Trust In Robots Research Articles

Trusting robots: a relational trust definition based on human intentionality

As robots are increasingly found in various aspects of human life, understanding how humans relate to and interact with robots is becoming more relevant. One important factor affecting the quality of human-robot interactions is trust, and in recent years there has been an increase in scientific interest in the nature of trust between humans and robots. However, it can be observed that a clear distinction between trust and its related constructs is not always made when examining trust in robots. To overcome these limitations, we propose a definition of trust in robots that clearly separates trust from its related constructs. To this end, we extend an established trust model for interpersonal trust, which we adapt to describe trust in HRI. The new definition describes trust as a particular way in which human trustor relates to a robot. By characterizing trust as an asymmetric relation with certain properties, the proposed definition adopts a perspective that focuses on the human trustor in describing trust. Furthermore, we argue that the proposed definition of trust is suitable for describing trust in all types of robots and for any form of human-robot interaction.

A Meta-Analysis of Vulnerability and Trust in Human–Robot Interaction

In human–robot interaction studies, trust is often defined as a process whereby a trustor makes themselves vulnerable to a trustee. The role of vulnerability however is often overlooked in this process but could play an important role in the gaining and maintenance of trust between users and robots. To better understand how vulnerability affects human–robot trust, we first reviewed the literature to create a conceptual model of vulnerability with four vulnerability categories. We then performed a meta-analysis, first to check the overall contribution of the variables included on trust. The results showed that overall, the variables investigated in our sample of studies have a positive impact on trust. We then conducted two multilevel moderator analysis to assess the effect of vulnerability on trust, including: (1) an intercept model that considers the relationship between our vulnerability categories and (2) a non-intercept model that treats each vulnerability category as an independent predictor. Only model 2 was significant, suggesting that to build trust effectively, research should focus on improving robot performance in situations where the users are unsure how reliable the robot will be. As our vulnerability variable is derived from studies of human–robot interaction and researcher reflections about the different risks involved, we relate our findings to these domains and make suggestions for future research avenues.

Do Children with Autism Spectrum Disorders Show Selective Trust in Social Robots?

Previous researches suggest that social robots can facilitate the learning of children with Autism Spectrum Disorder (ASD) by enhancing their interests, engagement, and attention. However, there is limited understanding regarding whether children with ASD can learn directly from the testimony of social robots and whether they can remain vigilant based on the perceived accuracy of these robots. Therefore, the present study was conducted to examine whether children with ASD demonstrated selective trust towards social robots. Twenty-nine children with ASD between ages of 4-7 years, and 38 typically-developing (TD) age and IQ-matched peers participated in classic selective trust tasks. During the tasks, they learned the names of novel objects from either a pair of social robots or a pair of human informants, where one informant had previously been established as accurate and the other inaccurate. Children with ASD trusted information from an accurate social robot over an inaccurate one, similar to their performance with human informants. However, compared to TD children, children with ASD exhibited lower levels of selective trust regardless of the type of informants they learned from. Our study suggests that children with ASD can selectively trust and acquire knowledge from social robots, shedding light on the potential use of social robots in supporting individuals with ASD.

Effects of Robots’ Character and Information Disclosure on Human–Robot Trust and the Mediating Role of Social Presence

Effects of Robots’ Character and Information Disclosure on Human–Robot Trust and the Mediating Role of Social Presence

