The utilization of user privacy data in the health data tracking system (HDTS) offers numerous benefits for businesses and public services, contingent upon users’ privacy sharing intentions. However, previous research neglected users’ preferences for public services and focused mainly on personalized services. Additionally, traditional privacy calculus theory has a limited focus on data security, leaving gaps in understanding individual and societal aspects. This study aims to fill these gaps by examining the influence of risk perception and factors like potential loss expectations, perceived personalized service benefits, group value identification, perceived public service utility, and perceived privacy on privacy sharing intentions in the context of personalized and public services. The results indicate a positive relationship between individual privacy protection perception and data sharing intention, as well as a positive relationship between group value identification and perceived public service utility with individuals’ privacy sharing intentions. Moreover, this research uncovers the moderating effect of information type sensitivity on the impact of perceived privacy and perceived public service utility on privacy sharing intentions, while there is no moderating effect of information type sensitivity on the relationship between group value identification and privacy sharing intentions. We recommend improving individual privacy education, ensuring data use transparency, and fostering identification with common group values to increase users’ privacy sharing intentions.

In today’s increasingly competitive globalization, innovation is crucial to technological development, and original innovations have become the high horse in the fight for market dominance by enterprises and governments. However, extracting original innovative technologies from patent data faces challenges such as anomalous data and lengthy analysis cycles, making it difficult for traditional models to achieve high-precision identification. Therefore, we propose a Multi-Dimensional Robust Stacking (MDRS) model to deeply analyze patent data, extract leading indicators, and accurately identify cutting-edge technologies. The MDRS model is divided into four stages: single indicator construction, robust indicator mining, hyper-robust indicator construction, and the pioneering technology analysis phase. Based on this model, we construct a technological development matrix to analyze core 3D-printing technologies across the industry chain. The results show that the MDRS model significantly enhances the accuracy and robustness of technology forecasting, elucidates the mechanisms of technological leadership across different stages and application scenarios, and provides new methods for quantitative analysis of technological trends. This enhances the accuracy and robustness of traditional patent data analysis, aiding governments and enterprises in optimizing resource allocation and improving market competitiveness.

Financial fraud, as a salient manifestation of corporate governance failure, erodes investor confidence and threatens the long-term sustainability of capital markets. This study aims to develop and validate SFG-2DCNN, a multimodal deep learning framework that adopts a configurational perspective to diagnose financial fraud under class-imbalanced conditions and support sustainable corporate governance. Conventional diagnostic approaches struggle to capture the higher-order interactions within covert fraud patterns due to scarce fraud samples and complex multimodal signals. To overcome these limitations, SFG-2DCNN adopts a systematic two-stage mechanism. First, to ensure a logically consistent data foundation, the framework builds a domain-adaptive generative model (SMOTE-FraudGAN) that enforces joint distribution alignment to fundamentally resolve the issue of economic logic coherence in synthetic samples. Subsequently, the framework pioneers a feature topology mapping strategy that spatializes extracted multimodal covert signals, including non-traditional indicators (e.g., Total Liabilities/Operating Costs) and affective dissonance in managerial narratives, into an ordered two-dimensional matrix, enabling a two-dimensional Convolutional Neural Network (2D-CNN) to efficiently identify potential governance failure patterns through deep spatial fusion. Experiments on Chinese A-share listed firms demonstrate that SFG-2DCNN achieves an F1-score of 0.917 and an AUC of 0.942, significantly outperforming baseline models. By advancing the analytical paradigm from isolated variable assessment to holistic multimodal configurational analysis, this research provides a high-fidelity tool for strengthening sustainable corporate governance and market transparency.