Differential Effects of Customers’ Regulatory Fit on Trust, Perceived Value, and M-Commerce Use among Developing and Developed Countries
Bibliographic record
Abstract
Despite promising growth, mobile commerce (m-commerce) still represents only a small proportion of the world's total e-commerce market. The research behind this article moves away from the predominantly single-country (typically developed) and utilitarian-focused market scope of past research to examine and provide a more nuanced understanding of customers’ motivations, whether utilitarian or hedonic, for using m-commerce across six countries. The six-country context, with data collected from 1,183 m-commerce users, offers a unique opportunity to advance mobile-retailing literature by comparing customers’ value perceptions, trust, and m-commerce use across disparate national markets. By treating motivations as conditions activated by individuals’ chronic regulatory orientations, our results show that hedonic motivation plays a more significant role in influencing customers’ value perceptions and trust for those who are promotion oriented (Australia and the United States), whereas utilitarian motivation plays a more important role for those who are prevention oriented (Bangladesh and Vietnam). Finally, both hedonic and utilitarian motivations play an important role in influencing customers’ value perceptions and trust for those who are moderately promotion and prevention oriented (India and Pakistan). These results offer insights to mobile retailers operating internationally in their decisions to standardize or adapt the mobile-shopping environment to deliver the most valuable, trustworthy, and engaging solutions to customers.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".