隐私计算理论在酒店机器人服务中的应用：应用研究概述、隐私侵入性边界条件、研究空白与案例发现

… need to overcome customers’ privacy concerns. Based on privacy calculus theory, this study investigated the nonlinear relationship between SRA and customer privacy concerns. Three …

Abstract Technological advancements have made artificial intelligence and robotic systems more functional, intelligent as well vulnerable. This development caused fresh discussions about service robots in tourism and hospitality service settings where privacy is crucial. One of these discussions is the privacy of personal information and the attitude toward service robots. With a focus on current discussions, this study aims to explore the relationship between the perceived benefits and risks of disclosing personal information and the intention to use service robots. This study also hypothesized that willingness to disclose information privacy influence the intention to use service robot. In this study, an online survey was used as a method of quantitative research. A total of 541 responses were received during the data collection process. Model and hypotheses were tested using partial least squares structural equation modelling. The results of this research showed that perceived personalization and personal innovativeness were associated with the perceived benefits of information disclosure. Meanwhile, it was found that the unauthorized secondary use of personal data and perceived policy effectiveness were negatively related to the perceived risks of information disclosure. Overall, the results indicated that perceptions of disclosing personal information under privacy calculus were related to the intention to use a service robot.

ABSTRACT Artificial intelligence in hospitality is increasingly transforming the way we travel. Service robots collect, store, analyze, and act upon a continuous stream of private information as a by-product of human-robot interaction. As such, they invade consumers’ virtual and physical space and raise privacy challenges in AI settings. We conduct a survey study(n = 576) and validate a mediating contextualized model of consumers’ adoption decisions on service robots in hospitality from privacy concerns. Our findings highlight the interplay between perceived risk and perceived benefit in shaping service robots’ adoption decisions is partially mediated by privacy concerns. Our findings also highlight the mechanism of privacy concerns, conceptualized as psychological constructs of collection, control and awareness of privacy practices, as an important addition to the established multiple chain mediating effect. The insights explain how consumers calculate privacy between perceived risk and benefit, and help reconcile a fundamental tension among consumers, how to avail benefits of privacy by improving the privacy awareness and control associated with the collection of private information in hospitality.

… Drawing on privacy calculus theory and a socio-technical interaction perspective, this study … settings with three types of service robots: a hospitality welcoming robot, a room service …

… address the customer uncontrollable privacy paradox posed by service robots in the tourism … at this hotel. Upon entering the hotel room, they were greeted by an intelligent robot shown …

ABSTRACT The paper aims to investigate the evolving role of service robots in the hospitality industry, particularly in light of the increasing demand for contactless services. The authors propose a mixed-method research approach to explore how tourist trust is formed regarding service robots and what risks tourists perceive when interacting with them. For this, two studies were conducted. Initially, themes were drawn based on qualitative research. Emerging themes were compared with extant literature, and clear mapping with privacy calculus theory, artificially intelligent device use acceptance, and TAM theory were observed. Data was collected from 512 respondents who traveled and availed of hotel services recently. Perceived benefits, Perceived Risks, and enjoyment positively influenced initial trust towards robots. Overall, this research seeks to provide insights into how tourist trust can be cultivated in the context of service robots, ultimately contributing to more effective implementation strategies within the hospitality sector.

… among mobile hotel booking users within the framework of Privacy Calculus Theory. Their … concerns mediate consumers' adoption decisions regarding service robots in the hospitality …

… privacy control. Results of this study also supported the privacy calculus theory in that customers weigh privacy risks … trust dynamics: Understanding tourist confidence in service robots in …

ABSTRACT The imperative data-driven digital transformation in tourism and hospitality has sparked renewed scholarly and practical urgency. However, research on this prominent topic remains fragmented and limited. This study systematically reviewed and evaluated 83 articles published on Q1 SSCI-listed tourism and hospitality journals over the past decade (2015–2024) to map the current landscape and highlight research deficits. This review reveals customers’ heterogenous privacy concerns across technologies and critically evaluates dominant theoretical frameworks. By synthesizing these insights, this study contributes to the research domain by highlighting underexplored research areas and suggesting future research avenues.

Service robots are already being used in various roles, such as hotel receptionists, retail sales assistants, and guides at museums and airports. Their potential is vast and continues to expand. With the advent of large language models, robots that were once technically sophisticated machines are becoming highly intelligent. They can now answer any question and make product usage suggestions to customers. This has opened up completely new possibilities, especially in customer service. While the benefits of service robots are evident, these developments also pose risks and raise ethical concerns. In an effort to better understand the fragmented research field, the purpose of this scoping review is to synthesize the last two decades of research on ethical considerations in customer–robot interactions. We analyzed the existing literature from a substantive and theoretical perspective to provide an overview of key concepts/theories and to discuss strengths and weaknesses of the reviewed literature. In addition, we used network visualization to create a knowledge structure of the research field and highlight developments over time. Integrating the findings of more than 55 studies, we developed an overarching framework with five key pillars and highlighted the importance of ethical customer–robot service interactions. Finally, we identified avenues for future research.

