Trust In Robots Research Articles

On the Role of Beliefs and Trust for the Intention to Use Service Robots: An Integrated Trustworthiness Beliefs Model for Robot Acceptance

With the increasing abilities of robots, the prediction of user decisions needs to go beyond the usability perspective, for example, by integrating distinctive beliefs and trust. In an online study (N = 400), first, the relationship between general trust in service robots and trust in a specific robot was investigated, supporting the role of general trust as a starting point for trust formation. On this basis, it was explored—both for general acceptance of service robots and acceptance of a specific robot—if technology acceptance models can be meaningfully complemented by specific beliefs from the theory of planned behavior (TPB) and trust literature to enhance understanding of robot adoption. First, models integrating all belief groups were fitted, providing essential variance predictions at both levels (general and specific) and a mediation of beliefs via trust to the intention to use. The omission of the performance expectancy and reliability belief was compensated for by more distinctive beliefs. In the final model (TB-RAM), effort expectancy and competence predicted trust at the general level. For a specific robot, competence and social influence predicted trust. Moreover, the effect of social influence on trust was moderated by the robot's application area (public > private), supporting situation-specific belief relevance in robot adoption. Taken together, in line with the TPB, these findings support a mediation cascade from beliefs via trust to the intention to use. Furthermore, an incorporation of distinctive instead of broad beliefs is promising for increasing the explanatory and practical value of acceptance modeling.

Assessing the Relationship between Cognitive Workload, Workstation Design, User Acceptance and Trust in Collaborative Robots

Collaborative robots are revolutionising the manufacturing industry and the way workers perform their tasks. When designing shared workspaces between robots and humans, human factors and ergonomics are often overlooked. This study assessed the relationship between cognitive workload, workstation design, user acceptance and trust in collaborative robots. We combined subjective and objective data to evaluate the cognitive workload during an assembly task in three different scenarios in which we manipulated various features of the workstation and interaction modalities. Our results showed that participants experienced a reduction in cognitive workload in each of the three trials, indicating an improvement in cognitive performance. Additionally, we found that user acceptance predicted perceived stress across the trials but did not significantly impact the cognitive workload. Trust was not found to moderate the relationship between cognitive workload and perceived stress. This study has the potential to make a significant contribution to the field of collaborative assembly systems by providing valuable insights and helping to bridge the gap between researchers and practitioners. This study can potentially impact companies looking to improve safety, productivity and efficiency.

Three Strikes and you are out!: The impacts of multiple human–robot trust violations and repairs on robot trustworthiness

Robots like human co-workers can make mistakes violating a human’s trust in them. When mistakes happen, humans can see robots as less trustworthy which ultimately decreases their trust in them. Trust repair strategies can be employed to mitigate the negative impacts of these trust violations. Yet, it is not clear whether such strategies can fully repair trust nor how effective they are after repeated trust violations. To address these shortcomings, this study examined the impact of four distinct trust repair strategies: apologies, denials, explanations, and promises on overall trustworthiness and its sub-dimensions: ability, benevolence, and integrity after repeated trust violations. To accomplish this, a between-subjects experiment was conducted where participants worked with a robot co-worker to accomplish a task. The robot violated the participant’s trust and then provided a particular repair strategy. Results indicated that after repeated trust violations, none of the repair strategies ever fully repaired trustworthiness and two of its sub-dimensions: ability and integrity. In addition, after repeated interactions, apologies, explanations, and promises appeared to function similarly to one another, while denials were consistently the least effective at repairing trustworthiness and its sub-dimensions. In sum, this paper contributes to the literature on human–robot trust repair through both of these original findings.

Zaufanie – istotny czynnik w human–robot collaboration

Purpose – The purpose of this article is to present factors affecting the quality of human–robot collaboration (HRI) in the workplace. The impact of trust is described in detail. According to the Polish Economic Institute, between 1993 and 2018, the number of industrial robots in the world increased from 557,000 to 2.4 million. Implemented robots, cease to be just tools in human hands. Thanks to their autonomy, they are becoming known as teammates. Research method – The article is based on a literature review conducted to identify factors, particularly trust, that affect human–robot collaboration. Results – It has been revealed that research conducted in the HRI area proves that human trust in robots, is one of the most important determinants of proper cooperation. Above that, the relevance of areas such as reliability, predictability of robots, the personality of people, or appearance of robots was pointed out. The mentioned areas have a direct impact on the formation and level of trust in human–robot teams. Originality / value / implications / recommendations – The publication highlights the relevance of the area of human–robot collaboration (HRC), as part of the implementation of robots in modern enterprises. The article details which elements in the formation of HRC play the most important role. The topic of HRC is vaguely known in Poland, and as the referenced literature indicates, the correct cooperation of humans and robots is an important element affecting the success of robotization. Learning how HRC works is one of the challenges facing today’s organizations.

