Priyam Porwal, Bibeth Sharma, Samrat Kumar Mukherjee and Ajeya Jha
Homestays have emerged as a popular alternative form of accommodation for tourists seeking authentic cultural experiences. It is expected that profile of home-stay tourists would be significantly different from that of general tourists. Literature review brings out four distinct psychographic latent constructs namely eco-centrism, sustainability importance, Global self-identity and altruism. This research work explores the possibility to successfully classify tourists as general and homestay on the basis of these variables. Study is empirical in nature and is conclusive by design. It is based on primary data that has been collected for the tourists visiting state of Sikkim, India. The sample size is 923 of which 710 are general tourists and 213 are homestay tourists. For classification neural network has been utilized through SPSS. Results indicate that while training 477 (out of 478) general tourists and 120 (out of 144) homestay tourists were correctly identified. This results in overall 96% accuracy. For the testing sample we find an almost equivalent accuracy. Out of 231 general tourists 226 have classified correctly. For homestay tourists these figures are 53 out of 67. Overall accuracy at this instance is 93.6%. It is therefore concluded that general tourists and homestay tourists differ in terms of their attitude towards eco-centrism, sustainability importance, Global self-identity and altruism and which can be exploited to identify the home stay tourist for targeting efficiency using these variables. Implications of the study are that Home-stay managers can reach out to their customer in a more effective manner by catering to their psychographic profile.