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

Credibility Maers: Motivations, Characteristics, and Inf luence Mechanisms of Cr ypto K ey Opinion Leaders Alexander Kropiunig Complexity Science Hub Vienna, A ustria kropiunig@csh.ac.at Svetlana Kremer A ustrian Institute of T echnology Vienna, A ustria Complexity Science Hub Vienna, A ustria abramova@csh.ac.at Bernhard Haslhofer Complexity Science Hub Vienna, A ustria haslhofer@csh.ac.at Abstract Crypto Key Opinion Leaders (KOLs) shap e W eb3 narratives and retail investment behaviour . In volatile, high-risk markets, their credibility becomes a key determinant of their inuence on follow- ers. Y et prior research has focused on lifestyle inuencers or generic nancial commentary , leaving cr ypto KOLs’ understandings of mo- tivation, credibility , and responsibility underexplored. Drawing on interviews with 13 KOLs and self-determination theory (SDT), we examine how psy chological needs are negotiated alongside mon- etisation and community expectations. Whereas prior work treats nuencer credibility as a set of static credentials, our ndings re- veal it to be a self-determined, ethically enacted practice . W e identify four community-recognised markers of credibility: self-regulation, bounded epistemic competence, accountability , and reexive self- correction. This r eframes credibility as socio-technical performance, extending SDT into high-risk crypto ecosystems. Methodologically , we employ a hybrid human–LLM thematic analysis. The study sur- faces implications for designing credibility signals that prioritise transparency over hype. CCS Concepts • Human-centered computing → Empirical studies in HCI . Ke ywords blockchain, credibility , crypto inuencer , cryptocurrency , nu- encer , opinion leader , social me dia 1 Introduction Over recent years, social media inuencers have emerged as pow- erful actors shaping consumer prefer ences and nancial decision- making. Global spending on inuencer marketing reached $24 bil- lion in 2024 [ 49 ], reecting their growing economic signicance. Au- diences often emulate their online opinion leaders, and inuencer- driven content has be en shown to signicantly aect consumer behaviour [ 35 ]. This inuence increasingly extends into nancial markets, where digital communities can shape investment decisions. The case of Reddit’s WallStreetBets illustrates this phenomenon, as collective attention drove wav es of high-risk retail trades [62]. In the blockchain and cryptocurrency sector , a distinct class of inuencers, often termed Ke y Opinion Leaders (KOLs) or “crypto- inuencers, ” has emerged. These individuals command large follow- ings and play a central role in shaping sentiment and investment behaviour . Empirical research shows that crypto inuencers’ com- munications can drive short-term cr yptocurrency price uctuations and trading volumes, underscoring their market-moving capacity [ 41 , 45 ]. Unlike mainstream lifestyle inuencers, crypto KOLs op- erate in highly volatile, loosely regulated markets characterise d by pseudonymous participation, complex tokenomics, and irreversible transactions. They often hold positions in the assets they discuss, promote or build products around, and communicate with com- munities that are both nancially and ideologically invested. In this setting, a single post about a low-cap token may move prices, expose followers to pump-and-dump schemes or rug pulls, and gen- erate substantial losses for retail investors with limited technical or nancial expertise. Credibility is therefor e not merely an abstract reputational asset but a determinant of real nancial risk. Crypto KOLs dier from generic nancial inuencers (nu- encers) and traditional nancial advisors. Whereas nuencers fre- quently comment on diversied portfolios or macro trends, crypto counterparts position themselves as domain specialists in decen- tralised nance (DeFi) and W eb3 protocols, translating highly tech- nical developments into actionable narratives. They may communi- cate under pseudonyms while simultaneously leaving public traces on-chain (e . g., via visible wallet holdings or governance activity ), creating new forms of transpar ency and conict of interest. Regu- latory guidance for such actors is still emerging and fragmented across jurisdictions, leaving many KOLs to self-interpret obliga- tions around disclosure, suitability , and market integrity . These socio-technical conditions make questions of credibility , ethics, and responsibility especially acute. Academic attention to nuencers remains still limited. Prior work has examined how inuencers build personal brands and motivations in mainstream contexts [59], how sponsorship disclo- sure and perceived authenticity aect p ersuasion [ 10 ], and how nuencers and celebrity gures can move cr ypto markets in the short run [ 6 , 41 ]. Parallel literatures in human-computer interac- tion (HCI) and usable security document how cryptocurrency users struggle with risks, scams, and security practices [ 3 , 33 , 39 , 53 ]. Howev er , there is still a limited understanding about how crypto KOLs become and remain opinion leaders in such high-stakes en- vironments, how they conceptualise and cultivate credibility , and how they navigate ethical tensions between education, promotion, and self-interest. Guided by self-determination the ory (SDT) [ 14 ] and using quali- tative research metho ds, this study e xamines the motivations, be- liefs, and communicative strategies of crypto K OLs. Specically , we investigate: 1 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer • RQ1: What extrinsic and intrinsic factors motivate indi- viduals to be come and remain key opinion leaders in the cryptocurrency ecosystem? • RQ2: What dening characteristics and ev eryday practices set crypto KOLs apart from other inuencers and members of the crypto community? • RQ3: How do crypto KOLs conceptualise and enact credi- bility , ethics, and responsibility toward their communities in high-risk, speculative markets? Contributions. This study advances understanding of crypto- inuencer dynamics through four interrelated contributions which are grounded in theory , empirical evidence , methodological reec- tion, and practical impact. Theoretical contribution. W e apply self-determination theory to high-risk nancial contexts by showing how the needs for auton- omy , competence, and relatedness are negotiate d under conditions of volatility , sponsorship pressure , and shared nancial exposure with followers. Our analysis surfaces tensions between autonomy and monetisation, between technical competence and communica- tive accessibility , and between relatedness and the risk of encour- aging speculation. W e further contribute to inuencer marketing and creator-labour research by theorising cr ypto K OLs as hybrid actors who simultaneously serve as educators, informal nancial advisors, entrepreneurs, and community stewar ds. Whereas prior work conceptualises credibility in terms of static cr edentials such as formal education, professional background, and nancial certi- cations [ 28 ], our ndings reveal credibility as a self-determined, ethically enacted practice rooted in SDT ne eds and shaped by K OLs’ own norms, constraints, and r esponsibilities. W e identify four emer- gent dimensions that function as community-recognise d mark- ers of trustworthiness: (i) self-r egulation and voluntary constraint , whereby KOLs decline misaligned sponsorships and impose per- sonal rules on promotion; (ii) bounded epistemic competence , ac- knowledging the limits of one ’s expertise and avoiding prognostica- tion; (iii) accountability , cultivating long-term trust through trans- parent disclosure and community stewar dship; and (iv) reexive self-correction , learning from past failures and continuously reassess- ing own practices. This reframes credibility as a socio-technical, self-regulated performance rather than a static credential. Empirical contribution. Drawing on thirte en inter views with crypto K OLs from Europe, the United States, and Asia (traders, edu- cators, founders, and analysts), we present a thematic analysis that details (i) extrinsic drivers ( e. g., sponsorship rev enue, monetisation of analytics, social capital), (ii) intrinsic motives ( e. g., enjoyment, educating newcomers, ideological commitment, desire for mastery and community), (iii) characteristics and practices that distinguish crypto K OLs from mainstream inuencers (e. g., technical exper- tise, regulatory literacy , use of on-chain signals), and (iv) ethical and community norms that guide their communication strategies (e . g., disclosure practices, self-imposed restrictions on pr omotions). These qualitative insights ll a notable gap in the literature on digi- tal opinion leadership by pr oviding rich, contextualised accounts of a previously understudied inuencer category . Methodological contribution. T o systematically analyse a complex and rapidly evolving domain, we describe and critically reect on a hybrid workow that couples conventional qualitativ e interview- ing with large language model–assisted thematic analysis. In our approach, a large language model (LLM) proposes candidate co des and SDT mappings on anonymised transcript segments, while hu- man r esearchers r etain full control ov er the codeb ook development, theme construction, and interpretation. W e show how this human- in-the-loop workow can broaden candidate theme coverage and support transparency without treating the LLM as an autonomous analyst or source of ground truth. Practical contribution. Our ndings yield actionable recommen- dations for industry and policy . For marketers, campaigns that resonate with K OLs’ community-oriented motives such as educa- tion and protection are more likely to produce credible and endur- ing partnerships than those based solely on one-o paid promo- tions. For platform designers, we suggest features such as trans- parent disclosure badges, longitudinal accuracy or “track record” indicators, and cross-platform reputation proles that help users identify credible actors and incentivise responsible behaviour . For regulators, the study highlights the need for proportionate disclo- sure rules, registration thr esholds for high-reach inuencers, and cross-jurisdictional coordination to addr ess scams, rug pulls, and other forms of market manipulation while pr eserving the partici- patory ethos of W eb3. T ogether , these measures oer a roadmap for strengthening trustworthy inuence in cr yptocurrency and for designing socio-technical infrastructures that support ethical, evidence-based content. Paper structure. The remainder of this paper is organised as fol- lows. Section 2 reviews rele vant literature on crypto currency user behaviour , social media inuencers, nuencers, creator lab our , and the SDT framework. Section 3 describes our qualitative method- ology , including interview procedures, participant demographics, and coding strategy . Section 4 presents the main results structured along the specied RQs. In Section 5, we discuss the implications of these ndings, limitations and future research directions, and Section 6 concludes with a summary of contributions. 