How customer relationship management and social media business profiles drive customer retention of MSMEs
Bibliographic record
Abstract
This study examines the impact of customer relationship management (CRM) practices and social media marketing (SMM) activities on customer retention among MSMEs in Aceh. It considers the dual role of social media in relationship management (CRM) and business engagement (SMM). Recognizing the widespread use of social media, the study explores different stages of its adoption and utilization in business. A formalized social media business profile is used as the moderating variable, defined by a firm's formal allocation of responsibility, outsourcing, funding, governance of social media, and broader changes to structure, processes, leadership, training, and culture. Data was collected from 565 MSMEs using questionnaires and analyzed with partial least squares structural equation modeling (PLS-SEM) and multi-group analysis. The results demonstrated a high predictive power of the model on customer retention. Within CRM, the findings indicated a significant difference in the effect of key customer focus on customer retention, with higher effects observed in MSMEs that do not formalize their social media business profiles. Additionally, technology-based CRM showed significantly higher effects on customer retention for those who formalize their social media profiles. Within SMM, the study revealed significant differences in the effects of customization and trendiness on customer retention, both of which were more pronounced in MSMEs without formalized social media profiles. Furthermore, word-of-mouth had a significantly higher impact on customer retention for MSMEs with formalized social media profiles. This research contributes theoretically by developing an integrated framework that identifies how key customer focus, CRM organization, knowledge management, technology-based CRM, customization, entertainment, interaction, trendiness, and word-of-mouth influence customer retention. It also explores the moderating effects of formalized social media business profiles on CRM practices and SMM activities within MSMEs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".