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The focus of this thesis is to develop a methodology that leads to both the explanation and prediction of the influences on acceptance of assistive social robots by elderly users. This drove us to find a method to explain and predict the influences on acceptance of assistive social robots by elderly users. We therefore answered the following sub-qu...
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Citations
... In detail, Xu and Dudek [52] investigated the robot's trustworthiness using an online trust inference model. Heerink et al. discussed the influence of trust in the elderly's healthcare context on their willingness to use intelligent machines to make decisions [95]. Some scholars have discussed the influence of human trust in service robots on their intention to use under the background of service marketing [18,96]. ...
... In certain contexts, AI interactions may be perceived as entertaining or enjoyable. Such positive experiences can enhance users' engagement with AI technologies, making them more willing to explore and utilize AI-driven decision-making tools [56,95]. Interactions with AI systems can also evoke feelings of social belonging, as AI technologies are designed to emulate human-like qualities [99]. ...
The emergence of artificial-intelligence (AI)-powered information technology, such as deep learning and natural language processing, enables human to shift their behaving or working diagram from human-only to human–AI synergy, especially in the decision-making process. Since AI is multidisciplinary by nature and our understanding of human–AI synergy in decision-making is fragmented, we conducted a literature review to systematically characterize the phenomenon. Adopting the affordance actualization theory, we developed a framework to organize and understand the relationship between AI affordances, the human–AI synergy process, and the outcomes of human–AI synergy. Three themes emerged from the review: the identification of AI affordances in decision-making, human–AI synergy patterns regarding different decision tasks, and outcomes of human–AI synergy in decision-making. For each theme, we provided evidence on the existing research gaps and proposed future research directions. Our findings provide a holistic framework for understanding human–AI synergy phenomenon in decision-making. This work also offers theoretical contributions and research directions for researchers studying human–AI synergy in decision-making.
... Social robots are currently being developed, tested and implemented in nursing homes to improve care for older adults and PlwD in the future. Social robots are a subtype of robots and robotic devices, as illustrated in Fig. 1; for social robots, the primary focus is not on productivity or efficiency, as is often the case with industrial robots, but on social interactions and companionships [4]. ...
... Social robots differ from other technologies, as they are embodied social actors that socially interact with humans. These general technology acceptance models do not capture all the factors influencing the acceptance of social robots [4]. Heerink et al. [42] instead introduced the Almere model specifically for assessing the use of social robots for older adults, and we have used this model to guide and structure our scoping review while also including acceptance literature in the review, which does not rely on the Almere model. ...
Social robots are increasingly used in the care of older adults, including people living with dementia. An important prerequisite for effective use is knowledge about which factors facilitate and hinder the acceptance of social robots. The aim of this review at the intersection of health care research and human–robot interaction research is to understand factors facilitating and hindering the acceptance of social robots for older adults and people living with dementia in nursing homes based on the Almere model and to identify research gaps. We conducted a scoping review, systematically searching MEDLINE, PsycINFO, CINAHL and Compendex. The Almere model of acceptance was used to guide the development and conduct of this review. We used the principles of deductive content analysis, and we narratively present the review results. Twenty-six studies were included in the review. We found facilitating and/or hindering factors for all constructs of the Almere model. Perceived usefulness, perceived ease of use and perceived enjoyment were the most studied constructs, whereas trust and intention to use were the least studied constructs. Across constructs, seeing robots positively impacting residents was one of the key facilitating factors, and practical difficulties were a hindering factor. The Almere model provided useful insight into the acceptance of social robots. Nevertheless, we found the concept of engagement, the nursing home context, the intervention characteristics and the individual user groups important for the acceptance of social robots in nursing homes and therefore argue for future research expanding on the model.
... People who are less neurotic and more agreeable are more likely to allow and feel more comfortable in greater proximity to a robot [7]. In robot acceptance models, experience is identified as a strongly moderating variable [30], [31]. This influence is also observed in proxemic behaviour studies, e.g., in [32] subjects with more than one year of experience accept a closer distance to the robot than inexperienced users. ...