… of data privacy. The study extends the traditional risk-benefit perspective of Privacy Calculus Theory … into privacy control. Insights from these findings can help hoteliers reconcile guests’ …

… customers' privacy concerns and behavioral intentions toward interacting with hospitality robots. … artificial intelligence-powered anthropomorphic robots were performed. The exploratory …

ABSTRACT The purpose of this study is to examine the relationships among customer trust toward voice Artificial Intelligence (AI) in guestrooms, information privacy concerns associated with this technology, intention to use, and intention to stay at a hotel providing this technology. This study further examined whether the research model can be applied to different cultures by collecting data from U.S. and Singaporean customers. The hypotheses were tested via structural equation modeling and multiple-group analysis. The findings indicated that trust toward voice AI had a positive effect on intention to use this technology in both groups, and this effect was stronger for the U.S. group. Concern of information collection via voice AI had a negative effect on intention to use in both groups, and this effect was stronger for the Singaporean group. Intention to use voice AI in guestrooms was positively related to intention to stay at a hotel providing this technology.

… the link between privacy concerns and the intention to use social robots. As social robots are … physical objects and spaces (eg, by entering private rooms and using personal objects). …

This study empirically and comprehensively explores consumers’ ethical perceptions of autonomous service robots (ASRs) in hotels. Under the triangulation approach, this study has identified eight themes of consumer perceived ethical issues (privacy, security, safety, transparency, fairness, socialization, autonomy, and responsibility). Each theme can be explained from two dimensions: ethical issues arise during the interaction (i.e., ubiquitous surveillance, excessive data, unidentified risks, service disclosure, inaccessibility, dehumanization, selection of services, and service recovery), and ethical issues can be raised by the characteristics of ASRs (i.e., privacy infringement, malicious use, malfunctions, untrustworthiness, biased features, job replacement, inflexibility, and self-identified solutions). This study is the first to propose ethical issues of ASRs from two dimensions with different intelligence levels, and to highlight ethical issues during hotel service interactions. The findings contribute to ethics studies of service robots from consumers’ perspectives and offer managerial insights to reduce ethical concerns and enhance ASRs usage in hotels.

… that the interaction effect between privacy concerns and service type … robots. The findings suggest that individuals with high privacy concerns are less willing to utilize service robots in …

This study proposed and tested a theoretical framework that investigated the influences of perceived risk and information security on hotel customers’ intention to use service robots. In …

Abstract In recognizing the increase in the use of service robots by service industries, identifying the structure of trust in intelligent robots is crucial for tourism studies. This paper first proposes a model of multifaceted trust in service robots comprised of three constructs – performance, process, and purpose – and, second, tests the trust model that considers institution-based trust, trusting belief, and intention. As a result, this paper identified a higher-order formative construct of trust in service robots with the highest importance for a performance construct (Study 1). The antecedents of the multifaceted trust in tourism service robots are then identified (Study 2). This study provides important theoretical and methodological contributions to the fields of information technology and tourism.

ABSTRACT This study employs mixed methods to investigate the impacts of consumer perceived ethical issues regarding service robots in tourism and hospitality. The qualitative stage reveals ethical issues under two critical dimensions: interaction-based and robot feature-based. These findings establish a second-order structural model based on the Unified Theory of Acceptance and Use of Technology and the Initial Trust Model. The results demonstrate that factors, except effort expectancy and interaction-based ethical issues, influence adoption. Initial trust mediates the impacts between ethical issues and adoption intention. Innovativeness moderates the relationship between initial trust and adoption intention, whereas age has no moderating effect.

Abstract The outbreak of COVID-19 in 2019 has posed an unprecedented threat to the global economy. Due to the pandemic, the hotel industry has undergone fundamental changes. In particular, social distancing technology (SDT) has become an indispensable tool in this industry and has played a vital role in its recovery. This study explores the factors that affect customer trust in using SDT in hotels during COVID-19 and how this trust affects their booking intention. The research uses social exchange theory (SET) as the theoretical basis and measures the benefits and perceived risks of using SDT through the benefit-risk method. In addition, the technology acceptance model (TAM) is introduced, and two attributes are added: perceived ease of use and perceived usefulness. A total of 437 questionnaires were distributed, of which 405 were valid. Research results show that benefits, perceived usefulness, and perceived ease of use positively affect trust in SDT, while privacy concerns have a negative impact. Health risks and social rewards are not directly related to trust in SDT.This study provides ideas for future research on the health aspects of SDT by studying the benefits and health risks brought about by its use. For relevant practitioners, hotels should reasonably incorporate SDT into their daily operations and conduct regular disinfection to reduce the risks caused by direct and indirect contact to protect the lives and health of both customers and workers.

