IoT biometric technology adoption in higher education: a TRAM-based analysis of users and non-users in Bangladesh

Incorporating IoT biometric in higher education is progressively being studied to improve campus security, modernize administrative activities, and boost student services. This research sheds light on understanding students' technology readiness and perceptions of IoT biometrics in Bangladesh's higher education institutions utilizing the Technology Readiness and Acceptance Model (TRAM). 216 data were elicited through purposive sampling and evaluated by Structural Equation Modelling (SEM) in SmartPLS 4.0. The study outcomes disclose that optimism and innovativeness as positive technology readiness (TR) significantly affect both perceived usefulness (PU) and perceived ease of use (PEOU). However, discomfort, as one of the negative TR, posits negative impact on PU. Additionally, PEOU, PU, and behavioral intentions (BI) in embracing IoT biometric technology have found significant positive relationships. A comparative analysis between users and non-users implies that users tend to be more innovative and express more significant concerns about security and comfort. On the other hand, non-users show greater optimism about new technology, underscoring their advantages and convenience. Besides, a multi-group analysis underlines a significant difference in the association between PU and BI, with non-users exhibiting a more significant influence than users. As an early implication of TRAM in this context, this research provides substantial insights into the effect of technology readiness on behavioral intentions in adopting IoT biometric technology in higher education institutions of Bangladesh. The empirical evidence recommends practitioners and education stakeholders in Bangladesh stressing the diverse roles of positive and negative technology readiness, adoption intention, and psychological distinction between users and non-users in embracing IoT biometric technology.