An empirical study of the effect of perceived risk upon intention to use online applications
By Hsi-Peng Lu, Chin-Lung Hsu, Hsiu-Ying Hsu. Inf. Manag. Comput. Security, 2005
316 citations recorded by the source corpus.
Subfields: Information Systems and Management, Organizational Behavior and Human Resource Management, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/54c6afd15b6bc9d964a44afa195c5bc5bdfcaa25
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Information Systems and Management
- Nripendra P. Rana
- Yogesh Kumar Dwivedi
- Ali Abdallah Alalwan
- Sheshadri Chatterjee
- Michael D. Williams
- Marc Clement
- Kuttimani Tamilmani
- Banita Lal
In Organizational Behavior and Human Resource Management
- Nripendra P. Rana
- Yogesh Kumar Dwivedi
- Sheshadri Chatterjee
- Michael D. Williams
- Thurasamy Ramayah
- Banita Lal
- Marc Clement
- Mirza Mohammad Didarul Alam
In Sociology and Political Science
- Nripendra P. Rana
- Yogesh Kumar Dwivedi
- Ali Abdallah Alalwan
- Sheshadri Chatterjee
- Michael D. Williams
- Marc Clement
- Thurasamy Ramayah
- Banita Lal
Citations in the corpus
- What Have We Learned about the Effects of the COVID-19 Pandemic on Consumer Behavior? (2021, background): “The rising consumption risks have effects on purchasing [23–25], such as physical risk [2,26–29] and financial risk [30], and this influences consumers’ attitudes and behavior in a time of crisis [30–32].”
- An empirical validation of a unified model of electronic government adoption (UMEGA) (2017, background): “The theoretical models on e-commerce adoption (e.g. Lu et al., 2005; Teo & Liu, 2007) have also found a negative and significant relationship between perceived risk and attitude.”
- Technology acceptance among micro-entrepreneurs in marginalized social strata: The case of social innovation in Bangladesh (2017, background): “Such feelings bring about a sense of risk associated with using a new technology (Kuo et al., 2013), and this risk perception influences PEOU and PU (Lu et al., 2005).”
- Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling (2020, background): “The theoretical underpinning of the other models reveals that perceived ease of use (which is similar in concept with EE) is considered to be a significant and effective predictor of Attitude (ATT) in the field of technology adoption research (Lu et al. 2005).”