Purpose Drawing on the Service Robot Acceptance Model (sRAM) proposed by Wirtz et al. (2018), this study aims to examine how functional and social-emotional antecedents affect relational elements and the critical functions that trust and rapport play in robot acceptance in hotel services. Additionally, this study incorporates customer characteristics into the modified sRAM. Design/methodology/approach Consistent partial least squares (PLSc) was used to test the proposed model utilizing data collected from 456 Chinese customers. Findings The results indicated that effort expectancy and performance expectancy positively affect hotel guests’ trust toward and rapport with service robots. However, the effect of social influence on trust and rapport is insignificant. Additionally, perceived humanness and perceived social interactivity positively influence rapport, and perceived social presence positively affects both trust and rapport. Furthermore, trust and rapport positively influence hotel guests’ acceptance of service robots. The results also revealed the moderating role of age. Originality/value This study contributes to service robot literature by providing insights into how functional and social-emotional factors affect relational factors and the key role of relational factors in robot acceptance based on the sRAM. This study also advances this body of knowledge by highlighting the moderating effect of age.

Abstract Although AI service robots have been widely deployed in service businesses, customer privacy concerns during AI-human interaction still need to be taken seriously. Drawing upon social response theory, this study explores how perceived experience influences customer privacy concerns during AI-human interaction from the mind perception perspective. Based on two-wave longitudinal survey, 263 valid data were collected. The results of SEM and bootstrapping approach analysis show that perceived experience negatively affects customer privacy concerns; hedonic value plays a mediating role in this negative relationship; the negative effect is also affected by the chain mediating role of hedonic value and trust; ideal self-congruence enhances this negative effect and the positive relationship between perceived experience and hedonic value. The findings broaden the idea of reducing customer privacy concerns during AI-human interaction and provide a theoretical reference for AI service robot designers.

… through the mediation of privacy concerns. Study 1 confirms the … AI devices trigger stronger privacy concerns than nonhumanoid AI … personal information and privacy issues effectively. …

PurposeService robots are expected to become increasingly common, but the ways in which they can move around in an environment with humans, collect and store data about humans and share such data produce a potential for privacy violations. In human-to-human contexts, such violations are transgression of norms to which humans typically react negatively. This study examines if similar reactions occur when the transgressor is a robot. The main dependent variable was the overall evaluation of the robot.Design/methodology/approachService robot privacy violations were manipulated in a between-subjects experiment in which a human user interacted with an embodied humanoid robot in an office environment.FindingsThe results show that the robot's violations of human privacy attenuated the overall evaluation of the robot and that this effect was sequentially mediated by perceived robot morality and perceived robot humanness. Given that a similar reaction pattern would be expected when humans violate other humans' privacy, the present study offers evidence in support of the notion that humanlike non-humans can elicit responses similar to those elicited by real humans.Practical implicationsThe results imply that designers of service robots and managers in firms using such robots for providing service to employees should be concerned with restricting the potential for robots' privacy violation activities if the goal is to increase the acceptance of service robots in the habitat of humans.Originality/valueTo date, few empirical studies have examined reactions to service robots that violate privacy norms.

Many Human-Robot Interaction (HRI) researchers are exploring the use of healthcare robots. Due to the sensitive nature of care, privacy concerns play a significant role in determining robot utility and adoption. While HRI research has explored some dimensions of privacy for robots in general, to our knowledge, no prior work has empirically studied how human-like robot design affects people ’ s privacy and utility perceptions of robots across different healthcare contexts and tasks. We conducted a 3 × 3 × 3 study (n = 239) to understand these relationships, varying robot Human Likeness (HL) (low, medium, and high) and scenario/task type (hospital waiting room/robot check-in support, hospital patient room/robot mobility support, home care/robot neuro-rehabilitation support) via a mixed between-within subjects design. To our knowledge, this is one of the first studies that operationalizes complex constructs of privacy, healthcare, and HL across multiple realistic healthcare contexts, with a high degree of cognitive fidelity. Our results suggest the tasks and contexts in which privacy is considered in healthcare contexts with robots is more impactful than other factors like robot HL appearance. In particular, some settings include more complex tradeoffs between privacy and utility for robots than others. For example, HRI researchers and practitioners who want to build healthcare robots intended for the home may encounter the greatest challenges for balancing privacy risks. Finally, for the community, we demonstrate that design fiction animations can be a useful way to facilitate cognitive fidelity for supporting studies in HRI and serving as a bridge between narrative methods and the use of real-world robots.

