Organizational learning and organizational citizenship behavior in higher education: a PLS-SEM model with gender moderation
DOI:
https://doi.org/10.63688/aprendizaje.v3.i2.32Keywords:
organizational learning, organizational citizenship behavior, strategic leadership, PLS-SEM, higher education.Abstract
Introduction: Organizational learning can foster voluntary behaviors that extend beyond formal job responsibilities and contribute to collective performance in higher education institutions. Objective: To analyze the relationship between organizational learning dimensions and organizational citizenship behavior and to assess the moderating effect of gender. Methods: A quantitative, non-experimental, cross-sectional, explanatory-correlational study was conducted with 246 faculty and administrative staff members from an Ecuadorian university, selected through non-probability convenience sampling. Data were collected through online questionnaires and analyzed using PLS-SEM. Results: The model explained 62.0% of the variance in organizational citizenship behavior (R² = 0.620). Strategic leadership showed the strongest association (β = 0.638; p < 0.05), followed by team learning (β = 0.227; p < 0.05) and connection with the environment (β = 0.165; p < 0.05). Inquiry and dialogue, learning systems, learning opportunities, and collective vision were not statistically significant. Gender did not show a significant moderating effect. Conclusions: The relationship between organizational learning and organizational citizenship behavior was selective across dimensions, with strategic leadership, team learning, and connection with the environment showing the most relevant associations.References
Liang J, Stephens JM, Brown GTL. A systematic review of the early impact of artificial intelligence on higher education curriculum, instruction, and assessment. Front Educ. 2025;10:1522841. https://doi.org/10.3389/feduc.2025.1522841
Xia Q, Weng X, Ouyang F, Lin TJ, Chiu TKF. A scoping review on how generative artificial intelligence transforms assessment in higher education. Int J Educ Technol High Educ. 2024;21:40. https://doi.org/10.1186/s41239-024-00468-z
Qian Y. Pedagogical applications of generative AI in higher education: a systematic review of the field. TechTrends. 2025;69:1105-1120. https://doi.org/10.1007/s11528-025-01100-1
Deng R, Jiang M, Yu X, Lu Y, Liu S. Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Comput Educ. 2025;227:105224. https://doi.org/10.1016/j.compedu.2024.105224
Chen S, Cheung ACK. Effect of generative artificial intelligence on university students' learning outcomes: a systematic review and meta-analysis. Educ Res Rev. 2025;49:100737. https://doi.org/10.1016/j.edurev.2025.100737
Carless D, Boud D. The development of student feedback literacy: enabling uptake of feedback. Assess Eval High Educ. 2018;43(8):1315-1325. https://doi.org/10.1080/02602938.2018.1463354
Dawson P, Yan Z, Lipnevich A, Tai J, Boud D, Mahoney P. Measuring what learners do in feedback: the feedback literacy behaviour scale. Assess Eval High Educ. 2024;49(3):348-362. https://doi.org/10.1080/02602938.2023.2240983
Zhan Y, Boud D, Dawson P, Yan Z. Generative artificial intelligence as an enabler of student feedback engagement: a framework. High Educ Res Dev. 2025;44(5):1289-1304. https://doi.org/10.1080/07294360.2025.2476513
Er E, Akçapınar G, Bayazıt A, Noroozi O, Banihashem SK. Assessing student perceptions and use of instructor versus AI-generated feedback. Br J Educ Technol. 2025;56(3):1074-1091. https://doi.org/10.1111/bjet.13558
Venter J, Coetzee SA, Schmulian A. Exploring the use of artificial intelligence (AI) in the delivery of effective feedback. Assess Eval High Educ. 2025;50(4):516-536. https://doi.org/10.1080/02602938.2024.2415649
Usher M. Generative AI vs. instructor vs. peer assessments: a comparison of grading and feedback in higher education. Assess Eval High Educ. 2025;50(6):912-927. https://doi.org/10.1080/02602938.2025.2487495
Dai W, Tsai YS, Lin J, Aldino A, Jin H, Li T, et al. Assessing the proficiency of large language models in automatic feedback generation: an evaluation study. Comput Educ Artif Intell. 2024;7:100299. https://doi.org/10.1016/j.caeai.2024.100299
Zhao X, Cox A, Chen X. The use of generative AI by students with disabilities in higher education. Internet High Educ. 2025;66:101014. https://doi.org/10.1016/j.iheduc.2025.101014
Stefaniak JE, Moore SL. The use of generative AI to support inclusivity and design deliberation for online instruction. Online Learn. 2024;28(3):181-206. https://doi.org/10.24059/olj.v28i3.4458
Tai J, Mahoney P, Ajjawi R, Bearman M, Dargusch J, Dracup M, et al. How are examinations inclusive for students with disabilities in higher education? A sociomaterial analysis. Assess Eval High Educ. 2023;48(3):390-402. https://doi.org/10.1080/02602938.2022.2077910
Steiss J, Tate T, Graham S, Cruz J, Hebert M, Wang J, et al. Comparing the quality of human and ChatGPT feedback of students' writing. Learn Instr. 2024;91:101894. https://doi.org/10.1016/j.learninstruc.2024.101894
Sembey R, Hoda R, Grundy J. Emerging technologies in higher education assessment and feedback practices: a systematic literature review. J Syst Softw. 2024;211:111988. https://doi.org/10.1016/j.jss.2024.111988
Weng X, Xia Q, Gu M, Rajaram K, Chiu TKF. Assessment and learning outcomes for generative AI in higher education: a scoping review on current research status and trends. Australas J Educ Technol. 2024;40(6):37-55. https://doi.org/10.14742/ajet.9540
Lee SS, Moore RL. Harnessing generative AI (GenAI) for automated feedback in higher education: a systematic review. Online Learn. 2024;28(3):82-106. https://doi.org/10.24059/olj.v28i3.4593
Essel HB, Vlachopoulos D, Essuman AB, Amankwa JO. ChatGPT effects on cognitive skills of undergraduate students: receiving instant responses from AI-based conversational large language models (LLMs). Comput Educ Artif Intell. 2024;6:100198. https://doi.org/10.1016/j.caeai.2023.100198
Escalante J, Pack A, Barrett A. AI-generated feedback on writing: insights into efficacy and ENL student preference. Int J Educ Technol High Educ. 2023;20:57. https://doi.org/10.1186/s41239-023-00425-2
Estévez-Ayres I, Callejo P, Hombrados-Herrera MA, Alario-Hoyos C, Delgado Kloos C. Evaluation of LLM tools for feedback generation in a course on concurrent programming. Int J Artif Intell Educ. 2025;35:774-790. https://doi.org/10.1007/s40593-024-00406-0
Belkina M, Daniel S, Nikolic S, Haque R, Lyden S, Neal P, et al. Implementing generative AI (GenAI) in higher education: a systematic review of case studies. Comput Educ Artif Intell. 2025;8:100407. https://doi.org/10.1016/j.caeai.2025.100407
Drinkwater Gregg K, Ryan O, Katz A, Huerta M, Sajadi S. Expanding possibilities for generative AI in qualitative analysis: fostering student feedback literacy through the application of a feedback quality rubric. J Eng Educ. 2025;114(3):e70024. https://doi.org/10.1002/jee.70024
Luo J, Zheng C, Yin J, Teo HH. Design and assessment of AI-based learning tools in higher education: a systematic review. Int J Educ Technol High Educ. 2025;22:42. https://doi.org/10.1186/s41239-025-00540-2
Published
Issue
Section
License
Copyright (c) 2026 Daniel Alejandro Mantilla González (Author)

This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.