Determinants of end-user acceptance of biometrics: Integrating the "Big 3" of technology acceptance with privacy context

By Caroline Lancelot Miltgen, Ales Popovic, Tiago Oliveira. Decision Support Systems, 2013

425 citations recorded by the source corpus.

The information systems (IS) literature has long emphasized the importance of user acceptance of computer-based IS. Evaluating the determinants of acceptance of information technology (IT) is vital to address the problem of underutilization and leverage the benefits of IT investments, especially for more radical technologies. This study examines individual acceptance of biometric identification techniques in a voluntary environment, measuring the intention to accept and further recommend the technology resulting from a carefully selected set of variables. Drawing on elements of technology acceptance model (TAM), diffusion of innovations (DOI) and unified theory of acceptance and use of technology (UTAUT) along with the trust-privacy research field, we propose an integrated approach that is both theoretically and empirically grounded. By testing some of the most relevant and well-tested elements from previous models along with new antecedents to biometric system adoption, this study produces results which are both sturdy and innovative. We first confirm the influence of renowned technology acceptance variables such as compatibility, perceived usefulness, facilitating conditions on biometrics systems acceptance and further recommendation. Second, prior factors such as concern for privacy, trust in the technology, and innovativeness also prove to have an influence. Third, unless innovativeness, the most important drivers to explain biometrics acceptance and recommendation are not from the traditional adoption models (TAM, DOI, and UTAUT) but from the trust and privacy literature (trust in technology and perceived risk). We propose an integrated approach of end-user acceptance of biometric system.The model is based on TAM, DOI and UTAUT along with trust-privacy literature.Technology adoption theory is extended by adding the potential recommendation power.Renowned technology acceptance variables influence acceptance and recommendation.Key drivers of acceptance and recommendation come from the trust-privacy literature.

Subfields: Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/f81d71a9f2a326e7cb92c77f58b92f9ede6ddd3d

How researchers see this paper

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

In Information Systems and Management

1. Puneet Kaur
2. Amandeep Dhir
3. Tripti Singh
4. Rahul Bodhi
5. Mohammad Almotairi

In Sociology and Political Science

1. Amandeep Dhir
2. Puneet Kaur
3. Tripti Singh
4. Rahul Bodhi
5. Mohammad Almotairi
6. Shalini Talwar

Citations in the corpus

• Why do people use and recommend m-wallets? (2020, positive): “First, previous literature confirmed a positive association, and second, researchers have argued that a high usage intention toward a technology is likely to increase users ’ ITR it (for example, Miltgen et al., 2013; Oliveira et al., 2016).”