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Whom Should a Leader Imitate in Multiple Competitor Settings? A Contingency Perspective
Author(s) -
Dmitry Sharapov,
Jan-Michael Roß
Publication year - 2015
Publication title -
academy of management proceedings
Language(s) - English
Resource type - Journals
eISSN - 2376-7197
pISSN - 0065-0668
DOI - 10.5465/ambpp.2015.199
Subject(s) - imitation , perspective (graphical) , contingency , position (finance) , work (physics) , economics , computer science , psychology , artificial intelligence , social psychology , engineering , epistemology , philosophy , finance , mechanical engineering
Can a leader's imitation of rival competitors lead to superior performance in multiple-competitor settings, and, if so, which rival should the leader's imitative efforts be focused on? Building on prior work on the performance consequences of imitation strategies from competitive dynamics and neo-computational scholars, this paper examines the effectiveness of different imitation strategies pursued by a leader in a multiple-competitor setting subject to exogenous environmental shocks. Findings from an NK simulation model of a three-competitor setting suggest that a leader's imitation of either the rival closest to her in terms of performance to date (her challenger) or of the rival closest to her in terms of position on the landscape (her neighbor) can outperform an independent search strategy in all environments apart from those in which large changes occur frequently. This is because an imitative leader may benefit from ratcheting dynamics that occur as a result of their focal rival being forced into distant search by the leader's imitative behavior, leaving the leader with the opportunity to make productive minor changes to their rival's configuration. The effectiveness of the challenger-imitation strategy relative to one of neighbor-imitation is found to decrease with increasing magnitude of environmental changes, and to increase with their increasing frequency. We proceed to evaluate the external validity of these findings using fine-grained data on multiple-competitor (fleet) sailing races from the America's Cup World Series 2011-2013, and find support for the propositions emerging from the simulation results.

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