Enhancement of Indonesia’s blue economy sector through innovation and competitive advantage based on Resource-Based View theory

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Indonesia’s blue economy holds immense potential due to the marine ecosystem and maritime area, encompassing the marine fisheries sector, marine tourism, and fisheries influencing the market. Despite this significant potential, these resources have not been optimally utilized to boost economic growth. Therefore, this study aims to investigate the competitive advantage of the Blue Economy sector by enhancing the outcomes of the tourism, processing, and sales of fisheries in Indonesia through the Resource-Based View (RBV) theory. The Resource-Based View theory explains the formulation and creation of competitive advantages by utilizing tangible and intangible resources such as innovation, strategy, capability, and culture. This study employs a quantitative method through an online survey with a total of 319 respondents working in the Blue Economy sector, including fisheries, marine product sellers, and marine tourism. The analytical methodology uses Structural Equation Modeling (SEM) with SMART PLS to assess the determination of competitive advantage. The findings of this study show that capabilities, operational capabilities, response capabilities, technological capabilities, innovation culture, innovation strategy, and strategic alignment significantly influence competitive advantage. Moreover, the competitive advantage in the blue economy sector is most influenced by innovation strategy with a beta of 0.375. This suggests that enhancing innovation strategies can positively impact Indonesia’s fisheries industry, as evidenced by fisheries tourism being the largest tourism sector in Indonesia, competing with international tourism. Additionally, Indonesia’s fisheries sector is a major contributor to exports.

Acknowledgment
The authors would like to thank all the stakeholders, including entrepreneurs, government officials, and academics, for their willingness to provide in-depth information on maritime matters in North Sulawesi and other assistance essential for the success of this study.

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    • Figure 1. Construct’s factor loading of SEM-PLS result
    • Table 1. Profile of respondents
    • Table 2. Reliability analysis of questionnaire items
    • Table 3. Model fit
    • Table 4. Correlation matrix
    • Table 5. Hypotheses testing
    • Table A1. Variable measurements
    • Conceptualization
      Andri Rianawati, Noviaty Kresna Darmasetiawan, Faizal Susilo Hadi, Joshua Oktavianus, Carissa Avelinda Utama
    • Formal Analysis
      Andri Rianawati, Noviaty Kresna Darmasetiawan, Faizal Susilo Hadi
    • Investigation
      Andri Rianawati, Noviaty Kresna Darmasetiawan
    • Methodology
      Andri Rianawati, Noviaty Kresna Darmasetiawan
    • Resources
      Andri Rianawati, Noviaty Kresna Darmasetiawan
    • Supervision
      Andri Rianawati, Noviaty Kresna Darmasetiawan
    • Validation
      Andri Rianawati, Noviaty Kresna Darmasetiawan
    • Visualization
      Andri Rianawati, Joshua Oktavianus, Carissa Avelinda Utama
    • Writing – original draft
      Andri Rianawati, Noviaty Kresna Darmasetiawan, Faizal Susilo Hadi, Joshua Oktavianus, Carissa Avelinda Utama
    • Writing – review & editing
      Andri Rianawati, Noviaty Kresna Darmasetiawan, Faizal Susilo Hadi, Joshua Oktavianus, Carissa Avelinda Utama
    • Data curation
      Faizal Susilo Hadi, Joshua Oktavianus, Carissa Avelinda Utama
    • Software
      Faizal Susilo Hadi
    • Project administration
      Joshua Oktavianus, Carissa Avelinda Utama