Optimization of Branding and Value Chain Mapping Using Artificial Intelligence for the Batik Village Clusters in Indonesia to Achieve Competitive Advantage
DOI:
https://doi.org/10.56294/dm2024.620Keywords:
Branding Optimization, Value Chain Mapping, Artificial Intelligence, Competitive Advantage, Batik Village ClustersAbstract
This research investigates the role of artificial intelligence (AI) in optimizing branding and mapping value chains to strengthen the competitive advantage of Batik Village Clusters in Indonesia. Employing a quantitative approach, the study analyzes survey data from stakeholders in the batik industry, focusing on their perceptions of AI's impact on branding and value chain processes. The study reveals that AI has a significant positive impact on branding optimization (t-statistic = 29.249, p = 0.000) and value chain mapping (t-statistic = 15.066, p = 0.000). Additionally, both branding optimization (t-statistic = 8.621) and value chain mapping (t-statistic = 16.853) were found to positively affect the competitive advantage of batik clusters. These findings suggest that AI can enhance branding efforts, improve value chain efficiency, and elevate the competitive positioning of Batik Village Clusters. The study provides actionable recommendations for batik entrepreneurs and policymakers, emphasizing the need to incorporate AI technologies to improve global competitiveness and ensure long-term sustainability in the batik industry
References
1. Zlatanov S, Popesku J. Current Applications of Artificial Intelligence in Tourism and Hospitality. In: Proceedings of the International Scientific Conference - Sinteza 2019 [Internet]. Novi Sad, Serbia: Singidunum University; 2019 [cited 2024 Dec 10]. p. 84–90. Available from: http://portal.sinteza.singidunum.ac.rs/paper/648
2. Zsarnoczky M. How does artificial intelligence affect the tourism industry? Vadyba. 2017;31(2):85–90.
3. Mandić A, Marković B, Mulović Trgovac A. Tools of Artificial Intelligence Technology as a Framework for Transformation Digital Marketing Communication. Teh glas (Online). 2024 Oct 14;18(4):660–5. DOI: https://doi.org/10.31803/tg-20240708161118
4. Khoiroh SM. Optimalisasi pengembangan kampung industri batik tulis daerah berdasarkan mapping value chain. In 2017. p. 125–35.
5. Ahmad SA, Mir MA. Impact of Artificial Intelligence on Marketing and Consumer Decision-Making: In: Gaur L, editor. Advances in Computational Intelligence and Robotics [Internet]. IGI Global; 2024 [cited 2024 Dec 10]. p. 169–88. Available from: https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-3691-5.ch008 DOI: https://doi.org/10.4018/979-8-3693-3691-5.ch008
6. Sujani S, Arif MS, Edi AS. Mapping Value Chain Sebagai Optimalisasi Pengembangan Kampung Batik Tulis di Surabaya. mahardhika. 2019 May 30;17(3):488. DOI: https://doi.org/10.29062/mahardika.v17i3.107
7. Wodecki A. Value Creation and Competitive Advantage Models. In: Artificial Intelligence in Value Creation [Internet]. Cham: Springer International Publishing; 2019 [cited 2024 Dec 10]. p. 1–70. Available from: https://link.springer.com/10.1007/978-3-319-91596-8_1 DOI: https://doi.org/10.1007/978-3-319-91596-8_1
8. Rahmawati M, Ruslan A, Bandarsyah D. The Era of Society 5.0 as the unification of humans and technology: A literature review on materialism and existentialism. JSD. 2021 Sep 7;16(2):151. DOI: https://doi.org/10.20473/jsd.v16i2.2021.151-162
9. Purwaningsih E, Fathurahman M, Basrowi B, Salim N, Agil AS. Branding, Artificial Intelligence, dan Tradisi Berdesa dalam Membangun Kinerja Inovasi BUMDesa. GYB. 2024 Sep 30;5(3):765–87. DOI: https://doi.org/10.33650/guyub.v5i3.9189
10. Kustiyahningsih Y, Anamisa DR, Mufarroha FA. The SME performance recommendation system facing the 4.0 industrial revolution uses the Fuzzy ANP method. J Phys: Conf Ser. 2021 Mar 1;1836(1):012036. DOI: https://doi.org/10.1088/1742-6596/1836/1/012036
11. Sugiono S. Peran E-Government dalam Membangun Society 5.0: Tinjauan Konseptual terhadap Aspek Keberlanjutan Ekonomi, Sosial, dan Lingkungan. mp. 2021 Nov 29;5(2):115–25. DOI: https://doi.org/10.21787/mp.5.2.2021.115-125
12. Purwaningsih E, Muslikh M, Suhaeri S, Basrowi B. Utilizing blockchain technology in enhancing supply chain efficiency and export performance, and its implications on the financial performance of SMEs. 105267/j.uscm. 2024;12(1):449–60. DOI: https://doi.org/10.5267/j.uscm.2023.9.007
13. Latan H, Ghozali I. Partial Least Squares Konsep, Metode dan Aplikasi Menggunakan Program WarpPLS 5.0 (Third). Universitas Diponegoro. 2017; DOI: https://doi.org/10.1007/978-3-319-64069-3
14. Vakili MM. Assessment of construct validity questionnaires in psychological, educational and Health research: Applications, Methods, and Interpretation of Exploratory factor analysis. J Med Educ Dev. 2018 Sep 1;11(30):4–19. DOI: https://doi.org/10.29252/edcj.11.30.4
15. Yusuf M, Andariana A, Bte Abustang P, Mannan A, Tabbu MAS, Qaiyimah D, et al. Construction Validity Testing on Blended Learning Implementation Evaluation Instruments. Yuliarto B, Susanto H, Becker MR, Nurjayadi M, Molnar G, Andriani Y, et al., editors. E3S Web Conf. 2023;400:01007. DOI: https://doi.org/10.1051/e3sconf/202340001007
16. Choodari-Oskooei B, Royston P, Parmar MK. A new measure of predictive ability for survival models. Trials. 2011 Dec;12(S1):A138. DOI: https://doi.org/10.1186/1745-6215-12-S1-A138
17. Rönkkö M, Cho E. An Updated Guideline for Assessing Discriminant Validity. Organizational Research Methods. 2022 Jan;25(1):6–14. DOI: https://doi.org/10.1177/1094428120968614
18. Kumari TL, Bambuwala S, Rajalakshmi (last). Methods for testing discriminant validity. Management & Marketing Journal. 2011;9(2):217–24.
