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Artificial intelligence (AI) is increasingly embedded into service firms' operations. However, production systems and operations management scholars have not yet examined if AI-empowered service operations are positively judged by service customers. To bridge this gap, this study draws on the three-factor theory of customer satisfaction applied to online review data, to capture the effect of AI-empowered service operations on overall customer satisfaction, operationalised by means of online review ratings. Based on text analytics techniques applied to a sample of more than 50,000 TripAdvisor ORs covering 35 international hotels in Asia and America, we develop a penalty–reward contrast analysis. The findings reveal that the effects of customer interaction with mechanical AI on customer satisfaction with service operations are asymmetric: positive customer interaction with mechanical AI positively and significantly influences overall customer satisfaction with AI-empowered service operations, whereas negative customer interaction with mechanical AI does not significantly alter customer satisfaction. Taken together, these findings suggest that mechanical AI constitutes a key element of resilient AI-empowered service operations. [ABSTRACT FROM AUTHOR]

The intelligent soft robot has received wide attention from both academia and the industry due to its remarkable adaptability. It performs intelligent behavioral learning and evolved morphologies in unpredictable environmental conditions. However, designing a soft robot with a well-adapted morphology involves searching through a large number of possible structures. Furthermore, to learn control tasks in diverse environments, the robot performs computationally intensive numerical simulations, which is time-consuming for evaluating the performance of robots. To address both issues, a multi-fidelity evolutionary algorithm is proposed, which consists of three main components. Firstly, a niching-based fidelity adjustment strategy is introduced to significantly reduce the evaluation cost by training the controller of each robot for only a small number of simulation steps. In particular, considering the estimation errors of the low-fidelity evaluation, the population is divided into multiple subpopulations with different fidelity levels for parallel optimization. Secondly, an effective morphology transfer strategy is proposed to improve the quality of offspring by transferring the local structure of robots in different subpopulations. Finally, a fast local search is developed to enhance the search efficiency of the algorithm without performing additional control simulations. The experimental results on 31 test tasks demonstrate that the proposed algorithm outperforms the SOTA design algorithms on 25 test tasks, especially when the computational budget is limited. Compared to the baseline algorithms, our algorithm reduces the computational cost by 60$\%$% while achieving similar performance. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

AbstractThis study examines the impact of robotic gastronomic experiences (RGE) on customer emotional and functional value, delight, and loyalty in service robot restaurants. Using validated scales from prior research, a 5-point Likert scale survey was conducted with 302 participants via Amazon Mechanical Turk, targeting individuals with experience in such establishments. The analysis, performed with SmartPLS 4.0, utilized partial least squares-path modelling (PLS-PM) to test the hypothesized relationships. Results show that RGE, characterized by competence and coolness, positively affects emotional and functional values, which significantly influence delight and loyalty. Methodological measures were implemented to address common method variance (CMV), confirming the robustness of the findings. This research underscores the potential of service robots to enhance customer satisfaction and loyalty through efficient and engaging service delivery, providing valuable insights for integrating advanced robotics in the hospitality industry. [ABSTRACT FROM AUTHOR]

Service robots could provide benefits for hotels in terms of productivity and even service quality. However, hotel managers in Spain (and Europe) have not implemented them as yet or are doing so only tentatively. This could be due to the fact that current literature is fragmented (i.e., dominated by hybrid models) and inconclusive and does not provide consistent guidelines for practitioners. This research narrows this gap by showing that a parsimonious application of the Theory of Planned Behavior is useful to predict attitude and buying intention toward robot-served hotels. Through an empirical analysis of 600 consumers in Spain, our findings indicate that hotel managers could focus on intrinsic motivations (enjoyment and challenge). They should also communicate the advantages of hybrid human–robot teams, in which the robots supplement humans instead of substituting them. The anthropomorphism of robots should not be emphasized as it seems to be perceived as a threat. Although utilitarian benefits matter, they seem to have a subordinate salience. [ABSTRACT FROM AUTHOR]

