How important is the “social” in social networking? A perceived value empirical investigation
Bibliographic record
Abstract
Purpose – The purpose of this paper is to report on a value-based empirical investigation of the adoption of Twitter social networking application. The unprecedented popularity of social networking applications in a short time period warrants exploring theory-based reasons of their success. Design/methodology/approach – A cross-sectional survey-based study to elicit user views on Twitter was conducted with participants recruited through the web site of a North-American university. Findings – All facets of perceived value considered in the study (utilitarian, hedonic and social) had a significant and relatively strong influence on consumer intent to use Twitter. Quite surprisingly for a social networking application, though, the social value facet had comparatively the weakest contribution in the use equation. Research limitations/implications – User value perception might have been influenced by the features of the actual social networking application under scrutiny (i.e. Twitter in this case). Practical implications – To maximize the chances of success of new social networking applications, developers and marketers of these media should focus on the hedonic and utilitarian sides of their perceived value. Social implications – Additional efforts are necessary to better understand the reasons and factors leading to a comparatively lower social value perception of a social networking application, compared to its hedonic and utilitarian values. Originality/value – Overall, the study opens the door for investigating user perceptions on popular social networking applications in an effort to understand the unparalleled success of these services in a short time period.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".