... The acceptance was assessed using the Almere Model Questionnaire (AMQ) [21], which made 39 items available at [37]. The questionnaire used in this study was based on the original test by Heerink and adapted for the Pharaon technologies. ...
Assistive devices could promote independent living and support the active and healthy aging of an older population; however, several factors can badly influence the long-term use of new technologies. In this context, this paper presents a two-step methodology called “pre-validation” that aims to identify the factors that can bias the use of new services, thus minimizing the risk of an unsuccessful longer trial. The proposed pre-validation methodology is composed of two main phases that aim to assess the usability and the reliability of the technology assessed in a laboratory environment and the usability, acceptability, user experience, and reliability of the technology in real environments. The tested services include the socialization scenario, in which older adults are better connected to the community via technological solutions (i.e., socialization applications), and the monitoring scenario, which allows for the introduction of timely interventions (technologies involved include environmental monitoring sensors, a telepresence robot, wearable sensors, and a personalized dashboard). The obtained results underline an acceptable usability level (average System Usability Scale score > 65) for the tested technologies (i.e., socialization applications and a telepresence robot). Phase Two also underlines the good acceptability, user experience, and usability of the tested services. The statistical analysis underlines a correlation between the stress related to the use of technology, digital skills, and intention of use, among other factors. Qualitative feedback also remarks on a correlation between older adults with low digital skills and an anxiety about using technology. Positive correlation indexes were highlighted between the trust and usability scores. Eventually, future long-term trials with assistive technology should rely on motivated caregivers, be founded on a strong recruitment process, and should reassure older adults—especially the ones with low digital literacy—about the use of technology by proposing personalized training and mentoring, if necessary, to increase the trust.
... SARs may provide motivation, encourage training, and support rehabilitation to various users using nonphysical interaction [2]. Heerink (2010) divided SARs into two subcategories: companions and service robots, and included functionalities such as a butler, a guide, and an information provider [3]. Since there may be many functional differences among SAR types, user experience is expected to vary with the context of use, functionality, user characteristics, and environmental conditions. ...
... SARs may provide motivation, encourage training, and support rehabilitation to various users using nonphysical interaction [2]. Heerink (2010) divided SARs into two subcategories: companions and service robots, and included functionalities such as a butler, a guide, and an information provider [3]. Since there may be many functional differences among SAR types, user experience is expected to vary with the context of use, functionality, user characteristics, and environmental conditions. ...
Socially assistive robots (SARs) aim to provide assistance through social interaction. Previous studies contributed to
understanding users` perceptions and preferences regarding existing commercially available SARs. Yet, very few
studies regarding SARs' appearance used designated SAR designs, and even fewer evaluated isolated visual qualities
(VQ). In this work, we aim to assess the effect of isolated VQs systematically. To achieve this, we first conducted
market survey and deconstructed the VQs attributed to SARs. Then, a reconstruction of body structure, outline, and
color scheme was done, resulting in the creation of 30 new SAR models that differ in their VQs, allowing us to isolate
one character at a time. We used these new designs to evaluate users' preferences and perceptions in two empirical
studies. Our empirical findings link VQs with perceptions of SAR characteristics. These can lead to forming guidelines
for the industrial design processes of new SARs to match user expectations.
... Learner characteristics include age and gender as well as personal characteristics such as interest in learning. The UTAUT (Unified Theory of Acceptance and Use of Technology) model has been used in acceptance of robots (De Ruyter et al. 2005;Heerink 2010;Looije et al. 2006), and states the influences of (i) performance expectancy, (ii) effort expectancy, and (iii) social influence as direct determinants of intention to use (De Ruyter et al. 2005;Heerink 2010;Looije et al. 2006;Venkatesh et al. 2003), with age, gender and interest as significantly moderating factors to behavioral intention. ...
... Learner characteristics include age and gender as well as personal characteristics such as interest in learning. The UTAUT (Unified Theory of Acceptance and Use of Technology) model has been used in acceptance of robots (De Ruyter et al. 2005;Heerink 2010;Looije et al. 2006), and states the influences of (i) performance expectancy, (ii) effort expectancy, and (iii) social influence as direct determinants of intention to use (De Ruyter et al. 2005;Heerink 2010;Looije et al. 2006;Venkatesh et al. 2003), with age, gender and interest as significantly moderating factors to behavioral intention. ...
