• Asia Marketing Journal
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• v.13 no.3
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• pp.265-274
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• 2011

The objective of this case study is to analyze how effectively KIA K5, which is a leading mid-size car brand, has positioned itself into the mid-size car market. Before KIA launched the K5, Sonata and SM5 were the leading brands in the mid-size car market. They had loyal customers who like their similar images. As many competitors keep launching new brands or new designs into the car industry, Sonata and SM5 were pressured to introduce new versions. But, the YF Sonata and the New SM5 failed to catch up with the new trends in the market. Whilst YF Sonata was perceived as too innovative, the New SM5 was treated as an old car by the target customers of the mid-size car. While the two leading brands struggled to attract customers, KIA K5 found a new market space by identifying and focusing on the lucrative replace and up-grade demand segment and filling the gap between the current product category values and the emerging mid-size car category values. The K5 found the right values that customers need and successfully articulated the values to the customers. This case study illustrates that a successful positioning strategy can be effectively employed to attract customers in the saturated car manufacturing industry. This case can be summarized as the successful positioning strategy of KIA K5 is comprised of four primary pillars: design innovation, market analysis, STP (segmentation, targeting, and positioning), and launch strategy. The KIA K5 case study provides valuable insights and implications for many other companies that are planning to find a proper positioning strategy for their own business.

• Asia Marketing Journal
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• v.13 no.3
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• pp.233-248
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• 2011

This case illustrates a story of passionate and innovative new market development by Pulmuone, a fresh food provider in Korea. The company has been successfully developing a new market by introducing a (non-frying) fresh ramen, 'Jayeonun Masitda - The Nature Is Tasty' in the packaged ramen market dominated by fried ones. In this case, a detailed new market development process by Pulmuone will be investigated including; company overview, a new product development process, marketing strategy formulation, marketing mix implementation, market performance, and future directions. Pulmuone has been making efforts to create a new product category by marketing non-frying ramens since 1995, but with a modest success. In 2011, Pulmuone finally succeeded to develop an innovative product, 'Jayeonun Masitda' that brought more health and nutrition conscious consumers' attention in the ramen market. The company intended to change the current competitive structure in the ramen market, i.e., from the strength of taste and the amount of ingredient to fried/non-fried and the freshness of ingredient. By this new positioning, Pulmuone aimed to reshaping the ramen market into competition between healthy and unhealthy ramens. Pulmuone has been successful in developing a new market. Sales revenue of 'Jayeonun Masitda' has been continuously increasing, and customers are found to be highly satisfied with the product resulting in a high repeat purchase rate. The company's successful new market development can be attributed to a faithful new product development process, innovative technology, an appropriate positioning strategy, and consistent marketing communication. In addition, Pulmuone's eco-friendly corporate image and the organization's passion to grow are also important factors for success of this new market development.

• Asia Marketing Journal
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• v.6 no.3
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• pp.59-81
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• 2004

Store loyalty is increasingly being recognized as a path to long-term business profitability. Customer contact employees deliver a service firm's promises and create an important image for the firm. A major purpose of this study is to investigate the effects of customer service and product value on store loyalty. In order to test research hypotheses, data were collected through surveys administered to 300 apparel store customers. Two hundred thirty nine usable data were used for the analysis. The findings of this research are as follows: First, a employee's voluntary service(EVS) has a positive impact on interpersonal r elationship, which then affects switching barrier and store loyalty. Second, a employee's regular service(ERS) has an influence on store satisfaction, which in turn affect store loyalty. Third, product value is shown to be a significant antecedent to store satisfaction, which have a direct effect on store loyalty. The study concludes with implications, contributions, and limitations of the research and the empirical findings of this research should be beneficial to marketing practitioners and retailing businessmen in developing effective marketing strategies.

• Asia Marketing Journal
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• v.11 no.2
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• pp.149-172
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• 2009

Prior research has investigated different effects of brand performance information such as premium price information and market share information on brand equity components - quality perception and brand preference. It was shown that the differential effects of brand performance information could depend on product-related variables like product category concept and quality variation in the product category. In this study, we conducted an experiment to find out how personal characteristics such as self-construal, price perception and brand commitment could influence the effects of different types of brand performance information. The results show that individuals who have independent self-construal, favorable price perception and emotional commitment with the brand develop more favorable evaluation of the premium price performance information resulting in more positive evaluations of the brand. However, individuals who have interdependent self-construal, unfavorable price perception and cognitive commitment with the brand develop more favorable evaluation of the market share performance information resulting in more positive evaluations of the brand. We discuss the theoretical and practical implications of this study and its limitations, along with future research interests.

• Asia Marketing Journal
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• v.11 no.4
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• pp.69-93
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• 2010

In this research, through experiments, we show that incongruity of color between mediums has positive influence on brand attitude in terms of integrated management of brand. We also present that self-image congruence of 'brand-consumer' has moderating effect on such influence of color incongruity. Mediums were limited to the ones that magnifying visual influence in order only to observe influence of color. With the same reason, visual factors other than color were coherently set or held constant and we chose brands with either low familarity or no previous knowledge. As a result, we find that brand attitude by the incongruity of color between mediums was higher compared to brand attitude by the congruence of color. In case with lower self-image congruence of brand-consumer we show higher change in attitude compared to the one with higher self-image congruence of brand-consumer. We believe our findings are interesting to note that brand may be enhanced by forming positive brand attitude through brand expression i.e., color of visual factors. In addition, we suggest that level of congruence and diversity of brand expression is in fact deeper or wider than that of brand manager's intuition. We see that it is possible for studying brands the incongruity which has been studied as a strategy to reposition mature brands can be a way of improving the recognition on new brands.

