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The paper aims to expand on the works well documented by Joy Boulamwini and Ruha Benjamin by expanding their critique to the African continent. The research aims to assess if algorithmic biases are prevalent in DALL-E 2 and Starry AI. The aim is to help inform better artificial intelligence (AI) systems for future use.

The paper utilised a desktop study for literature and gathered data from Open AI’s DALL-E 2 text-to-image generator and StarryAI text-to-image generator.

The DALL-E 2 significantly underperformed when it was tasked with generating images of “An African Family” as opposed to images of a “Family”. The pictures lacked any conceivable detail as compared to the latter of this comparison. The StarryAI significantly outperformed the DALL-E 2 and rendered visible faces. However, the accuracy of the culture portrayed was poor.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further. The implications, however, are that more inclusion is warranted to help address the issue of cultural inaccuracies noted in a few of the paper’s experiments.

The paper is useful for advocates who advocate for algorithmic equality and fairness by highlighting evidence of the implications of systemic-induced algorithmic bias.

The reduction in offensive racism and more socially appropriate AI can be a better product for commercialisation and general use. If AI is trained on diversity, it can lead to better applications in contemporary society.

The paper’s use of DALL-E 2 and Starry AI is an under-researched area, and future studies on this matter are welcome.

The rapid innovation of artificial intelligence (AI) technology promotes the prosperity of the AI product market. However, consumers seem to have negative attitudes (e.g. prejudice, aversion) toward AI products and services. Those negative attitudes are rooted in the fear that AI might replace humans. The authors thus propose that turning the image of AI from substitutes to facilitators can alleviate identity threat perception. This paper aims to examine how the image of AI products (facilitators vs substitutes) influences consumer evaluation and explores the underlying mechanism and boundary conditions.

This study uses four experiments with between-subjects designs to investigate whether the image of AI products (facilitators vs substitutes) will affect consumer evaluation in specific consumption and service scenarios. The same products (or services) were manipulated as “substitute” or “facilitator” through advertisement slogans. Participants were randomly assigned to a condition and read the advertisement, then they reported their evaluation. The mediator perceived identity threat and the moderator preconceived perceptions of AI risks were measured by scales. The moderator, self-affirmation, was manipulated through the instruction of the experiment.

This study demonstrates that consumers give higher evaluation of AI products in the image of the facilitator than in the image of the substitute (Study 1). The underlying mechanism is that the perceived identity threat caused by “facilitator” products is lower than “substitute” products (Study 2). The effect of AI image is moderated by consumers’ preconceived perceptions of AI risks (Study 3) and self-affirmation (Study 4). Specifically, for consumers who have a strong AI risk-perception, this effect exists, but it disappears for consumers who have a weak AI risk perception. When consumers are given a strong self-affirmation, the negative impact of the “substitute” image disappears.

This paper analyzes the psychological root of consumers’ negative evaluation of AI technology from the perspective of AI’s image. The proposed typology of “substitutes” and “facilitators” helps expand the vision on brand/product image and enriches the research on consumer self-identity in today’s highly informatized market. The findings shed light on how to choose appropriate image for AI products, which will be crucial for increasing consumers’ acceptance of AI products.

Artificial intelligence (AI) is experiencing growth and prosperity worldwide because of its convenience and other benefits. However, AI faces challenges related to consumer resistance. Thus, drawing on the user resistance theory, this study explores factors that influence consumers’ resistance to AI and suggests ways to mitigate this negative influence.

This study tested four hypotheses across four studies by conducting lab experiments. Study 1 used a questionnaire to verify the hypothesis that AI’s “substitute” image leads to consumer resistance to AI; Study 2 focused on the role of perceived threat as an underlying driver of resistance to AI. Studies 3–4 provided process evidence by the way of a measured moderator, testing whether AI with servant communication style and literal language style is resisted less.

This study showed that AI’s “substitute” image increased users' resistance to AI. This occurs because the substitute image increases consumers’ perceived threat. The study also found that using servant communication and literal language styles in the interaction between AI and consumers can mitigate the negative effects of AI-substituted images.

This study reveals the mechanism of action between AI image and consumers’ resistance and sheds light on how to choose appropriate image and expression styles for AI products, which is important for lowering consumer resistance to AI.

