Adoption of AI-driven personalization in digital news platforms
An integrative model of technology acceptance and perceived contingency
Bibliographic Data
| ID | 11239819 |
|---|---|
| Authors | Joon Soo Lim (0000-0003-0519-4169, Syracuse University), Jun Zhang (0000-0003-1706-1611, Middle Tennessee State University, corresponding author) |
| Year | 2022 |
| Volume | 69 |
| Pages | 101965 |
| Publication date | 2022-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Technology in Society (JOURNAL) |
| Journal identifiers | ISSN: 0160-791X • E-ISSN: 1879-3274 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.techsoc.2022.101965 |
| OpenAlex | W4221048843 |
| Language | EN |
| Citations received | 28 |
| References cited | 50 |
Business · Contingency · Human–computer interaction · Knowledge management · Personalization · Technology Acceptance Model · Usability · Behavioral Health and Interventions · Computer Science · Digital Marketing and Social Media · Psychology · Technology Adoption and User Behaviour · Marketing
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The importance of human interactivity in artificial intelligence use in advertising
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Uncovering the Mediating Effects of AI Trust and Self‐Efficacy on Engagement in Human‐ GenAI Communication for EFL Learning
Text classification for evaluating digital technology adoption maturity based on Bert
Bridging the digital divide
Beyond conventional adoption
Exploring students’ perceptions of Kenya during a study abroad programme
User Perceptions of News Recommender Systems and Trust in Media Outlets
Artificial Intelligence and News
The intention to adopt photovoltaic systems
Peeling back acceptance
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User acceptance of YouTube for procedural learning
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Theoretical Intersections Among Social Influences, Beliefs, and Intentions in the Context of 3G Mobile Services in Singapore
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An acceptance model for smart watches
Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)
Understanding users’ acceptance of smart homes
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Making ‘The Daily Me
Talking Health With a Machine
| Unique citing works | 28 |
|---|---|
| Citations per year | 9.33 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 28 |