Assessing social media and influential marketing on brand perception and selection of higher educational institute in India
Bibliographic record
Abstract
In the rapidly evolving landscape of higher education, Social media marketing (SMM) has evolved a critical factor in shaping brand perception (B.P.) and the decision-making process of prospective students in India. This paper intends to explore the intricate dynamics among Social media (S.M.), influencer marketing, and the selection of higher academic institutes (HEIs), focusing on understanding how these factors shape students' perceptions. We conducted this research on 560 students, who represented the research's population. SEM-PLS was applied to analyze the data and acquire procedures. Surveys on the Internet were used. Employing exogenous/endogenous elements to create sequences, the SEM approach examines causal associations among elements. It provides solutions to research into causation in dimensional and structural frameworks. According to the research's findings, selecting HEI is positively impacted by SMM initiatives. The aspects of SMM activities (electronic word-of-mouth (eWOM), personalization, interaction, and trendiness) affect the HEI selection. In addition, personalization and two have an impact on perceptions of the brand. Understanding and utilizing the efficacy of S.M. and influencer marketing will be crucial for HEIs looking to draw in and hold on to potential students as the higher education environment keeps evolving in the age of digitization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".