While the privacy implications of social robots have been increasingly discussed and privacy-sensitive robotics is becoming a research field within human–robot interaction, little empirical research has investigated privacy concerns about robots and the effect they have on behavioral intentions. To address this gap, we present the results of an experimental vignette study that includes antecedents from the privacy, robotics, technology adoption, and trust literature. Using linear regression analysis, with the privacy-invasiveness of a fictional but realistic robot as the key manipulation, we show that privacy concerns affect use intention significantly and negatively. Compared with earlier work done through a survey, where we found a robot privacy paradox, the experimental vignette approach allows for a more realistic and tangible assessment of respondents' concerns and behavioral intentions, showing how potential robot users take into account privacy as consideration for future behavior. We contextualize our findings within broader debates on privacy and data protection with smart technologies.

For robots to become integrated into our daily environment, they must be designed to gain sufficient trust of both users and bystanders. This is in particular important for social robots including those that assume the role of a mediator, working towards positively shaping relationships and interactions between individuals. One crucial factor influencing trust is the appropriate handling of personal information. Previous research on privacy has focused on data collection, secure storage, and abstract third-party disclosure risks. However, robot mediators may face situations where the disclosure of private information about one person to another specific person appears necessary. It is not clear if, how, and to what extent robots should share private information between people. This study presents an online investigation into appropriate robotic disclosure strategies. Using a vignette design, participants were presented with written descriptions of situations where a social robot reveals personal information about its owner to support pro-social human-human interaction. Participants were asked to choose the most appropriate robot behaviors, which differed in the level of information disclosure. We aimed to explore the effects of disclosure context, such as the relationship to the other person and the information content. The findings indicate that both the information content and relationship configurations significantly influence the perception of appropriate behavior but are not the sole determinants of disclosure-adequacy perception. The results also suggest that expected benefits of disclosure and individual general privacy attitudes serve as additional influential factors. These insights can inform the design of future mediating robots, enabling them to make more privacy-appropriate decisions which could foster trust and acceptance.

Privacy is crucial for healthy relationships, but robots will impact our privacy in new ways—this warrants a new area of research. This paper presents work from the first workshop on privacy-sensitive robotics. We identify the seven research themes that should comprise privacy-sensitive robotics research in the near future: data privacy; manipulation and deception; trust; blame and transparency; legal issues; domains with special privacy concerns; and privacy theory. We intend for the research directions proposed for each of these themes to serve as a roadmap for privacy-sensitive robotics research.

When encountering social robots, potential users are often facing a dilemma between privacy and utility. That is, high utility often comes at the cost of lenient privacy settings, allowing the robot to store personal data and to connect to the internet permanently, which brings in associated data security risks. However, to date, it still remains unclear how this dilemma affects attitudes and behavioral intentions towards the respective robot. To shed light on the influence of a social robot’s privacy settings on robot-related attitudes and behavioral intentions, we conducted two online experiments with a total sample of N  = 320 German university students. We hypothesized that strict privacy settings compared to lenient privacy settings of a social robot would result in more favorable attitudes and behavioral intentions towards the robot in Experiment 1. For Experiment 2, we expected more favorable attitudes and behavioral intentions for choosing independently the robot’s privacy settings in comparison to evaluating preset privacy settings. However, those two manipulations seemed to influence attitudes towards the robot in diverging domains: While strict privacy settings increased trust, decreased subjective ambivalence and increased the willingness to self-disclose compared to lenient privacy settings, the choice of privacy settings seemed to primarily impact robot likeability, contact intentions and the depth of potential self-disclosure. Strict compared to lenient privacy settings might reduce the risk associated with robot contact and thereby also reduce risk-related attitudes and increase trust-dependent behavioral intentions. However, if allowed to choose, people make the robot ‘their own’, through making a privacy-utility tradeoff. This tradeoff is likely a compromise between full privacy and full utility and thus does not reduce risks of robot-contact as much as strict privacy settings do. Future experiments should replicate these results using real-life human robot interaction and different scenarios to further investigate the psychological mechanisms causing such divergences.

