Determinants of end-user acceptance of biometrics

TLDR: 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.

Abstract: 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.

Figures

Table 2. Matrix of correlation constructs and the square root of AVE (in bold)

Table 1. Factor loadings, Average Variance Extracted, Composite Reliability and Alphas

Figure 1. Proposed theoretical framework

Table 3. Results for the structural model and hypothesis testing

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Determinants of end-user acceptance of biometrics:
Integrating the “Big 3” of technology acceptance with
privacy context
Caroline Lancelot Miltgen, Ales Popovic, Tiago Oliveira
To cite this version:
Caroline Lancelot Miltgen, Ales Popovic, Tiago Oliveira. Determinants of end-user acceptance of
biometrics: Integrating the “Big 3” of technology acceptance with privacy context. Decision Support
Systems, Elsevier, 2013, 56, pp.103-114. �10.1016/j.dss.2013.05.010�. �hal-01116141�

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Determinants of end-user acceptance of Biometrics:
Integrating the “Big 3” of technology acceptance with privacy context
Accepted for publication in
Decision Support Systems
Caroline Lancelot Miltgen (First and Corresponding Author)
Angers University
GRANEM Research Center
UFR de Droit, d’Economie et de Gestion
13, Allée François Mitterrand
BP 13633
49036 Angers Cedex 01
FRANCE
Tel: 00 33 665 012 704
Fax: 00 33 241 962 196
Email: caroline.miltgen@univ-angers.fr
Dr. Aleš Popovič
Assistant Professor of Information Management
Faculty of Economics, University of Ljubljana, Slovenia
& ISEGI, Universidade Nova de Lisboa, Lisboa, Portugal
Dr. Tiago Oliveira
Assistant Professor of Information Management
ISEGI, Universidade Nova de Lisboa, Lisboa, Portugal

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Highlights
- We propose an integrated approach of end-user acceptance of biometric system
- The model is based on TAM, DOI and UTAUT along with privacy-trust 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
Funding
This study was funded by the European Commission IPTS (Institute for Prospective
Technological Studies) Joint Research Centre (EC JRC IPTS Contract No. 150876-2007
F1ED-FR).
The authors thank Ioannis Maghiros, Wainer Lusoli, and Margherita Bacigalupo from the
European Commission IPTS Joint Research Centre for their support and confidence.

Caroline Lancelot Miltgen et al. – DSS 2013
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Determinants of end-user acceptance of Biometrics:
Integrating the “Big 3” of technology acceptance with privacy context
Abstract
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).
Keywords: Biometric system, technology acceptance, privacy, risk, trust, personal data

Caroline Lancelot Miltgen et al. – DSS 2013
4
Determinants of end-user acceptance of Biometrics:
Integrating the “Big 3” of Technology Acceptance with privacy context
1. Introduction
Information technology (IT) acceptance and use has been one of the priority issues of information
systems (IS) research and practice since the late 1980 [84, 88]. IT is becoming increasingly complex
and crucial for business operations, thus making the issue of acceptance an important challenge in IT
implementation. Despite impressive advances in technology capabilities, the problem of
underutilization of IT, especially for more radical technologies, is still present [3]. To leverage the
benefits of IT investments, firms increasingly show interest in factors facilitating the implementation
success, in particular factors affecting technology acceptance. Driven by extensive research to
understand IT acceptance [87, 88] therefore, different models and theories that incorporate a variety of
social, behavioral and other control factors were developed in the past to explain IT usage [i.e. 21, 84,
87, 88].
In personal identification and authentication field, the application of biometric technologies is
increasingly apparent [75]. Practical evidence shows that augmented interest in these technologies is
fuelled by anticipated decrease of technology costs, improved technical quality of the systems and
socio-political pressures for better security-related controls [71]. Nevertheless, an important issue
stemming the deployment of biometrics or leading to their underutilization is user resistance to utilize
such pervasive technology [71]. Most users feel fearful, hesitant, or uncomfortable around these
systems especially because they perceive them as means for potential infringements into their privacy
[75]. Such users’ feelings and perceptions increase the risk of rejection and can lead to biometrics
implementation failure [71]. The need to inform biometric technologies implementation with various
factors affecting biometrics acceptance is, therefore, of crucial importance [63, 71].
To date, only a few authors have discussed biometric systems from a consumer acceptance
perspective [63]. Yet, the perception and behavioral response of end users is an important

Frequently asked questions

Q1. What have the authors contributed in "Determinants of end-user acceptance of biometrics: integrating the ``big 3'' of technology acceptance with privacy context" ?

