Consumer self-construal modulates the relevance of E-tail socialness
Bibliographic record
Abstract
Purpose The growth in social content such as video facilitates consumer exposure to social information at e-tail settings. Research has recommended enhancing the e-store socialness. Focusing on focal consumer outcomes (flow and purchase intentions), the current research delineates a boundary condition, proposing that e-tail socialness improves outcomes when the consumer interdependent self, rather than the independent self, is activated. Design/methodology/approach The experimental approach is employed to test the research thesis. Two experiments (N1 = 303 Females 42.4%; N2 = 387 Females 51.4%) that used different manipulation for socialness and sample frames (USA and Canadian) are performed. Analysis of variance was applied. Findings The results generally support the research thesis, suggesting that e-tail socialness enhances consumer flow and purchase intentions when the interdependent self is activated. The effect, however, is marginal for segments with high brand preference. Practical implications As more information increase overload and reduce decision quality, e-tail practitioners should focus on providing social information predominately for consumers whose interdependent self is activated. This recommendation is particularly relevant for segments with low brand preference. Originality/value So far, studies recommend enhancing the e-store socialness, or increasing the social volume, to achieve better outcomes. Such research stream is giving rise to the “social is better in e-tail” conventional wisdom. The current work contributes by delineating a boundary condition based on consumer self-construal. This work suggests that the use of online socialness is fruitful predominantly for interdependent consumers.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".