Engagement with Search-Based Advertising on Search Engine Results Pages Varies Based on the User’s Prior Knowledge and Screen Size
Notice bibliographique
Résumé
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.”
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,020 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,029 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».