Currently, in the fishery industry, production of traditional fisheries is decreasing due to ① limitations in fish resources, ② pollution of the marine environment, ③ illegal overfishing, and ④ weakening of the foundation due to hollowing out and aging of fishing villages. As a result, aquaculture has been proposed as an alternative to traditional fishing methods.
In this paper, by identifying the factors that people interested in existing conventional fish farming, new fishermen who want to enter aquaculture, and fishermen who want to retire in a fishing village to spend their old age accepting AI-based smart aquaculture systems, It was intended to derive the direction of advancement and contribute to the development of the aquaculture industry.
To this end, the following three research questions were set. First, a research model was established to identify the acceptance intention of the artificial intelligence-based smart aquaculture system. Second, factors that directly or indirectly affect the acceptance intention of the AI-based smart farm system were set as independent variables and parameters, and measurement items were derived. Third, in order to find out the effect of demographic characteristics such as education, career, and age on acceptance intention, the study was conducted focusing on the relationship between independent and dependent variables.
The study was conducted in 5 stages. In the first stage, the overall direction of the thesis, such as the purpose of the study and the procedure for the factors affecting the acceptance intention of the AI-based smart farm system, was presented.
In the second step, previous studies were divided into three areas and analyzed. ① Analysis of preceding studies on the definition, configuration and theoretical background of AI-based smart aquaculture systems, environmental characteristics in terms of policy, economy, society, and technology through PEST technique, and future development direction, ② Acceptance intention of preceding studies related to artificial intelligence services Analysis ③ Prior to constructing the research model, previous research models such as TAM, UTAUT, IS Success Model, and ServQual were analyzed to derive measurement items such as independent variables, parameters, and dependent variables.
In the third step, the research model construction step, a model was constructed to conduct this study, hypotheses were established, and operational definitions and measurement items for direct, indirect, and moderating effects were derived.
In the 4th stage of empirical analysis, a questionnaire was constructed based on the measurement items derived in advance, and a total of 457 people were surveyed from December 2020 to February 2021. Using SPSS 26 and AMOS 26, the influence of parameters and the moderating effect of demographic characteristics were analyzed as follows.
Checked the result. ① Reliability, expertise, and availability were analyzed to have a positive (+) effect on perceived ease. Along with this, responsiveness and security had a positive effect on usability. Investment value and social influence had a positive (+) effect on acceptance intention. ② Reliability-perceived ease-acceptance-intention path, expertise-perceived ease-acceptance-intention path, and usability-perceived ease-perceived usefulness path had a complete mediating effect, and the perceived ease-acceptance-intention path It was analyzed that there was a partial mediating effect. mediating effect. ③ Regarding the moderating effect according to demographic characteristics, it was analyzed that gender, age, and service experience had a moderating effect.
Lastly, by analyzing the implications of this study, quality improvement through standardization of services and systems in terms of policy, contribution to the preparation of related legal and institutional measures, and academically analyzed the intention to accept artificial intelligence smart systems in other primary industries. It can provide value as a reference case for related studies such as smart farm and smart livestock farming.
Keywords
Artificial Intelligence, Limitation of fish resources, pollution of the marine environment, illegal overfishing and deterioration of fishing villages