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Service robots equipped with artificial intelligence are continuously advancing in both appearance and functionality. With these ongoing developments, there is a growing necessity to investigate consumer perceptions of service robots' anthropomorphism and functions within the food service industry using an optimal framework model. This study examines trust, perceived risk, and acceptance intentions concerning service robots based on their level of anthropomorphism in casual dining and fine dining restaurants. A one-way analysis of variance was conducted to assess differences in trust and perceived risk among service robots with varying levels of anthropomorphism (low, medium, and high). Additionally, multiple regression analysis was employed to explore the impact of trust and perceived risk on the intention to accept service robots. The findings revealed that trust in robots and perceived risk varied significantly between casual dining and fine dining establishments, contingent on the anthropomorphism level of the service robot. In line with human-robot interaction theory, this study delineates consumer perceptions regarding the utilization of restaurant service robots, taking into account the level of anthropomorphism. Moreover, it expands the framework concerning service robots by integrating restaurant consumers' trust, perceived risk, and acceptance intentions towards these robots. This research underscores the importance of considering human-robot interaction and addressing consumers' preferences, benefits, and safety in the deployment of service robots within the restaurant industry. It also offers practical implications for stakeholders in the restaurant service robot sector.

Robots for surgeons? Surgeons for robots? Exploring the acceptance of robotic surgery in the light of attitudes and trust in robots

BackgroundOver the last century, technological progress has been tremendous, and technological advancement is reflected in the development of medicine. This research assessed attitudes towards surgical robots and identified correlations with willingness to participate in robotic surgery based on factors influencing trust in automated systems.MethodUsing data from a survey, which included the Multi-dimensional Robot Attitude Scale (MdRAS) and a questionnaire consisting of attitude statements regarding the factors affecting trust in automated systems, the experiment assessed the attitudes of healthcare workers and potential patients towards surgery robots, and attempted to find a correlation between these attitudes, age, and gender.Results and ConclusionStatistical evaluation of the responses (N = 197) showed that positive attitude towards surgical robots showed a high correlation with the willingness to participate in robotic surgery and gave the strongest correlations with the MdRAS utility and negative attitude towards robots subscales. For the assessment of willingness, the MdRAS subscales alone did not provide a strong enough correlation. All factors examined showed a significant correlation with participation. Having faith in the surgery robot, the propensity to trust technology, the designer’s reputation, the ease of work that a surgical robot provides, positive experience with robots, and believing the surgeon is competent at operating the machine seemed to have been the most important positive correlations, while fear of errors gave the highest negative correlation. The healthcare workers and potential patients showed significant differences in the subscales of the questionnaire perceived risk and knowledge but no significant difference in the characteristics of the surgical robot. There was no difference in willingness to participate between the samples. Age did not show a significant correlation with the score achieved and willingness in any of the samples. Significant differences were found between male and female respondents, with men having more positive attitudes and being more likely to participate in surgeries using surgery robots than women. As a result, the research potentially sheds light on the factors that need to be considered when building trust in robotic surgery.

How service robots’ human-like appearance impacts consumer trust: a study across diverse cultures and service settings

PurposeThis study aims to compares the effects of different human-like appearances (low vs. medium vs. high) of service robots (SRs) on consumer trust in service robots (CTSR), examines the mediating role of perceived warmth (WA) and perceived competence (CO) and demonstrates the moderating role of culture and service setting.Design/methodology/approachThe research design includes three scenario-based experiments (Chinese hotel setting, American hotel setting, Chinese hospital setting).FindingsStudy 1 found SR’s human-like appearance can arouse perceived anthropomorphism (PA), which positively affects CTSR through parallel mediators (WA and CO). Study 2 revealed consumers from Chinese (vs. American) culture had higher CTSR. Study 3 showed consumers had higher WA and CO for SRs in the credence (vs. experience) service setting. The authors also had an exploratory analysis of the uncanny valley phenomenon.Practical implicationsThe findings have practical implications for promoting the diffusion of SRs in the hospitality industry. Managers can increase CTSR by augmenting the anthropomorphic design of SRs; however, they must consider the differences in this effect across all service recipients (consumers from different cultures) and service settings.Originality/valueThe authors introduce WA and CO as mediators between PA and CTSR and set the culture and service setting as moderators.

