Important literature on FinTech
| Focus | Study types | Source | Main findings |
|---|---|---|---|
| iGen Trust Perspective on FinTech | Empirical | Stewart and Jürjens (2018a, b) | The prospect of data security risk scares Gen Z into prioritising security when using FinTech |
| Case Study | Aseng, 2020; Kee et al. (2024a, b) | Security, usability and social influence are the key sources of trust and satisfaction in FinTech | |
| Descriptive | Memon et al. (2021), Anand and Sharma (2023) | Gen Z's FinTech adoption is initially low, it may increase with more financial literacy and awareness of the benefits of FinTech in their digital life | |
| Case Study | Vinichenko, Takigawa, Oseev, Rybakova, and Makushkin (2022) | iGen use AI-driven financial services with added worries of automation bias and transparency | |
| Descriptive | Sundararajan (2020), Yang (2025) | Gen Z values FinTech's speed but still trusts traditional banks more | |
| Report | Phuong et al. (2022a, b) | Social networks and peer influence drive their digital payment adoption | |
| Conceptual | Armansyah, Ardianto, and Rithmaya (2023), Adiputra and Nathaerwin (2024) | Gen Z's investment behavior in FinTech highly depends on trust | |
| Empirical | Alam et al. (2022), Dwi Fortuna and Sutrisno (2024) | Easier to use but concerned with data security | |
| Report | Acharya and Poudel (2023) | To build trust, financial literacy and transparency is what FinTech firms must market | |
| Conceptual | Syakinah (2024) | Ethical concerns, corporate responsibility and transparency shape Gen Z's trust in FinTech | |
| Dimensions | Empirical | Yousafzai et al. (2009a, b), Stewart and Jürjens (2018a, b), Aldboush and Ferdous (2023a, b, c), Pierson Martha (2023) | Security: The protection of financial transactions and personal data through encryption, authentication and fraud prevention |
| Privacy: The measure that confirms financial data of user is collected, stored and shared unambiguously and safely | |||
| Risk: The three major sources of risk in FinTech are perceived uncertainty of financial fraud, cyber threats and possible financial loss from FinTech usage | |||
| Descriptive | Pierson Martha (2023) | Transparency: FinTech providers are clear, open and provide fees, terms, conditions and data usage policies | |
| Systematic Review & Meta-Analysis | Zarifis and Cheng (2022), Aldboush and Ferdous (2023a, b, c), Margaroli (2023), Pierson Martha (2023) | User Experience: FinTech platforms should be easy, efficient and convenient to navigate and interact for User Experience | |
| Cross-Sectional | Hasan, Jalal Siam, and Haque (2023) | Customer Support: Availability and quality of support are provided to the users in case of query or issue | |
| Mixed-Method | Stewart and Jürjens (2018a, b), Pierson Martha (2023) | Awareness Campaign: The efforts for educating the users on the advantages and risks of FinTech technology and secure it | |
| Reports & Industry Studies | Zarifis and Cheng (2022) | Social Influence: The impact of peers, online communities and social networks on users' trust and adoption of FinTech | |
| Empirical | Stewart and Jürjens (2018a, b) | Attitude: Perception, propensity and preference of users for the FinTech services | |
| Trust and FinTech Adoption | Empirical | Zhang et al. (2023), Singh (2024) | Trust and adoption of FinTech services are highly influenced by considerations around data security and privacy, which results in encryption becoming a strong preference for data transfer as well as keeping the information transparent |
| Descriptive | Anand, Himani, Gautam, and Modi (2024), Acharya and Bhojak (2024) | The adoption of Fintech by Gen Z is primarily influenced by digital literacy, compatibility of technology and service and perceived benefits, but a lack of awareness inhibits their engagement | |
| Cross-Sectional | Arifin, Saputra, Puspitasari, and Bazen (2023), Subhani, Tahir, Naz, Nazir, and Chaudhry (2024) | Gen Z's acceptance of FinTech is influenced by social influence and technostress and moderated by their perceived ease of use and financial risk | |
| Mixed-Method | Grandhi, Wibowo, and Wells (2023), Srivastava, Mohta, and Shunmugasundaram (2024) | The effect of performance expectancy, effort expectancy and financial literacy on customer's behavioral intention to adopt FinTech is mediated by customer's satisfaction, with financial literacy moderating the effect | |
| Systematic Review | Firmansyah, Masri, Anshari, and Besar (2022), Maniam (2024) | Key determinants of the adoption of FinTech are trust, financial literacy and regulatory frameworks, while UTAUT and TAM are the main theoretical models underlying models finance the adoption | |
| Reports & Industry Studies | Kumar (2024), Al hazimeh et al. (2024) | Transparency, security and regulatory frameworks reinforce trust in FinTech and ultimately shapes long-term user engagement | |
| Methodological Approaches for scale development | Quantitative | Trinidad (2018) | Pre-Test and Post-Test measuring changes before and after the implementation of intervention; preserves internal validity. Statistical significance thresholds consist of p < 0.05 |
| Scale Development | Choi and You (2017) | EFA reduces multiple variables into their factors; it is CFA that confirms the factor structure. Good model is fit indices such as RMSEA <0.08 and CFI >0.9 | |
| Scale Validation | Lowe, Grumbein, and Raad (2011) | Psychometric properties analysis the Reliability. It is evaluated by the use of Cronbach's alpha (>0.7) and stability (>0.8) through test-retest reliability | |