Service robots are of paramount importance in both industrial and residential environments. Nevertheless, these robots frequently encounter obstacles in the form of perilous elements present in their surroundings, which may result in their malfunction and hinder their optimal performance. Hence, autonomous robot applications heavily rely on coverage path planning algorithms, which facilitate the execution of area coverage operations in a streamlined and economical manner. The article presents a coverage path planning algorithm for service robots that is based on a backtracking procedure. The proposed method attempts to strike a balance between the safety of the cleaning automaton and the extent of coverage. Following straight-forward instructions, the mobile robot (MR) initially generates a spiral or zigzag trajectory. It examines each position for possible backtracking locations. When obstacles and visited positions obstruct the path, the improved A* algorithm is utilized to return to known unvisited regions. Subsequently, the robot proceeds to sweep the regions until no remaining back-tracking sites remain. Simulation of the method on the Robot Operating System (ROS) verifies that it is possible to restrict the movement of high-risk robots. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Hospitality & Tourism Technology is the property of Emerald Publishing Limited and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study consolidates the existing literature and develops a conceptual framework for a holistic understanding of the tourist experience with AI-enabled technologies. It entails performance analysis of scientific actors, thematic analysis, and content analysis of the intellectual structure of the research. The key technologies shaping the tourist experience include service robots, personalized recommender systems, IoT, chatbots, AR/VR, big data analytics, text mining, text analytics, and NLP. The study contributes by offering performance and science mapping in the field of AI for the tourist experience. Accordingly, several future research directions and a consolidated conceptual framework are proposed. [ABSTRACT FROM AUTHOR]

The number of restaurants that have added service robots to their operation has increased recently; however, research on their impact on customers' perceptions and intentions is scarce. The purpose of this study was to investigate, while considering consumer innovativeness, the effects of customers' perceived service newness and hedonic benefits on their perceived service quality and value and, consequently, their willingness to select a restaurant that uses robot services. Data were collected from 675 respondents who watched a video about service robots at restaurants. Structural equation modeling and hierarchical regression were used to analyze data. The results indicated that participants' perceived service newness influenced their perceived hedonic benefits directly and their perceived service quality indirectly. Perceived service newness negatively influenced customers' perceived value, and perceived value enhanced their willingness to select a restaurant with service robots. It was also found that consumer innovativeness moderated the relationship between perceived hedonic benefits and perceived service quality. [ABSTRACT FROM AUTHOR]

The increasing introduction of intelligent, interactive robots in the service industry raises concerns about the potential dehumanization of service provision and its influences on corporate brand perceptions. To avoid adverse effects, new service development (NSD) managers seemingly favor service robots that feature anthropomorphic design metaphors, so they appear more human‐like. The current research investigates explicitly how customers' perception of a robot's anthropomorphic design metaphors might spill over to affect corporate brand perceptions. Study 1, a picture‐based scenario study with 109 participants, reveals the impact of anthropomorphic design metaphors on untested corporate brand outcomes, such as brand trust and brand experience. Then Study 2, a video‐based scenario study with 530 participants, addresses whether these effects depend on the service context. In Study 3, a field study of 393 participants, the authors examine how anthropomorphic design metaphors influence other firm‐related outcomes (e.g., shopping enjoyment, sales). The combined results confirm that anthropomorphic design metaphors strongly affect brand trust and brand experience, as well as other critical firm‐related outcomes; they also reveal notable context effects, such that customers of people‐processing (e.g., care services) and mental‐stimulus‐processing (e.g., shopping assistance) services appear more likely to use anthropomorphic design metaphors as corporate brand cues. Our research encourages NSD managers and scholars to consider the effects of introducing anthropomorphic service robots on corporate brands. [ABSTRACT FROM AUTHOR]

