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Cited by 312

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Strategy, advanced manufacturing technology and performance: empirical evidence from U.S. manufacturing firms

Kotha, Suresh, Swamidass, Paul M·2000·Journal of Operations Management

Abstract

This study investigates the complex relationships among strategy, advanced manufacturing technology (AMT) and performance using survey responses from 160 U.S. manufacturing firms. In contrast to previous studies that emphasize only the flexibility dimension of AMT, this study adopts a multidimensional view of AMT by stressing the information processing capability inherent in AMTs. The study found support for four dimensions of AMT: information exchange and planning technology (IEPT), product design technology (PDT), low‐volume flexible automation technology (LVFAT), and high‐volume automation technology (HVAT). The results found also indicate empirical support for the study's major premise that a fit between certain strategy–AMT dimensions will be associated with superior performance. Using the findings, the study discusses the implications of the findings and suggests several avenues for future research.

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Metadata

Authors
Kotha, S., & Swamidass, P. M.
Venue
Journal of Operations Management
Publisher
Wiley
Published
2000-03-27
Volume
18
Issue
3
Pages
257-277
ISSN
  • 0272-6963 (ISSN-L)
  • 1873-1317
OpenAlex
W2036378056
Cited by
312
References
1
FWCI
12.81
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Strategy, advanced manufacturing technology and performance: empirical evidence from U.S. manufacturing firms. https://app.cochat.ai/papers/10.1016/s0272-6963(99)00025-x
The paper
Kotha, S., & Swamidass, P. M. (2000). Strategy, advanced manufacturing technology and performance: empirical evidence from U.S. manufacturing firms. Journal of Operations Management. https://doi.org/10.1016/s0272-6963(99)00025-x

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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.