The Impact of AI Based Agricultural Advisory Services on Farmers Crop Management Decision Making in Bangladesh: The Mediating Role of Information Quality
DOI:
https://doi.org/10.54536/ijsa.v4i1.8974Keywords:
Agricultural Advisory Services, Artificial Intelligence, Bangladesh, Crop Management, Information Quality, Partial Least Squares Structural Equation ModellingAbstract
Farmers have alternative sources of crop management advice through artificial intelligence based agricultural advisory services. Their contribution will be determined by the availability and personalized advice that contains information that farmers would consider useful in assessing management options. This paper will look at AI advisory accessibility, AI advisory personalization, information quality and quality of crop management decisions among 430 users of agricultural advisory in Bangladesh. A reflective survey was created to measure four reflective constructs with the help of a questionnaire composed of 21 indicators. The measurement quality, direct relationships, indirect relationships, explanatory performance and predictive performance were measured using the partial least squares structural equation modelling. There was a positive relationship between accessibility and personalization and information quality with standardized coefficients of 0.4508 and 0.3880 respectively. Decision quality was directly related to information quality at the highest level of 0.5424. Smaller positive direct relationships of decision quality were also observed with accessibility and personalization. Information quality as the indirect coefficients were 0.2445 and 0.2104, which implies complementary partial mediation. The model was able to explain 42.19 percent of the variance of information quality and 49.55 percent of the variance of decision quality. All endogenous indicators were reported to have positive predictive relevance. Results place information quality at the heart of interconnection between advisory service design and perceived quality of decisions. The use of convenient access, farm specific recommendations and clear standards of information should be integrated to agricultural advisory development.
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