In a multi-objective problem, no single solution optimally satisfies all objectives. Thus, the challenge is to find a balance between conflicting objectives. The decision-making necessarily requires human intervention. The person responsible for selecting the most appropriate solution among all the trade-off solutions is the decision maker (DM). The DM seeks to approach only the solutions that best suit her/his preferences. Since there is plenty of specialized literature showing that emotions play a critical role in decision-making, we aim to incorporate them into the decision-making process. To elicit emotions that can be quantifiable, we propose the Emotional Assessment Method. The method presents a simulation of the objectives to be optimized that represents the consequences of each decision. Using this methodology, the decision maker assesses the emotions evoked by each presented simulation to guide his/her search for solutions that satisfy his/her preferences. As a case study, we aim to identify subjects’ preferences towards robot behaviors. Seventy-two subjects with varying levels of familiarity with robots (divided into two datasets) participated in the experiments. We concluded that the method elicits subjects’ emotions while observing the consequences of the robot’s performance. Also, we found out that it is possible to identify subjects’ preferences based on both the context and the emotions to select the robot’s behavior. [ABSTRACT FROM AUTHOR]

Service robots are becoming more prevalent in the service industry, yet the impact they have on customer experiences is not well-documented. This study identified consumer emotions toward service robots in hotels and restaurants, focusing on how specific attributes of these robots affect consumer emotions. Furthermore, it explored which countries generate the most comments on these service robot attributes and the nature of consumer emotions within these regions. Utilizing sentiment analysis, the study examined 52,513 English-language tweets about hotel and restaurant service robots. Additionally, logistic regression analysis helped quantify the impact of robot attributes – categorized as physical, functional, and relational – on different classes of emotions. The findings reveal that customers of restaurants and hotels were most influenced by the physical and relational attributes of service robots, respectively, with functional attributes having the least impact. The logistic regression analysis offered significant insights into how service robot attributes influence consumer experiences in hotels and restaurants. [ABSTRACT FROM AUTHOR]

Recent research on the use of robotics in hospitality and tourism has called attention to tourists' post-adoption behavior, including value co-creation. Guided by the stimulus–organism–response (SOR) framework, a mixed-method study was conducted to explore the antecedents and mechanisms of tourists' value co-creation behavior with robots. Qualitative interviews revealed robots' anthropomorphism and economic rewards as tourists' key concerns regarding robot-provided services. Subsequently, partial least squares structural equation modeling was employed to investigate how robot service attributes influenced tourist–robot co-creation, including participation and citizenship behaviors, and enhancing tourists' experiences at tourist attractions. Findings indicated that three types of perceived experiential value fully mediated the relationships between robots' degree of anthropomorphism and tourists' value co-creation behavior, as well as between economic rewards and participation behavior. However, the relationship between economic rewards and citizenship behavior was partially mediated. Theoretical and practical implications are discussed. [ABSTRACT FROM AUTHOR]

We investigate whether the perceived ethicalness of police actions changes when police follow an AI-robot's advice. We assess whether perceived ethicalness of police violence is higher when police follow robot advice to arrest a passer-by, compared to no robot advice to arrest the passer-by. Using neutralization theory, we test how blame-shifting occurs. When police violently arrest an innocent passer-by, the violence is neutralized when the decision was made following the AI-robot. Perceived ethicalness of police violence is higher when the passer-by is a terrorist, and police violence against a passer-by is neutralized through 'denial of victim' and 'denial of injury'. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of technology in tourism is increasing day by day. Service robots are now on the agenda. The success of developments that envisage a new experience for consumer tourists depends on the accurate prediction of tourists' possible attitudes and behaviors and the influencing antecedents. This study examined consumer tourists' attitudes and usage intentions towards service robots on the multidimensional basis of anthropomorphism and technological readiness (TR). A survey was conducted with 1018 tourists in Türkiye face‐to‐face and using QR codes. Findings showed that attitude towards service robots (AR) mediated the relationship between anthropomorphism, technological readiness and intention to use (IS). Participating tourists anthropomorphically focused on the physical appearance of the robots rather than their functions and internal structures. Service robot experience is still limited in the tourism industry. Therefore, the empirical results obtained are guiding theory and practice in terms of academic studies and tourism service providers. [ABSTRACT FROM AUTHOR]

