Pay-for-performance incentives in benchmarking with quasi S-shaped technology

Abstract

To elicit better performance or productivity, organizations often design and implement pay-for-performance incentive plans, involving ex-post performance evaluation and ex-ante management goals setting. In practice, the setting of ex-ante management goals is a challenging task because inappropriate goals lead to biased performance evaluations and unreasonable incentive payments, which may backfire on performance improvement. Some studies attempt to alleviate such challenges using data envelopment analysis. However, they rarely consider the realistic production process in the implementation of pay-for-performance incentive plans. To handle this issue, we propose a quasi S-shaped technology in the pay-for-performance incentives for performance improvement consistent with the realistic production process. The technology includes two segments, namely increasing returns to scale (IRS) and nonincreasing returns to scale (NIRS) segments. The former sacrifices convexity to exhibit the feature of S-shaped technology as possible, and the latter is consistent with the conventional variable returns to scale (VRS) frontier where convexity strictly holds. Then, the alternative benchmarks considering ex-ante management goals on the quasi S-shaped frontier are selected, from which ex-post performance evaluations and pay-for-performance incentive plans are conducted in tandem. In particular, to adapt specific contexts in practice, pay-for-performance incentive plans under decentralized and centralized settings are developed. Finally, the proposed approach is applied to the China's agricultural sector.