• The Journal of the Korea Contents Association
• /
• v.21 no.1
• /
• pp.426-433
• /
• 2021

Investment in public sector information services has been on the rise in recent years. The supply of high-speed Internet and smartphones has become more common, and the stability of the information system provided to the public in the public sector has become an important management factor. In other words, tasks such as handling civil complaints and issuing certificates by public institutions, financial transactions by banks, customs clearance work, and e-commerce by individuals or institutions are mostly done online. Therefore, how to deal with obstacles arising from the information system, which is in charge of important civil service affairs, is becoming a very important issue. In other words, in the case of a disability that does not function normally even for a short period of time, various problems can occur when the work is delayed, as well as causing serious financial damage to the civil petitioner. This could be accompanied by a decline in public confidence and various other damages such as filing civil complaints. The reasons for the occurrence of information system failures are very diverse and realistically difficult to predict when. Among the various measures to cope with disability, this paper proposed a plan to establish a disability situation notification system that can minimize confusion caused by disability in the event of a homepage malfunction. The proposed disability situation notification system was established in the public IDC environment to show the possibility of utilization.

• Journal of the Korea Institute of Building Construction
• /
• v.22 no.5
• /
• pp.519-530
• /
• 2022

As online commerce increases, the construction of large logistics buildings worldwide is exploding. Most of these buildings have the characteristics of long span and heavy loaded and use precast concrete components, a pin joint structure, for rapid construction. However, due to construction safety and structural stability requirements, the pin joint structure has many limitations in terms of the erection of the PC member, which increases the time and cost. A structural frame connected with steel joints between precast concrete components, called a SMART frame, has been developed, which addresses these constraints and risks. However, the effect of the appllication of a SMART frame on the time aspect has not been analysed. The study is a time reduction effect analysis of a SMART frame for long span and heavy loaded logistics buildings. For this study, the authors select a case site erected using existing PC components, and compare the time reduction with the SMART frame erection simulations. Through this analysis, it was found that a time reduciton about 4 months, approximately 48% of the conventional PC installation period could be achieved. If the SMART frame is applied when carrying out future large-scale logistics building projects, it can be expected to have the effect of significantly shortening the construction period compared to the conventional method.

• The Korean Journal of Franchise Management
• /
• v.13 no.2
• /
• pp.33-51
• /
• 2022

Purpose: The online market is growing the most in history due to the expansion of non-face-to-face commerce. In addition, as consumers' interest in health, food safety, and environment increases, interest in and consumption of eco-friendly agricultural products is also increasing. Therefore, in the case of a specialty store that sells eco-friendly organic agricultural products, a marketing strategy that can increase customer loyalty by reflecting these consumer needs is necessary. In this study, the store attributes of eco-friendly food stores are classified into location, assortment, price, quality, and employee service, and the effect of each store attribute on utilitarian and hedonic value is investigated. Research design, data, and methodology: The subjects of this study were customers who visited an eco-friendly food store. Of the 511 survey responses, 311 were used for statistical verification, excluding 200 who had not visited within the last 3 months. For statistical analysis, Smart PLS 3.0 was used, and after checking the validity and reliability of the items, hypothesis testing was performed. Result: As a result of the study, it was found that assortment, quality, and employee service among store attributes had a positive (+) effect on utilitarian and hedonic value. Second, location had no significant effect on utilitarian and hedonic value. Third, price did not appear to have a positive (+) effect on the utilitarian value, and it was found to have a positive (+) effect on the hedonic value. Fourth, It was investigated whether the presence or absence of delivery service had an effect on store attributes between utilitarian and hedonic value, and it was found that there was a significant effect between employee service and hedonic value. Conclusions: Among eco-friendly food store environment management will be required in order to provide food that meets the tastes and needs of consumers by diversifying the taste, standard, and quality grade of food, and to maintain or improve the quality. In order to unlike other stores, eco-friendly food stores have high price resistance from the point of view of consumers, so it is necessary to diversify promotional media such as YouTube and SNS to raise awareness of eco-friendly organic food.

• Journal of Internet of Things and Convergence
• /
• v.8 no.4
• /
• pp.85-96
• /
• 2022

The purpose of this study was to investigate how the management strategies and organizational culture required in the digital economy have an effect on business performance. It provided basic data on management strategies and organizational culture necessary to approach as a digital leading country. For data collection, a survey was conducted from March 1 to May 30, 2022 for companies located in J province and engaged in industries related to the digital economy. The survey was conducted online and non-face-to-face, and a total of 225 companies participated in the survey. For statistical analysis, frequency analysis, exploratory factor analysis and reliability analysis, cluster analysis, independent sample t-test, and multiple regression analysis were performed. The research results are as follows. First, organizational culture was classified into high and low groups according to preference in innovation oriented, relationship oriented, task oriented, and hierarchical oriented. Second, the 4 types of organizational culture showed differences in prospectors strategy, analyzers strategy, defenders strategy, differentiation strategy, cost leadership strategy, financial performance, and non-financial performance according to preference. Third, management strategies affecting financial performance were found to be analyzers strategy, differentiation strategy, prospectors strategy, and cost leadership strategy. Fourth, management strategies affecting non-financial performance were found to be differentiation strategy, defenders strategy, analysis strategy, offensive strategy, cost leadership strategy, and focus strategy. Fifth, organizational culture affecting financial performance was found to be task oriented. Sixth, organizational culture affecting non-financial performance was found to be innovation oriented and relationship oriented. Through these studies, it is expected that the economy will be revitalized in the domestic market and a growth ecosystem that can take a new leap forward is created in the global market.

