JOURNAL ARTICLE
Transcreation and AI in global marketing: A comparative case study of two telecom websites.
Published In: Digital Translation: International Journal of Translation & Localization, 2026, v. 13, n. 1. P. 29 1 of 3
Database: Communication Source 2 of 3
Authored By: Aulló-Saura, Emma; Gutiérrez-Artacho, Juncal; Díaz Millón, Mar 3 of 3
Abstract
This study investigates transcreation in global marketing through a comparative case study of selected sections of Apple's and Sunstech's multilingual websites (English<>Spanish). Using the FACT framework, it evaluates content adaptation, cultural localization, and linguistic strategies. Apple employs a hybrid strategy, blending standardization and transcreation, maintaining structural consistency while adapting key visual and textual elements for cultural relevance. Sunstech, however, relies heavily on standardization, with translation deficiencies affecting its cultural and linguistic resonance. The study also explores AI's role in transcreation, highlighting its efficiency in handling large volumes of content. However, AI-generated translations often lack cultural nuance and creative adaptation, requiring human intervention to ensure cultural and contextual appropriateness. Ultimately, the study advocates for a hybrid transcreation approach that combines technology with human creativity, enhancing global brand communication and positioning in increasingly diverse and competitive markets. [ABSTRACT FROM AUTHOR]
Additional Information
- Source:Digital Translation: International Journal of Translation & Localization. 2026/01, Vol. 13, Issue 1, p29
- Document Type:Article
- Subject Area:Computer Science
- Publication Date:2026
- ISSN:2949-6861
- DOI:10.1075/dt.25009.aul
- Accession Number:191434004
- Copyright Statement:Copyright of Digital Translation: International Journal of Translation & Localization is the property of John Benjamins Publishing Co. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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