User Behavior Assessment Towards Biometric Facial Recognition System: A SEM-Neural Network Approach

Abstract

A smart home is grounded on the sensors that endure automation, safety, and structural integration. The security mechanism in digital setup possesses vibrant prominence and the biometric facial recognition system is novel addition to accrue the smart home features. Understanding the implementation of such technology is the outcome of user behavior modeling. However, there is the paucity of empirical research that explains the role of cognitive, functional, and social aspects of end-user's acceptance behavior towards biometric facial recognition systems at homes. Therefore, a causal research survey was conducted to comprehend the behavioral intention towards the use of a biometric facial recognition system. Technology Acceptance Model (TAM) was implied with Perceived System Quality (PSQ) and Social Influence (SI) to hypothesize the conceptual framework. Data was collected from 475 respondents through online questionnaires. Structural Equation Modeling (SEM) and Artificial Neural Network (ANN) were employed to analyze the surveyed data. The results showed that all the variables of the proposed framework significantly affected the behavioral intention to use the system. The PSQ appeared as the noteworthy predictor towards biometric facial recognition system usability through regression and sensitivity analyses. A multi-analytical approach towards understanding the technology user behavior will support the efficient decision-making process in Human-centric computing.

A Scalable and Automated Framework for Tracking the Likely Adoption of Emerging Technologies

“…While a variety of sentiment analysis methods exist, in the work herein, Valence Aware Dictionary and Sentiment Reasoner (VADER) [38], a lexicon-based sentiment analysis tool, was employed. VADER not only aligns with other relevant studies in the field (e.g., [22][23][24][25]) and therefore ensures consistency and comparability with existing research but is also specifically tuned to classify sentiment expressed in social media language, such as the dataset collated in Section 3. VADER takes into account various features of social media language, such as the use of exclamation marks, capitalisation, degree modifiers, conjunctions, emojis, slang words, and acronyms, which can all impact the sentiment intensity and polarity of a tweet.…”

“…Efuwape et al [23] investigate the acceptance and adoption of digital collaborative tools for academic planning using a sentiment analysis of the responses gathered in a poll. Hizam et al [24] employ sentiment analysis to examine the correlations between numerous factors of technology adoption behaviour, such as perceived ease of use, perceived utility, and social impact. The research aims to understand the underlying variables driving Web 3.0 adoption and offers insights regarding how these factors influence users' decisions to accept or reject these emerging technologies by analysing user-generated content on social media sites.…”

Determinants of National Digital Identity Verification Platform Acceptance Among Young Investors in Malaysia

“…NDIV platform acceptance is the degree to which a person consents to the NDIV platform (Chong et al, 2021). There were several terms for the NDIV platform used in previous research studies, including digital identity (Rivera et al, 2017;Engeness, 2021;Korać et al, 2021;Madon and Schoemaker, 2021;Sule et al, 2021); privacy-preserving authentication technology (Harbach et al, 2013); digital identity system (Mir et al, 2021;; and biometric facial recognition system (Hizam et al, 2021). The different countries had different names for their digital identity verification platform.…”

Realizing environmental sustainability development through the efficiency of Web 3.0 on supply chain resilience

“…Moreover, besides enhancing information security, the consensus protocols of the decentralized mechanism also have a positive influence on participants' behavior and thinking, fostering cohesion, and centripetal force to address information security issues, including those caused by human error (Lacity & Lupien, 2022). Thus, firms perceive value in consensual thinking formation (Hizam et al, 2022) and believe it can facilitate trust building and promote green knowledge sharing.…”