DEVELOPING A CONCEPTUAL MODEL OF A MARKETING MECHANISM FOR ATTRACTING APPLICANTS
DOI:
https://doi.org/10.5281/zenodo.22823248Abstract
Conceptual models in higher education marketing are commonly proposed and illustrated rather than subjected
to explicit empirical testing. This article develops the Applicant Attraction Model, formulates three claims derived from the
model, and reports the results of testing each claim using panel data from twenty institutions. The model conceptualises
applicant attraction as a sequence of filters in which enrolment is expressed as the product of an exposure rate, a screening
rate, and a verification rate.
The first claim—that the composition of these rates is multiplicative—is an accounting identity and is therefore presented
as a structural property of the model rather than as an empirical finding. The second claim—that each driver acts primarily
on a specific block—is not supported in its original three-block specification. Across the three blocks, every driver records
a specificity index between 0.374 and 0.503, compared with a no-specificity floor of 0.333. When the model is simplified
to two blocks—exposure and conversion—specificity emerges for the information-related drivers only: the verifiable claim
share reaches 0.956 and evidence distance reaches 0.950, relative to a no-specificity floor of 0.500, whereas promotional
expenditure, tuition, and programme breadth remain between 0.540 and 0.648. The empirical test therefore supports a
more parsimonious two-block representation for the information-related drivers rather than the original three-block specification.
The third claim—that the model can localise the binding constraint—is supported by the observed panel patterns. Exposure
is identified as the binding constraint in sixteen institutions and verification in four. Raising the binding rate to the
panel median produces estimated gains ranging from zero to 172.7 per cent across institutions and an aggregate sector-
level gain of 8.4 per cent without additional capacity.
When compared with a single reduced-form equation, the Applicant Attraction Model provides a lower in-sample fit (R² =
0.969 versus R² = 0.996) but yields institution-specific diagnostic implications. By contrast, applying the largest coefficient
from the reduced-form specification as a decision rule would imply the same recommended action for all twenty institutions.
The comparison illustrates the distinction between maximising statistical fit and using a conceptual model to identify
institution-specific constraints and corresponding areas for intervention.
Keywords
conceptual model; applicant attraction; higher education marketing; marketing mechanism; block specificity; binding constraint; explanatory modelling; Uzbekistan.References
Cameron A.C., Miller D.L. A practitioner’s guide to cluster-robust inference // Journal of Human Resources. – 2015. –
Vol. 50, № 2. – P. 317–372.
Chapman D.W. A model of student college choice // Journal of Higher Education. – 1981. – Vol. 52, № 5. – P. 490–505.
Darby M.R., Karni E. Free competition and the optimal amount of fraud // Journal of Law and Economics. – 1973. – Vol.
, № 1. – P. 67–88.
Goldratt E.M., Cox J. The Goal: A Process of Ongoing Improvement / E.M. Goldratt, J. Cox. – Great Barrington: North
River Press, 1984.
Hauser J.R., Wernerfelt B. An evaluation cost model of consideration sets // Journal of Consumer Research. – 1990.
– Vol. 16, № 4. – P. 393–408.
Heckman J.J. Sample selection bias as a specification error // Econometrica. – 1979. – Vol. 47, № 1. – P. 153–161.
Hemsley-Brown J., Oplatka I. Universities in a competitive global marketplace: A systematic review of the literature
on higher education marketing // International Journal of Public Sector Management. – 2006. – Vol. 19, № 4. – P.
–338.
Hossler D., Gallagher K.S. Studying student college choice: A three-phase model and the implications for policymakers
// College and University. – 1987. – Vol. 62, № 3. – P. 207–221.
Lavidge R.J., Steiner G.A. A model for predictive measurements of advertising effectiveness // Journal of Marketing.
– 1961. – Vol. 25, № 6. – P. 59–62.
MacInnis D.J. A framework for conceptual contributions in marketing // Journal of Marketing. – 2011. – Vol. 75, № 4.
– P. 136–154.
Maringe F. University and course choice: Implications for positioning, recruitment and marketing // International Journal
of Educational Management. – 2006. – Vol. 20, № 6. – P. 466–479.
Nelson P. Information and consumer behavior // Journal of Political Economy. – 1970. – Vol. 78, № 2. – P. 311–329.
Perna L.W. Studying college access and choice: A proposed conceptual model // Higher Education: Handbook of
Theory and Research / ed. by J.C. Smart. – Dordrecht: Springer, 2006. – Vol. 21. – P. 99–157.
Roberts J.H., Lattin J.M. Development and testing of a model of consideration set composition // Journal of Marketing
Research. – 1991. – Vol. 28, № 4. – P. 429–440.
Shmueli G. To explain or to predict? // Statistical Science. – 2010. – Vol. 25, № 3. – P. 289–310.
Shocker A.D., Ben-Akiva M., Boccara B., Nedungadi P. Consideration set influences on consumer decision-making
and choice: Issues, models, and suggestions // Marketing Letters. – 1991. – Vol. 2, № 3. – P. 181–197.
Spence M. Job market signaling // Quarterly Journal of Economics. – 1973. – Vol. 87, № 3. – P. 355–374.
Welch B.L. The generalization of Student’s problem when several different population variances are involved //
Biometrika. – 1947. – Vol. 34, № 1–2. – P. 28–35.
Yadav M.S. The decline of conceptual articles and implications for knowledge development // Journal of Marketing. –
– Vol. 74, № 1. – P. 1–19.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 MUHANDISLIK VA IQTISODIYOT
This work is licensed under a Creative Commons Attribution 4.0 International License.
