Human-Machine Collaboration in Decision-Making

The use of ML-based decision support systems in business-related decision-making processes is a proven approach for companies to increase process performance and quality. To a certain extent, machines are capable of reproducing the cognitive abilities of humans in specific domains. In order to leverage the resulting potential, synergistic human-machine collaboration (HMC) is becoming increasingly important for companies. However, orchestrating HMC is dependent on a set of framework conditions that determine the success of the collaboration. This study examines the research question of how to utilize the concept of collaborative intelligence (CI) to enhance decision-making processes while using machine learning (ML) -based data prediction. The purpose is to identify success factors in the development, design, and implementation of an ML-based predictive analytics solution to orchestrate HMC in decision-making processes. These success factors state recommendations for companies to fulfil the necessary framework conditions for synergetic HMC orchestration. In total, five success factors were identified that represent a combination of theoretical findings and empirical insights. At the same time, further research needs were uncovered, which point out starting points for future research projects in the field of HMC. The use of ML-based decision support systems in business-related decision-making processes is a proven approach for companies to increase process performance and quality. To a certain extent, machines are capable of reproducing the cognitive abilities of humans in specific domains. In order to leverage the resulting potential, synergistic human-machine collaboration (HMC) is becoming increasingly important for companies. However, orchestrating HMC is dependent on a set of framework conditions that determine the success of the collaboration. This study examines the research question of how to utilize the concept of collaborative intelligence (CI) to enhance decision-making processes while using machine learning (ML) -based data prediction. The purpose is to identify success factors in the development, design, and implementation of an ML-based predictive analytics solution to orchestrate HMC in decision-making processes. These success factors state recommendations for companies to fulfil the necessary framework conditions for synergetic HMC orchestration. In total, five success factors were identified that represent a combination of theoretical findings and empirical insights. At the same time, further research needs were uncovered, which point out starting points for future research projects in the field of HMC.