• The Journal of the Convergence on Culture Technology
• /
• v.10 no.3
• /
• pp.891-899
• /
• 2024

The Fourth Industrial Revolution is centered on a personalized demand fulfillment economy and is all about transformation and flexible processing that can deliver what customers want in real time across space and time. This paper implements the construction and operation of a packaging platform that can instantly procure the required packaging products based on real-time orders and evaluates its performance. The components of customer satisfaction are flexible and dependent on the situation which requires efficient management of enterprise operational processes based on an e-SCM platform. An OMS optimized for these conditions plays an important role in maximizing and differentiating the efficiency of a company's operations and improving its cost advantage. OMS is a system of mass customization that provides efficient MOT(Moment of Truth) logistics services to meet the eco-friendly issues of many individual customers and achieve optimized logistics operation goals to enhance repurchase intentions and sustainable business. OMS precisely analyzes the collected data to support information and decision-making related to efficiency, productivity, cost and provide accurate reports. It uses data visualization tools to express data visually and suggests directions for improvement of the operational process through statistics and prediction analysis.

• Journal of Intelligence and Information Systems
• /
• v.21 no.4
• /
• pp.37-51
• /
• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.14 no.4
• /
• pp.199-209
• /
• 2019

The importance of business strategy for successful social media engagement has quickly increased as more businesses engage in social media. The importance is even greater for startup companies because startup companies are genuinely new to business, and they need to increase their presence in the market, and quickly access future customers. The objective of this paper lies in exploring key indicators of social media engagements by selected startup companies. The key indicators include two aspects of social media usages by the companies: i) overall social media activities, and ii) properties of network structure of the information flow platform provided by social media service. To better assess and evaluate the key indicators of social media usages by startup companies, the indicators will be compared with those of selected large established companies. Twitter is selected as a social media service for the analysis of this paper, and using Twitter REST API, data regarding the key indicators of overall Twitter activities and the Twitter follower-network of each company in the sample are collected. Then, the data are analyzed using social network analysis and hierarchical clustering analysis to examine the characteristics of the follower-network structures and to compare the characteristics between startup companies and established companies. The results show that most indicators are significantly different across startup companies and established companies. One key interesting finding is that the startup companies have proportionally more influencers in their follower-networks than the established companies have. Another interesting finding is that the follower-networks of startup companies in the sample have higher modularity and higher transitivity, suggesting that the startup companies tend to have a proportionally larger number of communities of users in their follower-networks, and the users in the networks are more tightly connected and cohesive internally. The key business implication for the future social media engagement efforts by startup companies in general is that startup companies may need to focus on getting more attention from influencers and promoting more cohesive communities in their follower-networks to appreciate the potential benefits of social media in the early stage of business of startup companies.

