Investigating Determinants of Software Developers' Intentions to Follow Methodologies

By Bill C. Hardgrave, Fred D. Davis, Cynthia K. Riemenschneider. Journal of Management Information Systems, 2003

306 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/31f23eb10320f0b74d6dce8f46e66a17e697428a

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Citations in the corpus

An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives (2016, background): “Past studies have confirmed that the perceived compatibility of an innovation has a positive influence of its adoption (Cooper & Zmud, 1990; Hardgrave et al., 2003; Tan & Teo, 2000). Moreover, Schilling (2002) indicated that a technology user's perceived benefit may increase with the number of users”
Technology Acceptance, Technological Self-Efficacy, and Attitude Toward Technology-Based Self-Directed Learning: Learning Motivation as a Mediator (2020, unknown): “%) demonstrated by this questionnaire survey highlighted the issue of educational compatibility, as previous studies have established compatibility as an important predictor of information system acceptance (Hardgrave et al., 2003; Liao and Lu, 2008).”
Integrated Theoretical Framework for a Homeowner's Decision in Favor of an Innovative Residential Heating System (2010, unknown): “…and Kiel, 2001, on online purchasing), whereas in other studies, attitude has the highest effect on the adoption (Moore and Benbasat, 1996, on IT; Karahanna et al., 1999; Hardgrave et al., 2003, on software developers; Hebert and Benbasat, 1994, on IT in hospitals; Chau and Hu, 2001, on IT).”
Technology acceptance model: a literature review from 1986 to 2013 (2014, background): “Factors from other theories: factors from other theories of technology acceptance for increasing predictive validity of the TAM like subjective norm [35], expectations [87], user participation [5], risk [29, 67], and trust [30, 31, 92]; 3.”
Examining the influence of online reviews on consumers' decision-making: A heuristic-systematic model (2014, background): “'s [37] work, we further examined the direct, indirect, and total effects of the determinants of behavioral intention in this study.”
Understanding consumer acceptance of mobile payment services: An empirical analysis (2010, background): “Extant research shows positive effects of perceived compatibility on both the attitude toward using a technology and perceived usefulness (Hardgrave et al. 2003).”
Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model (2018, background): “The study by Hardgrave et al. (2003) revealed that complexity had a significantly negative influence on PU when the software developers had to adopt formalized methodologies, while Hasan (2007) confirmed that complexity has significant direct effects on both PU and PEOU.”
Mobile computing acceptance factors in the healthcare industry: A structural equation model (2007, background): “[37] B.”
Adding Innovation Diffusion Theory to the Technology Acceptance Model: Supporting Employees' Intentions to use E-Learning Systems (2011, background): “These results were consistent with previous research findings (Yang, 2007; Hardgrave et al., 2003).”
Integrating Technology Acceptance Model With Innovation Diffusion Theory: An Empirical Investigation on Students’ Intention to Use E-Learning Systems (2019, background): “[32] revealed that complexity has a negative relationship with the perceived usefulness.”