Service encounters increasingly feature AI-powered inputs such as add-ons recommendations or aftercare solutions. These novel forms of customer service, provided by AI rather than humans, can shape customers’ sense of agency throughout the customer journey. Customers find themselves in a form of competitive collaboration with AI, sharing tasks, resources, inputs, and decisions. This research conceptualises and develops a scale to measure shared agency power during customer-AI interactions. Understanding the role of agency in AI- customer interactions is important, as agency represents a source, mechanism, delimiter and effect of a human’s or a machine’s actions. Agency may differ across various service encounters and with it, the type of perceived risks associated with human-AI interactions. Future research may use the shared agency power scale to better understand the nature and impact of customer-AI interactions in a service context on traditional marketing factors.

Artificial intelligence generated content (AIGC) refers to content produced by artificial intelligence that represents the perspectives of its users, and a new technique of content Generation. Continuous development in deep learning and algorithms have facilitated the adoption of AIGC. This research summarizes literature published under the topic of AIGC using bibliometric analysis method, aims to provide insightful research directions for future studies. 342 documents were collected from Database of Web of science, network visualization analysis among authors and citation analysis over publications are presented to scholars who wish to further research into this area.

Artificial intelligence (AI) is producing more and more branded content such as image, text, video and sound. This area of so-called generative AI became particularly popular with the public after the launch of ChatGPT. Furthermore, political correctness has been discussed in recent years, since society is becoming increasingly sensitive to certain issues surrounding topics such as racism or gender equality. Therefore, it is more important than ever for brands to communicate in a politically correct way. In the past, humans were responsible for negative brand communication and brand voice. However, with the development of these AI-tools and platforms, AI also creates brand voice and this AI-generated brand voice can similarly cause such negative feelings.

In addition to humanoid and robotic designs, an increasing number of AI-powered services are being represented by non-human species (i.e., zoonotic design). Yet, little is known about the consequential effects of such zoonotic AI on consumer adoption of these services. Drawing on the concept of speciesism and Cognitive Load Theory, the current research seeks to uncover how does using zoonotic (vs. robotic) designs affects consumer adoption.

AI technology has been increasingly integrated into a wider range of industries – from small-sized home appliances robots to service robots in the hospitality or education sectors. Subsequently, researchers are increasingly interested in understanding how consumers respond to AI robots. Adding to such stream of studies, this research delves into how consumer responses differ depending on the characteristics of AI robots, specifically, the degree of appearance resemblance to a real-life object and the benefit type that the robot is designed to provide. Particularly, this research focuses on the role of perceived fit in shaping consumers’ intention to adopt AI robots as the underlying mechanism.

Chatbot-based services in online travel agency (OTA) are rapidly spreading in order to respond more agilely to consumers' needs based on the digitalization of the travel industry. Although AI chatbots use anthropomorphism to provide social experiences on behalf of humans, research results on its effects are mixed. Therefore, based on construal level theory, this study suggests the degree of anthropomorphism (low vs. high) of chatbots prime mental representations of different construal levels (low vs. high) and the fit between anthropomorphism and communication context (communication types and conversation types) has a positive effect on use behavior. This research method consisted of sentimental analysis for exploring use behavior of AI chatbots and two experimental studies (study 1 and study 2) to examine the hypotheses. The results of this study expand construal level theory and avatar research to provide an understanding of the anthropomorphism of AI chatbots.

The following is not a conversation with a bank clerk. " Instead, let me introduce you to customized credit loans," "Do you want me to connect you to the screen of using COVID-19 support funds and checking balance?" These are the contents of consultations with AI chatbots at financial institutions. Chatbot, which used to be an additional tool for adding convenience to life, is now at the center of our lives.

Following a series of major breakthroughs in artificial intelligence (AI) technology, it is believed that the use of AI technology can fundamentally subvert many industries and business fields, one of which is marketing. For instance, AI is likely to become a key driver of how advertising and marketing activities are conducted (Qin and Jiang, 2019) and thus dramatically change marketing strategies and customer behaviors (Davenport et al., 2020).

