Non-manipulated mechanism of decision-making on terms of contracts with university faculty

One of the key tasks of managing contracts with university faculty (UF) is to determine the optimal term of contract. In this regard, it is of scientific and practical interest to develop an effective mechanism of decision-making on the terms of the contracts with the UF. The competition for the positions of UF is held by the collegiate body for managing the personnel policy of the university called personnel commission (PC). The decision on the terms of the contract is made, according to the key performance indicators (KPIs) of UF activities for a certain time period. PC members have opportunity, in case the teacher fails to meet all the required KPIs, to recommend a longer contract term for a teacher, guided by other (alternative) KPIs. Since this approach is applied selectively and often without a reasoned justification for the position of PC members, the UF perceives it as a manifestation of manipulation on the part of the PC when deciding on the term of the contract. To solve this problem, it is proposed to use a non-manipulative mechanism of decision-making on the terms of contracts with the UF. To implement the proposed mechanism, a machine learning tool is used, which generates a forecast for the implementation of an alternative KPI by the teacher. The source of data for forecasting is the teacher's passive digital footprint, which makes it possible to ensure the completeness and veracity of information about his/her scientific and pedagogical activities. Based on the forecast obtained, the PC makes a reasonable and transparent decision on the term of the contract with the teacher. Using the proposed mechanism will reduce the negative impact of the effect of manipulation on the decision-making process of the PC on the terms of contracts with the UF of the university and hence to ensure an increase their efficiency.