Social media marketing for student recruitment: an algorithmically sequenced literature review

Abstract

ABSTRACT The extant literature on the use of social media marketing for recruiting higher education students has an unstructured nature. To address this gap, this paper introduces a novel method called ‘algorithmic document sequencing’ (ADS) that links the key findings of 43 relevant articles procured from all databases to one another on the use of social media marketing for student recruitment in higher education. The key findings from these articles were sequenced through cosine similarity on R in an algorithmically structured way. This new method can help researchers connect the insights drawn from the use of social media marketing activities and developments in higher education literature and provide marketing professionals at universities with invaluable clues to develop social media marketing strategies to be implemented within their higher education institutions to develop their brand, become more competitive and meet their marketing goals. Furthermore, ADS can be applied to any domain to link extant literature or thematically homogeneous documents to each another for a systematically synthesized cohesive review.