… privacy in human-robot interaction, which we call “privacy-sensitive robotics.” Our understanding of “… [127] studied the trade-off between filtering the robot’s video feed to protect user …

… In addition, it is interesting to study the trade-off between respecting privacy and providing service … would or would not tolerate privacy invasions for the benefits from using the robot. …

Voice Activated Personal Assistants (VAPA) are unique and different from other Information Systems (IS) due to their personalized, intelligent, and human-like behavior. Given the unique characteristics of these VAPA’s, current technology adoption models are not comprehensive enough for explaining the usage of these systems. While trust and privacy have been identified as relevant issues affecting adoption of VAPA’s, both these have been treated in a simplistic fashion that is not effective keeping in mind the complex nature of these factors. Moreover, being “always on”, VAPA’s are intrusive by nature: another aspect that current research has overlooked. Drawing on current findings in IS and artificial intelligence, we propose two different types of trust (cognitive and emotional) together with their antecedents (anthropomorphism, intelligence, VAPA privacy concern, household privacy concern, vendor & third-party privacy concern, and government privacy concern). The moderation effect of perceived intrusiveness on usage behavior is also examined. The proposed research model is empirically validated with data obtained from 466 VAPA users in India using a Structure Equation Modelling approach. We observe that perceived anthropomorphism does not affect emotional trust, whereas the effect of perceived intelligence on cognitive trust is significant. Social privacy concerns like VAPA and household privacy affect both forms of trust, whereas the effect of institutional privacy category is weak with only vendor & third-party privacy concern affecting emotional trust. Additionally, the findings establish the moderating role of perceived intrusiveness in dampening and negatively influencing the usage of VAPA’s, with a stronger effect for large households.

Mobile robots are the only in-store technology capable of mimicking the typical approach tactics used by human agents. Yet this unique and crucial feature has been overlooked in previous research about robots in commercial contexts. Based on proxemics and personal space invasion frameworks, the current research investigates what happens when a robot emulates the behavior of a human assistant and approaches consumers to initiate an interaction in-store. Four experimental studies reveal that the approaching robot is considered more intrusive than the approaching salesperson, reducing satisfaction toward the retailer and leading to mitigated behavioral expectations such as reduced likelihood of seeking advice or increased likelihood of leaving the store. It is also perceived as more intrusive than the tested alternatives (standing still or reactive). Yet, practical solutions exist to reduce backfiring effects – a reactive robot, the ability to refuse interaction, or having a salesperson as a backup option. These findings offer significant theoretical implications for human–robot interaction research in a business context while also presenting key managerial considerations for robot design and implementation in retail.

ABSTRACT This study evaluates different levels of proactivity in socially assistive robots (SARs) within elderly care, focussing on user acceptance and comfort. Using the mPLiCA model, we categorise proactive behaviours from basic presence to full autonomy. A series of video scenarios was created to represent varying degrees of proactive interactions with SHARA, a socially assistive robot. Participants rated these scenarios based on usefulness, appropriateness, perceived intrusiveness and naturalness. Results indicate that higher levels of proactivity are perceived as more useful but also more intrusive. Intermediate levels, particularly dialogue-based interactions, were the most acceptable. This research offers insights into designing SARs that balance autonomy and user comfort in elderly care, contributing to the development of more effective assistive technologies.

PurposeConsumers' perceptions of product intelligence affect their willingness to accept smart offerings. This paper explores how people perceive various smart products based on their smartness profiles, composed of five distinct smartness facets. Additionally, the study investigates how these perceptions of product intelligence impact consumers' evaluation of factors that either promote or impede the adoption of smart products. These factors are examined as potential mediators in the adoption process. This paper aims to determine if the value-based adoption model can be applied to a broad range of smart service systems.Design/methodology/approachConsumers assessed one of 28 smart products in a scenario-based quantitative study. Multilevel structural equation modeling (SEM) is used to test the conceptual model, taking the nested data structure into account.FindingsThe findings show that product smartness essentially enhances usage intention via adoption drivers (enjoyment and usefulness) and reduces usage intention via adoption barriers (intrusiveness). In particular, the ability to interact in a humanlike manner increases the benefits consumers perceive, which in turn increases consumer acceptance. Only the smartness characteristic of awareness impairs usage intention, mediated by the perceived benefits of enjoyment and usefulness.Originality/valueIn contrast to previous research, which usually focuses on single smart products, this work examines a variety of different products, which allows for better transferability of the results to other smart offerings. Furthermore, prior research has mainly focused on single facets of product smartness or researched smartness on an aggregated level. By considering the consumer perception of each smartness facet, the authors gain deeper insights into the perceptual differences regarding product smartness and how this affects technology adoption via conflicting key acceptance drivers and barriers.