• Journal of Service Research and Studies
• /
• v.13 no.3
• /
• pp.39-56
• /
• 2023

Due to restrictions on offline activities caused by COVID-19, the use of mobile applications is increasing along with interest in online shopping, which are non-face-to-face commerce. Accordingly, mobile applications and various industries are combined, and the number of cases of using mobile applications in the fashion industry is increasing. In this study, the effect of e-service quality of fashion mobile applications on user's flow, user satisfaction, and service loyalty was examined. To conduct this study, a survey of 274 people who experienced the 'ABLY' fashion mobile application was used for analysis to verify the hypothesis. As a result of the analysis, it was found that informativity and responsiveness among the e-service quality of fashion mobile applications had a positive (+) effect on flow. And it has been confirmed that informativity, reliability, and responsiveness affect user satisfaction. In addition, flow has a positive (+) (+) effect on user satisfaction, and user satisfaction has a positive (+) effect on service loyalty. However, among the e-service quality of fashion mobile applications, reliability did not have a positive (+) effect on flow. And ease of use did not have a positive (+) effect on both flow and user satisfaction. Finally, it was confirmed that flow did not directly affect service loyalty. Through this study, we intend to contribute to the establishment of marketing strategies for fashion mobile application users, who are increasing with the development of mobile technology, and provide practical implications for the post-COVID-19 era.

• Information Systems Review
• /
• v.23 no.3
• /
• pp.51-75
• /
• 2021

Global e-commerce websites offer personalized recommendation services to gain sustainable competitiveness. Existing studies have offered personalized recommendation services using quantitative preferences such as ratings. However, offering personalized recommendation services using only quantitative data has raised the problem of decreasing recommendation performance. For example, a user gave a five-star rating but wrote a review that the user was unsatisfied with hotel service and cleanliness. In such cases, has problems where quantitative and qualitative preferences are inconsistent. Recently, a growing number of studies have considered review data simultaneously to improve the limitations of existing personalized recommendation service studies. Therefore, in this study, we identify review and rating mismatches and build a new user profile to offer personalized recommendation services. To this end, we use deep learning algorithms such as CNN, LSTM, CNN + LSTM, which have been widely used in sentiment analysis studies. And extract sentiment features from reviews and compare with quantitative preferences. To evaluate the performance of the proposed methodology in this study, we collect user preference information using real-world hotel data from the world's largest travel platform TripAdvisor. Experiments show that the proposed methodology in this study outperforms the existing other methodologies, using only existing quantitative preferences.

• Information Systems Review
• /
• v.22 no.4
• /
• pp.75-92
• /
• 2020

As population and generation structures change, more and more customers tend to avoid facing relation due to the development of information technology and spread of smart phones. This phenomenon consists with efficiency and immediacy, which are the consumption patterns of modern customers who are used to information technology, so offline network-oriented distribution companies actively try to switch their sales and services to untact patterns. Recently, untact services are boosted in various fields, but beauty products are not easy to be recommended through untact services due to many options depending on skin types and conditions. There have been many studies on recommendations and development of recommendation systems in the online beauty field, but most of them are the ones that develop recommendation algorithm using survey or social data. In other words, there were not enough studies that classify segments based on user information such as skin types and product preference. Therefore, this study classifies customer segments using machine learning technique K-prototypesalgorithm based on customer information and search log data of mobile application, which is one of untact services in the beauty field, based on which, untact marketing strategy is suggested. This study expands the scope of the previous literature by classifying customer segments using the machine learning technique. This study is practically meaningful in that it classifies customer segments by reflecting new consumption trend of untact service, and based on this, it suggests a specific plan that can be used in untact services of the beauty field.

• Smart Media Journal
• /
• v.13 no.6
• /
• pp.54-61
• /
• 2024

Recently, due to the increase in domestic and international online e-commerce platforms and the increase in container traffic at domestic ports, the operating ratio of large trucks has increased, and the number of truck fires is continuously increasing. In particular, spontaneous combustion is the most common cause of truck fires. Various academic approaches have been attempted to prevent truck fires, but due to the lack of research on the spontaneous tire ignition phenomenon that occurs during braking, this research directly designed and manufactured an experimental device to establish an environment similar to the braking system of a truck. A non-contact temperature sensor was installed on the brake device of the experimental device to collect temperature data generated from the brake device. Based on the data collected from the temperature sensor of the brake device and the temperature sensor on the tire surface, the ARIMA model among the time series prediction models was used to Appropriate parameters were selected to suit the temperature change trend, and as a result of comparing and analyzing the measured and predicted data, an accuracy of over 90% was obtained. Based on this, a plan was proposed to reduce the rate of fires in trucks by providing real-time warnings and support for truck drivers to respond to overheating phenomena occurring in the braking system.