This study examined how to form behavioural intentions in regards to using the extended Theory of Planned Behaviour (TPB) model. This study more specifically proposed the effect of the overall service quality on attitude. In addition, it was hypothesized that attitude, subjective norm and perceived behavioural control have a positive influence on intention to use. Lastly, this study investigated the moderating role of the employee type, which included robots and humans, in the proposed model. The study collected data from 320 travellers who received services from only service robots and from 316 travellers who received services only from human staff at Incheon Airport in South Korea. The data analysis indicated that the overall service quality has a positive influence on attitude. In addition, it was discovered that intentions to use is positively affected by attitude, subjective norm and the perceived behavioural control. Finally, the type of employee moderated the relationship between the overall service quality and attitude. [ABSTRACT FROM AUTHOR]

Karel Čapek’s play R.U.R. (Rossum’s Universal Robots) was a big hit when it was first performed in the 1920s. Written in Czech, it was soon translated into English and other languages and performed throughout Europe and the Anglo-American world. It was quickly adapted for radio and television as well. Through this success, it had a significant influence on technologists, writers, and general audiences. [ABSTRACT FROM AUTHOR]

Among the many challenges robots encounter in the mining industry, exploring confined environments receives significant attention. This work tackles problems associated with robot communication in hazardous and confined environments, where its cluttered and extensive nature frequently precludes traditional cable-based and wireless solutions. Our methods resort to off-the-shelf long-range radio frequencies to profile the signal propagation behaviour over the geometrical map to assist navigation algorithms that seek to preserve the connection. We consider mathematical models to predict signal power behaviour and serve as input to path planning. We also propose a semi-autonomous leader-follower scheme, with signal repeater units forming a mobile wireless network to enable inspection in hard-to-reach locations. Finally, we present a multi-robot connection-aware system, combining path planning based on radio signal power with multiple robot navigation. Results show the applicability of the proposed solutions, generating single and multi-robot paths for optimal signal reception based on power estimation, thus enabling operations in remote and isolated areas with no line-of-sight between the command base and the robotic inspection device. Experiments conducted in long corridors and in a representative mining environment using the EspeleoRobô and Pioneer platforms demonstrate significant improvements over the traditional communication methods for robotic operation regarding communication quality and inspection range limits. [ABSTRACT FROM AUTHOR]

To aid humans in everyday tasks, robots need to know which objects exist in the scene, where they are, and how to grasp and manipulate them in different situations. Therefore, object recognition and grasping are two key functionalities for autonomous robots. Most state-of-the-art approaches treat object recognition and grasping as two separate problems, even though both use visual input. Furthermore, the knowledge of the robot is fixed after the training phase. In such cases, if the robot encounters new object categories, it must be retrained to incorporate new information without catastrophic forgetting. To resolve this problem, we propose a deep learning architecture with an augmented memory capacity to handle open-ended object recognition and grasping simultaneously. In particular, our approach takes multi-views of an object as input and jointly estimates pixel-wise grasp configuration as well as a deep scale- and rotation-invariant representation as output. The obtained representation is then used for open-ended object recognition through a meta-active learning technique. We demonstrate the ability of our approach to grasp never-seen-before objects and to rapidly learn new object categories using very few examples on-site in both simulation and real-world settings. Our approach empowers a robot to acquire knowledge about new object categories using, on average, less than five instances per category and achieve 95 % object recognition accuracy and above 91 % grasp success rate on (highly) cluttered scenarios in both simulation and real-robot experiments. A video of these experiments is available online at: [ABSTRACT FROM AUTHOR]

