Salient Predictors of Behavioural Intention to Use Picture This Artificial Intelligence Technology for Plant Identification by Undergraduate Students in Public Universities in Uganda
DOI:
https://doi.org/10.47672/ajep.2759Keywords:
Predictors, Behavioural intention PictureThis AI, Plant identification, UTAUT2Abstract
Purpose: The purpose of the study was to examine the influence of behavioural intention (BI) predictors on the intention to use artificial intelligence (AI) technology, Picture This, for plant identification by undergraduate students in public universities in Uganda.
Materials and Method: The study was carried out using a correlational, cross-sectional survey method based on a research instrument developed from the extended unified theory of acceptance and use of technology (UTAUT2).
Findings: The findings of the study show that five BI predictors namely, performance expectancy (β = 0.278, p = 0.000 < 0.05), effort expectancy (β = 0.118, p = 0.006 < 0.05), social influence (β = 0.104, p = 0.009 < 0.05), hedonic motivation (β = 0.292, p = 0.000 < 0.05) and habit (β = 0.106, p = 0.011 < 0.05) positively and significantly influence intention to use Picture, This AI by undergraduate students in Uganda’s public universities.
Unique Contribution to Theory, Practice and Policy: The study was informed by UTAUT2 which posits that use behaviour of technology is influenced by behavioural intention, coupled with habit and facilitating conditions. To the university management, we suggest that appropriate facilities should be put in their institutions to enhance AI’s contribution to performance expectancy, effort expectancy, hedonic motivation, social influence and price value in order to improve students’ behavioural intention and ultimately use behaviour of AI technology for better learning experiences and improved learning outcomes. To the student community pursuing biological and agricultural sciences, we recommend the adoption of Picture. This AI as a learning tool for use during plant identification.
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Abengmeneng, C. S., Korankye, S., & Cobbinah, P. (2023). Comparative Analysis of the Accuracy of Thirteen Photo-based Applications in the Identification of Trees Species in Northern Ghana. International Journal for Multidisciplinary Research, 5(6), 1- 10.
Admassu, W., & Gorems, K. (2024). Analyzing health service employees’ intention to use e-health systems in southwest Ethiopia: using UTAUT-2 model. BMC Health Services Research, 24(1), 1136. https://doi.org/10.1186/s12913-024-11567-y
Akbar, M. R., Irianto, G., & Rofiq, A. (2018). Purchase behaviour determinants on online mobile game in Indonesia. International Journal of Multicultural and Multireligious Understanding, 5(6), 16-27. http://dx.doi.org/10.18415/ijmmu.v5i6.457
Atas, A. H., & Çelik, B. (2019). Smartphone use of university students: Patterns, purposes, and situations. Malaysian Online Journal of Educational Technology, 7(2), 59-70. http://dx.doi.org/10.17220/mojet.2019.02.004
Athambawa, A., Johar, M. G. M., & Khatibi, A. (2023). Behavioural intention to adopt cloud computing: A quantitative analysis with a mediatory factor using bootstrapping. Indonesian Journal of Electrical Engineering and Computer Science, 32(1), 458-467. http://doi.org/10.11591/ijeecs.v32.i1.pp458-467
Barbosa, H. F., Garcia-Fernandez, J., Pedragosa, V., & Cepeda-Carrion, G. (2021). The use of fitness centre apps and its relation to customer satisfaction: a UTAUT2 perspective. International Journal of Sports Marketing and Sponsorship, 23(5), 966-985. https://doi.org/10.1108/IJSMS-01-2021-0010
Bawingan, P. A., Montevirgen Jr, E. M., Canuto, P. P. L., Lucas, L. E., & Pumaras, J. C. (2024). Plant identification mobile apps: Users’ difficulties and impressions. The Asian Journal of Biology Education, 16, 15-21.
Berges-Puyo, J. G. (2024). The use of technology in education: 10 concerns that should not be overlooked. International Journal of Research-GRANTHAALAYAH, 12(3).
Canuto, P. P. (2023). Perceptions of primary pre-service teachers in the utilization of plant identification apps as educational tools. Journal of Baltic Science Education, 22(5), 799-812.
