Whom should a leader imitate? Using rivalry-based imitation to manage strategic risk in changing environments

• 成果类型: Article
• 署名作者: Sharapov, Dmitry; Ross, Jan-Michael
• 署名单位: Imperial College London
• 刊物名称: STRATEGIC MANAGEMENT JOURNAL
• ISSN/ISSBN: 0143-2095
• DOI: 10.1002/smj.3120
• 发表日期: 2023
• 页码: 311-342
• 关键词: COMPETITIVE DYNAMICS; environmental shocks; rivalry-based imitation; search; Strategic risk

摘要

Research Summary We study the performance implications of dynamic environments for a leader's rivalry-based imitation efforts in a setting with multiple rivals. We disentangle competitive interactions from environmental changes to show that a leader's simple rules to either imitate the closest rival in terms of attributes (her neighbor) or the closest rival in terms of rank (her challenger) can help to maintain the performance gap to her competitors. Using a computational model and an empirical test, we find that environmental changes alter the trade-offs between imitation accuracy and the responsiveness to threats from distant rivals. Consequently, when environmental changes are infrequent and minor, neighbor imitation is more effective in maintaining the lead, whereas challenger imitation prevails as environmental changes become more frequent and substantial. Managerial Summary By showing that imitating a lower-ranked rival can help a leader to stay ahead, recent research has overturned the common thinking that imitation is only useful for those trying to catch up with the leader. However, these insights come from contexts in which the leader has only one competitor. Can imitation also be effective for a leader competing against multiple rivals, and whom should the leader imitate? We find that imitation can indeed help the leader to maintain their lead against multiple rivals, but that the choice of imitation target matters and should take the competitive environment into account. In relatively stable environments, imitating your most similar rival works best, while imitating whoever is in second place is a more effective approach in changeable environments.