19. Asher AD. Approaches for Improving Validity in Quantitative Research Articles. pla. 2024 Apr;24(2):209–15. DOI: https://doi.org/10.1353/pla.2024.a923703
20. Hutchinson B, Rostamzadeh N, Greer C, Heller K, Prabhakaran V. Evaluation Gaps in Machine Learning Practice. In: 2022 ACM Conference on Fairness, Accountability, and Transparency [Internet]. Seoul Republic of Korea: ACM; 2022 [cited 2024 Dec 11]. p. 1859–76. Available from: https://dl.acm.org/doi/10.1145/3531146.3533233
21. Wang X, Gao R, Jain A, Edge G, Ahuja S. How Well do Offline Metrics Predict Online Performance of Product Ranking Models? In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval [Internet]. Taipei Taiwan: ACM; 2023 [cited 2024 Dec 11]. p. 3415–20. Available from: https://dl.acm.org/doi/10.1145/3539618.3591865 DOI: https://doi.org/10.1145/3539618.3591865
22. Sarstedt M, Ringle CM, Hair JF. Partial Least Squares Structural Equation Modeling. In: Homburg C, Klarmann M, Vomberg A, editors. Handbook of Market Research [Internet]. Cham: Springer International Publishing; 2022 [cited 2024 Dec 11]. p. 587–632. Available from: https://link.springer.com/10.1007/978-3-319-57413-4_15 DOI: https://doi.org/10.1007/978-3-319-57413-4_15
23. Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J of the Acad Mark Sci. 2015 Jan;43(1):115–35. DOI: https://doi.org/10.1007/s11747-014-0403-8
24. Ringle CM, Sarstedt M, Mitchell R, Gudergan SP. Partial least squares structural equation modeling in HRM research. The International Journal of Human Resource Management. 2020 Jul 3;31(12):1617–43. DOI: https://doi.org/10.1080/09585192.2017.1416655
25. Asiabar MG, Asiabar MG, Asiabar AG. Analyzing the Role of Artificial Emotional Intelligence in Personalizing Human Brand Interactions: A Mixed-Methods Approach [Internet]. 2024 [cited 2024 Dec 11]. Available from: https://www.researchsquare.com/article/rs-5037977/v1 DOI: https://doi.org/10.21203/rs.3.rs-5037977/v1
26. Dai X, Liu Q. Impact of artificial intelligence on consumer buying behaviors: Study about the online retail purchase. J Infras Policy Dev. 2024 Sep 4;8(9):7700. DOI: https://doi.org/10.24294/jipd.v8i9.7700
27. Eyo-Udo NL. Leveraging artificial intelligence for enhanced supply chain optimization. Open Access Res J Multidiscip Stud. 2024 Apr 30;7(2):001–15. DOI: https://doi.org/10.53022/oarjms.2024.7.2.0044
28. Komandrovska V, Sozynova I, Kovpik V. Branding in the context of innovative development and sustainable marketing. UJAET. 2024 Jan 31;9(1):195–9. DOI: https://doi.org/10.36887/2415-8453-2024-1-32
29. Harikrishnan V, P. Vikraman Dr. INNOVATIVE BRANDING: CREATIVE APPROACHES FOR CAPTURING TOMORROW’S CONSUMER. In: Futuristic Trends in Management Volume 3 Book 2 [Internet]. First. Iterative International Publisher, Selfypage Developers Pvt Ltd; 2024 [cited 2024 Dec 11]. p. 34–40. Available from: https://www.iipseries.org/viewpaper.php?pid=1625&pt=innovative-branding-creative-approaches-for-capturing-tomorrow-s-consumer DOI: https://doi.org/10.58532/V3BHMA2P2CH3
30. Al-Shammari MM. Production Value Chain Model for Sustainable Competitive Advantage. Management Systems in Production Engineering. 2023 Mar 1;31(1):27–32. DOI: https://doi.org/10.2478/mspe-2023-0004
31. Mvn N, Chandrika Reddy P. AI-driven Business Model Innovation - Where Technology Meets Strategy. rvim. 2024 Sep 9;16(1):5–17 DOI: https://doi.org/10.70599/rvim/2024/306
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