<![CDATA[This research investigates search engine algorithm update effects on digital content performance during 2020-2025 using sequential explanatory mixed-method design. Analyzing 1,247 content pieces from 512 websites across 10 industry verticals through interrupted time series analysis, multiple regression, and machine learning classification, combined with 40 in-depth practitioner interviews. Findings reveal average organic traffic declined 14.2% post-update, creating trimodal distributions: 28% severe decline, 57% moderate fluctuation, and 15% significant growth. Temporal analysis identifies three phases: immediate shock (35.4% decline), volatile adjustment, and stabilization at 18% below baseline, with only 12.4% fully recovering. E-E-A-T (Experience, Expertise, Authoritativeness, Trustworthiness) emerges as strongest resilience predictor (B=0.386, p<0.001), alongside content depth and original research. Qualitative findings expose pervasive algorithm anxiety (92.5% practitioners) and paradigm shifts from technical optimization toward authentic expertise. The research provides evidence-based strategies emphasizing E-E-A-T implementation, content depth over volume, and strategic diversification. This study uniquely integrates quantitative performance patterns with practitioner phenomenology across 18 major updates, advancing understanding of algorithmic governance dynamics in digital content ecosystems.]]>
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