The Effects of Robot Managers’ Reward-Punishment Behaviours on Human–Robot Trust and Job Performance

The Effects of Robot Managers’ Reward-Punishment Behaviours on Human–Robot Trust and Job Performance

Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications

Human Trust in Robots: A Survey on Trust Models and Their Controls/Robotics Applications

Trust in service robot: the role of appearance anthropomorphism

ABSTRACT An increasing number of hotels and restaurants are using service robots as front-line employees, yet the lack of consumer trust in service robots affects the service experience. To enhance trust, companies have added anthropomorphism designs for service robots. This study investigated the effects of service robot anthropomorphism on trust and psychological mechanisms of trust in service robot. This study conducted three scenario-based experiments to explore the mechanisms of the service robot appearance anthropomorphism effect on trust. The results show that consumers are more likely to trust service robots with a higher degree of appearance anthropomorphism. The anthropomorphism appearance of service robots affects consumers’ perceived interpersonal attraction, including physical attraction, task attraction and social attraction, which in turn affects trust. In addition, consumers’ technology readiness moderates the effect of service robot anthropomorphism on interpersonal attraction. This paper develops research on consumer trust in service robots, and provides guidance for marketing practices of service robots.

Trust in Robot–Robot Scaffolding

The study of robot trust in humans and other agents is not explored widely despite its importance for the near future human-robot symbiotic societies. Here we propose that robots should trust partners that tend to reduce their computational load, which is analogous to human cognitive load. We test this idea by adopting an interactive visual recalling task. In the first set of experiments, the robot can get help from online instructors with different guiding strategies to decide which one it should trust based on the computational load it experiences during the experiments. The second set of experiments involves robot-robot interactions. Akin to the robot-online instructor case, the Pepper robot is asked to scaffold the learning of a less capable ‘infant’ robot (Nao) with or without being equipped with the cognitive abilities of theory of mind and task experience memory to assess the contribution of these cognitive abilities to scaffolding performance. Overall, the results show that robot trust based on computational/cognitive load within a sequential decision-making framework leads to effective partner selection and robot-robot scaffolding. Thus, using the computational load incurred by the cognitive processing of a robot may serve as an internal signal for assessing the trustworthiness of interaction partners.

Are Friendly Robots Trusted More? An Analysis of Robot Sociability and Trust

Older individuals prefer to maintain their autonomy while maintaining social connection and engagement with their family, peers, and community. Though individuals can encounter barriers to these goals, socially assistive robots (SARs) hold the potential for promoting aging in place and independence. Such domestic robots must be trusted, easy to use, and capable of behaving within the scope of accepted social norms for successful adoption to scale. We investigated perceived associations between robot sociability and trust in domestic robot support for instrumental activities of daily living (IADLs). In our multi-study approach, we collected responses from adults aged 65 years and older using two separate online surveys (Study 1, N = 51; Study 2, N = 43). We assessed the relationship between perceived robot sociability and robot trust. Our results consistently demonstrated a strong positive relationship between perceived robot sociability and robot trust for IADL tasks. These data have design implications for promoting robot trust and acceptance of SARs for use in the home by older adults.

Customer experience quality with social robots: Does trust matter?

Although service providers increasingly adopt social robots, much remains to be learned about what influences customers' experiences with robots. To address this issue, this study investigates the relationships among customer equity drivers (i.e., value equity, brand equity and relationship equity), trust in social robots, and trust in service providers. Specifically, we hypothesize that customer equity drivers influence trust in social robots and trust in service providers. We also propose that customer equity drivers influence customer experience quality in the context of social robots and that trust in social robots and trust in service providers mediate these relationships. The study used a two-stage hybrid partial least squares structural equation modelling (PLS-SEM)-artificial neural network (ANN) analysis to examine the proposed relationships. Findings show that while all the customer equity drivers influence trust in service providers, only brand and relationship equity influence trust in social robots. Results also suggest that trust in service providers mediates the relationship between customer equity drivers and customer experience quality. In addition, we find that consumers' trust in service providers helps generate trust in social robots. Theoretical and managerial implications are discussed.

