อิทธิพลของความคล้ายคลึงเชิงรูปลักษณ์และการรับรู้การควบคุมได้ที่มีผลต่อการรับรู้ความเหมือนมนุษย์ ความเชื่อใจ และการยอมรับต่อหุ่นยนต์

งานวิจัยนี้มีจุดประสงค์เพื่อศึกษาอิทธิพลของการรับรู้ความเหมือนมนุษย์ที่มีต่อความไว้วางใจและการยอมรับหุ่นยนต์ตามแนวคิดทฤษฎี S-E-E-K ที่นำมาใช้สร้างกรอบแนวคิดหลักในการวิจัยนี้ โดยมีปัจจัยทางด้านความคล้ายคลึงเชิงรูปลักษณ์เป็นตัวแปรต้น การรับรู้การควบคุมได้เป็นตัวแปรกำกับ และแรงจูงใจทางสังคมเป็นตัวแปรควบคุม กลุ่มตัวอย่าง คือ นิสิต จำนวน 200 คน อายุระหว่าง 18-25 ปี ใช้การสุ่มอย่างเป็นระบบร่วมกับการสุ่มอย่างง่ายเข้าหนึ่งใน 4 เงื่อนไข โดยให้ผู้เข้าร่วมการวิจัยดูรูปภาพและอ่านข้อความคุณสมบัติของหุ่นยนต์ที่มีการจัดกระทำให้มีความคล้ายคลึงมนุษย์และพฤติกรรมที่คาดเดา/ควบคุมได้ของหุ่นยนต์ที่แตกต่างกันในแต่ละเงื่อนไข จากนั้นตอบแบบสอบถามการรับรู้ความเหมือนมนุษย์ ความไว้วางใจหุ่นยนต์การยอมรับหุ่นยนต์ ผลจากการวิเคราะห์เส้นทางพบว่า ความคล้ายคลึงเชิงรูปลักษณ์มีผลต่อการรับรู้ความเหมือนมนุษย์ที่มีต่อหุ่นยนต์อย่างมีนัยสำคัญทางสถิติ โดยในเงื่อนไขที่มีการรับรู้การควบคุมและคาดเดาไม่ได้ทำให้อิทธิพลของความคล้ายคลึงเชิงรูปลักษณ์ที่มีต่อการรับรู้ความเหมือนมนุษย์เพิ่มมากขึ้น ยิ่งไปกว่านั้นหุ่นยนต์ที่มีความคล้ายคลึงมนุษย์สูง และบุคคลไม่สามารถควบคุมหรือคาดเดาพฤติกรรมของหุ่นยนต์ได้ ทำให้บุคคลรู้สึกว่าหุ่นยนต์มีความเหมือนมนุษย์มากขึ้น ซึ่งส่งผลต่อความไว้วางใจรวมถึงการยอมรับหุ่นยนต์คำสำคัญ: ทฤษฎี S-E-E-K  ความคล้ายคลึงเชิงรูปลักษณ์  การรับรู้การควบคุมได้  ความเหมือนมนุษย์  ความไว้วางใจ  การยอมรับ  หุ่นยนต์This research aimed to investigate the effect of perceived anthropomorphism toward trust and acceptance in robots based on S-E-E-K theory; physical appearance similarity as independent variable, perceived of controllability as moderator and social motivation as controlled variable. Sample of 200 undergraduate and postgraduate students, aged between 18-25 years old were systematic and simple randomly assigned to one out of four conditions which were manipulated by a picture and a short description of the robot. The robots of each condition were designed to have different appearance (humanlike and not humanlike) and also behavior (predicted/controlled and not predicted/controlled). Afterward, participants completed the perceived anthropomorphism, trust in robot, acceptance in robot questionnaires. Results from Paths analysis showed that physical appearance similarity had relationship with a statistically significant on anthropomorphism. The effect of physical appearance similarity increased in the condition of unpredicted and uncontrollable robot. Furthermore, robot with highly humanlike appearance with unpredicted and uncontrolled behavior had the effect on participants’ feeling and it also increased trust and acceptance toward robots.Keywords: S-E-E-K Theory, Physical Appearance Similarity, Controllability, Anthropomorphism, Trust, Acceptance, Robot

