PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 26, 2026SHILAP Revista de lepidopterología2 citationsOpen Access

Determinants and effects of academic engagement in university–industry collaboration: a PLS-SEM approach

VBVladimir Alfonso Ballesteros-BallesterosRZRodrigo Arturo Zárate-Torres

Key Points

  • This research explores how academic engagement facilitates the connection between university research and societal needs, analyzing its determinants and effects.
  • Cross-sectional survey administered to 147 full-time faculty members in Colombia.
  • Utilized a validated 32-item instrument measured on five-point Likert scales.
  • Applied partial least squares structural equation modelling to assess model performance.
  • Satisfactory psychometric properties with standardized loadings above 0.70.
  • All antecedents positively linked to academic engagement, notably institutional support (β = 0.373) and epistemic motivation (β = 0.320).
  • Academic engagement correlated positively with knowledge transfer (β = 0.688) and scientific productivity (β = 0.563), explaining 66.4%, 47.4%, and 31.7% of variance respectively.

Abstract

Introduction This study examines academic engagement as a mechanism through which universities connect research with societal use. Drawing on a stimulus–organism–response perspective, it investigates the determinants of academic engagement and its effects on knowledge transfer and scientific productivity. Methods We administered a cross-sectional survey to 147 full-time faculty members in Colombia using a validated 32-item instrument measured on five-point Likert scales. The model specifies epistemic motivation, instrumental motivation, prior U–I experience, institutional support, and perceived social norms as antecedents; academic engagement as the focal construct; and knowledge transfer and scientific productivity as outcomes. Partial least squares structural equation modelling was used to assess reliability, validity, collinearity, and predictive performance. Results The measurement model showed satisfactory psychometric properties, with standardised loadings above 0.70, AVE values ranging from 0.590 to 0.715, composite reliability between 0.852 and 0.899, and a maximum HTMT of 0.829. All antecedents were positively and significantly associated with academic engagement, with institutional support ( β = 0.373) and epistemic motivation ( β = 0.320) showing the strongest effects. Academic engagement was positively associated with knowledge transfer ( β = 0.688) and scientific productivity ( β = 0.563). The model explained 66.4% of the variance in academic engagement, 47.4% in knowledge transfer, and 31.7% in scientific productivity. Discussion The findings position academic engagement as a robust mechanism for translating academic work into external use while sustaining scholarly output. They suggest that universities can strengthen both societal impact and research performance by recognising engagement in workload and promotion systems, reinforcing faculty support structures, and embedding collaboration more systematically into institutional strategy.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Ballesteros-Ballesteros et al. (2026) studied this question.

synapsesocial.com/papers/69edaa9b4a46254e215b314dhttps://doi.org/10.3389/fpsyg.2026.1745917
Ask AI
Helpful
Bookmark
Share
View Full Paper