Today, decision makers face the challenge of making sense of the mass of data generated by digital applications and processes. A sophisticated approach to data analysis, however, can foster a lead time advantage in innovation management and is likely to enhance corporates’ competitive strength. Data-driven decision-making and decision-support-systems (DSS) are stated helpful for overcoming the cognitive constraints of decision makers. Nevertheless, critics claim that data-driven decision-making and the underlying algorithms might be prone to biases and likely to introduce deviations from rational judgement in organizations. While this is a crucial issue that needs to be addressed, it seems as if research on this topic remains scarce and scattered. To understand how scientific research has gone about this inquiry so far, we conduct a bibliometric analysis based on 78 scientific articles. We hence aim to outline focus topics of the past and areas for future research. The bibliometric mapping indicates a strong focus on algorithm aversion and the role of trust and performance expectations herein. Research on other cognitive biases, however, remains scarce and inconsistent. As digitalization continues and technologically advanced as well as sophisticated DSS gain user acceptance, future research is expected to become more differentiated regarding both, research design and foci.     «

Today, decision makers face the challenge of making sense of the mass of data generated by digital applications and processes. A sophisticated approach to data analysis, however, can foster a lead time advantage in innovation management and is likely to enhance corporates’ competitive strength. Data-driven decision-making and decision-support-systems (DSS) are stated helpful for overcoming the cognitive constraints of decision makers. Nevertheless, critics claim that data-driven decision-making...     »