Between fear and trust: Older adults’ evaluation of socially assistive robots

Socially Assistive Robots (SARs) are expected to support autonomy, aging in place, and wellbeing in later life. Acceptance and successful assimilation of SARs among older adults depend ontheir Quality Evaluations (QEs), namely, the pragmatic and hedonic evaluations and overall attractiveness. Previous studies showed that trust in robots significantly enhances QE, while technophobia considerably decreases it. However, so far, these key factors have always been explored separately. Applying a case study approach, the current research aimed to examine the relative impact of these two factors on older persons’ QE of SARs. The study was based on an online survey of 384 individuals aged 65 and above. Respondents were presented with a video of Gymmy, a robotic system for physical and cognitive training, and filled out a questionnaire relating to that system. The results indicated a positive association between trust and QE and a negative association between technophobia and QE. A simultaneous exploration demonstrated that the relative impact of technophobia was significantly more substantial than that of trust. In addition, the pragmatic qualities of the robot were found to be more crucial to its QE than the social aspects of use. The findings suggest that implementing robotics technology in later life strongly depends on reducing older adults’ technophobia regarding the convenience of using SARs. The study also highlights the importance of simultaneous explorations of facilitators and inhibitors to SARs use.

The Influence of Robots' Fairness on Humans' Reward-Punishment Behaviors and Trust in Human-Robot Cooperative Teams.

Based on social exchange theory, this study investigates the effects of robots' fairness and social status on humans' reward-punishment behaviors and trust in human-robot interactions. In human-robot teamwork, robots show fair behaviors, dedication (altruistic unfair behaviors), and selfishness (self-interested unfair behaviors), but few studies have discussed the effects of these robots' behaviors on teamwork. This study adopts a 3 (the independent variable is the robot's fairness: self-interested unfair behaviors, fair behaviors, and altruistic unfair behaviors) × 3 (the moderator variable is the robot's social status: superior, peer, and subordinate) experimental design. Each participant and a robot completed the experimental task together through a computer. When robots have different social statuses, the more altruistic the fairness of the robot, the more reward behaviors, the fewer punishment behaviors, and the higher human-robot trust of humans. Robots' higher social status weakens the influence of their fairness on humans' punishment behaviors. Human-robot trust will increase humans' reward behaviors and decrease humans' punishment behaviors. Humans' reward-punishment behaviors will increase repaired human-robot trust. Robots' fairness has a significant impact on humans' reward-punishment behaviors and trust. Robots' social status moderates the effect of their fair behavior on humans' punishment behavior. There is an interaction between humans' reward-punishment behaviors and trust. The study can help to better understand the interaction mechanism of the human-robot team and can better serve the management and cooperation of the human-robot team by appropriately adjusting the robots' fairness and social status.

Going under Dr. Robot’s knife: the effects of robot anthropomorphism and mortality salience on attitudes toward autonomous robot surgeons

Objective This study sought to experimentally test two potential factors that could affect the consumer acceptance of autonomous robot surgeons: anthropomorphism and mortality salience. The study also investigated the effect of gender and its interaction with anthropomorphism on attitudes toward autonomous robot surgeons. Design and Main Outcome Measures A between-subjects experiment with a 2 (anthropomorphism: low vs. high) x 2 (mortality salience: no vs. yes) factorial design was conducted (N = 196). The trust in the autonomous surgical robot and the willingness to undergo autonomous robotic surgery served as the dependent variables. Results When death thoughts were not active, the human-likeness of the autonomous surgical robot significantly increased the trust in the robot and the willingness to undergo autonomous robotic surgery. Activating death thoughts did not further increase the positive attitudes toward the higher-anthropomorphic robot, while it significantly increased the trust in and the willingness to be operated on by the lower-anthropomorphic robot, rendering both robots comparable. This study also found that women had less positive attitudes toward the autonomous robot surgeon, regardless of the robot’s human-likeness. Conclusion Anthropomorphism and mortality salience can both positively affect the acceptance of autonomous robotic surgery but only in the absence of one another.