... Main features are stability in functioning, ease of use and no risk to children. Many terms have been used to describe robotic acceptance (Heerink 2010;Kidd et al. 2006;Taggart et al. 2005). Most focus robotic features as being easy on the eye and "easy to use" by people who are unfamiliar with robots (Leite et al. 2013). ...
Educational robots have been used in many countries as teaching assistants in elementary schools but robotic education quality is not well established in Thailand. The primary objective of this study was to identify and confirm quality dimensions in robotic education from the teachers’ perspectives. The sample size was 510 teachers who were observed in Thai elementary schools. Confirmatory Factor Analysis (CFA) indicated a good fit of a six-factor model to the observed data. The construct of CFA revealed six dimensions of robotic education quality as Social interaction, Cognitive function, Teaching method, Learner characteristics, Main features and Content. Results were similar to previous studies. Prototype development of an educational robot was proposed in relation to the Thai educational context. Further research, including large random comparative studies, needs to be performed.
... This theoretical model was developed by using the essential elements of the eight theoretical models to study both consumer usage intention and actual use within new technology acceptance in both organizational and consumer contexts. UTAUT was established with four key variables (1) performance expectancy, (2) effort expectancy, (3) social influence, and (4) facilitating conditions with various individual moderators such as age, gender, experience, and voluntariness to make the effect of a construct on usage intention stronger or weaker (Heerink, 2010). This theoretical model provides a basic construct and definitions of consumer technology acceptance and uses context. ...
As service robots continue to automate and transform the food and beverage industry, these technological innovations will fundamentally alter the existing practices of service practitioners, inherently changing service management and marketing strategies. The current study investigates the relationship between trust, performance expectancy, perceived value, social influence and perceived enjoyment towards consumers' usage intention of service robots in the food and beverage industry. A total of 300 sets of questionnaires were distributed through online platforms with 264 sets used for subsequent analysis. Four constructs were significant in influencing consumers' intention to use service robots (trust, perceived value, social influence, and perceived enjoyment); while performance expectancy did not. The outcome provides meaningful theoretical and practical insights which will contribute towards sustainable development of the F&B industry in the future.
... Cheung & Vogel, 2013;Tarhini, Hone, & Liu, 2015) and the studies about the acceptance of social robots (e.g. Fridin & Belokopytov, 2014;Heerink, 2010;Heerink et al, 2009). ...
This paper adopts a technology acceptance model used for studying Robot’s acceptance and focuses on the acceptance of robotic technologies. Despite a wide range of studies on the acceptance and usage of robotics technologies in different fields, there is lacuna of empirical evidence on the acceptance of robotics technologies in the educational context. We contribute to the scholarship on robotics technologies in an educational context, by using qualitative semi-structured interviews, and proposing a research model to empirically explore the main factors affecting the acceptance of robotics technologies, and particularly among university students. We contribute to practice by offering insights on users' expectations and intentions toward the potential use of robot services to both robot developers, and educational institutions alike. The results revealed a potential impact of effort expectancy, performance expectancy, social influence, and facilitating conditions on the intention behavior towards using robots as academic advisors. Additionally, an emergent dimension (i.e. emotions) was found to have an influence on the behavioral intentions, via its proposed impact on performance and effort expectancies. Overall, social characteristics of robots ought to be considered when investigating their acceptance, specifically when used as social entities in a human environment.
... 1) the impact of the apps described above, when they were delivered by MARIO; 2) the impact of robot embodiment and how this affected the interactions between PLWD and the robot [26][27][28][29][30]; 3) to present the key results that were obtained across the three different pilot sites, specifically in the final evaluation phase of the project; 4) to report how the PLWD interacted with the MARIO robot; 5) to assess the acceptability and efficacy of the MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience capacity, quality of life in PLWD, and burden level of the caregivers. ...
Background:
In the EU funded MARIO project, specific technological tools are adopted for the people living with dementia (PLWD). In the final stage of the project, a validation of the MARIO companion robot was performed from August to October 2017.