The dynamics of human–robot trust attitude and behavior — Exploring the effects of anthropomorphism and type of failure

In two experiments centered around trust dynamics in human–robot interaction, we explored the interplay between attribute-based characteristic of robots (i.e., anthropomorphism and type of failure) and trust attitude, perceived reliability, as well as trust behavior. In general, trust attitude is considered to be one of the most important factors for successful collaboration. However, although previous research has shown that performance and attribute-based characteristics of robots lead to changes in trust attitudes, the question remains whether this is also linked to behavioral adaptations. To investigate this relationship, participants collaborated with robots via voice commands that failed. In the first experiment, participants worked with either an anthropomorphic or technical robot. In the second experiment, all participants worked with the technical robot, which made a failure in either information acquisition/processing or action implementation. Participants trusted the technical robot significantly more than the anthropomorphic one. Moreover, failures in information acquisition/processing seemed to lead to more trust dissolution than failures in action implementation. In both experiments, the dynamics of trust attitudes as a function of interaction experience were shown, in particular, trust dissolution due to failure. However, there was no evidence of behavioral adaptation following a failure experience. In general, the effects on the attitudinal level did not clearly translate into behavioral adaptations. For trust attitude, purely decorative anthropomorphic robot design and failures related to the cognitive abilities of robots should be avoided.

How Do We Trust Our Robots?

Trust plays the most important role in accepting social robots in our communities, especially in an era where robots take part in daily activities in close distance with humans. While some communities accept the presence of robots in their surrounding, others are less open to them. Furthermore, this acceptance depends on a large number of factors which are specific to the communities as well as the deployed robots. We conducted a survey to explore the aspects which people would consider if they had an option to deploy a robot at home and assign responsibilities to it. The study will help direct future researches on embodied intelligence in robots towards a more human-accepted level. The paper presents a human study conducted to evaluate the trust between humans and robots. We present the feedback we received from the participants to assess the level of trust participants have on their robots and their personal preferences upon the abilities of robots. Human responses and their decisions observed during the study are analyzed and critical observations are highlighted. From the analysis we derive a set of guidelines to improve human trust in future robots by adjusting their humanlike social behaviors.

A Multi-Robot-Based Architecture and a Trust Model for Intelligent Fault Management and Control Systems

One of the most important challenges in robotics is the development of a Multi-Robot-based control system in which the robot can make intelligent decisions in a changing environment. This paper proposes a robot-based control approach for dynamically managing robots in such a widely distributed production system. A Multi-Robot-based control system architecture is presented, and its main features are described. Such architecture facilitates the reconfiguration (either self-reconfiguration ensured by the robot itself or distributed reconfiguration executed by the Multi-Robot-based system). The distributed reconfiguration is facilitated through building a trust model that is based on learning from past interactions between intelligent robots. The Multi-Robot-based control system architecture also addresses other specific requirements for production systems, including fault flexibility. Any out-of-control fault occurring in a production system results in the loss of production time, resources, and money. In these cases, robot trust is critical for successful job completion, especially when the work can only be accomplished by sharing knowledge and resources among robots. This work introduces research on the construction of trust estimation models that experimentally calculate and evaluate the trustworthiness of robots in a Multi-Robot system where the robot can choose to cooperate and collaborate exclusively with other trustworthy robots. We compare our proposed trust model with other models described in the literature in terms of performance based on four criteria, which are time steps analysis, RMSD evaluation, interaction analysis, and variation of total feedback. The contribution of the paper can be summarized as follows: (i) the Multi-Robot-based Control Architecture; (ii) how the control robot handles faults; and (iii) the trust model.

