Credibility Matters: Motivations, Characteristics, and Influence Mechanisms of Crypto Key Opinion Leaders

Paper Title

Credibility Matters: Motivations, Characteristics, and Influence Mechanisms of Crypto Key Opinion Leaders

Publication Info

• Topic area: Crypto influencers' motivations, credibility practices, and ethical responsibilities in high-risk financial ecosystems.
• Keywords: Crypto influencers, credibility, self-determination theory, Web3, decentralized finance, ethics, community norms, financial regulation, parasocial interaction, thematic analysis.

Background and Problem

• Problem / challenge: Existing research on influencers and finfluencers focuses on lifestyle contexts or general financial commentary, neglecting the unique socio-technical and ethical challenges faced by crypto key opinion leaders (KOLs) in volatile, loosely regulated markets.
• Significance: Crypto KOLs significantly impact retail investment behavior and market sentiment, making their credibility and ethical practices crucial for protecting users from financial harm and scams.
• Motivation and related work: Prior studies explore influencer culture, finfluencer sentiment effects, and crypto user struggles but lack in-depth analysis of crypto KOLs' motivations, practices, and ethical reasoning. This paper addresses these gaps using self-determination theory (SDT) and qualitative methods.

Solution

• Proposed approach: A qualitative study combining interviews with 13 crypto KOLs and a hybrid human–LLM thematic analysis to examine motivations, practices, and credibility enactment through the lens of SDT.
• Novelty:
  1. Application of SDT to high-risk financial contexts, highlighting autonomy, competence, and relatedness as drivers of KOL behavior.
  2. Reconceptualization of credibility as a socio-technical, self-determined practice rather than static credentials.
  3. Identification of four community-recognized markers of credibility: self-regulation, bounded epistemic competence, accountability, and reflexive self-correction.
  4. Methodological innovation using LLM-assisted thematic analysis while retaining human oversight.
• Procedure and key techniques:
  • Conducted semi-structured interviews with 13 KOLs across Europe, the U.S., and Asia.
  • Used SDT to analyze intrinsic and extrinsic motivations, ethical practices, and influence mechanisms.
  • Employed a hybrid thematic analysis workflow combining human coding and LLM-suggested candidate themes, with iterative refinement and validation.

Results

• Concrete findings:
  • KOL motivations evolve from extrinsic incentives (e.g., sponsorships) to intrinsic drivers like education, mastery, and ideological commitment.
  • Credibility is enacted through self-determined practices: self-regulation, bounded competence, transparency, and reflexive correction.
  • KOLs balance educational missions with algorithmic visibility constraints, tailoring content across platforms like YouTube, Telegram, and LinkedIn.
• Advantage over baselines: Challenges traditional views of credibility based on static credentials by demonstrating its dynamic, ethically enacted nature in decentralized, pseudonymous environments.
• Experiments / evaluation:
  • Interviews with 13 KOLs, analyzed using SDT and thematic coding.
  • Inter-annotator reliability (Krippendorff’s alpha = 0.78) confirmed coding consistency.
  • LLM-assisted thematic analysis broadened candidate theme coverage while maintaining human interpretive control.
• Limitations and future work:
  • Sample skewed toward established, male KOLs from Western and Asian contexts; lacks diversity in gender and smaller creators.
  • Self-reported data not cross-validated with behavioral indicators like on-chain transactions.
  • Focused solely on KOL perspectives, excluding follower and regulator viewpoints.
  • Findings may be transient due to the volatility of crypto markets and rapid platform evolution.

Summary

This study investigates crypto KOLs' motivations, practices, and ethical responsibilities using self-determination theory and qualitative interviews. It reconceptualizes credibility as a dynamic, self-determined practice rooted in autonomy, competence, and relatedness, identifying four markers of trustworthiness. Results highlight how KOLs navigate ethical tensions, platform dynamics, and community stewardship in high-risk, decentralized markets. The hybrid human–LLM thematic analysis demonstrates the potential of AI-assisted workflows while emphasizing the necessity of human oversight. Findings inform platform design, community norms, and regulatory frameworks to enhance transparency and accountability in crypto ecosystems.