• Journal of Intelligence and Information Systems
• /
• v.27 no.2
• /
• pp.1-15
• /
• 2021

If it is difficult for us to make decisions, we ask for advice from friends or people around us. When we decide to buy products online, we read anonymous reviews and buy them. With the advent of the Data-driven era, IT technology's development is spilling out many data from individuals to objects. Companies or individuals have accumulated, processed, and analyzed such a large amount of data that they can now make decisions or execute directly using data that used to depend on experts. Nowadays, the recommender system plays a vital role in determining the user's preferences to purchase goods and uses a recommender system to induce clicks on web services (Facebook, Amazon, Netflix, Youtube). For example, Youtube's recommender system, which is used by 1 billion people worldwide every month, includes videos that users like, "like" and videos they watched. Recommended system research is deeply linked to practical business. Therefore, many researchers are interested in building better solutions. Recommender systems use the information obtained from their users to generate recommendations because the development of the provided recommender systems requires information on items that are likely to be preferred by the user. We began to trust patterns and rules derived from data rather than empirical intuition through the recommender systems. The capacity and development of data have led machine learning to develop deep learning. However, such recommender systems are not all solutions. Proceeding with the recommender systems, there should be no scarcity in all data and a sufficient amount. Also, it requires detailed information about the individual. The recommender systems work correctly when these conditions operate. The recommender systems become a complex problem for both consumers and sellers when the interaction log is insufficient. Because the seller's perspective needs to make recommendations at a personal level to the consumer and receive appropriate recommendations with reliable data from the consumer's perspective. In this paper, to improve the accuracy problem for "appropriate recommendation" to consumers, the recommender systems are proposed in combination with context-based deep learning. This research is to combine user-based data to create hybrid Recommender Systems. The hybrid approach developed is not a collaborative type of Recommender Systems, but a collaborative extension that integrates user data with deep learning. Customer review data were used for the data set. Consumers buy products in online shopping malls and then evaluate product reviews. Rating reviews are based on reviews from buyers who have already purchased, giving users confidence before purchasing the product. However, the recommendation system mainly uses scores or ratings rather than reviews to suggest items purchased by many users. In fact, consumer reviews include product opinions and user sentiment that will be spent on evaluation. By incorporating these parts into the study, this paper aims to improve the recommendation system. This study is an algorithm used when individuals have difficulty in selecting an item. Consumer reviews and record patterns made it possible to rely on recommendations appropriately. The algorithm implements a recommendation system through collaborative filtering. This study's predictive accuracy is measured by Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE). Netflix is strategically using the referral system in its programs through competitions that reduce RMSE every year, making fair use of predictive accuracy. Research on hybrid recommender systems combining the NLP approach for personalization recommender systems, deep learning base, etc. has been increasing. Among NLP studies, sentiment analysis began to take shape in the mid-2000s as user review data increased. Sentiment analysis is a text classification task based on machine learning. The machine learning-based sentiment analysis has a disadvantage in that it is difficult to identify the review's information expression because it is challenging to consider the text's characteristics. In this study, we propose a deep learning recommender system that utilizes BERT's sentiment analysis by minimizing the disadvantages of machine learning. This study offers a deep learning recommender system that uses BERT's sentiment analysis by reducing the disadvantages of machine learning. The comparison model was performed through a recommender system based on Naive-CF(collaborative filtering), SVD(singular value decomposition)-CF, MF(matrix factorization)-CF, BPR-MF(Bayesian personalized ranking matrix factorization)-CF, LSTM, CNN-LSTM, GRU(Gated Recurrent Units). As a result of the experiment, the recommender system based on BERT was the best.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.18 no.1
• /
• pp.141-158
• /
• 2023

From the perspective of an entrepreneur, one of the most important factors for understanding the inherent limitations of a startup, reducing the risk of failure, and succeeding is the composition of the talent, that is, the founding team. Therefore, a common concern experienced by entrepreneurs in the pre-entrepreneurship stage or the early stage of startup is the choice between independent startups and co-founding start-up. Nonetheless, in Korea, the share of independent entrepreneurship is significantly higher than that of co-founding start-up. On the other hand, focusing on the fact that many successful global innovative companies are in the form of co-founding start-up, the success factors of co-founding start-up were examined. Most of the related preceding studies are studies that identify the capabilities and characteristics of individual entrepreneurs as factors influencing the survival and success of entrepreneurship, and there is a lack of research on partnerships, that is, co-founding start-up, which are common in the field of entrepreneurship ecosystems. Therefore, this study attempted a multi-case study through in-depth interviews, collection of relevant data, analysis of contextual information, and consideration of previous studies targeting co-founders of domestic startups that succeeded in opportunistic startups. Through this, a model for deriving the phased characteristics and key success factors of co-founding start-up was proposed. As a result of the study, the key element of the preliminary start-up stage was 'opportunity', and the success factors were 'opportunity recognition through entrepreneur's experience' and 'idea development'. The key element in the early stages of start-up is "start-up team," and the success factor is "trust and complement of start-up team," and synergy is shown when "diversity and homogeneity of start-up team" are harmonized. In addition, conflicts between co-founders may occur in the early stages of start-ups, which has a large impact on the survival of start-ups. The conflict between the start-up team could be overcome through constant "mutual understanding and respect through communication" and "clear division of work and role sharing." It was confirmed that the core element of the start-up growth stage was 'resources', and 'securing excellent talent' and 'raising external funds' were important factors for success. These results are expected to overcome the limitations of start-up companies, such as limited resources, lack of experience, and risk of failure, in entrepreneurship studies, and prospective entrepreneurs preparing for a start-up in a situation where the form of co-founding start-up is attracting attention as one of the alternatives to increase the success rate. It has implications for various stakeholders in the entrepreneurial ecosystem.