The importance and effectiveness of the applications of service robots in human daily life is constantly increasing. It is used in various fields, such as healthcare, education, shopping, tourism, hospitality and safety, to name a few, however employing this advanced technology for use in traditional environments is not a simple matter. Users must first be prepared to accept dealing with these social bots to obtain the desired services, and here a positive user experience is vital, and should be considered an important part of the bot development process. Excessive use of service robots applications and work to expand their reach in society, without a serious study of the potential negative effects as a result of incompatibility with user experience, may lead to many problems that may affect society as a whole. This has already happened in a number of cases as a result of bot developers neglecting to define UX goals due to lack of knowledge or time constraints. Therefore, the aim of this research is to focus on studying the user experience as a basic determinant of the service robots design process, and identifying each of the ways to benefit from the results of evaluating the user experience towards service robots, as well as the challenges that the industrial designer must deal with, leading to the most common and effective ways to evaluate User experience in relation to social robots and service delivery. To provide early feedback in a way that allows dealing with potential errors at an early stage of design. And to highlight one of those modern areas of products, which is the service robot, and to determine the role of the industrial designer in the design of the service robot, especially with regard to studying and monitoring user behavior and its impact on service robot design and improvement. Targeted service quality. This study emphasizes the need for bot developers to acquire the required theoretical and practical knowledge of how to conduct successful user experience assessment.

Robotic services’ popularity continues to increase due to technological advancements, labour shortages, and global crises. Yet, while providing these services, robots are subject to occasional physical interruption by humans to them, thus restricting their functioning and, at times, leading to failure. To investigate this issue, the present study examined the role of third-party human interference in service robot failures and its effects on the observers’ attitudes towards and willingness to engage with the robot. We manipulated human interference resulting in different robotic service failures in two online scenario-based experiments. The results revealed that individuals held less favourable attitudes towards a failed service robot without (vs. with) physical human interference, and they were less willing to engage with the failed service robot without (vs. with) physical human interference. The perceived deservingness of the robot accounted for this effect, moderated by the person’s self-efficacy regarding robots. The results are discussed with their implications for not only the theory of service failures and human-service robot interactions but also for robotic service providers. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive study on fall recognition and forecasting for reconfigurable stair-accessing robots by leveraging deep learning techniques. The proposed framework integrates machine learning algorithms and recurrent neural networks (RNNs), specifically Long Short-Term Memory (LSTM) and Bidirectional LSTM (BiLSTM), for fall detection of service robots on staircases. The reconfigurable stair-accessing robot sTetro serves as the platform, and the fall data required for training models are generated in a simulation environment. The two machine learning algorithms are compared and their effectiveness on the fall recognition task is reported. The results indicate that the BiLSTM model effectively classifies falls with a median categorical accuracy of 94.10% in simulation and 90.02% with limited experiments. Additionally, the BiLSTM model can be used for forecasting, which is practically valuable for making decisions well before the onset of a free fall. This study contributes insights into the design and implementation of fall detection systems for service robots used to navigate staircases through deep learning approaches. Our experimental and simulation data, along with the simulation steps, are available for reference and analysis via the shared link. [ABSTRACT FROM AUTHOR]

Service scholars seem to have empirically overlooked the impact of service robots in the overall customer evaluation of tourism services. This study addresses this gap by leveraging three-factor theory and electronic Word-Of-Mouth data to assess human-robot interaction' influence on customer satisfaction. Text analytics are deployed alongside a penalty-reward contrast technique on almost 70,000 online reviews spanning 44 hotels worldwide that incorporated service robots into their operations. Customer satisfaction with hospitality services is significantly increased by positive service robots' performance, while no significant effect is associated with negative service robots' performance. The traveler type does not moderate the relationship between service robots' performance and customer satisfaction. These findings, confirmed through Propensity Score Matching, reveal that service robots constitute an "excitement factor" in hospitality service offerings, thus providing a strong incentive for their integration into the workforce. Policymakers are urged to proactively facilitate the transition to a more automated service economy. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Xi'an Jiaotong University is the property of Editorial Office of Journal of Xi'an Jiaotong University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: Service robot technology is increasingly gaining prominence in the field of artificial intelligence. However, persistent limitations continue to impede its widespread implementation. In this regard, human motion pose estimation emerges as a crucial challenge necessary for enhancing the perceptual and decision-making capacities of service robots. Method: This paper introduces a groundbreaking model, YOLOv8-ApexNet, which integrates advanced technologies, including Bidirectional Routing Attention (BRA) and Generalized Feature Pyramid Network (GFPN). BRA facilitates the capture of inter-keypoint correlations within dynamic environments by introducing a bidirectional information propagation mechanism. Furthermore, GFPN adeptly extracts and integrates feature information across different scales, enabling the model to make more precise predictions for targets of various sizes and shapes. Results: Empirical research findings reveal significant performance enhancements of the YOLOv8-ApexNet model across the COCO and MPII datasets. Compared to existing methodologies, the model demonstrates pronounced advantages in keypoint localization accuracy and robustness. Discussion: The significance of this research lies in providing an efficient and accurate solution tailored for the realm of service robotics, effectively mitigating the deficiencies inherent in current approaches. By bolstering the accuracy of perception and decision-making, our endeavors unequivocally endorse the widespread integration of service robots within practical applications. [ABSTRACT FROM AUTHOR]

