GILMAR Enrollment Acceleration Model and Student Application Intention in Higher Education
DOI:
https://doi.org/10.35870/ijmsit.v6i2.8692Keywords:
GILMAR, Campus Attractiveness, Social Media Exposure, Higher Education Marketing, Stimulus–Organism–Response, PLS-SEMAbstract
Private higher education institutions increasingly compete through institutional values, perceived attractiveness, and digital visibility rather than through promotion alone. Drawing on the stimulus–organism–response (S–O–R) paradigm, this study develops and tests the GILMAR Enrollment Acceleration Model, which positions five value-based institutional marketing dimensions (resilience, adaptability, communicativeness, total commitment, and innovativeness) as antecedents of campus attractiveness, campus attractiveness as the mechanism driving prospective students' behavioral intention to apply, and social media exposure as a moderator of that relationship. A quantitative explanatory design was applied to 300 senior high school students and graduates in Surabaya, Indonesia, who had actively searched for campus information and were selected through purposive sampling. The data were analyzed using Partial Least Squares Structural Equation Modeling in SmartPLS 4 with 5,000 bootstrap subsamples. Adaptability (β = 0.337), innovativeness (β = 0.306), total commitment (β = 0.279), and resilience (β = 0.163) significantly increase campus attractiveness, whereas communicativeness does not (β = 0.055; p = 0.133). Campus attractiveness strongly increases behavioral intention to apply (β = 0.468), and social media exposure positively moderates this relationship (β = 0.255; p < 0.001), raising the simple slope from 0.212 at low exposure to 0.723 at high exposure. The model explains 56.3% of the variance in campus attractiveness and 57.0% of the variance in behavioral intention to apply. Enrollment intention is therefore driven by visible institutional substance rather than by communication alone, with social media exposure operating as a boundary condition that amplifies campus attractiveness rather than as a direct driver of enrollment. Universities should prioritize adaptive programs, innovation, and service commitment, supported by consistent and evidence-based social media content.
Downloads
References
Abbas, J. (2020). HEISQUAL: A modern approach to measure service quality in higher education institutions. Studies in Educational Evaluation, 67, Article 100933. https://doi.org/10.1016/j.stueduc.2020.100933
Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. SAGE Publications.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T
Amoozegar, A., Esohwode, O. E., Yujiao, W., Pee, W. H., Ismaeil, A., Yadav, M., & Harun, M. T. (2025). Employee creativity and innovation in higher education institutions: Applying the dynamic componential model of creativity and innovation. Frontiers in Psychology, 16, Article 1614751. https://doi.org/10.3389/fpsyg.2025.1614751
Becker, J.-M., Cheah, J.-H., Gholamzade, R., Ringle, C. M., & Sarstedt, M. (2023). PLS-SEM’s most wanted guidance. International Journal of Contemporary Hospitality Management, 35(1), 321–346. https://doi.org/10.1108/IJCHM-04-2022-0474
Dul, J. (2016). Necessary condition analysis (NCA): Logic and methodology of “necessary but not sufficient” causality. Organizational Research Methods, 19(1), 10–52. https://doi.org/10.1177/1094428115584005
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104
Gudergan, S. P., Ringle, C. M., Wende, S., & Will, A. (2008). Confirmatory tetrad analysis in PLS path modeling. Journal of Business Research, 61(12), 1238–1249. https://doi.org/10.1016/j.jbusres.2008.01.012
Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM) (3rd ed.). SAGE Publications.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. https://doi.org/10.1108/EBR-11-2018-0203
Henseler, J., & Sarstedt, M. (2013). Goodness-of-fit indices for partial least squares path modeling. Computational Statistics, 28(2), 565–580. https://doi.org/10.1007/s00180-012-0317-1
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
Jacoby, J. (2002). Stimulus-organism-response reconsidered: An evolutionary step in modeling (consumer) behavior. Journal of Consumer Psychology, 12(1), 51–57. https://doi.org/10.1207/S15327663JCP1201_05
Juhaidi, A. (2024). Social media marketing of Islamic higher education institution in Indonesia: A marketing mix perspective. Cogent Business & Management, 11(1), Article 2374864. https://doi.org/10.1080/23311975.2024.2374864
