Perceived Values and Motivations Influencing M-Commerce Use
Bibliographic record
Abstract
Mobile commerce (m-commerce) has become increasingly important for organizations attempting to grow revenue by expanding into international markets. However, for multinational mobile retailers (m-retailers), one of the greatest challenges lies in carefully managing their websites across multiple national markets. This work advances cross-national research on m-retailing by (1) examining how value dimensions shape m-shoppers’ motivations, (2) analyzing differential effects of hedonic and utilitarian motivations on intention and habit, and (3) examining the competing roles of conscious (intentional) and unconscious (habitual) m-commerce use drivers across developed and developing countries. This research also examines the moderating role of m-commerce readiness at the country level on the effect of motivation on intention and habit, along with their impact on m-commerce use. Based on data from 1,975 m-shoppers in nine countries (Australia, Bangladesh, Brazil, India, Pakistan, Singapore, the United Kingdom, the United States, and Vietnam) across four continents, the results demonstrate differential relationships: consumers at an advanced (early) readiness stage are more likely to be hedonism-motivated (utility-motivated) when using m-commerce and tend to use it intentionally/consciously (habitually/unconsciously). In addition to advancing knowledge about m-commerce from a scientific perspective, the findings can help multinational firms decide whether to standardize or adapt m-shopping experiences when internationalizing.
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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 | 0.001 | 0.009 |
| 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) | 0.003 | 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".