The university-generated social media content and students’ eWOM behavior: the mediating role of student-university identification
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DOIhttp://dx.doi.org/10.21511/im.21(3).2025.20
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Article InfoVolume 21 2025, Issue #3, pp. 266-276
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Type of the article: Research Article
Abstract
This study investigates the influences of university-generated social media content on students’ e-word-of-mouth behavior and student-university identification, and student-university identification on students’ e-word-of-mouth behavior. Understanding these relationships is crucial for enhancing institutional engagement and communication strategies in higher education, particularly in the Philippine context. Using predictive-causal and quantitative research design, the direct and indirect links between the latent variables of the structural model were estimated using partial least squares of 487 students at Tarlac State University in Tarlac City, Philippines, administered in November 2024. Results revealed significant relationship of university-generated social media content and e-word-of-mouth (β = 0.11, p < .001), university-generated social media content and student-university identification (β = 0.69, p < .001), student-university identification and e-word-of-mouth (β = 0.60, p < .001), and mediation of student-university identification between university-generated social media content and e-word-of-mouth (β = 0.42, p < .001), thereby supporting all the hypotheses. This research provides significant statistical evidence that social media content being generated by the university has an influence on the student-university’s identification and eWOM behavior. More importantly, the study reveals that student-university identification as mediator between university-generated social media content and e-word-of-mouth.
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JEL Classification (Paper profile tab)M30, M31, M39
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References49
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Tables5
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Figures3
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- Figure 1. Proposed theoretical model
- Figure 2. Minimum sample size required using inverse square root and gamma-exponential methods
- Figure 3. Path analysis
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- Table 1. Demographic profile of the respondents
- Table 2. Social media accounts of the respondents
- Table 3. Assessment of the convergent validity of the constructs
- Table 4. Assessment of the divergent validity of the constructs
- Table 5. Path analysis and hypotheses testing
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