Understanding the Interaction between Older Adults and Soft Service Robots: Insights from Robotics and the Technology Acceptance Model

As the world’s population increasingly ages, we need technological solutions such as robotics technology to assist older adults in their daily tasks. In this regard, we examine soft service robots’ potential to help care for the elderly. To do so, we developed and tested the degree to which they would accept a soft service robot that catered to their functional needs in the home environment. We used embodied artificial to develop an in-house teleoperated human-sized soft service robot that performed object-retrieval tasks with a soft gripper. Using an extended technology acceptance model as a theoretical lens, we conducted a study with 79 older adults to examine the degree to which they would accept a soft service robot in the home environment. We found perceived ease of use, perceived usefulness, and subjective norms as significant predictors that positively influenced older adults’ intention to adopt and use soft service robots. However, we also found that perceived anxiety and perceived likability did not significantly predict older adults’ intention to adopt and use soft service robots. We discuss the implications, limitations, and future research directions that arise from these findings

The Role of Social and Technological Predispositions in Participation in the Sharing Economy

This study contributes to the growing body of research on drivers of participation in the sharing economy. We extend the well-established technology acceptance model and include layers of personality architecture related to the social nature of these markets (extraversion) and their technology intermediation (technology proclivity). Findings from a cross-sectional survey (n = 292) show that extraversion is related directly to the intention to use sharing economy applications, such as in home gig services, and related indirectly to likelihood to use these technologies and to engage as a provider of such services, through technology proclivity and the technology’s perceived usefulness

Analyse des prédicteurs de l’attribution de caractéristiques humaines à l’intelligence artificielle dans un contexte criminologique

