A Successful International Joint Venture: Exploring the Critical Success Factors of Starbucks Korea

International joint venture has become a key foreign entry mode among global service firms. Scholars have devoted significant attention to the theory of international joint venture in the past three decades. However, despite growing interest from academics and practitioners alike, research that would synthesize the model of a successful international joint venture in the service industry has not been established. To close this gap, we undertook a qualitative study using Starbuck Korea case. This study investigates how Starbucks Korea, an international joint venture between the Starbucks Corporation and the Shinsegae Corporation (the Korean joint venture partner of Starbucks Korea) has been successful in the competitive Korean coffee market. Specifically, the study highlights how the partners involved in Starbucks Korea have successfully collaborated and developed mutual trust while bridging cultural, geographic and language gaps

Domain Adaptive Transfer Attack (DATA)-based Segmentation Networks for Building Extraction from Aerial Images

Semantic segmentation models based on convolutional neural networks (CNNs) have gained much attention in relation to remote sensing and have achieved remarkable performance for the extraction of buildings from high-resolution aerial images. However, the issue of limited generalization for unseen images remains. When there is a domain gap between the training and test datasets, CNN-based segmentation models trained by a training dataset fail to segment buildings for the test dataset. In this paper, we propose segmentation networks based on a domain adaptive transfer attack (DATA) scheme for building extraction from aerial images. The proposed system combines the domain transfer and adversarial attack concepts. Based on the DATA scheme, the distribution of the input images can be shifted to that of the target images while turning images into adversarial examples against a target network. Defending adversarial examples adapted to the target domain can overcome the performance degradation due to the domain gap and increase the robustness of the segmentation model. Cross-dataset experiments and the ablation study are conducted for the three different datasets: the Inria aerial image labeling dataset, the Massachusetts building dataset, and the WHU East Asia dataset. Compared to the performance of the segmentation network without the DATA scheme, the proposed method shows improvements in the overall IoU. Moreover, it is verified that the proposed method outperforms even when compared to feature adaptation (FA) and output space adaptation (OSA).Comment: 11pages, 12 figure