Purpose Applying the Innovation Resistance Theory, this study aims to examine the effects of contextual and trait stressors on consumer engagement and wellbeing in the context of service robots. Design/methodology/approach Data were collected from 560 users who interacted with a service robot at a library and a museum. The data were analyzed using Smart PLS 4.0. Findings The findings show the significant negative effects of perceived intrusion on consumer engagement and wellbeing. In addition, technology anxiety exhibited a significant positive effect on consumer engagement and wellbeing, whereas consumer engagement predicted wellbeing. Moreover, the findings highlight the importance of consumer engagement as a key mediator between the stressors and wellbeing. Practical implications The findings equip service managers with the necessary information to effectively integrate service robots in an inclusive manner that resonates with consumer engagement and wellbeing. Originality/value This research uses field data to empirically validate the effects of contextual and trait stressors on consumer resistance to service robots.

People’s successful coexistence with robots strictly depends on people’s acceptance of robots’ presence in their daily activities. This is particularly relevant when the robot’s actions may interfere with or intrude on people’s activities, creating discomfort and possible rejection. We believe that people’s acceptance of a robot may vary depending on the activities they are involved in. In this study, we investigate the impact of a robot’s actions on people’s engagement in an activity while the robot has the task of monitoring them. We observed the behaviours of 18 older adults with respect to the robot while they were carrying out tasks that require different cognitive workloads (e.g., working at the PC, talking on the phone). We used subjective and objective metrics, such as social cues, to evaluate people’s engagement in the robot and their disengagement in their own tasks. We observed that people were distracted by the robot’s behaviours based on the cognitive loads required by their activity. Our results show that variation in people’s engagement in the robot and the task is affected by their perception of the usefulness of and trust in the robot, and by individuals’ personality traits and acceptance of the robot. People with higher trust in the robot, and a higher degree of conscientiousness and emotional stability, tend to continue with their task, paying less attention to the robot. We observed, in contrast, that a robot perceived as a social entity caught more easily their attention when people have a higher extroverted personality. Our findings also showed that variations in the affective and emotional demeanour of the participants are a predictor of their distraction to an external observer.

… Data collection is not unique to HRI and is adopted in many (if not … In contrast, robots adopt a two-way interaction (eg request and … and intrusive, increasing the complexity of interaction. …

While research in human–robot interaction is beginning to focus on the acceptance of domestic robots, there is little research on the potential adoption of these agents. Technology adoption is a complex phenomenon requiring not only positive perceptions of technology but also its value, along with product desire strong enough to lead to desired adoption behavior. Adoption of innovations also occurs in phases, from early adopters to mainstream consumers, then laggards. While the characteristics of technology early adopters generally have been researched extensively, there is no previous work that seeks to validate some of these variables for domestic robots specifically, and which draws from HRI research to further amend them. In this work, we determine how various consumer and robot characteristics affect assessments of home robot liking, privacy concerns, and purchasing intent. We find that five main consumer characteristics are associated with robot early adopters, and that surprisingly, income has a negative correlation with purchasing intent, specifically for the companion robot. We further compare predicted product liking to purchasing intent, showing that although robot acceptance is reasonably high for those in the mainstream, purchasing intent is low. For all market segments, perceptions of high price accounted for about 20–30% of the variance in intended purchasing, intended use 6–7%, belief in performance as advertised 4–6%, and design 3%. While privacy concerns were not influential to purchasing intentions held by early adopters, they were to mainstream users and laggards.

… adoption/acceptance. However, little is known about consumer reactance to service robots … This study, based on reactance theory, examines how perceived threat to freedom triggers …

Domestic robots and agents are widely sold to the grand public, leading us to ethical issues related to the data harvested by such machines. While users show a general acceptance of these robots, concerns remain when it comes to information security and privacy. Current research indicates that there’s a privacy-security trade-off for better use, but the anthropomorphic and social abilities of a robot are also known to modulate its acceptance and use. To explore and deepen what literature already brought on the subject we examined how users perceived their robot (Replika, Roomba©, Amazon Echo©, Google Home©, or Cozmo©/Vector©) through an online questionnaire exploring acceptance, perceived privacy and security, anthropomorphism, disclosure, perceived intimacy, and loneliness. The results supported the literature regarding the potential manipulative effects of robot’s anthropomorphism for acceptance but also information disclosure, perceived intimacy, security, and privacy.

Robots powered by Artificial Intelligence require continuous sensing to function and interact autonomously with their environment. This may create concerns for their users. Here, we describe a study in the context of an office environment equipped with autonomous delivery robots. Our study aims to understand the employees’ current comprehension of the data captured by the robots, the nature of their concerns and ways to mitigate those. We found that the employees have little knowledge about data capture and that providing such knowledge can go either way, reassuring or generating new concerns which are often contextual. We qualitatively analysed reasons for having or not having concerns. Our findings include that trust in the employer is an important factor limiting them.

