Adapting to the digital marketplace: manufacturer channel selection in the age of consumer migration
Bibliographic record
Abstract
Purpose This study aims to examine manufacturers’ strategic responses to consumer migration from offline to online channels, focusing on how these shifts affect their channel selection and business strategies. Design/methodology/approach This research uses a theoretical framework using a Stackelberg game model to analyze manufacturers’ decision-making processes amid evolving consumer behaviors. It intricately explores the strategic implications across three distinct channel structures: manufacturer direct sales (MD), retailer resale (RR) and retailer agency (RA), focusing on their economic outcomes and market dynamics. This approach is instrumental in decoding the multifaceted nature of channel migration and its impact on manufacturer–retailer relationships in the digital marketplace. Findings The research reveals that in MD and RA scenarios, as channel migration intensifies, manufacturers tend to lower both wholesale and online retail prices. Conversely, in the RR scenario, the set wholesale price is intricately linked to the market share, with higher prices set for smaller offline market shares. From a strategic standpoint, MD emerges as the optimal choice for maximizing manufacturer profits, while RA takes precedence when considering the entire supply chain’s profitability, particularly under high commission costs. Originality/value This research illuminates the impact of channel migration on manufacturers’ pricing strategies and channel selection. It not only advances the understanding of consumer behavior in multichannel retail environments but also offers practical insights for businesses in effectively managing online and offline channels.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".