Role of trust in customer attitude and behaviour formation towards social service robots

This research proposes a model that identifies the antecedents of customers’ attitudes and behaviours towards the utilization of artificially intelligent (AI) social service robots in hospitality and tourism service delivery. The model highlights the importance of trust and its determinants on customers’ attitudes and behaviours towards social service robots. The proposed model and the hypotheses are tested utilizing data collected from the users of two distinctly different hospitality and tourism services. Data were analysed adopting a PLS-SEM approach. Results indicate that the level of acceptance of the use of social robots in service delivery is determined by a multistage process, in which trust perceptions play critical roles. Heuristic (i.e., hedonic motivation) and the individual (i.e., innovativeness) factors positively influence trust in social robots during service delivery. Findings, however, suggest significant differences in different service contexts: anthropomorphism has no effect on trust construct and effort expectancy does not influence positive emotion in hedonic service contexts while these relationships are significant in functional service contexts. Practical and theoretical implications of the findings are discussed.

The theory of mind and human–robot trust repair

Nothing is perfect and robots can make as many mistakes as any human, which can lead to a decrease in trust in them. However, it is possible, for robots to repair a human’s trust in them after they have made mistakes through various trust repair strategies such as apologies, denials, and promises. Presently, the efficacy of these trust repairs in the human–robot interaction literature has been mixed. One reason for this might be that humans have different perceptions of a robot’s mind. For example, some repairs may be more effective when humans believe that robots are capable of experiencing emotion. Likewise, other repairs might be more effective when humans believe robots possess intentionality. A key element that determines these beliefs is mind perception. Therefore understanding how mind perception impacts trust repair may be vital to understanding trust repair in human–robot interaction. To investigate this, we conducted a study involving 400 participants recruited via Amazon Mechanical Turk to determine whether mind perception influenced the effectiveness of three distinct repair strategies. The study employed an online platform where the robot and participant worked in a warehouse to pick and load 10 boxes. The robot made three mistakes over the course of the task and employed either a promise, denial, or apology after each mistake. Participants then rated their trust in the robot before and after it made the mistake. Results of this study indicated that overall, individual differences in mind perception are vital considerations when seeking to implement effective apologies and denials between humans and robots.

Acceptance of service robots: a meta-analysis in the hospitality and tourism industry

ABSTRACT Robot services have become an essential competitive differentiator in the hospitality and tourism industry. Seminal studies indicate the need for more profound knowledge about the factors that can increase the acceptance of service robots. This paper investigates the relationship between informational cues and acceptance of service robots through a meta-analysis. Our study also analyzes the acceptance of service robots through contextual characteristics moderators. The results showed that perceived robot trust influences the relationship between informational cues (anthropomorphism and social influence) and acceptance of service robots. By analyzing previous studies, our framework demonstrates that acceptance of service robots has increased more after the COVID pandemic than before the COVID pandemic. Furthermore, the relationship between attitude toward robots and robot usage intention tends to be greater in humanoid service robots (than social robots) and analytical intelligence (than mechanical intelligence).

Industry 4.0 and Industrial Robots: A Study from the Perspective of Manufacturing Company Employees

Background: Human–robot collaboration is essential for efficient manufacturing and logistics as robots are increasingly used. Using industrial robots as part of an automation system results in many competitive benefits, including improved quality, efficiency, productivity, and reduced waste and errors. When robots are used in production, human coworkers’ psychological factors can disrupt operations. This study aims to examine the effect of employees’ negative attitudes toward robots on their acceptance of robot technology in manufacturing workplaces. Methods: A survey was conducted with employees in manufacturing companies to collect data on their attitudes towards robots and their willingness to work with them. Data was collected from 499 factory workers in Istanbul using a convenience sampling method, which allowed for the measurement of variables and the analysis of their effects on each other. To analyze the data, structural equation modeling was used. Results: The results indicate that negative attitudes towards robots have a significant negative effect on the acceptance of robot technology in manufacturing workplaces. However, trust in robots was found to be a positive predictor of acceptance. Conclusions: These findings have important implications for manufacturing companies seeking to integrate robot technology into their operations. Addressing employees’ negative attitudes towards robots and building trust in robot technology can increase the acceptance of robots in manufacturing workplaces, leading to improved efficiency and productivity.