Bundling and pricing strategies for integrating physical and online stores: A game-theoretic approach considering effect of network externalities
Bibliographic record
Abstract
The rapid advancement of the Internet has significantly reshaped traditional business models, enabling firms to leverage both online and offline channels to enhance competitiveness and profitability. This study explores the interplay between bundling and pricing strategies within a dual-channel retail system that integrates a physical store and an online shop, utilizing a game-theoretic approach while accounting for network externalities. A two-stage model is proposed: in stage 1, a manufacturer supplies two products with differing network externalities to a retailer, who must decide whether to sell them individually or as a bundle. In stage 2, the manufacturer considers launching its online channel to complement the existing physical channel. Four distinct scenarios are analyzed, examining the bundling and pricing strategies employed by both the manufacturer and the retailer across both channels to maximize their profits. The findings indicate that the integration of physical and online channels is mutually beneficial for both parties, resulting in increased profits that grow alongside stronger network externalities. Moreover, the optimal bundling strategy is contingent upon the nature of the products and their respective externalities. Specifically, when both products demonstrate high network externalities, the manufacturer should implement a mixed strategy—offering the products as a bundle online while selling them individually in physical stores. Numerical analysis emphasizes the importance of network externalities in shaping bundling decisions and profit outcomes, providing actionable insights for firms operating in multi-channel retail environments.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".