• Journal of the Korea Safety Management & Science
• /
• v.15 no.4
• /
• pp.407-416
• /
• 2013

This paper proposes an approach to analyzing the relationship between technology and services, and to identifying promising service areas for technology-based firms with the analysis of business model (BM) patents. First, BM patents and technology patents are collected and classified into their relevant categories, respectively. Second, patent citation analysis is conducted to analyze the linkage and impacts between each technology and service field at macro level. Third, as a micro level analysis, patent co-classification analysis is employed to identify the interrelationships among specific technology and service areas. Finally, the promising service areas for technology-based firms seeking service areas for diversification is investigated with portfolio analysis. The working of the proposed approach is provided with the help of a case study of IT and mobile services. The proposed approach could guide and help managers of technology-based firms to discover the opportunity of the diversification to new areas in emerging service fields.

• The Journal of Society for e-Business Studies
• /
• v.6 no.3
• /
• pp.181-196
• /
• 2001

This paper presents a fundamental study to establish a standardization system of the Construction Continuous Acquisition and Life-cycle Support(CALS). A state of the art in standardization of the Construction CALS is reviewed to find some defects in developing CALS system in construction industry. It is analyzed that three major parts were needed to set up a standardization system for the Construction CALS. Firstly, the range of Construction CALS standardization is set up to identifying Construction CALS and defining the standards and standardization. Secondly, the strategy to carry out more effectively in Construction CALS standardization and make the relationship of the concerned system presented here can be used to establish the Construction CALS standardization system. In addition, the spread and application device are proposed to use Construction CALS standards at public institution and construction related companies. Conclusively, a classification of the Construction CALS standards was proposed and some objects to be standardized were represented in that. Results studied in this paper will provide the primary information and basic model to develop a guideline for standardization of the Construction CALS.

• The Journal of Society for e-Business Studies
• /
• v.8 no.3
• /
• pp.143-164
• /
• 2003

Because of the wide scale in EC area, the use of Electronic Commerce spreads out quickly. This paper proposes the curriculum applied to the department of EC in University reflecting the recent social trend. EC has some relations with the several areas like Computer Science ＆ Engineering, Management and Industrial Engineering etc. after analyzing the domestic curriculums, we propose the propriate curriculums and academic classification standard. Also according to the standard, We want to propose the separate curriculums to three important majors and describe the introduction to each major.

• Current Research on Agriculture and Life Sciences
• /
• v.29
• /
• pp.63-74
• /
• 2011

The purpose of this study is a classification of farm typology and an analysis of characteristic e-commerce type of farm enterprises. The classification of sample farm enterprises thus results in six distinctively agribusiness type. The six identified types can be characteristic as follows: production type, processing type and distribution type, e-commerce type, export-agricultural type, amenity-tourism type. This article attempted to come up with workable strategies to solve these problems affecting e-commerce type farm enterprises. The main results this paper are as follows: 1) to make organization of e-commerce type farm enterprises to accomplish business goals 2) to find out solution for urgent problems and subjects of farm management 3) to practice profitable business model for e-commerce type. E-commerce type farm enterprises needs are not only technology transference but also farm management.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.4090-4102
• /
• 2018

After emerging online communications, text mining and sentiment analysis has been frequently applied into analyzing electronic word-of-mouth. This study aims to develop a domain-specific lexicon of sentiment analysis to predict box office success in Korea film market and validate the feasibility of the lexicon. Natural language processing, a machine learning algorithm, and a lexicon-based sentiment classification method are employed. To create a movie domain sentiment lexicon, 233,631 reviews of 147 movies with popularity ratings is collected by a XML crawling package in R program. We accomplished 81.69% accuracy in sentiment classification by the Korean sentiment dictionary including 706 negative words and 617 positive words. The result showed a stronger positive relationship with box office success and consumers' sentiment as well as a significant positive effect in the linear regression for the predicting model. In addition, it reveals emotion in the user-generated content can be a more accurate clue to predict business success.

