The Design of a Bayesian Network Model for Increasing the Number of Graded Tourism Establishments

Abstract

Research has been conducted on the grading of tourism establishments but little research has been conducted on the implementation of Artificial Intelligence (AI) to increase the number of graded tourism establishments. The objective of this study was to identify variables influencing tourism grading and to use them to construct a Bayesian Model for increasing the number of tourism establishments. Data was collected using an online survey questionnaire developed using the Survey Monkey tool. A total of 87 responses were received from 60 non-graded and 27 graded tourism establishments. The results indicate six factors affecting tourism grading, namely cost of grading, grading benefits, simplicity/complexity of grading application process, government funding, training of prospective grading applicants and computer literacy. The results further indicate grading cost and grading benefits as the most important factors for increasing the number of tourism establishments. The study implies that using this model will assist grading professionals to make informed decisions on initiatives aimed at increasing the number of graded tourism establishments. The study is among the first on implementation of AI to increase tourism grading establishments.