Sy Syms School of Business News ONLINE AND ON-CAMPUS IN NEW YORK CITY Apply to undergraduate program Apply to graduate program Artificial Intelligence Biotechnology Computer Science Cybersecurity Data Analytics and Visualization Digital Marketing and Media Mathematics Occupational Therapy Physician Assistant Physics Speech-Language Pathology Faculty News Dr. Travis Oh has papers accepted into two major journals: ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐๐ซ๐ค๐๐ญ๐ข๐ง๐ and ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐ฌ๐ฌ๐จ๐๐ข๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก Read the SSRN version for the GenAI paper here: https://bit.ly/4mAJsgA Read excerpts from both papers here Dr. Travis Oh has papers accepted into two major journals: ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐๐ซ๐ค๐๐ญ๐ข๐ง๐ and ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐ฌ๐ฌ๐จ๐๐ข๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐๐ซ๐ค๐๐ญ๐ข๐ง๐ : ๐๐๐ฐ ๐๐จ๐จ๐ฅ๐ฌ, ๐๐๐ฐ ๐๐ฎ๐ฅ๐๐ฌ: ๐ ๐๐ซ๐๐๐ญ๐ข๐๐๐ฅ ๐๐ฎ๐ข๐๐ ๐ญ๐จ ๐๐๐๐๐๐ญ๐ข๐ฏ๐ ๐๐ง๐ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐๐ฅ๐ ๐๐๐ง๐๐ ๐๐ฌ๐ ๐๐จ๐ซ ๐๐ฎ๐ซ๐ฏ๐๐ฒ๐ฌ ๐๐ง๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐ฆ๐๐ง๐ญ๐ฌ ๐๐๐ฌ๐๐๐ซ๐๐กโฃGenerative Artificial Intelligence (GenAI) tools based on Large Language Models (LLMs) are quickly reshaping how researchers conduct surveys & experiments. From reviewing the literature & designing instruments, to administering studies, coding data, & interpreting results, these tools offer substantial opportunities to improve research productivity & advance methodology. Yet with this potential comes a critical challenge: researchers often use these systems without fully understanding how they work. This article aims to provide a practical guide for effective & responsible GenAI use in primary research. We begin by explaining how GenAI systems operate, highlighting the gap between their intuitive interfaces & the underlying model architectures. We then examine different use cases throughout the research process, both the opportunities and associated risks at each stage. Throughout our review, we provide flexible tips for best practice & firm, non-negotiable rules for effective & responsible GenAI use, particularly in areas pertaining to ensuring the validity of GenAI coded responses. In doing so, we hope to help researchers integrate GenAI into their workflows in a transparent, rigorous, & ethically sound manner. Our accompanying website (questionableresearch.ai) provides supporting materials, including reproducible coding templates in R and SPSS and sample pre-registrations.โฃโฃ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐ฌ๐ฌ๐จ๐๐ข๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก: ๐๐ก๐ ๐๐๐ญ๐๐ซ๐ข๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐๐ฌ: ๐๐จ๐ฐ ๐๐๐ซ๐๐๐ข๐ฏ๐๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐ ๐๐๐ฉ๐ญ๐ก ๐๐ง๐๐ซ๐๐๐ฌ๐๐ฌ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ๐ฌโ ๐๐ซ๐๐๐๐ซ๐๐ง๐๐ ๐๐จ๐ซ ๐๐ง๐ข๐ช๐ฎ๐ ๐๐๐ฃ๐๐๐ญ๐ฌ โฃWhile consuming experiences, people often acquire objects associated with those experiences. This investigation identifies an aspect of experiences that reliably predicts the type of experience-related objects consumers prefer to acquire: perceived experience depth. Evidence from five studies and four replications (N=2036) examining past, imagined, and real-time consumption of experiences indicates that, when consumers perceive to have had a deep (vs. basic) experience consumption, they are more prone to purchasing unique (vs. typical) objects related to that experience. Further, we find that the conversational value of the object helps explain this effect. These results add to the experiential consumption literature a novel finding associated with an aspect present in