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Kellogg, Kate Kessinger, Raquel Sloan School of Management 2022-01-14T15:08:22Z 2022-01-14T15:08:22Z 2021-06 2021-06-03T17:55:18.788Z https://hdl.handle.net/1721.1/139385 0000-0002-2341-0358 How are employers using big data and algorithms to measure employee performance, and with what consequences for employees? Current literature suggests that organizations are increasingly engaging in algorithmic evaluation by using finely-grained, real-time, interactive, and visible data to measure employee performance. In our field study of a digital marketing organization, we find that managers may mitigate some of the negative employee outcomes that scholars have found to be associated with algorithmic evaluation—stress, perceived pressure to constantly improve individual outcomes at the expense of collaborating or learning, and fear of disclosing bad news to managers. Just as the firm used algorithmic evaluation, many managers simultaneously engaged in a type of relational work with employees we call “orchestrating friendship.” Managers provided employees with socioemotional support, used hyper-personalization to create a communal atmosphere, and engaged in voluntary and informal self-disclosure to purposefully soften some of the negative employee experiences associated with the algorithmic evaluation. Yet, these managerial practices carried a set of unintended negative consequences for the middle managers themselves and, in turn, for the senior managers who employed them. S.M. Massachusetts Institute of Technology In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ ORCHESTRATING FRIENDSHIP WITHIN A FIRM: SOFTENING THE EDGES OF ALGORITHMIC EVALUATION Thesis application/pdf Master Master of Science in Management Research Publication unknown unknown <Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="9203a29b-8114-4a33-a7f5-9cf428dd9119"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Title>ORCHESTRATING FRIENDSHIP WITHIN A FIRM: SOFTENING THE EDGES OF ALGORITHMIC EVALUATION</Title> <PublishedIn> <Publication> </Publication> </PublishedIn> <PublicationDate>2021-06</PublicationDate> <Authors> <Author> <DisplayName>Kessinger, Raquel</DisplayName> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Massachusetts Institute of Technology</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>http://rightsstatements.org/page/InC-EDU/1.0/</License> <Abstract>How are employers using big data and algorithms to measure employee performance, and with what consequences for employees? Current literature suggests that organizations are increasingly engaging in algorithmic evaluation by using finely-grained, real-time, interactive, and visible data to measure employee performance. In our field study of a digital marketing organization, we find that managers may mitigate some of the negative employee outcomes that scholars have found to be associated with algorithmic evaluation—stress, perceived pressure to constantly improve individual outcomes at the expense of collaborating or learning, and fear of disclosing bad news to managers. Just as the firm used algorithmic evaluation, many managers simultaneously engaged in a type of relational work with employees we call “orchestrating friendship.” Managers provided employees with socioemotional support, used hyper-personalization to create a communal atmosphere, and engaged in voluntary and informal self-disclosure to purposefully soften some of the negative employee experiences associated with the algorithmic evaluation. Yet, these managerial practices carried a set of unintended negative consequences for the middle managers themselves and, in turn, for the senior managers who employed them.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication>