Extending the Technology Acceptance Model to assess automation
By Mahtab Ghazizadeh, John D. Lee, Linda Ng Boyle. Cognition, Technology & Work, 2011
485 citations recorded by the source corpus.
Subfields: Information Systems and Management, Management Science and Operations Research, Social Psychology
View Paper: https://www.semanticscholar.org/paper/e36f53ffad3e5273e372b153933c6de47ccce8e2
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
- Michael A. Nees
- Ella Glikson
- Anita Williams Woolley
- Linda Ng Boyle
- Mahtab Ghazizadeh
- Lesley J. Strawderman
- V. Pitardi
- John D. Lee
In Management Science and Operations Research
- V. Pitardi
- John D. Lee
- Linda Ng Boyle
- Mahtab Ghazizadeh
- Ellen Enkel
- Hannah R. Marriott
- Arto Salonen
- Noora Haavisto
In Social Psychology
- Michael A. Nees
- Anita Williams Woolley
- Ella Glikson
- Linda Ng Boyle
- Mahtab Ghazizadeh
- Lesley J. Strawderman
- V. Pitardi
- John D. Lee
Citations in the corpus
- Applied artificial intelligence and trust—The case of autonomous vehicles and medical assistance devices (2016, background): “In academic literature, two different research streams on trust in the context of automation and innovation have emerged: First, there is prior work on trust in the technology (Ghazizadeh et al., 2012; Lee and Moray, 1992; Lee and See, 2004; Zuboff, 1988).”
- Assessing the utility of TAM, TPB, and UTAUT for advanced driver assistance systems. (2017, result): “Similar attempts to augment the TAM model (Davis, 1989) were also found in the literature (Xu et al., 2010; Ghazizadeh et al., 2012b).”
- Augmenting the Technology Acceptance Model with Trust: Commercial Drivers’ Attitudes towards Monitoring and Feedback (2012, background): “Based on the TAM literature, it is hypothesized that PU and PEOU each have a positive effect on BI and that PEOU has a positive effect on PU (Davis, et al., 1989; Ghazizadeh, Lee, & Boyle, 2012).”
- Human Trust in Artificial Intelligence: Review of Empirical Research (2020, background): “More recent approaches have further added the concept of trust as a predictor of technology acceptance (Ghazizadeh, Lee, & Boyle, 2012; Hoff & Bashir, 2015; Lee & See, 2004; Pavlou, 2003).”
- Acceptance of Self-driving Cars (2016, background): “…(e) perceived usefulness of automation; (f) perceived ease of use of automation; (g) experience with automation; and (h) intention to use automation (see Davis, Bagozzi, & Warshaw, 1989; Ghazizadeh et al., 2012; Madigan et al., 2016; Peng, Ghazizadeh, Lee, & Boyle, 2012; Venkatesh et al., 2003).”
- Customer experiences in the age of artificial intelligence (2020, background): “Two streams of research have emerged on trusting technology-mediated services: trust in the technology (Ghazizadeh, Lee, & Boyle, 2012), and trust in the innovating firm, including their communication and processes (Chiesa & Frattini, 2011; Nienaber & Schewe, 2014).”
- Towards Autonomous Transportation. Passengers’ Experiences, Perceptions and Feelings in a Driverless Shuttle Bus in Finland (2019, background): “It is important that driverless shuttle buses serve the real needs of people; because mere good performance does not guarantee the acceptance of new technology [62], and other incentives are also required.”
- Alexa, she's not human but… Unveiling the drivers of consumers' trust in voice‐based artificial intelligence (2021, background): “Trust has been acknowledged as a key influencer of human – machine interactions (Ghazizadeh et al., 2012; McLean et al., 2020).”