No Evidence for an Effect of the Smell of Hexanal on Trust in Human–Robot Interaction

The level of interpersonal trust among people is partially determined through the sense of smell. Hexanal, a molecule which smell resembles freshly cut grass, can increase trust in people. Here, we ask the question if smell can be leveraged to facilitate human–robot interaction and test whether hexanal also increases the level of trust during collaboration with a social robot. In a preregistered double-blind, placebo-controlled study, we tested if trial-by-trial and general trust during perceptual decision making in collaboration with a social robot is affected by hexanal across two samples (n = 46 and n = 44). It was hypothesized that unmasked hexanal and hexanal masked by eugenol, a molecule with a smell resembling clove, would increase the level of trust in human–robot interaction, compared to eugenol alone or a control condition consisting of only the neutral smelling solvent propylene glycol. Contrasting previous findings in human interaction, no significant effect of unmasked or eugenol-masked hexanal on trust in robots was observed. These findings indicate that the conscious or nonconscious impact of smell on trust might not generalise to interactions with social robots. One explanation could be category- and context-dependency of smell leading to a mismatch between the natural smell of hexanal, a smell also occurring in human sweat, and the mechanical physical or mental representation of the robot.

Physiological and perceptual consequences of trust in collaborative robots: An empirical investigation of human and robot factors

Measuring trust is an important element of effective human-robot collaborations (HRCs). It has largely relied on subjective responses and thus cannot be readily used for adapting robots in shared operations, particularly in shared-space manufacturing applications. Additionally, whether trust in such HRCs differ under altered operator cognitive states or with sex remains unknown. This study examined the impacts of operator cognitive fatigue, robot reliability, and operator sex on trust symptoms in collaborative robots through both objective measures (i.e., performance, heart rate variability) and subjective measures (i.e., surveys). Male and female participants were recruited to perform a metal surface polishing task in partnership with a collaborative robot (UR10), in which they underwent reliability conditions (reliable, unreliable) and cognitive fatigue conditions (fatigued, not fatigued). As compared to the reliable conditions, unreliable robot manipulations resulted in perceived trust, an increase in both sympathetic and parasympathetic activity, and operator-induced reduction in task efficiency and accuracy but not precision. Cognitive fatigue was shown to correlate with higher fatigue scores and reduced task efficiency, more severely impacting females. The results highlight key interplays between operator states of fatigue, sex, and robot reliability on both subjective and objective responses of trust. These findings provide a strong foundation for future investigations on better understanding the relationship between human factors and trust in HRC as well as aid in developing more diagnostic and deployable measures of trust.

Trust in humanoid robots in footwear stores: A large-N crisp-set qualitative comparative analysis (csQCA) model

Based upon Computers-Are-Social-Actors and Social Exchange theories, this study investigated causal configurations of Human-Robot Trust (HRT), social intelligence, and retail assistance performance of humanoid robots that may increase consumers’ intention to visit a footwear store. A pretest and main study (n = 455) were conducted with a video-based stimulus. Using Crist-Set Qualitative Comparative Analysis (csQCA), the study took a holistic approach to the emerging topic of humanoid robots, and proposed a QCA-based conceptual framework. The results revealed that consumers will always demonstrate a strong intention to visit a robot-operated store when the configurations of HRT and retail assistance performance or HRT and social intelligence are present (sufficient conditions). Most importantly, consumers will never demonstrate a strong intention to visit the store in the absence of HRT (necessary condition). This study serves as an example of new alternative analytical methods, such as large-N csQCA applications, to traditional multivariate analyses.

I vs. robot: Sociodigital self-comparisons in hybrid teams from a theoretical, empirical, and practical perspective

This article in the journal Gruppe. Interaktion. Organisation. (GIO) introduces sociodigital self-comparisons (SDSC) as individual evaluations of own abilities in comparison to the knowledge and skills of a cooperating digital actor in a group. SDSC provide a complementary perspective for the acceptance and evaluation of human-robot interaction (HRI). As social robots enter the workplace, in addition to human-human comparisons, digital actors also become objects of comparisons (i.e., I vs. robot). To date, SDSC have not been systematically reflected in HRI. Therefore, we introduce SDSC from a theoretical perspective and reflect its significance in social robot applications. First, we conceptualize SDSC based on psychological theories and research on social comparison. Second, we illustrate the concept of SDSC for HRI using empirical data from 80 hybrid teams (two human actors and one autonomous agent) who worked together in an interdependent computer-simulated team task. SDSC in favor of the autonomous agent corresponded to functional (e.g., robot trust, or team efficacy) and dysfunctional (e.g., job threat) team-relevant variables, highlighting the two-sidedness of SDSC in hybrid teams. Third, we outline the (practical) potential of SDSC for social robots in the field and the lab.

