Understanding algorithmic transference in digital governance: a social identity theory perspective on public trust and policy responses

Purpose As digital government initiatives advance, algorithmic technologies are increasingly integrated into public governance. However, the phenomenon of “algorithmic transference” introduces complex challenges for digital governance, including erosion of public trust, service engagement decline and broader governance doubts. Drawing on social identity theory and the concept of outgroup homogeneity bias, this study aims to examine the mechanisms underlying algorithmic transference (AT) and their implications for public administration. By synthesizing empirical evidence and existing literature, the authors develop a three-dimensional governance framework – prevention, intervention and repair – aimed at reconciling technological efficiency with public trust. This study further extends the application of social psychological theory to digital government governance, highlighting the critical role of cognitive, emotional and social factors in shaping citizen responses to algorithmic systems. Design/methodology/approach The study uses a mixed-method approach. It combines theoretical analysis based on social identity theory and outgroup homogeneity bias with 13 preregistered experiments. Samples from various platforms cover different groups. Statistical methods like t-tests and structural equation models are used to explore AT. Findings The research finds that the public classifies artificial intelligence (AI) systems as outgroups and generalizes single algorithm errors. This causes trust erosion, service suppression and governance doubt. Group cognition, technical experience and human supervision play important roles and a three-dimensional governance framework is proposed. Research limitations/implications Limitations include that experimental data mainly come from American samples, and people’s perception of algorithm homogeneity may not match reality. Implications suggest the need for large-scale localization verification, exploring related factors and better understanding AI knowledge’s regulatory role in AT. Practical implications For digital government, it implies constructing interpretable algorithm governance systems, enhancing public participation and setting manual intervention thresholds. This can mitigate AT, improve public trust and optimize algorithm applications, ensuring the healthy development of digital governance. Social implications AT affects social stability, as it undermines public trust in government services and governance. By understanding and addressing it, society can avoid the decline of public service acceptance, maintain harmonious government-public relations and promote positive social development. Originality/value This study is original in using social identity theory to explain AT in digital government. It uncovers the mechanism and proposes a three-dimensional governance framework, which expands the perspective of social psychology in digital government research and offers practical guidance for governance.