A Systematic Literature Review of Social CRM’s Impact on Customer Loyalty
Bibliographic record
Abstract
We systematically review how Social CRM (SCRM) affects customer loyalty by synthesizing antecedents, processes, and outcomes (APO) across mainstream marketing outlets. Using a PRISMA-guided search with dual screening, we identify 130 peer-reviewed studies. A subset of 58 empirical SCRM investigations maps to our coding framework, and 28 report effect sizes eligible for meta-analysis. We conceptualize SCRM as an integrated strategy that uses social-platform data and dialogic engagement to augment CRM processes. Thematic synthesis reveals four process mechanisms—social listening, engagement, personalization, and co-creation—linking organizational/technological antecedents to outcomes. A sample-size-weighted meta-analysis estimates a moderate positive association between SCRM and customer loyalty (r ≈ 0.41). Effects are stronger when personalization and community co-creation co-occur and attenuated when loyalty is measured purely behaviorally (habit). We articulate boundary conditions (platform–market fit, data governance, switching costs) and practical implications for designing SCRM programs that measurably improve loyalty. This review contributes (i) a coherent APO framework grounded in mainstream literature, (ii) a quantitative benchmark for SCRM→loyalty, and (iii) a research agenda that reconciles differing conceptualizations of loyalty.
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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 | 0.013 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".