Role of community trust in driving brand loyalty in large online B2B communities
Bibliographic record
Abstract
Purpose Online customer communities have become a strategic tool for business-to-business (B2B) firms to drive collaboration among customers around the company’s products and services. This paper aims to argue that the three social capital dimensions, that is, structural, relational and cognitive, themselves driven by brand community trust, can affect brand loyalty for the organization. Design/methodology/approach The authors use a survey to collect data and structural equation modeling to test the conceptual framework by collecting data from 214 participants across three online B2B communities operated by three technology firms in India. Findings Brand community trust is found to have a strong association with social network ties, identification and norm of reciprocity and shared vision. These three have concomitant effects on the quality of customer-to-customer (C2C) interactions. Such communication generates functional, emotional and social benefits, which, in turn, curate brand loyalty. Practical implications The authors’ findings guide community managers in leveraging such conversations in shaping customer loyalty for the corporate brand. Originality/value This work provides an integrated framework to explain the important role of C2C interactions in B2B online brand communities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".