The DOI matches its Crossref record.
A New Product Growth for Model Consumer Durables
Abstract
(This article originally appeared in Management Science, January 1969, Volume 15, Number 5, pp. 215–227, published by The Institute of Management Sciences.) A growth model for the timing of initial purchase of new products is developed and tested empirically against data for eleven consumer durables. The basic assumption of the model is that the timing of a consumer's initial purchase is related to the number of previous buyers. A behavioral rationale for the model is offered in terms of innovative and imitative behavior. The model yields good predictions of the sales peak and the timing of the peak when applied to historical data. A long-range forecast is developed for the sales of color television sets.
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.
Integrity signals
Registry
Cites
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.
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
Cited by
Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations
GREENHALGH, T., ROBERT, G., MACFARLANE, F., BATE, P., & KYRIAKIDOU, O. (2004) · The Milbank Quarterly · doi:10.1111/j.0887-378x.2004.00325.x · Cited by 7,448
Using Online Conversations to Study Word-of-Mouth Communication
Godes, D., & Mayzlin, D. (2004) · Marketing Science · doi:10.1287/mksc.1040.0071 · Cited by 2,601
Everyone's an influencer: quantifying influence on twitter
Bakshy, E., Hofman, J. M., Mason, W. A., & Watts, D. J. (2011) · Proceedings of the fourth ACM international conference on Web search and data mining · doi:10.1145/1935826.1935845 · Cited by 1,689
Marketing Analytics for Data-Rich Environments
Wedel, M., & Kannan, P. K. (2016) · Journal of Marketing · doi:10.1509/jm.15.0413 · Cited by 1,261
Huang, R., Riddle, M., Graziano, D., Warren, J., Das, S., Nimbalkar, S., Cresko, J., & Masanet, E. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2015.04.109 · Cited by 805
Metadata
- Authors
- Bass, F. M.
- Venue
- Management Science
- Published
- 2004-12
- Volume
- 50
- Issue
- 12_supplement
- Pages
- 1825-1832
- ISSN
- 0025-1909 (ISSN-L)
- 1526-5501
- OpenAlex
- W2130067743
- Cited by
- 2,414
- References
- 0
- FWCI
- 86.45
- Type
- journal article
- Language
- en
- Open access
- closed
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: A New Product Growth for Model Consumer Durables. https://app.cochat.ai/papers/10.1287/mnsc.1040.0264
- The paper
- Bass, F. M. (2004). A New Product Growth for Model Consumer Durables. Management Science. https://doi.org/10.1287/mnsc.1040.0264
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.
