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The current research examines how cultural factors shape consumer preferences for anthropomorphic AI services. The authors investigate the impact of cultural tightness‐looseness, a measure of the strictness of social norms and the tolerance for nonconformity, on the acceptance of anthropomorphic AI services. Through analysis of more than 12,000 product reviews and four experiments, the findings indicate that cultural tightness reduces consumer preference for AI services with anthropomorphic characteristics. The results pinpoint social interaction anxiety as a mediator, whereby increased anxiety due to high cultural tightness subsequently lowers the preference for anthropomorphic AI services. Perceived human identity threat as an alternative explanation for this effect is ruled out. The results further show that the effect of cultural tightness is context‐dependent, affecting preferences in public settings but not private ones. This research bridges a critical gap in knowledge about consumer preferences for AI services, highlighting the significant impact of cultural factors. These findings are crucial for designing products, crafting marketing strategies, and shaping policies in a world increasingly influenced by AI. [ABSTRACT FROM AUTHOR]

AI is considered a key driver of industrial transformation and a strategic technology that will shape future development. With AI services continuing to permeate various sectors, concerns have emerged about the ethics of AI. This study investigates the effects of different types of AI services (mechanical, thinking, and affective AI services) on consumers' attitudes through offline and online AI service experiments. We also construct a model to explore the mediating roles of identity threat and perceived control. The findings reveal that mechanical AI services negatively affect consumers' attitudes while thinking and affective AI services have a positive effect. Additionally, we explore how consumers' attitudes vary across different service scenarios and ethical judgments (utilitarianism and deontology). Our findings could offer practical guidance for enterprises providing AI services. [ABSTRACT FROM AUTHOR]

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization. [ABSTRACT FROM AUTHOR]

AI is considered a key driver of industrial transformation and a strategic technology that will shape future development. With AI services continuing to permeate various sectors, concerns have emerged about the ethics of AI. This study investigates the effects of different types of AI services (mechanical, thinking, and affective AI services) on consumers’ attitudes through offline and online AI service experiments. We also construct a model to explore the mediating roles of identity threat and perceived control. The findings reveal that mechanical AI services negatively affect consumers’ attitudes while thinking and affective AI services have a positive effect. Additionally, we explore how consumers’ attitudes vary across different service scenarios and ethical judgments (utilitarianism and deontology). Our findings could offer practical guidance for enterprises providing AI services.

Massive computational resources are required by a booming number of artificial intelligence (AI) services in the communication network of the smart grid. To alleviate the computational pressure on data centers, edge computing first network (ECFN) can serve as an effective solution to realize distributed model training based on data parallelism for AI services in smart grid. Due to AI services with diversified types, an edge data center has a changing workload in different time periods. Selfish edge data centers from different edge suppliers are reluctant to share their computing resources without a rule for fair competition. AI services-oriented dynamic computational resource scheduling of edge data centers affects both the economic profit of AI service providers and computational resource utilization. This letter mainly discusses the partition and distribution of AI data based on distributed model training and dynamic computational resource scheduling problems among multiple edge data centers for AI services. To this end, a mixed integer linear programming (MILP) model and a Deep Reinforcement Learning (DRL)-based algorithm are proposed. Simulation results show that the proposed DRL-based algorithm outperforms the benchmark in terms of profit of AI service provider, backlog of distributed model training tasks, running time and multi-objective optimization.

In recent times hotels have embraced innovative AI services to enhance the guest experience. These services encompass a wide range of applications, from automated check-in and check-out processes to AI- powered chatbots that provide instant responses to guest inquiries. Moreover guests can access and share their experiences with these AI services through online reviews, social media platforms and travel websites. Through this study the guest has accepted the new change in hospitality industry and has positive impact on their stay in hotels. Researcher talked about the positive aspects of technology; could do in addition to the negative. We also keep updated on what is new in the world of artificial intelligence. There are always new things happening with AI, just as there are new gadgets and apps. This research has answered the questions, How does AI affect guests when they stay in the hotel? Do guests appreciate it when AI is used to enhance their stay in a hotel, or do they disagree with it? We asked the guests about their preference. This research suggests that, it was noticed health had been a significant concern and as a result many activities smoothly transitioned from in-persons to online ones, guests wanted to do more things by themselves online, rather to interact with people. They may also be more concerned with being healthy and safe. As a result, this research helps us understand comprehending the necessity of Artificial Intelligence on guest experiences in the current scenario. [ABSTRACT FROM AUTHOR]

