Exploring the Continuous Motivation of Algorithm Engineers under Multiple Objectives: A Mixed-Methods Study

Abstract

There is a multi-objective coordination relationship between online platform enterprises and algorithm engineers. Based on principal–agent theory, this study builds a multi-objective coordination incentive model for the two using a mixed-methods approach. Qualitative analysis reveals three main attributes of algorithm items: completion time, difficulty, and quality. The quantitative analysis had two results: first, the level of effort of algorithm engineers on the three indicators—time, difficulty coefficient, and quality—is correlated positively with their own technical competence and negatively with the change rate of their marginal effort costs. Second, the company’s incentive coefficient for algorithm engineers depends on two factors: (1) comprehensive technical level, risk aversion coefficient, and marginal effort cost change rate of each algorithm engineer; and (2) the importance of the project for the company. The research findings suggest that enterprises adopt different incentive methods for different projects and enact hierarchical incentives for algorithm engineers with different levels of technical competence.