L’omniprésence de l’intelligence artificielle (IA) est indéniable, que ce soit dans son utilisation de tous les jours jusqu’à son utilisation dans divers domaines comme la médecine ou le service à la clientèle. Cette technologie fera sans aucun doute partie intégrante de la vie de chaque individu dans un futur rapproché. Par conséquent, il est aussi indéniable de penser que ces technologies feront éventuellement partie intégrante du domaine de la criminologie, que ce soit par le biais de la police prédictive, d’algorithmes d’aide à la décision en termes de récidive, de l’utilisation de la reconnaissance faciale dans les tâches policières ou peut-être de l’accompagnement aux citoyens dans les processus judiciaires. En conséquence, il est important de comprendre comment les individus faisant partie du domaine perçoivent l’intelligence artificielle afin de mieux comprendre comment les individus percevront l’IA lors de l’implantation potentielle d’une telle technologie. Cette étude vise donc à mettre en lumière l’effet des facteurs sociodémographiques, des perceptions de l’intelligence artificielle en général ainsi que des facteurs reliés aux types de personnalités en relation avec le fait d’attribuer des caractéristiques humaines à l’IA. Pour y arriver, des données ont été colligées à l’aide d’un questionnaire en ligne posant des questions notamment sur les facteurs sociodémographiques, les différents aspects de la personnalité ainsi que sur l’utilisation et les perceptions générales des technologies d’intelligence artificielle. Les résultats ont permis de souligner que les facteurs sociodémographiques tels que le genre et le domaine d'activité professionnelle, soit de travailler dans la police, sont corrélés à l’échelle Attribution de caractéristiques humaines à l’IA (ACHIA). En effet, le genre est associé à une corrélation positive faible indiquant que le fait d’être un homme a une légère influence sur l’échelle ACHIA. Ensuite, le fait de travailler dans la police est inversement corrélé signifiant que ceux travaillant dans la police peuvent avoir une légère tendance à répondre en défaveur de l’échelle ACHIA. En revanche, les traits de personnalité n'ont pas montré d'effet significatif sur cette perception, ce qui permet de mettre en lumière que certaines recherches supplémentaires sont nécessaires pour approfondir cette relation complexe. De plus, l’analyse de régression linéaire multiple a permis d’obtenir un modèle de prédiction de l’attribution de caractéristiques humaines à l’IA. Le modèle de prédiction de l’ACHIA est présenté dans le tableau 9 du chapitre résultats. Ce modèle a permis de faire ressortir que seules les variables « Bonnes connaissances par rapport à l’intelligence artificielle » et « IA a le potentiel de remplacer les tâches professionnelles » sont capable de prédire le fait d’attribuer des caractéristiques humaines à l’IA. Ainsi, plus une personne a de bonnes connaissances par rapport à l’intelligence artificielle, plus celle-ci aura tendance à attribuer des caractéristiques humaines à l’intelligence artificielle. De même, plus une personne pense que l’IA a le potentiel de remplacer ses tâches professionnelles, plus celle-ci attribuera des caractéristiques humaines à l’IA. Enfin, la recherche sur l’attribution de caractéristiques humaines à l’intelligence artificielle devrait être explorée davantage dans le futur afin d’approfondir notre compréhension de la relation complexe entre les humains et cette technologie émergente.The omnipresence of artificial intelligence (AI) is undeniable, whether in its everyday use or in various fields such as medicine or customer service. This technology will undoubtedly become an integral part of every individual's life in the near future. Therefore, it is also undeniable to think that these technologies will eventually become an integral part of the field of criminology, whether through predictive policing, decision-making algorithms regarding recidivism, the use of facial recognition in police tasks, or perhaps assisting citizens in judicial processes. Consequently, it is important to understand how individuals in the field perceive artificial intelligence to better understand how individuals will perceive AI during the potential implementation of such technology. This study aims to shed light on the effect of sociodemographic factors, perceptions of artificial intelligence in general, and personality-related factors in relation to attributing human characteristics to AI. To achieve this, data was collected using an online questionnaire, posing questions on sociodemographic factors, different aspects of personality, as well as the use and perceptions of artificial intelligence technologies. The results emphasized that sociodemographic factors such as gender and professional field, specifically working in the police, are correlated with the Attribution of Human-like Characteristics to AI (ACHIA) scale. Indeed, gender is associated with a weak positive correlation indicating that being a man slightly influences the ACHIA scale. Furthermore, working in the police is inversely correlated, meaning that those working in the police may have a slight tendency to respond unfavorably to the ACHIA scale. On the other hand, personality traits did not show a significant effect on this perception, highlighting the need for further research to deepen this complex relationship. Additionally, multiple linear regression analysis yielded a prediction model for attributing human characteristics to AI. The ACHIA prediction model is presented in Table 9 of the results chapter. This model revealed that only the variables "Good knowledge of artificial intelligence" and "AI has the potential to replace professional tasks" are capable of predicting the attribution of human characteristics to AI. Thus, the more a person has good knowledge of artificial intelligence, the more likely they are to attribute human characteristics to artificial intelligence. Similarly, the more a person thinks that AI has the potential to replace their professional tasks, the more they will attribute human characteristics to AI. Finally, research on attributing human characteristics to artificial intelligence should be further explored in the future to deepen our understanding of the complex relationship between humans and this emerging technology

Examining Successful Management Practices Among Senior Women Using AI Technology

Artificial intelligence (AI) technology innovations are envisioned to intensify the digital ecosystem affecting management practices and the quality of life for female senior business leaders in the United States. The purpose of this qualitative, transcendental phenomenology study was to examine the lived experiences that some female senior business leaders, ages 55 - 95, face using AI technology in decision making. The conceptual framework included the technology acceptance model and the mindspace model. Data was collected through semistructured interviews with 12 successful female senior business leaders from nine different industries in the United States. The Van Kaam method, as supported by Moustakas\u27 theoretical process, was used to analyze the data. Descriptive and inductive coding was used to uncover and categorize the found themes: (a) AI technology is beneficial, (b) leadership and change management, (c) technology adaptation and acceptance, (d) decision making and communication, and (e) information sharing and privacy. This study may contribute to positive social change as a benefit to other seniors by strengthening their AI technology decision making experiences, leadership, and supporting community awareness in addition to influencing positive social change across management and business platforms

