@inproceedings{,
    author = {Schropp, Theresa; Martini, Melanie; Kaiser, Stephan; John, Marcus},
    title = {Cognitive Biases in Data-Driven Decision-Making : A Literature Review},
    editor = {},
    booktitle = {XXXV ISPIM Innovation Conference "Local Innovation Ecosystems for Global Impact" 2024 : 9-12 June 2024, Tallinn, Estonia},
    series = {},
    journal = {},
    address = {},
    publisher = {International Society for Professional Innovation Management - ISPIM},
    edition = {},
    year = {2024},
    isbn = {978-952-65069-6-8},
    volume = {},
    number =  {},
    pages = {1-10},
    url = {},
    doi = {10.24406/publica-3583},
    keywords = {Cognitive bias ; data-driven decision-making ; decision-support-systems ; decision-making ; bibliometric analysis},
    abstract = {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 a dataset of 70 scientific publications we hence aim to outline focus topics of the past and areas for future research.  },
    note = {},
    institution = {Universität der Bundeswehr München, Fakultät für Wirtschafts- und Organisationswissenschaften, WOW 2 - Institut für Entwicklung zukunftsfähiger Organisationen, Professur: Kaiser, Stephan},
    
    
}