How and why humans trust: A meta-analysis and elaborated model.

Trust exerts an impact on essentially all forms of social relationships. It affects individuals in deciding whether and how they will or will not interact with other people. Equally, trust also influences the stance of entire nations in their mutual dealings. In consequence, understanding the factors that influence the decision to trust, or not to trust, is crucial to the full spectrum of social dealings. Here, we report the most comprehensive extant meta-analysis of experimental findings relating to such human-to-human trust. Our analysis provides a quantitative evaluation of the factors that influence interpersonal trust, the initial propensity to trust, as well as an assessment of the general trusting of others. Over 2,000 relevant studies were initially identified for potential inclusion in the meta-analysis. Of these, (n = 338) passed all screening criteria and provided therefrom a total of (n = 2,185) effect sizes for analysis. The identified dependent variables were trustworthiness, propensity to trust, general trust, and the trust that supervisors and subordinates express in each other. Correlational results demonstrated that a large range of trustor, trustee, and shared, contextual factors impact each of trustworthiness, the propensity to trust, and trust within working relationships. The emphasis in the present work on contextual factors being one of several trust dimensions herein originated. Experimental results established that the reputation of the trustee and the shared closeness of trustor and trustee were the most predictive factors of trustworthiness outcome. From these collective findings, we propose an elaborated, overarching descriptive theory of trust in which special note is taken of the theory's application to the growing human need to trust in non-human entities. The latter include diverse forms of automation, robots, artificially intelligent entities, as well as specific implementations such as driverless vehicles to name but a few. Future directions as to the momentary dynamics of trust development, its sustenance and its dissipation are also evaluated.

Trust in Artificial Intelligence: Meta-Analytic Findings.

OBJECTIVE: The present meta-analysis sought to determine significant factors that predict trust in artificial intelligence (AI). Such factors were divided into those relating to (a) the human trustor, (b) the AI trustee, and (c) the shared context of their interaction. BACKGROUND: There are many factors influencing trust in robots, automation, and technology in general, and there have been several meta-analytic attempts to understand the antecedents of trust in these areas. However, no targeted meta-analysis has been performed examining the antecedents of trust in AI. METHOD: Data from 65 articles examined the three predicted categories, as well as the subcategories of human characteristics and abilities, AI performance and attributes, and contextual tasking. Lastly, four common uses for AI (i.e., chatbots, robots, automated vehicles, and nonembodied, plain algorithms) were examined as further potential moderating factors. RESULTS: Results showed that all of the examined categories were significant predictors of trust in AI as well as many individual antecedents such as AI reliability and anthropomorphism, among many others. CONCLUSION: Overall, the results of this meta-analysis determined several factors that influence trust, including some that have no bearing on AI performance. Additionally, we highlight the areas where there is currently no empirical research. APPLICATION: Findings from this analysis will allow designers to build systems that elicit higher or lower levels of trust, as they require.

Evolving Trust in Robots: Specification Through Sequential and Comparative Meta-Analyses.

OBJECTIVE: The objectives of this meta-analysis are to explore the presently available empirical findings on the antecedents of trust in robots and use this information to expand upon a previous meta-analytic review of the area. BACKGROUND: Human-robot interaction (HRI) represents an increasingly important dimension of our everyday existence. Currently, the most important element of these interactions is proposed to be whether the human trusts the robot or not. We have identified three overarching categories that exert effects on the expression of trust. These consist of factors associated with (a) the human, (b) the robot, and (c) the context in which any specific HRI event occurs. METHOD: The current body of literature was examined and all qualifying articles pertaining to trust in robots were included in the meta-analysis. A previous meta-analysis on HRI trust was used as the basis for this extended, updated, and evolving analysis. RESULTS: Multiple additional factors, which have now been demonstrated to significantly influence trust, were identified. The present results, expressed as points of difference and points of commonality between the current and previous analyses, are identified, explained, and cast in the setting of the emerging wave of HRI. CONCLUSION: The present meta-analysis expands upon previous work and validates the overarching categories of trust antecedent (human-related, robot-related, and contextual), as well as identifying the significant individual precursors to trust within each category. A new and updated model of these complex interactions is offered. APPLICATION: The identified trust factors can be used in order to promote appropriate levels of trust in robots.

The Effects of Virtual Reality, Augmented Reality, and Mixed Reality as Training Enhancement Methods: A Meta-Analysis.

OBJECTIVE: The objective of this meta-analysis is to explore the presently available, empirical findings on transfer of training from virtual (VR), augmented (AR), and mixed reality (MR) and determine whether such extended reality (XR)-based training is as effective as traditional training methods. BACKGROUND: MR, VR, and AR have already been used as training tools in a variety of domains. However, the question of whether or not these manipulations are effective for training has not been quantitatively and conclusively answered. Evidence shows that, while extended realities can often be time-saving and cost-saving training mechanisms, their efficacy as training tools has been debated. METHOD: The current body of literature was examined and all qualifying articles pertaining to transfer of training from MR, VR, and AR were included in the meta-analysis. Effect sizes were calculated to determine the effects that XR-based factors, trainee-based factors, and task-based factors had on performance measures after XR-based training. RESULTS: Results showed that training in XR does not express a different outcome than training in a nonsimulated, control environment. It is equally effective at enhancing performance. CONCLUSION: Across numerous studies in multiple fields, extended realities are as effective of a training mechanism as the commonly accepted methods. The value of XR then lies in providing training in circumstances, which exclude traditional methods, such as situations when danger or cost may make traditional training impossible.

The Relationship Between Extroversion and the Tendency to Anthropomorphize Robots: A Bayesian Analysis.

Personality variables play an important role in how individuals relate to the world around them, including how they view their peers. Such peers now include machine entities, such as robots. This study examined the relationship between the personality trait of extroversion and the tendency to view a robot as anthropomorphic in an experimental setting. To evaluate this relationship, 486 participants were required to complete measures of the big five personality traits (The Mini IPIP) and Negative Attitudes to Robots Scale (NARS). Participants then viewed videos and images of robots performing common jobs (i.e., warehouse technician, IED detection), and then rated these robots via an assessment instrument scaling anthropomorphism. A significant positive relationship between extroversion and the tendency toward anthropomorphization of the robots was found. A Bayesian regression analysis was performed, which indicated the strength of extroversion as a predictor of the tendency to anthropomorphize. We conclude that personality dimensions influence how an individual views the robot that they interact with. These findings are important, as the relationship between personality and the tendency to anthropomorphize robots is likely to influence the acceptance and use of robots.