Influencer Transparency and Audience Trust: A Case Study of ByteCom Media Group

Thesis Abstract

The rapid expansion of influencer marketing has intensified scrutiny of transparency practices and their impact on audience trust, yet there exists a gap in understanding how disclosure norms within a single media conglomerate shape user perceptions, engagement, and brand credibility. This study investigates how ByteCom Media Group’s influencer ecosystem communicates sponsorships, product placements, and affiliate relationships, and how these practices influence audience trust, perceived authenticity, and behavioral intentions. The aim is to elucidate the mechanisms by which source credibility, disclosure clarity, and consistency of ethical guidelines affect consumer attitudes and actions in digital environments. Specific objectives are to (i) examine the prevalence and modalities of sponsorship disclosures across ByteCom’s influencer networks; (ii) assess the relationship between disclosure transparency and audience trust using constructs of source credibility, perceived authenticity, and message transparency; (iii) identify moderating effects of viewer characteristics (age, platform type, and prior trust in ByteCom) on disclosure–trust linkages; (iv) determine whether trust translates into engagement outcomes such as willingness to pay, repeat engagement, and sharing behaviors; and (v) generate evidence-based recommendations for governance of influencer disclosures within large media groups. The study is anchored in the Theory of Planned Behavior and the Elaboration Likelihood Model to explain cognitive processing of disclosures and its impact on attitudes and intentions, supplemented by Transparency Theory to frame disclosure practices. A mixed-methods design integrates quantitative and qualitative strands. The quantitative component surveys a stratified sample of 1,200 ByteCom audience members across YouTube, Instagram, and TikTok platforms, employing validated scales for disclosure clarity, perceived credibility, authenticity, trust, and purchase/engagement intentions. Structural equation modelling (SEM) will test hypothesized pathways from disclosure clarity to trust and from trust to engagement, with multi-group invariance testing to explore platform and demographic moderators. The qualitative component uses 40 in-depth interviews with ByteCom audience members and 12 focus groups (6–8 participants each) to explore interpretive nuances of disclosures, including perceived sincerity, coercive inference, and brand alignment. Thematic analysis will identify recurrent patterns and divergent interpretations of sponsorship disclosures. Data collection instruments include a structured online questionnaire with a 7-point Likert scale, cross-validated scales for source credibility (expertise, trustworthiness, goodwill), authenticity (perceived genuineness and transparency), and behavioral intentions; interview and focus group guides are crafted to probe contextual factors such as platform affordances, content genre, and prior experiences with ByteCom campaigns. Validity and reliability will be ensured through pilot testing (n=120) and confirmatory factor analysis to establish construct validity. Data analysis will apply SEM to quantify relationships and mediation effects, with bootstrapped bias-corrected confidence intervals; qualitative data will be coded using NVivo, employing open, axial, and selective coding to develop a theoretical synthesis of disclosure-driven trust dynamics. The study is expected to reveal that explicit, consistent, and context-appropriate disclosures within ByteCom’s influencer campaigns significantly enhance audience trust, particularly when disclosures are visible pre-content and reinforced by post-content clarifications. It is anticipated that higher perceived transparency will mediate positive attitudes toward influencers and brands, increasing engagement intentions and willingness to pay a premium for endorsed products. The research will also delineate differential effects by platform and audience segment, with younger viewers and fast-scrolling platforms showing heightened sensitivity to disclosure clarity. The findings will contribute to knowledge by operationalizing transparency constructs within large-scale influencer networks, bridging theory and practice for digital media governance. The study’s contribution to knowledge lies in (i) advancing an empirically validated model linking disclosure transparency to trust and engagement in a corporate influencer ecosystem, (ii) identifying contextual moderators relevant to platform and demographic heterogeneity, and (iii) offering actionable governance guidelines for media groups to standardize ethical disclosure practices while preserving audience trust. Practical recommendations include standardized disclosure templates, platform-specific disclosure placements, routine audits of influencer content, training programs for creators on ethical marketing, and a governance framework integrating corporate policy, legal compliance, and audience feedback mechanisms. The main conclusion posits that transparent, coherent, and user-centered disclosure practices within ByteCom Media Group robustly foster trust and constructive consumer behaviors, advocating for an integrated transparency strategy as a core organizational capability.