KMAN Publication Inc. AI and Tech in Behavioral and Social Sciences In Press 2026 10 25 Identification and Prioritization of Digital Content Marketing Factors for Brand Equity in the Leather Industry Identification and Prioritization of Digital Content Marketing Factors for Brand Equity in the Leather Industry EN Journal Article <p>To identify and prioritize factors perceived by experts as relevant to digital content marketing intended to support brand equity in the leather industry. This applied exploratory study used purposive expert selection and three Delphi rounds. Literature-derived indicators were rated on a five-point importance scale and revised using expert feedback. The initial questionnaire contained 52 indicators, of which 49 were retained after round one. Descriptive means were recalculated from the available aggregate response frequencies. Separately reported hierarchical priority weights were treated as supplementary descriptive outputs. The indicators were organized into two domains and nine components. External factors comprised consumer characteristics, cultural readiness, and customer participation. Organizational factors comprised brand personality, organizational culture, content production strategies, strategic planning, content creation, and advertising. Customer satisfaction had the highest final-round mean (4.90), followed by customer motivation, willingness to innovate, employee support for change, and low cost (each 4.80). In the separate weighting output, customer satisfaction ranked first (0.110) and willingness to innovate second (0.094); external and organizational factors received weights of 70.2% and 29.8%, respectively. These weights could not be independently reproduced from the available records. The findings provide an exploratory framework linking customer-oriented priorities with organizational capabilities for digital content marketing. They represent expert judgments rather than demonstrated effects on brand equity. Incomplete methodological documentation and irregularities in the aggregate data limit the precision and reproducibility of the prioritization.</p>