• Journal of Distribution Research
• /
• v.16 no.4
• /
• pp.65-93
• /
• 2011

As relationship between buyer and seller has been brought closer and long-term relationship has been more important in B2B markets, the importance of service and service convenience increases as well as product. In homogeneous markets, where service offerings are similar and therefore not key competitive differentiator, providing greater convenience may enable a competitive advantage. Service convenience, as conceptualized by Berry et al. (2002), is defined as the consumers' time and effort perceptions related to buying or using a service. For this reason, B2B customers are interested in how fast the service is provided and how much save non-monetary cost like time or effort by the service convenience along with service quality. Therefore, this study attempts to investigate the impact of service convenience on relationship factors such as relationship satisfaction, relationship commitment, and relationship performance. The purpose of this study is to find out whether service convenience can be a new antecedent of relationship quality and relationship performance. In addition, this study tries to examine how five-dimensional service convenience constructs (decision convenience, access convenience, transaction convenience, benefit convenience, post-benefit convenience) affect customers' relationship satisfaction, relationship commitment, and relationship performance. The service convenience comprises five fundamental components - decision convenience (the perceived time and effort costs associated with service purchase or use decisions), access convenience(the perceived time and effort costs associated with initiating service delivery), transaction convenience(the perceived time and effort costs associated with finalizing the transaction), benefit convenience(the perceived time and effort costs associated with experiencing the core benefits of the offering) and post-benefit convenience (the perceived time and effort costs associated with reestablishing subsequent contact with the firm). Earlier studies of perceived service convenience in the industrial market are none. The conventional studies that have dealt with service convenience have usually been made in the consumer market, or they have dealt with convenience aspects in the service process. This service convenience measure for consumer market can be useful tool to estimate service quality in B2B market. The conceptualization developed by Berry et al. (2002) reflects a multistage, experiential consumption process in which evaluations of convenience vary at each stage. For this reason, the service convenience measure is good for B2B service environment which has complex processes and various types. Especially when categorizing B2B service as sequential stage of service delivery like Kumar and Kumar (2004), the Berry's service convenience measure which reflect sequential flow of service deliveries suitable to establish B2B service convenience. For this study, data were gathered from respondents who often buy business service and analyzed by structural equation modeling. The sample size in the present study is 119. Composite reliability values and average variance extracted values were examined for each variable to have reliability. We determine whether the measurement model supports the convergent validity by CFA, and discriminant validity was assessed by examining the correlation matrix of the constructs. For each pair of constructs, the square root of the average variance extracted exceeded their correlations, thus supporting the discriminant validity of the constructs. Hypotheses were tested using the Smart PLS 2.0 and we calculated the PLS path values and followed with a bootstrap re-sampling method to test the hypotheses. Among the five dimensional service convenience constructs, four constructs (decision convenience, transaction convenience, benefit convenience, post-benefit convenience) affected customers' positive relationship satisfaction, relationship commitment, and relationship performance. This result means that service convenience is important cue to improve relationship between buyer and seller. One of the five service convenience dimensions, access convenience, does not affect relationship quality and performance, which implies that the dimension of service convenience is not important factor of cumulative satisfaction. The Cumulative satisfaction can be distinguished from transaction-specific customer satisfaction, which is an immediate post-purchase evaluative judgment or an affective reaction to the most recent transactional experience with the firm. Because access convenience minimizes the physical effort associated with initiating an exchange, the effect on relationship satisfaction similar to cumulative satisfaction may be relatively low in terms of importance than transaction-specific customer satisfaction. Also, B2B firms focus on service quality, price, benefit, follow-up service and so on than convenience of time or place in service because it is relatively difficult to change existing transaction partners in B2B market compared to consumer market. In addition, this study using partial least squares methods reveals that customers' satisfaction and commitment toward relationship has mediating role between the service convenience and relationship performance. The result shows that management and investment to improve service convenience make customers' positive relationship satisfaction, and then the positive relationship satisfaction can enhance the relationship commitment and relationship performance. And to conclude, service convenience management is an important part of successful relationship performance management, and the service convenience is an important antecedent of relationship between buyer and seller such as the relationship commitment and relationship performance. Therefore, it has more important to improve relationship performance that service providers enhance service convenience although competitive service development or service quality improvement is important. Given the pressure to provide increased convenience, it is not surprising that organizations have made significant investments in enhancing the convenience aspect of their product and service offering.

• Journal of Hospice and Palliative Care
• /
• v.1 no.1
• /
• pp.39-46
• /
• 1998

Purpose : Hospice Care is considered as one of the most perfect solutions for the problems brought up as the number of chronically ill patients are increasing rapidly and most of social welfare oriented countries are seeking the quality of life. Our former studies(1996, 1997) were to find out the current status of the hospice care in Korea by surveying terminally ill patients and their family members as well as medical professionals. The former study was also to conduct the operation research by developing an information service system for training of hospice care teams and volunteers, and hospice patients management. The purpose of this study was that hospice information service system was tested by home visiting hospice care through visiting nurses. Methods : From October 1, 1997 to March 31, 1998, Twenty six terminal cancer patients were included in this study from Seoul National University Hospital and other hospital. Databases and homepage, hospice information service system were designed and developed for the information needed for the hospice care before this study by our research team and this services were available through the internet. Visiting nurses were trained about this system and they visited the patients with PC notebook and provided them hospice care with hospice information system. They collected physical, psychiatric, social data of the subjects at the first visit and during hospice care at home. Results : Sixteen subjects(61.5%) died during the study and the mean survival was 20.7 days. Anorexia(96.2%), immobility(88.5%) and pain(84.6%) were the major symptom in the 26 subjects, Altered nutrition(26.1%) and pain(12.4%) were the most frequent diagnoses in 226 nursing diagnoses of the subjects. Families understood and demanded the hospice care more than patients. And most patients and families didn't demand spiritual or social care. Conclusion : Through this demonstration study, it was found that we have to provide the information of pain management and nutritional support for patients by the nurses and visiting hospice nurse. The information service system needs to be upgraded with information and manpower of spiritual and social care according to the findings.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.49 no.4
• /
• pp.15-29
• /
• 2021

