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.

The purpose of this research was to investigate the characteristics of Korean college students’ writings, which have been produced without or with the help of machine translation tool in the classroom. Specifically, this research attempted to investigate the linguistic characteristics of the students’ writings, and types of errors identified in the writings. Twelve pieces of writings from three college students were collected for analysis. Two online word analysis programs, Word Counter (2023) and LIWC-22 (2023), were employed for data analysis. The findings of data analysis found out that 1) The students’ drafts consisted of 22.8 sentences including 303.9 words in 3.6 paragraphs on average. 2) In the students’ drafts, ‘unique’ words (46.8%) were included a lot more than ‘difficult’ words (27%), and students tended to write their essay writings in an unfiltered or impromptu way rather than an analytical way regardless of their English language proficiency levels. 3) The highest frequency of errors was seen in grammatical errors (41.7%) followed by lexical errors (31.6%). Based on the research findings, pedagogical implications and suggestions for the effective use of machine translation in English writing classes were presented.