The Role of M-Commerce Readiness in Emerging and Developed Markets
Bibliographic record
Abstract
Although mobile commerce (m-commerce) growth provides ample potential for retailers around the globe, several studies have shown that it has failed to attract potential customers across different countries. This study advances the literature by comparing m-commerce customers’ behavioral intentions and actual behaviors using data from 812 m-commerce users across four countries (Australia, India, the United States, and Pakistan). This context offers a unique opportunity for understanding how m-commerce consumers’ behaviors differ across disparate national markets. The authors propose a conceptual framework linking m-commerce users’ behaviors (intentions and actual usages) to key drivers (ubiquity and habit), and they develop hypotheses about the moderating roles of m-commerce readiness and habit in these linkages. The results reveal important asymmetries between m-commerce readiness stage and between habit: users at an early m-commerce readiness stage assign more importance to ubiquity relative to habit in influencing purchase intentions, whereas the opposite is true for the users who are at an advanced stage. Habit moderates the influence of ubiquity such that its importance in determining intention decreases as the behavior in question takes a more habitual nature. The authors outline how m-retailers operating across developed and developing countries should adapt their marketing strategies to customers at different m-commerce readiness stages.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".