Toward preprototype user acceptance testing of new information systems: implications for software project management

By Fred D. Davis, V. Venkatesh. IEEE transactions on engineering management, 2004

567 citations recorded by the source corpus.

Subfields: Computer Science Applications, Information Systems, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/bac6ed8196e3c293d67e383acc63c78c095ea9b5

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Citations in the corpus

Technology Acceptance Model 3 and a Research Agenda on Interventions (2008, methodology): “Perceivedeaseofusehasbeentheorizedtobecloselyassociatedwith individuals’self-efﬁcacybeliefsandproceduralknowledge,whichrequireshands-onexperienceandexecutionofskills(Davisetal.,1989;Venkatesh,2000;Davis &Venkatesh,2004).”
Understanding information systems continuance: The case of Internet-based learning technologies (2008, background): “In IS, Davis and Venkatesh [11] found that users’ hands-on experience was the key driver of sustained usage.”
Evaluating the Role of Trust in Consumer Adoption of Mobile Payment Systems: An Empirical Analysis (2010, background): “Numerous studies over the past decade have validated the significant relationship of PEOU with the AI [Davis et al., 1989; Venkatesh, 2000; Davis and Venkatesh, 2004].”
Technology acceptance model: a literature review from 1986 to 2013 (2014, background): “Usage measures: usage measures for operationalization of actual system usage like attitude toward technology [23], usage perception [37, 61, 77], and actual usage of technology [26].”
Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology (2012, unknown): “…recent work has challenged the role of behavioral intention as the key predictor of technology use and introduced a new theoretical construct (i.e., habit) as another critical predictor of technology use (e.g., Davis and Venkatesh 2004; Kim and Malhotra 2005; Kim et al. 2005; Limayem et al. 2007).”
Mobile food ordering apps: An empirical study of the factors affecting customer e-satisfaction and continued intention to reuse (2020, background): “…in terms of using smartphones and the attached apps or in terms of ordering food from restaurants, could be a very critical component in shaping customer intention and behaviour towards MFOAs (Alalwan, Dwivedi, Rana, & Algharabat, 2018; Davis & Venkatesh, 2004; Eriksson, Kerem, & Nilsson, 2008).”
The role of task-technology fit as users' motivation to continue information system use (2009, background): “…indirectly supported through studies where the argument is that prior behavior (i.e., acceptance of functions) is the most important antecedent of future behavior (i.e., continued use of the functions) (Bagozzi & Kimmel, 1995; Conner & Armitage, 1998; Davis & Venkatesh, 2004; Norman & Smith, 1995).”
Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) (2017, background): “According to Davis and Venkatesh (2004), habit has been recognized as an alternative determinant of technology usage along with behavioural intention.”
Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk (2018, background): “Also, Venkatesh et al. (2012) argued that while behavioural intention has been identified as the most significant factor predicting technology adoption, habit, according to Davis and Venkatesh (2004), has been recognised as an alternative determinant to technology usage.”
Empirical investigation of e-learning acceptance and assimilation: A structural equation model (2016, background): “Prior individual information and perceptions – about how easy a system is to use from important referents – are unlikely to be stable as individuals need to form these stable perceptions based on their own general computer beliefs and hands-on experience with the sys-tem [21].”