Community and Consumer Dynamics in NFTs: Understanding Digital Asset Value Through Social Engagement

Journal of Consumer Behaviour, Journal Year: 2025, Volume and Issue: unknown

Published: March 18, 2025

ABSTRACT While non‐fungible tokens (NFTs) have emerged as a significant blockchain application, research has largely focused on market dynamics rather than consumer behavior. Through in‐depth interviews with 21 NFT consumers and netnographic analysis of Discord interactions (109,517 words), this study develops comprehensive framework explaining the evolution from initial purchase to sustained or discontinued interest in NFTs. The findings reveal that while profit expectations drive purchases, strong community bonds social identity formation are crucial for maintaining engagement. Specifically, active participation, both before after creates self‐reinforcing cycle where engagement directly influences valuation. However, unfulfilled perceived abandonment by project leaders often lead disillusionment. extends Need‐to‐Belong Social Identity Theory digital asset context, demonstrating how communities serve platforms expression emotional support, transcending purely financial motivations. For practitioners, suggest sustainable projects should prioritize building transparent leadership over short‐term speculation. This provides first longitudinal behavior, offering insights into assets can create enduring value through merely speculative trading.

Language: Английский

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Evolution of artificial intelligence in medical sciences: a comprehensive scientometrics analysis

Global Knowledge Memory and Communication, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Purpose The purpose of this study is to analyze the trend scientific publications, geographic and organizational distribution, examine keyword cooccurrence map in field artificial intelligence (AI) medical sciences. Design/methodology/approach applied research has used scientometrics method data AI were extracted from WOSCC database. Data analysis was performed using bibliometrix software. Findings According results, 41,352 documents sciences extracted, growth which increased significantly since 2000. USA, China England identified as leaders field, universities, such Harvard University California, contributed most related knowledge production. Moreover, terms “machine learning” “deep have been proposed key concepts field. Practical implications findings highlight significant role advancing healthcare systems. By fostering international collaboration focusing on emerging trends, integration can lead improved outcomes development innovative solutions that address pressing challenges. Originality/value This contributes existing body by providing a comprehensive distribution associated with scientometric methods software, offers unique perspective evolution within identifying leading institutions pivotal learning.”

Language: Английский

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Teacher empathy messages: The role of teacher enthusiasm and student outcomes

British Journal of Educational Psychology, Journal Year: 2025, Volume and Issue: unknown

Published: March 25, 2025

Recent research has increasingly focused on the role of teachers' empathy in classrooms. However, due to inconsistencies observed its conceptualization and assessment, whether this competence is key for effective teaching remains unknown. Grounding previous approaches understanding emotions, such as control-value theory, could be assess messages, understood their demonstration an students' context, appraisals emotions. Moreover, reaching how teacher motivation might shape instructional practices (i.e., messages) these student outcomes also crucial. This study aimed develop a framework examined use across academic year, contextual classroom characteristics, enthusiasm grades related usage. Participants included 45 teachers 1370 students distributed 66 classrooms 24 high schools. Teacher messages were assessed through audio recording speech during lessons. Messages extracted from transcriptions with help large language models. was T1 T3. Student's collected records at end course (T3). Overall, number per class increased emotion used by teacher. Teachers' associated whereas no significant relation between grades. presents practical messages. Findings highlight enthusiasm) can practices.

Language: Английский

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Are chatbots reliable text annotators? Sometimes

PNAS Nexus, Journal Year: 2025, Volume and Issue: 4(4)

Published: March 27, 2025

Abstract Recent research highlights the significant potential of ChatGPT for text annotation in social science research. However, is a closed-source product, which has major drawbacks with regards to transparency, reproducibility, cost, and data protection. advances open-source (OS) large language models (LLMs) offer an alternative without these drawbacks. Thus, it important evaluate performance OS LLMs relative standard approaches supervised machine learning classification. We conduct systematic comparative evaluation range alongside ChatGPT, using both zero- few-shot as well generic custom prompts, results compared classification models. Using new dataset tweets from US news media focusing on simple binary tasks, we find variation across tasks that classifier DistilBERT generally outperforms both. Given unreliable challenges poses Open Science, advise caution when substantive tasks.

Language: Английский

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New opportunities and challenges for conservation evidence synthesis from advances in natural language processing

Conservation Biology, Journal Year: 2025, Volume and Issue: 39(2)

Published: April 1, 2025

Abstract Addressing global environmental conservation problems requires rapidly translating natural and social science evidence to policy‐relevant information. Yet, exponential increases in scientific production combined with disciplinary differences reporting research make interdisciplinary syntheses especially challenging. Ongoing developments language processing (NLP), such as large models, machine learning (ML), data mining, hold the promise of accelerating cross‐disciplinary primary research. The evolution ML, NLP, artificial intelligence (AI) systems computational provides new approaches accelerate all stages synthesis science. To show how processing, AI can help automate scale science, we describe methods that querying literature, process unstructured bodies textual evidence, extract parameters interest from studies. Automation translate other agendas by categorizing labeling at scale, yet there are major unanswered questions about use hybrid AI‐expert ethically effectively conservation.

Language: Английский

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