From Online to Mobile: Linking Consumers’ Online Purchase Behaviors with Mobile Commerce Adoption
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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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".