2 Background and Related W ork This section synthesises prior research to situate cr ypto key opinion leaders (KOLs) as a distinct category of digital workers. W e organise the literature around four themes: (1) inuencers, nuencers, and creator labour; (2) why crypto constitutes a qualitatively dierent context; (3) crypto KOLs as digital creators under nancialised conditions; and (4) the research gap motivating our study . 2.1 Inuencers, Finuencers, and Creator Labour 2.1.1 Inf luencer Culture and Creator Labour . Inuencer research conceptualises so cial media cr eators as micro-celebrities who strate- gically curate authenticity and intimacy to attract audiences [ 18 , 32 , 40 ]. Ethnographic accounts reveal how inuencers cultivate personas that are simultaneously relatable and aspirational, with authenticity understood as performative, carefully crafted to sustain attention and trust [8, 40]. T wo concepts capture the digital labour involved. Abidin’s visi- bility labour describes the ongoing work of p osting, curation, and in- teraction through which inuencers maintain presence [ 1 ]. Baym’s 2 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders relational labour emphasises the eort invested in sustaining inti- mate audience relationships [ 8 ]. Both are consequential because parasocial interaction makes followers receptive to r ecommenda- tions; credibility emerges from aective bonds, not just expertise [34, 37, 50]. From a political-economy perspective, Duy and colleagues de- scribe “nested precarities” of creators, which denotes overlapping insecurities at the level of platforms, industries, and algorithms [ 16 , 17 ]. Creators engage in constant self-monitoring, cr oss-posting, and brand management to remain visible [ 15 ], “playing the visibil- ity game” to satisfy opaque ranking systems [ 12 ]. Success depends on algorithmic regimes and r evenue str eams that can shift abruptly . 2.1.2 Finf luencers and Social-Media Finance. Recent scholarship extends inuencer culture into personal nance. Guan characterises “nuencers” as online nancial advice-givers who mix analysis, sto- rytelling, and entertainment, yet often operate outside regulatory frameworks [ 26 ]. Hayes and Ben-Shmuel show how nuencers contribute to the nancialisation of everyday life by weaving in- vesting into lifestyle narratives, normalising spe culative b ehaviours [ 29 ]. This work highlights diverse motivations, ranging fr om edu- cation and community building to self-promotion, and identies conicts of interest when creators prot fr om endorsed products. Quantitative studies demonstrate that nuencer activity shapes crowd sentiment. Haase et al. nd that nuencers’ sentiment Granger-causes broader crowd sentiment, particularly in crypto discussions [ 27 ]. Meyer et al. document emotional contagion in Y ouT ube crypto content, with inuencers’ tone mirror ed in audi- ence comments [ 43 ]. These ndings suggest nuencers modulate aect, amplifying herd dynamics in volatile markets. 2.1.3 Credibility in Finf luencer Research. Existing research on n- uencer credibility has predominantly adopted a credential-based perspective, conceptualising trustworthiness in terms of static at- tributes such as formal education, professional background, nan- cial certications, and practitioner experience [ 28 ]. This framing treats credibility as a property that individuals p ossess by virtue of their qualications, implicitly privileging institutional markers over situated practices. Howe ver , such an approach may not fully capture how credibility operates in decentralised, pseudonymous environments like cr ypto, where formal credentials are often absent, unveriable, or irrele vant to community norms. In these contexts, credibility may instead emerge from observable b ehaviours (such as transparent disclosure, r estraint in promotion, and responsiveness to community feedback) rather than from static signals of expertise. This gap motivates our inquiry into how crypto KOLs enact credi- bility through self-determined practices, an approach w e analyze empirically in Section 4.3 and theorise in Section 5. 2.1.4 Creator Lab our Under Financialisation. Duy’s work on aspi- rational labour shows how creators invest substantial unpaid time building visibility in hope of future opportunities [ 15 ]. Financiali- sation intensies these dynamics. Alacovska and Chalcraft analyse NFT artists’ work as “speculative labour , ” where creative outputs become nancial assets subject to extreme volatility [ 5 ]. Internet celebrity dynamics [ 2 ] illustrate how rapid visibility generates both opportunities and vulnerability to backlash when advice fails. In such environments, creators’ incomes, reputations, and portfolios become entangled with promoted products. This literature establishes that inuencers are not merely commu- nicators but workers under conditions of nested precarities. Crypto KOLs inherit these dynamics but operate in a high-risk nancial domain where attention uctuations translate dir ectly into gains or losses, foregrounding questions of credibility and responsibility . 2.2 Crypto as a Distinct Context Cryptocurrency presents a qualitatively dierent context for inu- encer work. W e synthesise research on usability , regulation, scams, and community trust to explain why crypto K OLs occupy a partic- ularly consequential position. 2.2.1 Fragile Mental Models and Usability Struggles. HCI research underscores that crypto currencies remain dicult to use safely . Many users struggle with private-key management and rely on third-party services, exposing them to custodial risk [ 19 , 33 ]. Studies reveal fragile mental models of custody , anonymity , and transaction nality , including assumptions about reversibility that rarely hold [ 3 , 39 ]. This suggests that K OL audiences often lack foundational knowledge to critically evaluate advice. 2.2.2 Regulatory Gaps and W eak Consumer Protections. Cr yptocur- rency promotion falls between or outside conventional investor- protection regimes. Finuencers’ undisclosed sponsorships and pump-and-dump schemes evade rules designed for licensed ad- visors [ 57 ]. Regulatory agencies have issued guidelines targeting misleading promotions [ 4 , 22 , 61 ], yet enforcement remains patchy and jurisdictionally fragmented. Cr ypto KOLs often operate in even murkier environments, promoting speculative assets that cross na- tional b oundaries and challenge securities law . This intensies both KOLs’ impact and ethical tensions ar ound disclosure. 2.2.3 Scams and Community T rust. Oak and Shaq analyse scam- related discourse on Reddit, identifying roles of victims, vigilantes, and advice-givers that underscore grassroots demand for trustwor- thy guidance [ 47 ]. On-chain analyses reveal ecosystems of scam tokens and rug pulls that exploit naive investors [ 11 ]. Research on user trust highlights a “centralised trust in decentralised sys- tems” paradox: users anchor trust in exchanges and charismatic community gures, precisely the interme diaries decentralisation was meant to obviate [7, 31, 53]. T ogether , these strands establish that crypto is a socio-technical environment where users struggle with operations, protections are weak, and scams are per vasive . Users often “outsour ce” due diligence to community gures, including KOLs whose endorse- ments function as trust shortcuts, akin to, but less r egulated than, traditional nancial advisors. 2.3 Crypto K OLs as Digital W orkers W e now reframe crypto KOLs as a form of digital creator labour at the intersection of inuencer culture and crypto’s specic risks. 2.3.1 Double Exposure: Platform and Market Precarity . Crypto KOLs exemplify intersecting forms of precarity . Like other creators, they depend on platform algorithms and constant content production 3 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer [ 12 , 17 ], engaging in visibility and relational lab our [ 1 , 8 ] while navigating nested precarities [16]. Howev er , unlike lifestyle inuencers, crypto KOLs’ success is tied directly to volatile asset prices. Their work constitutes specu- lative labour [ 5 ], with reputations and portfolios entangle d with promoted products. Lacking institutional safeguards or licensing, they face a double exposure to both platform dynamics and nan- cial markets, rendering their labour uniquely precarious. Internet celebrity dynamics [ 2 ] apply forcefully: rapid visibility generates opportunities but also vulnerability to backlash when projects fail. 2.3.2 Self-Determination Theory and KOL Motivations. SDT pro- vides a framework for understanding motivations bey ond “greed” versus “altruism. ” Central to SDT is a continuum of relative au- tonomy , ranging from amotivation through external regulation to fully internalised, intrinsic motivation; the degree to which external incentives become self-endorse d determines the quality and persis- tence of engagement [ 52 ]. SDT also p osits three basic psychological needs: autonomy , competence , and relatedness , whose satisfaction supports this internalisation process and fosters w ell-being [ 13 , 52 ]. SDT has been applied to online communities and digital work. Nov nds Wikipedians driven by intrinsic enjoyment, learning, and prosocial motives [ 46 ]. T yack and Mekler show that supporting SDT needs fosters sustained engagement [ 60 ]. Studies of content creators nd that Y ouTube pr oducers value autonomy , competence, and relatedness, with monetary rewards often secondary [36, 59]. Applying SDT to crypto KOLs, we conceptualise how they may experience autonomy in cho osing projects, competence in decoding protocols for lay audiences, and relatedness in cultivating commu- nities. Simultaneously , sponsorships and portfolio performance exert external pressures. An SDT lens distinguishes internalised motivations (education, community stewardship) fr om controlled ones (sponsor obligations, trading gains), pro viding a framework for analysing credibility practices. 2.3.3 Market-Level Inf luence. Research links online inuence to nancial outcomes. Studies of r/W allStreetBets show how social dynamics moved markets during the GameStop sque eze [ 38 , 62 ]. In crypto, Musk’s tweets produce signicant price changes [ 6 ], and nuencer endorsements channel capital into tokens [ 27 , 29 , 41 , 43 ]. Across this literature, inuencers are modelled as sources of signals correlated with market movements. Much less attention is paid to their experiences as workers, or how motivations and ethical commitments shape their practices. 2.3.4 Conceptual Summary: Creator-Labour and SDT Concepts A p- plied to Crypto KOLs. T able 1 summarises how core concepts from creator-labour scholarship and SDT map onto the w ork of crypto KOLs. This conceptual mapping guides our empirical analysis by identifying the forms of labour , precarity , and motivation that char- acterise KOL w ork. 2.4 Research Gap A comprehensive mapping of the literatures, their relevance to crypto K OLs, and remaining gaps is pro vided in T able 5 (Appendix). Despite this rich body of work, a crucial gap remains. Existing re- search focuses on crypto users and retail investors , examining their struggles and trust practices; on market-level conse quences , mod- elling inuencers as sentiment signals; or on lifestyle inuencers and generic nuencers , without attending to crypto’s specic con- ditions. W e lack an in-depth, SDT -informed qualitative account of crypto KOLs themselves . This gap matters because cr ypto K OLs occupy a critical, under- researched position as community gures whose motivations and practices have material consequences for followers’ nancial well- being. Understanding KOLs as workers, rather than merely as mar- ket signals, is essential for theory , intervention design, and policy . Our research questions (Section 1) address this gap: KOL moti- vations (RQ1), distinguishing practices (RQ2), and credibility and ethical reasoning (RQ3). 