• Journal of Distribution Science
• /
• v.12 no.6
• /
• pp.15-20
• /
• 2014

Purpose - In general, researchers try to abide by the code of research ethics, but many of them are not fully aware of plagiarism, unintentionally committing the research misconduct when they write a research paper. This research aims to introduce researchers a clear and easy guideline at a conference, which helps researchers avoid accidental plagiarism by addressing the issue. This research is expected to contribute building a climate and encouraging creative research among scholars. Research design, data, methodology & Results - Plagiarism is considered a sort of research misconduct along with fabrication and falsification. It is defined as an improper usage of another author's ideas, language, process, or results without giving appropriate credit. Plagiarism has nothing to do with examining the truth or accessing value of research data, process, or results. Plagiarism is determined based on whether a research corresponds to widely-used research ethics, containing proper citations. Within academia, plagiarism goes beyond the legal boundary, encompassing any kind of intentional wrongful appropriation of a research, which was created by another researchers. In summary, the definition of plagiarism is to steal other people's creative idea, research model, hypotheses, methods, definition, variables, images, tables and graphs, and use them without reasonable attribution to their true sources. There are various types of plagiarism. Some people assort plagiarism into idea plagiarism, text plagiarism, mosaic plagiarism, and idea distortion. Others view that plagiarism includes uncredited usage of another person's work without appropriate citations, self-plagiarism (using a part of a researcher's own previous research without proper citations), duplicate publication (publishing a researcher's own previous work with a different title), unethical citation (using quoted parts of another person's research without proper citations as if the parts are being cited by the current author). When an author wants to cite a part that was previously drawn from another source the author is supposed to reveal that the part is re-cited. If it is hard to state all the sources the author is allowed to mention the original source only. Today, various disciplines are developing their own measures to address these plagiarism issues, especially duplicate publications, by requiring researchers to clearly reveal true sources when they refer to any other research. Conclusions - Research misconducts including plagiarism have broad and unclear boundaries which allow ambiguous definitions and diverse interpretations. It seems difficult for researchers to have clear understandings of ways to avoid plagiarism and how to cite other's works properly. However, if guidelines are developed to detect and avoid plagiarism considering characteristics of each discipline (For example, social science and natural sciences might be able to have different standards on plagiarism.) and shared among researchers they will likely have a consensus and understanding regarding the issue. Particularly, since duplicate publications has frequently appeared more than plagiarism, academic institutions will need to provide pre-warning and screening in evaluation processes in order to reduce mistakes of researchers and to prevent duplicate publications. What is critical for researchers is to clearly reveal the true sources based on the common citation rules and to only borrow necessary amounts of others' research.

• Journal of Intelligence and Information Systems
• /
• v.18 no.4
• /
• pp.19-42
• /
• 2012

Consumer consumption patterns are shifting rapidly as buyers migrate from offline markets to e-commerce routes, such as shopping channels on TV and internet shopping malls. In the offline markets consumers go shopping, see the shopping items, and choose from them. Recently consumers tend towards buying at shopping sites free from time and place. However, as e-commerce markets continue to expand, customers are complaining that it is becoming a bigger hassle to shop online. In the online shopping, shoppers have very limited information on the products. The delivered products can be different from what they have wanted. This case results to purchase cancellation. Because these things happen frequently, they are likely to refer to the consumer reviews and companies should be concerned about consumer's voice. E-commerce is a very important marketing tool for suppliers. It can recommend products to customers and connect them directly with suppliers with just a click of a button. The recommender system is being studied in various ways. Some of the more prominent ones include recommendation based on best-seller and demographics, contents filtering, and collaborative filtering. However, these systems all share two weaknesses : they cannot recommend products to consumers on a personal level, and they cannot recommend products to new consumers with no buying history. To fix these problems, we can use the information which has been collected from the questionnaires about their demographics and preference ratings. But, consumers feel these questionnaires are a burden and are unlikely to provide correct information. This study investigates combining collaborative filtering with the centrality of social network analysis. This centrality measure provides the information to infer the preference of new consumers from the shopping history of existing and previous ones. While the past researches had focused on the existing consumers with similar shopping patterns, this study tried to improve the accuracy of recommendation with all shopping information, which included not only similar shopping patterns but also dissimilar ones. Data used in this study, Movie Lens' data, was made by Group Lens research Project Team at University of Minnesota to recommend movies with a collaborative filtering technique. This data was built from the questionnaires of 943 respondents which gave the information on the preference ratings on 1,684 movies. Total data of 100,000 was organized by time, with initial data of 50,000 being existing customers and the latter 50,000 being new customers. The proposed recommender system consists of three systems : [+] group recommender system, [-] group recommender system, and integrated recommender system. [+] group recommender system looks at customers with similar buying patterns as 'neighbors', whereas [-] group recommender system looks at customers with opposite buying patterns as 'contraries'. Integrated recommender system uses both of the aforementioned recommender systems to recommend movies that both recommender systems pick. The study of three systems allows us to find the most suitable recommender system that will optimize accuracy and customer satisfaction. Our analysis showed that integrated recommender system is the best solution among the three systems studied, followed by [-] group recommended system and [+] group recommender system. This result conforms to the intuition that the accuracy of recommendation can be improved using all the relevant information. We provided contour maps and graphs to easily compare the accuracy of each recommender system. Although we saw improvement on accuracy with the integrated recommender system, we must remember that this research is based on static data with no live customers. In other words, consumers did not see the movies actually recommended from the system. Also, this recommendation system may not work well with products other than movies. Thus, it is important to note that recommendation systems need particular calibration for specific product/customer types.