Table 1 Brief summary of previous relevant studies
From: Net valence analysis of iris recognition technology-based FinTech
Source | Cultural context | Biometric technology in use | Use case | Benefits/gains | Risks/concerns | Methodology/approach | Underpinning theory |
|---|---|---|---|---|---|---|---|
James et al. (2006) | USA | Various biometric technologies (retinal scanners, fingerprint scanners, hand geometry scanners, signature biometrics, and facial recognition devices) | Different use cases (physical access, virtual access, e-commerce, and covert surveillance) | Perceived need for security Perceived need for privacy | Perceived physical invasiveness | Quantitative research design using a survey instrument | Extended TAM model |
Byun and Byun (2013) | USA | Fingerprints | Financial Transactions via ATMs | Increased security Cognitive effort saving Time convenience Perceived enjoyment | Performance risk Information privacy risk Physical risk | Quantitative research design using a survey instrument | Value-based adoption/Net Valence Framework (NVF) |
Breward et al. (2017) | USA | Fingerprints | Financial Transactions via ATMs | Account security Convenience | Privacy concerns Security concerns | Mixed Methodology (qualitative and quantitative) | Cognitive-rational consumer decision-making paradigm/ Net Valence Framework (NVF) |
Moriuchi (2020) | USA | Facial recognition | Payment method either through laptop cameras or smartphone cameras | Performance expectancy Effort expectancy | Risk | Two quantitative studies—study 1: general validation of the model; study 2: Validate the model using two distinct modes of customer shopping (online versus in-person) | UTAUT and ToM |
Wang (2021) | N/A | Face recognition, fingerprint recognition, iris recognition, and voice recognition | Biometric identification in FinTech Applications | Perceived ease of use Perceived usefulness Perceived trust Perceived privacy | N/A | Analytic Hierarchy Process (AHP) | Extended TAM model |
Liu et al. (2021) | China | Facial recognition | Mobile payment services | Perceived benefits | Perceived privacy risk | Quantitative research design using a survey instrument | Privacy calculus |
Liébana Cabanilla et al. (2022) | Spain | Iris recognition technology | Payment method utilizing mobile phones | Convenience Effort expectancy | N/A | Quantitative research design using a survey instrument | Stimulus- Organism- Response (S–O-R) framework |
Palash et al. (2022) | China | Facial recognition | Payment method via smartphones or point of sale terminals | Relative advantage Initial trustPerceived playfulness Need for uniqueness | Perceived risk Technophobia Perceived complexity | Quantitative research design using a survey instrument | Net Valence Framework (NVF) |
Lee and Pan (2022) | China | Facial recognition | Mobile payment services | N/A | System feature overload Information overload Technological uncertainty Perceived risk Privacy concern | Quantitative research design using a survey instrument | Stressor–Strain–Outcome (S–S–O) framework |