The Fintech business, which was initially focused on the payment sector, is becoming a global issue due to the entry of nonfinancial firms into the banking business. With the advent of the “mobile age in your hand”, global ICT companies are actively entering the banking business through alliances and competitions with existing financial companies. Classifying the alliance companies of Apple Pay and Samsung Pay into the downstream alliance and the upstream alliance, this study analyzed the signaling effect of service opening and its impact on the firm value. To analyze the effect of a specific event on firm value, this study adopted the event study. Additionally, ordinary least squares regression analysis was carried out to examine the influence of up- and downstream alliance on the firm value. The result shows that Apple Pay’s service launch in the USA. has a positive impact on stock prices of up- and downstream alliance companies, providing new experience and satisfaction to users through active alliance with credit card companies. On the other hand, downstream alliance companies that showed a negative response to the launch of Korean services turned to a positive response to USA service launch because to the difference in the specificity of credit card penetration rate and the portion of premium smartphones. Analyzing the impact of the expansion of the service area toward the payment platform on the firm value, research results provide important implications for establishing technology management strategies to ensure the sustainability in rapidly changing technical advances by comparing the different market response of Apple Pay and Samsung Pay.
Considering the lifecycle of products, firms are releasing new products through diversified strategic partnerships via the global supply chain. As the uncertainty about the future increases and strategic partnership grows more important, part suppliers are becoming more and more significant in assessing firm value. From the perspective of the signaling effect, this study analyzed the impact of partner volatility (new partner, old partner, revocation partner) on firm value in terms of global supply chain management. Regarding both Apple and Samsung which have bisected the premium smart phone market, research results reveal that companies eliminated from partnership selection are found to show negative signaling effect, and the newly selected companies have the stronger innovative capacity and higher signaling effect of higher excess earning rate than that of re-selected companies. The findings indicate that the partner volatility of partner companies work as a reliable investment signal for investors to recognize as an investment indication, contributing to firm value. In particular, it is meaningful to confirm that a new partner's differentiated R&D capacity is a key factor of new product launching and a significant variable capable of determining a firm's survival in the smart phone market.
With rapid changes driven by technical advances, innovative technology capacity is a strategic asset unique to a company allocating various tangible and intangible resources, and it promotes technological innovation. This study analyzed the technology applied to iPhone series by Apple from 2007 to 2017 and measured the information effect of innovative technology exploration on the firm value for managing global supply chain (USA, Taiwan, Japan, Korea, and Europe). Adopting the pooled OLS (Ordinary least square) and panel analysis, this study revealed that exploration technology exploring new technologies shows a positive market response in the information effect of sustaining innovation. Results identified that exploitation and exploration can give different results depending on a construct (exploration and exploitation technologies) or congruence (combination and balance). In addition, the results indicate the importance of the balance between exploitation and exploration technologies and rational part supplies management in Apple’s new product development strategy. Analyzing the impact of innovative technology exploration on the firm value for global supply chain management, this study suggests significant implications for strategic decision making for the company to build continuous innovation path through technology search and to secure sustainability of organizations facing rapid changes in technical advances.
Human is one of the most essential classes in visual recognition tasks such as detection, segmentation, and pose estimation. Although much effort has been put into individual tasks, multi-task learning for these three tasks has been rarely studied. In this paper, we explore a compact multi-task network architecture that maximally shares the parameters of the multiple tasks via object-centric learning. To this end, we propose a novel query design to encode the human instance information effectively, called humancentric query (HCQ). HCQ enables for the query to learn explicit and structural information of human as well such as keypoints. Besides, we utilize HCQ in prediction heads of the target tasks directly and also interweave HCQ with the deformable attention in Transformer decoders to exploit a well-learned object-centric representation. Experimental results show that the proposed multi-task network achieves comparable accuracy to state-of-the-art task-specific models in human detection, segmentation, and pose estimation task, while it consumes less computational costs.
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