Ties that bind tourists: embedding destination motivators to destination attachment: a study in the context of Kumbh Fair,India

ABSTRACT

Despite the deep cultural value and strong appeal to global tourists, the Kumbh Fair has not been explored much by researchers. This is even though the Kumbh Fair is crafting the tourism of India, thereby leading to its overall socio-economic development. This study aims to examine the determinants of tourist motivation, such as ads awareness, destination image and online-communities, which provoke tourists to have attachment with the destination more closely. The study found the tourists’ attitude as a complementary mediation and spiritual stimuli as a mediating moderator are positively impacting on the significant relationship of destination motivators and destination attachment.     

Prediction Model for Bollywood Movie Success: A Comparative Analysis of Performance of Supervised Machine Learning Algorithms

The main purpose of this paper is to do a comparative analysis of prediction models using various machine learning techniques. The models will be used to predict whether a movie will be a hit or flop before it is actually released. The techniques used for comparisons are decision tree, random forest (RF), support vector machine, logistics regression, adaptive tree boosting, and artificial neural network algorithms. The major predictors used in the models are the ratings of the lead actor, IMDb ranking of a movie, music rank of the movie, and total number of screens planned for the release of a movie. The results of most models indicated a reasonable accuracy, ranging from 80 to 90%. However, models based on two techniques, RF and logistic regression, achieved an accuracy of 92%. From the results, the most important predictors of a movie’s success are music rating, followed by its IMDb rating and total screens used for release.