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Humankind is becoming increasingly dependent on algorithms in their everyday life. Algorithmic decision support has existed since the entrance of computers but are becoming more sophisticated with elements of Articial Intelligence (AI). Though many decision support systems outperform humans in many areas, e.g. in forecasting task, the willingness to trust and use algorithmic decision support is lower than in a corresponding human. Many factors have been investigated to why this algorithm aversion exists but there is a gap in research about the eects of scenario characteristics. Results provided by this study showed that people prefer recommendations from a human expert over algorithmic decision support. This was also re ected in the self-perceived likelihood of keeping a choice when the decision support recommended the other option, where the likelihood was lower for the group with human expert as the decision support. The results also showed that the decision supports, regardless of type, are more trusted by the user in an acquisition framed scenario than in a forfeiture framed. However, very limited support was found for the hypothesized interaction between decision support and scenario type, where it was expected that algorithm aversion would be stronger for forfeiture than acquisition scenarios. Moreover, the results showed that, independent of the experimental manipulations, participants with a positive general attitude towards AI had higher trust in algorithmic decision support. Together, these new results may be valuable for future research into algorithm aversion but must also to be extended and replicated using dierent scenarios and situations.

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Although past research finds that auditors support data analytics and artificial intelligence to enhance audit quality in their daily work, in reality, only a small number of audit firms, who innovated and invested in the two sophisticated technologies, utilize it in their auditing process. This paper analyzes three factors, including three individual theories, that may influence the adoption of data analytics and artificial intelligence in auditing: regulation (Institutional theory: explaining the catch-22 between the auditors and policymakers), knowledge barrier (Technology acceptance model's theory: explore the concept of ease of use), and people (algorithm aversion: a phenomenon that auditors believe in human decision makers more than technology). Among the three barriers, this paper focuses more on the people factor, which firms can start to overcome early. Past research has shown the existence of algorithm aversion in audit, so it is important to identify ways to decrease algorithm aversion. This study conducted a survey with four attributes: transparency-efficiency-trade-off, positive exposure, imperfect algorithm, and company's training. The study results shows that transparency-efficiency-trade-off can be a potential solution for decreasing algorithm aversion. When auditor firms implement transparency-efficiency-trade-off in their company training, auditors may give more trust to the technologies. The trust may lead to the increase of data analytics and artificial intelligence in audit.

Algorithms refer to the software programs designed to support problem solving in a wide range of decision domains. Given the Artificial Intelligence (AI) revolution, algorithms have become an integral part of our personal, social, and professional lives. As technology rapidly advances, these algorithms are not only becoming more capable but are also finding a growing array of applications in managerial and consumer decision support. Despite their increasing presence, reactions to algorithms are mixed. While some research highlights a preference for algorithms over human judgment ("algorithm appreciation"), other studies reveal a contrary preference ("algorithm aversion"), where people favor human expertise. This research provides a conceptual framework and empirical evidence regarding factors that may influence preference for algorithmic versus human expert recommendations in business decision contexts. We use experimental psychological methods to investigate how algorithm characteristics, decision-maker psy / Doctor of Philosophy / Amid the AI revolution, algorithms have become central to our personal, social, and professional lives, evolving rapidly in both capability and application. Reactions to these algorithms are mixed: some studies show a preference for algorithms over human judgment, known as "algorithm appreciation," while others reveal a preference for human judgment, or "algorithm aversion." Understanding these preferences is essential. Our research helps to clarify this issue by examining the factors that influence whether people prefer algorithms or human experts in business decisions. Using experimental methods, we explore how algorithm features, decision-maker psychology, and situational factors impact these preferences. We focus on scenarios where algorithms and human experts are presented as competing options rather than complementary ones. Our findings, detailed in two empirical essays, aim to advance marketing literature on algorithms and decision-making, identify future research opportunities, and offer insights for

The relevance of AI is ever increasing. The goal of the wide implementation is usually either to boost task efficiency or for public comfort. To fuel this progression, more personal data is being used and Artificial intelligence inhabits the role of the human expert, in many different applications. This study investigated the attitudes and rates of acceptance to said AI applications and if they differed in relation to each other. Additionally, this study explored if general positive and negative attitude towards AI influence AI acceptance. Applications studied came from two different domains, E-commerce/Marketing and Healthcare. It was found that acceptance levels did in fact not significantly differ between the two domains. However, a significant positive correlation was found between positive attitude and acceptance rates, while an inverse significant correlation was found between negative attitude and acceptance rates.

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AI applications are today implemented in a wide range of settings with the goal of achieving greater efficiency. However, these implementations can not be guaranteed to be free of risks. This study investigated how people perceive these risks and benefits, and whether there were any notable differences to be found between the domains in which they appear, in this case e-commerce/marketing and healthcare. Additionally, the relationship between the perceptions and individual positive and negative attitudes towards AI were examined by utilizing an affect heuristic framework. The findings showed that the two domains did differ from one another, as ratings of both perceived risks and benefits were higher for the healthcare domain compared to the e-commerce/marketing domain. Further, an inverse correlation between ratings of risks and benefits were found within each domain, which is consistent with the affect heuristic framework.