This study examines the relationships between consumption values and car-sharing service adoption in Korean users. We used structural equation modeling to analyze data from 266 Korean individuals who had experience with car-sharing services. The results showed that functional value, social value, and conditional value positively predicted the use intention of car-sharing services. We also found that car-sharing use intention was positively related to the use behavior of car-sharing services. The findings suggest that both proximal and distal factors of consumption values explain the experimental use of car-sharing services. This study yields theoretical and practical insights into understanding the role of consumption values in customer use intention and behavior in the context of collaborative and experimental car-sharing service consumption.

This study aims to develop a success prediction model for crowdfunding by integrating ESG (Environmental, Social, and Governance) factors using machine learning techniques. Crowdfunding, a modern financing method conducted through online platforms, has become a popular avenue for raising funds, particularly for creative projects, startups, and social enterprises. Incorporating ESG factors into crowdfunding success prediction models is crucial, as these factors represent environmental responsibility, social accountability, and ethical governance, which are increasingly important to investors and sponsors. To achieve the research objectives, this study employed advanced machine learning algorithms, including XGBoost, LightGBM, AdaBoost, CatBoost, and NGBoost, to analyze data from a prominent reward-based crowdfunding platform in Korea, 'Wadiz.' Text mining techniques were used to quantify unstructured ESG-related data, and the predictive performance of models including ESG factors was compared with models excluding them. The results demonstrated that prediction accuracy significantly improved when ESG factors were incorporated. Among the ESG dimensions, environmental information contributed the highest prediction performance, followed by social activity information and governance information. The findings of this study contribute to academia by expanding the understanding of non-financial factors in crowdfunding success prediction and integrating ESG elements into machine learning models. This research also provides practical implications by offering insights for platform operators, project creators, and investors to improve decision-making processes through the strategic use of ESG factors. The proposed model enhances crowdfunding platforms' reliability and success rates, supports project makers in designing impactful campaigns, and aids investors in making sustainable and data-driven investment decisions. This study advances theoretical understanding and provides actionable strategies for enhancing the effectiveness of crowdfunding in practice.

In the field of urban environment analysis research, image segmentation technology that groups important objects in the urban landscape image in pixel units has been the subject of increased attention. However, since a dataset consisting of a huge amount of image and label pairs is required to utilize this technology, in most cases, a model trained with a dataset having similar characteristics is used for analysis, and as a result, the quality of segmentation is poor. To overcome this limitation, we propose a hybrid model to leverage the strengths of each model in predicting specific classes. In particular, we first introduce a pre-processing operation to reduce the differences between the collected urban dataset and public dataset. Subsequently, we train several segmentation models with a pre-processed dataset then, based on the weight rule, the segmentation results are fused to create one segmentation map. To evaluate our proposal, we collected Google Street View images that do not have any labels and trained a model using the cityscapes dataset which contains foregrounds similar to the collected images. We quantitatively assessed its performance using the cityscapes dataset with ground truths and qualitatively evaluated the results of GSV data segmentation through user studies. Our approach outperformed existing methods and demonstrated the potential for accurate and efficient urban environment analysis using computer vision technology.