Perceived fit and satisfaction on web learning performance: IS continuance intention and task-technology fit perspectives

By Wen-Shan Lin. Int. J. Hum. Comput. Stud., 2012

297 citations recorded by the source corpus.

Subfields: Communication, Education, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/ea066ecf5fc37e8be5a677baca817077660e5c78

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. Judy Chuan-Chuan Lin
2. Chin-Lung Hsu
3. Tseng-Lung Huang
4. Shuling Liao
5. Tao Zhou

In Education

1. Judy Chuan-Chuan Lin
2. Chin-Lung Hsu

In Information Systems and Management

1. Judy Chuan-Chuan Lin
2. Chin-Lung Hsu
3. Chia-Chen Chen
4. Tao Zhou
5. Kuo-Lun Hsiao
6. Tseng-Lung Huang
7. Shuling Liao
8. Juho Hamari

Citations in the corpus

• Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy (2024, unknown): “For example, Lin (2012) utilized task-technology fit (TTF) theory to explore factors influencing students’ intention to continue using virtual learning systems.”
• What drives purchase intention for paid mobile apps? - An expectation confirmation model with perceived value (2015, background): “Several studies have demonstrated the validity of ECM across a wide range of IT products and services (Lin et al. 2005, Chen 2008, Vatanasombut et al. 2008, Lin 2012, Stone and Baker-Eveleth 2013).”
• What factors determining customer continuingly using food delivery apps during 2019 novel coronavirus pandemic period? (2020, unknown): “…the Task-Technology Fit model with UTAUT and ECM to verify the TTF plays an essential role not only on determining users’ premier behaviour intention of adopting technology (Zhou et al., 2010), but on explaining users’ continuance intention (Larsen et al., 2009; Lin, 2012; Yuan et al., 2016).”