Mobile-based assessment: Investigating the factors that influence behavioral intention to use

By Stavros A. Nikou, Anastasios A. Economides. Comput. Educ., 2017

345 citations recorded by the source corpus.

Subfields: Information Systems, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/ce699755a58ea6e8d4c63d4e92f12318d75e66d2

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Citations in the corpus

Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model (2019, methodology): “…m-learning for educational use has become increasingly common, and thus has received increasing attention from researchers and educators (Briz-Ponce et al., 2017; Kim et al., 2017; Nikou and Economides, 2017; Crompton and Burke, 2018; Hamidi and Chavoshi, 2018; Hamidi and Jahanshaheefard, 2019).”
Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator (2020, background): “Previous studies have discussed learners’ acceptance of different types of technology, such as mobile technology (Pindeh et al., 2016; Nikou and Economides, 2017), computer-based communication technology (Park et al., 2014), social media (Hsieh et al., 2017), and MOOCS courses (Joo et al., 2018).”
Using an extended Technology Acceptance Model to understand students’ use of e-learning during Covid-19: Indonesian sport science education context (2020, neutral): “Other studies in e-learning integration also reported that when respondents’ perceived that technology benefits instructional activities, the intention to use will be more likely to be improved (Nikou and Economides, 2017; Ramírez-Correa et al., 2015; Teo et al., 2018; Zhang et al., 2008).”
Determinants of Intention to Use ChatGPT for Educational Purposes: Findings from PLS-SEM and fsQCA (2023, positive): “The personal innovativeness and IU items were adapted from the study of Nikou and Economides (2017).”