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This study aims to understand how travelers evaluate travel destination recommendations received from either artificial intelligence (AI) or human travel experts (TEs) based on the size of recommendation and their travel involvement.

This study used a 2 (agent type: AI vs TE) × 2 (size of recommendation: small vs large) × 2 (travel involvement: low vs high) between-subjects design.

When AI recommends destinations, less-involved travelers perceive the recommendations as more credible and trust the system when AI offers larger recommendations than smaller ones. Meanwhile, when TEs offer recommendations, travelers consider the recommendations as equally credible and similarly trust the system, regardless of the recommendation size and travel involvement.

This study sheds light on the design of human-centered AI travel destination recommendation services.

本研究旨在了解旅行者如何根据推荐的规模和他们的旅行参与度来评估从人工智能 (AI) 或人类旅行专家 (TE) 收到的旅行目的地推荐。

本研究使用 2（代理类型：AI 与 TE）×2（推荐数量：小与大）×2（旅行参与：低与高）受试者间设计。

当 AI 推荐目的地时, 参与度较低的旅行者认为推荐更可信, 并且当 AI 提供的建议比较小的建议大时信任系统。 同时, 当 TE 提供推荐时, 无论推荐数量大小和旅行参与度如何, 旅行者都认为这些推荐同样可信并且同样信任系统。

这项研究揭示了以人为本的人工智能旅游目的地推荐服务的设计。

The increase in the number of healthcare wearable (Internet of Things) IoT options is making it difficult for individuals, healthcare experts and physicians to find the right smart device that best matches their requirements or treatments. The purpose of this research is to propose a framework for a recommender system to advise on the best device for the patient using machine learning algorithms and social media sentiment analysis. This approach will provide great value for patients, doctors, medical centers, and hospitals to enable them to provide the best advice and guidance in allocating the device for that particular time in the treatment process.

This data-driven approach comprises multiple stages that lead to classifying the diseases that a patient is currently facing or is at risk of facing by using and comparing the results of various machine learning algorithms. Hereupon, the proposed recommender framework aggregates the specifications of wearable IoT devices along with the image of the wearable product, which is the extracted user perception shared on social media after applying sentiment analysis. Lastly, a proposed computation with the use of a genetic algorithm was used to compute all the collected data and to recommend the wearable IoT device recommendation for a patient.

The proposed conceptual framework illustrates how health record data, diseases, wearable devices, social media sentiment analysis and machine learning algorithms are interrelated to recommend the relevant wearable IoT devices for each patient. With the consultation of 15 physicians, each a specialist in their area, the proof-of-concept implementation result shows an accuracy rate of up to 95% using 17 settings of machine learning algorithms over multiple disease-detection stages. Social media sentiment analysis was computed at 76% accuracy. To reach the final optimized result for each patient, the proposed formula using a Genetic Algorithm has been tested and its results presented.

The research data were limited to recommendations for the best wearable devices for five types of patient diseases. The authors could not compare the results of this research with other studies because of the novelty of the proposed framework and, as such, the lack of available relevant research.

The emerging trend of wearable IoT devices is having a significant impact on the lifestyle of people. The interest in healthcare and well-being is a major driver of this growth. This framework can help in accelerating the transformation of smart hospitals and can assist doctors in finding and suggesting the right wearable IoT for their patients smartly and efficiently during treatment for various diseases. Furthermore, wearable device manufacturers can also use the outcome of the proposed platform to develop personalized wearable devices for patients in the future.

In this study, by considering patient health, disease-detection algorithm, wearable and IoT social media sentiment analysis, and healthcare wearable device dataset, we were able to propose and test a framework for the intelligent recommendation of wearable and IoT devices helping healthcare professionals and patients find wearable devices with a better understanding of their demands and experiences.

The objective of this study was to examine how high-social and low-social virtual live streamers affect consumers' experiential value (utilitarian value and hedonic value) and the mechanism and boundary conditions behind the effect.

The research consisted of four laboratory experiments.

The results showed that socialness has a positive significant effect on experiential value. Social presence mediated the effect of socialness on utilitarian value and hedonic value. In the relationship between socialness and experiential value, the moderating effects of communication style and situation were significant.

