Engagement with Search-Based Advertising on Search Engine Results Pages Varies Based on the User’s Prior Knowledge and Screen Size
Bibliographic record
Abstract
A Review of: Schultheiß, S., & Lewandowski, D. (2021). How users’ knowledge of advertisements influences their viewing and selection behavior in search engines. Journal of the Association for Information Science and Technology, 72(3), 285–301. https://doi.org/10.1002/asi.24410 Abstract Objective – To examine how users’ understanding of ads on search engine results pages (SERPs) influences their viewing and selection behaviour on computers and smartphones. Design – Mixed methods approach consisting of pre-study interview, eye-tracking experiment, and post-study questionnaire. Setting – Usability lab at a university in Germany. Subjects – 50 students enrolled at the Hamburg University of Applied Sciences and 50 non-students recruited in Hamburg. Methods – After giving informed consent and receiving payment, participants provided information on demographics as well as how they use search engines as part of a pre-study interview. For the eye-tracking experiment, each participant completed 10 tasks each on a desktop computer and smartphone. Both the device condition order and task order were randomized. Tasks were broken down into five informational tasks (e.g., how do I build a desktop computer?), three transactional tasks (e.g., how would I go about buying a refrigerator?), and two navigational tasks (e.g., I need to go to the Apple website). The software displayed clickable screenshots of SERPs, and all clicks were recorded. iMotions eye-tracking software recorded eye fixations on areas of the page featuring organic search results and paid ads. A post-experiment questionnaire asked participants about Google’s business model and probed them about the extent to which they were able to differentiate between organic results and ads. Answers to the questionnaire were weighted and normalized to form a 0–100 scale. Main Results – The first set of research hypotheses examining the correlation between participants’ knowledge of ads and viewing and clicking behaviour was partially confirmed. There was no significant correlation between participants’ questionnaire score and visual fixations on ads, but there was a significant negative correlation between questionnaire score and the number of clicks on ads. Users with questionnaire scores in the bottom quartile paid significantly less attention to organic results than those in the top quartile, but users in the top quartile still fixated on ads and did so comparably to users in the bottom quartile. The second set of research hypotheses examining the relationship between viewing and clicking behaviour and device (desktop versus mobile) was also partially confirmed. Users on a smartphone had significantly higher fixation rates on ads than users on a desktop computer, although click rates on ads did not differ significantly between the two conditions. Conclusion – Knowledge about ads on SERPs influences selection behaviour. Users with a low level of knowledge on search advertising are more likely to click on ads than those with a high level of knowledge. Users on smartphones are also more likely to pay visual attention to ads, probably because the smaller screen size narrows content “above the fold.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".