The study aims to grasp the perception and appraisal of urban park users through text analysis. This study used Google review data provided by Google Maps. Google Maps Review is an online review platform that provides information evaluating locations through social media and provides an understanding of locations from the perspective of general reviewers and regional guides who are registered as members of Google Maps. The study determined if the Google Maps Reviews were useful for extracting meaningful information about the user perceptions and appraisals for parks management plans. The study chose three urban parks in Seoul, South Korea; Seoul Forest, Boramae Park, and Olympic Park. Review data for each of these three parks were collected via web crawling using Python. Through text analysis, the keywords and network structure characteristics for each park were analyzed. The text was analyzed, as were park ratings, and the analysis compared the reviews of residents and foreign tourists. The common keywords found in the review comments for the three parks were "walking", "bicycle", "rest" and "picnic" for activities, "family", "child" and "dogs" for accompanying types, and "playground" and "walking trail" for park facilities. Looking at the characteristics of each park, Seoul Forest shows many outdoor activities based on nature, while the lack of parking spaces and congestion on weekends negatively impacted users. Boramae Park has the appearance of a city park, with various facilities providing numerous activities, but reviewers often cited the park's complexity and the negative aspects in terms of dog walking groups. At Olympic Park, large-scale complex facilities and cultural events were frequently mentioned, emphasizing its entertainment functions. Google Maps Review can function as useful data to identify parks' overall users' experiences and general feelings. Compared to data from other social media sites, Google Maps Review's data provides ratings and understanding factors, including user satisfaction and dissatisfaction.

• Journal of Service Research and Studies
• /
• v.13 no.2
• /
• pp.181-203
• /
• 2023

YouTube exhibits a hybrid personality, incorporating traits of both over-the-top (OTT) and personal broadcasting platforms. However, limited research has investigated these hybrid characteristics, particularly in the context of paid YouTube channel memberships. Therefore, building upon consumption value theory and prior literature, this study examines the influence of consumption value factors associated with paid YouTube channel memberships on usage satisfaction and continuance intention. Specifically, the study identifies four perceived consumption value factors (functional, social, emotional, and epistemic values) within the paid YouTube channel membership context and assesses their impact on usage satisfaction and continuance intention. Additionally, the study explores the moderating role of conditional value (the experience of watching live streams on paid YouTube channels) in these relationships. Data was collected via an online survey from Korean adults who subscribed to multiple paid YouTube channel memberships, resulting in 274 responses. The proposed hypotheses were tested using structural equation modeling (SEM). The SEM results indicate that all four consumption value factors significantly influence usage satisfaction, with usage satisfaction in turn positively affecting continuance intention. Furthermore, the study reveals that conditional value moderates the relationships between functional/emotional values and usage satisfaction, as well as between usage satisfaction and continuance intention. This study is the first to focus on YouTube channel paid memberships, which encompass characteristics from both OTT and personal broadcasting platforms. It is anticipated that this research will offer insights to personal broadcasters and stakeholders regarding the motivational factors that impact user satisfaction and encourage subscriptions to channel memberships.

• Journal of Intelligence and Information Systems
• /
• v.22 no.1
• /
• pp.119-138
• /
• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• Journal of Korean Society for Geospatial Information Science
• /
• v.22 no.3
• /
• pp.57-69
• /
• 2014

As of 2013, Korean population density is 505 persons per $1km^2$ and is ranked 3rd place in the most densely populated countries exception of city-states. It shows clearly the population is concentrated in the city area. To fulfil this urban concentration population demand, the enlargement and complexation of buildings, subway and other underground spaces connection tendency has been intensified, and it is need to construct the indoor spatial information DB as well as the accurate indoor surveying DB to promote people's safety and social welfare. In this study, Sadang station and Incheon National Airport were aimed for the construction of Wi-Fi AP location DB and RadioMap DB by collecting the indoor AP raw datas by using mobile device and those collected results were ran through the process of verification, supplementation, and analyzation. To evaluate the performance of constructed DB, 10 points in Incheon Airport- 3rd flr in block A, and 9 points in Sadang station-B1 were selected and calculated the estimated points and ran evaluation experiment using survey positioning error, which is distance between real position and the estimated position. The result shows that Incheon international airport's average and standard deviation was separately 17.81m, 17.79m and Sadang station's average and standard deviation was separately 22.64m, 23.74m. In Sadang station's case, the areas near the exit has low performance of surveying position due to fewer visible AP points than other areas. As total datas were examined except those position, it was verified that the user's location was mapping close position in surveying positioning by using constructed DB. It means that constructed DB contains correct Wi-Fi AP locations and radio wave patterns in object region, so it is considered that the indoor spatial information service based on constructed DB would be available.

• Journal of Intelligence and Information Systems
• /
• v.20 no.4
• /
• pp.89-105
• /
• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.