corrected nonparametric estimator proposed by Yao (2012). The di erent resulting regression estimates are evaluated by minimising a global discrepancy measure, i.e. the mean integrated squared error (MISE). In the machine learning context various improvement methods, in terms of the precision and accuracy of an estimator, exist. The rst two improvement methods introduced in this study are bootstrapped based. Bagging is an acronym for bootstrap aggregating and was introduced by Breiman (1996a) from a machine learning viewpoint and by Swanepoel (1988, 1990) in a functional context. Bagging is primarily a variance reduction tool, i.e. bagging is implemented to reduce the variance of an estimator and in this way improve the precision of the estimation process. Bagging is performed by drawing repetitive bootstrap samples from the original sample and generating multiple versions of an estimator. These replicates of the estimator are then used to obtain an aggregated estimator. Bragging stands for bootstrap robust aggregating. A robust estimator is obtained by using the sample median over the B bootstrap estimates instead of the sample mean as in bagging. The third improvement method aims to reduce the bias component of the estimator and is referred to as boosting. Boosting is a general method for improving the accuracy of any given learning algorithm. The method starts of with a sensible estimator and improves iteratively, based on its performance on a training dataset. Results and conclusions verifying existing literature are provided, as well as new results for the new methods. / MSc (Statistics), North-West University, Potchefstroom Campus, 2015