• Journal of The Korean Association For Science Education
• /
• v.28 no.5
• /
• pp.495-505
• /
• 2008

The purpose of this study was to analyze the level of evidence used in gifted elementary students' argumentation. The subjects were 15, 5th and 6th grade students selected in the Science Education Institute for Gifted Youth in K University. After the argumentation task was given to students 2 weeks ago, the students grouped themselves in the affirmative and negative and took part in a debate for 2 hours. Their argumentation process was observed, recorded and transcribed for analysis. Transcribed data was given a Protocol Number according to priority and was examined to find out what were the characteristics when students participated in the task. The evidence used in argumentation was graded from level 1 to level 6 according to Perella's Hierarchy of Evidence and the rate of frequency classified by the level was expressed in graph. Students used Level 1- Level 2 evidence above 50% without for or against task. They had weak argumentation making use of low-level evidence such as individual experience, opinion and another person's experience rather than objective evidences. On the other hand, students commented on the lack of opponent's evidence when they could not trust an opponent's evidence. If one team asked the other to present more evidence but could not, they disregarded the question and turned to another topic. And in cases where the opponent team refuted with evidences of high level, the other team just repeated their claim or evaded the rebuttal. The students tended to complete the argument without the same conclusions with some interruptions. The results show that we need an educational programs including scientific argumentation for science-gifted elementary school students.

• Journal of Distribution Science
• /
• v.9 no.4
• /
• pp.5-13
• /
• 2011

This study provides analysis of the effectiveness of domestic marketing strategies of the Korean coffee shop "Caffe Bene". It bases its evaluation on statistical outputs of 'choice attributes,' "market segmentation," demographic characteristics," and "satisfaction differences." The results are summarized in four points. First, five choice attributes were extracted from factor analysis: price, atmosphere, comfort, taste, and location; these are related to coffee shop selection behavior. Based on these five factors, cluster analysis was conducted, with statistical results classifying customers into three major groups: atmosphere oriented; comfort oriented; and taste oriented. Second, discriminant analysis tested cluster analysis and showed two discriminant functions: location and atmosphere. Third, cross-tabulation analysis based on demographic characteristics showed distinctive demographic characteristics within the three groups. Atmosphere oriented group, early-20s, as women of all ages was found to be 'walking down the street 'and 'through acquaintances' in many cases, as the cognitive path, and mostly found the store through 'outdoor advertising', and 'introduction'. Comfort oriented group was mainly women who are students in their early twenties or professionals, and appeared as a group to be very loyal because of high recommendation to other customers compared to other groups. Taste oriented group, unlike the other group, was mainly late-20s' college graduates, and was confirmed, as low loyalty, with lower recommendation activity. Fourth, to analyze satisfaction differences, one-way ANOVA was conducted. It shows that groups which show high satisfaction in the five main factors also show high menu satisfaction and high overall satisfaction. This results show that segmented marketing strategies are necessary because customers are considering price, atmosphere, comfort, taste, location when they choose coffee shop and demographics show different attributes based on segmented groups. For example, atmosphere oriented group is satisfied with shop interior and comfort while dissatisfied with price because most of the customers in this group are early 20s and do not have great financial capability. Thus, price discounting marketing strategies based on individual situations through CRM system is critical. Comfort oriented group shows high satisfaction level about location and shop comfort. Also, in this group, there are many early 20s female customers, students, and self-employed people. This group customers show high word of mouth tendency, hence providing positive brand image to the customers would be important. In case of taste oriented group, while the scores of taste and location are high, word of mouth score is low. This group is mainly composed of educated and professional many late 20s customers, therefore, menu differentiation, increasing quality of coffee taste and price discrimination is critical to increase customers' satisfaction. However, it is hard to generalize the results of study to other coffee shop brand, because this study have researched only one domestic coffee shop, Caffe Bene. Thus if future study expand the scope of locations, brands, and occupations, the results of the study would provide more generalizable results. Finally, research of customer satisfactions of menu, trust, loyalty, and switching cost would be critical in the future study.

