Meeting Stevie: Perceptions of a Socially Assistive Robot by Residents and Staff in a Long-Term Care Facility

McGinn, Conor; Bourke, Eamonn; Murtagh, Andrew; Donovan, Cian; Cullinan, Michael F.

doi:10.1109/hri.2019.8673161

Cited by 18 publications

(14 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HRI is a multi-disciplinary area and there are five key dimensions tested by developing the Godspeed questionnaire: ANM, PNT, likeability, animacy and perceived safety (Bartneck et al , 2009). This scale is widely used to understand the HRI (Sheehan, 2018; Yu, 2020; Ho and Macdorman, 2010; Stark et al , 2018; Weiss and Bartneck, 2015; Werner et al , 2012; Willemse and Wykowska, 2019; Zarouali et al , 2018; McGinn et al , 2019; Petisca et al , 2015; Hughes et al , 2015). This study mainly focuses on tourism chatbots; hence, this study has considered ANM and PNT from the Godspeed questionnaire.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The AI has developed the concept of PNT (Ho and Macdorman, 2010; Warner and Sugarman, 1986) and it mainly concentrates on competence, knowledge delivery, sensibility, intelligence and responsible reaction of robot or chatbot (Weiss and Bartneck, 2015; Petisca et al , 2015; McGinn et al , 2019; Hughes et al , 2015). It is challenging for robots to interact with customers intelligently (Bartneck et al , 2009).…”

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%

See 2 more Smart Citations

Adoption of AI-based chatbots for hospitality and tourism

Pillai¹,

Sivathanu

2020

IJCHM

466

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

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Adoption of AI-based chatbots for hospitality and tourism

Pillai¹,

Sivathanu

2020

IJCHM

466

269

View full text Add to dashboard Cite

show abstract

“…A further line of research could implement more standardized measures designed for measuring dimensions of human-robot interactions. Robinson et al [17] developed psychometric tools to assess incentives, intentions, and selfefficacy to use social robots. Based on data obtained from such measures, Stevie's features and characteristics can be refined to reflect such dimensions as they may predict acceptability and use of Stevie [18].…”

Section: V111 Discussionmentioning

confidence: 99%

“…Secondly, Stevie has a range of facial expressions designed to convey unambiguous emotions. Prior to this study, Stevie had only been evaluated in the US [17]; this study was the first trial of Stevie in the United Kingdom (UK). Importantly, in the current study, Stevie was tested at a day center for older adults living with dementia, unlike the US deployment where users were receiving support for a continuum of other age-related care needs.…”

Section: Introductionmentioning

confidence: 99%

Exploring the applicability of the socially assistive robot Stevie in a day center for people with dementia

Taylor¹,

Downing²,

Noury³

et al. 2021

2021 30th IEEE International Conference on Robot &Amp; Human Interactive Communication (RO-MAN)

Self Cite

View full text Add to dashboard Cite

Socially assistive robots have the potential to help keep people with dementia cognitively active and entertained. This is important for their wellbeing. We explored staff perceptions of the acceptability of a new humanoid robot, Stevie, in an adult day care center for people with dementia. Stevie was deployed over 2 weeks in the day center to entertain 40 guests with dementia with three activities: (i) musical bingo, (ii) quizzes, and (iii) meet and greet. Nine staff members were asked what went well, whether the robot operated as expected, experiences of any technical issues, and the extent to which it hindered or aided their duties. Staff also rated guest engagement, guest enjoyment, and whether they were able to spend more time with guests. The sessions were successfully delivered. Staff reported that Stevie operated as expected, guest engagement with the robot was high, interactions with the robot were natural and the robot's novelty helped engagement. Use of the robot gave staff more time to attend to guests' needs. Suggested improvements included improvements to the robot's voice and adding a pause function during activities. With greater autonomy and/or extended use of its telepresence functionality, use of Stevie could become cost effective in such settings. Metropolitan University, and Conor McGinn worked for Trinity College Dublin. Angela Downing is Owner and Manager of the Reflections Day Center in Camborne, Cornwall, hosted the study and supervised the care staff and day center guests. Conor McGinn is the co-inventor of the Stevie Robot providing technical support in the set up of the study but was not involved in the operation or data collection for the evaluation. CONFLICT OF INTERESTConor Mc Ginn is a co-inventor of Stevie. However, he was not directly involved with the data collection and analysis of this study.

show abstract

Artificial Intelligence Community and Self

2021

AI Magazine

View full text Add to dashboard Cite

The means by which artificial intelligence systems will both interact and influence individuals and community are considered, with a description of current capabilities such as video analytics, agency, natural language processing, and online data analysis. Open research questions such as the roles of analogies, associative memory, and the grounding problem are discussed, with speculation presented regarding the possible ramification of this research agenda. Such topics will include how artificial intelligence and humanity might co‐evolve; artificial creativity; and life without work.

show abstract

Meeting Stevie: Perceptions of a Socially Assistive Robot by Residents and Staff in a Long-Term Care Facility

Cited by 18 publications

References 1 publication

Adoption of AI-based chatbots for hospitality and tourism

Adoption of AI-based chatbots for hospitality and tourism

Exploring the applicability of the socially assistive robot Stevie in a day center for people with dementia

Artificial Intelligence Community and Self

Contact Info

Product

Resources

About