The study examines the determinants of users’ removal behavior of mobile apps from an integrated perspective of the theory of reasoned action, innovation diffusion theory, technology acceptance model, and perceived risks. The data collected from a sample of 275 valid respondents were used to validate the research model and hypotheses using the partial least squares structural equation modeling (PLS-SEM). The results indicate that compatibility and perceived ease of use significantly affect removal attitude; removal attitude and performance risk significantly affect removal intention, and removal intention significantly affects removal behavior. This study is a pioneering effort to investigate the determinants of users’ removal behavior of mobile apps. The findings of this study provide several important theoretical and practical implications for how to promote users’ continuous mobile app usage.

The rise of 6G networks brings ultra-fast communication and broad connectivity, enabling intelligent applications such as IoT, smart cities, and autonomous vehicles. However, training AI models in these environments often faces privacy concerns and limited labeled data. Federated Learning (FL) provides a privacy-preserving solution by allowing clients to collaboratively train models without sharing raw data. Yet, FL still struggles with the scarcity of labeled data due to privacy constraints. Semi-Supervised Learning (SSL) can alleviate this issue by leveraging both labeled and unlabeled data. While combining SSL and FL (FSSL) offers promise, it also introduces new challenges, such as confirmation bias and degraded performance under non-IID data distributions. Most existing FSSL methods assume uniform labeling capabilities across clients, which is rarely the case in practice. This leads to a new challenge only occur in FSSL called annotation heterogeneity, where some clients have many labeled samples while others have few or none. When combined with data heterogeneity, this dual-data heterogeneity (data and annotation heterogeneity) severely affects global model performance. Therefore, FSSL must learn under label scarcity while preserving privacy, remain stable when non-IID data is compounded by annotation heterogeneity, and stay communication-efficient for mobile deployment. In this work, we propose Federated Fisher Focus Filtering (FedF3), an enhanced framework built upon our previous SynFMPL method, to address the combination effect of dual-data heterogeneity. FedF3 introduces a two-stage strategy, Adaptive Loss Filtering stabilizes early training by suppressing unreliable contributions under dual heterogeneity, and Fisher Focus Selection preserves accuracy with Fisher-guided sparsity to meet communication budgets. Our method demonstrates robust performance improvements across diverse and heterogeneous FL settings, mitigating the negative effects of dual-data heterogeneity while preserving personalization and privacy.

Marketing channels have become a key factor in competitive success. Businesses should know the potential strengths of major channels and integrate them to develop effective multi-channel business models. Internet and physical stores are the most popular channels. Therefore, this study explores the effects of channel characteristics on channel attitudes of multi-channel customers who have experiences with the two channels to find and purchase products and services. We used survey to collect data from a sample of 191 multi-channel customers. The findings suggest that businesses should concentrate their efforts on channel characteristics that influence customers' channel attitudes in order to increase cross-channel synergy. The results have implications for channel management and marketing strategy design.