• Journal of Global Scholars of Marketing Science
• /
• v.20 no.2
• /
• pp.188-198
• /
• 2010

This study attempts to examine the influence that negative WOM (NWOM) has in an online context. It specifically focuses on the impact of the service failure description and the perceived intention of the communication provider on consumer evaluations of firm competence, attitude toward the firm, positive word of mouth and behavioral intentions. Studies of communication persuasiveness focus on "who says what; to whom; in which channel; with what effect (Chiu 2007)." In this research study, we examine electronic web posting, particularly focusing on two aspects of "what": the level of service failure communicated and perceived intention of the individual posting. It stands to reason electronic NWOM that appears to be trying to damage a product’s or firm's reputation will be viewed as more biased and will thus be considered as less credible. According to attribution theory, people search for the causes of events especially those that are negative and unexpected (Weiner 2006). Hennig-Thurau and Walsh (2003) state "since the reader has only limited knowledge and trust of the author of an online articulation the quality of the contribution could be expected to serve as a potent moderator of the articulation-behavior relationship. We therefore posit the following hypotheses: H1. Subjects exposed to electronic NWOM describing a high level of service failure will provide lower scores on measures of (a) firm competence, (b) attitude toward the firm, (c) positive word of mouth, and (d) behavioral intention than will subjects exposed to electronic NWOM describing a low level of service failure. H2. Subjects exposed to electronic NWOM with a warning intent will provide lower scores on measures of (a) firm competence, (b) attitude toward the firm, (c) positive word of mouth, and (d) behavioral intention than will subjects exposed to electronic NWOM with a vengeful intent. H3. Level of service failure in electronic NWOM will interact with the perceived intention of the electronic NWOM, such that there will be a decrease in mean response on measures of (a) firm competence, (b) attitude toward the firm, (c) positive word of mouth, and (d) behavioral intention from electronic NWOM with a warning intent to a vengeful intent. The main study involved a2 (service failure severity) x2 (NWOM with warning versus vengeful intent) factorial experiment. Stimuli were presented to subjects online using a mock online web posting. The scenario described a service failure associated with non-acceptance of a gift card in a brick-and-mortar retail establishment. A national sample was recruited through an online research firm. A total of 113 subjects participated in the study. A total of 104 surveys were analyzed. The scenario was perceived to be realistic with 92.3% giving the scenario a greater than average response. Manipulations were satisfactory. Measures were pre-tested and validated. Items were analyzed and found reliable and valid. MANOVA results found the multivariate interaction was not significant, allowing our interpretation to proceed to the main effects. Significant main effects were found for post intent and service failure severity. The post intent main effect was attributable to attitude toward the firm, positive word of mouth and behavioral intention. The service failure severity main effect was attributable to all four dependent variables: firm competence, attitude toward the firm, positive word of mouth and behavioral intention. Specifically, firm competence for electronic NWOM describing high severity of service failure was lower than electronic NWOM describing low severity of service failure. Attitude toward the firm for electronic NWOM describing high severity of service failure was lower than electronic NWOM describing low severity of service failure. Positive word of mouth for electronic NWOM describing high severity of service failure was lower than electronic NWOM describing low severity of service failure. Behavioral intention for electronic NWOM describing high severity of service failure was lower for electronic NWOM describing low severity of service failure. Therefore, H1a, H1b, H1c and H1d were all supported. In addition, attitude toward the firm for electronic NWOM with a warning intent was lower than electronic NWOM with a vengeful intent. Positive word of mouth for electronic NWOM with a warning intent was lower than electronic NWOM with a vengeful intent. Behavioral intention for electronic NWOM with a warning intent was lower than electronic NWOM with a vengeful intent. Thus, H2b, H2c and H2d were supported. However, H2a was not supported though results were in the hypothesized direction. Otherwise, there was no significant multivariate service failure severity by post intent interaction, nor was there a significant univariate service failure severity by post intent interaction for any of the three hypothesized variables. Thus, H3 was not supported for any of the four hypothesized variables. This study has research and managerial implications. The findings of this study support prior research that service failure severity impacts consumer perceptions, attitude, positive word of mouth and behavioral intentions (Weun et al. 2004). Of further relevance, this response is evidenced in the online context, suggesting the need for firms to engage in serious focused service recovery efforts. With respect to perceived intention of electronic NWOM, the findings support prior research suggesting reader's attributions of the intentions of a source influence the strength of its impact on perceptions, attitude, positive word of mouth and behavioral intentions. The implication for managers suggests while consumers do find online communications to be credible and influential, not all communications are weighted the same. A benefit of electronic WOM, even when it may be potentially damaging, is it can be monitored for potential problems and additionally offers the possibility of redress.