The Role of Local Wisdom in Driving Innovation and Green Economic Growth through Digital Platforms

Authors

  • Intan Oktaviani Universitas Duta Bangsa Surakarta
  • Pipin Widyaningsih Universitas Duta Bangsa Surakarta
  • Triana Universitas Duta Bangsa Surakarta
  • Husna Sarirah Husin Taylor’s University Malaysia

DOI:

https://doi.org/10.47701/99zprq92

Keywords:

Local Wisdom, Culture, Digital, Green Economic

Abstract

This study investigates the transformative potential of integrating local wisdom encompassing indigenous knowledge, cultural values, and traditional ecological practices into digital platforms to drive innovation and foster green economic growth, aligning with global sustainability agendas. It highlights how local wisdom, often manifested in heritage crafts, regenerative agriculture, and community-based resource management, serves as a foundation for eco-friendly production systems, ethical trade, and inclusive entrepreneurship when amplified through digital technologies such as e-commerce, traceability systems, and financial technology. Employing a mixed-method approach involving case studies from Asia, Africa, and Latin America alongside quantitative analyses of export growth, job creation, and supply chain resilience, the research reveals that embedding cultural identity and sustainable practices into digital commerce enhances product authenticity, attracts premium markets, reduces carbon footprints through localized production, and empowers marginalized groups, including women and indigenous communities. The findings underscore that such integration not only contributes to fair trade and decent work but also strengthens biodiversity conservation, promotes circular economy principles, and mitigates socio-economic inequalities. The study recommends implementing policy incentives, digital literacy programs, and multi-stakeholder collaborations between governments, platform developers, and cultural institutions to scale these initiatives, ultimately positioning local wisdom as a strategic driver for environmentally responsible, socially inclusive, and innovation-driven economic transformation in the digital era.

References

R. S., -, S. S., & -, S. F. (2023). E-Commerce and Digital Transformation: Trends, Challenges, and Implications. International Journal For Multidisciplinary Research, 5(5), 1–9. https://doi.org/10.36948/ijfmr.2023.v05i05.7128

Anderson, J., & Johnson, D. (2024). EasyChair Preprint The Role of Artificial Intelligence in Enhancing E-Commerce Customer Experience The Role of Artificial Intelligence in Enhancing E-commerce Customer Experience.

Atmaja, R. D., Faizah, N. M., & Kambry, M. A. (2023). Aplikasi E – Commerce Toko Sinar Bella dengan Metode Rapid Application Development ( RAD ) menggunakan Framework CodeIgniter 4. 1(1), 26–37.

Chandramana, S. (2023). AI in Retail Industry: Reshaping Shopping Experience and Business Profitability. Ushus Journal of Business Management, 21(4), 41–50. https://doi.org/10.12725/ujbm/61.3

da Silva, A., & Gil, M. M. (2020). Industrial processes optimization in digital marketplace context: A case study in ornamental stone sector. Results in Engineering, 7(April), 100152. https://doi.org/10.1016/j.rineng.2020.100152

DUBEL, M. (2022). Modeling of Forecast for the Development of International Electronic Commerce. Herald of Khmelnytskyi National University. Economic Sciences, 304(2(1)), 145–153. https://doi.org/10.31891/2307-5740-2022-304-2(1)-20

Kumar, B. . P., & Sree, D. K. S. (2021). Virtualization-Based Digitization of a Retail Store: An Enhanced Implementation of Digital Transformation. International Journal of Innovative Technology and Exploring Engineering, 10(11), 133–136. https://doi.org/10.35940/ijitee.k9488.09101121

Mourtzis, D., Angelopoulos, J., & Panopoulos, N. (2020). A survey of digital B2B platforms and marketplaces for purchasing industrial product service systems: A conceptual framework. Procedia CIRP, 97(March), 331–336. https://doi.org/10.1016/j.procir.2020.05.246

MYKYTENKO, N., & RZAIEVA, S. (2024). Application of artificial intelligence in retail. International Scientific-Practical Journal Commodities and Markets, 50(2), 4–20. https://doi.org/10.31617/2.2024(50)01

Oktaviani, I., Atina, V., & Nugroho, D. (2019). E-Farm Marketplace In Hasanah SMEs. International Conference of Health, Science & Technology (ICOHETECH) 2019, 198–200.

Oktaviani, I., Prajadi, B., Duta, U., & Surakarta, B. (2023). E-Marketplace Pada Kelompok Ternak Kambing. 5(1), 138–143.

Oktaviani, I., & Purawanto, E. (2024). ANALYSIS OF AI-BASED BIG DATA FOR STRATEGIC DECISION-MAKING IN E-COMMERCE. 266–278.

Rahman, S. S., & Dekkati, S. (2022). Revolutionizing Commerce: The Dynamics and Future of E-Commerce Web Applications. Asian Journal of Applied Science and Engineering, 11(1), 65–73. https://doi.org/10.18034/ajase.v11i1.58

Raihan, M., & Hidayat, A. T. (2025). Rapid Application Development ( RAD ) in the Development of Mobile Based E-Commerce Application Rapid Application Development ( RAD ) dalam Pengembangan Aplikasi E-Commerce Berbasis Mobile. 5(January), 93–100.

Ruan, S., & Zhao, T. (2024). JungleGPT: Designing and Optimizing Compound AI Systems for E-Commerce. https://huggingface.co/croissantllm/

S., D., & R., S. (2022). Digital transformation in Retail Industry. Cardiometry, 24, 859–866. https://doi.org/10.18137/cardiometry.2022.24.859866

Serrano, W. (2023). The Deep Learning Generative Adversarial Random Neural Network in data marketplaces: The digital creative. Neural Networks, 165, 420–434. https://doi.org/10.1016/j.neunet.2023.05.028

Sharma, S. (2023). Implementing Automation and AI in Small Businesses. AI for Small Business Leveraging Automation to Stay Ahead, 28–56. https://doi.org/10.46679/978819573223403

Teknologi, J., & Open, D. A. N. (2025). Web-Based E-Commerce System Design Using RAD Method : A Case Study of PT Muda Jaya Export. 8(1), 295–306. https://doi.org/10.36378/jtos.v8i1.4420

Toonen, R. J., Jesse, N., & Bird, C. E. (2014). Demystifying the RAD fad. 5937–5942.

Yildiz, Z. O., & Beloff, N. (2020). The Emerging AI Policy for e-commerce Industry. ACM International Conference Proceeding Series, 66–70. https://doi.org/10.1145/3385209.3385210

Zabranskyi, M. (2024). Challenges and Prospects of Implementing Ai in Strategic Management. 74–75. https://doi.org/10.36074/logos-24.05.2024.013

Zeitlin, D. M. H. C., & Martin, R. F. W. S. B. C. (2012). The Radiation Assessment Detector ( RAD ) Investigation. https://doi.org/10.1007/s11214-012-9913-1

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Published

2025-09-25