Selection at the Gate: Difficult Cases, Spillovers, and Organizational Learning

Mihaela Stan () and Freek Vermeulen ()
Additional contact information
Mihaela Stan: Management Science and Innovation, University College London, London WC1E 6BT, United Kingdom
Freek Vermeulen: London Business School, London NW1 4SA, United Kingdom

Organization Science, 2013, vol. 24, issue 3, 796-812

Abstract: We analyze longitudinal data on British fertility clinics to examine the impact of “selection at the gate,” i.e., the attempts of organizations to improve the success rate of their output by selecting promising cases as input. In contrast to what might be expected, we argue that more stringent input selection is likely to lead to lower overt performance compared with those firms that admit difficult cases, because the latter develop steeper learning curves. That is, difficult cases enable greater learning from prior experience because they promote experimentation, communication among various actors, and the codification of new knowledge. Our results confirm this prediction and provide clear evidence that organizations with more difficult cases in their portfolios gradually begin to display performance figures that compare favorably with those of firms that do select at the gate.

Keywords: organizational learning; longitudinal research; organizational capabilities (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

Downloads: (external link)
http://dx.doi.org/10.1287/orsc.1120.0763 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:ororsc:v:24:y:2013:i:3:p:796-812

Access Statistics for this article

More articles in Organization Science from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().