Trusting Robocop: Gender-Based Effects on Trust of an Autonomous Robot

Gallimore, Darci; Lyons, Joseph B.; Vo, Thy; Mahoney, Sean; Wynne, Kevin T.

doi:10.3389/fpsyg.2019.00482

Cited by 57 publications

(28 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Finally, the sample size in the conditions of the TC phase, in which feedback and reliability influences on human trust were investigated, is relatively small to draw generalizable conclusions. This might also be the reason why we were not able to find any statistically significant differences between men and women in trust which does not conform with previous studies where women reported higher trust in Gallimore et al (2019) and lower trust in Schuster et al (2015) than men. A larger sample size would better represent the population allowing for the use of parametric tests and enhancing the power and generalizability of the results.…”

Section: Discussioncontrasting

confidence: 97%

“…We will refer to the first group as G1 and the second one as G2 . Different research work suggests that there is a difference between men and women in terms of trust [e.g., trust games in economics ( Dittrich, 2015 ), human robot interaction ( Gallimore et al, 2019 ), and human automation interaction ( Schuster et al, 2015 )]. Therefore, we made sure that the number of men and women was equal in the groups.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Alhaji

Prilla

Rausch

2021

Front. Artif. Intell.

View full text Add to dashboard Cite

Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot’s reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot’s feedback accuracy has a conditional effect on trust depending on the robot’s reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.

show abstract

Section: Discussioncontrasting

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Alhaji

Prilla

Rausch

2021

Front. Artif. Intell.

View full text Add to dashboard Cite

show abstract

“…PTR is discussed as the extent to which the consumers sense that the chatbot/robot is reliable and credible (Yagoda and Gillan, 2012; You et al , 2018; Gallimore et al , 2019). Consumers feel safe when trust is developed toward robots (You et al , 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Along with TAM, context-specific variables concerning HRI (Bartneck et al , 2009; Deshmukh et al , 2018; Ho and Macdorman, 2010; Yu, 2020; Sheehan, 2018; Bartneck et al , 2007; Gray and Wegner, 2012) are considered to provide better explanatory power. The considered variables of HRI are ANM (Ruijten et al , 2019), PNT (Weiss and Bartneck, 2015; Petisca et al , 2015; McGinn et al , 2019; Hughes et al , 2015), PTR (Yagoda and Gillan, 2012; You et al , 2018; Gallimore et al , 2019) and technology anxiety (Evanschitzky et al , 2015; Mani and Chouk, 2018; Meuter et al , 2005). This study also explores the AUE of chatbots in tourism by investigating the association of AIN and AUE (Gupta et al , 2017; Ghanem et al , 2017).…”

Section: Literature Reviewmentioning

confidence: 99%

Adoption of AI-based chatbots for hospitality and tourism

Pillai¹,

Sivathanu

2020

IJCHM

470

269

View full text Add to dashboard Cite

Purpose This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots. Practical implications This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.

show abstract

“…In addition, we are interested in exploring the role of user personality in perception of robot employment fit, as personality has been shown to be an individual difference that is predictive of responses to automation (Szalma & Taylor, 2011). Further, there has been very little research on individual differences in HRI, and what does exist examines constructs like robotic control (Chen & Barnes, 2012), and trust (Gallimore, Lyons, Vo, Mahoney, & Wynne, 2019;Oleson, Billings, Kocsis, Chen, & Hancock, 2011). Thus, we have included a measure of personality in our study of the influence of robot human-likeness on perceptions of robot fit across a range of job types to explore the how personality influences these perceptions.…”

Section: Introductionmentioning

confidence: 99%

Robot Career Fair: An Exploratory Evaluation of Anthropomorphic Robots in Various Career Categories

Tenhundfeld

Phillips

Davis

2020

Proceedings of the Human Factors and Ergonomics Society Annual

View full text Add to dashboard Cite

Robots are being used in a host of different work environments currently. However, to date there has been very little broad exploration into the designs of systems and how that affects users’ perception of fit for the robots in different job categories. In the present experiment we showed participants images of 252 robots and asked them to make assignments of the robots into 16 potential job categories taken from the U.S. Department of Labor. The robots’ overall human likeness, as well as four contributory components of anthropomorphism were used to predict job category assignment. Results indicate that participants expect higher levels of anthropomorphism in jobs with more direct human interactions (such as education and hospitality), whereas they expect minimal levels in jobs with less human interaction (e.g. agriculture and architecture). Results also indicate that there is more nuance required for these judgments than general human likeness.

show abstract

Trusting Robocop: Gender-Based Effects on Trust of an Autonomous Robot

Cited by 57 publications

References 38 publications

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Trust Dynamics and Verbal Assurances in Human Robot Physical Collaboration

Adoption of AI-based chatbots for hospitality and tourism

Robot Career Fair: An Exploratory Evaluation of Anthropomorphic Robots in Various Career Categories

Contact Info

Product

Resources

About