Knowledge Resource · Open access
Research Summary: Adoption of generative artificial intelligence by university marketing students
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Summary & Analysis prepared by
- Aziz Shuaib Ausi
- Resource type
- Research Summary / Knowledge Resource
- Resource published on AZIZ OS
- 30 October 2026
- Reading time
- 1 min
- Publication type
- Knowledge Resource
- Availability
- Open access
About this Summary & Analysis
AZIZ OS provides independently prepared summaries and analytical interpretations of externally published research and knowledge sources. The underlying works remain attributable to their original authors and rights holders. This resource is intended to improve accessibility and understanding and does not replace the original publication.
A cross-sectional empirical study examined the determinants of Generative Artificial Intelligence (GenAI) adoption among 310 university marketing students across two Latin American institutions in Peru and Costa Rica. Applying the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) framework and Partial Least Squares Structural Equation Modeling (PLS-SEM), the research evaluates drivers influencing student integration of GenAI tools into academic settings.
Why it matters
This research matters because GenAI is rapidly reshaping skill requirements and educational delivery in marketing and business disciplines. Understanding empirical drivers of adoption enables academic leaders to guide emerging workforce capabilities rather than react to ad-hoc technology use. Proactive institutional alignment ensures pedagogical integrity while meeting the changing demands of technology-driven industries.
Key insights
- The study utilized the UTAUT2 framework to investigate factors governing academic GenAI adoption among higher education marketing students.
- Empirical analysis sampled 310 marketing undergraduates from institutions in Peru and Costa Rica using purposive non-probability sampling during early 2026.
- Structural modeling was executed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to measure predictive behavioral constructs.
Source
Frontiers in Education — https://www.frontiersin.org/articles/10.3389/feduc.2026.1892057
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- Verification ID
- ASA-EXE-2026-01493
- Version
- v1.0 · r0
- Issued
- 30 October 2026
- Resource prepared by
- Aziz Shuaib Ausi
- Resource status
- Research Summary / Knowledge Resource
- Underlying work
- Adoption of generative artificial intelligence by university marketing students
- Original authors
- Attribution requires verification
- Original source
- Frontiers in Education
- Provenance status
- Attribution requires verification
- Rights
- Underlying publication rights remain with the respective copyright holder(s). Refer to the original source for authoritative publication and licensing information.
This verification confirms the AZIZ OS resource record and its documented provenance. It does not establish authorship of the underlying external work.