3 Methodology W e employ ed a qualitative design comprising semi-structured in- terviews with thirteen cr ypto KOLs and a hybrid thematic analysis guided by self-determination the ory (SDT). T wo traine d researchers conducted and code d the interviews, and we used a large language model in a human-in-the-lo op workow to propose additional can- didate codes that were subsequently curated and veried by humans. W e assessed inter-annotator reliability between the two human coders and integrated the resulting themes into an SDT -aligne d framework. Detailed procedures follow . 3.1 Ethical Considerations The study protocol was reviewed and approved by the T U Wien Research Ethics Committee (T UW REC, Case No. 080/27062025). All participants were adults ( ≥ 18 years) and provided informed con- sent. Safeguards included strict pseudonymization (e. g., “K OL01”), removal of direct identiers in transcripts and publications, and GDPR-compliant retention limits (audio deleted after one year; anonymized transcripts after three). Participants could skip ques- tions, pause, or withdraw until October 31, 2025. Quotations were anonymized and cleaned prior to publication, research materials were stored securely , and protocols governed the handling of sen- sitive disclosures to ensure condentiality unless imminent harm was reported. For language editing, large language models were used only with fully anonymized excerpts containing no identiers; no raw data were transferred externally , and all core processing occurred within the controlled research environment. Model use and additional safeguards. W e used OpenAI GPT -4 as a supplementary assistant for candidate theme suggestion (tem- perature = 0.2; top_p = 1.0). Before any model input, all processed excerpts were fully anonymized. Only short, de-identied segments were processed; no raw datasets or identiers left the research en- vironment. All model outputs were reviewed by human coders and accepted, revised, or discarded. 3.2 Qualitative Interviews W e conducte d 13 semi-structured interviews with Key Opinion Leaders (K OLs) within the blockchain ecosystem. Participants were recruited through targeted sampling of individuals who met sp ecic criteria: (1) active presence on social media platforms with crypto- focused content, (2) demonstrated inuence within the cr ypto com- munity through follower engagement or industry recognition, and 4 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders T able 1: Conceptual framework mapping creator-labour constructs and SDT needs to the work of cr ypto key opinion leaders. Concept Denition Application to Crypto KOLs Creator-labour constructs Visibility lab our [1] Ongoing work of posting, curation, and interaction to maintain public presence Continuous content production, audience engagement, and cross-platform presence management Relational lab our [8] Eort invested in building and maintaining intimate audience relationships Cultivating parasocial bonds through community engagement and sharing personal experiences Aspirational labour [15] Unpaid or under-compensated work invested in hope of future opportunities Heavy early-career investment in content and community before monetisation Nested precarities [16] Overlapping insecurities at platform, industry , and economic levels Algorithmic volatility , market crashes, sponsorship instability , and regulatory uncertainty Speculative lab our [5] Creative work entangled with volatile nancial assets Reputations and incomes tied to token prices; portfolios often include promoted projects Internet celebrity [2] Rapid visibility generating opportunities and vulnerabilities Successful calls attract followers and sponsorships; failed calls invite backlash SDT basic needs † A utonomy Need to feel self-directed and volitional Freedom to choose projects, content style, and community norms Competence Need to feel eective and capable Satisfaction from accurate analyses, successful predictions, and audience learning Relatedness Need for meaningful social connections Community ties and peer networks; prosocial motives coexist with commercial ones † All three needs drawn from the self-determination theory [13, 52]. (3) consistent content creation related to blockchain technology , cryptocurrency , or de centralized nance. Initial participants wer e identied through systematic searches of pr ominent social media accounts, and additional participants were r ecruited through snow- ball sampling based on referrals from initial interviewees. Each interview lasted between 30 and 60 minutes and was con- ducted via video conferencing platforms to accommodate partic- ipants’ global distribution. Interview questions were organised around three main areas: (1) motivations for becoming a cr ypto KOL, (2) strategies for building and maintaining inuence, and (3) perspectives on ethics and responsibility within the cr ypto com- munity . All inter views were r ecorded with participant consent and transcribed verbatim for thematic analysis. Building on this design, we implemented an inter view protocol explicitly aligned with our resear ch questions (RQs) to elicit rich narratives while preserving comparability across the inter views. The interview questions are provided in Section D. Each question also includes an identier mapping it to the associated RQ(s). 3.3 Participant Prole The study examined a heterogeneous cohort of key opinion leaders (KOLs), ranging fr om early crypto adopters to post-2020 entrants shaped by DeFi and W eb3 infrastructures. Participants occupy hy- brid roles: educator , builder , researcher , and entrepreneur , combin- ing academic training with market practice. This hybridity positions KOLs as intermediar y nodes that translate between technical dis- course, retail education, and regulatory concerns. Geographically , participants were based in Europe, the United States, and Asia. Platform repertoires are multi-sited. Core channels include X (formerly T witter), LinkedIn, Y ouT ube, and T elegram, often com- plemented by Instagram, TikT ok, and specialize d academic or DA O channels. Content formats span short-form explainers, livestreams, research threads, technical updates, and policy commentary . T able 2 synthesises entr y timing, roles, content orientation, plat- form portfolios, professional backgrounds, and topical foci. The dis- tribution re veals thr ee overlapping archetypes: (i) educator-curators standardising retail-facing kno wledge; (ii) researcher-practitioners linking academic and technical insights to market narratives; and (iii) builder-entrepreneurs emb edding communication within prod- uct and e cosystem development. These archetypes recur across platforms, indicating that inuence is less channel-sp ecic than orchestrated across multiple venues. All participants were adults. T o reduce re-identication risks in this relatively small and specialised population, we do not report more granular demographic breakdowns ( e. g., precise age ranges or gender identities) beyond the regional and role information summarised in T able 2. Participants rst engaged with crypto cur- rencies between 2015 and 2021 (T able 2), meaning that at the time of interview they spanned early adopters with nearly a decade of experience and more recent entrants who became active during the expansion of DeFi and W eb3 infrastructures. T o balance b oth depth and diversity while remaining feasible within our resources, we aimed for a sample of approximately a dozen KOLs. Recruitment and analysis proceeded iteratively; after the thirteenth interview , new accounts primarily reinforced exist- ing patterns in motivations, practices, and ethical reections, and we therefore judged that we had reached the oretical saturation 5 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer T able 2: Anonymize d overview of KOLs: entry timing, self-dene d role, content style, platforms, background, and focus. Abbrev .: YT =Y ouT ube, TG=T elegram, X=X (formerly T witter), IG=Instagram, T T =TikT ok, LI=LinkedIn. Profile Pla tform Context KOL Entry Role Content YT TG X IG T T LI Background Focus KOL-01 2019 Educator (nance) Videos, tips ✓ ✓ ✓ ✓ ✓ Coaching / studies Retail investing / education KOL-02 2015 Educator Short videos, news ✓ ✓ ✓ ✓ Insurance Crypto education KOL-03 2015 Advocate / educator Essays, commentary ✓ Research / arts Blockchain advocacy KOL-04 2017 Researcher / educator Research, lectures ✓ Academic background Crypto-economic research KOL-05 2017 Educator / researcher Articles, scholarly ✓ ✓ Academic + industry T eaching / research KOL-06 2016 Builder / founder T echnical up dates ✓ ✓ T ech background DeFi infrastructure KOL-07 pre-2016 Educator / inuencer Daily posts, newsletter ✓ ✓ ✓ Finance / teaching Retail nance education KOL-08 2016 Builder / academic Research, commentary ✓ ✓ Markets / tech DeFi & DA O research KOL-09 2017 Educator / inuencer T utorials, guides ✓ ✓ ✓ ✓ Entrepreneurial W eb3 education KOL-10 2015 Builder / founder Startup commentary ✓ T ech / consulting W eb3 products KOL-11 2015 Entrepreneur / expert W ebinars, blogs T ech / auditing Regulation / compliance KOL-12 2021 Investor / mentor Investment insights ✓ Entrepreneurship / V C W eb3 investment KOL-13 2018 Researcher / investor Resear ch threads ✓ ✓ T ech / nance Crypto ventures for our focal population. This sampling strategy , however , intro- duces several limitations. Participants were drawn from Europe, the United States, and Asia, and produced content on major global platforms (e. g., X, Y ouTube, Linke dIn, T elegram), which under- represents KOLs from other regions, language communities, or platforms. Moreover , most inter view ees already had substantial audiences and multi-platform presences, so our ndings may over- state the perspe ctives of relatively established and successful KOLs compared to smaller or emerging creators. These biases constrain the generalisability of our ndings to the broader population of crypto content creators; we return to these limitations in Section 5. 