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Artificial intelligence (AI) fosters economic growth and opens up new directions for innovation. However, the diffusion of AI proceeds very slowly and falls behind, especially in comparison to other technologies. An important path leading to better adoption rates identified is trust-building. Particular requirements for trust and their relevance for AI adoption are currently insufficiently addressed.

To close this gap, the authors follow a qualitative approach, drawing on the extended valence framework by assessing semi-structured interviews with experts from various companies.

The authors contribute to research by finding several subcategories for the three main trust dimensions ability, integrity and benevolence, thereby revealing fundamental differences for building trust in AI compared to more traditional technologies. In particular, the authors find access to knowledge, transparency, explainability, certification, as well as self-imposed standards and guidelines to be important factors that increase overall trust in AI.

The results show how the valence framework needs to be elaborated to become applicable to the AI context and provide further structural orientation to better understand AI adoption intentions. This may help decision-makers to identify further requirements or strategies to increase overall trust in their AI products, creating competitive and operational advantage.

Current developments in the FinTech payment industry have shown a rapid revolution in Industry 4.0, and understanding the factors affecting individual acceptance of facial recognition payment (FRP) is crucial. Hence, this study aims to evaluate the benefits and risks of FRP system adoption in Malaysia.

The perceived risks and benefits framework is adopted as the foundation in this study to examine the various risks and benefits that users perceive, along with the trust factor, to study the relationships between these variables. Data were collected via an online questionnaire, and the hypotheses were tested using Partial Least Squares analysis on 277 responses.

The results revealed that perceived risk is a significant predictor of users' intention to use the FRP system. Privacy risk and financial risk significantly influence perceived risks, while security risk does not. Although convenience, perceived ease of use and perceived trust positively influence perceived benefits, perceived benefits do not significantly influence adoption intention. Moreover, perceived trust negatively affects perceived risks while positively affecting both perceived benefits and adoption intention. Additionally, personal innovativeness moderates the relationship between perceived risks and the intention to use the FRP system.

This study helps policymakers and service providers understand individuals’ concerns and expectations regarding FRP systems. It aids practitioners in developing strategies to build trust, address innovativeness differences and mitigate risks, serving as a roadmap for integrating these systems into Malaysia's financial landscape.

This study distinguishes itself from prior research by evaluating FRP system adoption in Malaysia through the lens of perceived risks and benefits framework. It also explores personal innovativeness as a moderator, examining its impact on the relationship between usage intention and perceived risks and benefits. Additionally, it highlights perceived trust as a crucial factor influencing individuals' intention to adopt FRPs.