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Cited by 494

The DOI matches its Crossref record; 128 of the 128 references it cites were found in a registry or an index.

Bridging Yesterday, Today, and Tomorrow: CEO Temporal Focus, Environmental Dynamism, and Rate of New Product Introduction

Nadkarni, Sucheta, Chen, Jianhong·2014·Academy of Management Journal

Abstract

Using a multidimensional framework of CEO temporal focus (the degree to which CEOs characteristically devote attention to perceptions of the past, present, and future), we propose that a company's rate of new product introduction (NPI) is predicted by a CEO's focus on each of the three distinct time frames in interaction with environmental dynamism. Based on a longitudinal (from 1996 to 2003) analysis of 221 firms in 19 industries, we show that, in stable environments, new products are introduced faster in firms headed by CEOs with high past focus, high present focus, and low future focus. In dynamic environments, new products are introduced faster in firms headed by CEOs with low past focus, high present focus, and high future focus. These findings demonstrate how CEO temporal attentional bias shapes the rate of NPI.

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Metadata

Authors
Nadkarni, S., & Chen, J.
Venue
Academy of Management Journal
Published
2014-12
Volume
57
Issue
6
Pages
1810-1833
ISSN
  • 0001-4273 (ISSN-L)
  • 1948-0989
OpenAlex
W2079243136
Cited by
494
References
128
FWCI
53.59
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Bridging Yesterday, Today, and Tomorrow: CEO Temporal Focus, Environmental Dynamism, and Rate of New Product Introduction. https://app.cochat.ai/papers/10.5465/amj.2011.0401
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Nadkarni, S., & Chen, J. (2014). Bridging Yesterday, Today, and Tomorrow: CEO Temporal Focus, Environmental Dynamism, and Rate of New Product Introduction. Academy of Management Journal. https://doi.org/10.5465/amj.2011.0401

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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.