This study presents a comprehensive evaluation of vision-language models (VLMs) for skin lesion classification, comparing them with conventional convolutional neural networks (CNNs) and Vision Transformer (ViT) architectures. Using the HAM10000 dataset, we evaluated six models in four key dimensions: classification accuracy, robustness to visual perturbations, zero-shot generalization to unseen lesion types, and the quality of semantic explanations. Although traditional vision-only models achieve higher accuracy in clean images, their performance degrades significantly under various types of image distortion. In contrast, VLMs—particularly Qwen2.5—demonstrate stronger robustness and generate more coherent and clinically relevant explanations. However, they still fail to achieve overall classification performance and exhibit limited generalization in zero-shot settings. These findings highlight the trade-offs between task-specific accuracy and multimodal adaptability, offering practical insights into the current capabilities and limitations of VLMs in dermatology-focused artificial intelligence applications.

In organizations, the amount of attention that user‐generated knowledge receives in knowledge management systems (KMSs) may not imply its potential for benefiting organizational activities in terms of accelerating innovation and product development. To optimize the utilization of knowledge in organizations, it is crucial to identify factors that influence knowledge popularity. From a network perspective, this study proposes a model to evaluate knowledge popularity by investigating 2 attributes of contextual information (i.e., authors and tags) that are embedded in a heterogeneous knowledge network, and how they interact to impact knowledge popularity. Objective data obtained through the interaction history of a KMS in a global telecommunication company was applied to test the hypotheses. This paper contributes to the extant literature on knowledge popularity by identifying contextual attributions of knowledge, and empirically tests the impact of their interactions on knowledge popularity.

This study investigated use of social media to enable public opinion convergence in activities with wide-scale interaction, such as in political events. Using theoretical foundations in elaboration likelihood, we explored the process of opinion convergence by analyzing Twitter data of Singapore General Election 2011. Our quantitative analyses showed that informative tweets were more effective than affective tweets in opinion convergence, and their interactive effect on social impact was significant. In addition, qualitative analyses using semantic network maps enabled us to distinguish the central and peripheral routes of influence. We discussed both theoretical and practical implications of our findings with respect to social impact and new media.

In order to take advantage of lower costs and wider availability of talent, managers often staff software development projects globally. While this practice may be economically appealing there are often hidden costs. This paper reports the results of a study that used social network analysis to study how people in three global software teams acquired and used available expertise through communication and acquisition strategies. We found that frequency of communication was associated with awareness and familiarity of the other person especially awareness of the person's current work. When it came to acquiring information, members of these teams were more likely to seek specific technical information or administrative help from people outside their software team. People use others on their team with whom they have strong ties to exploit preexisting knowledge but they go to people they know uniquely outside the team for innovative ideas. We discuss the implications of these results for tools that encourage transparent work and for work and management practices

Under-contribution is a problem for many online communities. Social psychology theories of social loafing and goal-setting can provide mid-level design principles to address this problem. We tested the design principles in two field experiments. In one, members of an online movie recommender community were reminded of the uniqueness of their contributions and the benefits that follow from them. In the second, they were given a range of individual or group goals for contribution. As predicted by theory, individuals contributed when they were reminded of their uniqueness and when they were given specific and challenging goals, but other predictions were not borne out. The paper ends with suggestions and challenges for mining social science theories as well as implications for design.