The present study is designed to assess the ethical-moral effectiveness of three levels of AI implementations in the hospitality industry, which include mechanical AI for transactional services (automated service), thinking AI for functional services (algorithms search), and feeling AI for hedonic services (biometric sensors) when compared to consumers’ interactions with human. AI service robots are ethically challenging in use and morally controversial in its acceptance of labor replacement in hospitality contexts. In the hospitality industry, service robots have been rapidly adopted replacing frontline human services (Park et al., 2021). Service robots refer to “system-based autonomous and adaptable interfaces that interact, communicate and deliver service to an organization’s customers” (Wirtz et al., 2018, p.909). On the one hand, the applications of artificial intelligence (AI)-based service robots are promising in this field owing to remarkable accuracy in error reduction, portion control, and cost control in service operation and delivery (Berezina et al., 2019). However, on the other hand, there has been a debate on ethical and moral principles and values regarding service robots replacing human labor (Cowls et al., 2021). Nevertheless, restaurants’ adoption of service robots seems inevitable in the current marketplace as labor shortages and rising wages have challenged them to invest in automation (Tanzi, 2021). While prior research focused on the benefits of AI-based service offerings (e.g., Cristou et al., 2023; Huang & Rust, 2021; Park et al., 2021), this study explored the extent to which AI-based service robots are accepted by consumers without rising concern about service robots replacing human labors. To this end, we adopted Huang and Rust’s framework that identifies three levels of AI applications: mechanical, thinking, and feeling AIs. Mechanical AIs refer to the automation of repetitive and routine tasks (e.g., self-service technologies); thinking AIs facilitate rational decision-making based on data processing (e.g., conversational intelligent systems such as Siri); feeling AIs are able to interact with human emotions (e.g., humanlike robots that respond to human emotions such as Sophia). Further, we adopted the construal level theory (Trope & Liberman, 2003) to examine how different levels of AIs’ service capabilities influence the way that people think about AI-based service robots. In brief, this study demonstrated how different levels of AI benefits influence consumers’ moral concerns about AI-based service robotization’s replacement of human labor and social acceptance.

In this article, we address this shortcoming by exploring the concept of AI-based sustainable service—an offering that embeds artificial intelligence in ways that meet the needs of current consumers by contributing to socio-economic equalities and conserving the natural environment.

This paper aims to examine the effect of AI framing (i.e., Scientific AI vs Magic AI) on consumers' product evaluation. This study shows that magically framed AI technology may be more beneficial to appeal to product innovativeness when subjective product properties (e.g., personal taste) becomes important. On the contrary, when objective product properties (e.g., functionality) becomes important, scientifically framed AI technology is more likely to generate higher perceived product attractiveness and purchase intention.

Artificial Intelligence (AI) technology offers many opportunities for use in influencer marketing. There is however, no standardised ethical frameworks for use in this specific field. We offer a foundation framework to emphasise the social well-being goal and relate it to stakeholders involved.

This study constructs a model to predict ad attitude when AI influencers act as ad endorsers. In the results, search products and rational ad appeal have more positive ad attitude, perceived empathy and perceived expertise as mediator. These three variables can be reinforced by the consistency of ad appeals and product categories.

Non-fungible tokens (NFTs) exploded onto the global digital landscape in 2020, spurred by pandemic-related lockdowns and government stimulus (Ossinger, 2021). An NFT is a unit of data stored on a blockchain that represents or authenticates digital or physical items (Nadini, 2021). Since it resides on a blockchain, NFTs carry the benefits of decentralization, anti-tampering, and traceability (Joy et al., 2022). Fashion brands quickly capitalized on these features, launching fashion NFT collections and garnering significant profits from the sale of fashion NFTs in 2021 (Zhao, 2021). For example, Nike’s December 2021 acquisition of RTFKT (pronounced “artifact”) resulted in USD 185 million in sales less than a year after their acquisition (Marr, 2022).

With the development of WEB 3.0 and the metaverse, the emergence of chat GPT, and AI is attracting attention across all industries. AI technologies such as robotics, advanced analytics, and in-store applications created a sensation in the fashion industry, as well as created an exceptional customer experience. In this study, fashion AI types (e.g. AI models: generative, conversational, AI applications: design, production, sales, retail, marketing) and case analysis (e.g. concepts, characteristics, benefits, risks) are examined. Consumer experiences with fashion AI are also discussed for future research directions. Finally, the Fashion AI research framework and research agenda are discussed for future research.

This study suggests that using AI chatbots with highly human-like characteristics could reduce the effectiveness of personalized AI chatbot advertising because they will likely worsen consumer concerns about privacy. Conversely, using AI chatbot with less human-like characteristics will not heighten consumer privacy concerns, thereby increasing the impact of personalized AI advertising.

