The Impact of Mobile Device Use on Shopper Behaviour in Store: An Empirical Research on Grocery Retailing
Bibliographic record
Abstract
Over the last decade, retailers and manufacturers alike are increasing their attention to the role of instore mobile technology use with the aim to understand its impact on consumers’ decision making process. The rise of the mobile channel, in fact, has produced disruptive changes in shopping habits designed to gradually reduce the effectiveness of in-store marketing levers in influencing shopping behaviour.This topic is of paramount importance in grocery sector since retailers and manufacturers devote a lot of investments in instore marketing activities with the aim to influence consumers’ decisions and stimulate impulse purchases. Nevertheless, there are few contributions about the influence of the mobile technology in a retail setting and its effects on buying behavior inside the store.Our research intends to explore the impact of in-store mobile technology use on shopper behavior instore in order to understand its effects on planned versus unplanned purchases. According to our preliminary results, consumers using mobile technology instore make less unplanned items and fail to purchase more planned items. Moreover, the use of mobile technology negatively impacts shoppers’ ability to recall in-store stimuli. Our findings are interesting for both retailers and manufacturers who are looking for new ways to better address their marketing efforts and increase consumers’ engagement instore.
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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 | 0.011 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".