Predicting Collaboration Technology Use: Integrating Technology Adoption and Collaboration Research

By Susan A. Brown, Alan R. Dennis, V. Venkatesh. Journal of Management Information Systems, 2010

601 citations recorded by the source corpus.

The paper presents a model integrating theories from collaboration research (i.e., social presence theory, channel expansion theory, and the task closure model) with a recent theory from technology adoption research (i.e., unified theory of acceptance and use of technology, abbreviated to UTAUT) to explain the adoption and use of collaboration technology. We theorize that collaboration technology characteristics, individual and group characteristics, task characteristics, and situational characteristics are predictors of performance expectancy, effort expectancy, social influence, and facilitating conditions in UTAUT. We further theorize that the UTAUT constructs, in concert with gender, age, and experience, predict intention to use a collaboration technology, which in turn predicts use. We conducted two field studies in Finland among (1) 349 short message service (SMS) users and (2) 447 employees who were potential users of a new collaboration technology in an organization. Our model was supported in both studies. The current work contributes to research by developing and testing a technology-specific model of adoption in the collaboration context.

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

View Paper: https://www.semanticscholar.org/paper/d9f6b31bdc398a3b2e12c3deb2a394068192fb4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Communication

1. V. Venkatesh
2. Hartmut Hoehle
3. James Y. L. Thong
4. Björn Niehaves
5. Ralf Plattfaut
6. Yogesh Kumar Dwivedi
7. Nripendra P. Rana
8. Frank K. Y. Chan

In Information Systems and Management

1. Yogesh Kumar Dwivedi
2. Nripendra P. Rana
3. Ali Abdallah Alalwan
4. V. Venkatesh
5. Michael D. Williams
6. James Y. L. Thong
7. Frank K. Y. Chan
8. John S. Edwards

In Sociology and Political Science

1. Yogesh Kumar Dwivedi
2. Nripendra P. Rana
3. V. Venkatesh
4. Ali Abdallah Alalwan
5. James Y. L. Thong
6. Michael D. Williams
7. Frank K. Y. Chan
8. Yanqing Duan

Citations in the corpus

• The unified theory of acceptance and use of technology (UTAUT): a literature review (2015, background): “…Raaij and Schepers (2008), Wills et al. (2008) Single IS 12 Only a Single IS for the research Aggelidis and Chatzoglou (2009), Aoun et al. (2010), Brown et al. (2010), Carlsson et al. (2006), Chang et al. (2007), Chiu and Wang (2008), Gupta et al. (2008), Im et al. (2008), Sambasivan et al.…”
• Mobile Application Usability: Conceptualization and Instrument Development (2015, methodology): “As such, our work follows several calls for developing more precise and context-specific theories (Alvesson and Kärreman 2007; Bamberger 2008; Brown et al. 2010; Hong et al. 2014; Johns 2006; Van der Heijden 2004; Venkatesh et al. 2012) because there is “a general tendency to seek causal explanation”
• Impact of perceived value on the satisfaction of supermarket customers: developing country perspective (2020, background): “The literature on theory building highlights the importance of testing the generalisability of instruments and research models to understand the better the context and how it may differently influence theories (Brown et al., 2010; Sharma et al., 2020b; Venkatesh et al., 2012)”
• Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead (2016, background): “Other UTAUT-based studies have mainly measured technology use by usage duration, frequency, and intensity or a subset of these three measures on a self-report basis (e.g., Brown et al., 2010, Liang et al., 2010; Venkatesh et al., 2008).”
• Internet adoption by the elderly: employing IS technology acceptance theories for understanding the age-related digital divide (2014, methodology): “The additional inclusion of (four) socio-demographic variables can further increase the models ’ power to determine whether elderly people (65 and older) ultimately adopt the Internet and to what extent.”
• A Framework and Guidelines for Context-Specific Theorizing in Information Systems Research (2014, background): “For example, social influence can be decomposed into peer influence and superior influence in the context of collaboration technology use in the workplace (Brown et al. 2010), whereas it can be decomposed into friends and family influences, secondary sources’ influences, and workplace referent’s…”
• The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency (2018, background): “Additionally, although UTAUT is a predictive model, it does not provide guidance for steps to foster technology adoption [34].”
• Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, methodology): “Those findings from the present study in the AI context are largely in agreement with the findings of prior studies conducted in different IT contexts (e.g. Brown et al., 2010; Hong et al., 2011; Wang et al., 2014).”