This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator.

This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants.

The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred.

The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff.

To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.

As artificial intelligence (AI) has become increasingly popular and accessible, most companies have recognized its far-reaching potential. However, despite numerous research papers on organizational adoption of new technologies including AI, little is known about individual employees’ intentions to use them. Given that organizational innovations are of limited value if they are not adopted by employees, the purpose of this study is to understand the underlying factors that push employees to make use of these new technologies in the workplace.

This study builds on previously developed technology acceptance models to provide a new theoretical model. The model is then tested using data collected from a survey of 203 employees and analyzed through structural equation modeling.

Findings show that five factors affect employees’ intention to use AI either directly or as mediators. Organizational culture and habit exert a positive impact on employees’ intention to use AI, whereas job insecurity has a negative impact. Perceived self-image and perceived usefulness fully mediate the relation between job insecurity and intention to use. Moreover, perceived self-image and perceived usefulness partially mediate the relationship between habit and intention to use.

To the best of the authors’ knowledge, this study is among the first to determine the factors that influence employees’ intention to use AI in general and more particularly chatbots within the workplace.

This paper aims to describe several artificial intelligence (AI)-based applications that librarians can use to serve and design virtual library instruction, so it will be more effective and efficient.

The approach involves a comprehensive review of AI-based applications that bring benefits to librarian to enhance the virtual instructional services (AI). This study explores the existing papers to reveal the potential use of AI for research consultation, designing the instructional services and conducting evaluation of the program.

There are some AI-based applications that are available for free that will help instructional librarian jobs. Librarians use the AI to increase effectiveness of the services. The AI-based applications that can be used to support instructional services on research inquiries include virtual assistance, knowledge mapping and note making, and to support designing virtual instruction, librarians can use design apps, image generators, voice generator, grammar checker and paraphrasing.

There are many studies on AI at the library; however, it’s still rare a paper studied AI-based application that potentially will bring benefit for virtual instructional services. This paper will give overview of AI application that will help instructional librarian on transactions with users and help librarians to create innovative instructional media.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The impact of artificial intelligence (AI) and extended reality (XR, including virtual reality [VR], augmented reality [AR], and mixed reality [MR]) on marketing in Industry 5.0 and Society 5.0 is explored with systematic literature review in this chapter. AIXR is becoming a necessary aspect of marketing, driven by efficiency, productivity, and innovation. Despite AI's capabilities, the human touch in marketing is preferred due to superior adaptive, creative, and innovative abilities. The use of fully automated marketing systems is limited to specific tasks. This research will benefit both practitioners and academics focusing on AIXR in marketing and is limited by the number of included literature.

This column aims to provides an overview of the techniques that make text-to-image generators possible.

This column will be the first in a two-part series, exploring the impacts of text-to-image software with an emphasis on libraries.

It shows how advances in machine learning and the particular technique of diffusion are critical building blocks for artificial intelligence.

Understanding these technologies and large data sets, which are often scraped by public users, help information professionals understand how this software may further develop, as well as how to help guide their patrons.

This study profiles and segments potential tourists on the basis of their various attitudes toward artificial intelligence (AI) services. Furthermore, this study distinguishes descriptors among the different clusters, such as preference for using diverse AI services, overall image of AI services, willingness to use AI services (WUAI), willingness to pay more for AI services (WPAI) in tourism and hospitality, and characteristics of respondents.

An online survey was conducted in South Korea. Data on 758 potential tourists were used for K-means cluster analysis.

This study identified three distinct tourist segments with differentiated attitudes toward AI services: the group aspiring to use or fantasizing about AI services (Cluster 1), the group being knowledgeable and supportive of AI services (Cluster 2), and the group having low interest about AI services (Cluster 3).

Members of Cluster 2 were the most marketable as this segment exhibited the greatest knowledge of and support for AI services, while Cluster 1 would be an ideal segment to launch and test novel AI services.

This study extends the authors’ knowledge of AI scholarship by unpacking the existing market segments, which could be tapped to sustain AI penetration in the tourism industry. Hence, this study contributes to existing debates on AI scholarship, which is predominated by conceptual reflections and issues of AI services in the tourism and hospitality field.