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Using an experimental approach, Maximilian Eberl evaluates the role of implicit learning (CBM/AAT) for the modification of organizational routines. Taking a vertical perspective on the (collective) entities in organizations shows an increasing role of impulsive processes the lower the level gets. The horizontal perspective demonstrates the potential of implicit learning for the replication of routines. Finally, the time perspective highlights the contributions of implicit learning strategies for change in and of routines, as well as the contributions of implicit learning to deal with the path-dependence of routines. Contents Organizational Routines Replication Organizational Change and Learning Dual Process Theory Implicit Learning Approach-Avoidance Task Training (AAT) Target Groups Researchers and students in the fields of organizational theory, evolutionary economics, and experimental psychology Practitioners in the areas of knowledge management, human resources development, and learning & development The Author Dr. Maximilian Eberl did his doctorate under the supervision of Prof. Dr. Stephan Kaiser at the chair for Human Resources & Organization at the Bundeswehr University Munich. He began his career as an officer at the German Air Force and is currently working as a knowledge manager in an internationally operating logistics enterprise, being responsible for knowledge, digitalization, and transformation topics.--

Artificial intelligence can provide organizations with prescriptive options for decision-making. Based on the notions of algorithmic decision-making and user involvement, we assess the role of artificial intelligence in workplace decisions. Using a case study on the implementation and use of cognitive software in a telecommunications company, we address how actors can become distanced from or remain involved in decision-making. Our results show that humans are increasingly detached from decision-making spatially as well as temporally and in terms of rational distancing and cognitive displacement. At the same time, they remain attached to decision-making because of accidental and infrastructural proximity, imposed engagement, and affective adhesion. When human and algorithmic intelligence become unbalanced in regard to humans' attachment to decision-making, three performative effects result: deferred decisions, workarounds, and (data) manipulations. We conceptualize the user interface that presents decisions to humans as a mediator between human detachment and attachment and, thus, between algorithmic and humans' decisions. These findings contrast the traditional view of automated media as diminishing user involvement and have useful implications for research on artificial intelligence and algorithmic decision-making in organizations.