Information disclosure on mobile devices: Re-examining privacy calculus with actual user behavior

By Mark Jeffrey Keith, Samuel C. Thompson, Joanne E. Hale, Paul Benjamin Lowry, Chapman Greer. Int. J. Hum. Comput. Stud., 2013

358 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/39b7775b841d586d203845eb2aad7e50abb640ca

How researchers see this paper

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

In Artificial Intelligence

1. Daniel Wentzel
2. Tobias Kowatsch
3. Elgar Fleisch
4. Flavius Kehr
5. Keng-Boon Ooi
6. Voon‐Hsien Lee

In Sociology and Political Science

1. Voon‐Hsien Lee
2. Jun-Jie Hew
3. June Wei
4. Daniel Wentzel
5. Tobias Kowatsch
6. Elgar Fleisch
7. Ardion D. Beldad
8. Sabrina M. Hegner

Citations in the corpus

• What catalyses mobile apps usage intention: an empirical analysis (2015, background): “10, as suggested by Kline (2005). Furthermore, Table 4 also reported the correlation coefficients between variables to be less than 0.”
• Blissfully ignorant: the effects of general privacy concerns, general institutional trust, and affect in the privacy calculus (2015, methodology): “Also, Keith et al. (2013) found similar effects when studying attitudes, intentions and actual disclosing behavior simultaneously.”
• Expanding the Technology Acceptance Model with the Inclusion of Trust, Social Influence, and Health Valuation to Determine the Predictors of German Users’ Willingness to Continue using a Fitness App: A Structural Equation Modeling Approach (2018, background): “Risks and uncertainties also abound in mobile app usage, as some apps could compromise users ’ information privacy (Giota & Kleftaras, 2014; Keith, Thompson, Hale, Lowry, & Greer, 2013; Lewis & Wyatt, 2014), while others might provide erroneous information (Lewis & Wyatt, 2014).”