Jacob Stevy Seleky
Universitas Matana

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Integrated Force Field Analysis (FFA) and Inferential Statistics Method for Student Recruitment Strategy Evaluation Wiwik Wiyanti; Jacob Stevy Seleky; Rambu Marshanda Tamu Ina Paa
Journal of Mathematics, Computations and Statistics Vol. 9 No. 3 (2026): Volume 09 Issue 03 (September 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/ebwk5a42

Abstract

This study applies an integrated approach combining inferential statistics, specifically inferences for two population means, with the Force Field Analysis (FFA) method. This study aims to analyze marketing strategies to increase new student enrollment at a university in Banten Province, Indonesia. The strategies analyzed in this study were classified into two categories: driving forces and restraining forces. The subjects of this study were employees from the Sales and Marketing Division of the university. The data analyzed in this study consisted of primary data collected through the administration of a structured questionnaire. The data analyzed in this study were primary data collected through a questionnaire. The questionnaire responses were categorized into two groups: driving forces and restraining forces. Data were analyzed using an integrated approach that combined FFA with inferential statistics, specifically the independent-samples t-test. Prior to its administration to the research subjects, the questionnaire underwent an expert validation process to ensure the relevance and appropriateness of its items. Descriptively, the mean score for the driving forces was 8.3, which was higher than that of the restraining forces was 5.4. The t-test yielded a calculated  ( ) of 11.4, which was greater than the critical t-value ( ) of 1.67. It’s means  that marketing strategies classified as driving forces had significantly higher mean scores than those classified as restraining forces in efforts to recruit prospective new students to the university over the past three years. Furthermore, the findings revealed two restraining forces with higher mean scores than the driving forces, specifically for Items 32 and 33, which concerned heavy workload and an inadequate compensation and incentive system, respectively.