The DOI matches its Crossref record; 62 of the 62 references it cites were found in a registry or an index.
Towards a Theory of Competitive Progression: Evidence from High‐Tech Manufacturing
Abstract
This study replicates and extends Ferdows and De Meyers' observed ‘sand cone’ model of cumulative competitive capabilities by means of Roth's related competitive progression theory (CPT). Using path analysis, we model and test the relationships among the generic competitive capability constructs of conformance quality, delivery reliability, volume flexibility, and low cost as predicted by CPT. Our results, drawn from a sample of high‐tech manufacturers, provide further evidence that on average, these four capabilities are acquired both cumulatively and in that sequence. We also find that each generic capability increases operational know‐how and reduces non‐value‐added directly and/or indirectly through the enhancement of successive capabilities in the progression, which in turn improves profitability. The paper contributes a theoretical rationale for the observed sand cone effect, describes how the competitive progression acts to influence accelerated organizational learning over an innovation cycle, and offers evidence that combinative capabilities have strategic value for high‐tech manufacturers.
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
Cites
(1994) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(94)92216-0 · Cited by 45,910
Absorptive Capacity: A New Perspective on Learning and Innovation
Cohen, W. M., & Levinthal, D. A. (1990) · Administrative Science Quarterly · doi:10.2307/2393553 · Cited by 35,728
(1993) · Journal of the American Dietetic Association · doi:10.1016/0002-8223(93)91697-o · Cited by 18,154
Armstrong, J. S., & Georgoff, D. M. (1988) · International Journal of Forecasting · doi:10.1016/0169-2070(88)90019-2 · Cited by 17,894
A Dynamic Theory of Organizational Knowledge Creation
Nonaka, I. (1994) · Organization Science · doi:10.1287/orsc.5.1.14 · Cited by 17,809
Structural Equations with Latent Variables.
Clogg, C. C., & Bollen, K. A. (1991) · Contemporary Sociology · doi:10.2307/2072165 · Cited by 17,442
Structural Equations with Latent Variables
Bollen, K. A. (1989) · Wiley series in probability and statistics · doi:10.1002/9781118619179 · Cited by 17,277
Knowledge of the firm, combinative capabilities, and the replication of technology
Kogut, B., & Zander, U. (1992) · Organization Science · doi:10.1287/orsc.3.3.383 · Cited by 12,939 · cited 8×
“Organizational learning is path dependent , for what a firm has done in the past tends to predict what it can do in the future (Clark 1996; Corbett and van Wassen-hove 1993; Hayes 1992; Hayes and Pisano 1996; Hayes et al. 2004; Kogut and Zander 1992).”
BackgroundInfluentialMail and Telephone Surveys: The Total Design Method.
Babbie, E., & Dillman, D. A. (1982) · Contemporary Sociology · doi:10.2307/2068433 · Cited by 6,852
The machine that changed the world
(1992) · Long Range Planning · doi:10.1016/0024-6301(92)90400-v · Cited by 5,805
Cited by
Yang, M. G. (., Hong, P., & Modi, S. B. (2011) · International Journal of Production Economics · doi:10.1016/j.ijpe.2010.10.017 · Cited by 961
Linking routines to operations capabilities: A new perspective
Peng, D. X., Schroeder, R. G., & Shah, R. (2008) · Journal of Operations Management · doi:10.1016/j.jom.2007.11.001 · Cited by 612
Kristal, M. M., Huang, X., & Roth, A. V. (2010) · Journal of Operations Management · doi:10.1016/j.jom.2009.12.002 · Cited by 489
Self-citationMunir, M., Jajja, M. S. S., Chatha, K. A., & Farooq, S. (2020) · International Journal of Production Economics · doi:10.1016/j.ijpe.2020.107667 · Cited by 452
KRAUSE, D. R., VACHON, S., & KLASSEN, R. D. (2009) · Journal of Supply Chain Management · doi:10.1111/j.1745-493x.2009.03173.x · Cited by 399
Metadata
- Authors
- Rosenzweig, E. D., & Roth, A. V.
- Venue
- Production and Operations Management
- Publisher
- SAGE Publications
- Published
- 2004-12
- Volume
- 13
- Issue
- 4
- Pages
- 354-368
- ISSN
- 1059-1478 (ISSN-L)
- 1937-5956
- OpenAlex
- W2122965070
- Cited by
- 264
- References
- 62
- FWCI
- 33.72
- 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: Towards a Theory of Competitive Progression: Evidence from High‐Tech Manufacturing. https://app.cochat.ai/papers/10.1111/j.1937-5956.2004.tb00223.x
- The paper
- Rosenzweig, E. D., & Roth, A. V. (2004). Towards a Theory of Competitive Progression: Evidence from High‐Tech Manufacturing. Production and Operations Management. https://doi.org/10.1111/j.1937-5956.2004.tb00223.x
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.