Development and Validation of the System Trustworthiness Scale.

OBJECTIVE: We created and validated a scale to measure perceptions of system trustworthiness. BACKGROUND: Several scales exist in the literature that attempt to assess trustworthiness of system referents. However, existing measures suffer from limitations in their development and validation. The current study sought to develop a scale based on theory and methodological rigor. METHOD: We conducted exploratory and confirmatory factor analyses on data from two online studies to develop the System Trustworthiness Scale (STS). Additional analyses explored the manipulation of the factors and assessed convergent and divergent validity. RESULTS: The exploratory factor analyses resulted in a three-factor solution that represented the theoretical constructs of trustworthiness: performance, purpose, and process. Confirmatory factor analyses confirmed the three-factor solution. In addition, correlation and regression analyses demonstrated the scale's divergent and predictive validity. CONCLUSION: The STS is a psychometrically valid and predictive scale for assessing trustworthiness perceptions of system referents. APPLICATIONS: The STS assesses trustworthiness perceptions of systems. Importantly, the scale differentiates performance, purpose, and process constructs and is adaptable to a variety of system referents.

Differential biases in human-human versus human-robot interactions.

The research on human-robot interactions indicates possible differences toward robot trust that do not exist in human-human interactions. Research on these differences has traditionally focused on performance degradations. The current study sought to explore differences in human-robot and human-human trust interactions with performance, consideration, and morality trustworthiness manipulations, which are based on ability/performance, benevolence/purpose, and integrity/process manipulations, respectively, from previous research. We used a mixed factorial hierarchical linear model design to explore the effects of trustworthiness manipulations on trustworthiness perceptions, trust intentions, and trust behaviors in a trust game. We found partner (human versus robot) differences across all three trustworthiness perceptions, indicating biases towards robots may be more expansive than previously thought. Additionally, there were marginal effects of partner differences on trust intentions. Interestingly, there were no differences between partners on trust behaviors. Results indicate human biases toward robots may be more complex than considered in the literature.

The Effect of Asset Degradation on Trust in Swarms: A Reexamination of System-Wide Trust in Human-Swarm Interaction.

OBJECTIVE: The effects of asset degradation on trust in human-swarm interaction were investigated through the lens of system-wide trust theory. BACKGROUND: Researchers have begun investigating contextual features that shape human interactions with robotic swarms-systems comprising assets that coordinate behavior based on their nearest neighbors. Recent work has begun investigating how human trust toward swarms is affected by asset degradation through the lens of system-wide trust theory, but these studies have been marked by several limitations. METHOD: In an online study, the current work manipulated asset degradation and measured trust-relevant criteria in a within-subjects design and addressed the limitations of past work. RESULTS: Controlling for swarm performance (i.e., target acquisition), asset degradation and trust (i.e., reliance intentions) in swarms were negatively related. In addition, as degradation increased, perceptions of swarm cohesion, obstacle avoidance, target acquisition, and terrain exploration efficiency decreased, the latter two of which (coupled with the reliance intentions criterion) support the tenets of system-wide trust theory as well as replicate and extend past work on the effects of asset degradation on trust in swarms. CONCLUSION: Human-swarm interaction is a context in which system-wide trust is relevant, and future work ought to investigate how to calibrate human trust toward swarm systems. APPLICATIONS: Based on these findings, design professionals should prioritize ways to depict swarm performance and system health such that humans do not abandon trust in systems that are still functional yet not over-trust those systems which are indeed performing poorly.