ALGORITHMIC MANAGEMENT: AN EMPIRICAL STUDY

Abstract

The paper addresses algorithmic management within mechanistic and organic organizational paradigms, and discusses problems associated with algorithmic management from the socio-technical perspective. Based on the empirical data collected during the experimental implementation of algorithmic monitoring and evaluation of sales representatives in a FMCG company, the study aims to answer two research questions framed from the perspectives of technical and social sub-system: first, what is the effect of algorithmic management on job performance of sales representatives, and, second, how do they perceive algorithmic management. Methods of data collection and analysis included in-depth interviews and thematic analysis. Concerning the first research question, findings indicate that job performance of experimental groups of algorithmically managed sales representatives has improved as compared to the past period and the control group, primarily in terms of standards compliance. As for the second research question, two main themes were identified: trust in the algorithm and perception of man-algorithm interaction. Our findings indicate that trust in algorithmic assessment depends on whether the algorithm is perceived as an instrument rather than an independent agent; on the ability to appeal algorithmic evaluation; and on the algorithmic transparency. Key conclusion of the study is that algorithmic management creates a potential mismatch between technical and social subsystems of an organization, and its implementation requires that decision-makers keep their focus not just on the tools and anticipated goals, but on the in-depth understanding of the assumptions behind these goals.