Towards a Development of a Learners’ Ratified Acceptance of Multi-biometrics Intentions Model (RAMIM): Initial Empirica

1. Towards a Development of a Learners’ Ratified Acceptance of Multi-biometrics Intentions Model (RAMIM): Initial Empirical Results By: Yair LevyNova Southeastern University and Michelle M. Ramim Nova Southeastern University

2. Outline • Introduction • Statement of the problem • Objective • Theoretical background • Proposed initial model and propositions • Methodology and sample • Data analysis and results • Implications • Proposed revised model

3. Introduction • Growing use of organizational electronic records • Growing use e-learning • Valid authentication of users is a perpetual challenge amongst organizations • Authentication should expand beyond the limited username/password verification • Reduce misuse and unethical conduct * Source: United States Department of Education, National Center of Educational Statistics (NCES) (2005)

4. Introduction (Cont.) • The use of information security related devices in highly secured environments: • Financial institutions • Government agencies • Military facilities • Expanded utility for management • Employee attendance • Track employee daily activities

5. Statement of the problem • A growing concern • Invasiveness of biometric devices • Effective safeguarding of biometric information • Potential misuse of information captured by biometric devices • Increase of identity fraud crimes • Compromising of existing identification methodologies

6. Statement of the problem (Cont.) • Context of e-learning systems • Issues with academic misconduct • A new trend • Integrate more than a single biometric method of authentication • Increase accuracy • Transparency • Reliability beyond the initial point of entry • Monitoring real-time users’ activity in a non-intrusive manner

7. Objectives • Multi-biometric authentication method • Initial empirical results • Develop and validate a learners’ Ratified Acceptance of Multi-biometrics Intentions Model (RAMIM). • The multi-biometric authentication • Two devices: fingerprint scanner and Web-cam head geometry scanner

8. Theoretical Background • Authentication Theory • Authentication is the process whereby the system verifies the user’s identity as declared (Liebl, 1993). • Two principal authentication elements: • Identification - the user declares their identity • Verification - the identity is validated • There are several established authentication protocols (password authentication protocol (PAP), encryption, etc.)

9. Theoretical Background (Cont.) • User authentication: (Furnell et al., 2000) • Something the user knows (e.g. password or personal identification number (PIN)) • Something the user has (e.g. a card, token, etc) • Something the user is (e.g. a biometric characteristic) • Passwords are the most common authentication method (Oorschot & Thorpe, 2008; Rodwell, Furnell, & Reynolds, 2007) • Passwords tend to be undermined by users • Promote additional authentication methods • Physiological and behavioral biometrics • Multiple means of authentications

10. Theoretical Background (Cont.) • Biometrics - a process that examines unique biological characteristics of humans (James et al., 2006) • DNA, voice, retinal and iris, fingerprints, facial images, hand prints, or other unique biological characteristics • Biometric technologies operate by scanning a biological characteristic and matching it with the stored data • Behavioral characteristics - keystrokes dynamics, and mouse clicks (Sasamoto et al., 2008; Pusara & Brodley, 2004) • Further work is needed to commercialize such methods to large scale systems.

11. Multi-Biometrics • A multilateral model scheme that utilizes a mixture of two or more biological and/or physiological characteristics that the end-user has in a secured environment • Multi-biometrics can enable ongoing non-intrusive verification not only at the point of entry but also throughout the logged-in session. • Use in various corporate and e-learning situations. • The context outlined here is during e-exams.

12. Users’ Acceptance of Technology • Challenges in experiments with biometrics • Before moving to experiments with actual systems, seek better understanding of the acceptance of such systems by the users (Venkatesh et al., 2003) • Users’ perceived usefulness and ease-of-use are strong predictors of technology acceptance (Davis, 1989; Simon & Paper, 2007; Venkatesh et al., 2003; Viswanath & Hillol, 2008). • Theory of Reasoned Action (TRA) (Ajzen & Fishbein, 1980). • Intention to use a technology is a significant predictor of actual use (Bagozzi, 2007; Gefen et al., 2003)

13. Ethical Decision Making • Users’ ethical decisions making • Ethical issues with the use of e-learning systems have been a growing concern • Users who are more ethically driven will be open to accept multi-biometrics • Learners who are more ethically driven will be more inclined to use multi-biometrics during e-exams.

14. Familiarity with Code of Conduct • Users’ familiarity with code of conduct (Chonko, 2003; Harris, 2002). • Users who are more familiar with the organization’s code of conduct will be open to accept security devices as they understand the need to maintain proper conduct (Wotruba et al., 2001).

15. Proposed Initial Model

16. Propositions P1: Learners’ familiarity with university’s code of conduct will have a significant positive contribution to their intention to use multi-biometrics for authentication during e-learning exams. P2: Learners’ perceived ease-of-use will have a significant positive contribution to their intention to use multi-biometrics for authentication during e-learning exams. P3: Learners’ perceived usefulness will have a significant positive contribution to their intention to use multi-biometrics for authentication during e-learning exams. P4: Learners’ ethical decision making will have a significant positive contribution to their intention to use multi-biometrics for authentication during e-learning exams.

17. Methodology • Use of validated measures from prior literature • Perceived ease-of-use, perceived usefulness, and intention to use were adopted (Gefen et al., 2003; James et al., 2006). • Familiarity with the code of conduct and ethical decision making (Ramim, 2007). • All items used 5-point Likert-type scale. • Survey items were specifically in the context of intention to use multi-biometrics for authentication during e-learning exams. • Used Ordinal Logistic Regression (OLR) for initial data. Plan to use Structural Equations Modeling (SEM) in the future.

18. Sample • The sample: 97 entry level managers from service oriented organization and government agencies in USA who attended e-learning Masters of Business Administration (MBA) and Masters of Public Administration (MPA) courses. • Gender distribution: 61% males and 39% females • About 60% between the ages of 19-34. • Majority (above 81%) had previously taken two or more e-learning courses

19. Data Analysis and Results Overall Ordinal Logistic Regression Model Fit (N= 97) Ordinal Logistic Regression Parameter Estimates Results (N= 97)

20. Data Analysis and Results (Cont.)

21. Proposed Revised Model

22. Thank you! • Questions?

23. Contact Information - Michelle Michelle M. Ramim, Ph.D. Part-time ProfessorNova Southeastern University H. Wayne Huizenga School of Business and Entrepreneurship The DeSantis Building 3301 College Avenue Ft. Lauderdale, FL 33314 E-mail: ramim@nova.edu Site: http://www.nova.edu/~ramim/

24. Contact Information - Yair Yair Levy, Ph.D.Associate ProfessorNova Southeastern UniversityGraduate School of Computer and Information SciencesThe DeSantis Building - Room 40583301 College AvenueFort Lauderdale, FL 33314Tel.: 954-262-2006        Fax: 954-262-3915E-mail: levyy@nova.edu Site: http://scis.nova.edu/~levyy/