How Perceived University Support Shapes Marketing Students’ AI Self-Efficacy: A Multigroup Dual-Mediation TAM Analysis Across Three Chinese Universities

The proliferation of artificial intelligence (AI) in marketing has prompted higher education institutions to invest substantially. However, a critical pedagogical question remains: whether and how these institutional investments translate into students’ psychological readiness to deploy AI tools effectively. Students’ AI self-efficacy constitutes a foundational prerequisite for technology adoption and career adaptability. A lack of such confidence can precipitate technology avoidance behaviors and diminish learn…

By the Truvace deskOctober 22, 20261 min read1 sourcePeer-reviewedTRV-2026-2198· cite & verify

In brief

A structural equation modeling study surveyed 538 marketing students at three Chinese universities. It tested whether the support students feel from their university translates into confidence in using AI tools, through two beliefs: that AI is useful and that it is easy to use.

The findings suggest that spending on AI infrastructure alone may not build student confidence, and that teaching which makes AI tools seem less complex and shows their professional relevance may matter more. The data are cross-sectional and self-reported, so the causal pathway and its wider applicability remain uncertain.

Main points

The sample was 538 marketing students from three Chinese universities, analysed with structural equation modeling.
Perceived university support significantly increased both perceived usefulness and perceived ease of use in every sample.
The indirect effect through ease of use was consistently stronger than the one through usefulness.
The direct support-to-self-efficacy path stayed significant at two of the three institutions.

The gain

Perceived university support increased marketing students' perceived usefulness and ease of use of AI, which in turn raised their AI self-efficacy, with ease of use the stronger pathway.

The rundown

The study uses the Technology Acceptance Model as a framework for how institutional resources shape students' psychological readiness to use AI. It asks whether the mechanism holds across institutionally different settings.

Both usefulness and ease-of-use beliefs carried the effect of support onto AI self-efficacy, which makes this a dual-mediation pathway. The direct effect of support persisted at two of the three institutions, so it did not hold uniformly.

The authors recommend that universities channel investment into pedagogical strategies that demonstrably reduce the perceived complexity of AI tools and illustrate their professional relevance, rather than into infrastructure alone.