Metachoice for benchmarking: a case study

Purpose – In a previous paper the authors emphasized the advantages of multicriteria methodologies to evaluate business performance. The purpose of this paper is to highlight the metachoice problem that always arises in a benchmark multicriteria analysis that can be synthesized as follows: “how to choose an algorithm to choose?” Design/methodology/approach – In order to perform a benchmark analysis, a set of criteria must be chosen. In the Balanced Scorecard approach, for example, key performance indicators (KPIs) are grouped in four different perspectives: financial, customer, internal processes and learning and growth. In this paper, the authors focus on multicriteria benchmark analysis applied to KPIs of the financial perspective. The paper considers a set of criteria used in financial statement analysis based on balance sheet, income statement and cash flow statement. A case study is described. Findings – The main findings of the paper are when the evaluation of a firm is based on different genuine criteria, a metachoice problem arises: multicriteria ranking algorithms cannot be selected using a multicriteria algorithm; the choice of an algorithm ultimately depends on the subjective preference of the policy maker; and the authors metachoice solution to the benchmarking problem is in accordance with Simon’s satisfacing solution, describing a non-maximizing performance measurement methodology. Practical implications – The paper provides several practical implications in all cases in which a ranking has to be assigned to a group of firms based on financial performances. More in general the problem is very relevant when a ranking has to be carried out with respect to a set of projects, a set of strategies, a set of organizational units, etc. Originality/value – The adoption of a set of criteria is certainly an advantage to avoid uni-criterial myopic evaluation. However, this also creates some methodological problems. The paper demonstrates the “relativity” (subjectivity) of results of the evaluation process when there are many evaluation criteria, as in a benchmark context. This is a metachoice problem that cannot be solved by using another multicriteria algorithm.