ANTINO KIM

Abstract Summary: Minority-owned business markers can increase preference for such businesses, but mainly among motivated consumers. For others, effects are mixed and may even backfire, especially when businesses defy stereotypes. Results from Yelp data and online experiments highlight the nuanced, context-dependent impact of these identity markers.

Author's Comment: We explore the impact of online platforms' practice of highlighting minority-owned businesses with markers. Our findings suggest that these markers tend to benefit businesses primarily when consumers already champion the cause of minority empowerment. For consumers who are neutral or indifferent, the effect diminishes significantly. In some cases, if the business doesn't align with existing biases about minority ownership (i.e., operate outside the boundaries of existing stereotypes), these markers can actually backfire, reducing consumer interest. This raises important questions about the overall effectiveness of these markers and whether they inadvertently reinforce certain biases. Ultimately, our research highlights the need for platforms to consider these nuanced effects to ensure desired outcomes for all businesses.

Abstract Summary: Chatbots outperform traditional search tools in self-service IT support, yielding higher user satisfaction across three experiments. This is driven by perceived assistance and co-creation of queries. Chatbots enable faster answers or more effective support, suggesting they meaningfully enhance AI-assisted self-service experiences.

Author's Comment: This paper is one of the first to compare AI-assisted self-service IT support, using a chatbot, against traditional search-based self-service methods. Our findings reveal that users significantly prefer the chatbot-assisted approach, primarily because it gives a sense of personalized attention rather than leaving them to figure things out on their own. This shift essentially transforms self-service support into a more AI-assisted experience, with broad implications for other self-service domains. Additionally, we explored the concept of co-creation, where users and the AI collaboratively identify problems and solutions. While co-creation did improve user satisfaction, the chatbot's impact was limited, mainly because many issues were straightforward and didn't require multiple interactions. However, when we took a closer look at user trascripts from chatbot interactions, for more complex issues, co-creation was a key factor in successful problem resolution. Overall, this research is timely, especially as traditional search engines begin integrating generative AI, making conversational AI a crucial dimension to consider.

Abstract Summary: Despite expectations, chatbots were consistently perceived as more judgmental than humans in mental health screening, reducing user engagement. This judgment was linked to a lack of emotional understanding and validation, challenging the assumption that chatbots lower stigma-related barriers to care.

Author's Comment: We began this project with the hypothesis that chatbots, by lacking human biases, would be perceived as less judgmental and therefore lower the barrier to seeking mental health screening by reducing stigma. However, across multiple experiments involving varied participants, scenarios, and measurement items, we consistently found the opposite: chatbots were perceived as more judgmental than human professionals, even though their behavior was identical. This challenges the common assumption that AI offers a judgment-free alternative in sensitive contexts. Through qualitative and quantitative analysis, we developed new constructs related to perceived judgmentalness, stigma, and understanding, which provide a foundation for future research.

Abstract Summary: This study compares the effectiveness of chatbots versus traditional web forms in collecting user reviews and feedback. We analyzed response quality, completion rates, and user satisfaction across both methods. Our findings suggest that while chatbots offer conversational advantages, traditional forms may be more efficient for certain types of data collection tasks.

Abstract Summary: This research examines how different levels of detail in news source evaluations affect users' ability to identify misinformation on social media platforms. We investigated whether providing summary information versus detailed source evaluations better helps users distinguish between credible and unreliable news sources. Our findings reveal important implications for designing effective misinformation detection tools.

Abstract Summary: This study investigates how users evaluate source credibility when encountering misinformation in internet search results. We examined the effectiveness of various source evaluation techniques in helping users identify and avoid misleading information. Our research provides insights into designing better search interfaces that support critical evaluation of information sources.

Abstract Summary: This research explores how the timing of algorithmic errors affects user trust and reliance on automated systems. We conducted experiments comparing user responses to early versus late errors in algorithmic recommendations. Our findings reveal that early errors have a more significant impact on long-term user trust, with important implications for algorithm design and deployment strategies.

Abstract Summary: This study examines whether asking users to rate the credibility of news stories affects their susceptibility to fake news on social media platforms. We conducted experiments to understand how the act of rating influences user beliefs and sharing behavior. Our findings suggest that prompting users to evaluate news credibility can serve as an effective intervention against misinformation spread.

Abstract Summary: This research analyzes the strategic implications of the secondhand video game market for manufacturers and retailers. We examined how the presence of used game markets affects new game pricing, sales, and overall market dynamics. Our findings reveal how manufacturers can strategically position themselves to benefit from secondhand markets rather than being disadvantaged by them.

Abstract Summary: This study evaluates the effectiveness of identity-based messaging in public health communications during the COVID-19 pandemic. We conducted a randomized controlled experiment to assess how different messaging approaches affect compliance with health guidelines. Our research provides evidence for the effectiveness of identity-based appeals in promoting public health behaviors during crisis situations.

Abstract Summary: This research examines user attitudes and responses to COVID-19 screening chatbots deployed by reputable healthcare providers. We analyzed user feedback and engagement patterns to understand acceptance and trust in AI-powered health screening tools. Our findings highlight the importance of provider reputation in user acceptance of health-related AI applications.

Abstract Summary: This study explores interventions targeting different cognitive processing systems to combat fake news on social media platforms. We developed and tested interventions based on dual-process theory, examining both intuitive (System 1) and analytical (System 2) approaches to detecting misinformation. Our research demonstrates the effectiveness of cognitive-based interventions in reducing fake news sharing behavior.

Abstract Summary: This article presents a counterintuitive finding that moderate levels of piracy can actually benefit content creators and distributors. We analyzed market dynamics and consumer behavior to understand when piracy serves as a form of product sampling that ultimately increases legitimate sales. Our analysis suggests that completely eliminating piracy may not always be the optimal strategy for content industries.

Abstract Summary: This research investigates the effectiveness of source rating systems in combating fake news on social media platforms. We compared user-generated ratings with expert-provided ratings to understand their relative impact on news credibility assessment. Our findings reveal important design considerations for implementing effective source rating systems that can help users identify reliable information sources.

Abstract Summary: This study examines how different presentation formats and source ratings affect users' ability to identify fake news on social media platforms. We conducted experiments testing various combinations of visual presentation and credibility indicators. Our research provides insights into designing effective interfaces that help users distinguish between credible and unreliable news sources.

Abstract Summary: This research analyzes the complex relationship between publishers and retailers in digital content markets, focusing on the coexistence of wholesale and agency business models. We examined how these different models create interdependencies and strategic considerations for both parties. Our findings reveal the nuanced dynamics that lead to the adoption of hybrid business model approaches in digital marketplaces.

Abstract Summary: This study provides an economic analysis of how piracy affects the information goods supply chain, drawing parallels to Adam Smith's concept of the "invisible hand." We developed theoretical models to understand the complex interactions between piracy, pricing strategies, and market outcomes. Our analysis reveals how piracy can sometimes lead to market outcomes that benefit consumers and legitimate businesses through indirect mechanisms.

Abstract Summary: This research examines the effectiveness of extended enforcement mechanisms in combating online piracy. We analyzed how broader enforcement strategies, including international cooperation and technological measures, affect piracy rates and legitimate content consumption. Our findings suggest that extended enforcement approaches can be more effective than traditional localized efforts in reducing piracy while promoting legitimate content markets.