• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.135-145
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• 2022

Due to COVID 19, we are facing unprecedented phenomena. Among various industries, the aviation industry, as it is directly affected by the corona crisis is actively establishing various survival strategies, such as expanding cargo transportation. The market size of air cargo transportation is continuously increasing due to the characteristics that distinguish it from other transportation, and as individualism and selfishness deepen in the aftermath of COVID 19, it can be inferred that the concept of reciprocity within distribution channels will become important in the post-corona era. The specific contents of this study(research question) are as follows. First, as a central member of the air logistics distribution channel, logistics service companies business process and the sub-dimensions of trust of are identified, and how trust is built with transportation companies is investigated. Second, the effect of such trust on the various performances of logistics service companies is analyzed. Third, we examine whether the influences of the sub-dimensions of trust change according to the perceived reciprocity of logistics service companies. In addition, we investigate whether the perceived reciprocity changed before and after the corona situation. In particular, this study theoretically integrates the concepts of trust and commitment, which have been distinguished in many prior studies, to improve the parsimony and practicality of the research model. This study will be able to present useful academic and practical implications by empirically examining how trust is built between members of the air logistics distribution channel and furthermore, how much it affects the performance of logistics service companies, and by identifying the moderating effect of reciprocity.

• Journal of Service Research and Studies
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• v.14 no.1
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• pp.13-26
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• 2024

Multi-modal generation is the process of generating results based on a variety of information, such as text, images, and audio. With the rapid development of AI technology, there is a growing number of multi-modal based systems that synthesize different types of data to produce results. In this paper, we present an AI system that uses speech and text recognition to describe a person and generate a montage image. While the existing montage generation technology is based on the appearance of Westerners, the montage generation system developed in this paper learns a model based on Korean facial features. Therefore, it is possible to create more accurate and effective Korean montage images based on multi-modal voice and text specific to Korean. Since the developed montage generation app can be utilized as a draft montage, it can dramatically reduce the manual labor of existing montage production personnel. For this purpose, we utilized persona-based virtual person montage data provided by the AI-Hub of the National Information Society Agency. AI-Hub is an AI integration platform aimed at providing a one-stop service by building artificial intelligence learning data necessary for the development of AI technology and services. The image generation system was implemented using VQGAN, a deep learning model used to generate high-resolution images, and the KoDALLE model, a Korean-based image generation model. It can be confirmed that the learned AI model creates a montage image of a face that is very similar to what was described using voice and text. To verify the practicality of the developed montage generation app, 10 testers used it and more than 70% responded that they were satisfied. The montage generator can be used in various fields, such as criminal detection, to describe and image facial features.