3.4 LLM- Assisted Thematic Analysis Rationale for LLM assistance. W e introduced LLM supp ort to explore on a small interview corpus whether mo del-suggested can- didate themes could broaden the space of co des considered in stan- dard qualitative analysis without reducing interpretiv e quality . The aims were to (i) br oaden multi-label coverage, (ii) impr ove consis- tency and auditability of the working codebook, and (iii) accelerate SDT -oriented theor y linkage. In line with our ethics protocol, the LLM was strictly supplementary: all suggestions wer e treated as hypotheses, reviewed by human coders against the raw text, and retained only when clearly grounded in participant accounts. Our analysis employed a hybrid thematic analysis that com- bined traditional human-led analysis with LLM-suggested candi- date themes to systematically identify and rene patterns across the interview transcripts. This approach lev eraged the analytical expertise of trained qualitative researchers while using the pattern- recognition capabilities of large language models to surface addi- tional codes for human consideration. W e used OpenAI GPT -4 ( June 2025) with low-temperature settings (temperature = 0.2, top_p = 1.0); prompts and example outputs are documented in Appendix 6, Sec- tion 2 (Figures 3 and 4). The coding process followed a multi-stage approach (as illus- trated in Fig. 1): Stage 1: Transcript Segmentation. Inter view transcripts were divided into segments of ca. 200–300 words. Segment b oundaries were aligned with natural conversational pauses and thematic shifts to preserve coherence. This approach provided sucient detail for ne-graine d analysis while retaining the broader context of participants’ narratives. Stage 2: Dual Human Descriptive Coding. T wo trained quali- tative researchers independently annotated all transcript segments using descriptive coding techniques. Both analysts had experience in qualitative methodology and were familiar with self-determination theory and inuencer marketing literature . They worked indepen- dently to identify key concepts, motivations, behaviours, and at- titudes expressed within each segment, generating candidate the- matic labels that remained close to participants’ language. Stage 3: LLM-Suggested Candidate Themes. In parallel to human analysis, each transcript chunk was processed with a large language model (OpenAI GPT -4) using prompts designed to sug- gest additional candidate themes and potential SDT mappings that might capture patterns not imme diately apparent to human ana- lysts. In the rst pass, the LLM suggeste d approximately 60 unique labels. The research team then inspecte d this list, remo ved obvi- ous duplicates, collapsed near-synonyms, and discarded lab els that moralised participants’ accounts, introduce d speculative psy cholog- ical diagnoses, or were insuciently grounded in the underlying text. This process yielded 32 candidate labels for further considera- tion. The exemplary prompt as well as an excerpt of the model’s JSON output structure are pr ovided in Appendix 6, Section 2 (see Figures 3 and 4). Stage 4: Theme Integration and Reconciliation. The research team systematically compared human-generated labels with the ltered set of LLM-suggested candidates and incorporated those that added analytical depth or coverage. This process resulted in a combine d set of 51 labels in total. Of these, 32 originated from LLM suggestions that sur vived human curation, while 19 were generated independently by the two human co ders. Discrepan- cies between human analysts were resolved through discussion rounds and consensus, with LLM suggestions ser ving solely as supplementary inputs during reconciliation. This process yielded a comprehensive pool that blended human interpretive expertise 6 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders Transcript Segmentation Dual Human Descriptive Coding LLM Candidate Themes Theme Integration & Reconciliation Iterative Renement & V alidation Apply Rened Thematic Frame Themes & SDT Mapping Human Coding LLM Assistance Synthesis & V alidation Outcomes Legend Primary ow LLM-assisted input Feedback / iteration Figure 1: LLM-assisted thematic analysis workow combining human analysis and LLM-suggested candidate themes. with machine-assisted pattern recognition while keeping humans in full control of coding decisions. Stage 5: Iterative Renement and V alidation. Through iter- ative reduction, the pool of 51 labels was distilled into ve overar- ching themes and sixteen subthemes . The integrated set underwent multiple rounds of renement: (1) labels wer e analysed for patterns and redundancies, (2) similar labels were consolidated for concep- tual clarity and distinctiveness, (3) the rened thematic framework was reapplied to transcript segments to test consistency and cov er- age, and (4) nal validation was achiev ed through manual review of supporting quotations to conrm accuracy . Throughout this process, only human-generated and human-curated lab els entered the nal framework; LLM outputs functioned exclusively as in- puts to early coding discussions. This proce dure produced a stable and transparent thematic framework, grounded in both systematic procedure and qualitative standards. Bias and hallucination mitigation. At each stage , we treated LLM outputs as tentative hypotheses rather than authoritative analy- ses. Candidate labels that could not b e substantiated by multiple excerpts, that relied on speculative attributions of intent, or that introduced moralising language were removed. W e also monitor ed for systematic dierences in how the model described participants from dierent regions or roles, revising or discarding labels that appeared to reect such biases. For instance, we r ejected codes such as “LinkedIn for Professional Use ” (too platform-spe cic to gener- alise), “Critical Evaluation of Inuencers” (conated self-reection with external critique), and “Industry Credibility and Expertise” ( at- tributed domain-level authority without textual grounding). Each rejection was logge d with a brief rationale to maintain an auditable curation trail; the complete code curation pipeline, including re- jection categories and counts, is documente d in Appendix C. Final themes and interpretations were based solely on human review of the transcripts and the curated codebo ok. 3.5 Inter- Annotator Reliability W e assessed inter-annotator reliability for the two human coders on a stratied random sample of 120 transcript segments drawn across all interviews. Annotation was multi-label : each segment could receive zero , one, or multiple candidate themes from the working label set. Reliability was computed as label × segment binary deci- sions (presence/absence) per annotator , and we r eport agreement using Krippendor ’s alpha (nominal metric), which is appropriate for multi-label categorical annotation. Pairwise agreement metrics for the two human coders are re- ported in T able 3. Across the working set of 36 candidate themes and 120 segments, Krippendor ’s 𝛼 = 0 . 78 , indicating substantial reliability for the nal codebook. The LLM was not treated as an annotator; its suggestions were used only upstream to expand the candidate label set before human coding. T able 3: Pairwise inter-annotator agreement under multi- label annotation acr oss 36 candidate themes for the two hu- man coders. Agreement p ercentage and Krippendor ’s alpha (nominal) are reported for the annotator pair . Pairwise Comparison % Agreement Krippendor ’s 𝛼 Anno. 1 vs Anno. 2 82% 0.78 Anno. 1 vs LLM-assisted 58% 0.55 Anno. 2 vs LLM-assisted 62% 0.59 3.6 Theme Construction and SDT Integration Following the coding stages described above, the human resear ch team conducted thematic analysis guided by self-determination the- ory (SDT) to organise labels into coherent themes addressing our research questions. The analysis proceeded through three phases: (1) descriptive re view to identify surface-level patterns and ex- plicit motivations, (2) interpretiv e synthesis to uncover deeper psychological needs and implicit drivers, and (3) theoretical inte- gration to connect emergent themes with SDT’s core constructs of motivation, autonomy , competence, and relatedness. Thr ough- out these phases, we engaged in iterative memo writing and team discussions to challenge early interpretations and to clarify how themes mapped onto RQ1–RQ3 and the SDT needs. For an o verview of the synthesis, see Appendix 6 (T able 4), which links overarching themes and subthemes to the research questions and provides concise denitions and repr esentative quotations. 7 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer 4 Results Our thematic analysis of 13 inter views with crypto KOLs produces three sets of empirical ndings aligned with the research questions. W e interpret the recurring patterns and characteristics through the lens of SDT , providing conceptual grounding. Themes and sub- themes are summarized in T able 4 with representative quotes and RQ mapping. 4.1 RQ1: Motivational Factors for Cr ypto K OLs Our analysis rev eals a blend of extrinsic and intrinsic motivators positioned along a continuum of relative autonomy (see Figure 2). Early entry is often encouraged by extrinsic factors ( e. g., sponsor- ship income, visibility , access to projects). Sustained engagement is driven by mor e self-determined forms of motivation. Interpreted through SDT , K OLs describe trajectories in which external incen- tives are gradually internalised into self-endorsed values and iden- tities organised around autonomy , competence, and relatedness. These qualitatively distinct forms of motivation are discussed be- low . Recalibrating incentives over time. Consistent with SDT’s ac- count of internalisation, many K OLs recount a shift from externally regulated engagement (e . g., taking any sponsorship that pays) to- wards more self-regulated practices in which they reject misaligned opportunities in order to protect their autonomy and credibility: “The more I progress in this space and the more mature it gets, I have less and less of an appetite to endorse crypto projects or coins. ” — KOL10 Credibility over cash. Chasing money erodes credibility; auton- omy sustains trust. Several participants explicitly describe self- imposed rules, such as declining projects they would not personally invest in, that prioritise long-term relatedness with their communi- ties over short-term re venues. “Even when a sponsor appr oaches me, I only cover it if I would recommend it to my community anyway . Otherwise, it undermines everything I’ve built. ” — KOL07 Education as a w orthwhile mission. Many participants empha- sise an educational mission and community ser vice, describing sat- isfaction from helping newcomers avoid mistakes and make sense of a complex, high-stakes ecosystem. This combination points to competence (mastery and explanation) and relatedness ( care for an imagined community of followers). “Educating people that they have to invest, other wise they will be broke and poor , is the biggest motivation behind my work. ” — KOL01 Ideological commitment and master y . Inter viewees link opin- ion leadership to personal values (e. g., critiques of incumbent - nance, decentralisation ideals) and to curiosity and mastery in the fast-moving domain. These narratives foreground autonomy , as participants emphasise self-endorse d commitments to being “at the forefront” of technological change rather than simply chasing short- term gains. Nine participants explicitly highlighted the dynamic nature of the crypto market as a eld for impact: “My motivation was to be at the forefr ont of this new technological revolution and have a chance to shape it. ” — KOL11 Ke y takeaway (RQ1). Initial extrinsic incentives give way to more internalised motivations. As expertise and recognition grow , KOLs increasingly foreground autonomy (editorial independence and self- imposed standards), competence (continuous learning and transla- tion work), and relatedness (educational stewardship of their com- munities) as reasons to remain activ e in high-risk crypto markets, even when this constrains short-term nancial opportunities. 