Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 247

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Absorptive Capacity in High-Technology Markets: The Competitive Advantage of the Haves

Narasimhan, Om, Rajiv, Surendra, Dutta, Shantanu·2006·Marketing Science

Abstract

The rapid rate of knowledge obsolescence in many high-technology markets makes it imperative for firms to renew their technological bases constantly. Given its critical importance, excellence in renewal of technological base would serve as a dynamic capability. Drawing on past literature, we identify this dynamic capability associated with acquiring and utilizing external technological know-how with the notion of absorptive capacity (AC). We ask the following questions: (a) What would cause some firms to have a higher AC than others? and, (b) What is the impact of AC on a firm’s profitability? We build a conceptual framework suggesting that marketing, R&D, and operations capabilities have a significant positive impact on a firm’s AC. We test our framework on a data set of firms in high-technology markets. Using an econometric technique called stochastic frontier estimation, we infer the AC of firms from an observation of the know-how they actually absorb. We find that firm-specific capabilities significantly impact AC. Also, we find that AC has a significant impact on profitability and that this impact is moderated by the pace of technological change: the greater the pace of change, the greater the impact.

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/mksc.1060.0219

Cites

3 of 3 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 3 references

Metadata

Authors
Narasimhan, O., Rajiv, S., & Dutta, S.
Venue
Marketing Science
Published
2006-09
Volume
25
Issue
5
Pages
510-524
ISSN
  • 0732-2399 (ISSN-L)
  • 1526-548X
OpenAlex
W2092146150
Cited by
247
References
3
FWCI
10.04
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: Absorptive Capacity in High-Technology Markets: The Competitive Advantage of the Haves. https://app.cochat.ai/papers/10.1287/mksc.1060.0219
The paper
Narasimhan, O., Rajiv, S., & Dutta, S. (2006). Absorptive Capacity in High-Technology Markets: The Competitive Advantage of the Haves. Marketing Science. https://doi.org/10.1287/mksc.1060.0219

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.