Social Media and Customization of the Relationship: The Case of Coca Cola Tunisia’s Facebook Fun Page
Bibliographic record
Abstract
This paper aims to investigate the role of social media in the customization of the relationship between a company and their customers. Customizing the relationship is determined by four observable variables: the pre-purchase phase (Co-creation), customer knowledge, speed of feedback and online interactivity. Personalized relationship may be facilitated by the use of new technologies and especially social networks. By using this media, company can create databases allowing it to know perfectly the customer. In this context, the contribution of employing such media by company, led us to ask about the effect of using this social network on customizing relationship with client. Based on an empirical study on a sample of 136 Tunisian students, we show that the use of social media haven’t directs and positive effects on customizing relationship with client. Indeed, the obtained results confirm that social media have positive and statistically significant impact only on co-creation and online interactivity between the company and its customers. So, customizing customer relationship should go through co-creation and online interactivity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".