4.2 RQ2: Dening Characteristics of Cr ypto K OLs W e outline characteristics as themes aligne d with SDT needs (cf. T able 4), showing how autonomy , competence, and relatedness are enacted in high-risk crypto ecosystems and under algorithmic visibility constraints. A utonomy . Autonomy is often e xpressed in an individual’s deci- sion to take a deep dive into cr ypto- and blockchain-related topics and activities through a self-directed learning path. This process typically involves intensive reading, hands-on experimentation and asset trading, active browsing, and engaging in conv ersations with others in the community . Most participants described them- selves as digital natives and reporte d technical, nancial, or con- sulting backgrounds, which facilitated both their onboarding and continued interest in this eld. Over time, few KOLs have intro- duced premium content and community memb erships as strategies to achieve greater (nancial) autonomy and to transition from platform-controlled monetisation to community-driven value cre- ation and exchange. Once excellence in subject matter and a follower base are se cured, KOLs place a high value on the free dom to articulate their own perspectives and share their opinions with a broader audience: “[A s a KOL], you have the inuence of letting pe ople know what you think. I can curate an ocial cr ypto narrative and put it in the way I feel like it’s the right way . ” — K OL01 A utonomy is also expressed through self-imposed policies and guiding principles that many of the inter viewed participants devel- oped to govern their content cr eation and sponsorship decisions. In particular , crypto KOLs tend to be highly-selective, prioritising value-adding, meaningful, and solution-oriented me dia publica- tions. By contrast, non-serious or contro versial posts are perceived as harmful to authentic self-expression: “I don’t like putting out content to entertain. ” — KOL10 In rare cases, the autonomy of crypto KOLs extends beyond individual self-expression towards actively shaping the broader institutional environment. By positioning themselves as intermedi- aries between regulators and the cr ypto community , KOLs demon- strate both autonomy and competence, thereby aligning their self- endorsed values with inuence at the systemic level: “When it comes to crypto regulation, we lead regu- lators down a path that is sustainable, because they are often lacking technological know-how . W e exert some inuence on the e cosystem—not so much on the 8 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders Low autonomy High autonomy Amotivation External regulation Introjection Identied regulation Intrinsic motivation Sponsorship rewards Self-regulated choice of promotion content Education, ideology , personal beliefs Enjoyment, curiosity , exploring new ideas Figure 2: Motivational factors for crypto KOLs ( in italics ) through the lens of SDT . masses, but indirectly through contributing to the formation of regulation. ” — KOL11 Competence. Crypto KOLs tend to maintain an enduring com- mitment to developing highly-specialized expertise, often by con- centrating on a specic domain or a narrower subset of topics (e. g., nancial advisory , the dynamics b etween macroeconomics and cryptocurrencies, cr ypto-asset taxation, prediction or regional markets). Their competence is primarily demonstrated through the ability to simplify and communicate complex issues in an accessible, timely , and transparent manner: “If you want to b e trusted, you have to b e able to articulate quite complicated things in a relatively simple fashion, b ecause blockchain [te chnology] is a multidisciplinary , quite complicated matter . Nobody can profess you to know it all, but you have to be able to communicate well about all the little bits and pieces that you slice out p er your preference . ” — K OL08 Some KOLs, in particular those oering advisory ser vices, further perceive themselves as curators and interpreters of nancial infor- mation, synthesizing multiple sources ( e. g., on-chain and market data, technical documentation, analytical reports, research pub- lications) and data points to provide clarity and actionable guid- ance for their followers. These skills distinguish cr ypto K OLs from other inuencers, as their authority is grounded not in p opular- ity , entertainment or promotion value, but in demonstrable expert knowledge, analytical rigour , and the capacity to translate highly technical content into practical insights in a volatile, high-risk en- vironment. In SDT terms, competence is experienced not only as private mastery but as the ability to help others navigate complex infrastructures without resorting to hype. Beyond knowledge creation and dissemination, crypto inu- encers invest considerable eort into proactive management of their reputation and p ersonal image. As KOL08 described, main- taining their social reputation inv olves transparent communication, careful selection of topics and partnerships, and adherence to self- imposed ethical standards. Ultimately , a K OL’s reputation serves as a signal of e xpertise, r einforcing the credibility and trustworthiness of the online content they share. In terms of content creation strategies, crypto KOLs note that blending educational materials and investment ideas with emotion- triggering narratives (e . g., personal stories, analogies, or provoca- tive framings) elicit the strongest r esponses from their audiences. This balancing act b etween education and engagement also mir- rors the competitive dynamics of the broader inuencer market, in which celebrities and inuential gures compete for visibility and audience attention [25]: “There is a ght for attention, but it’s not hostile; people focus on a topic and aim to attract attention through quality content rather than by undermining others. ” — KOL11 Relatedness. Given the p ositive eect of interpersonal connections on human well-being [ 51 ], crypto KOLs actively pursue a sense of belonging and social recognition through their online and oine interactions with like-minded followers. Some invest considerable eort into community building and mutual support, striving to cultivate online environments that foster connecte dness, shared purpose, and collective enthusiasm. Others adopt a more conserva- tive approach, prioritising in-person connections and confer ence participations while using social media primarily as a post hoc amplier of their oine activities. Building on these mo des of interaction, our participants also highlighted the importance of strategically navigating through var- ious social media platforms to maintain their inuence and visibility . Crypto KOLs dierentiate their use of platforms, leveraging Insta- gram as a medium for outreach and audience acquisition, while employing Y ouTube for extended content formats with in-depth know-how: “On Y ouT ube, the community is usually smaller but the content is longer , more binding, and has a strong opinion-shaping eect. By contrast, on X (formerly T witter) or T elegram the exchanges are made up of short, regular inputs signals that quickly fade away . If people actually engage with Y ouT ube, you have a much stronger inuence on them than through these eeting messages. ” — K OL11 Despite its declining popularity , X (formerly Twitter ) continues to serve as an important channel for many of the interviewed partic- ipants, primarily due to its established role in the ecosystem, the concentration of crypto-interested communities, and networking opportunities. At the same time , K OLs increasingly adopt T elegram and Discord, which are regarded as platforms oering more direct community interaction, private modes of engagement, and an ac- cess to niche audiences. Many interviewees describe d TikT ok as the least favourable platform, largely due to its popularity among young audiences and the frequent promotion of meme coins and speculative projects. A voiding such environments was portrayed as a deliberate choice, allowing the interviewed KOLs to protect their professional credibility . While T elegram, Discord, and X are generally associated with instant and high-volume exchanges, some interviewees emphasised that LinkedIn fulls a dierent role within their communication strategies. In particular , Linke dIn distinguishes itself through its 9 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer perceived professionalism and the prevalence of authentic, non- fake accounts. For many K OLs, maintaining a presence on LinkedIn complements their credibility-building strategies, as it aligns with their aspiration to be recognised as trustworthy experts rather than mere entertainers. Furthermore , LinkedIn enables crypto KOLs to reach a dierent audience (e. g., companies, entrepreneurs, and developers) and engage in more high-quality discussions compared to the community-driven spaces such as T elegram or Discord. At the same time, several participants noted that LinkedIn’s dynamic algorithms require continuous adaptation of content and framing to sustain impressions and ensure that their messages reach intended audiences, highlighting how platform-level engagement metrics shape day-to-day communication choices. Finally , beyond the platform diversication, K OLs also employ specic communicative practices to reinforce their credibility and strengthen audience ties. Beyond consistent publishing crypto- related materials, participants described a range of dialogic inter- actions as crucial for sustaining trust. Common practices include replying to comments, engaging in respectful debates, hosting live Q&A sessions, and organising webinars and po dcasts. Such inter- active formats invite feedback and collective sensemaking, thereby strengthening bonds with online communities. Key takeaway (RQ2). Authoritative K OLs align autonomy (inde- pendence, self-imposed rules), competence (specialised expertise, translation, curation, reputation), and relatedness (community stew- ardship, dialogic practices, platform navigation) while selectively engaging with platform algorithms and attention dynamics. They dierentiate between channels, avoiding spaces that normalise meme-coin speculation and tailoring formats to sustain inuence and credibility under shifting visibility metrics. 