Security robots increasingly assume authoritative roles, but the underlying mechanisms for why humans cooperate with them is not well understood. This study proposed and tested a cooperation model based on legitimacy theory, focusing on how distributive fairness (outcome equity) and interactional fairness (treatment equity) influence robot legitimacy and cooperation. Using a 2 × 2 online video-based experiment with 372 U.S. participants, the authors found that both fairness types promote cooperation through value alignment, with a non-significant path through obligation to obey; meanwhile, perceived effectiveness was strongly associated with both value alignment and obligation to obey. These findings extend legitimacy theory to human–robot interaction in a U.S. context, emphasizing fairness and perceived effectiveness as key to fostering cooperation and informing ethical robot design.

This study examined an important debate in Human-Robot Interaction (HRI) research: the suitability of non-physically non-collocated robots instead of physically collocated robots for HRI research. This meta-analysis ($\mathrm{N}=34$studies) examined the equivalence of physically and non-physically collocated robots in HRI research, focusing on anthropomorphism, social presence, and user engagement. No significant differences were found, suggesting that non-physical representations are viable alternatives. However, observed heterogeneity indicates potential moderating factors (e.g., task complexity, user characteristics, design features) warranting further investigation. These findings inform choices in resource-constrained environments.

The increasing deployment of robots in authority roles, such as security, necessitates understanding public acceptance of robot-exercised power. This study investigated the relationship between perceived power bases (expert, legitimate, referent, reward, coercive) and public acceptance of security robots. One hundred participants viewed videos depicting robot- citizen interactions. Results revealed positive correlations between perceived expert and legitimate power and acceptance and a negative correlation between perceived coercive power and acceptance; reward power showed no significant relationship. These findings contribute to the human-robot interaction (HRI) literature by demonstrating the influence of perceived power on public acceptance and offering design guidelines for enhancing the acceptance of security robots by emphasizing expertise and legitimate authority while minimizing coercive tactics.

Security robots are becoming increasingly prevalent for maintaining law and order, offering cost efficiencies and safety benefits in hazardous environments. Despite these advantages, significant questions remain regarding the public acceptance of robots as replacements for human security agents. This paper presents a systematic literature review to explore whether there is a discernible public preference between human security personnel and their robotic counterparts. The review identifies a contextual pattern: individuals tend to prefer human agents in citizen-initiated interactions, and security robots in police-initiated ones. This paper offers valuable insights to guide the future design and deployment of security robots.

As advances in robotics continue, security robots are increasingly integrated into public and private security, enhancing protection in locations such as streets, parks, and shopping malls. To be effective, security robots must interact with civilians and security personnel, underscoring the need to enhance our knowledge of their interactions with humans. To investigate this issue, the authors systematically reviewed 47 studies on human interaction with security robots, covering 2003 to 2023. Papers in this domain have significantly increased over the last 7 years. The article provides three contributions. First, it comprehensively summarizes existing literature on human interaction with security robots. Second, it employs the Human–Robot Integrative Framework (HRIF) to categorize this literature into three main thrusts: human, robot, and context. The framework is leveraged to derive insights into the methodologies, tasks, predictors, and outcomes studied. Last, the article synthesizes and discusses the findings from the reviewed literature, identifying avenues for future research in this domain.

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.

The rapid growth in the use of security robots makes it critical to better understand their interactions with humans. The impacts of anthropomorphism and interaction scenarios were examined via a 3 x 2 between-subjects experiment. Sixty participants were randomly assigned to interact with one of three security robots (Knightscope, RAMSEE, or Pepper) in either an indoor hallway or an outdoor parking lot scenario in a virtual reality cave. There were significant differences only between Pepper and Knightscope with Pepper rated higher in anthropomorphism, ability, integrity, and desire to use than Knightscope but the interaction scenario has no effect.