In most offices a raft of mainly pointless, cumbersome tools are used to assess performance, including “competency matrices”, appraisal interviews and psychometric testing. Together they are so ineffective that according to a delightful piece of research by the University of Catania, companies would be no worse off if they promoted people at random.
A lot of people try to hide behind these performance management systems, development courses, and leadership programmes. Their justification is that this introduces fairness, a meritocracy where your real performance leads to recognition and promotion. The principle of promotion on merit is not the only one. We have no qualms that Prince William will succeed to the throne eventually and Harry will not. In democracies, we give the decision to the electorate, in the bizarre assumption that they can understand the subtle nuances of political policy as well as the delicate nature of interpersonal relationships that make things happen in Government. Even the judicial system has begun to accept that some crimes are so complex that specially trained juries are necessary.
HR professionals often don’t wish to stand up and account for their role in colluding with those in power who are inclined to make subjective decisions based on their own preferences (and not only for boys from the same school and girls with shorter skirts). In some cases, they therefore disguise the process by subjecting applicants to a battery of tests with ambiguous conclusions, and then go along with the original selection. If they do wish to influence the choices to be more inclusive, but are afraid to confront their ‘sponsors’, then, again, they tend to resort to this smorgasbord of pseudo-scientific ‘instruments’.
It was Lawrence Peter and Raymond Hull, who first flagged these paradoxes to us back in 1969. There is a general principle that in human life that anything that works will be used in progressively more challenging applications until it fails. Peter and Hull applied this to the management of enterprises, observing that the selection of a candidate for a position is based on their performance in their current role rather than on their abilities relevant to the intended role and, therefore, that people will tend to be promoted until they reach their “position of incompetence”.
What the latest researchers have shown is that mathematical modelling backs this principle up – attempts to select the performers for a particular piece of work make it less likely to be successful than using people at random.
There’s more to be done, but it is certainly refreshing to start thinking this way.
Here is the abstract from the piece of research;
“In the late sixties the Canadian psychologist Laurence J. Peter advanced an apparently paradoxical principle, named since then after him, which can be summarized as follows: ‘Every new member in a hierarchical organization climbs the hierarchy until he/she reaches his/her level of maximum incompetence’. Despite its apparent unreasonableness, such a principle would realistically act in any organization where the mechanism of promotion rewards the best members and where the competence at their new level in the hierarchical structure does not depend on the competence they had at the previous level, usually because the tasks of the levels are very different to each other. Here we show, by means of agent based simulations, that if the latter two features actually hold in a given model of an organization with a hierarchical structure, then not only is the Peter principle unavoidable, but also it yields in turn a significant reduction of the global efficiency of the organization. Within a game theory-like approach, we explore different promotion strategies and we find, counterintuitively, that in order to avoid such an effect the best ways for improving the efficiency of a given organization are either to promote each time an agent at random or to promote randomly the best and the worst members in terms of competence.”
Pluchino A, Rapisarda A, and Garofalo C (2010) The Peter principle revisited: A computational study. Physica A: Statistical mechanics and its applications. 389(3): 467-472.