virtually every experienceโperceived experience depthโand an outcome of theoretical and practical relevanceโconsumersโ acquisitions of objects linked to their experiences. Dr. Travis Oh has papers accepted into two major journals: ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐๐ซ๐ค๐๐ญ๐ข๐ง๐ and ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐ฌ๐ฌ๐จ๐๐ข๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก Read the SSRN version for the GenAI paper here: https://bit.ly/4mAJsgA Read excerpts from both papers here Dr. Travis Oh has papers accepted into two major journals: ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐๐ซ๐ค๐๐ญ๐ข๐ง๐ and ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐ฌ๐ฌ๐จ๐๐ข๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐๐ซ๐ค๐๐ญ๐ข๐ง๐ : ๐๐๐ฐ ๐๐จ๐จ๐ฅ๐ฌ, ๐๐๐ฐ ๐๐ฎ๐ฅ๐๐ฌ: ๐ ๐๐ซ๐๐๐ญ๐ข๐๐๐ฅ ๐๐ฎ๐ข๐๐ ๐ญ๐จ ๐๐๐๐๐๐ญ๐ข๐ฏ๐ ๐๐ง๐ ๐๐๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐๐ฅ๐ ๐๐๐ง๐๐ ๐๐ฌ๐ ๐๐จ๐ซ ๐๐ฎ๐ซ๐ฏ๐๐ฒ๐ฌ ๐๐ง๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐ฆ๐๐ง๐ญ๐ฌ ๐๐๐ฌ๐๐๐ซ๐๐กโฃGenerative Artificial Intelligence (GenAI) tools based on Large Language Models (LLMs) are quickly reshaping how researchers conduct surveys & experiments. From reviewing the literature & designing instruments, to administering studies, coding data, & interpreting results, these tools offer substantial opportunities to improve research productivity & advance methodology. Yet with this potential comes a critical challenge: researchers often use these systems without fully understanding how they work. This article aims to provide a practical guide for effective & responsible GenAI use in primary research. We begin by explaining how GenAI systems operate, highlighting the gap between their intuitive interfaces & the underlying model architectures. We then examine different use cases throughout the research process, both the opportunities and associated risks at each stage. Throughout our review, we provide flexible tips for best practice & firm, non-negotiable rules for effective & responsible GenAI use, particularly in areas pertaining to ensuring the validity of GenAI coded responses. In doing so, we hope to help researchers integrate GenAI into their workflows in a transparent, rigorous, & ethically sound manner. Our accompanying website (questionableresearch.ai) provides supporting materials, including reproducible coding templates in R and SPSS and sample pre-registrations.โฃโฃ๐๐จ๐ฎ๐ซ๐ง๐๐ฅ ๐จ๐ ๐๐ฌ๐ฌ๐จ๐๐ข๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ ๐๐๐ฌ๐๐๐ซ๐๐ก: ๐๐ก๐ ๐๐๐ญ๐๐ซ๐ข๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง ๐จ๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐๐ฌ: ๐๐จ๐ฐ ๐๐๐ซ๐๐๐ข๐ฏ๐๐ ๐๐ฑ๐ฉ๐๐ซ๐ข๐๐ง๐๐ ๐๐๐ฉ๐ญ๐ก ๐๐ง๐๐ซ๐๐๐ฌ๐๐ฌ ๐๐จ๐ง๐ฌ๐ฎ๐ฆ๐๐ซ๐ฌโ ๐๐ซ๐๐๐๐ซ๐๐ง๐๐ ๐๐จ๐ซ ๐๐ง๐ข๐ช๐ฎ๐ ๐๐๐ฃ๐๐๐ญ๐ฌ โฃWhile consuming experiences, people often acquire objects associated with those experiences. This investigation identifies an aspect of experiences that reliably predicts the type of experience-related objects consumers prefer to acquire: perceived experience depth. Evidence from five studies and four replications (N=2036) examining past, imagined, and real-time consumption of experiences indicates that, when consumers perceive to have had a deep (vs. basic) experience consumption, they are more prone to purchasing unique (vs. typical) objects related to that experience. Further, we find that the conversational value of the object helps explain this effect. These results add to the experiential consumption literature a novel finding associated with an aspect present in virtually every experienceโperceived experience depthโand an outcome of theoretical and practical relevanceโconsumersโ acquisitions of objects linked to their experiences. 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