Scholarlink

This study examines the effectiveness of combining AI-optimized mobile design with Bootstrap performance improvements and ChatGPT-driven text analysis for Micro, Small, and Medium Enterprise (MSME) websites. A mixed-methods approach was utilized to assess the effects of these interventions. Performance measures, particularly Largest Contentful Paint (LCP), exhibited a significant 40% enhancement following modification. Simultaneously, text analysis performed with ChatGPT-4 attained an accuracy of 86.7% in detecting significant content concerns. This research fills a notable vacuum in the literature by offering a comprehensive analysis of technical and AI-based solutions for enhancing the online presence of MSMEs. The results highlight the capability of AI to improve the technical performance and content quality of mobile-optimized websites, providing practical insights for MSME digital strategies. Subsequent research will investigate wider geographical contexts. These findings are further contextualized within the Technology Acceptance Model (TAM), emphasizing the perceived usefulness and ease of use of AI-enhanced mobile websites.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Evaluating AI-Optimized Mobile Design: A Mixed-Method Study on Bootstrap Performance and ChatGPT Text Analysis for MSME Websites

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Sabo Hermawan,
  • Ryna Parlyna,
  • Surya Anugrah,
  • Inkreswari Retno Hardini,
  • Ferry Setyadi Atmadja,
  • Fajri Hamdani,
  • Alifah Nur Rahmawati,
  • Cornellius Seno Adriano

摘要

This study examines the effectiveness of combining AI-optimized mobile design with Bootstrap performance improvements and ChatGPT-driven text analysis for Micro, Small, and Medium Enterprise (MSME) websites. A mixed-methods approach was utilized to assess the effects of these interventions. Performance measures, particularly Largest Contentful Paint (LCP), exhibited a significant 40% enhancement following modification. Simultaneously, text analysis performed with ChatGPT-4 attained an accuracy of 86.7% in detecting significant content concerns. This research fills a notable vacuum in the literature by offering a comprehensive analysis of technical and AI-based solutions for enhancing the online presence of MSMEs. The results highlight the capability of AI to improve the technical performance and content quality of mobile-optimized websites, providing practical insights for MSME digital strategies. Subsequent research will investigate wider geographical contexts. These findings are further contextualized within the Technology Acceptance Model (TAM), emphasizing the perceived usefulness and ease of use of AI-enhanced mobile websites.