Predicting m-shopping in the two largest m-commerce markets: The United States and China
Bibliographic record
Abstract
This research examines the factors affecting consumers’ mobile shopping (m-shopping) intentions in China and the United States. Drawing on the hedonic-motivation system adoption model (HMSAM), it is proposed that perceived ease of use affects m-shopping intentions; furthermore, this relationship is mediated by perceived usefulness, perceived enjoyment, and control. A survey-based cross-sectional analysis involving a total of 720 respondents constitutes the methodology of this study. In the United States, 409 responses from American citizens or residents were obtained from surveys administered online by MTurk. In China, 311 responses from Chinese consumers were obtained from surveys administered online by Sojump. Perceived usefulness, an extrinsic motive, directly affects behavioral intentions, especially for Chinese consumers, and this effect is also much stronger and complemented by an indirect effect for the Chinese (relative to American) consumers. In contrast, intrinsic motives of joy and control, which are strongly affected by perceived ease of use, do not influence intentions in either market. However, joy exerts an indirect influence on m-shopping intentions, but only for Chinese consumers. These results pertain to the specific context of m-shopping and establish further the importance of distinguishing between utilitarian and hedonic factors, especially across different markets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".