Sanaz Nabavian
Curious about solving complex problems, Sanaz Nabavian has built a career shaped by both research and real-world practice.
Her interest in using analytical thinking to understand systems and business problems was shaped during her early experiences as an undergraduate student at the Iran University of Science Technology. Studying industrial engineering with a focus on systems analysis, she saw the concepts she was learning in class applied in real organizational settings through her work as a part-time employee.
While completing her master’s degree in entrepreneurship at University of Tehran, she explored how ideas can grow into practical solutions. It was there where she first became interested in innovation and value creation. Guided by this interest, Sanaz spent several years working across various industries, including banking, manufacturing and service sectors where she built more than a decade of experience in data and business analytics.
Her industry experience meant that by the time Sanaz started her PhD in Management Information Systems at Memorial University in Newfoundland, she was deeply familiar with the many challenges organizations face when using data, technology, and analytics. She brought those experiences into her research and academic work during her doctoral studies.
Today, as an Assistant Professor at University of Niagara Falls Canada, Sanaz continues to connect real-world business challenges with teaching and research in business and data analytics, emerging technology, and entrepreneurship.
Courses taught
Information Systems Design, Introduction to Information Systems, Business Process Modeling, Data Management and Database Modeling, Business Analytics, Statistics
Areas of academic interest
Design science research, Knowledge Quality in Human–AI collaboration, Meaningful Human Control and AI application in research
Publications
- Nabavian, S., Rostami, E., Gao, S., Karlsson, F., khando, K. (2026). Thematic Analysis in the Age of LLMs: Human–Machine Differences and the Role of Context. I3E 2026.
- Nabavian, S., & Parsons, J. (2026). Applicability of Design Principles for AI Agents in Human–AI Co-Design. DESRIST.
- Nabavian, S., Parsons, J., & Ogunseye, S. (2025). Design Principle Reusability Scale. ICIS.
- Nabavian, S., Grabis, J., & Dohan, M. (2025). Balancing Anthropomorphic Design in Healthcare AI Agents. AMCIS.
- Nabavian, S., Parsons, J., & Robertson, C. (2025). Suspicious Transaction Reports—Applying Two Contradictory Design Principles in Practice. AMCIS.
- Nabavian, S., & Parsons, J. (2025). Scale Development for Reusability (Poster). DESRIST.
- Nabavian, S., & Parsons, J. (2024). A Design-Principle-Friendly Conceptual Model of Observational Crowdsourcing. Lecture Notes in Computer Science.
- Doyle, M., & Nabavian, S. (2024). Supporting Students’ AI Literacy Development. Teaching & Learning Conference.
- Nabavian, S. (2023). Design Principle Structure in Observational Crowdsourcing. DESRIST Doctoral Consortium.
- Nabavian, S., & Parsons, J. (2021). Guidelines for Conceptual Modeling to Support Data Repurposability. ER Conference.