Assistive robotic systems could be a suitable solution to support a variety of health and care services, help independent living, and even simulate affection, to reduce loneliness. However, adoption is limited by several issues, as well as user concerns about ethics, data security, and privacy. Other than the common threats related to internet connectivity, personal robotic systems have advanced interaction possibilities, such as audio, video, touch, and gestures, which could be exploited to gain access to private data that are stored in the robot. Therefore, novel, safer methods of interaction should be designed to safeguard users’ privacy. To solicit further research on secure and private multimodal interaction, this article presents a thorough study of the state-of-the-art literature on data security and user privacy in interactive social robotic systems for health and care. In our study, we focus on social robotics to assist older people, which is a global challenge that is receiving a great deal of attention from the robotics and social care communities. This application will have a significant positive impact on the economy and society, but poses various security and privacy issues. This article analyses the key vulnerable areas where data leakage could occur during a multimodal interaction with a personal assistive robotic system. Thus, blockchain with a resource-aware framework, along with a continuous multifactor authentication mechanism, are envisaged as a potential solution for making such systems secure by design; therefore, increasing trust, acceptability, and adoption. Among the key cybersecurity research challenges, it is crucial to create an intelligent mechanism that autonomously determines the right trade-off between continuous user prompts and system usability, according to data types and personal preferences.

… This chapter explores how the mainstreaming of robots might specifically affect privacy.It is not hard to imagine why robots raise privacy concerns. Practically by definition, robots are …

… Therefore, this paper examines the moderating role of robot anthropomorphism on the … Therefore, this study explores the public's usage intention for hotel service robots. The objectives …

This research investigates the factors influencing the intention to adopt AI-chatbots within the tourism industry considering the moderating role of privacy concerns, addressing a literature gap in adoption determinants. Hypotheses were tested using partial least squares (PLS) structural equation modelling, based on a sample of 1800 respondents who had engaged in tourism activities within the previous two years. The results reveal that perceived ease of use, perceived usefulness, interactivity, anthropomorphism and perceived intelligence positively influence attitudes towards AI-chatbots. Furthermore, the latter strongly predicts the intention to adopt these technologies. Privacy concerns were found to moderate the relationship between attitudes and the intention to adopt AI-chatbots. The findings provide actionable recommendations for tourism service providers, highlighting the need to enhance the interactive and smart characteristics of AI-chatbots while managing privacy concerns to foster user adoption. These recommendations are crucial for developing effective service strategies in the tourism sector. This research contributes to the literature by providing a comprehensive model that explains the factors influencing AI-chatbot adoption in the tourism field, filling the gap and giving practical guidance for decision-makers.

… Additionally, there may be concerns about the privacy and security of data collected by service robots [5], as well as the potential for technical malfunctions or errors, such as cutler …

… human) agents’ passive or mere presence affects consumer information privacy concerns—… express lesser privacy concerns in the presence of AI (vs. human) service agents, which in …

Enabled by technological advances, robot teachers have entered educational service frontlines. Scholars and policymakers suggest that during Human-Robot Interaction (HRI), human teachers should remain “in-the-loop” (i.e., oversee interactions between students and robots). Drawing on impression management theory, we challenge this belief to argue that robot teacher confidentiality (i.e., robot teachers not sharing student interactions with the human teacher) lets students make more use of the technology. To examine this effect and provide deeper insights into multiple mechanisms and boundary conditions, we conduct six field, laboratory and online experiments that use virtual and physical robot teachers (Total N = 2,012). We first show that students indeed make more use of a confidential (vs. nonconfidential) robot teacher (both physical and virtual). In a qualitative study (Study 2), we use structural topic modeling to inductively identify relevant mediators and moderators. Studies 3 through 5 provide support for these, showing two key mediators (i.e., social judgment concern and interaction anxiety) and two moderators (i.e., student prevention focus and teacher benevolence) for the effect of robot teacher confidentiality. Collectively, the present research introduces the concept of service robot confidentiality, illustrating why and how not sharing HRI with a third actor critically impacts educational service encounters. Graphical Abstract

The role of artificial intelligence (AI)‐based DoctorBots in improving healthcare and the medical industry is expected to increase significantly in the coming decades. However, contrary to the general view that AI can gradually replace human work, user acceptance of DoctorBots has become an obstacle to the development of AI medical diagnosis. Building on the service robot acceptance model (sRAM), this study investigates the potential of the functional, socioemotional, and relational elements of DoctorBots to reconcile the personalization–privacy paradox, thus enhancing user acceptance. Via two scenario‐based experiments with 398 participants, this study reveals that the negative influence of the personalization–privacy paradox on user acceptance is exacerbated when users' technology anxiety is high. In addition, an online survey of 400 DoctorBot users indicates that ease of use, subjective social norms, social presence, and rapport are effective in addressing both nonpersonalization (NPC) and privacy concerns (PVC). These findings suggest that the healthcare industry can leverage DoctorBots to implement self‐diagnosis. Specifically, DoctorBots' functional elements are effective in mitigating users' NPC, and their relational elements are effective in extenuating users' PVC.

