Channel Capabilities, Product Characteristics, and the Impacts of Mobile Channel Introduction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Drawing on the notion of channel capability, we develop a theoretical ramework for understanding the interactions between mobile and traditional online channels for products with different characteristics. Specifically, we identify two channel capabilities—access and search capabilities—that differentiate mobile and online channels, and two product characteristics that are directly related to the channel capabilities—time criticality and information intensity. Based on this framework, we generate a set of predictions on the differential effects of mobile channel introduction across different product categories. We test the predictions by applying a counterfactual analysis based on vector autoregression to a large panel data set from a leading e-market in Korea that covers a 28-month period and contains all of the transactions made through the online and mobile channels before and after the mobile channel introduction. Consistent with our theoretical predictions, our results suggest that the performance impact of the mobile channel depends on the two product characteristics and the resulting product-channel fit. We discuss implications for theory and multichannel strategy.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it