This study provides managerial implications for online stores about the use of virtual live streamers.

The finding of this paper extends the literature on virtual humans or avatars, enriches the literature on the characteristics of virtual humans and tests the explanatory power of social response theory.

Fact-checking is a process of seeking and displaying facts to confirm or counter uncertain information, which reduces the spread of fake news. However, little is known about how to promote fact-checking posts to online users on social media. Through uncertainty reduction theory and message framing, this first study examines the effect of fact-checking posts on social media with an avatar on online users' trust, attitudes, and behavioral intentions. The authors further investigate the congruency effects between promotional message framing (gain/loss/neutral) and facial expressions of the avatar (happy/angry/neutral) on online users' trust, attitudes, and behavioral intentions in the second study.

The authors conducted two studies and statistically analyzed 120 samples (study 1) and 519 samples (study 2) from Facebook users.

Results showed that including the neutral facial expression avatar in fact-checking posts leads to online users' greater trust and more positive attitudes. Furthermore, the congruency effects between loss message framing and the angry facial expression of the avatar can effectively promote online users' trust and attitudes as well as stronger intentions to follow and share.

This study offers theoretical implications for fact-checking studies, and practical implications for online fact-checkers to apply these findings to design effective fact-checking posts and spread the veracity of information on social media.

This study investigates how artificial intelligence (AI) chatbots persuade customers to accept their recommendations in the online shopping context.

Drawing on the elaboration likelihood model, this study establishes a research model to reveal the antecedents and internal mechanisms of customers' adoption of AI chatbot recommendations. The authors tested the model with survey data from 530 AI chatbot users.

The results show that in the AI chatbot recommendation adoption process, central and peripheral cues significantly affected a customer's intention to adopt an AI chatbot's recommendation, and a customer's cognitive and emotional trust in the AI chatbot mediated the relationships. Moreover, a customer's mind perception of the AI chatbot, including perceived agency and perceived experience, moderated the central and peripheral paths, respectively.

This study has theoretical and practical implications for AI chatbot designers and provides management insights for practitioners to enhance a customer's intention to adopt an AI chatbot's recommendation.

1. The study investigates customers' adoption of AI chatbots' recommendation.

2. The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

3. The central and peripheral cues are generalized according to cooperative principle theory.

4. Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

5. Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

6. Customers' mind perception positively moderates the central and peripheral paths.

The study investigates customers' adoption of AI chatbots' recommendation.

The authors develop research model based on ELM theory to reveal central and peripheral cues and paths.

The central and peripheral cues are generalized according to cooperative principle theory.

Central cues include recommendation reliability and accuracy, and peripheral cues include human-like empathy and recommendation choice.

Central and peripheral cues affect customers' adoption to recommendation through trust in AI.

Customers' mind perception positively moderates the central and peripheral paths.

Artificial intelligence (AI) is transforming consumers' experiences and how firms identify, create, nurture and manage interactive marketing relationships. However, most marketers do not have a clear understanding of what AI is and how it may mutually benefit consumers and firms. In this paper, the authors conduct an extensive review of the marketing literature, develop an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships, identify research gaps and offer a future research agenda.

The authors first conduct an extensive literature review in 16 top marketing journals on AI. Based on this review, an AI framework for understanding value co-creation in interactive buyer–seller marketing relationships was conceptualized.

The literature review led to a number of key research findings and summary areas: (1) an historical perspective, (2) definitions and boundaries of AI, (3) AI and interactive marketing, (4) relevant theories in the domain of interactive marketing and (5) synthesizing AI research based on antecedents to AI usage, interactive AI usage contexts and AI-enabled value co-creation outcomes.

This is one of the most extensive reviews of AI literature in marketing, including an evaluation of in excess or 300 conceptual and empirical research. Based on the findings, the authors offer a future research agenda, including a visual titled “What is AI in Interactive Marketing? AI design factors, AI core elements & interactive marketing AI usage contexts.”

This study aims to examine hotel guests’ acceptance of technology agency – the extent to which they would let artificial intelligence (AI)-based systems make decisions for them when staying in hotels. The examination was conducted through the prism of several antecedents of acceptance of technology agency, including perceived ethics, benefits, risks and convenience orientation.

