Developing a Big Data Analytics Tool to Enhance the Efficiency of Hotel Fee Collection by Provincial Administrative Organizations in the Northeast of Thailand

Abstract

Provincial Administrative Organizations (PAOs) play a critical role in delivering basic public services. However, many PAOs continue to face challenges in efficiently collecting revenues, particularly in managing hotel fees. This study addresses the question of whether enhancing hotel databases through the use of big data can improve PAOs’ fee collection performance. Using a participatory action research approach in ten PAOs across Northeastern Thailand, the findings reveal several issues that hinder effective hotel fee collection, including outdated and incomplete hotel lists, underreporting of revenue by establishments, and lack of cooperation from hotel operators. In response, the researchers developed the “PAO Hotel 360” platform to extract hotel data from booking websites, thereby increasing the accuracy and comprehensiveness of hotel information and enabling better revenue estimation. The implementation of this tool led to substantial improvements in database coverage: that is, Kalasin PAO saw a 165.38% increase in hotel listings, Khon Kaen 62.42%, Chaiyaphum 58.96%, Nakhon Phanom 82.12%, Nakhon Ratchasima 177.62%, Roi Et 37.56%, Loei 74.04%, Nong Khai 106.09%, and Udon Thani 129.58%, with an average increase of 91.91% across all cases—corresponding to an estimated 19 million THB in previously uncollected revenue. These findings demonstrate that web scraping technology can effectively reduce information gaps and significantly improve the efficiency of hotel fee collection at the local government level.