Abstract

Having based his incentivisation strategy on cost reimbursement, Bogetoft developed an incentive formula. Given that inputs are typically costs, different input-oriented methods were proposed to estimate the parameters of the incentive formula. Recently, several radial-based input-oriented super-efficiency measures have been developed in the centralized resource allocation (CRA) context for estimation of the incentive formula parameters. We show, using examples, that input-oriented incentive methods can lead to unfair incentive mechanisms. It transpires that fair incentive mechanisms can be achieved via non-oriented methods. To this end, we propose an non-oriented slacks-based super-efficiency method in the CRA context using which we estimate the incentive formula parameters. Given that the slacks-based measures can distinguish between weak and strong efficient units, such measures do not suffer from the known deficiencies of the radial CRA-based incentive methods. The validity and applicability of the proposed approach are demonstrated using a real dataset. We consider percentage of relative returns, and use statistical indices and visualization techniques to compare the incentive plans obtained from the proposed method with those resulted from other existing CRA-based incentive methods.

摘要

Bogetoft 基于成本补偿的激励策略制定了激励公式。鉴于投入通常是成本，因此提出了不同的投入导向方法来估计激励公式的参数。最近，在集中资源分配 (CRA) 环境中开发了几种基于径向的投入导向的超效率措施，用于估计激励公式参数。我们通过例子表明，以投入为导向的激励方法会导致不公平的激励机制。可以看出，公平的激励机制可以通过非导向的方法来实现。为此，我们在 CRA 上下文中提出了一种基于非定向松弛的超效率方法，我们使用该方法估计激励公式参数。鉴于基于松弛的措施可以区分弱有效单位和强有效单位，这些措施不会受到基于径向 CRA 激励方法的已知缺陷的影响。使用真实数据集证明了所提出方法的有效性和适用性。我们考虑相对回报的百分比，并使用统计指标和可视化技术来比较从提出的方法获得的激励计划与从其他现有的基于 CRA 的激励方法得到的激励计划。