• Asia Marketing Journal
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• v.15 no.3
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• pp.53-77
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• 2013

As advertising and promotions are categorized as operating expenses, managers tend to reduce marketing budget to improve their short term profitability. Gauging the value and accountability of marketing spending is therefore considered as a major research priority in marketing. To respond this call, recent studies have documented that financial market reacts positively to a firm's marketing activity or marketing related outcomes such as brand equity and customer satisfaction. However, prior studies focus on the relation of marketing variable and financial market variables. This study suggests a channel about how marketing activity increases firm valuation. Specifically, we propose that a firm's marketing activity increases the level of the firm's product market information and thereby the dispersion in financial analysts' earnings forecasts decreases. With less uncertainty about the firm's future prospect, the firm's managers and shareholders have less information asymmetry, which reduces the firm's cost of capital and thereby increases the valuation of the firm. To our knowledge, this is the first paper to examine how informational benefits can mediate the effect of marketing activity on firm value. To test whether marketing activity contributes to increase in firm value by mitigating information asymmetry, this study employs a longitudinal data which contains 12,824 firm-year observations with 2,337 distinct firms from 1981 to 2006. Firm value is measured by Tobin's Q and one-year-ahead buy-and-hold abnormal return (BHAR). Following prior literature, dispersion in analysts' earnings forecasts is used as a proxy for the information gap between management and shareholders. For model specification, to identify mediating effect, the three-step regression approach is adopted. All models are estimated using Markov chain Monte Carlo (MCMC) methods to test the statistical significance of the mediating effect. The analysis shows that marketing intensity has a significant negative relationship with dispersion in analysts' earnings forecasts. After including the mediator variable about analyst dispersion, the effect of marketing intensity on firm value drops from 1.199 (p < .01) to 1.130 (p < .01) in Tobin's Q model and the same effect drops from .192 (p < .01) to .188 (p < .01) in BHAR model. The results suggest that analysts' forecast dispersion partially accounts for the positive effect of marketing on firm valuation. Additionally, the same analysis was conducted with an alternative dependent variable (forecast accuracy) and a marketing metric (advertising intensity). The analysis supports the robustness of the main results. In sum, the results provide empirical evidence that marketing activity can increase shareholder value by mitigating problem of information asymmetry in the capital market. The findings have important implications for managers. First, managers should be cognizant of the role of marketing activity in providing information to the financial market as well as to the consumer market. Thus, managers should take into account investors' reaction when they design marketing communication messages for reducing the cost of capital. Second, this study shows a channel on how marketing creates shareholder value and highlights the accountability of marketing. In addition to the direct impact of marketing on firm value, an indirect channel by reducing information asymmetry should be considered. Potentially, marketing managers can justify their spending from the perspective of increasing long-term shareholder value.

• Smart Structures and Systems
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• v.30 no.5
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• pp.521-535
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• 2022

Efficient management of deteriorating civil infrastructure is one of the most important research topics in many developed countries. In particular, the remote displacement measurement of bridges using linear variable differential transformers, global positioning systems, laser Doppler vibrometers, and computer vision technologies has been attempted extensively. This paper proposes a remote displacement measurement system using closed-circuit televisions (CCTVs) and a computer-vision-based method for in-situ bridge bearings having relatively large displacement due to temperature change in long term. The hardware of the system is composed of a reference target for displacement measurement, a CCTV to capture target images, a gateway to transmit images via a mobile network, and a central server to store and process transmitted images. The usage of CCTV capable of night vision capture and wireless data communication enable long-term 24-hour monitoring on wide range of bridge area. The computer vision algorithm to estimate displacement from the images involves image preprocessing for enhancing the circular features of the target, circular Hough transformation for detecting circles on the target in the whole field-of-view (FOV), and homography transformation for converting the movement of the target in the images into an actual expansion displacement. The simple target design and robust circle detection algorithm help to measure displacement using target images where the targets are far apart from each other. The proposed system is installed at the Tancheon Overpass located in Seoul, and field experiments are performed to evaluate the accuracy of circle detection and displacement measurements. The circle detection accuracy is evaluated using 28,542 images captured from 71 CCTVs installed at the testbed, and only 48 images (0.168%) fail to detect the circles on the target because of subpar imaging conditions. The accuracy of displacement measurement is evaluated using images captured for 17 days from three CCTVs; the average and root-mean-square errors are 0.10 and 0.131 mm, respectively, compared with a similar displacement measurement. The long-term operation of the system, as evaluated using 8-month data, shows high accuracy and stability of the proposed system.