… our theoretical understanding of consumer behavior and provide managers with a practical framework for addressing privacy challenges in the deployment of service robots. Second, …

Purpose This paper aims to explore the overall research question “How can artificial intelligence (AI) influence consumer information disclosure?”. It considers how anthropomorphism of AI, personalisation and privacy concerns influence consumers’ attitudes and encourage disclosure of their private information. Design/methodology/approach This research draws upon the personalisation-privacy paradox (PPP) and privacy calculus theory (PCT) to address the research question and examine how AI can influence consumer information disclosure. It is proposed that anthropomorphism of AI and personalisation positively influence consumer attitudes and intentions to disclose personal information to a digital assistant, while privacy concerns negatively affect attitude and information disclosure. Findings This paper develops a conceptual model based on and presents seven research propositions (RPs) for future research. Originality/value Building upon PPP and PCT, this paper presents a view on the benefits and drawbacks of AI from a consumer perspective. This paper contributes to literature by critically reflecting upon on the question how consumer information disclosure is influenced by AI. In addition, seven RPs and future research areas are outlined in relation to privacy and consumer information disclosure in relation to AI.

The prevalence of social robots is increasing, with examples such as customer service robots in malls and airports. This trend highlights the importance of transparency, particularly in data-sharing interactions with social robots operating in public spaces, where users may be asked to provide personal information to receive personalized experiences. This article investigates how design transparency influences user trust and data-sharing behavior in human-robot interactions. We conducted an experiment with 143 participants who interacted with the social robot ARI under two transparency conditions: low and high transparency. In the low-transparency condition, participants were informed about the data being collected and could choose to save or delete it. In the high-transparency condition, the robot additionally indicated the sensitivity level of each data item: low (e.g., scenario preference), medium (e.g., name and e-mail), and high (e.g., religious beliefs), allowing participants to make more informed decisions. Participants were presented with two scenarios: exploring city events and discovering local attractions. They received personalized recommendations based on their preferences, with the option to provide personal data (name, phone number, e-mail) for possible future communication. After the interaction, participants decided whether to save or delete the data they had shared. The results indicated that while transparency did not significantly affect trust in the robot, it influenced data-sharing behavior. In particular, participants in the high-transparency condition demonstrated more cautious behavior, opting to save less data and delete more. Furthermore, the results showed that both sensitivity levels and transparency influenced the participants’ data-sharing choices. Low-level sensitivity data led to the highest rates of saving and the lowest rates of deleting, while medium-level sensitivity data showed the opposite pattern. These findings highlight the need to align data categorization with user perceptions to address data sharing concerns more effectively.

Social robots are a type of robotics that focuses on creating intelligent and embodied machines capable of interacting and communicating with humans in a socially acceptable manner. However, these robots' potential to capture user data, including emotions, biometrics, and behavioural habits, raises significant privacy concerns that could influence users' intention to use and trust social robots. Therefore, there is a pressing need to synthesize a privacy model that helps unravel the complex behavioural processes underlying current and future HRI technologies. This work aims to contribute to the growing body of privacy-friendly robot design by proposing comprehensive guidelines that enable the development of trustworthy and transparent social robots that respect user privacy. We have established a set of theoretical constructs to address people's concerns regarding privacy across four dimensions: physical, informational, psychological, and social. Finally, guidelines are provided in each construct to enhance transparency and trust through compliance with laws like GDPR.

Trust is a very popular concept in human-robot interaction (HRI) to explain why and how people interact with robots. However, the definition of trust and the methods used to study the concept vary widely, often leading to confusion instead of insight. In this position paper, we discuss possible reasons for the confusion and disagreement by reviewing theory and methods. Our main criticism is that HRI researchers have recently taken an oversimplified approach by not adhering to the process model of trust. Instead, we have primarily measured perceived trustworthiness (often as a proxy for trust) and assumed that it accurately predicts behavior or is satisfactory as an end goal. In addition, many experimental paradigms fail to account for the critical elements of risk and vulnerability that are essential for trust to guide behavior. With this position paper, we aim to shed light on these ”trust issues” in HRI and to improve the HRI community’s approach by providing suggestions for enhancing the quality of future research.