Mobile app localization-based advertising: Effects of spatial and social-cultural distances on consumer behavior
Bibliographic record
Abstract
This article studies how and why localized mobile applications shape consumer responses toward mobile advertising in the context of ridesharing. Drawing from construal level theory and extant research on cultural customization of website design, we hypothesize that consumers respond more favorably when the app’s design closely matches their immediate social-cultural surroundings, compared to designs that are culturally distant. We conducted a laboratory experiment with a custom-built ridesharing app that manipulated spatial and social-cultural distances in a controlled setting. Findings show that exposure to localized mobile ads on apps that reflect the consumer’s immediate social-cultural environment (near social-cultural distance) enhances app usage intentions and purchasing intentions compared to apps with culturally distant designs. The preference for apps with local design intensifies when the advertised location is closer to the immediate location. We also find that app atmospherics (informativeness, entertainment, and effectiveness) mediate the positive effects of social-cultural distance in application design on consumer reactions in conditions of a low spatial distance to the promoted destination. Considering atmospherics offers a new explanation for why a local application design affects consumer reactions positively, beyond accounts made by construal level theory. We discuss the theoretical implications of our findings, offer managerial insights for developers and designers of geo-localized applications, and outline the study’s limitations along with directions for future research.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".