A thorough literature review provided the foundation of the structural model, which was tested using confirmatory factor analysis, followed by structural equation modeling. Data were collected from 400 US hotel guests.

The most important determinant of acceptance of technology agency was perceived ethics, followed by benefits. Risks of using AI-based systems to make decisions for consumers had a negative impact on acceptance of technology agency. In addition, perceived loss of competence and unpredictability had relatively strong impacts on risks.

The results provide a conceptual foundation for research on systems that make decisions for consumers. As AI is increasingly incorporated in the business models of hotel companies to make decisions, ensuring that the decisions are perceived as ethical and beneficial for consumers is critical to increase the utilization of such systems.

Most research on AI in hospitality is either conceptual or focuses on consumers’ intentions to stay in hotels that may be equipped with AI technologies. Occupying a unique position within the literature, this study discusses the first time AI-based systems that make decisions for consumers. The value of this study stems from the examination of the main concept of technology agency, which was never examined in hospitality.

This article proposes an architecture of an intelligent and autonomous recommendation system to be applied to any virtual learning environment, with the objective of efficiently recommending digital resources. The paper presents the architectural details of the intelligent and autonomous dimensions of the recommendation system. The paper describes a hybrid recommendation model that orchestrates and manages the available information and the specific recommendation needs, in order to determine the recommendation algorithms to be used. The hybrid model allows the integration of the approaches based on collaborative filter, content or knowledge. In the architecture, information is extracted from four sources: the context, the students, the course and the digital resources, identifying variables, such as individual learning styles, socioeconomic information, connection characteristics, location, etc. Tests were carried out for the creation of an academic course, in order to analyse the intelligent and autonomous capabilities of the architecture.

We attempt to construct a grey three-way conflict analysis model with constraints to deal with correlated conflict problems with uncertain information.

In order to address these correlated conflict problems with uncertain information, considering the interactive influence and mutual restraints among agents and portraying their attitudes toward the conflict issues, we utilize grey numbers and three-way decisions to propose a grey three-way conflict analysis model with constraints. Firstly, based on the collected information, we introduced grey theory, calculated the degree of conflict between agents and then analyzed the conflict alliance based on the three-way decision theory. Finally, we designed a feedback mechanism to identify key agents and key conflict issues. A case verifies the effectiveness and practicability of the proposed model.

The results show that the proposed model can portray their attitudes toward conflict issues and effectively extract conflict-related information.

By employing this approach, we can provide the answers to Deja’s fundamental questions regarding Pawlak’s conflict analysis: “what are the underlying causes of conflict?” and “how can a viable consensus strategy be identified?”

This study aims to conceptualize the relationship of perceived artificial intelligence (AI) credibility with consumer-AI experiences. With the widespread deployment of AI in marketing and services, consumer-AI experiences are common and an emerging research area in marketing. Various factors affecting consumer-AI experiences have been studied, but one crucial factor – perceived AI credibility is relatively underexplored which the authors aim to envision and conceptualize.

This study employs a conceptual development approach to propose relationships among constructs, supported by 34 semi-structured consumer interviews.

This study defines AI credibility using source credibility theory (SCT). The conceptual framework of this study shows how perceived AI credibility positively affects four consumer-AI experiences: (1) data capture, (2) classification, (3) delegation, and (4) social interaction. Perceived justice is proposed to mediate this effect. Improved consumer-AI experiences can elicit favorable consumer outcomes toward AI-enabled offerings, such as the intention to share data, follow recommendations, delegate tasks, and interact more. Individual and contextual moderators limit the positive effect of perceived AI credibility on consumer-AI experiences.

This study contributes to the emerging research on AI credibility and consumer-AI experiences that may improve consumer-AI experiences. This study offers a comprehensive model with consequences, mechanism, and moderators to guide future research.

The authors guide marketers with ways to improve the four consumer-AI experiences by enhancing consumers' perceived AI credibility.

This study uses SCT to define AI credibility and takes a justice theory perspective to develop the conceptual framework.