The Impact of Power Distance Beliefs on Resistance Toward AI (vs Human Service): An Abstract
Book Chapter
Hossain, T, Lee, J. (2023). The Impact of Power Distance Beliefs on Resistance Toward AI (vs Human Service): An Abstract
. 335-336. 10.1007/978-3-031-24687-6_138
Hossain, T, Lee, J. (2023). The Impact of Power Distance Beliefs on Resistance Toward AI (vs Human Service): An Abstract
. 335-336. 10.1007/978-3-031-24687-6_138
With the rapid advances of technology enabled healthcare services in the last few decades, Artificial intelligence (AI) can provide cost-effective healthcare services with equal precision to human expert-delivered health services (Chatterjee, 2019). AI can diagnose various diseases and provides medical suggestions which may help to enhance patients’ well-being. For example, AI can readily track Covid-19 patients and assists with infection management by providing real-time data (Vaishya et al., 2020). However, many customers have shown their apathy to adopt AI-enabled patient care services. This research investigates why customers resist to adopt AI delivered patient services. Using identity process theory, this study demonstrates that power distance belief (PDB) dimension influences customers to resist AI delivered health services. We reason that people with high PDB demonstrate resistance toward AI delivered health services because they believe that AI may fail to consider their unique problems. Consequently, their uniqueness motives get activated and create anxiety among them, resulting in resistance toward AI delivered medical services. Further, this study proposes a boundary condition for this effect. We argue that when high (vs low) PDB people perceive threat, they demonstrate lower need for uniqueness. However, when they don’t perceive threat, they show higher need for uniqueness. To examine our assertions above, we used 2 (PDB: High vs Low) × 2 (Perceived threat: present vs control) between subject experimental design. Findings demonstrate that people with high PDB show less need for uniqueness when they perceive threat, which impact their adoption of AI delivered health services. However, they demonstrate higher need for uniqueness in the control condition. Results show the importance of threat in consumer decisions as well as the need to emphasize on the tailored and individualized care in the AI delivered health services to enhance customers’ preference. These results have important implications for healthcare marketers, as customers with high PDB resist AI delivered health services for their preference toward equality. Therefore, hospitals can design AI delivered health services in a way that attenuates customers’ concerns for their uniqueness.
