In the post-COVID-19 era, agricultural services face significant challenges in adopting smartphone-based technologies, which impact their effectiveness in extension activities. Previous studies in the agricultural domain have primarily focused on technology acceptance theories, lacking a comprehensive view of the interaction and interplay of constructs on the ultimate performance of smartphone usage. This study aims to: (1) identify the key determinants influencing Agricultural Extension Agents' (AEAs) behavioral intention to adopt smartphone-based applications (SBAs) for agricultural services in Khuzestan Province, Iran; and (2) examine how facilitating conditions and behavioral intention translate into actual usage behavior. This research employs a quantitative approach, integrating established models for technology acceptance to explore the behavior of agricultural extension agents in Khuzestan Province, a key agricultural region in southwestern Iran. The data were examined through the application of Partial Least Squares Structural Equation Modeling (PLS-SEM) and Importance-Performance Map Analysis (IPMA). PLS-SEM results indicate that Performance Expectancy, Social Influence, Perceived Uncertainty, and Trust significantly influence Behavioral Intention, whereas Effort Expectancy and Facilitating Conditions do not. Facilitating Conditions and Behavioral Intention significantly predict Actual Usage behavior. IPMA results reveal that while Performance Expectancy and Behavioral Intention exhibit high performance scores, Facilitating Conditions and Trust represent high-priority areas requiring targeted improvement. This study contributes theoretically by validating an integrated acceptance framework in an underexplored agricultural extension context and methodologically by demonstrating the complementary value of combining PLS-SEM with IPMA for actionable insight generation. Practically, findings suggest that policymakers and developers should prioritize infrastructure investment, localized application design, and trust-building mechanisms to enhance SBA adoption among extension agents. The knowledge generated supports evidence-based strategies to overcome technology adoption barriers and promote sustainable digital transformation in agricultural communities.
Sadeghizadeh et al. (Mon,) studied this question.