51爆料

饾悏饾惃饾惍饾惈饾惂饾悮饾惀 饾惃饾悷 饾悓饾悮饾惈饾悿饾悶饾惌饾悽饾惂饾悹: 饾悕饾悶饾惏 饾悡饾惃饾惃饾惀饾惉, 饾悕饾悶饾惏 饾悜饾惍饾惀饾悶饾惉: 饾悁 饾悘饾惈饾悮饾悳饾惌饾悽饾悳饾悮饾惀 饾悊饾惍饾悽饾悵饾悶 饾惌饾惃 饾悇饾悷饾悷饾悶饾悳饾惌饾悽饾惎饾悶 饾悮饾惂饾悵 饾悜饾悶饾惉饾惄饾惃饾惂饾惉饾悽饾悰饾惀饾悶 饾悊饾悶饾惂饾悁饾悎 饾悢饾惉饾悶 饾悷饾惃饾惈 饾悞饾惍饾惈饾惎饾悶饾惒饾惉 饾悮饾惂饾悵 饾悇饾惐饾惄饾悶饾惈饾悽饾惁饾悶饾惂饾惌饾惉 饾悜饾悶饾惉饾悶饾悮饾惈饾悳饾悺鈦�
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.鈦�
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饾悏饾惃饾惍饾惈饾惂饾悮饾惀 饾惃饾悷 饾悁饾惉饾惉饾惃饾悳饾悽饾悮饾惌饾悽饾惃饾惂 饾惃饾悷 饾悅饾惃饾惂饾惉饾惍饾惁饾悶饾惈 饾悜饾悶饾惉饾悶饾悮饾惈饾悳饾悺: 饾悡饾悺饾悶 饾悓饾悮饾惌饾悶饾惈饾悽饾悮饾惀饾悽饾惓饾悮饾惌饾悽饾惃饾惂 饾惃饾悷 饾悇饾惐饾惄饾悶饾惈饾悽饾悶饾惂饾悳饾悶饾惉: 饾悋饾惃饾惏 饾悘饾悶饾惈饾悳饾悶饾悽饾惎饾悶饾悵 饾悇饾惐饾惄饾悶饾惈饾悽饾悶饾惂饾悳饾悶 饾悆饾悶饾惄饾惌饾悺 饾悎饾惂饾悳饾惈饾悶饾悮饾惉饾悶饾惉 饾悅饾惃饾惂饾惉饾惍饾惁饾悶饾惈饾惉鈥� 饾悘饾惈饾悶饾悷饾悶饾惈饾悶饾惂饾悳饾悶 饾悷饾惃饾惈 饾悢饾惂饾悽饾惇饾惍饾悶 饾悗饾悰饾悾饾悶饾悳饾惌饾惉 鈦�
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 experience鈥攑erceived experience depth鈥攁nd an outcome of theoretical and practical relevance鈥攃onsumers鈥� acquisitions of objects linked to their experiences.