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Managerial motivation dynamics and incentives

Author: Kocabiyikoglu, Ayse ; Popescu, IoanaINSEAD Area: Decision SciencesIn: Management Science, vol. 53, no. 5, May 2007 Language: EnglishDescription: p. 834-848.Type of document: INSEAD ArticleNote: Please ask us for this itemAbstract: Firms can increase profitability by appropriately motivating managers. The authors investigate drivers of managerial motivation, and propose how firms can use performance-pay to alter motivational patterns. They focus on the agent's optimal effort decision in trading off compensation utility with effort cost in a static and dynamic setting. Surprisingly, they find that lower risk aversion or increased pay are not necessarily motivating factors, and identify the relevant motivational drivers underlying the agent's utility and compensation plan. They characterize properties of agents' preferences for performance lotteries (risk aversion, aggressiveness, prudence) that trigger systematic motivational patterns with respect to a variety of factors, such as agent's productivity and past performance, time to evaluation, firm's capabilities and market factors. Our insights are robust, holding under very general modeling assumptions on preferences, rewards and the stochastic effort-performance function.
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Firms can increase profitability by appropriately motivating managers. The authors investigate drivers of managerial motivation, and propose how firms can use performance-pay to alter motivational patterns. They focus on the agent's optimal effort decision in trading off compensation utility with effort cost in a static and dynamic setting. Surprisingly, they find that lower risk aversion or increased pay are not necessarily motivating factors, and identify the relevant motivational drivers underlying the agent's utility and compensation plan. They characterize properties of agents' preferences for performance lotteries (risk aversion, aggressiveness, prudence) that trigger systematic motivational patterns with respect to a variety of factors, such as agent's productivity and past performance, time to evaluation, firm's capabilities and market factors. Our insights are robust, holding under very general modeling assumptions on preferences, rewards and the stochastic effort-performance function.

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