Social media has transformed how people communicate, access information and shop. Despite its widespread use, digital content remains inaccessible for blind and/or deaf consumers due to missing or inaccurate accessibility features like alt text and closed captioning. This study examines the utilization and accuracy of these features on social media platforms and highlights the challenges vulnerable consumers face.
The findings reveal that many social media posts lack proper accessibility features, resulting in lost opportunities for consumers and organizations. The study emphasizes the need for proactive interventions and changes in public policy to enhance digital accessibility for all users.
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When a slur is used, people hear it. Usually, it is either morally wrong or used in a negative way. However, certain nonprofit organizations have turned these words into tools to grab attention in provocative prosocial advertising campaigns. This study examines how such sexist and homophobic slurs can influence consumer engagement and cognitive elaboration. The findings suggest that advertisements containing offensive language can increase the perceived importance of the issue and motivate individuals to seek help or register for training sessions. However, the effectiveness of such advertisements depends on the audience's prior exposure to sexism or homophobia and their perception of the issue's importance.
The study also highlights the potential risks and benefits of using offensive language in social marketing. While offensive advertisements can capture attention and provoke thought, they may also alienate certain audiences. Social marketers should carefully consider their target audience and the context in which offensive language is used to maximize the positive impact of their campaigns.
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This study examines user interactions with AI assistants to infer purchase intent. By analyzing the text of user-initiated interactions, researchers build a bipartite network of nouns and verbs and measure the distance of specific words to "golden" purchasing words like "purchase," "buy" or "order." The study uses large language models, specifically Chat-GPT4, to annotate data with a measure of purchase intent and validates this method by comparing the results with cost-per-click (CPC) for keywords in Google Ads. The findings suggest that words used in an exchange with an AI assistant can predict purchase intent without customer tracking across interactions.
These findings have implications for using customized small versus large language models and can potentially inform advertising decisions. The study highlights the importance of understanding consumer behaviors in interactions with AI assistants. It provides a method to predict purchase intent based solely on the textual content of these interactions.
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The study explores the relationship between consumers' AI literacy and their receptivity to this emerging technology. What is AI literacy you ask? This refers to a person's degree of objective knowledge about AI, while receptivity refers to the extent to which a consumer is interested in having AI complete tasks. The study finds that contrary to popular belief, people with lower AI literacy exhibit greater receptivity towards AI-based products and services. What’s more, this relationship persists across a broad range of receptivity measures.
The research offers both theoretical and practical contributions. Theoretically, it contributes to the growing literature on psychological responses to AI, by focusing on understanding whether systematic differences across individuals predict differences in AI receptivity. Practically, the results suggest that attempts to increase the adoption of AI-based products and services through targeting consumers with greater AI literacy or increasing knowledge of AI may not be the most effective.
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