LAUSR

Evaluating knowledge workers performance has always been one of the main concerns of knowledge-based organizations. The purpose of this article is to present two new versions of the additive ratio assessment (ARAS) method called Augmented ARAS (A-ARAS1 and A-ARAS2) that improve the rank reversal phenomenon of ARAS using some iterative methods to evaluate the optimal performance of knowledge workers. The study population consisted of the Academic Center for Education, Culture, and Research in Mashhad, Iran, which has been contributing to national development through the expansion of knowledge and applied research since 1995. The performance appraisal index was weighted using the best-worst method and then analyzed by the ARAS, A-ARAS1, and A-ARAS2. The results indicated that the A-ARAS1 and A-ARAS2 models outperformed the previous version of ARAS in terms of the performance appraisal of knowledge workers and that the ARAS model needed to be revised to improve performance appraisal.