Social value and content value in social media: Two paths to psychological well-being
Bibliographic record
Abstract
The unique characteristics and universal popularity of social media enable consumers to experience their customer value and thus improve their psychological well-being. Hence, it is important for researchers to investigate how consumers experience their customer value in this new media and demonstrate how managers can use the ensuing knowledge for designing appropriate marketing strategies. This article is a step toward that direction. We explore the antecedents and consequences of customer value in social media so as to provide a deeper insight into consumer behavior and subsequently discuss its managerial implications. Specifically, we develop a model that incorporates psychological well-being as the endogenous variable, interdependence self-construal and independent self-construal as exogenous variables, and social value, content value, social identity, self-esteem, and flow as mediating variables. Based on data from a sample of 437 social media consumers collected by an online survey and through analysis of the data by SPSS 22.0 and Amos 22.0 programs, the study revealed that consumers can gain psychological well-being by either of two paths: consumers with a higher degree of interdependent self-construal will have a higher degree of social value experience, thus leading to a higher degree of psychological well-being through a positive mediating role of social identity; whereas consumers with a higher degree of independent self-construal will have a higher degree of content value experience, thus leading to a higher degree of psychological well-being through a positive mediating role of self-esteem. Managers can segment the consumers based on their self-construal and design appropriate customer relationship strategies.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".