Human-machine collaboration in managerial decision making

Haesevoets, Tessa; Cremer, David De; Dierckx, Kim; Hiel, Alain Van

doi:10.1016/j.chb.2021.106730

Cited by 93 publications

(74 citation statements)

References 53 publications

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“…At the same time, however, our results also point out that employees are open and willing to establish a partnership with the automated algorithm in which the algorithm is considered a member of the decision-making team, but importantly led by a human. Interestingly, our findings show that the leadership role that humans see for themselves in this partnership is not one of leading with full control but rather one where they have the smallest majority in the decision vote (see also Haesevoets et al, 2021). Specifically, findings from Study 2 showed that employees are open to finding an equilibrium of cooperation in the human-algorithm partnership, which seems to center around 60% human participation and 40% algorithm participation (Study 2).…”

Section: Theoretical and Practical Implicationsmentioning

confidence: 67%

“…According to Davenport (2016), human intervention and guidance is inevitable to achieve a well-functioning human-machine partnership, which implies that it is necessary for humans to have the majority vote in the partnership (Haesevoets et al, 2021). This suggestion also complements well with a remark from Venema (2018), who notes that, "one weakness remains apparent: algorithms are poor at interacting with humans in scenarios where subtle forms of cooperation are required" (p. 173).…”

Section: Study 2: Further Examining the Phenomenonmentioning

confidence: 94%

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Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

Cremer

McGuire

2021

Soc Just Res

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Autonomous algorithms are increasingly being used by organizations to reach ever increasing heights of organizational efficiency. The emerging business model of today therefore appears to be one where autonomous algorithms are gradually expanding their occupation into becoming a leading decision-maker, and humans by default become increasingly more subordinate to such decisions. We address the question of whether this business perspective is consistent with the sort of collaboration employees want to have with algorithms at work. We explored this question by investigating in what way humans preferred to collaborate with algorithms when making decisions. Using two experimental studies (Study 1, n = 237; Study 2, n = 684), we show that humans consider the collaboration with autonomous algorithms as unfair when the algorithm leads decision-making and will even incur high financial costs in order to avoid this. Our results also show that humans do not want to exclude algorithms entirely but seem to prefer a 60-40% human-algorithm partnership. These findings contrast the position taken by today's emerging business model on the issue of automated organizational decision-making. Our findings also provide support for the existence of an implicit theory-held by both present and future employees-that humans should lead and algorithms follow.

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Section: Theoretical and Practical Implicationsmentioning

confidence: 67%

Section: Study 2: Further Examining the Phenomenonmentioning

confidence: 94%

Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

Cremer

McGuire

2021

Soc Just Res

Self Cite

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“…One major factor driving this discussion is the ongoing development of artificial intelligence (AI), which that can be described as "the ability of a machine to perform cognitive functions that we associate with human minds, such as perceiving, reasoning, learning, interacting with the environment, problem solving, decision-making, and even demonstrating creativity" (Rai et al, 2019, p. iii). In digital work, AI is applied for various functions, such as (managerial) decision making (Haesevoets et al, 2021), data analysis and prediction work (Grønsund & Aanestad, 2020;Marabelli et al, 2021), or (human-AI) collaboration (Brachten et al, 2020;Mirbabaie et al, 2020Mirbabaie et al, , 2021. Therefore, AI will change workplaces and professions persistently, potentially threatening the livelihoods of individuals whose jobs are taken over (Aleksander, 2017;Haenlein & Kaplan, 2019).…”

Section: Introductionmentioning

confidence: 99%

The rise of artificial intelligence – understanding the AI identity threat at the workplace

et al. 2021

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Artificial intelligence (AI) is being increasingly integrated into enterprises to foster collaboration within humanmachine teams and assist employees with work-related tasks. However, introducing AI may negatively impact employees’ identifications with their jobs as AI is expected to fundamentally change workplaces and professions, feeding into individuals’ fears of being replaced. To broaden the understanding of the AI identity threat, the findings of this study reveal three central predictors for AI identity threat in the workplace: changes to work, loss of status position, and AI identity predicting AI identity threat in the workplace. This study enriches information systems literature by extending our understanding of collaboration with AI in the workplace to drive future research in this field. Researchers and practitioners understand the implications of employees’ identity when collaborating with AI and comprehend which factors are relevant when introducing AI in the workplace.

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“…AI Contribution To The Field can be implemented in several aspects of the financial DM process, such as information collection and analysis, standardization of criteria of investments, and automation of customer interaction services. Nevertheless, recent findings show that the acceptance of AI-based solutions in DM by management is still an open issue within financial organizations since attitudes of manager toward intelligent agents are still unbalanced regarding human intervention in DM ( Haesevoets et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

“The Flow in the Funnel”: Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems

2021

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Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as “financial brain” aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.

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Human-machine collaboration in managerial decision making

Cited by 93 publications

References 53 publications

Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

Human–Algorithm Collaboration Works Best if Humans Lead (Because it is Fair!)

The rise of artificial intelligence – understanding the AI identity threat at the workplace

“The Flow in the Funnel”: Modeling Organizational and Individual Decision-Making for Designing Financial AI-Based Systems

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