The Impact of Capabilities and Prior Investments on Online Channel Commitment and Performance
Bibliographic record
Abstract
Attracted by the promise of greater market exposure and increased revenues, firms across a wide variety of industries have undertaken significant investments in online channels. However, while some firms' entire business models revolve around this initiative, others have made only limited commitments to online channel ventures. What accounts for these marked differences in commitment to online initiatives, and do firms reap the performance benefits of increased levels of commitment? Furthermore, how do firms' internal and external capabilities affect their propensity to establish and succeed with online channel ventures? Drawing on marketing, innovation, and information systems perspectives, along with insights from the resource-based view of the firm, we propose an integrative conceptual framework that helps answer these questions. We ground our hypotheses in the context of retailers' online channel development efforts, and test our conceptual framework with data collected via a Web-based survey of 550 retailers. We find evidence of significant positive returns to investments in online channels. Furthermore, we observe the divergent effects of different sets of capabilities on commitment and performance. Importantly, although we find that the direct effect of firms' information systems capabilities on online performance appears to be negative, the indirect effect (mediated by commitment) is positive. Our study also examines the impact of firms' established distribution channels on levels of commitment to, and performance of, the online channel. We find that firms' established distribution channels act as double-edged swords, with divergent effects on commitment and performance. We also find evidence of diminishing returns to commitment as a function of established distribution presence, thereby suggesting that the rewards of commitment do not accrue equally to all firms.
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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 | 0.004 | 0.041 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".