Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 430

The DOI matches its Crossref record.

EXPLORING THE LIMITS OF THE TECHNOLOGY S‐CURVE. PART I: COMPONENT TECHNOLOGIES

CHRISTENSEN, CLAYTON M.·1992·Production and Operations Management

Abstract

The technology S‐curve is a useful framework describing the substitution of new for old technologies at the industry level. In this paper I use information from the technological history of the disk drive industry to examine the usefulness of the S‐curve framework for managers at the firm level in planning for new technology development. Because improvements in over‐all disk drive product performance result from the interaction of improved component technologies and new architectural technologies, each of these must be monitored and managed. This paper focuses on component technology S‐curves, and a subsequent paper, also published in this issue of the journal, examines architectural technology Scurves. Improvement in individual components followed S‐curve patterns, but I show that the flattening of S‐curves is a firm‐specific, rather than uniform industry phenomenon. Lack of progress in conventional technologies may be the result, rather than the stimulus, of a forecast that the conventional technology is maturing, and some firms demonstrated the ability to wring far greater levels of performance from existing component technologies than other firms. Attacking entrant firms evidenced a distinct disadvantage versus incumbent firms in developing and using new component technologies. Firms pursuing aggressive Scurve switching strategies in component technology development gained no strategic advantage over firms whose strategies focused on extending the life of established component technologies.

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 · 10.1111/j.1937-5956.1992.tb00001.x

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.

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

Cited by

Metadata

Authors
CHRISTENSEN, C. M.
Venue
Production and Operations Management
Published
1992-12
Volume
1
Issue
4
Pages
334-357
ISSN
  • 1059-1478 (ISSN-L)
  • 1937-5956
OpenAlex
W1966572897
Cited by
430
References
0
FWCI
1.22
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

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.