Perceived Risk and Performance Expectancy Drive Neobanking Adoption

Why It Matters

Understanding the psychological drivers behind technology adoption is crucial for designing user-friendly and trustworthy digital services. By addressing user concerns about privacy and demonstrating clear performance advantages, designers can significantly influence the uptake of new financial technologies.

Key Finding

Users are more inclined to adopt neobanking services if they believe the service will perform as expected and if they feel their privacy and the service's reliability are not at significant risk.

Key Findings

• Behavioral intention positively influences neobanking adoption, usage, and recommendation.
• Performance expectancy (perceived benefits of using the service) is a key driver of adoption.
• Perceived privacy risk and perceived performance risk negatively influence adoption.

Research Evidence

Aim: To investigate the factors influencing the adoption, usage, and recommendation of neobanking services, specifically examining the roles of behavioural intentions, perceived risks, and performance expectations.

Method: Quantitative survey research using Structural Equation Modeling (SEM).

Procedure: A conceptual model integrating the UTAUT-3 framework with perceived risk constructs was proposed. Hypotheses were formulated and empirically tested using survey data collected from neobanking users.

Sample Size: 680 participants

Context: Financial technology (Fintech) adoption, specifically neobanking services in India.

Design Principle

Design for trust and perceived value to drive user adoption of digital services.

How to Apply

When designing new digital financial products, conduct user research to identify key performance benefits and potential risk perceptions. Use this information to shape the product's features, communication strategy, and security protocols.

Limitations

The study was cross-sectional and conducted during the COVID-19 pandemic, potentially limiting generalizability. Geographic scope was limited to Delhi NCR.

Student Guide (IB Design Technology)

Simple Explanation: People are more likely to use new online banks if they think the bank works well and is safe, especially regarding their personal information.

Why This Matters: This research shows that for new digital services, it's not just about how easy it is to use, but also about whether users believe it will work well and if they trust it with their information.

Critical Thinking: How might the cultural context of India, or specific demographic factors, further influence the perception of risk and performance expectancy in neobanking adoption?

IA-Ready Paragraph: Research into neobanking adoption highlights that user trust and perceived value are paramount. Studies integrating behavioral theories with risk perception, such as the work by Bhatnagr and Rajesh (2023), demonstrate that users are more likely to adopt new financial technologies when they perceive high performance expectancy and low risks related to privacy and service functionality. This underscores the importance of designing secure, transparent, and benefit-driven digital financial solutions.

Project Tips

• Clearly define the user group and the specific technology or service being investigated.
• Use established theoretical frameworks like TAM or UTAUT to structure your research.
• Consider incorporating perceived risk as a factor in technology adoption studies.

How to Use in IA

• Reference this study when discussing user adoption models and the importance of perceived risk in technology design.

Examiner Tips

• Ensure your research clearly links user perceptions (like risk and performance) to their behavioral intentions.

Independent Variable: ["Performance Expectancy","Effort Expectancy","Perceived Privacy Risk","Perceived Performance Risk"]

Dependent Variable: ["Behavioral Intention to Use","Neobanking Adoption","Recommendation"]

Controlled Variables: ["Demographics","Technological familiarity"]

Strengths

• Integrates multiple theoretical constructs (UTAUT-3 and perceived risk).
• Empirically validates the proposed model using a robust statistical method (PLS-SEM).

Critical Questions

• To what extent do these findings generalize to other types of digital services beyond neobanking?
• How do actual user experiences, rather than just perceptions, shape long-term adoption and loyalty?

Extended Essay Application

• Investigate the long-term impact of perceived risk on user retention and loyalty in digital service adoption.
• Explore how different risk mitigation strategies (e.g., two-factor authentication, clear data usage policies) affect user adoption rates.

Source

Neobanking adoption – An integrated UTAUT-3, perceived risk and recommendation model · South Asian Journal of Marketing · 2023 · 10.1108/sajm-06-2022-0040