Adapting robot behavior to user preferences in assistive scenarios

Robotic assistants have inspired numerous books and science fiction movies. In the real world, these kinds of devices are a growing need in amongst the elderly, who while life continue requiring more assistance. While life expectancy is increasing, life quality is not necessarily doing so. Thus, we may find ourselves and our loved ones being dependent and needing another person to perform the most basic tasks, which has a strong psychological impact. Accordingly, assistive robots may be the definitive tool to give more quality of life by empowering dependent people and extending their independent living. Assisting users to perform daily activities requires adapting to them and their needs, as they might not be able to adapt to the robot. This thesis tackles adaptation and personalization issues through user preferences. We 'focus on physical tasks that involve close contact, as these present interesting challenges, and are of great importance for he user. Therefore, three tasks are mainly used throughout the thesis: assistive feeding, shoe fitting, and jacket dressing. We first describe a framework for robot behavior adaptation that illustrates how robots should be personalized for and by end- users or their assistants. Using this framework, non-technical users determine how !he robot should behave. Then, we define the concept of preference for assistive robotics scenarios and establish a taxonomy, which includes hierarchies and groups of preferences, grounding definitions and concepts. We then show how the preferences in the taxonomy are used with Al planning systems to adapt the robot behavior to the preferences of the user obtained from simple questions. Our algorithms allow for long-term adaptations as well as to cope with misinformed user models. We further integrate the methods with low-level motion primitives that provide a more robust adaptation and behavior while lowering the number of needed actions and demonstrations. Moreover, we perform a deeper analysis in Planning and preferences with the introduction of new algorithms to provide preference suggestions in planning domains. The thesis then concludes with a user study that evaluates the use of the preferences in the three real assistive robotics scenarios. The experiments show a clear understanding of the preferences of users, who were able to assess the impact of their preferences on the behavior of the robot. In summary, we provide tools and algorithms to design the robotic assistants of the future. Assistants that should be able to adapt to the assisted user needs and preferences, just as human assistants do nowadays.Els assistents robòtics han inspirat nombrosos llibres i pel·lícules de ciència-ficció al llarg de la història. Però tornant al món real, aquest tipus de dispositius s'estan tornant una necessitat per a una societat que envelleix a un ritme ràpid i que, per tant, requerirà més i més assistència. Mentre l'esperança de vida augmenta, la qualitat de vida no necessàriament ho fa. Per tant, ens podem trobar a nosaltres mateixos i als nostres estimats en una situació de dependència, necessitant una altra persona per poder fer les tasques més bàsiques, cosa que té un gran impacte psicològic. En conseqüència, els robots assistencials poden ser l'eina definitiva per proporcionar una millor qualitat de vida empoderant els usuaris i allargant la seva capacitat de viure independentment. L'assistència a persones per realitzar tasques diàries requereix adaptar-se a elles i les seves necessitats, donat que aquests usuaris no poden adaptar-se al robot. En aquesta tesi, abordem el problema de l'adaptació i la personalització d'un robot mitjançant preferències de l'usuari. Ens centrem en tasques físiques, que involucren contacte amb la persona, per les seves dificultats i importància per a l'usuari. Per aquest motiu, la tesi utilitzarà principalment tres tasques com a exemple: donar menjar, posar una sabata i vestir una jaqueta. Comencem definint un marc (framework) per a la personalització del comportament del robot que defineix com s'han de personalitzar els robots per usuaris i pels seus assistents. Amb aquest marc, usuaris sense coneixements tècnics són capaços de definir com s'ha de comportar el robot. Posteriorment definim el concepte de preferència per a robots assistencials i establim una taxonomia que inclou jerarquies i grups de preferències, els quals fonamenten les definicions i conceptes. Després mostrem com les preferències de la taxonomia s'utilitzen amb sistemes planificadors amb IA per adaptar el comportament del robot a les preferències de l'usuari, que s'obtenen mitjançant preguntes simples. Els nostres algorismes permeten l'adaptació a llarg termini, així com fer front a models d'usuari mal inferits. Aquests mètodes són integrats amb primitives a baix nivell que proporcionen una adaptació i comportament més robusts a la mateixa vegada que disminueixen el nombre d'accions i demostracions necessàries. També fem una anàlisi més profunda de l'ús de les preferències amb planificadors amb la introducció de nous algorismes per fer suggeriments de preferències en dominis de planificació. La tesi conclou amb un estudi amb usuaris que avalua l'ús de les preferències en les tres tasques assistencials. Els experiments demostren un clar enteniment de les preferències per part dels usuaris, que van ser capaços de discernir quan les seves preferències eren utilitzades. En resum, proporcionem eines i algorismes per dissenyar els assistents robòtics del futur. Uns assistents que haurien de ser capaços d'adaptar-se a les preferències i necessitats de l'usuari que assisteixen, tal com els assistents humans fan avui en dia

