• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1125-1139
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• 2022

In an unmanned aerial vehicles (UAVs) system, a physical offset can be existed between the global positioning system/inertial measurement unit (GPS/IMU) sensor and the observation sensor such as a hyperspectral sensor, and a lidar sensor. As a result of the physical offset, a misalignment between each image can be occurred along with a flight direction. In particular, in a case of multi-sensor system, an observation sensor has to be replaced regularly to equip another observation sensor, and then, a high cost should be paid to acquire a calibration parameter. In this study, we establish a precise sensor model equation to apply for a multiple sensor in common and propose an independent physical offset estimation method. The proposed method consists of 3 steps. Firstly, we define an appropriate rotation matrix for our system, and an initial sensor model equation for direct-georeferencing. Next, an observation equation for the physical offset estimation is established by extracting a corresponding point between a ground control point and the observed data from a sensor. Finally, the physical offset is estimated based on the observed data, and the precise sensor model equation is established by applying the estimated parameters to the initial sensor model equation. 4 region's datasets(Jeon-ju, Incheon, Alaska, Norway) with a different latitude, longitude were compared to analyze the effects of the calibration parameter. We confirmed that a misalignment between images were adjusted after applying for the physical offset in the sensor model equation. An absolute position accuracy was analyzed in the Incheon dataset, compared to a ground control point. For the hyperspectral image, root mean square error (RMSE) for X, Y direction was calculated for 0.12 m, and for the point cloud, RMSE was calculated for 0.03 m. Furthermore, a relative position accuracy for a specific point between the adjusted point cloud and the hyperspectral images were also analyzed for 0.07 m, so we confirmed that a precise data mapping is available for an observation without a ground control point through the proposed estimation method, and we also confirmed a possibility of multi-sensor fusion. From this study, we expect that a flexible multi-sensor platform system can be operated through the independent parameter estimation method with an economic cost saving.

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.

• Knowledge Management Research
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• v.21 no.3
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• pp.249-269
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• 2020

Recently, as the online market has matured, it is facing many problems to prevent the growth. The most common problem is the homogenization of online products, which fails to increase the number of customers any more. Moreover, although the portion of the online market has increased significantly, it now becomes essential to expand offline for further development. In response, many online firms have recently sought to expand their businesses and marketing channels by securing offline spaces that can complement the limitations of online platforms, on top of their existing advantages of online channels. Based on their competitive advantage in terms of analyzing large volumes of customer data utilizing information technologies (e.g., big data and artificial intelligence), they are reinforcing their offline influence as well through this online for offline (O4O) business model. On the other hand, most of the existing research has primarily focused on online to offline (O2O) business model, and there is still a lack of research on O4O business models, which have been actively attempted in various industrial fields in recent years. Since a few of O4O-related studies have been conducted only in an experience marketing setting following a case study method, it is critical to conduct an empirical study on O4O selection attributes and their impact on customer satisfaction and loyalty. Therefore, focusing on China's representative O4O business model, 'Fresh Hema,' this study attempts to identify some key selection attributes specialized for O4O services from the customers' viewpoint and examine the impact of these attributes on customer satisfaction and loyalty. The results of the structural equation modeling (SEM) with 300 O4O (Fresh Hema) experienced customers, reveal that, out of seven O4O selection attributes, four (mobile app quality, mobile payment, product quality, and store facilities) have an impact on customer satisfaction, which also leads to customer loyalty (reuse intention, recommendation intention, and brand attachment). This study would help managers in an O4O area well adapt to rapidly changing customer needs and provide them with some guidelines for enhancing both customer satisfaction and loyalty by allocating more resources to more significant selection attributes, rather than less significant ones.