Perceived critical mass effect on groupware acceptance

By Hao Lou, Wenhong Luo, Diane M. Strong. European Journal of Information Systems, 2000

390 citations recorded by the source corpus.

Subfields: Computer Science Applications, Management Science and Operations Research, Social Psychology

View Paper: https://www.semanticscholar.org/paper/6902023c30625f0525b8e0b0bf3eb240c96378ea

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In Computer Science Applications

In Management Science and Operations Research

In Social Psychology

Citations in the corpus

Dead Or Alive? The Development, Trajectory And Future Of Technology Adoption Research (2007, methodology): “TAM was tested among various types of information systems, including email (Karahanna and Straub 1999; Straub 1994), groupware (Lou, Luo, and Strong 2000), expert systems (Keil, Beranek, and Konsynski 1995), CASE tools (Dishaw and Strong 1999), voice mail (Karahanna and Limayem 2000), calculator…”
Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning (2013, background): “This is consistent with previous studies on the direct effect of subjective norms on behavioral intention to use a collaborative technology (Lou et al., 2000).”
Technology acceptance model (TAM) and social media usage: an empirical study on Facebook (2014, neutral): “In summary, theories in social psychology, economics, and diffusion of innovations support the notion that a perceived critical mass is a key variable for new technology acceptance (Lou et al, 2000).”
Why people use social networking sites: An empirical study integrating network externalities and motivation theory (2011, methodology): “…(2009) Online social support Social effects Lin and Bhattacherjee (2008) Interactive information technologies Compatibility effects, Social effects Lou et al. (2000) Groupware Social effects Pae and Hyun (2002) Personal computer operating system Market size effects Wu et al. (2007) End user…”
The role of moderating factors in user technology acceptance (2006, background): “Users who have adopted the technology have the incentive to enlist more users for the technology, and they may be eager to promote the technology by sharing their experience with and offering help to potential adopters (Lou et al., 2000).”
Understanding digital inequality: comparing continued use behavioral models of the socio-economically advantaged and disadvantaged (2008, background): “(2005) represents a related construct, which refers to the degree to which a person believes that most of his or her peers are using a particular innovation (Lou et al. 2000).”
A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects (2007, background): “Electronic and voice mail [1], transactional web sites [7], electronic supermarkets [31], groupware [56], and electronic payment systems [70].”
Enhancing perceived interactivity through network externalities: An empirical study on micro-blogging service satisfaction and continuance intention (2012, result): “The dimension of control was significantly affected by perceived network size, which is consistent with thefindings fromprevious research [37,39].”
An empirical examination of consumer adoption of Internet of Things services: Network externalities and concern for information privacy perspectives (2016, methodology): “…compatibility and perceived complementarity), perceived benefits, attitude and intention, measures adapted from past research were used (Chiu et al., 2013; Davis, 1989; Fishbein& Ajzen, 1975; Lin& Bhattacherjee, 2008; Lou et al., 2000; Rogers, 1983), with modifications to suit the context of IoT.”
Understanding Individual Adoption of Instant Messaging: An Empirical Investigation (2005, background): “However, an individual may use a communication technology based on the perception of the number of users rather than the actual number, as evidenced by the empirical study in Lou et al. (2000).”
Do I really have to? User acceptance of mandated technology (2002, background): “…usefulness and ease of use are primary drivers of user intentions to adopt new information technologies (IT) (eg, Davis, 1989, 1993; Davis et al , 1989; Mathieson, 1991; Adams et al , 1992; Szajna, 1994, 1996; Taylor & Todd, 1995; Davis & Venkatesh, 1996; Lou et al , 2000; Venkatesh & Davis, 2000).”
Why do people play on-line games? An extended TAM with social influences and flow experience (2004, background): “Furthermore, to develop a scale for measuring social norms and perceived critical mass, we utilized measures of Fishbein and Ajzen and Luo and Strong, with modifications to suit the setting of on-line games.”
Factors affecting hotels' adoption of mobile reservation systems: A technology-organization-environment framework (2016, background): “In their study of groupware acceptance, Lou, Luo, and Strong (2000) suggest that, in order to achieve a critical mass of users, the choice of appropriate individuals to participate in the initial introduction is crucial.”
An empirical study of the effect of perceived risk upon intention to use online applications (2005, background): “Actually, many extended perception variables have been added to TAM in previous studies in specific contexts, such as perceived critical mass in groupware usage (Luo and Strong, 2000), perceived playfulness in the WWW context (Moon and Kim, 2001), perceived user resources in a bulletin board system ”