Adapting robot behavior to user preferences in assistive scenarios

Aplicat embargament des de la data de defensa fins el 24 de juliol de 2020Robotic assistants have inspired numerous books and science fiction movies. In the real world, these kinds of devices are a growing need in amongst the elderly, who while life continue requiring more assistance. While life expectancy is increasing, life quality is not necessarily doing so. Thus, we may find ourselves and our loved ones being dependent and needing another person to perform the most basic tasks, which has a strong psychological impact. Accordingly, assistive robots may be the definitive tool to give more quality of life by empowering dependent people and extending their independent living. Assisting users to perform daily activities requires adapting to them and their needs, as they might not be able to adapt to the robot. This thesis tackles adaptation and personalization issues through user preferences. We 'focus on physical tasks that involve close contact, as these present interesting challenges, and are of great importance for he user. Therefore, three tasks are mainly used throughout the thesis: assistive feeding, shoe fitting, and jacket dressing. We first describe a framework for robot behavior adaptation that illustrates how robots should be personalized for and by end- users or their assistants. Using this framework, non-technical users determine how !he robot should behave. Then, we define the concept of preference for assistive robotics scenarios and establish a taxonomy, which includes hierarchies and groups of preferences, grounding definitions and concepts. We then show how the preferences in the taxonomy are used with Al planning systems to adapt the robot behavior to the preferences of the user obtained from simple questions. Our algorithms allow for long-term adaptations as well as to cope with misinformed user models. We further integrate the methods with low-level motion primitives that provide a more robust adaptation and behavior while lowering the number of needed actions and demonstrations. Moreover, we perform a deeper analysis in Planning and preferences with the introduction of new algorithms to provide preference suggestions in planning domains. The thesis then concludes with a user study that evaluates the use of the preferences in the three real assistive robotics scenarios. The experiments show a clear understanding of the preferences of users, who were able to assess the impact of their preferences on the behavior of the robot. In summary, we provide tools and algorithms to design the robotic assistants of the future. Assistants that should be able to adapt to the assisted user needs and preferences, just as human assistants do nowadays.Els assistents robòtics han inspirat nombrosos llibres i pel·lícules de ciència-ficció al llarg de la història. Però tornant al món real, aquest tipus de dispositius s'estan tornant una necessitat per a una societat que envelleix a un ritme ràpid i que, per tant, requerirà més i més assistència. Mentre l'esperança de vida augmenta, la qualitat de vida no necessàriament ho fa. Per tant, ens podem trobar a nosaltres mateixos i als nostres estimats en una situació de dependència, necessitant una altra persona per poder fer les tasques més bàsiques, cosa que té un gran impacte psicològic. En conseqüència, els robots assistencials poden ser l'eina definitiva per proporcionar una millor qualitat de vida empoderant els usuaris i allargant la seva capacitat de viure independentment. L'assistència a persones per realitzar tasques diàries requereix adaptar-se a elles i les seves necessitats, donat que aquests usuaris no poden adaptar-se al robot. En aquesta tesi, abordem el problema de l'adaptació i la personalització d'un robot mitjançant preferències de l'usuari. Ens centrem en tasques físiques, que involucren contacte amb la persona, per les seves dificultats i importància per a l'usuari. Per aquest motiu, la tesi utilitzarà principalment tres tasques com a exemple: donar menjar, posar una sabata i vestir una jaqueta. Comencem definint un marc (framework) per a la personalització del comportament del robot que defineix com s'han de personalitzar els robots per usuaris i pels seus assistents. Amb aquest marc, usuaris sense coneixements tècnics són capaços de definir com s'ha de comportar el robot. Posteriorment definim el concepte de preferència per a robots assistencials i establim una taxonomia que inclou jerarquies i grups de preferències, els quals fonamenten les definicions i conceptes. Després mostrem com les preferències de la taxonomia s'utilitzen amb sistemes planificadors amb IA per adaptar el comportament del robot a les preferències de l'usuari, que s'obtenen mitjançant preguntes simples. Els nostres algorismes permeten l'adaptació a llarg termini, així com fer front a models d'usuari mal inferits. Aquests mètodes són integrats amb primitives a baix nivell que proporcionen una adaptació i comportament més robusts a la mateixa vegada que disminueixen el nombre d'accions i demostracions necessàries. També fem una anàlisi més profunda de l'ús de les preferències amb planificadors amb la introducció de nous algorismes per fer suggeriments de preferències en dominis de planificació. La tesi conclou amb un estudi amb usuaris que avalua l'ús de les preferències en les tres tasques assistencials. Els experiments demostren un clar enteniment de les preferències per part dels usuaris, que van ser capaços de discernir quan les seves preferències eren utilitzades. En resum, proporcionem eines i algorismes per dissenyar els assistents robòtics del futur. Uns assistents que haurien de ser capaços d'adaptar-se a les preferències i necessitats de l'usuari que assisteixen, tal com els assistents humans fan avui en dia.Postprint (published version