| Quantitative | Ab Hamid et al. (2017) | Fornell-Larcker Criterion evaluates discriminant validity; AVE should be > 0.5; square root of its value should be greater than the correlation with other variables | |
| Qualitative | Hung, Liao, & Wang (2021) | Penel discussion mainly conduct for the qualitative assessment of the scale content to ensure the face validity | |
| Qualitative using Delphi method | Zeng et al. (2023) | Expert opinion refines new measurement tools through multiple rounds to ensure content validity and construct clarity | |
| Scale Development | Ab Hamid et al. (2017) | Validity (discriminant and convergent) Convergent validity: AVE >0.5; Discriminant validity: Square root of AVE > inter-variable correlations | |
| Quantitative | Cecen (2023) | A Pearson's Correlation analysis r above 0.5 implies strong correlation, while it indicates a high association of variables for numbers near 1 | |
| Qualitative | Liao, Huang, and Wang (2022) | Content validation by a subject expert, and they will identify relevant, clear and necessary items and the average content validity index (CVI) should be > 0.8 |
| Focus | Study types | Source | Main findings |
|---|---|---|---|
| iGen Trust Perspective on FinTech | Empirical | The prospect of data security risk scares Gen Z into prioritising security when using FinTech | |
| Case Study | Security, usability and social influence are the key sources of trust and satisfaction in FinTech | ||
| Descriptive | Gen Z's FinTech adoption is initially low, it may increase with more financial literacy and awareness of the benefits of FinTech in their digital life | ||
| Case Study | iGen use AI-driven financial services with added worries of automation bias and transparency | ||
| Descriptive | Gen Z values FinTech's speed but still trusts traditional banks more | ||
| Report | Social networks and peer influence drive their digital payment adoption | ||
| Conceptual | Gen Z's investment behavior in FinTech highly depends on trust | ||
| Empirical | Easier to use but concerned with data security | ||
| Report | To build trust, financial literacy and transparency is what FinTech firms must market | ||
| Conceptual | Ethical concerns, corporate responsibility and transparency shape Gen Z's trust in FinTech | ||
| Dimensions | Empirical | ||
| Descriptive | |||
| Systematic Review & Meta-Analysis | |||
| Cross-Sectional | |||
| Mixed-Method | |||
| Reports & Industry Studies | |||
| Empirical | |||
| Trust and FinTech Adoption | Empirical | Trust and adoption of FinTech services are highly influenced by considerations around data security and privacy, which results in encryption becoming a strong preference for data transfer as well as keeping the information transparent | |
| Descriptive | The adoption of Fintech by Gen Z is primarily influenced by digital literacy, compatibility of technology and service and perceived benefits, but a lack of awareness inhibits their engagement | ||
| Cross-Sectional | Gen Z's acceptance of FinTech is influenced by social influence and technostress and moderated by their perceived ease of use and financial risk | ||
| Mixed-Method | The effect of performance expectancy, effort expectancy and financial literacy on customer's behavioral intention to adopt FinTech is mediated by customer's satisfaction, with financial literacy moderating the effect | ||
| Systematic Review | Key determinants of the adoption of FinTech are trust, financial literacy and regulatory frameworks, while UTAUT and TAM are the main theoretical models underlying models finance the adoption | ||
| Reports & Industry Studies | Transparency, security and regulatory frameworks reinforce trust in FinTech and ultimately shapes long-term user engagement | ||
| Methodological Approaches for scale development | Quantitative | Pre-Test and Post-Test measuring changes before and after the implementation of intervention; preserves internal validity. Statistical significance thresholds consist of | |
| Scale Development | EFA reduces multiple variables into their factors; it is CFA that confirms the factor structure. Good model is fit indices such as RMSEA <0.08 and CFI >0.9 | ||
| Scale Validation | Psychometric properties analysis the Reliability. It is evaluated by the use of Cronbach's alpha (>0.7) and stability (>0.8) through test-retest reliability | ||
| Quantitative | Fornell-Larcker Criterion evaluates discriminant validity; AVE should be > 0.5; square root of its value should be greater than the correlation with other variables | ||
| Qualitative | Penel discussion mainly conduct for the qualitative assessment of the scale content to ensure the face validity | ||
| Qualitative using Delphi method | Expert opinion refines new measurement tools through multiple rounds to ensure content validity and construct clarity | ||
| Scale Development | Validity (discriminant and convergent) Convergent validity: AVE >0.5; Discriminant validity: Square root of AVE > inter-variable correlations | ||
| Quantitative | A Pearson's Correlation analysis r above 0.5 implies strong correlation, while it indicates a high association of variables for numbers near 1 | ||
| Qualitative | Content validation by a subject expert, and they will identify relevant, clear and necessary items and the average content validity index (CVI) should be > 0.8 |
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