• Asia Marketing Journal
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• v.10 no.2
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• pp.71-98
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• 2008

Considerably many numbers of studies on country-of-origin(hereafter COO) effects have been presented in international business and marketing areas. Recent studies have been included the effects of COO of manufacture, parts, and design, as well as the effects of brand origin, reflected by the accelerating convergent manufacture circumstances and increasingly competitive environments. Moderating constructs such as knowledge of product category and involvement as individual variables, have been also introduced and researched in various angles. In addition, how the effects of COO occur as processes is also argued in previous studies. This research has attempted to explain business corporation's strategic decisions on choosing a domain of its product manufacturing for several critical reasons, for cost reduction or better image. We displayed two constructs of brand and manufacture in a positive and negative country image group to reconfirm the existence of the effects of COO. Additionally, the effects of respondents' regulatory fit between their motivational focus and the contents of product messages, have been declared. Furthermore the respondents' motivational focus moderates the main effect of COO on product evaluations in a positive 'made-in' combination, while, surprisingly, it does not statistically moderate in a negative, except attitude. Based on the results, implications and suggestions on how to plan and execute more effective marketing strategies regarding COO dimensions, especially COO of manufacture, are separately presented for each situations when it has already been determined and when it is to be.

• 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.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.92-92
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• 2022

In preparation for the ever-changing climate and unification of North Korea and South Korea, it is necessary to increase the grain self-sufficiency rate by selecting crops with good utilization in high-altitude regions. The principle is to sow pastures at the end of August. However, sowing occurs in spring because the sowing period is missed when the weather is bad or when the workforce is insufficient. Sometimes when the grassland is completely devastated, it is frequently sowed in spring. In addition, North Korea consists of a high-altitude regions, and has been devastated in a general mountainous region. As a result, the landscape is not good and it is vulnerable to natural disasters such as landslides. Therefore, to prevent this, pasture must be sowed in the high-altitude regions. The goal of this study was to evaluate and compare yield and feed value of pasture species and varieties by spring sowing in high-latitude regions. The study was conducted in Pyeongchang, Gangwon-do, which is 700m height above sea level. The pasture species and varieties was sown on April 24, 2022. Each treatment was carried out by sowing 30 kg/ha, the experiment field size was 1 m²(1m×1m), and randomized block design with tri-repeat. The total of 14 varieties was used in the study, 6 varieties of Orchardgrass (OG), 6 varieties of Tall fescue (TF) and 2 varieties of Perennial ryegrass (PRG). The grassland composition fertilization using (N:P₂O₅:K₂O at 80:200:70 kg/ha) was conducted and management fertilizer was N:P₂O₅:K₂O at 210:150:180 kg/ha. The first harvest was June 26,2022 and the second harvest was on August 16, 2022. For statistical analysis of the data, an Analysis of Variance (ANOVA) was performed using the R3.6.3 software program, and all data was subjected to analysis using Duncan's multiple range test. Significance was set at the 5% level. The dry matter yield at the first harvest was the highest in PRG, and second harvest was the highest in TF (p < 0.05). Overall, PRG showed a trend of gradually decreasing growth, OG and TF showed a trend of gradually improving growth. This showed that PRG was considerably weaker to summer depression than other pasture species. Comparing the total dry matter yield, TF was the highest (4,565.45 kg/ha), but there was no significance difference with PRG (4,487.24 kg/ha) (p < 0.05). In addition, comparing the total TDN (total digestible nutrient) yield, TF was the highest (3147.33 kg/ha), second in PRG (2975.67 kg/ha) and third in OG (2052.33 kg/ha). Since this result is the data of the second harvests, if the result is derived by the end of next year, it will be provided as basic data for selection of pasture species and varieties suitable for spring seeding in high-altitude regions.

• Smart Media Journal
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• v.12 no.10
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• pp.55-62
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• 2023

Innovation and change are occurring rapidly in the agriculture and livestock industry, and new technologies such as smart bams are being introduced, and data that can be used to control equipment is being collected by utilizing various sensors. However, there are various challenges in the operation of bams, and virtual sensor technology is needed to solve these challenges. In this paper, we define various data items and sensor data types used in livestock farms, study cases that utilize virtual sensors in other fields, and implement and design a virtual sensor system for the final smart livestock farm. MBE and EVRMSE were used to evaluate the finalized system and analyze performance indicators. As a result of collecting and managing data using virtual sensors, there was no obvious difference in data values from physical sensors, showing satisfactory results. By utilizing the virtual sensor system in smart livestock farms, innovation and efficiency improvement can be expected in various areas such as livestock operation and livestock health status monitoring. This paper proposes an innovative method of data collection and management by utilizing virtual sensor technology in the field of smart livestock, and has obtained important results in verifying its performance. As a future research task, we would like to explore the connection of digital livestock using virtual sensors.