Strategies to Protect Against Security Violations During the Adoption of the Internet of Things by Manufacturers

Security violations have been one of the key factors affecting manufacturers in adopting the Internet of Things (IoT). The corporate-level information technology (IT) leaders in the manufacturing industry encounter issues when adopting IoT due to security concerns because they lack strategies to protect against security violations. Grounded in Roger’s diffusion of innovations theory, the purpose of this qualitative multiple case study was to explore strategies corporate-level IT leaders use in protecting against security violations while adopting IoT for manufacturers. The participants were senior IT leaders in the eastern region of the United States. The data collection process included interviews with corporate-level IT leaders (n = 6) and examination of company documents (n = 10). The data analysis process involved searching patterns for words, codes, or themes and their relationships to confirm the findings. During analysis, four major themes emerged: relevance of securing IoT devices in IoT adoption, identifying and separating personal and confidential data from analytical data, adequate budget for securing IoT network devices and infrastructure as key factors in IoT adoption, and risk mitigation policy relevant to securing IoT devices. The implications for positive social change include the potential for corporate-level IT leaders to develop tools that will detect threats, prevent malicious attacks, and monitor IoT networks for any IoT device vulnerabilities. Improved protection from security violations may result in more efficient ways for people to use natural resources. Additionally, there may be a wider usage of smartphones connected to IoT to simplify people’s lives

Robots in Nursing - False Rhetoric or Future Reality?: How might robots contribute to hospital nursing in the future? A qualitative study of the perspectives of roboticists and nurses

Introduction. The challenge of the global nursing shortage coupled with a rising healthcare demand prompts consideration of technology as a potential solution. Technology in the form of robots is being developed for healthcare applications but the potential role in nursing has not been researched in the UK. Methods A three-phased qualitative study was undertaken: interviews with 5 robotic developers (Phase 1); nine focus groups /interviews with 25 hospital Registered Nurses (RN) in Phase 2, and 12 nurse leaders in four focus groups (Phase 3). Data was analysed using framework analysis for Phase 1 and reflexive thematic analysis for Phase 2 and 3 data based on the Fundamentals of Care framework. Results Roboticist interviews confirmed that a taxonomy of potential robotic automation was a useful tool for discussing the role of robots. In Phase 2, RNs described activities that robots might undertake and commented on those which they should not. RNs more readily agreed that robots could assist with physical activities than relational activities. Six potential roles that robots might undertake in future nursing practice were identified from the data and which have been labelled as advanced machine, social companion, responsive runner, helpful co-worker, proxy nurse bot, and feared substitute. Three cross-cutting themes were identified: • a fear of the future; • a negotiated reality and • a positive opportunity. In phase 3, nurse leaders considered the RN results and four themes were identified from their discussions: • First impressions of robot in nursing; • The essence of nursing; • We must do something and • Reframing the future. Conclusions Robots will be a future reality in nursing, playing an assistive role. Nursing must become technically proficient and engage with the development and testing of robots. Nurse leaders must lead policy development and reframe the narrative from substitution to assistance. A number of navigational tools have been developed including a taxonomy of nursing automation and the six robotic roles which may be useful to inform future debate in nursing