ANTINO KIM

This study examines the evolution of IT support services from traditional self-service models to AI-assisted approaches. We conducted a mixed-method investigation comparing the effectiveness of search tools and chatbots in providing IT support. Our findings reveal significant improvements in user satisfaction and problem resolution rates when AI assistance is integrated into support workflows.

This research investigates the effectiveness of conversational agents in mental health screening applications. We examine how chatbots can be designed to provide empathetic and accurate preliminary mental health assessments. Our study reveals important design considerations for creating trustworthy AI-based mental health screening tools.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.