• Journal of Internet of Things and Convergence
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• v.10 no.1
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• pp.69-75
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• 2024

This paper proposes an innovative cognitive IoT framework specifically designed for fog computing (FC) in the context of industrial Internet of Things (IIoT). The discourse in this paper is centered on the intricate design and functional architecture of the Cognitive IoT platform. A crucial feature of this platform is the integration of machine learning (ML) and artificial intelligence (AI), which enhances its operational flexibility and compatibility with a wide range of industrial applications. An exemplary application of this platform is highlighted through the Predictive Maintenance-as-a-Service (PdM-as-a-Service) model, which focuses on real-time monitoring of machine conditions. This model transcends traditional maintenance approaches by leveraging real-time data analytics for maintenance and management operations. Empirical results substantiate the platform's effectiveness within a fog computing milieu, thereby illustrating its transformative potential in the domain of industrial IoT applications. Furthermore, the paper delineates the inherent challenges and prospective research trajectories in the spheres of Cognitive IoT and Fog Computing within the ambit of Industrial Internet of Things (IIoT).

• Journal of Digital Policy
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• v.2 no.4
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• pp.1-10
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• 2023

This study conducted preliminary study to identify effective ways to use ChatGPT in traffic policing by analyzing ChatGPT's responses to the driver's license test and the road traffic accident appraiser test. I collected ChatGPT responses for the driver's license test item pool and the road traffic accident appraiser test using the OpenAI API with Python code for 30 iterative experiments, and analyzed the percentage of correct answers by test, year, section, and consistency. First, the average correct answer rate for the driver's license test and the for road traffic accident appraisers test was 44.60% and 35.45%, respectively, which was lower than the pass criteria, and the correct answer rate after 2022 was lower than the average correct answer rate. Second, the percentage of correct answers by section ranged from 29.69% to 56.80%, showing a significant difference. Third, it consistently produced the same response more than 95% of the time when the answer was correct. To effectively utilize ChatGPT, it is necessary to have user expertise, evaluation data and analysis methods, design a quality traffic law corpus and periodic learning.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.6
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• pp.385-391
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• 2023

As the use of electric vehicles has increased to minimize carbon emissions, the analyzing the state and performance of lithium-ion batteries that is instrumental in electric vehicles have been important. Comprehensive analysis using not only the voltage, current and temperature of the battery pack, which can affect the condition and performance of the battery, but also the driving data and charging pattern data of the electric vehicle is required. Therefore, a thorough analysis is imperative, utilizing electric vehicle operation data, charging pattern data, as well as battery pack voltage, current, and temperature data, which collectively influence the condition and performance of the battery. Therefore, collection and preprocessing of battery data collected from electric vehicles, collection and preprocessing of data on driver driving habits in addition to simple battery data, detailed design and modification of artificial intelligence algorithm based on the analyzed influencing factors, and A battery analysis and evaluation model was designed. In this paper, we gathered operational data and battery data from real-time electric buses. These data sets were then utilized to train a Random Forest algorithm. Furthermore, a comprehensive assessment of battery status, operation, and charging patterns was conducted using the explainable Artificial Intelligence (XAI) algorithm. The study identified crucial influencing factors on battery status, including rapid acceleration, rapid deceleration, sudden stops in driving patterns, the number of drives per day in the charging and discharging pattern, daily accumulated Depth of Discharge (DOD), cell voltage differences during discharge, maximum cell temperature, and minimum cell temperature. These factors were confirmed to significantly impact the battery condition. Based on the identified influencing factors, a battery analysis and evaluation model was designed and assessed using the Random Forest algorithm. The results contribute to the understanding of battery health and lay the foundation for effective battery management in electric vehicles.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.99-114
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• 2022

Crowdfunding is one of new financing alternatives and is innovative and creative. In order to proceed with crowdfunding, various functions are required, such as design for screen composition, marketing and promotion for the public, accounting to manage the collected funds, and product production and purchase for reward. In addition, since it is a project that must be completed in a short period of time, cooperation between team members is important. This paper studied how the characteristics of the team conducting crowdfunding affect the team performance in crowdfunding. In this study, we set functional background diversity and shared leadership necessary for crowdfunding as team characteristic variables and crowdfunding amount, completion of work and team innovation as team performance variables. This study tests the hypotheses from 220 university students in 79 teams. The findings suggest that functional diversity and shared leadership are positively related to the completion of work and team innovation but not related to crowdfunding amount. To date, few studies have studied the relationship between characteristics of the crowdfunding team and performance. Therefore, the study on functional diversity and shared leadership in crowdfunding can expand existing crowdfunding study area.