Chang, C. M., Liu, L. W., Huang, H. C., & Hsieh, H. H. (2019). Factors influencing online hotel booking: Extending UTAUT2 with age, gender, and experience as moderators. Information, 10(9), 281. https://doi.org/10.3390/info10090281
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modeling. Education and Information Technologies, 25, 3443-3463. https://doi.org/10.1007/s10639-020-10159-7
Chen, L. Y., & Chen, Y. J. (2021). A Study of the Use Behavior of Line Today in Taiwan Based on the UTAUT2 Model. Revista de Administração de Empresas, 61(6), 1 - 19. https://doi.org/10.1590/S0034-759020210607
Choi, S. (2016). The flipside of ubiquitous connectivity enabled by smartphone-based social networking service: Social presence and privacy concern. Computers in Human Behavior, 65, 325-333.
Cioc, M. M., Popa, Ș. C., Olariu, A. A., Popa, C. F., & Nica, C. B. (2023). Behavioral intentions to use energy efficiency smart solutions under the impact of social influence: an extended TAM approach. Applied Sciences, 13(18), 10241. https://doi.org/10.3390/app131810241
Cukurova, M., Miao, X., & Brooker, R. (2023, June). Adoption of artificial intelligence in schools: Unveiling factors influencing teachers’ engagement. In International conference on artificial intelligence in education (pp. 151-163). Cham: Springer Nature Switzerland.
Dagnoush, S. M., & Khalifa, G. S. (2021). The Effect of Users’ Effort Expectancy on Users’ behavioral Intention to Use m-Commerce Applications: Case Study in Libya. International Journal on Recent Trends in Business and Tourism (IJRTBT), 5(4), 1-7. https://doi.org/10.31674/ijrtbt.2021.v05i04.001
Dash, G., & Paul, J. (2021). CB-SEM vs PLS-SEM methods for research in social sciences and technology forecasting. Technological Forecasting and Social Change, 173, 121092. https://doi.org/10.1016/j.techfore.2021.121092
Dhingra, S., & Gupta, S. (2020). Behavioural intention to use mobile banking: An extension of UTAUT2 model. International Journal of Mobile Human Computer Interaction (IJMHCI), 12(3), 1-20. https://doi.org/10.4018/IJMHCI.2020070101
Frank, D., & Milković, M. (2018). Evaluation of electronic programme guide adoption using UTAUT2 based model. Tehnički vjesnik, 25(3), 884-890. https://doi.org/10.17559/TV-20180601095518
Funmilola, B. O., Olalere, Y. M., Oluwole, O. M., Modupe, A. E., & Ayodeji, A. G. (2019). Examining performance expectancy and effort expectancy as determinants of secondary school teachers’ behavioural intention to use mobile technologies for instruction in Kaduna state, Nigeria. Association for Innovative Technology Integration in Education, 31-39. https://www.wjir.org/download_data/WJIR0802018.pdf
Gharaibeh, N., Gharaibeh, M. K., Gharaibeh, O., & Bdour, W. (2020). Exploring intention to adopt mobile commerce: Integrating UTAUT2 with social media. International Journal of Scientific and Technology Research, 9(3), 3826-3833. https://www.ijstr.org/final-print/mar2020/Exploring-Intention-To-Adopt-Mobile-Commerce-Integrating-Utaut2-With-Social-Media.pdf
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature. https://doi.org/10.1007/978-3-030-80519-7
Hair Jr, J. F., Howard, M. C., & Nitzl, C. (2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of business research, 109, 101-110. https://doi.org/10.1016/j.jbusres.201 9.11.069
Hartelina, H., Batu, R., & Hidayanti, A. (2021). What can hedonic motivation do on decisions to use online learning services? International Journal of Data and Network Science, 5(2), 121-126. https://www.growingscience.com/ijds/Vol5/ijdns_2021_5.pdf
Hendrajaya, C. T., Brahmasari, I. A., & Ratih, I. A. B. (2024). The influence of effort expectancy, performance expectancy, and social influence on perceived risk, behavioral intention, and actual use moderated by user trust in social commerce in Indonesia. Edelweiss Applied Science and Technology, 8(6), 4683-4699. https://doi.org/10.55214/25768484.v8i6.3017
Hill, E. (2024, March 26). Plant Identification? There’s an App for That – actually several!. Michigan State University Extension. https://www.canr.msu.edu/news/plant-identification-theres-an-app-for-that-actually-several