Objective:
The aims of the present study are: 1) to illustrate the key results and evidence obtained in the final evaluation phase of the project across the three different pilot sites; 2) to assess the engagement dimensions of the PLWD who interacted with the MARIO robot; and 3) to assess the acceptability and efficacy of the MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience, quality of life in PLWD, and burden level of the caregivers.
Methods:
38 people (M = 14; F = 24) with Alzheimer's disease were screened for eligibility and all were included. The following tests were administered Pre and Post interactions with MARIO: Observational Measurement of Engagement (OME), Mini-Mental State Examination (MMSE), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Neuropsychiatric Inventory (NPI), Cornell Scale for Depression in Dementia (CSDD), Multidimensional Scale of Perceived Social Support (MSPSS), 14-item Resilience Scale (RS-14), Quality of Life in Alzheimer's Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out.
Results:
In Post-MARIO interactions, significant improvements were observed in RS-14 (p = 0.020).Considering the age of the people, PLWD with 68-76 years perceived that they had major social support (MSPSS Total: p = 0.016) and friends to support them (MSPSS Fri: p = 0.014). Indeed, the younger people (55-67 years) were less depressed (CSDD: p = 0.033), and more resilient (RS-14: p = 0.003). The people aged 77-85 years perceived they had major family support (MSPSS Fam: p = 0.018). The participants were gender and education matched without any statistically significant difference.
Conclusion:
MARIO may be a useful tool in mitigating depression and loneliness, while enhancing social connectedness, resilience, and overall quality of life for people with dementia.
... 1) the impact of the apps described above, when they were delivered by MARIO; 2) the impact of robot embodiment and how this affected the interactions between PLWD and the robot [26][27][28][29][30]; 3) to present the key results that were obtained across the three different pilot sites, specifically in the final evaluation phase of the project; 4) to report how the PLWD interacted with the MARIO robot; 5) to assess the acceptability and efficacy of the MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience capacity, quality of life in PLWD, and burden level of the caregivers. ...
In the EU funded MARIO project, specific technological tools are adopted for the patient with dementia (PWD). At this stage of the project, the experimentation phase is under way, and the first two trials were completed as shown below: the first trial was performed in November 2016, and second trial was performed in April 2017. The current implemented and assessed applications (apps) are My Music app, My News app, My Games app, My Calendar app, My Family and Friends app, and Comprehensive Geriatric Assessment (CGA) app. The aim of the present study was to provide a preliminary analysis of the acceptability and efficacy of MARIO companion robot on clinical, cognitive, neuropsychiatric, affective and social aspects, resilience capacity, quality of life in PWD, and burden level of the caregivers. Thirteen patients [5 patients (M = 3; F = 2) in first trial, and 8 patients (M = 6; F = 2) in second trial] were screened for eligibility and all were included. At admission and at discharge, the following tests were administered: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Clock Drawing Test (CDT), Frontal Assessment Battery (FAB), Hachinski Ischemic Scale (HIS), Neuropsychiatric Inventory (NPI), Geriatric Depression Scale (GDS), Hamilton Rating Scale for Depression (HDRS-21), Multidimensional Scale of Perceived Social Support (MSPSS), Social Dysfunction Rating Scale (SDRS), Brief Resilience Scale (BRS), Quality of Life in Alzheimer’s Disease (QOL-AD), Caregiver Burden Inventory (CBI), Tinetti Balance Assessment (TBA), and Comprehensive Geriatric Assessment (CGA) was carried out. A questionnaire based on the Al-mere Acceptance model was used to evaluate the acceptance of the MARIO robot. During the first trial, My Music, My Games and My News apps were used. At discharge, no significant improvement was shown through the above questionnaires. During the second trial, My Music, My Games, My News, My Calendar, My Family and Friends, and CGA apps were used. At discharge, significant improvements were observed in the following parameters: NPI (p = 0.027), GDS-15 (p = 0.042), and BRS (p = 0.041), CBI (p = 0.046). Instead, the number of medications is increased at discharge (p = 0.038). The mean of hospitalization days is 5.6 ± 3.9 (range = 3–13 days). The Almere Model Questionnaire suggested, a higher acceptance level was shown in first and second trial.
