ฬวะฤฦฦฝโฐๆ

๐‰๐จ๐ฎ๐ซ๐ง๐š๐ฅ ๐จ๐Ÿ ๐Œ๐š๐ซ๐ค๐ž๐ญ๐ข๐ง๐ : ๐๐ž๐ฐ ๐“๐จ๐จ๐ฅ๐ฌ, ๐๐ž๐ฐ ๐‘๐ฎ๐ฅ๐ž๐ฌ: ๐€ ๐๐ซ๐š๐œ๐ญ๐ข๐œ๐š๐ฅ ๐†๐ฎ๐ข๐๐ž ๐ญ๐จ ๐„๐Ÿ๐Ÿ๐ž๐œ๐ญ๐ข๐ฏ๐ž ๐š๐ง๐ ๐‘๐ž๐ฌ๐ฉ๐จ๐ง๐ฌ๐ข๐›๐ฅ๐ž ๐†๐ž๐ง๐€๐ˆ ๐”๐ฌ๐ž ๐Ÿ๐จ๐ซ ๐’๐ฎ๐ซ๐ฏ๐ž๐ฒ๐ฌ ๐š๐ง๐ ๐„๐ฑ๐ฉ๐ž๐ซ๐ข๐ฆ๐ž๐ง๐ญ๐ฌ ๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐กโฃ
Generative 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 () provides supporting materials, including reproducible coding templates in R and SPSS and sample pre-registrations.โฃ
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๐‰๐จ๐ฎ๐ซ๐ง๐š๐ฅ ๐จ๐Ÿ ๐€๐ฌ๐ฌ๐จ๐œ๐ข๐š๐ญ๐ข๐จ๐ง ๐จ๐Ÿ ๐‚๐จ๐ง๐ฌ๐ฎ๐ฆ๐ž๐ซ ๐‘๐ž๐ฌ๐ž๐š๐ซ๐œ๐ก: ๐“๐ก๐ž ๐Œ๐š๐ญ๐ž๐ซ๐ข๐š๐ฅ๐ข๐ณ๐š๐ญ๐ข๐จ๐ง ๐จ๐Ÿ ๐„๐ฑ๐ฉ๐ž๐ซ๐ข๐ž๐ง๐œ๐ž๐ฌ: ๐‡๐จ๐ฐ ๐๐ž๐ซ๐œ๐ž๐ข๐ฏ๐ž๐ ๐„๐ฑ๐ฉ๐ž๐ซ๐ข๐ž๐ง๐œ๐ž ๐ƒ๐ž๐ฉ๐ญ๐ก ๐ˆ๐ง๐œ๐ซ๐ž๐š๐ฌ๐ž๐ฌ ๐‚๐จ๐ง๐ฌ๐ฎ๐ฆ๐ž๐ซ๐ฌโ€™ ๐๐ซ๐ž๐Ÿ๐ž๐ซ๐ž๐ง๐œ๐ž ๐Ÿ๐จ๐ซ ๐”๐ง๐ข๐ช๐ฎ๐ž ๐Ž๐›๐ฃ๐ž๐œ๐ญ๐ฌ โฃ
While 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 experienceโ€”perceived experience depthโ€”and an outcome of theoretical and practical relevanceโ€”consumersโ€™ acquisitions of objects linked to their experiences.