Social networking sites as a learning tool
Bibliographic record
Abstract
Purpose – Over the past few years, social networking sites (SNSs) have become very useful for firms, allowing companies to manage the customer–brand relationships. In this context, SNSs can be considered as a learning tool because of the brand knowledge that customers develop from these relationships. Because of the fact that knowledge in organisations is embodied in the concept of the learning organisation, customers may create brand knowledge as a consequence of two learning facilitators: informational and instrumental value. Then, the purpose of this paper is to identify the role played by brand knowledge in the process of creating customer capital, in the context of SNSs. Design/methodology/approach – A total of 259 users of SNSs, who were followers or fans of brand pages, participated in this study. Data were collected through an online survey and they were analysed using structural equation modelling. Findings – The results of the study show that brand pages at SNS can perform brand knowledge by providing purposive gratifications to its customers. Moreover, they can also develop an indirect effect on customer capital, through the direct effect that brand knowledge has on it. Therefore, the results of the study will help managers design their learning strategies in relation to SNS and confirm the need of using SNS as a learning tool. Originality/value – Few, if any, studies have analysed whether gratifications, usually related to media, work as learning facilitators in the context of brand pages at SNS.
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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 | 0.002 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".