4.3 RQ3: Ethics, So cial Responsibility , and Community Our participants articulated a nuance d sense of ethical duty and community accountability in their roles as blockchain inuencers. They acknowledged that their communication can meaningfully sway token prices and investment sentiment and therefore r equires careful self-regulation and transparent disclosur e in thin, specula- tive markets where low-cap tokens can move sharply on limited liquidity . Read through SDT , these accounts show how autonomy (self-imposed rules), competence ( bounded claims and careful trans- lation), and relatedness (care for followers’ outcomes) underpin KOLs’ ethical self-understandings. Self-restraint and responsible promotion. Se veral KOLs recog- nise that their ability to move markets obliges restraint. KOL01 explained that their follower count gives them pow er to inuence low-cap tokens and thus obliges them to avoid fraudulent practices: “It’s denitely my responsibility to not recklessly in- side trade. I notice d I can move an asset that has a market cap of 200,000 ( USD)—I can move it up 10 – 20% easily . ” — KOL01 KOL03 similarly reected on the past episo de in which they pro- moted an adult-content project that turned into a publicity disaster . They now r efuse requests to shill tokens 1 , noting that short-term gains would damage their reputation: “I have still sometimes requests . . . would we give you some token? Could you please push a bit? I don’t, because this actually harms my image. ” — KOL03 KOL9 and K OL11 avoid direct market commentary altogether . The former stated that their organisation only p osts after results and “ don’t shill tokens, we educate users about the projects , ” while the latter emphasised that as a regulatory expert they refrain from opining on whether particular coins are scams, positioning themself as a neutral actor rather than a promoter . T aken together , these practices exemplify self-regulation: KOLs voluntarily constrain their own promotional options to protect both their reputations and their communities’ nancial well-being. Transpar ency , disclosur e, and honesty . Most interviewees de- scribe strict disclosure norms. K OL10 recounted that during the NFT b oom they lab elled every sponsored thread as paid content and that this transparency protecte d them from backlash when projects underperformed: “I would disclose that, hey , this is paid . . . and it would have market impact . . . I never got any blame, b ecause if you are upfront and honest . . . had I not disclosed, I probably would have gotten blamed. ” — K OL10 They adde d that they have “less and less of an app etite ” to rec- ommend specic assets and now stick to high-level portfolio al- locations, holding themself to “a much higher standard. ” KOL2 characterised themself as “too honest” and note d that they always warn followers about risks, even though “people always blame you as an inuencer” regar dless of the outcome. K OL12, representing a venture fund, state d that the y will always disclose when a post concerns a portfolio company: “W e will always write a disclaimer . . . you have to disclose that y ou are an investor . . . this is part of our policy . ” — K OL12 These statements underscore a shared conviction that undisclosed conicts of interest violate community norms, yet they also rev eal tensions between transparency and accountability: even when dis- closures and risk warnings ar e explicit, inuencers anticipate being blamed if followers incur losses. Community norms and trust building. Inuencers link ethical practice to long-term relationship building. KOL08 argued that trust hinges on the ability to translate comple x concepts without feeding speculation: “If you want to b e trusted, you have to b e able to articulate quite complicated things in a relatively simple fashion . . . I explicitly never engage in any sort of commentary ar ound the markets. ” — K OL8 KOL9 emphasised authenticity through “ meeting people in real life ” and being “sup er doxxe d” in or der to show that the y are “really hon- est” about what they do. K OL13 recalled that leaving a traditional nance job was partly motivated by the sector’s openness; they 1 In the context of cryptocurrency discourse, to shill denotes the act of aggressively or uncritically promoting a digital asset, often with undisclosed nancial incentives or conicts of interest. The term carries a negative connotation, implying manipulative or deceptive endorsement rather than impartial evaluation. 10 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders highlighted that in the early days “ everyone was helping each other ” and that they have “not seen that good of a knowledge transfer in W eb2. ” Such accounts portray a community that values mutual support and education ov er hype and illustrates how relatedness is sustained through mutual visibility and knowledge sharing. Managing controversies and reective practice. Participants describe strategies for navigating controversies and learning from mistakes. When asked about a controversial presale, KOL10 ex- plained that they chose not to respond publicly b ecause defending themself would only iname anger . KOL3’s reection on their failed promotion taught them to decline paid advertisements and to pri- oritise social impact over money . They summarised their credo succinctly: “Trust is the most important thing. How can I promote the trust technology when I cannot be truste d . . . I don’t do any paid ads anymore . . . it harms my image. ” — KOL3 KOL13, who regularly publishes long-form research threads, ob- served that even accurate analyses can b e misconstrue d as buy signals. They now avoid validating their own calls and resist the role of a prognosticator: “I don’t think I’ve ever revalidated my own thesis . . . I don’t want to end up being a K OL on T witter at all. I want to have like 300–400 followers . . . because then I’ll be doing back and forth with [researchers], and people would realize . . . that’s the contradiction we live in—people don’t want me to make sense, they want me to give advices. ” — K OL13 They added that even prominent gur es “ not even Vitalik Buterin 2 . . . not ev en the b est traders ” can reliably pick winning tokens; thus “ everyone gets to start from the same line , ” reinforcing the need for humility . Such reections illustrate ongoing internalisation of ethical standards, as KOLs adapt their practices to align more closely with their self-perception of b eing trustworthy educators rather than forecasters. Key takeaway (RQ3). K OLs position themselves as custodians of norms in high-risk, attention-driven markets, enacting credibility through four self-determined practices: (i) self-r egulation and vol- untary constraint , whereby KOLs decline misaligned sponsorships and imp ose personal rules on promotion; (ii) b ounded epistemic competence , acknowledging limits and avoiding prognostication; (iii) accountability , cultivating long-term trust through transparent disclosure; and (iv ) reexive self-correction , learning from failures and continuously reassessing practices. These practices reframe credibility as an ongoing, ethically enacted performance rather than possessing a set of static credentials. 5 Discussion Our study she ds light on the diverse motivations, beliefs, and mech- anisms of inuence that characterise cr ypto K OLs operating within the W eb3 ecosystem. Beyond describing individual practices, these ndings carry broader implications for theories of opinion lead- ership as well as for the design of socio-technical systems that 2 Vitalik Buterin is a Russian–Canadian programmer best known as the co-founder of Ethereum, one of the most widely used blockchain platforms. mediate visibility , trust, and regulation in digital nance. T o situate our contributions, we structure the discussion along two perspe c- tives: (1) theoretical implications, where we extend understandings of perceived credibility and its management under conditions of uncertainty and volatility , and (2) practical implications, where we outline design recommendations for so cial me dia platforms and supervisor y authorities seeking to foster trustworthy , transparent, and community-oriented practices. W e further reect on the appli- cation of LLMs in qualitative coding and discuss limitations as well as directions for future research. 5.1 Theoretical Implications One important theoretical contribution of our study is to show that the professional trajectories and day-to-day practices of crypto KOLs can b e eectively interpreted through the lens of SDT . This framework has enabled us to e xamine how K OLs sustain long-term motivation, inuence, and legitimacy within decentralised, high- stakes ecosystems characterised by shared nancial exposure with followers, irreversible transactions, and thin liquidity . Thus, our ndings provide evidence consistent with SDT’s applicability by illustrating how the fundamental needs for autonomy , competence, and relatedness manifest in nancially risky , technologically me- diated, and algorithmically curated environments that dier from the domains where SDT has traditionally been applied [ 9 , 48 , 55 ]. A utonomy is exercised through selective sponsorships, transpar- ent nancial disclosures, e xplicit refusal of misaligned deals, and the rejection of pr omotional opportunities that could undermine credibility . Competence is demonstrated by simplifying complex knowledge, bounding claims in the face of uncertainty , producing educational content, and cultivating reputations as trusted sources of expertise. Relatedness emerges through dialogic interactions, mu- tual knowledge sharing, community-building practices, and main- taining social bonds across online and oine contexts. Second, our ndings reconceptualise credibility in the W eb3 in- uencer space. Prior research has treated nuencer credibility as a function of static credentials, namely formal education, pro- fessional background, and nancial certications [ 28 ], reecting broader source credibility traditions that emphasise expertise and trustworthiness as sender attributes [ 23 , 24 , 42 , 58 , 63 ]. Our ndings challenge this vie w . In high-risk crypto environments characterised by widespread misinformation [ 11 ], per vasive scepticism, and nan- cial and se curity risks [ 56 ], cr edibility emerges not fr om credentials but from self-determined, ethically enacted practices rooted in SDT needs. W e identify four dimensions of this practice-based credibility that function as community-recognised markers of trustworthiness: (1) Self-regulation and voluntar y constraint: KOLs decline misaligned sponsorships, impose personal rules on what they will promote, and reject short-term monetisation that could erode trust (Section 4.3). (2) Bounded epistemic competence: Rather than claiming omni- science, K OLs acknowledge the limits of their expertise, avoid prognostication, and emphasise that “not even the best traders” can reliably pick winners. 11 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer (3) Accountability through transparency: KOLs cultivate long- term trust through consistent disclosure of sponsorships, port- folio holdings, and conicts of interest, accepting that trans- parency invites scrutiny . (4) Reexive self-correction: KOLs learn from past failures, pub- licly reassess their practices, and iteratively rene their norms, as illustrated by participants who abandoned paid advertise- ments after reputational setbacks. These dimensions re-conceptualize credibility as a socio-technical, self-regulated performance rather than a static credential. Credibil- ity is enacted through ongoing behavioural signals, reinforced by community feedback, and embe dded in platform aordances (e . g., disclosure badges, accuracy indicators). This perspective extends source credibility theory into high-risk, decentralised contexts and oers a framework for understanding trust in environments where formal qualications are absent, unveriable, or irr elevant. 