{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T00:29:40Z","timestamp":1783384180637,"version":"3.54.6"},"reference-count":99,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T00:00:00Z","timestamp":1740441600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To gain a competitive advantage, enterprises are increasingly leveraging digital technologies to create unique value through innovative business models, streamlined operations, and enhanced customer experiences. Despite the widespread adoption of digital technology (DT) to drive digital transformation, research on the factors influencing the public\u2019s intention to use DT for engaging in innovative business activities remains sparse. This study explores the determinants of the public\u2019s intention to adopt DT by integrating the Diffusion of Innovations (DOI) theory and the Technology Acceptance Model (TAM), alongside dynamic individual differences such as perceived risk, computer self-efficacy, and computer anxiety. The findings demonstrate that perceived innovative characteristics and dynamic individual differences significantly impact the public\u2019s intention to use DT through their attitude toward using DT. Finally, this study offers specific recommendations for enhancing the public\u2019s intention to use DT, providing valuable insights for both industry practitioners and academic researchers.<\/jats:p>","DOI":"10.3390\/info16030170","type":"journal-article","created":{"date-parts":[[2025,2,25]],"date-time":"2025-02-25T03:40:12Z","timestamp":1740454812000},"page":"170","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Determinants of the Intention to Use Digital Technology"],"prefix":"10.3390","volume":"16","author":[{"given":"Shu-Mei","family":"Tseng","sequence":"first","affiliation":[{"name":"Department of Hospitality Management, I-Shou University, Kaohsiung 84001, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,2,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"15","DOI":"10.2753\/JEC1086-4415160202","article-title":"Modeling Consumer Purchasing Behavior in Social Shopping Communities with Clickstream Data","volume":"16","author":"Olbrich","year":"2011","journal-title":"Int. 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