Artificial Intelligence Adoption with Technology Acceptance Model 2400-ZEWW1040
This course covers the skills required for effective research design (based on TAM and UTAUT) and analysis of data (Quantitative Method – Structural Equation Modeling PLS-SEM). You will be exposed to contemporary digital technology adoption literature, case studies, data collection, and analysis methods to build your analytical and practical skills to develop research models. A special emphasis will be placed on how Technology Acceptance Models are being used to study the adoption, trust, and integration of Artificial Intelligence (AI) in various sectors such as business, education, healthcare, and governance.
Through real-world AI-related use cases and user-centric studies, students will explore how AI technologies challenge traditional acceptance frameworks and how TAM and UTAUT can be adapted or extended to assess AI-driven systems. This course enables students to critically examine the evolving landscape of AI adoption from a behavioral, technological, and methodological perspective.
Lecture topics include the following:
1-Scientific method and role of theory in Technology Acceptance research
2-Basic principles of research design on the basis of TAM and UTAUT
3-Critical literature review – identifying research gaps, especially in emerging AI applications
4-Sample selection (Case Studies/Published Papers) for replication of results
5-Structural Equation Modelling PLS-SEM (SmartPLS)
6-Design of new research models (Group Activity with potential AI applications)
7-Measurement: classical test theory, reliability, validity, factor analysis and item response theory, structural equation modeling (Final)
8-Presentations – Research designs and data analysis by students to pass this course
Course coordinators
Type of course
Learning outcomes
On successful completion of this unit, you should be able to:
1. equip with the ability to critically appraise theories, methods, and evidence for understanding behavior and behavior change in a technology acceptance context.
2. make new research design, implementation, and evaluation of digital user’ behavior change.
3. support the translation of evidence into practice across disciplines and sectors.
Assessment criteria
Participation: Your participation in class and tutorials (including short in-class quizzes) will determine 25% of your course grade.
Mid-term exam: The in-class mid-term examination will cover all materials from weeks 1 through 6. Expect Multiple Choice Question and statistical application questions to be completed on a computer. This will determine 25% of the course grade.
Final Project Paper (Group Activity): You will be asked to write a design TAM based research paper (no more than 15 pages) in a group. The final paper will determine 50% of the course grade.