The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Structural interdependence within top management teams: A key moderator of upper echelons predictions
Abstract
Studies of the effects of top management team ( TMT ) composition on organizational outcomes have yielded mixed and confusing results. A possible breakthrough resides in the reality that TMT s vary in how they are fundamentally structured. Some are structured such that members operate independently of each other, while others are set up such that roles are highly interdependent. We examine the potential for three facets of structural interdependence—horizontal, vertical, and reward interdependence—to resolve ambiguities regarding effects of TMT heterogeneity. Based on a sample of TMT s in technology firms, we find that the three facets of structural interdependence are potent moderators of two classic predictions: the positive association between TMT heterogeneity and member departures, and between TMT heterogeneity and firm performance . Copyright © 2014 John Wiley & Sons, Ltd.
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Cites
Tightening the Iron Cage: Concertive Control in Self-Managing Teams
Barker, J. R. (1993) · Administrative Science Quarterly · doi:10.2307/2393374 · Cited by 2,305
Cited by
Virtual teams in organizations.
Dulebohn, J. H., & Hoch, J. E. (2017) · Human Resource Management Review · doi:10.1016/j.hrmr.2016.12.004 · Cited by 342
Metadata
- Authors
- Hambrick, D. C., Humphrey, S. E., & Gupta, A.
- Venue
- Strategic Management Journal
- Publisher
- Wiley
- Published
- 2014-02-11
- Volume
- 36
- Issue
- 3
- Pages
- 449-461
- ISSN
- 0143-2095 (ISSN-L)
- 1097-0266
- OpenAlex
- W1572409317
- Cited by
- 296
- References
- 1
- FWCI
- 19.07
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
