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Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

Purpose Past research has emphasised the potential for conversational artificial intelligence (AI) to disrupt services. Conversely, the literature recognises customer expectations as fundamental to service quality and customer satisfaction. However, the understanding of users’ expectations for conversational AI services is currently limited. Building upon previous research that has underscored the importance of users’ expertise, this study aims to provide valuable insights into the expectations of users with varying levels of expertise. Design/methodology/approach Forty-five semi-structured interviews were conducted, on three populations: experts, quasi-experts and non-experts from various countries including Japan, France and the USA. This includes 10 experts and 11 quasi-experts, as in professionals in conversational AI and related domains. And 25 non-experts, as in individuals without professional or advanced academic training in AI. Findings Findings suggest that users’ expectations depend on their expertise, how much they value human contact and why they are using these services. For instance, the higher the expertise the less anthropomorphism was stated to matter compared to technical characteristics, which could be due to a disenchantment effect. Other results include expectations shared by all users such as a need for more ethics including public interest. Originality/value The study provides insights into a key yet relatively unexplored area: it defines three major expectations categories (anthropomorphic, technical and ethical) and the associated expectations of each user groups based on expertise. To the best of the authors’ knowledge, it also highlights expectations never detected before as such in the literature such as explainability.

Purpose This study aims to evaluate Artificial Intelligence (AI) research in the hospitality industry based on the service AI framework (mechanical-thinking-feeling) and highlight prospective avenues for future inquiry in this growing domain. Design/methodology/approach This paper conceptualizes timely concepts supported by research spanning multiple domains. Findings This research introduces a novel classification for the domain of AI hospitality research. This classification encompasses prediction and pattern recognition, computer vision, NLP, behavioral research, and synthetic data generation. Based on this classification, this study identifies and elaborates upon five emerging research topics, each linked to a corresponding set of research questions. These focal points encompass the realms of interpretable AI, controllable AI, AI ethics, collaborative AI, and synthetic data generation. Originality/value This viewpoint provides a foundational framework and a directional compass for future research in AI within the hospitality industry. It pushes the industry forward with a balanced approach to leveraging AI to augment human potential and enrich customer experiences. Both the classification and the research agenda would contribute to the body of knowledge that will guide the industry toward a future where technology and human service coalesce to create unparalleled value for all stakeholders.

PurposeThe application of artificial intelligence (AI) in the customer market has completely changed customer behaviors. This study aims to investigate the customers' co-creation experiences with AI in the digital age.Design/methodology/approachAn online survey was used to collect data from 699 customers who had used AI-enabled banking services. Hypotheses were validated using partial least squares modeling.FindingsThe findings indicate that the customer response capabilities (e.g. perceived response expertise and perceived response speed) serve as the intermediate processes between the AI service quality and the overall co-creation experience with AI. Moreover, AI function-customer ability fit negatively moderates the direct relationship between the AI service quality and the overall co-creation experience with AI.Originality/valueThis study improves the current understanding of co-creation by investigating the human–machine co-creation (e.g. customer–AI co-creation) instead of human–human co-creation.

There is a growing demand for Green AI (Artificial Intelligence) technologies in the market and society, as it emerges as a promising technology. Green AI technologies are used to create sustainable solutions and reduce the environmental impact of AI. This paper focuses on describing the services of Green AI and the challenges associated with it at the community level. This article also highlights the accuracy levels of machine learning algorithms for various time periods. The process of choosing the appropriate input parameters for weather, locations, and complexity is outlined in this paper to examine the ML algorithms. For correcting the algorithm performance parameters, metrics like RMSE (root mean square error), MSE (mean square error), MAE (mean absolute error), and MPE (mean percentage error) are considered. Considering the performance and results of this review, the LSTM (long short-term memory) performed well in most cases. This paper concludes that highly advanced techniques have dramatically improved forecasting accuracy. Finally, some guidelines are added for further studies, needs, and challenges. However, there is still a need for more solutions to the challenges, mainly in the area of electricity storage.

PurposeArtificial intelligence (AI) customer service chatbots are a new application service, and little is known about this type of service. This study applies service quality, trust and satisfaction to predict users' continuance intention to use a food-ordering chatbot.Design/methodology/approachThe proposed model and hypotheses are tested using online questionnaire responses to collect users' perceptions of such services. One hundred and eleven responses of actual users were received.FindingsEmpirical results show that anthropomorphism and service quality, such as problem-solving, are the antecedents of trust and satisfaction, while satisfaction has the most significant direct effect on the users' intention.Originality/valueThe results provide further useful insights for service providers and chatbot developers to improve services.

Purpose This study aims to investigate how information quality and system quality influence the effectiveness of artificial intelligence (AI)-based recommendation service platforms. It integrates traditional information technology service quality (SQ) metrics with recommendation SQ measures, focusing on their impact on user satisfaction and behavior. This study further examines the moderating effects of standardization and customization on these relationships. Design/methodology/approach This study uses structural equation modeling to analyze data from 978 users of AI recommendation services. It evaluates the direct impacts of information quality (completeness, accuracy and format) and system quality (reliability, flexibility and timeliness) on recommendation quality (RQ). Findings The findings show significant positive effects of information quality and system quality on the quality of AI-generated recommendations, enhancing user satisfaction. This satisfaction is crucial for promoting continuous intention to use and positive word-of-mouth (WOM). This study also finds that standardization positively moderates the impact of RQ on WOM, whereas customization strengthens the relationship between satisfaction and continuous intention to use. Originality/value This research emphasizes the importance of quality metrics in shaping the efficacy of AI-based recommendation systems and highlights the need for a balance between standardization and customization to optimize user engagement and satisfaction. The findings offer valuable insights for AI service developers and marketers, emphasizing the significance of customized, high-quality recommendations to ensure sustained user engagement.

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.

Artificial intelligence technology can be utilized in scholarly communication to enhance research efficiency and productivity in various aspects, such as exploring research topics, recommending academic information, reviewing literature, and analyzing and visualizing data. ScienceON, a public information service for science and technology operated by a government-supported research institute in Korea, has developed a service that summarizes and translates research papers and provides explanations for keywords. Additionally, it enables in-depth exploration of the paper through additional questions about research topics, methods, results, and more. According to user feedback, the service has received excellent evaluations in terms of convenience, appropriateness, usability, and other aspects. In the future, there are plans to add a literature review function that allows for quick review of multiple documents to support users’ research activities. It is expected to enable users to conduct research more efficiently and effectively.

In recent years, with the continuous expansion of artificial intelligence (AI) application forms and fields, users’ acceptance of AI applications has attracted increasing attention from scholars and business practitioners. Although extant studies have extensively explored user acceptance of different AI applications, there is still a lack of understanding of the roles played by different AI applications in human–AI interaction, which may limit the understanding of inconsistent findings about user acceptance of AI. This study addresses this issue by conducting a systematic literature review on AI acceptance research in leading journals of Information Systems and Marketing disciplines from 2020 to 2023. Based on a review of 80 papers, this study made contributions by (i) providing an overview of methodologies and theoretical frameworks utilized in AI acceptance research; (ii) summarizing the key factors, potential mechanisms, and theorization of users’ acceptance response to AI service providers and AI task substitutes, respectively; and (iii) proposing opinions on the limitations of extant research and providing guidance for future research.