Hong, C., Choi, E. K. C., & Joung, H. W. D. (2023). Determinants of customer purchase intention toward online food delivery services: The moderating role of usage frequency. Journal of Hospitality and Tourism Management, 54, 76-87. https://doi.org/10.1016/j.jhtm.2022.12.005
Huang, T. (2023). Expanding the UTAUT2 framework to determine the drivers of mobile shopping behaviour among older adults. Plos One, 18(12), 1- 29. | https://doi.org/10.1371/journal.pone.0295581
Hungilo, G. G., & Setyohadi, D. B. (2020). Factors influencing acceptance of online shopping in Tanzania using UTAUT2. Journal of Internet Banking and Commerce, 25(1), 1-23. https://www.icommercecentral.com/open-access/factors-influencing-acceptance-of-online-shopping-in-tanzania-using-utaut2.php?aid=87829
Ismail, M. N. I., Hanafiah, M. H., Hemdi, M. A., Sumarjan, N., & Azdel, A. A. (2020). Determinants of customer acceptance and usage of mobile hotel reservation apps (MHRA): an exploratory factor analysis. Journal of Tourism, Hospitality & Culinary Arts (JTHCA), 12(1), 231 - 247. https://fhtm.uitm.edu.my/images/jthca/Vol12Issue1/Chap_15.pdf
Kahn, J. H. (2006). Factor analysis in counseling psychology research, training, and practice: Principles, advances, and applications. The counseling psychologist, 34(5), 684-718. https://doi.org/10.1177/0011000006286347
Keivani, M., Mazloum, J., Sedaghatfar, E., & Tavakoli, M. B. (2020). Automated analysis of leaf shape, texture, and color features for plant classification. TRAITEMENT du Signal, 37(1), 17-28. https://doi.org/10.18280/ts.370103
Khalid, B., Lis, M., Chaiyasoonthorn, W., & Chaveesuk, S. (2021). Factors influencing behavioural intention to use MOOCs. Engineering Management in Production and Services, 13(2), 83-95. https://doi.org/10.2478/emj-2021-0014
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration (ijec), 11(4), 1-10. https://doi.org.10.4018/ijec.2015100101
Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607-610. https://doi.org/10.1177/001316447003000308
Kuleto, V., Ilić, M., Dumangiu, M., Ranković, M., Martins, O. M., Păun, D., & Mihoreanu, L. (2021). Exploring opportunities and challenges of artificial intelligence and machine learning in higher education institutions. Sustainability, 13(18), 10424. https://doi.org/10.3390/su131810424
Latif, M. I., & Zakaria, Z. (2020). Factors determine the behavioural intention in adopting the blockchain technology by Malaysian Public Sector Officers. Journal of Advanced Research in Business and Management Studies, 20 (1), 34-43. https://doi.org/10.37934/arbms.20.1.3443
Malik, O. A., Ismail, N., Hussein, B. R., & Yahya, U. (2022). Automated real-time identification of medicinal plants species in natural environment using deep learning models—a case study from Borneo Region. Plants, 11(15), 1952. https://doi.org/10.3390/plants11151952
McCaffrey, S. A., Zdinak, J. P., Plunkett, S., Becker, E., Lewis, J. N., & Black, R. A. (2021). Development and validation of behavioral intention measures of an E-vapor product: intention to try, use, dual use, and switch. Health and Quality of Life Outcomes, 19, 1-13. https://doi.org/10.1186/s12955-021-01764-2
Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 1 - 12. https://doi.org/10.1016/j.techsoc.2019.101151
Michels, M., Bonke, V., & Musshoff, O. (2020). Understanding the adoption of smartphone apps in crop protection. Precision Agriculture, 21, 1209-1226. https://doi.org/10.1007/s11119-020-09715-5
Moorthy, K., Chun T'ing, L., Ming, K. S., Ping, C. C., Ping, L. Y., Joe, L. Q., & Jie, W. Y. (2019). Behavioral intention to adopt digital library by the undergraduates. International Information & Library Review, 51(2), 128-144. https://doi.org/10.1080/10572317.2018.1463049
Moya, M., & Engotoit, B. (2017). Behavioural intentions: A mediator of performance expectancy and adoption of mobile communication technologies by Ugandan commercial farmers. ORSEA Journal, 7(1), 1 -20.