In recent years, the trend of customer demand and personalization has become more and more obvious. The previous innovation model can no longer meet the diversified needs of consumers. Therefore, firms vigorously develop open innovation to promote internal and external innovation (von Hippel, 1988). With the rapid development of AI technology, open innovation communities have more interactions with the users. Organizations continue to rely on their open innovation community to collect innovative ideas from non-professional customers and then integrate them into their new product development process to produce innovative products that are more in line with customer preferences (Bayus, 2013). At present, the research on user design focuses on how to increase user design implementation and the idea popularity (Yang et al., 2022; Zhang et al., 2022). Few studies discussed how to motivate consumers to participate in innovative content output from the source. In addition, academic research on user design is mostly limited to management comments, lacking in-depth empirical research (Franke et al. 2008). Previous studies have proved that the number of leading users in the open innovation community is far less than that of non-leading users (Hofstetter et al., 2018), so it is very necessary to improve the willingness of users to participate in community creative activities. With the vigorous development of the new technology, it is an urgent problem to be solved to encourage users to participate in innovation activities and improve the innovation performance of firms (Chesbrough, 2012). Today, firms pay more and more attention on the implementation of AI technology. With AI and user design as the research background, “AI recommendation” and “willingness to design” as the key variables, and the “S-O-R model” and “Self-determination Theory” as the basis, this paper deeply explores whether AI recommendation can be used as a factor affecting user’s participation in design activities from the perspective of users, focusing on the intermediary role of user’s inspiration, competency and self-expression. It also puts forward that product involvement and aesthetic experience openness (Donghwy and Youn, 2018) are the boundary conditions that affect user’s willingness to participate in design. The results show that user’s willingness to participate in design is higher when providing AI recommedation, and the sense of inspiration, competence and self-expression play a mediating role in it. Furthermore, the results show that when product involvement is high, users are more willing to participate in design. Similarly, users with a high degree of aesthetic experience openness are more willing to participate in design activities. This study enriches the theory of enterprise community management, promote the internal information flow of the open innovation community, and provide theoretical guidance and reference for firms to optimize the new product design process.

딥러닝(DL: Deep Learning)의 발전으로 오늘날 다양한 분야에서 AI 모델이 만들어지고 사용되고 있다. 오늘날, 컴퓨터의 발전과 DL 알고리즘의 발전에 의해, DL 기반 AI 모델은 수많은 데이터를 학습하고 스스로 규칙을 찾을 수 있다. DeepMind의 Alphago는 학습 데이터 만으로 게임의 규칙을 스스로 판단하고 고수준의 게임 플 레이를 할 수 있다는 가능성을 보여준다. 이런 다양한 DL 알고리즘이 게임 분야에 적용되고 있지만, 스포츠 게임 같이 팀의 전술과 개인 플레이가 공존하는 분야에서는 단일 AI 모델만으로 성공적인 플레이를 이끌어 내기에는 한계가 존재한다. 오늘날, 고품질의 스포츠 게임은 쉽게 접할 수 있다. 하지만, 게임 AI 연구자들이 이런 고품질의 스포츠 게임에 맞는 AI 모델을 개발하기 위해서는 게임 코드 소스를 받거나 게임 회사에서 테 스트용 시뮬레이터를 제공해줘야만 할 수 있다. 게임 AI 연구자들이 활발한 스포츠 게임 분야의 AI 모델을 개 발하기 위해서는 스포츠 게임의 규칙과 특징이 반영되고 접근하기 쉬운 테스트 환경(Test Environment)이 필요 하다. 본 논문에서는 팀의 전술과 개인 플레이가 중요한 스포츠 게임 분야에서 AI 모델을 만들고 테스트할 수 있는 규칙기반 축구 게임 프레임워크를 제안한다.

Governments around the world are enacting laws mandating explainable traceability when using AI(Artificial Intelligence) to solve real-world problems. HAI(Human-Centric Artificial Intelligence) is an approach that induces human decision-making through Human-AI collaboration. This research presents a case study that implements the Human-AI collaboration to achieve explainable traceability in governmental data analysis. The Human-AI collaboration explored in this study performs AI inferences for generating labels, followed by AI interpretation to make results more explainable and traceable. The study utilized an example dataset from the Ministry of Oceans and Fisheries to reproduce the Human-AI collaboration process used in actual policy-making, in which the Ministry of Science and ICT utilized R&D PIE(R&D Platform for Investment and Evaluation) to build a government investment portfolio.