Companies and managers pay large amounts of money for costly advice and expensive consultants. Prior literature identifies two central advice-taking motives: (1) increasing decision accuracy and (2) sharing responsibility with the advisor (e.g., Bonaccio & Dalal, 2006). In contrast to previous studies, which mainly focus on factors influencing decision accuracy, this thesis analyzes whether managers use advisors to share responsibility by blaming them as scapegoats. Moreover, I analyze which factors influence managerial advice-taking with a blame avoiding intention. Specifically, I study the impact of human advisors and nonhuman advisors in the form of algorithmic decision aids on managerial blame avoiding decision-making. The first part of the thesis focuses on discussing important findings of advice-taking literature and blame avoidance literature. I explain the importance and empirical relevance of the advice-taking motive sharing responsibility with advisors and theoretically link it to a blame avoiding strategy, which focuses on delegating difficult decisions with high blame potential to others, while demonstrating the lack of prior research on this motive. The empirical part of this thesis consists of two experiments with managers from German-speaking countries analyzing factors (i.e., managers’ and advisors’ characteristics) which influence managerial advice-taking with a blame avoiding intention. Specifically, Study 1 examines whether managers increasingly utilize advice provided by potential human scapegoats (advisors’ characteristics) to blame them and avoid personal blame. Additionally, the influence of managers’ individual risk perceptions (managers’ characteristics) on their blame avoiding decision-making is studied. Results of an online experiment with managers in an investment decision context are that potential scapegoats increase managerial advice-taking in an economic boom but decrease it in an economic crisis due to managers’ varying risk perceptions. Risk-averse managers – caused by a gain framed decision context in an economic boom – focus on avoiding personal blame by increasing advice-taking, whereas risk-seeking managers – caused by a loss framed decision context in an economic crisis – focus on avoiding financial losses and ignore potential scapegoats. Additionally, Study 2 analyzes whether managers use nonhuman advisors in the form of algorithmic decision aids (advisors’ characteristics) as scapegoats to share responsibility and avoid personal blame. In an online experiment with managers in a forecasting context, I find that managers exhibit algorithm aversion in regard to scapegoat selection by preferring to use human scapegoats compared to nonhuman scapegoats due to a perceived lack of social competence of algorithmic decision aids. However, managers also blame algorithmic decision aids and reduce their algorithm aversion when perceiving a higher level of human-likeness in the form of higher social competence of algorithmic decision aids.