Abstract

Purpose- This study investigates the structural relationships between data-driven characteristics, marketing innovation, sustainability practices, and their impact on brand credibility, customer loyalty, and business performance in fisheries startups. The research is grounded in the Resource-Based View (RBV) and Dynamic Capabilities Theory, emphasizing the strategic role of data culture and innovation in startup growth. Design/Methodology/Approach-A structured questionnaire was developed based on validated measurement scales from existing literature, translated using forward–backward translation, and pre-tested with 40 respondents. Data were collected from 380 startup stakeholders in Iran's aquaculture sector. The instrument demonstrated high reliability (Cronbach’s alpha ≥ 0.81) and convergent validity (AVE ≥ 0.59). Exploratory and Confirmatory Factor Analyses (EFA/CFA) were conducted to validate the measurement model. Structural Equation Modeling (SEM) using SmartPLS was applied to test the hypothesized paths (see Figure 1). Findings-The results reveal that data-driven characteristics significantly influence marketing innovation (β = 0.35, p < 0.001). In turn, marketing innovation enhances brand credibility (β = 0.48, p < 0.001), which positively affects customer loyalty (β = 0.52, p < 0.001). Loyalty and sustainability practices both significantly contribute to business performance (β = 0.43 and 0.39, respectively; p < 0.001). All proposed hypotheses were supported, and model fit indices confirmed the robustness of the structural model. Practical Implications- This research provides actionable insights for startup managers in emerging industries, particularly in aquaculture, emphasizing the integration of data analytics, innovative marketing, and sustainability to build resilient brand performance. Originality/Value- This is among the first studies to empirically examine the integrated role of data culture, innovation, and sustainability in determining brand and performance outcomes within fisheries startups, combining theory-driven modeling with real-world entrepreneurial data.