• IE interfaces
• /
• v.19 no.1
• /
• pp.86-96
• /
• 2006

Some customer data of manufacturing industries have a large number of incomplete data set due to the customer's infrequent purchasing behavior and the limitation of customer profile data gathered from sales representatives. So that, most sophisticated data analysis methods may not be applied directly. This paper proposes a heuristic data analysis method to classify customers in household appliances industries. The proposed PD (percent of difference) method can be used for the discriminant analysis of incomplete customer data with simple mathematical calculations. The method is composed of variable distribution estimation step, PD measure and cluster score evaluation steps, variable impact construction step, and segment assignment step. A real example is also presented.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2005.11a
• /
• pp.295-303
• /
• 2005

Semantic Web and its related technologies have been opening the era of information sharing via Web. In the meantime, there are several huddles to overcome toward the new era and one of the major huddles is information integration issue unless we build and use a single unified but huge ontology which address everything in the world. Particularly in e-business area, information integration problem must be a great concern in search and comparison of products from various internet shopping sites and e-marketplaces. To overcome such an information integration problem, we propose an ontology driven mapping algorithm between heterogeneous product classification and description frameworks. We also perform comparative evaluation of the proposed mapping algorithm against a well-known ontology mapping tool, PROMPT.

• The Journal of Fisheries Business Administration
• /
• v.53 no.4
• /
• pp.51-67
• /
• 2022

The Korean tariff rate for fishery products is a single tax rate of 10% for live fish and frozen seafood, and 20% for all others. Since FTAs have been concluded with several countries, the tariffs is not an appropriate means to protect domestic fishery producers. The differential tariff rate according to the scientific name (genus) of the fishery products, which was implemented 30 years ago to protect fishery products produced in the Korean coastal waters has lost its original purpose. It seems that future fishery trade policy should focus on IUU prevention, hygiene and safety of consumers rather than protecting fishery producers through customs tariffs. This paper suggest that a paradigm shift in the fishery producers protection policies such as direct financial support from the state, protection and development of fishery resources, and support for fostering the 6th industry rather than indirect protection through tariffs.

• Asia pacific journal of information systems
• /
• v.34 no.2
• /
• pp.372-399
• /
• 2024

Online reviews play a significant role in consumer purchase decisions on e-commerce platforms. To address information overload in the context of online reviews, factors that drive review helpfulness have received considerable attention from scholars and practitioners. The purpose of this study is to explore the differential effects of discrete emotions (anger, disgust, fear, joy, sadness, and surprise) on perceived review helpfulness, drawing on cognitive appraisal theory of emotion and expectation-confirmation theory. Emotions embedded in 56,157 hotel reviews collected from TripAdvisor.com were extracted based on a transfer learning model to measure emotion variables as an alternative to dictionary-based methods adopted in previous research. We found that anger and fear have positive impacts on review helpfulness, while disgust and joy exert negative impacts. Moreover, hotel star-classification significantly moderates the relationships between several emotions (disgust, fear, and joy) and perceived review helpfulness. Our results extend the understanding of review assessment and have managerial implications for hotel managers and e-commerce vendors.

• Asia pacific journal of information systems
• /
• v.29 no.1
• /
• pp.35-49
• /
• 2019

The paper attempts to document the application of relevant Machine Learning (ML) models on Yelp (a crowd-sourced local business review and social networking site) dataset to analyze, predict and recommend business. Strategically using two cloud platforms to minimize the effort and time required for this project. Seven machine learning algorithms in Azure ML of which four algorithms are implemented in Databricks Spark ML. The analyzed Yelp business dataset contained 70 business attributes for more than 350,000 registered business. Additionally, review tips and likes from 500,000 users have been processed for the project. A Recommendation Model is built to provide Yelp users with recommendations for business categories based on their previous business ratings, as well as the business ratings of other users. Classification Model is implemented to predict the popularity of the business as defining the popular business to have stars greater than 3 and unpopular business to have stars less than 3. Text Analysis model is developed by comparing two algorithms, uni-gram feature extraction and n-feature extraction in Azure ML studio and logistic regression model in Spark. Comparative conclusions have been made related to efficiency of Spark ML and Azure ML for these models.