User Acceptance Factors Related to Biometric Recognition Technologies of Examination Attendance in Higher Education: TAM Model

Meennapa Rukhiran, Sethapong Wong-In and Paniti Netinant ()
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Meennapa Rukhiran: Faculty of Social Technology, Rajamangala University of Technology Tawan-OK, Chanthaburi 22210, Thailand
Sethapong Wong-In: College of Digital Innovation Technology (DIT), Rangsit University, Pathum Thani 12000, Thailand
Paniti Netinant: College of Digital Innovation Technology (DIT), Rangsit University, Pathum Thani 12000, Thailand

Sustainability, 2023, vol. 15, issue 4, 1-18

Abstract: Identity recognition is influenced at all educational levels by biometric technology. The invention of facial recognition technology has added new efficiencies to the traditional method of tracking student examination attendance. This study aims to determine whether biometric recognition technologies could be utilized to enhance undergraduate examination attendance systems. The study examined the perceptions of first-year college students regarding the system’s use of face recognition technologies. Based on the proposed framework, experimental results were obtained by developing and deploying unimodal and multimodal face recognition methods. Using a quasi-practical design with sample groups, undergraduate students’ perceptions of traditional and biometric examination attendance were compared. Adopting the Theory for Reasoned Action and the Technology Acceptance Model, a questionnaire was distributed and analyzed to determine perception factors. The findings reveal that perceived ease of use, and trust and security significantly impact perceived usefulness. It was discovered that perceived usefulness significantly affects behavioral intention to use a system. According to the research findings, multimodal biometric recognition receives significantly more positive ratings than unimodal biometric recognition. This study proposes that universities utilize biometric technology, particularly facial recognition, to assess users’ acceptance of the system.

Keywords: biometric; examination attendance; face recognition; user perception; framework (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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