With the advancement of artificial intelligence technology, service robots have become increasingly prevalent in various consumption scenarios. While prior studies have investigated the link between the anthropomorphism of service robots and consumers' interaction willingness, the results have been inconsistent. Therefore, it is necessary to examine the impact of specific features of service robots on consumers' interaction willingness. This research employs a pre-test and three experiments to test how service robots' cuteness influences consumers' willingness to interact. Specifically, Study 1 used a single factor between-subject design (19.5% male, Mage = 21.74, SD = 0.69, Nhigh cuteness = 39, Nlow cuteness = 38), revealing that service robots' cuteness increases consumers' willingness to interact. Study 2 also employed a single factor between-subject design (27.5% male, Mage = 20.14, SD = 0.53, Nhigh cuteness = 69, Nlow cuteness = 73), indicating that perceived pleasure mediates the influence of service robots' cuteness on consumers' interaction willingness. Moreover, Study 3 (26.5% male, Mage = 20.65, SD = 0.59, N = 170) showed that service type moderates the impacts of service robots' cuteness on consumers' willingness to interact through a two-factor between-subject design. Specifically, for hedonic services, the higher cuteness of service robots leads to a greater willingness to interact. In contrast, for functional services, service robots' cuteness does not significantly affect consumers' willingness to interact. This research makes key contributions to the field of human-robot interaction and provides valuable insights for enterprises to strengthen the implementation of service robot strategies. [ABSTRACT FROM AUTHOR]

The need to improve service delivery efficiency and enhance passenger satisfaction is driving most airport operators to increasingly adopt robotics as part of their service delivery infrastructure. This study examined the factors influencing the adoption of service robots in Nigerian airports. It specifically explored the effects of UTAUT dimensions on the adoption of service robots in selected Nigerian Airports. The study adopted cross-sectional survey design, and data were obtained from 241 airport staff using structured questionnaires. Hypothesized relationships were tested using multiple regression analysis. Results showed that performance expectancy, effort expectancy, social influence and facilitating conditions had significant positive effects on the adoption of service robots in Nigerian airports, although social influence and facilitating conditions respectively had greater influence in the adoption decision. It was, therefore, concluded that marketers of aviation service robots should consistently promote the unique benefits and performance expectations of their products in order to demonstrate the potential capabilities of these technologies in improving operational efficiencies. [ABSTRACT FROM AUTHOR]

Humanoid store staff is increasingly common. Many retailers rely on automation to address the mounting pressure for cost savings. Meanwhile, humanoid store staff can influence customer experience negatively. People feel stressed and eerie about interacting with humanoids. In this article, I investigate one way to improve people's attitudes toward humanoids. People's negative attitudes towards humanoids, such as fear or a sense of eeriness, can be substantially mitigated when they are in a crowded environment. People in a crowded environment do not fear robots or find robots uncanny while people in a relatively uncrowded environment do. I refer to this phenomenon as the "crowd effect." Being in a crowded environment substantially changes how people evaluate and respond to humanoids. The effect is mediated by risk aversion tendency. [ABSTRACT FROM AUTHOR]