Human-machine collaboration for decision making: the interplay among human reasoning, data, and technology

• Ano de defesa: 2021
• Autor(a) principal: Omura, Suely Fischer
• Orientador(a): Sanchez, Otávio Próspero
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso embargado
• Idioma: eng
• Palavras-chave em Português: Intuição; Lógica; Dados; Tecnologia; Capacidade de decisão homem-máquina
• Palavras-chave em Inglês: Human-machine decision capability; Intuition; Logic; Data; Technology
• Link de acesso: https://hdl.handle.net/10438/31441

Since companies increasingly perform in highly unstable environments, decision making has become more challenging. Simultaneously, multiple technologies and massive data are available to improve executives’ decisions. However, although the access to such resources has grown exponentially, the decision outcomes do not seem to lead to more consistent choices or higher business value. A potential explanation for this is that most executives tend to opt for intuitive decisions instead of using logic and data, which can result in biased inferences and questionable choices. Although the literature has shown that intuition and logic are keystones of decision making, there is still an opportunity for complementary research on the supporting role of information technology in broadening human reasoning and enriching sensemaking. Hence, through an online survey with 202 US-based executives of distinct segments and roles, this study aims to answer the following research question: How do human reasoning and data insights enabled by technology relate and contribute to effective decision making? Therefore, we propose a human-machine decision capability to integrate intuitive and logical judgments with data insights to obtain improved decisions. The main results entail an interesting finding: the newlyproposed construct not only leads to faster and more trustworthy decisions, but also fully mediates the relationship between human reasoning and decision outcomes, leveraging results. Thus, this study contributes to academic knowledge and society by bringing novel insights that extend prior research on the theme and may encourage practitioners to invest in human-machinedriven decisions to minimize cognitive biases, thereby achieving higher business value.