• Asia Marketing Journal
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• v.9 no.2
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• pp.49-95
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• 2007

The "cause-related marketing (CRM)," generally defined as a mutually beneficial relationship between a company and a non-profit relationship or a social cause, which is perhaps the most progressive outgrowth of marketing trend. This paper contributes to, and looks at the practical issues of CRM and its effect on the brand attitude of the customer. To do so, following three broad research questions have been addressed. Which cause-related orientation is effective on customer's attitude of the brand? Which type of cause-related message provides crucial impact on customer's attitude of the brand? How product category acts upon and brings about different consequences on CRM? To address these questions, a causal model has been developed incorporating message type, product relevance, social significance, and brand attitude. The study model was tested with survey data collected from 400 career professionals and students in Seoul and statistically processed the 176 valid ones. The results of the study considerably supported the conceptual model. The analysis also revealed that the study population was not able to detect the differences in CRM strategies but tend to conceptualize them as a whole.

• Journal of IKEEE
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• v.27 no.4
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• pp.486-493
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• 2023

In this paper, we propose an automatic hand gesture area extraction and recognition technique using FMCW radar-based point cloud and LSTM. The proposed technique has the following originality compared to existing methods. First, unlike methods that use 2D images as input vectors such as existing range-dopplers, point cloud input vectors in the form of time series are intuitive input data that can recognize movement over time that occurs in front of the radar in the form of a coordinate system. Second, because the size of the input vector is small, the deep learning model used for recognition can also be designed lightly. The implementation process of the proposed technique is as follows. Using the distance, speed, and angle information measured by the FMCW radar, a point cloud containing x, y, z coordinate format and Doppler velocity information is utilized. For the gesture area, the hand gesture area is automatically extracted by identifying the start and end points of the gesture using the Doppler point obtained through speed information. The point cloud in the form of a time series corresponding to the viewpoint of the extracted gesture area is ultimately used for learning and recognition of the LSTM deep learning model used in this paper. To evaluate the objective reliability of the proposed technique, an experiment calculating MAE with other deep learning models and an experiment calculating recognition rate with existing techniques were performed and compared. As a result of the experiment, the MAE value of the time series point cloud input vector + LSTM deep learning model was calculated to be 0.262 and the recognition rate was 97.5%. The lower the MAE and the higher the recognition rate, the better the results, proving the efficiency of the technique proposed in this paper.

• Korean Journal of Remote Sensing
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• v.40 no.1
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• pp.71-80
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• 2024

In this study, a proposed ensemble learning technique aims to enhance the semantic segmentation performance of images captured by Unmanned Aerial Vehicles (UAVs). With the increasing use of UAVs in fields such as urban planning, there has been active development of techniques utilizing deep learning segmentation methods for land cover segmentation. The study suggests a method that utilizes prominent segmentation models, namely U-Net, DeepLabV3, and Fully Convolutional Network (FCN), to improve segmentation prediction performance. The proposed approach integrates training loss, validation accuracy, and class score of the three segmentation models to enhance overall prediction performance. The method was applied and evaluated on a land cover segmentation problem involving seven classes: buildings,roads, parking lots, fields, trees, empty spaces, and areas with unspecified labels, using images captured by UAVs. The performance of the ensemble model was evaluated by mean Intersection over Union (mIoU), and the results of comparing the proposed ensemble model with the three existing segmentation methods showed that mIoU performance was improved. Consequently, the study confirms that the proposed technique can enhance the performance of semantic segmentation models.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1099-1110
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• 2023

The recognition system of autonomous driving and robot navigation performs vision work such as object recognition, tracking, and lane detection after multi-sensor fusion to improve performance. Currently, research on a deep learning model based on the fusion of a camera and a lidar sensor is being actively conducted. However, deep learning models are vulnerable to adversarial attacks through modulation of input data. Attacks on the existing multi-sensor-based autonomous driving recognition system are focused on inducing obstacle detection by lowering the confidence score of the object recognition model.However, there is a limitation that an attack is possible only in the target model. In the case of attacks on the sensor fusion stage, errors in vision work after fusion can be cascaded, and this risk needs to be considered. In addition, an attack on LIDAR's point cloud data, which is difficult to judge visually, makes it difficult to determine whether it is an attack. In this study, image scaling-based camera-lidar We propose an attack method that reduces the accuracy of LCCNet, a fusion model (camera-LiDAR calibration model). The proposed method is to perform a scaling attack on the point of the input lidar. As a result of conducting an attack performance experiment by size with a scaling algorithm, an average of more than 77% of fusion errors were caused.

• Journal of Internet Computing and Services
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• v.24 no.6
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• pp.119-125
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• 2023

Through the Ukraine-Russia war, the military importance of drones is being reassessed, and North Korea has completed actual verification through a drone provocation towards South Korea at 2022. Furthermore, North Korea is actively integrating artificial intelligence (AI) technology into drones, highlighting the increasing threat posed by drones. In response, the Republic of Korea military has established Drone Operations Command(DOC) and implemented various drone defense systems. However, there is a concern that the efforts to enhance capabilities are disproportionately focused on striking systems, making it challenging to effectively counter swarm drone attacks. Particularly, Air Force bases located adjacent to urban areas face significant limitations in the use of traditional air defense weapons due to concerns about civilian casualties. Therefore, this study proposes a new passive air defense method that aims at disrupting the object detection capabilities of AI models to enhance the survivability of friendly aircraft against the threat posed by AI based swarm drones. Using laser-based adversarial examples, the study seeks to degrade the recognition accuracy of object recognition AI installed on enemy drones. Experimental results using synthetic images and precision-reduced models confirmed that the proposed method decreased the recognition accuracy of object recognition AI, which was initially approximately 95%, to around 0-15% after the application of the proposed method, thereby validating the effectiveness of the proposed method.