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This study aims to examine the effects of multidimensional factors of platform features, group effects and emotional attitudes on social media users’ privacy disclosure intention.

This study collected the data from 426 respondents through an online questionnaire survey and conducted two approaches of structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for theoretical hypothesis testing and configuration analysis of the data.

The results show that social media platform features (rewards of information disclosure, personalized service quality and data transparency), group effects (group similarity, group information interaction and network externality), individual emotional attitudes (trust and privacy concern) and control variable (gender) have a significant impact on privacy disclosure intention, as well as trust and privacy concern play mediating roles. Additionally, the fsQCA method reveals five causal configurations that explain high privacy disclosure intentions. Furthermore, the study reveals that male users pay more attention to platform features, while female users are more inclined to group effects.

This study attempts to construct a comprehensive model to examine the factors that affect users' intention to disclose their privacy on social media platforms. Drawing on the cognition-affect-conation model and multidimensional development theory, the model integrates multidimensional factors of platform features, group effects, trust and privacy concern to complement existing theoretical frameworks and privacy disclosure literature. By understanding the complex dynamics behind privacy disclosure, this study helps platform providers and policymakers develop effective strategies to ensure the vitality and momentum of the social media ecosystem.

To address the issue of large visual measurement errors caused by insufficient information collected by monocular vision when performing six-degree-of-freedom (6DOF) position measurements on metal castings, which hinders the robot’s ability to visually guide grasping, this paper aims to propose a 6DOF position measurement method that integrates monocular vision with deep neural networks.

This method enhances the robot’s ability to visually grasp small-sample industrial objects with high accuracy. By establishing a mapping relationship between the two-dimensional (2D) position of the object’s image and its three-dimensional (3D) position in space, the proposed approach achieves 6DOF position measurement of the target workpiece using monocular vision. An image enhancement algorithm based on a generative adversarial network (GAN) is introduced to improve robustness in industrial environments by addressing the challenge of acquiring image data for small-sample objects. Additionally, the method combines single-phase object detection using deep neural networks with 2D-3D affine transformation to achieve accurate 3D position measurements.

The introduction of the GAN-based image enhancement algorithm significantly mitigates the robustness issues posed by the difficulties in obtaining image data for small-sample objects in industrial settings. The integration of single-phase object detection and 2D–3D affine transformation allows for precise 3D position measurement of the workpiece. Experimental results demonstrate that the proposed method provides high accuracy in 6DOF position measurements for industrial objects.

This approach overcomes the limitations of traditional vision algorithms for 3D position measurement of industrial objects, such as high cost and poor robustness. The experimental validation confirms that the proposed method achieves excellent 6DOF position measurement accuracy for industrial objects.

The study aims to enhance energy efficiency within the high-energy consuming construction industry. It explores the spatial-temporal dynamics and distribution patterns of total factor energy efficiency (TFEE) across China’s construction industry, aiming to inform targeted emission reduction policies at provincial and city levels.

Utilizing a three-stage super-efficiency SBM-DEA model that integrates carbon emissions, the TFEE in 30 Chinese provinces and cities from 2004 to 2019 is assessed. Through kernel density estimation and exploratory spatial data analysis, the dynamic evolution and spatial patterns of TFEE are examined.

Analysis reveals that environmental investments positively impact TFEE, whereas Gross Regional Product (GRP) exerts a negative influence. R&D expenditure intensity and marketization show mixed effects. Excluding environmental and random factors, TFEE averages declined, aligning more closely with actual development trends, showing a gradual decrease from east to west. TFEE exhibited fluctuating growth with a trend moving from inefficient clusters to a more even distribution. Spatially, TFEE demonstrated aggregation effects and characteristics of space-time transition.

This research employs the three-stage super-efficiency SBM-DEA model to measure the total factor energy efficiency of the construction industry, taking into account external environment, random disturbances, and multiple effective decision-making units. It also evaluates energy efficiency changes before and after removing disturbances and comprehensively examines regional and temporal differences from static and dynamic, overall and phased perspectives. Additionally, Moran scatter plots and LISA cluster maps are used to objectively analyze the spatial agglomeration and factors influencing energy efficiency.

This study aims to explore how transformational leadership influences employees' creativity by considering the role of the environment and psychology. The study aims to provide insights into the impact of transformational leadership on team innovation climate, team reflexivity, psychological capital and employee creativity while also examining the moderating effect of environmental dynamism on these relationships.

This study employed a multi-source, multi-wave approach, utilizing data from 618 participants in 118 teams. It constructed a multilevel structural equation model and estimated the confidence intervals of the mediated and moderated effects using the Markov chain Monte Carlo method.

The results of the multilevel analyses indicated that transformational leadership positively influenced the team innovation climate, team reflexivity, psychological capital and employee creativity. Moreover, the study found that environmental dynamism positively moderates the relationships among transformational leadership, team reflexivity, psychological capital and employee creativity.

Drawing on social cognitive theory and the motivated information processing in groups model, this study offers new insights into the interplay between transformational leadership and creativity. It examines the moderating role of cross-level process linkages and environmental dynamism, thereby validating and extending relevant theories.

The study aims to provide concrete service remediation and enhancement for LLM developers such as getting user forgiveness and breaking through perceived bottlenecks. It also aims to improve the efficiency of app users' usage decisions.

This paper takes the user reviews of the app stores in 21 countries and 10 languages as the research data, extracts the potential factors by LDA model, exploratively takes the misalignment between user ratings and textual emotions as user forgiveness and perceived bottleneck and uses the Word2vec-SVM model to analyze the sentiment. Finally, attributions are made based on empathy.

The results show that AI-based LLMs are more likely to cause bias in user ratings and textual content than regular APPs. Functional and economic remedies are effective in awakening empathy and forgiveness, while empathic remedies are effective in reducing perceived bottlenecks. Interestingly, empathetic users are “pickier”. Further social network analysis reveals that problem solving timeliness, software flexibility, model updating and special data (voice and image) analysis capabilities are beneficial in breaking perceived bottlenecks. Besides, heterogeneity analysis show that eastern users are more sensitive to the price factor and are more likely to generate forgiveness through economic remedy, and there is a dual interaction between basic attributes and extra boosts in the East and West.

The “gap” between negative (positive) user reviews and ratings, that is consumer forgiveness and perceived bottlenecks, is identified in unstructured text; the study finds that empathy helps to awaken user forgiveness and understanding, while it is limited to bottleneck breakthroughs; the dataset includes a wide range of countries and regions, findings are tested in a cross-language and cross-cultural perspective, which makes the study more robust, and the heterogeneity of users' cultural backgrounds is also analyzed.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.