Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 188

The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.

Strategies to Fight Ad-Sponsored Rivals

Open access
Casadesus-Masanell, Ramon, Zhu, Feng·2010·Management Science

Abstract

We analyze the optimal strategy of a high-quality incumbent that faces a low-quality ad-sponsored competitor. In addition to competing through adjustments of tactical variables such as price or the number of ads a product carries, we allow the incumbent to consider changes in its business model. We consider four alternative business models: a subscription-based model; an ad-sponsored model; a mixed model in which the incumbent offers a product that is both subscription based and ad sponsored; and a dual model in which the incumbent offers two products, one based on the ad-sponsored model and the other based on the mixed business model. We show that the optimal response to an ad-sponsored rival often entails business model reconfigurations. We also find that when there is an ad-sponsored entrant, the incumbent is more likely to prefer to compete through the subscription-based or the ad-sponsored model, rather than the mixed or the dual model, because of cannibalization and endogenous vertical differentiation concerns. We discuss how our study helps improve our understanding of notions of strategy, business model, and tactics in the field of strategy.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1287/mnsc.1100.1199

Cites

4 of 4 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 4 references

Metadata

Authors
Casadesus-Masanell, R., & Zhu, F.
Venue
Management Science
Published
2010-09
Volume
56
Issue
9
Pages
1484-1499
ISSN
  • 0025-1909 (ISSN-L)
  • 1526-5501
OpenAlex
W1589811976
Cited by
188
References
4
FWCI
23.92
Type
journal article
Language
en
Open access
green

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Strategies to Fight Ad-Sponsored Rivals. https://app.cochat.ai/papers/10.1287/mnsc.1100.1199
The paper
Casadesus-Masanell, R., & Zhu, F. (2010). Strategies to Fight Ad-Sponsored Rivals. Management Science. https://doi.org/10.1287/mnsc.1100.1199

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.