GILMAR Enrollment Acceleration Model and Student Application Intention in Higher Education

Authors

  • Muhammad Andri Radiany Sekolah Tinggi Ilmu Ekonomi Mahardhika
  • Dodit Cahyo Nugroho Sekolah Tinggi Ilmu Ekonomi Mahardhika
  • Asruni Sekolah Tinggi Ilmu Ekonomi Pancasetia image/svg+xml
  • Zainal Nur Mustofa Sekolah Tinggi Ilmu Ekonomi Mahardhika

DOI:

https://doi.org/10.35870/ijmsit.v6i2.8692

Keywords:

GILMAR, Campus Attractiveness, Social Media Exposure, Higher Education Marketing, Stimulus–Organism–Response, PLS-SEM

Abstract

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.

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Author Biographies

  • Muhammad Andri Radiany, Sekolah Tinggi Ilmu Ekonomi Mahardhika

    Master of Management Study Program, Sekolah Tinggi Ilmu Ekonomi Mahardhika, Surabaya City, East Java Province, Indonesia

  • Dodit Cahyo Nugroho, Sekolah Tinggi Ilmu Ekonomi Mahardhika

    Management Study Program, Sekolah Tinggi Ilmu Ekonomi Mahardhika, Surabaya City, East Java Province, Indonesia

  • Asruni, Sekolah Tinggi Ilmu Ekonomi Pancasetia

    Management Study Program, Sekolah Tinggi Ilmu Ekonomi Pancasetia, Banjarmasin City, South Kalimantan Province, Indonesia

  • Zainal Nur Mustofa, Sekolah Tinggi Ilmu Ekonomi Mahardhika

    Management Study Program, Sekolah Tinggi Ilmu Ekonomi Mahardhika, Surabaya City, East Java Province, Indonesia

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

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Published

2026-09-27

How to Cite

Radiany, M. A., Nugroho, D. C., Asruni, A., & Mustofa, Z. N. (2026). GILMAR Enrollment Acceleration Model and Student Application Intention in Higher Education. International Journal of Management Science and Information Technology, 6(2), 2618-2636. https://doi.org/10.35870/ijmsit.v6i2.8692

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