Designing for Diagnosticity and Serendipity: An Investigation of Social Product-Search Mechanisms
Bibliographic record
Abstract
Users are increasingly sharing their product interests and experiences with others on e-commerce websites. For example, users can “tag” products using their own words, and these “product tags” then serve as navigation cues for other users who want to search for products. Also, socially endorsed information contributors are sometimes highlighted on websites and serve as direct information sources. This study examines the effects of these two distinct social product search cues, product tags and socially endorsed people, on users’ perceived diagnosticity and serendipity of their product search experience. While product tags support product navigation via a variety of product features tagged by the community, access to socially endorsed people enables users to browse diverse and high-quality alternatives favored by these individuals. We constructed an experimental website using real data from one of the largest social-network-based product-search websites in China to conduct an empirical study. The results of this study show that product tags help users to locate and evaluate relevant alternatives, thus enhancing the perceived diagnosticity of product search, whereas the integration of product tags and access to socially endorsed people enables users to conduct even more serendipitous searches. In addition, both perceived diagnosticity and perceived serendipity of a search experience positively affect users’ decision satisfaction. The online appendix is available at https://doi.org/10.1287/isre.2017.0695 .
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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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".