Insights into the relationship between usability and willingness to use a robot in the future workplaces: Studying the mediating role of trust and the moderating roles of age and STARA.

Background and aimHuman–robot collaboration is the key component of the fourth industrial revolution concept. Workers’ willingness to collaborate with industrial robots is a basic requirement for an efficient and effective interaction. The roles of human-robot trust and technology affinity as mediators in the relationship between robot usability and worker willingness were analyzed in this study. As other critical variables, the mediator roles of Age and STARA were also calculated.Materials and methodsThis study included 400 workers from a car company who interacted with industrial robots in their daily work activities. After examining the questionnaires’ validity and reliability, the main variables were determined to be willingness to use robots and robot usability. AMOS software also considered human-robot trust and worker technology affinity as mediators. The bootstrapping method was used to evaluate indirect relationships. A set of goodness-of-fit indices were presented to determine the adequacy of the goodness of fit between the proposed model and the data.ResultsBased on model fit indices, an overall satisfactory model fit was obtained for the direct/indirect relationship between robot usability and worker willingness to use it (with mediating role of human-robot trust). Workers’ age and fear of Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA) were identified as moderators in the relationship between usability and willingness.ConclusionAttention to the robot usability and the role of workers’ trust in robots appears to be required to ensure workers’ willingness to use robots and the success of human-robot collaboration in future workplaces. As the workers age and their fear of robots grows, usability can play a larger role in increasing their willingness to put robots to work.

Trusting social robots

In this paper, I argue that we need a more robust account of our ability and willingness to trust social robots. I motivate my argument by demonstrating that existing accounts of trust and of trusting social robots are inadequate. I identify that it is the feature of a façade or deception inherent in our engagement with social robots that both facilitates, and is in danger of undermining, trust. Finally, I utilise the fictional dualism model of social robots to clarify that trust in social robots, unlike trust in humans, must rely on an independent judgement of product reliability.

Computational Model of Robot Trust in Human Co-Worker for Physical Human-Robot Collaboration

Trust is key to achieving successful Human-Robot Interaction (HRI). Besides trust of the human co-worker in the robot, trust of the robot in its human co-worker should also be considered. A computational model of a robot’s trust in its human co-worker for physical human-robot collaboration (pHRC) is proposed. The trust model is a function of the human co-worker’s performance which can be characterized by factors including safety, robot singularity, smoothness, physical performance and cognitive performance. Experiments with a collaborative robot are conducted to verify the developed trust model.

The Role of Decision Authority and Stated Social Intent as Predictors of Trust in Autonomous Robots.

Prior research has demonstrated that trust in robots and performance of robots are two important factors that influence human-autonomy teaming. However, other factors may influence users' perceptions and use of autonomous systems, such as perceived intent of robots and decision authority of the robots. The current study experimentally examined participants' trust in an autonomous security robot (ASR), perceived trustworthiness of the ASR, and desire to use an ASR that varied in levels of decision authority and benevolence. Participants (N = 340) were recruited from Amazon Mechanical Turk. Results revealed the participants had increased trust in the ASR when the robot was described as having benevolent intent compared to self-protective intent. There were several interactions between decision authority and intent when predicting the trust process, showing that intent may matter the most when the robot has discretion on executing that intent. Participants stated a desire to use the ASR in a military context compared to a public context. Implications for this research demonstrate that as robots become more prevalent in jobs paired with humans, factors such as transparency provided for the robot's intent and its decision authority will influence users' trust and trustworthiness.

