Consumer Engagement in Social Media: A Systematic Literature Review of Surveys and Experiments
Bibliographic record
Abstract
Over the last decade, social media has become a ubiquitous platform for brands and influencers to bolster the reach and impact of their content, aided by customer interactions such as likes, shares, and comments. In recent years, a growing number of academic research from various disciplines examined social media engagement (SM-Engagement) primarily based on secondary data analysis by scrapping data from different social media platforms. Although this kind of research is very useful for observing the patterns in the real world, it fails to unpack the drivers of SM-Engagement. Following the PRISMA protocol, this systematic literature review aims to synthesize and combine the experimental and survey research to identify the variables and methods explored in SM-Engagement and the future research directions. By analyzing 58 academic articles consisting of 46 experiments and 40 surveys, the current review proposes a taxonomy of antecedents, mechanisms, and consequences. This review ends up with the assertion that SM-Engagement is an umbrella term encompassing a whole family of cognitive, affective, and behavioral responses representing how the audiences interact over social media.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.094 | 0.267 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".