Musakwa, I. S., & Petersen, F. (2023). Factors affecting consumer acceptance and use of mobile delivery applications in South Africa. South African Journal of Information Management, 25(1), 1585-1592. https://doi.org/10.4102/sajim.v25i1.1585
Nassar, A. A., Othman, K., & Nizah, M. A. B. M. (2019). The impact of the social influence on ICT adoption: Behavioral intention as mediator and age as moderator. International Journal of Academic Research in Business and Social Sciences, 9(11), 963-978. https://doi.org/6007/IJARBSS/v9-i11/6620
National Council for Higher Education (2022). The State of Higher Education and Training in Uganda 2019/2020: A report on higher education delivery and institutions. Kampala, Uganda. National Council for Higher Education (NCHE). https://unche.or.ug/wp-content/uploads/2023/02/State-for-Higher-Education-Report-2019-2022.pdf
Otter, J., Mayer, S., & Tomaszewski, C. A. (2021). Swipe right: a comparison of accuracy of plant identification apps for toxic plants. Journal of Medical Toxicology, 17, 42-47. https://doi.org/10.1007/s13181-020-00803-6
Papaneophytou, C., & Nicolaou, S. A. (2025). Promoting Critical Thinking in Biological Sciences in the Era of Artificial Intelligence: The Role of Higher Education. Trends in Higher Education, 4(2), 24. https://doi.org/10.3390/higheredu4020024
Park, Y., Gi Seo, D., Martin, M., & Lee, J. (2025). An Exploration of Causal Relationships between Behavioural/Emotional Difficulties and Academic Achievement: A Path Model Approach. International Journal of Disability, Development and Education, 72(2), 355-373. https://doi.org/10.1080/1034912X.2024.2337172
Purwanto, A., & Sudargini, Y. (2021). Partial least squares structural equation modeling (PLS-SEM) analysis for social and management research: a literature review. Journal of Industrial Engineering & Management Research, 2(4), 114-123. https://doi.org/10.7777/jiemar.v2i4
Rahi, S., Othman Mansour, M. M., Alghizzawi, M., & Alnaser, F. M. (2019). Integration of UTAUT model in internet banking adoption context: The mediating role of performance expectancy and effort expectancy. Journal of Research in Interactive Marketing, 13(3), 411-435. https://doi.org/10.1108/JRIM-02-2018-0032
Rwendeire, A. J. (2012). The Big Dream, A Glimpse at Uganda’s Future: Highlights of Vision 2040. The Celebration of Golden Jubilee of Uganda’s Independence. https://land.igad.int/index.php/documents-1/countries/uganda/investment-7/1334-the-big-dream-a-glimpse-at-uganda-s-future-highlights-of-vision-2040/file
Sarstedt, M., Ringle, C. M., & Hair, J. F. (2021). Partial least squares structural equation modeling. In Handbook of market research (pp. 587-632). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319- 57413-4_15
Susilowati, A. (2021). Effects of UTAUT 2 model on the use of BCA mobile banking in Indonesia. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(10), 686-694. https://turcomat.org/index.php/turkbilmat/article/view/2183
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48, 1273-1296. https://doi.org/10.1007/s11165-016-9602-2
Tamilmani, K., Rana, N. P., & Dwivedi, Y. K. (2021). Consumer acceptance and use of information technology: A meta-analytic evaluation of UTAUT2. Information Systems Frontiers, 23(4), 987-1005. https://doi.org/10.1007/s10796-020-10007-6
Tang, K. L., Aik, N. C., & Choong, W. L. (2021). A modified UTAUT in the context of m-payment usage intention in Malaysia. Journal of Applied Structural Equation Modeling, 5(1), 40-59. https://doi.org/10.47263/JASEM.5(1)05
Tavakol, M., & Dennick, R. (2011). Making sense of Cronbach's Alpha. International Journal of Medical Education, 2, 53 - 55. https://doi.org/10.5116/ijme.4dfb.8dfd
The New York Times Wirecutter ([NYTW], 2025, March 17). The Best Plant Identification App. https://www.nytimes.com/wirecutter/reviews/best-plant-identification-app/
Treibergs, K. A., Esparza, D., Yamazaki, J. A., & Smith, M. K. (2023). Journal reflections shed light on challenges students face in an introductory field biology course. Ecosphere, 14(4), 1 -14. https://doi.org/10.1002/ecs2.4509 e4509
Uno, G. E. (2009). Botanical literacy: What and how should students learn about plants? American journal of botany, 96(10), 1753-1759. https://doi.org/10.3732/ajb.0900025
Valle, N. N., Kilat, R. V., Lim, J., General, E., Cruz, J. D., Colina, S. J., ... & Valle, L. (2024). Modeling learners' behavioral intention toward using artificial intelligence in education. Social Sciences & Humanities Open, 10, 101167. https://doi.org/10.1016/j.ssaho.2024.101167
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS Quarterly, 157-178. https://ssrn.com/abstract=2002388
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