http://eprints.uny.ac.id/id/eprint/92125 2026-09-17T08:10:05Z info:eu-repo/semantics/descriptiveMetadata http://eprints.uny.ac.id/92125/ Cloud Commerce Learning Ecosystem: A Multilingual Vocational Model For Developing Digital Trade Competencies in Multi-Zone and Cross-Cultural Contexts. Adi, Priyo Nugroho Triyono, Mochamad Bruri Pendidikan Kejuruan Kurikulum dan Teknologi Pendidikan Teknologi Informasi This study develops and preliminarily evaluates a conceptual model for a multilingual, cross-cultural Cloud Commerce Learning Ecosystem (CCLE) designed to strengthen digital commerce literacy, entrepreneurial readiness, and vocational competence in increasingly globalized online marketplaces. Addressing persistent challenges, including rural–urban disparities in access to digital learning, skill mismatch between academic preparation and e-commerce workforce demands, and language–cultural barriers that constrain participation in digital trade, the model integrates constructivism, connectivism, and the German Dual System into a unified pedagogical framework. CCLE is positioned not merely as a digital learning platform but as a holistic ecosystem that embeds multilingual instruction, cross-cultural competence, and authentic digital commerce tasks. Academic validation by six experts (4 from Indonesia and 2 from Germany) ensured theoretical soundness, cultural relevance, and contextual transferability. To operationalize the conceptual model, a technical architecture was developed to translate pedagogical principles into modular system components, resulting in a functional prototype focused on training and assessing product listing competencies: an essential task within real digital commerce workflows. The research employed a modified Borg and Gall R&D approach encompassing problem identification, model design, expert validation, system development, modular prototyping, international alpha testing, refinement, and academic dissemination. The intervention was conducted with 100 undergraduate Information Systems and Technology students using a pre-test → intervention → post-test design across cognitive, affective, and psychomotor domains. Learning activities incorporated curated instructional videos, ethical narratives, multilingual navigation, and authentic commerce simulations. Data sources included expert validation forms, system logs, short-answer tests, attitudinal surveys, and rubric-based product listing assessments, with quantitative analysis supported by Wilcoxon Signed-Rank, McNemar, Mann–Whitney U, effect size (Cohen’s d), and logistic regression. Results demonstrate the pedagogical feasibility, operational robustness, and cross-cultural relevance of the CCLE model. Significant improvements were observed across all domains: cognitive scores increased from 9.17 to 10.78 (d = 0.73), driven by gains in product description skills; affective outcomes rose from 1.10 to 2.66 (d = 0.94), reflecting a 40–46 percent increase in ethical orientations related to transparency, fair pricing, and responsible digital trade; and psychomotor performance improved markedly, particularly in visual digital skills (+18 percent). The areas of pricing numeracy and platform compliance show no significant improvement, indicating priorities for future instructional refinement. The study concludes that CCLE demonstrates potential as an inclusive, adaptive, and ethically grounded learning model for platform-based vocational learning. The validated conceptual model stands as the primary scholarly contribution, offering a scalable foundation for future research in digital commerce education, cross-cultural learning ecosystems, and TVET digital transformation. 2026-06-19 Thesis NonPeerReviewed text en http://eprints.uny.ac.id/92125/1/disertasi_priyo%20nugroho%20adi_22702261025.pdf Adi, Priyo Nugroho and Triyono, Mochamad Bruri (2026) Cloud Commerce Learning Ecosystem: A Multilingual Vocational Model For Developing Digital Trade Competencies in Multi-Zone and Cross-Cultural Contexts. S3 thesis, Sekolah Pascasarjana. info:eu-repo/semantics/objectFile info:eu-repo/semantics/humanStartPage