Anthropomorphism—the attribution of human-like characteristics to non-human entities— has been widely studied for its influence on user interaction and technology acceptance. However, much of the existing research focuses on functional design elements or surface-level outcomes, resulting in fragmented understanding of how anthropomorphism shapes psychological and behavioral outcomes. As a result, a comprehensive integration and cohesive understanding of its strategic role in technology development, integration, and use remains limited. This assessing review synthesizes Information Systems (IS) research on anthropomorphism to clarify its implications for technology design and adoption. We categorize types of anthropomorphic design stimuli, examine the cognitive mechanisms that trigger anthropomorphic perception, and analyze user responses across a spectrum of perceptual and behavioral outcomes. We also identify key variables that moderate the effectiveness of anthropomorphic design, offering insights into how organizations can strategically leverage or mitigate its effects. Building on these insights, we propose a conceptual framework and research agenda to guide IS scholars and industry leaders in optimizing anthropomorphism for enhanced technology adoption.

The rapid deployment of security robots across our society calls for further examination of their acceptance. This study explored human acceptance of security robots by theoretically extending the technology acceptance model to include the impact of autonomy and risk. To accomplish this, an online experiment involving 236 participants was conducted. Participants were randomly assigned to watch a video introducing a security robot operating at an autonomy level of low, moderate, or high, and presenting either a low or high risk to humans. This resulted in a 3 (autonomy) × 2 (risk) between-subjects design. The findings suggest that increased perceived usefulness, perceived ease of use, and trust enhance acceptance, while higher robot autonomy tends to decrease acceptance. Additionally, the physical risk associated with security robots moderates the relationship between autonomy and acceptance. Based on these results, this paper offer recommendations for future research on security robots.

Human–robot work collaboration can be complementary where both the robot and employee use their strengths to complete the task at hand. But who is ultimately responsible for the task? Employees may assume more or less responsibility when working with robots, which can have a direct impact on job outcomes. In this paper the authors use the job characteristics model as a framework to investigate changes in job characteristics and their impact on job responsibility and worker outcomes as a result of robot implementation in restaurants. The results identify changes in task significance, job autonomy, and feedback from the job as important predictors of changes in experienced responsibility toward work. The study also found a significant positive relationship between job responsibility and motivation to work.