Advances in artificial intelligence (AI) have shaped today's user services, enabling enhanced personalization and better support. As such AI-based services inevitably require user data, the resulting privacy implications are de facto the unacceptable face of this technology. In this article, we categorize and survey the cuttingedge research on privacy and data protection in the context of personalized AI services. We further review the different protection approaches at three different levels, namely, the management, system, and AI levels-- showing that (i) not all of them meet our identified requirements of evolving AI services and that (ii) many challenges are addressed separately or fragmentarily by different research communities. Finally, we highlight open research challenges and future directions in data protection research, especially that comprehensive protection requires more interdisciplinary research and a combination of approaches at different levels. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) technology is rapidly changing the service industry. Automated social presence (ASP) is an essential construct in AI services. However, there is a lack of conceptualization and a valid measurement scale of ASP. Hence, the objective of the present study is to conceptualize, develop, and validate an ASP measurement scale. With four phases and 1027 Chinese respondents, this study followed rigorous scale development steps. Phase 1 conceptualized the ASP based on previous research. Phase 2 involved the development of measures, including generating items and validating content through individual interviews and expert assessments. Phase 3 involved ASP scale purification and refinement via exploratory factor analysis. In Phase 4, a new survey sample was collected to test the reliability, and validity through confirmatory factor analysis and structural equation modeling. The findings indicated that ASP is a multidimensional construct with five dimensions: social affability, empathy, responsiveness, communication versatility, and competence. Additionally, the developed scale showed sufficient reliability and validity. This study guides the design, development, and deployment of AI beings to enhance the consumer experience in AI services. • ASP captures user experience under functionalism and anthropomorphism in AI services. • Social affability, empathy, responsiveness, communication versatility, and competence • Individual interviews, expert assessments, and factor analyses • ASP positively influences continuance intention. • Organizations can use the ASP scale to detect and enhance user experience. [ABSTRACT FROM AUTHOR]

Background: Artificial intelligence (AI) is a term used to describe the use of computers and technology to emulate human intelligence mechanisms. Although AI is known to affect health services, the impact of information provided by AI on the patient-physician relationship in actual practice is unclear., Objective: The purpose of this study is to investigate the effect of introducing AI functions into the medical field on the role of the physician or physician-patient relationship, as well as potential concerns in the AI era., Methods: We conducted focus group interviews in Tokyo's suburbs with physicians recruited through snowball sampling. The interviews were conducted in accordance with the questions listed in the interview guide. A verbatim transcript recording of all interviews was qualitatively analyzed using content analysis by all authors. Similarly, extracted code was grouped into subcategories, categories, and then core categories. We continued interviewing, analyzing, and discussing until we reached data saturation. In addition, we shared the results with all interviewees and confirmed the content to ensure the credibility of the analysis results., Results: A total of 9 participants who belonged to various clinical departments in the 3 groups were interviewed. The same interviewers conducted the interview as the moderator each time. The average group interview time for the 3 groups was 102 minutes. Content saturation and theme development were achieved with the 3 groups. We identified three core categories: (1) functions expected to be replaced by AI, (2) functions still expected of human physicians, and (3) concerns about the medical field in the AI era. We also summarized the roles of physicians and patients, as well as the changes in the clinical environment in the age of AI. Some of the current functions of the physician were primarily replaced by AI functions, while others were inherited as the functions of the physician. In addition, "functions extended by AI" obtained by processing massive amounts of data will emerge, and a new role for physicians will be created to deal with them. Accordingly, the importance of physician functions, such as responsibility and commitment based on values, will increase, which will simultaneously increase the expectations of the patients that physicians will perform these functions., Conclusions: We presented our findings on how the medical processes of physicians and patients will change as AI technology is fully implemented. Promoting interdisciplinary discussions on how to overcome the challenges is essential, referring to the discussions being conducted in other fields., (©Masashi Tanaka, Shinji Matsumura, Seiji Bito. Originally published in JMIR Formative Research (https://formative.jmir.org), 18.05.2023.)