Undergraduate Students’ Intention to Use Artificial Intelligence Technology for Plant Identification: Validating the Research Instrument (85657)

Session Information: Higher Education
Session Chair: Zhaotong Li

Wednesday, 27 November 2024 11:50
Session: Session 2
Room: Room 608 (6F)
Presentation Type: Oral Presentation

All presentation times are UTC + 9 (Asia/Tokyo)

Use of Artificial Intelligence (AI) in plant taxonomy is an emerging scholarly field that has attracted less scholarly attention. In order to understand the predictors of undergraduate students’ behavioral intention (BI) to use AI technology for plant identification, we developed a research instrument based on the extended unified theory of acceptance and use of technology (UTAUT2) and three additional BI predictors/constructs. Following a cross-sectional survey involving 500 respondents from basic and agricultural science faculties in five Ugandan public universities after one month of using AI applications (PictureThis) to identify plants, a self-administered questionnaire was administered. Data were analyzed with structural equation modelling, partial least squares (SEM-PLS) using SmartPLS4 software. Confirmatory Factor Analysis showed student agreement with intention to use and intention to switch to AI applications, particularly PictureThis app (Eigenvalues > 1.00). Exploratory Factor Analysis gave a Bartlett’s test of Sphericity of 0.001, indicating highly significant correlations among the independent variable (IV) constructs. Average Variance Extracted (AVE) values of above 0.5 for all constructs confirmed convergent validity. HTMT ratios of correlation of less than 0.9 for all IV constructs confirmed their independent influence on behavioral intention to use AI PictureThis app. Cronbach’s alpha of above 0.70 for all constructs confirmed construct reliability. Composite reliability values of above 0.70 confirmed high reliability of the constructs. Value Inflation Factor (VIF) values of below 3.00 confirmed non-existance of multicollinearity and that all the ten IV constructs in the research framework could independently determine students’ BI to use AI technology for plant identification.

Authors:
John Bukenya, Makerere University, Uganda
Paul Muyinda, Makerere University, Uganda
Maurice Isabwe, University of Agder, Norway
Godfrey Mayende, Makerere University, Uganda
James Kalema, Makerere University, Uganda


About the Presenter(s)
Mr John Bukenya is a University Doctoral Student at Makerere University in Uganda

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Posted by Clive Staples Lewis

Last updated: 2023-02-23 23:45:00