Committing to interdependence: Implications from game theory for human–robot trust

AbstractHuman–robot interaction (HRI) and game theory have developed distinct theories of trust for over three decades in relative isolation from one another. HRI has focused on the underlying dimensions, layers, correlates, and antecedents of trust models, while game theory has concentrated on the psychology and strategies behind singular trust decisions. Both fields have grappled to understand over-trust and trust calibration, as well as how to measure trust expectations, risk, and vulnerability. This article presents initial steps in closing the gap between these fields. By using insights and experimental findings from interdependence theory and social psychology, this work starts by analyzing a large game theory competition data set to demonstrate that the strongest predictors for a wide variety of human–human trust interactions are the interdependence-derived variables for commitment and trust that we have developed. It then presents a second study with human subject results for more realistic trust scenarios, involving both human–human and human–machine trust. In both the competition data and our experimental data, we demonstrate that the interdependence metrics better capture social “overtrust” than either rational or normative psychological reasoning, as proposed by game theory. This work further explores how interdependence theory – with its focus on commitment, coercion, and cooperation – addresses many of the proposed underlying constructs and antecedents within human–robot trust, shedding new light on key similarities and differences that arise when robots replace humans in trust interactions.

Exploring Social and Assistive Domestic Robots for Older Adults: Robot Sociability, Trust, and Acceptance

Older adults prefer to age in place, to live independently while maintaining social connection and engagement with the community. Though older adults can encounter barriers to these goals, social and assistive domestic robots hold promise for promoting independence and online/offline social engagement. However, social robots must be designed to meet their needs and preferences. Open questions remain regarding how to facilitate the development of trust and acceptance in robot support. We investigated the associations between robot social characteristics, sociability, trust, and acceptance for instrumental activities of daily living. We used an online survey to assess older adults’ perceptions towards social and assistive robots. Robots with more social abilities were rated as more acceptable and trustworthy across different task types. We discuss design implications that may promote the development of robot trust and acceptance by older adults, and ultimately help enable aging in place and social engagement.

Trust, Acceptance and Social Cues in Human–Robot Interaction (SCRITA)

This workshop aimed for a deeper exploration of trust and acceptance in human-robot interaction (HRI) from a multidisciplinary perspective including robots' capabilities of sensing and perceiving other agents, the environment, and human-robot dynamics. The workshop was held online in conjunction with IEEE RO-MAN 2021 (see this https URL). Three invited speakers and six position papers analysed/discussed different aspects of human-robot interaction that can affect, enhance, undermine, or recover humans' trust in robots, such as the use of social cues or behaviour transparency. The attendees of different backgrounds engaged in a dynamic conversation about the relevant challenges of effectively supporting the design and development of socially acceptable and trustable robots. Website: this https URL

Do People Trust in Robot-Assisted Surgery? Evidence from Europe.

(1) Background: The goal of the paper was to establish the factors that influence how people feel about having a medical operation performed on them by a robot. (2) Methods: Data were obtained from a 2017 Flash Eurobarometer (number 460) of the European Commission with 27,901 citizens aged 15 years and over in the 28 countries of the European Union. Logistic regression (odds ratios, OR) to model the predictors of trust in robot-assisted surgery was calculated through motivational factors, using experience and sociodemographic independent variables. (3) Results: The results obtained indicate that, as the experience of using robots increases, the predictive coefficients related to information, attitude, and perception of robots become more negative. Furthermore, sociodemographic variables played an important predictive role. The effect of experience on trust in robots for surgical interventions was greater among men, people between 40 and 54 years old, and those with higher educational levels. (4) Conclusions: The results show that trust in robots goes beyond rational decision-making, since the final decision about whether it should be a robot that performs a complex procedure like a surgical intervention depends almost exclusively on the patient’s wishes.

A reference architecture for social robots

Social robotics poses tough challenges to software designers who are required to take care of difficult architectural drivers like acceptability, trust of robots as well as to guarantee that robots establish a personalized interaction with their users. Moreover, in this context recurrent software design issues such as ensuring interoperability, improving reusability and customizability of software components also arise. Designing and implementing social robotic software architectures is a time-intensive activity requiring multi-disciplinary expertise: this makes it difficult to rapidly develop, customize, and personalize robotic solutions. These challenges may be mitigated at design time by choosing certain architectural styles, implementing specific architectural patterns and using particular technologies. Leveraging on our experience in the MARIO project, in this paper we propose a series of principles that social robots may benefit from. These principles lay also the foundations for the design of a reference software architecture for social robots. The goal of this work is twofold: (i) Establishing a reference architecture whose components are unambiguously characterized by an ontology thus allowing to easily reuse them in order to implement and personalize social robots; (ii) Introducing a series of standardized software components for social robots architecture (mostly relying on ontologies and semantic technologies) to enhance interoperability, to improve explainability, and to favor rapid prototyping.