The Polarization of NFTs: Association between Personality Traits and Perceived Value
Abstract
The rise of Internet 3.0, the metaverse, and virtual realities is accelerating the shift from a physical economy to one that is digital, decentralized, and globally accessible. While the benefits and detriments of virtual assets like non-fungible tokens (NFTs) have received attention, individuals’ opinions about them remain polarized. This study investigates how personality traits shape users’ perceived value of NFTs. Using survey data from 805 respondents, we examine how the Big Five traits (openness, conscientiousness, extraversion, agreeableness, and neuroticism) are associated with 14 value dimensions spanning technology, art, and product aspects. The findings indicate that perceptions of NFTs vary among users. Of note, individuals high in agreeableness and conscientiousness perceive NFTs more favorably across the spectrum of value dimensions, whereas those high in neuroticism exhibit opposite tendencies. Extraverted individuals are drawn to the subjective norms and financial gains related to NFTs, while those high in openness value their information transparency.
Study specs
- Institution
- Tampere University
- Year
- 2026
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
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