Authors

Abstract

This research investigated the factors determining the adoption of AI-based applications in Thailand and Poland's agricultural sectors. The study explored the sector's adoption of AI technology and its contributions to driving the market and business performance. Despite the potential of AI in the agricultural sector, its adoption rate still needs to be clarified, and its potential needs to be better understood, hence the need for the study. The research applied primary data collected from respondents working in the agricultural sector in Thailand and Poland using a structured questionnaire. A sample of 356 and 377 Why respondents were representative samples in Thailand and Poland, respectively. The research was driven by the hypotheses evaluated using the Structural Equation Model (SEM). The findings indicated that organizational size was the most influential determinant of AI-based applications in both countries. Another significant determinant was technological competence in both countries. Additionally, social influence was a significant determinant in Thailand, while facilitating conditions and effort expectancy were significant determinants in Poland. The multi-group analysis revealed that the two countries were not invariant; hence, the effect of independent variables on behavioral intention to adopt AI between the two countries was different. The research recommended that each country's policymakers consider its contexts differently in AI-based application adoption policies. However, improving the organizational size and technological competence would enhance the adoption of AI-based applications across the board.

Abstract in English

This research investigated the factors determining the adoption of AI-based applications in Thailand and Poland's agricultural sectors. The study explored the sector's adoption of AI technology and its contributions to driving the market and business performance. Despite the potential of AI in the agricultural sector, its adoption rate still needs to be clarified, and its potential needs to be better understood, hence the need for the study. The research applied primary data collected from respondents working in the agricultural sector in Thailand and Poland using a structured questionnaire. A sample of 356 and 377 Why respondents were representative samples in Thailand and Poland, respectively. The research was driven by the hypotheses evaluated using the Structural Equation Model (SEM). The findings indicated that organizational size was the most influential determinant of AI-based applications in both countries. Another significant determinant was technological competence in both countries. Additionally, social influence was a significant determinant in Thailand, while facilitating conditions and effort expectancy were significant determinants in Poland. The multi-group analysis revealed that the two countries were not invariant; hence, the effect of independent variables on behavioral intention to adopt AI between the two countries was different. The research recommended that each country's policymakers consider its contexts differently in AI-based application adoption policies. However, improving the organizational size and technological competence would enhance the adoption of AI-based applications across the board.