Juhaidi, A., Fitria, A., Hidayati, N., & Saputri, R. A. (2025). Examining factors influencing enrolment intention in Islamic higher education in Indonesia, does Islamic senior high school matter? Social Sciences & Humanities Open, 11, Article 101243. https://doi.org/10.1016/j.ssaho.2024.101243
Kango, U., Kartiko, A., & Maarif, M. A. (2021). The effect of promotion on the decision to choose a higher education through the brand image of education. Al-Ishlah: Jurnal Pendidikan, 13(3), 1611–1621. https://doi.org/10.35445/alishlah.v13i3.852
Kaur, M., Verma, A., & Kaushik, M. B. (2026). Exploring the influence of university website usability and brand attributes on application intentions: The mediating role of university attractiveness. Asian Education and Development Studies. Advance online publication. https://doi.org/10.1108/AEDS-04-2025-0182
Kock, N. (2015). Common method bias in PLS-SEM: A full collinearity assessment approach. International Journal of e-Collaboration, 11(4), 1–10. https://doi.org/10.4018/ijec.2015100101
Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in PLS-SEM: The inverse square root and gamma-exponential methods. Information Systems Journal, 28(1), 227–261. https://doi.org/10.1111/isj.12131
Li, S., Quan, Y., Xiao, L., Ren, H., & Abinova, A. Y. (2025). Exploring the influence of social media communication and brand image on international student enrollment intentions in higher education. Frontiers in Education, 10, Article 1618524. https://doi.org/10.3389/feduc.2025.1618524
Liengaard, B. D., Sharma, P. N., Hult, G. T. M., Jensen, M. B., Sarstedt, M., Hair, J. F., & Ringle, C. M. (2021). Prediction: Coveted, yet forsaken? Introducing a cross-validated predictive ability test in partial least squares structural equation modeling. Decision Sciences, 52(2), 362–392. https://doi.org/10.1111/deci.12445
Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.
Pangarso, A., Astuti, E. S., Raharjo, K., & Afrianty, T. W. (2024). Enhancing sustained competitive advantage in Indonesian non-vocation private tertiary education institutions. SAGE Open, 14(2), 1–17. https://doi.org/10.1177/21582440241256316
Pawar, S. K. (2024). Social media in higher education marketing: A systematic literature review and research agenda. Cogent Business & Management, 11(1), Article 2423059. https://doi.org/10.1080/23311975.2024.2423059
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. https://doi.org/10.1037/0021-9010.88.5.879
Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865–1886. https://doi.org/10.1108/IMDS-10-2015-0449
Rizard, S. R., Waluyo, B., & Jaswir, I. (2023). Impact of brand equity and service quality on the reputation of universities and students’ intention to choose them: The case of IIUM and UIN [version 3; peer review: 2 approved]. F1000Research, 11, Article 1412. https://doi.org/10.12688/f1000research.122386.3
Ruangkanjanases, A., Sivarak, O., Wibowo, A., & Chen, S.-C. (2022). Creating behavioral engagement among higher education’s prospective students through social media marketing activities: The role of brand equity as mediator. Frontiers in Psychology, 13, Article 1004573. https://doi.org/10.3389/fpsyg.2022.1004573
Rutter, R., Roper, S., & Lettice, F. (2016). Social media interaction, the university brand and recruitment performance. Journal of Business Research, 69(8), 3096–3104. https://doi.org/10.1016/j.jbusres.2016.01.025
Shaya, N., Abukhait, R., Madani, R., & Khattak, M. N. (2023). Organizational resilience of higher education institutions: An empirical study during COVID-19 pandemic. Higher Education Policy, 36, 529–555. https://doi.org/10.1057/s41307-022-00272-2
Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322–2347. https://doi.org/10.1108/EJM-02-2019-0189
Siemsen, E., Roth, A., & Oliveira, P. (2010). Common method bias in regression models with linear, quadratic, and interaction effects. Organizational Research Methods, 13(3), 456–476. https://doi.org/10.1177/1094428109351241
Spence, M. (1973). Job market signaling. The Quarterly Journal of Economics, 87(3), 355–374. https://doi.org/10.2307/1882010
Sriyanto, A., Purwanto, & Muttaqin, Z. (2024). From Instagram to campus: The impact of social media marketing on students’ interest through trust and brand recognition. Kalijaga Journal of Communication, 6(2), 139–170. https://doi.org/10.14421/kjc.62.02.2024
Yaping, X., Huong, N. T. T., Nam, N. H., Quyet, P. D., Khanh, C. T., & Anh, D. T. H. (2023). University brand: A systematic literature review. Heliyon, 9(6), Article e16825. https://doi.org/10.1016/j.heliyon.2023.e16825
Downloads
Published
Issue
Section
License
Copyright (c) 2026 Muhammad Andri Radiany, Dodit Cahyo Nugroho, Asruni, Zainal Nur Mustofa

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.
