The influence of social capital through social media: a study of the creation of value in shopping behaviour
Bibliographic record
Abstract
When consumers access information from groups through social network sites (SNSs), they develop social capital in the form of bonding and bridging ties with these groups. The purpose of this study is to investigate the influence of these bonding and bridging behaviours on consumers’ use of the social network information (SNI) gained from SNSs in their purchasing decisions. The study integrates constructs from the Technology Acceptance Model and the concept of flow to examine how these behaviours influence consumers’ perceptions of how useful the SNI is, of how easy the SNI is to use, and how they engage with SNI. The study utilizes structural equation modelling to examine questionnaire data from a random sample of social network users. The findings demonstrate that bonding and bridging ties influence consumers’ perceptions of the usefulness and ease of use of the information provided by SNSs, and therefore influence their use of the information when making shopping decisions. In addition, consumers who access SNI through bonding ties are likely to have flow experiences which further contribute to their use of the information. This study makes a theoretical contribution by expanding knowledge of the social capital influences on consumers’ perceptions of the value of the social media shopping experience.
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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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".