5.2 Design Recommendations Our ndings yield actionable insights for platforms, creators, and regulators seeking to foster credible communication and respon- sible innovation within the evolving W eb3 ecosystem. While SDT illuminates why crypto KOLs pursue autonomy , competence, and relatedness, the implications here highlight how such motivations can be translated into governance, practice, and policy design. Platforms: Designing for credibility and accountability . P latforms occupy a pivotal role in shaping socio-technical conditions for cred- ibility . Moving beyond popularity and engagement counts, they should implement multidimensional credibility metrics that reward transparent practices, consistent disclosures, and educational rather than sensationalist content. Practical aordances include badges for veried sponsorships or investment statements, indicators that track predictive accuracy over time, and feedback systems enabling users to ag misleading or undisclosed promotions. Equally crit- ical is addressing the fragmentation of identity across channels: interoperable “trust proles” could consolidate disclosure histo- ries, accuracy scores, and community fee dback into a portable reputation system. Coupled with analytical tooling—such as au- tomated detection of undisclosed promotions, sentiment analysis, and cross-platform inuence mapping—these infrastructures would make credibility more transparent and comparable, assisting users in distinguishing between hype and substantive guidance while providing structural incentives for ethical behaviour . Creators and communities: Professional norms and self-regulation. Crypto KOLs already exhibit forms of self-regulation, including refusing misaligned promotions, labelling sponsored content, and avoiding nancial fraud or similar market manipulation schemes. Formalising these practices into a community-endorsed nuencer charter would pro vide a normative benchmark for responsible en- gagement. Such a charter could spe cify disclosure formats, risk warnings, and principles for mitigating potential market impact. Be- yond codication, community-governed mechanisms—peer-review panels, deliberation forums, or recognition systems led by experi- enced KOLs—can adjudicate contested cases, surface ethical dilem- mas, and publicly acknowledge exemplary conduct. These practices would not only guide new entrants but also embed a culture of ac- countability that scales with the growth of W eb3 communities, com- plementing platform infrastructures and regulatory frameworks. Policy and regulation: Safeguarding users in volatile markets. In light of the transnational and decentralise d character of cr ypto assets, formal oversight remains challenging, y et the potential for retail harm necessitates robust intervention. Regulators could ex- tend existing nancial promotion and consumer pr otection rules to encompass crypto inuencers while adapting them to blockchain’s distinctive features. Measures might include standardised disclosure statements for compensated content, registration requirements for actors surpassing dened thresholds of inuence, and coordinated enforcement through entities such as the International Organization of Securities Commissions (IOSCO) or regional consortia; guidance from the US Federal Trade Commission (FTC) on endorsements and testimonials, the US Securities and Exchange Commission (SEC) on crypto asset securities, the UK Financial Conduct A uthority (FCA) and Advertising Standards Authority/CAP on nuencer promo- tions, and the EU’s European Securities and Markets Authority (ESMA) oer relevant precedents and enforcement levers [ 4 , 20 – 22 , 30 , 61 ]. Platforms can assist by identifying persistent oenders and sharing intelligence with regulators. Alongside enforcement, public agencies should invest in educational initiativ es that build crypto literacy among retail investors, reducing susceptibility to misleading guidance. T aken together , harmonised regulation and proactive education would help mitigate risks while supporting responsible innovation. Collectively , these implications extend beyond individual inu- encer strategies to encompass the design of accountability infras- tructures, cross-platform reputation systems, community norms, and regulatory frameworks. By recognising cr edibility as a socio- technical outcome that demands coordinated action from platforms, creators, communities, and regulators, the W eb3 ecosystem can progress towar d more trustworthy and inclusive communication. 5.3 Reection on LLMs In terms of methodology , this study is among the rst to empiri- cally demonstrate how LLMs can support qualitative coding in HCI research without displacing human oversight and interpretation. Our hybrid approach aimed to preserve interpretiv e rigour while reducing the cognitive and temporal load associated with labour- intensive thematic analysis. In our workow , the LLM operated strictly upstream, suggesting additional candidate codes and SDT mappings on anonymised transcript segments for human revie w , rather than lab elling data directly or participating in reliability calculations. Our experience shows that LLMs can assist human coders in surfacing initial themes, identifying semantic overlaps, and broadening the space of codes under consideration. Neverthe- less, they remain prone to generating overly general, repetitive, or normatively loaded suggestions, justifying the ne ed for a dual approach to coding in which humans lter , rene , and ultimately decide which labels enter the co debook. Their use also raises impor- tant questions about transparency , reproducibility , ethics, and data privacy [ 54 ]; we mitigated some of these concerns through strict anonymisation and human-in-the-lo op validation, but model biases and versioning remain limitations. For the HCI community , these 12 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders ndings underscore the need for r esearch tools and workows that integrate LLMs responsibly , ensuring they augment rather than compromise the interpretive depth of qualitativ e inquiry . 5.4 Limitations This study exhibits se veral limitations that constrain the general- isability of its ndings. First, the sample comprised thirteen self- selected cr ypto K OLs and leaned W estern, with many participants operating within European and U.S. contexts; ne vertheless, the co- hort also included Asian representation. A dditionally , all conrmed participants were male despite activ e eorts to recruit female K OLs, reecting the male-dominated composition of the crypto inuencer space. Although purposive recruitment aorded heterogeneity in roles and experiences, the modest sample size, geographic skew , and lack of gender diversity restrict the extent to which the ob- servations may be extrapolate d to the wider population of crypto KOLs. Second, the r eliance on semi - structured interviews means that data are self - reported and subject to recall and social desirabil- ity biases; participants’ narratives were not cross - validated against behavioural indicators such as on - chain transaction records or fol- lower engagement statistics. Third, the analysis focused exclusively on inuencers’ p erspectives; the views of follow ers, regulators, and other ecosystem stakeholders were not solicited, limiting the abil- ity to triangulate perceptions of cr edibility and inuence. Fourth, while LLM - assisted coding expedite d early stages of thematic anal- ysis by suggesting additional candidate labels, it may still have introduced biases inherent in large language models; despite our l- tering and human verication steps, model outputs remain shap ed by opaque training data and cannot substitute for human interpre- tive judgement. Fifth, recruitment likely exhibits survivorship and self - selection bias: KOLs with stronger r eputations, clearer disclo- sure norms, or more reective practices may have b een more willing to participate, potentially underrepresenting highly promotional or opaque actors. Finally , the inherent volatility of cr ypto markets and the rapid evolution of W eb3 platforms imply that the practices and norms documented here may be transient, underscoring the need for longitudinal replication. 5.5 Future Research Directions Future investigations should address these limitations and advance the eld in several directions. Comparative and cross - cultural stud- ies drawing on larger and more diverse samples would help to assess the universality of the present ndings and rev eal how regu- latory and cultural contexts shape inuencer behaviour . Longitu- dinal mixed - methods designs that integrate interviews with trace data—such as social media analytics, on - chain transactions, and algorithmic amplication metrics—could illuminate how credibility develops over time and how KOLs’ recommendations correlate with market dynamics. Incorporating follower perspectives through sur- veys and ethnographic obser vation would elucidate parasocial inter- actions and audience responses to disclosure practices. Methodolog- ical innovation is warranted to develop and validate computational measures of credibility , transparency , and inuence that enable systematic cross - platform assessment; such tools could draw on machine learning and natural language processing to dete ct undis- closed promotions and sentiment. Examining the implications of emerging technologies—such as generative AI and decentralised identity infrastructures—for content production, reputation man- agement, and compliance represents another imp ortant avenue. Finally , comparative analyses across blockchain and other inu- encer domains could help distinguish domain - specic behaviours from broader trends in the cr eator economy . 6 Conclusion This study has examined how cr ypto key opinion leaders under- stand their motivations, credibility , and responsibilities. W e con- ceptualise KOLs as hybrid gures: educators, informal nancial advisors, entrepreneurs, and community stewards, whose credi- bility emerges from motivational orientations, cross-platform and on-chain practices, and collectively enforced norms. Drawing on self-determination theor y and interviews with 13 KOLs, we show ed how autonomy , competence, and relatedness needs are negotiated alongside monetisation pressur es, token holdings, and shared nan- cial exposure with followers. Our hybrid human–LLM worko w for thematic analysis demonstrates the potential of large language models to broaden candidate theme coverage while underscoring the necessity of human oversight to mitigate hallucinations and hidden biases. Regulatory implications. These insights carry practical impli- cations at a moment when inuencer regulation around nancial content is tightening globally , with jurisdictions such as China, the European Union, and Singapore introducing stricter o versight of crypto-related promotion [ 20 , 44 ]. Our ndings suggest that eec- tive governance will nee d to account for the blurred boundaries between education, promotion, and speculation that dene crypto KOL work. Rather than treating KOLs simply as advertisers or nan- cial advisors, regulators may benet from recognising the hybrid nature of their r oles and the community dynamics that sustain their inuence. Designing mechanisms that support transparent incen- tives, encourage responsible signalling, and reduce asymmetries of nancial exposure (such as the disclosur e badges, accuracy indica- tors, and interoperable trust proles discussed in Section 5) may strengthen both creator credibility and user protection as crypto markets continue to evolve . Acknowledgments This research was funded by the FFG project Finuencer (grant agreement number 924721). This paper was accepted at CHI 2026. References [1] Crystal Abidin. 