How Digital Word-of-Mouth Affects Consumer Decision Making: Evidence from Doctor Appointment Booking
Bibliographic record
Abstract
We use detailed clickstream data on online word-of-mouth (WOM) to uncover mechanisms underlying its influence on consumer decision making. A feature launch on a major doctor appointment booking platform allows us to examine the effects of online WOM on three dimensions of a consumer’s choice process: the consideration set size, the time taken to consider alternatives (web session duration), and the geographic dispersion of the choices considered. Results indicate that the effects of WOM on decision-making processes are not monotonic but rather are contingent on the abundance of WOM (number of rated doctors) in a market. When the abundance of WOM is high, the introduction of WOM makes patients consider fewer doctors, browse for a shorter duration, and focus on doctors that are geographically more proximate. In contrast, when the abundance of WOM is low, the introduction of WOM makes patients consider more doctors, browse for longer duration, and consider doctors that are geographically more dispersed. We also find that WOM can lead to a cannibalization effect: when ratings are published, the highly rated doctors reap the benefits (in the form of increased demand) at the expense of unrated doctors. Our study contributes to the extant literature on online WOM by providing new insights into how WOM influences consumer decision making and by examining this question at a more granular level than prior work. This paper was accepted by Anandhi Bharadwaj, information systems.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".