• Journal of Intelligence and Information Systems
• /
• v.22 no.3
• /
• pp.129-142
• /
• 2016

Customer product reviews have become one of the important factors for purchase decision makings. Customers believe that reviews written by others who have already had an experience with the product offer more reliable information than that provided by sellers. However, there are too many products and reviews, the advantage of e-commerce can be overwhelmed by increasing search costs. Reading all of the reviews to find out the pros and cons of a certain product can be exhausting. To help users find the most useful information about products without much difficulty, e-commerce companies try to provide various ways for customers to write and rate product reviews. To assist potential customers, online stores have devised various ways to provide useful customer reviews. Different methods have been developed to classify and recommend useful reviews to customers, primarily using feedback provided by customers about the helpfulness of reviews. Most shopping websites provide customer reviews and offer the following information: the average preference of a product, the number of customers who have participated in preference voting, and preference distribution. Most information on the helpfulness of product reviews is collected through a voting system. Amazon.com asks customers whether a review on a certain product is helpful, and it places the most helpful favorable and the most helpful critical review at the top of the list of product reviews. Some companies also predict the usefulness of a review based on certain attributes including length, author(s), and the words used, publishing only reviews that are likely to be useful. Text mining approaches have been used for classifying useful reviews in advance. To apply a text mining approach based on all reviews for a product, we need to build a term-document matrix. We have to extract all words from reviews and build a matrix with the number of occurrences of a term in a review. Since there are many reviews, the size of term-document matrix is so large. It caused difficulties to apply text mining algorithms with the large term-document matrix. Thus, researchers need to delete some terms in terms of sparsity since sparse words have little effects on classifications or predictions. The purpose of this study is to suggest a better way of building term-document matrix by deleting useless terms for review classification. In this study, we propose neutrality index to select words to be deleted. Many words still appear in both classifications - useful and not useful - and these words have little or negative effects on classification performances. Thus, we defined these words as neutral terms and deleted neutral terms which are appeared in both classifications similarly. After deleting sparse words, we selected words to be deleted in terms of neutrality. We tested our approach with Amazon.com's review data from five different product categories: Cellphones & Accessories, Movies & TV program, Automotive, CDs & Vinyl, Clothing, Shoes & Jewelry. We used reviews which got greater than four votes by users and 60% of the ratio of useful votes among total votes is the threshold to classify useful and not-useful reviews. We randomly selected 1,500 useful reviews and 1,500 not-useful reviews for each product category. And then we applied Information Gain and Support Vector Machine algorithms to classify the reviews and compared the classification performances in terms of precision, recall, and F-measure. Though the performances vary according to product categories and data sets, deleting terms with sparsity and neutrality showed the best performances in terms of F-measure for the two classification algorithms. However, deleting terms with sparsity only showed the best performances in terms of Recall for Information Gain and using all terms showed the best performances in terms of precision for SVM. Thus, it needs to be careful for selecting term deleting methods and classification algorithms based on data sets.

• Knowledge Management Research
• /
• v.22 no.3
• /
• pp.273-293
• /
• 2021

Due to the COVID-19 pandemic, the size of the e-commerce has been increased rapidly. This pandemic, which made contact-less communication culture in everyday life made the e-commerce market to be opened even to the consumers who would hesitate to purchase and pay by electronic device without any personal contacts and seeing or touching the real products. Consumers who have experienced the easy access and convenience of the online purchase would continue to take those advantages even after the pandemic. During this time of transformation, however, the size of information source for the consumers has become even shrunk into a flat screen and limited to visual only. To provide differentiated and competitive information on products, companies are adopting AR/VR and steaming technologies but the reviews from the honest users need to be recognized as important in that it is regarded as strong as the well refined product information provided by marketing professionals of the company and companies may obtain useful insight for product development, marketing and sales strategies. Then from the consumer's point of view, if the ratings of reviews are widely diverged how consumers would process the review information before purchase? Are non-converged ratings always unreliable and worthless? In this study, we analyzed how consumer's regulatory focus moderate the attitude to process the diverged information. This experiment was designed as a 2x2 factorial study to see how the variance of product review ratings (high vs. low) for cosmetics affects product attitudes by the consumers' regulatory focus (prevention focus vs. improvement focus). As a result of the study, it was found that prevention-focused consumers showed high product attitude when the review variance was low, whereas promotion-focused consumers showed high product attitude when the review variance was high. With such a study, this thesis can explain that even if a product with exactly the same average rating, the converged or diverged review can be interpreted differently by customer's regulatory focus. This paper has a theoretical contribution to elucidate the mechanism of consumer's information process when the information is not converged. In practice, as reviews and sales records of each product are accumulated, as an one of applied knowledge management types with big data, companies may develop and provide even reinforced customer experience by providing personalized and optimized products and review information.