Understanding User Acceptance of Information System for Sweet Potato Variety and Disease Classification: An Empirical Examination with an Extended Technology Acceptance Model

Abstract

Information systems for classifying crop variety and disease using machine learning are progressively introduced in the agriculture sector to assist farmers and experts. However, in developing countries, such as the Philippines, no study has been conducted to investigate its acceptance among users. This study aims to understand users acceptance of an information system to classify sweet potato variety and disease using the technology acceptance model. Results show that most of the factors influence each other. This study also shows that security, reliability, and portability plays a significant role in user acceptance. However, technological complexity does not affect perceived usefulness. This study confirms the roles of the different factors on users acceptance of information system for sweet potato variety and disease classification. This study also presents practical and research implications.