Let's Shop Online Together: An Empirical Investigation of Collaborative Online Shopping Support
Bibliographic record
Abstract
Prior studies investigating business-to-consumer e-commerce have focused predominantly on online shopping by individuals on their own, although consumers often desire to conduct their shopping activities with others. This study explores the important, but seldom studied, topic of collaborative online shopping. It investigates two design components that are pertinent to collaborative online shopping support tools, namely, navigation support and communication support. Results from a laboratory experiment indicate that compared to separate navigation, shared navigation effectively reduces uncoupling (i.e., the loss of coordination with one's shopping partner) incidents per product discussed and leads to fewer communication exchanges dedicated to resolving each uncoupling incident, thereby enhancing coordination performance. Compared to text chat, voice chat does not help reduce the occurrence of uncoupling, but likely increases the efficiency in resolving uncoupling. The results further show that shared navigation and voice chat can significantly enhance the collaborative shoppers' perceptions of social presence derived from their online shopping experiences. The interaction effect on social presence implies that the benefit of shared navigation is higher in the presence of text chat than in the presence of voice chat.
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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 | 0.010 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".