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, the authors propose an integrated approach that is both theoretically and empirically grounded. By testing some of the most relevant and welltested elements from previous models along with new antecedents to biometric system adoption, this study produces results which are both sturdy and innovative. The authors first confirm the influence of renowned technology acceptance variables such as compatibility, perceived usefulness, facilitating conditions on biometrics systems acceptance and further recommendation. 

Q2. What future works have the authors mentioned in the paper "Determinants of end-user acceptance of biometrics: integrating the ``big 3'' of technology acceptance with privacy context" ?

This research has its own limitations that should encourage further research in this area. There are other limitations which could also form the basis for future work in this area to transcend the work conducted here. Moreover, there are many exogenous factors that might influence responses which should be considered and explored in future research. As a complementary view, it would also be interesting to study how ( instead of why ) they adopt such kinds of technologies, as the agency of the end-user does not end with the decision to adopt or reject the technology, but continues to actively shape how the technology is used, in what contexts and for what purposes, which may be rather different from the uses, contexts and purposes envisaged by the originators of the technology. 

Q3. What is the effect of trust on the consumer's intentions to accept a biometric system?

The influence of trustTrust is one of the most effective tools for reducing uncertainty [6], the sense of risk and generating a sense of safety [69] and consumer trust is believed to play a pivotal role in consumers’ intentions to accept a biometric system [9] by reducing the perceived risks [45] and uncertainty associated with the acceptance [69]. 

Q4. What role should facilitating conditions play in biometrics intention to use?

Adequate facilitating conditions (continuous training and technical support to users) should also play an important role in biometrics intention to use. 

Q5. What is the effect of trust on the consumer's intention to accept a biometric system?

As trust increases, consumers are likely to perceive less risk than if trust were absent; the effect of trust on the consumer's intention to accept biometric technologies is thus mediated by risk as already suggested by [46]. 

Q6. Why is trust an important factor in biometrics acceptance?

Both because of the dominance of trust in the existing literature, and because biometric system demands the cooperation of individuals with little ability to monitor or control those operating it, trust is an important factor when considering biometric technology acceptance. 

Q7. What is the second area of practical implications of biometric systems?

The second area of practical implications deals with antecedent factors of biometric systems acceptance and comprises factors such as perceived ease of use, perceived usefulness, compatibility and perceived risks. 

Q8. What is the importance of a study aimed at identifying non-technical issues?

A study aiming to identify relevant non-technical issues such as the perceptions of future end-users’ fears and anticipations is likely to be a prerequisite for the development of a strategy to support the acceptance of such a pervasive innovation. 

Q9. What criteria were used to discern possible participations in line with demographic data?

In particular, the two criteria that were used to discern possible participations in line with demographic data were: gender (male/female) and age (split into two groups 15-18 year olds and 19-25 year olds). 

Q10. What is the significance of compatibility in predicting acceptance of a biometric system?

The research of Koenig-Lewis et al. [49] acknowledges that compatibility not only had a strong direct effect but was also identified as an important antecedent for perceived ease of use and perceived usefulness. 

Q11. What is the causal link between biometrics and the intention of the user?

The authors hypothesize, alongside some of their contemporaries [e.g. 17], a causal link exists between facilitating conditions and users intentions, so that greater facilitating conditions will increase the likelihood to accept biometrics. 

Q12. What is the relationship between social influence and acceptance of biometrics?

In their study, the authors will also consider social influence to be related to notions of peer pressure in the context of the biometric technology acceptance.