Factors Determining the Behavioral Intention to Use the Geographic Information System for Managing Marine Resources in the Coastal Area of Bandon Bay, Surat Thani Province

This research emphasizes the examination of factors influencing behavioral intention to use a Geographic Information System (GIS) for coastal marine resource management in Bandon Bay, Surat Thani Province. The study employs a survey research design with a population comprising residents along the Bandon Bay coastline. A sample of 398 participants was selected through simple random sampling using a questionnaire (Cronbach's Alpha = 0.857). The research utilizes an extended the Unified Theory of Acceptance and Use of Technology 2 (UTAUT 2) model with seven factors: Behavioral Intention (BI), Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI), Facilitating Conditions (FC), Hedonic Motivation (HM), and Price Value (PV). Data analysis was conducted by using mean, standard deviation, correlation, and multiple regression analysis. The findings reveal that Behavioral Intention is the factor with the highest mean level, with an overall mean in the highest category (x̄ = 4.623, S.D. = 0.447), followed by Price Value with an overall mean also in the highest category (x̄ = 4.612, S.D. = 0.464). The remaining five factors have means in the high category: Social Influence (x̄ = 4.474, S.D. = 0.658), Performance Expectancy (x̄ = 4.463, S.D. = 0.515), Hedonic Motivation (x̄ = 4.376, S.D. = 0.506), Effort Expectancy (x̄ = 4.366, S.D. = 0.446), and Facilitating Conditions (x ̅ = 4.154, S.D. = 0.504), respectively. The multiple regression analysis results indicate anvalue of 0.637, accounting for 63.70% of the total variance from five factors. This demonstrates that Performance Expectancy, Effort Expectancy, Social Influence, Facilitating Conditions, and Hedonic Motivation have statistically significant effects. The resulting predictive equation is as follows: BI = 1.463 + 0.540(PE) - 0.126(EE) + 0.200(SI) - 0.042(FC) + 0.088(HM). The findings can be applied to support the development of a geographic information system for managing marine and coastal resources in Bandon Bay, Surat Thani Province. The system development should focus on the factor weights specified in the predictive equation.