2016. Visibility Labour: Engaging with Inuencers’ Fashion Brands and #OOTD Campaigns on Instagram. Media International Australia 161, 1 (2016), 86–100. doi:10.1177/1329878X16665177 [2] Crystal Abidin. 2018. Internet Celebrity: Understanding Fame Online . 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C LLM Prompts and Outputs This appendix provides the LLM prompt used for thematic analysis as well as an exemplary excerpt of LLM output. Figure 3: LLM prompt used for thematic assistance. system_prompt = """You are a qualitative researcher assisting with thematic analysis of interview transcripts about crypto- influencers ' motivations. Your task is to: 1. Suggest candidate themes (labels) that capture emerging patterns in the text 2. Provide a brief description of each theme 3. Map each theme to one of the three Self- Determination Theory (SDT) needs: - Autonomy: The need to feel volitional and self-directed in one ' s actions - Competence: The need to feel effective and capable of achieving desired outcomes - Relatedness: The need to feel connected to others and experience belongingness Focus on identifying motivations, behaviours, and psychological needs expressed by the participants. Return your analysis as a JSON array of objects with these fields: - "theme": the candidate thematic label - "description": what the theme means in this context - "sdt_category": one of "Autonomy", "Competence", or "Relatedness" Example format: [ { "theme": "Financial Independence Seeking", "description": "Desire to achieve financial freedom through crypto investments and avoid traditional employment constraints", "sdt_category": "Autonomy" } ] """ user_prompt = f"""Analyze this interview transcript excerpt and suggest candidate themes: Context: Interview transcript from {filename}, chunk {chunk_index + 1} Text to analyze: {text_chunk} Generate 3-7 candidate themes that capture the key motivations, practices, and needs expressed in this text segment.""" LLM transparency note. W e used the best available GPT -4 model ( June 2025) with temperature = 0 . 2 and top_p = 1 . 0 . All excerpts were fully anonymised prior to processing; no raw data or identiers were transferred e xternally . Outputs were review ed and veried by human coders. For completeness, an example excerpt of the model’s JSON out- put structure is shown in Figure 4. Code curation summary . T able 6 summarises the code reduction pipeline. The overall r ejection rate of LLM-suggested codes (47%) 15 Alexander Kropiunig, Svetlana Kremer, and Bernhard Haslhofer T able 4: Thematic tree: overar ching themes and subthemes mapped to the research questions (RQ). Overarching Theme Subtheme Denition ( ≤ 20w) Representative Quote RQ(s) Autonomy Editorial independence Choosing topics/sponsors to preser ve credibility and control. “I only cover it if I’d recommend it to my com- munity . ” (KOL07) RQ2 Autonomy Self-directed learning Deep dive via reading, experimenting, trading, conversations. “I can curate a narrative and put it the right way . ” (KOL01) RQ2 Autonomy System-shaping advocacy Inuencing regulators/ecosystem in line with val- ues. “W e lead regulators down a sustainable path. ” (KOL11) RQ2 Competence Translating complexity Make technical topics clear without oversimpli- fying. “ Articulate complicated things in a simple fash- ion. ” (K OL08) RQ2 Competence Evidence curation Synthesize on-chain, docs, reports for guidance. “People focus on quality content to attract atten- tion. ” (K OL11) RQ2 Competence Reputation management Transparent standar ds signal expertise and trust. “Social reputation” via careful top- ics/partnerships. (KOL08) RQ2 Relatedness Community stewardship Education and long-term ties over hype. “Everyone was helping each other . ” (KOL13) RQ3 Relatedness Dialogic practices Comments, debates, Q&A, webinars, podcasts. “Meeting people in real life. . . b e ‘super doxxed’. ” (KOL09) RQ3 Relatedness Platform navigation Match platforms to audiences and norms. “LinkedIn felt more authentic, professional. ” (Synth. from interviews) RQ2,RQ3 Motivation Intrinsic—education/altruism Desire to inform, raise literacy , serve community. “Educating people. . . is the biggest motivation. ” (KOL01) RQ1 Motivation Intrinsic—curiosity/mastery Enjoyment, curiosity , shaping a new eld. “Be at the forefront of this rev olution. ” (K OL11) RQ1 Motivation Extrinsic—sponsorship/visibility Early income/visibility; wanes as values internal- ize. “Less app etite to endorse coins over time. ” (KOL10) RQ1 Credibility Practices † Self-regulation & voluntary constraint Decline misaligned sponsorships; impose per- sonal rules on promotion. “I don’t. . . because it harms my image. ” (K OL03) RQ3 Credibility Practices † Bounded epistemic comp e- tence Acknowledge limits; avoid pr ognostication and overclaiming. “Not e ven Vitalik. . . not even the best traders. ” (KOL13) RQ3 Credibility Practices † Accountability & transpar ency Clear sponsorship/investment disclaimers; ac- cept scrutiny . “I would disclose that. . . this is paid. ” (KOL10) RQ3 Credibility Practices † Reexive self-correction Learn from controversies; continuously reassess practices. “Trust is the most important thing. . . no paid ads. ” (KOL03) RQ3 † These four dimensions constitute self-determined credibility practices that function as community-recognised markers of trustworthiness. T able 5: Synthesis of prior research streams. Research Stream Ke y Constructs Central Findings Relevance to Crypto KOLs Gap Addressed Inuencer culture & paraso- ciality [1, 8, 18, 32, 34, 37, 40, 50] Visibility labour; relational labour; authenticity; paraso- cial interaction Credibility cultivated through curated authenticity and aective bonds; trust based on perceived closeness KOLs engage in similar visibility and relational labour; credibility central to inuence Does not addr ess nancial stakes or speculative contexts Finuencers & social-media - nance [26, 27, 29, 43, 57] Finuencer; emotional conta- gion; sentiment transfer; nan- cialisation Finuencers shap e crowd sentiment; emotional contagion amplies her d dy- namics KOLs are cr ypto-specic nuencers with intensied risks Focuses on audiences and markets, not on nuencers as workers Creator labour & nancialisa- tion [2, 5, 12, 15–17] Aspirational labour; nested precarities; speculative labour Creators face layered precarities; - nancialisation entangles creative work with volatile markets KOLs experience double exposure to algorithmic and nancial volatility Does not examine crypto-specic contexts Crypto users & communities [3, 7, 19, 31, 33, 39, 53] Mental models; usability; cen- tralised trust; user motivations Users hold fragile mental models; para- doxically trust centralised actors in de- centralised systems Users “ outsource” due diligence to KOLs as trust anchors Focuses on users, not intermedi- aries Regulation & consumer protec- tion [4, 22, 57, 61] Investor protection; disclosure; regulatory fragmentation Finuencer promotion evades tradi- tional regimes; crypto regulation is patchy KOLs operate in regulatory grey zones; accountability unclear Does not e xamine KOL perspe ctives on regulation Scams & fraud [11, 47] Rug pulls; scam discourse; community vigilance Scams are pervasive; communities de- velop grassroots guidance roles KOLs may help or harm users navi- gating scams Does not examine KOL role or re- sponsibility SDT & online work [13, 36, 46, 52, 59, 60] Autonomy; competence; relat- edness; intrinsic vs. extrinsic motivation SDT needs predict sustained, high- quality engagement; prosocial motives common among creators SDT oers lens on KOL motivations beyond “greed vs. altruism” Has not b een applied to crypto in- uencers Market-level inuence [6, 38, 41, 49, 62] Sentiment; attention; capital ows; price eects Online communities and inuencers aect trading volumes and prices KOLs’ endorsements can mov e mar- kets and aect follower wealth Models inuencers as signals, not as workers 16 Motivations, Characteristics, and Inf luence Me chanisms of Crypto Key Opinion Leaders reects a conservative curation strategy in which human judgement remained the nal arbiter . T able 6: Code curation pipeline: counts and rejection rates at each stage. Stage Codes in Retained Rejected Rej. Rate LLM rst pass 60 32 28 47% Human codes added +19 19 0 — Combined pool 51 36 15 29% Final codebook 36 36 — — LLM-suggested codes were rejected for: platform-specicity (4 codes, e. g., “LinkedIn for Professional Use”); conceptual cona- tion (6 codes, e. g., “Critical Evaluation of Inuencers”); insuf- cient grounding (9 codes, e. g., “Global Conne ctivity through Gaming”); and redundancy with human co des (9 codes, e. g., “Commitment to Transparency”). Each rejection was logged with a rationale to maintain an auditable curation trail. Figure 4: Example model output ( excerpt). [ { "theme": "Educational Mission", "description": "Motivation to translate complex crypto concepts and help newcomers avoid mistakes", "sdt_category": "Relatedness" }, { "theme": "Autonomy in Content Choices", "description": "Emphasis on independence from sponsors and freedom to express critical views", "sdt_category": "Autonomy" }, { "theme": "Mastery Through Continuous Learning", "description": "Drive to keep up with fast-moving protocols and improve analytical skills", "sdt_category": "Competence" }, { "theme": "Reputation and Recognition", "description": "Seeking status and credibility as a knowledgeable KOL within the ecosystem", "sdt_category": "Competence" } ] D Interview Questions 1. Background and Role in the Ecosystem Q1. Can you briey describe your professional background and how you got inv olved in the crypto assets indus- try? (RQ2) Q2. What led you to b ecome an inuential gure in the crypto space, and how would you dene your role within it? (RQ2) 2. Motivations and Goals Q3. What motivates your active participation in decentral- ized ledger–related discussions and project endorse- ments? (RQ1) Q4. Based on your role as a [self-dened ROLE fr om Q2], what drives you to engage with the crypto space in this particular way? (RQ1) 3. Inuence Mechanisms and Engagement Q5. Through what channels do you believe you achieve the most inuence (e. g., X, Y ouTube, podcasts, confer- ences, direct investments), and why? (RQ2) Q6. How do you engage with your audience, and what type of content do you believe generates the most engage- ment and resonance? (RQ2) 4. Community and Industr y Dynamics Q7. How do you interact with other opinion leaders in the decentralized ledger sphere? W ould you describe your relationships as collaborative, competitive, or neutral? Q8. Have you ever engaged in public debates or contro- versies within the digital ledger space? How do you navigate such interactions? (RQ2, RQ3) 5. Inuence on Investment, Adoption & Ethics Q9. Have you obser ved cases where your recommenda- tions signicantly impacted market trends or invest- ment behaviour? How do you vie w this responsibility? (RQ3) Q10. How do you ensure transparency and credibility in your public statements, particularly regarding nancial incentives and endorsements? (RQ1, RQ3) 17