Construal Matching in Online Search: Applying Text Analysis to Illuminate the Consumer Decision Journey
Bibliographic record
Abstract
As consumers move through their decision journey, they adopt different goals (e.g., transactional vs. informational). In this research, the authors propose that consumer goals can be detected through textual analysis of online search queries and that both marketers and consumers can benefit when paid search results and advertisements match consumer search–related goals. In bridging construal level theory and textual analysis, the authors show that consumers at different stages of the decision journey tend to assume different levels of mental construal, or mindsets (i.e., abstract vs. concrete). They find evidence of a fluency-driven matching effect in online search such that when consumer mindsets are more abstract (more concrete), consumers generate textual search queries that use more abstract (more concrete) language. Furthermore, they are more likely to click on search engine results and ad content that matches their mindset, thereby experiencing more search satisfaction and perceiving greater goal progress. Six empirical studies, including a pilot study, a survey, three lab experiments, and a field experiment involving over 128,000 ad impressions provide support for this construal matching effect in online search.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".