From Online to Mobile: Linking Consumers’ Online Purchase Behaviors with Mobile Commerce Adoption
Why this work is in the frame
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Bibliographic record
Abstract
With the growing popularity of mobile commerce (m-commerce), it becomes vital for both researchers and practitioners to understand consumers’ mobile commerce adoption behavior. In this study, we empirically investigate the drivers of consumers’ mobile commerce adoption behavior based on a cost and benefit framework. Based on consumers' browsing and purchase behaviors at the e-commerce site before the addition of mobile commerce channel, we constructed behavioral proxy variables which capture the underlying cost and benefit of mobile commerce channel relative to the pre-existing e-commerce channel. We collected two large datasets from of a large e-marketplace in South Korea that introduced mcommerce to its existing e-commerce offering in 2011. Based on the analysis of browsing and purchase behaviors of 29,283 subjects over a period of 28 months, we find that the need for ubiquity plays a significant role in the m-commerce adoption decision. The two proxies for ubiquity need— Purchase frequency and Purchase time irregularity—were found to have a positive impact on mcommerce adoption. The results also suggest that search cost influences the decision to adopt mcommerce. Specifically, we find that the consumers who search multi-item or categories at a time, engage in active search, and conduct thorough search, are less likely to adopt m-commerce. Finally, the results show that the risk preference of the consumer is related to the adoption decision. Risk aversion, as measured by the two proxies—Reliance on secure log-in system, and Need for receiving confirmations—lowers the likelihood of m-commerce adoption. These results highlight the importance of the unique features of mobile platform in influencing the consumers’ adoption of m-commerce. We discuss the implications of our findings for academics and practitioners.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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 it