• Trans-
• /
• v.10
• /
• pp.73-113
• /
• 2021

Previous studies show that VR images can influence consumers' attitudes and behaviors by evoking imagination. In this study, we introduce a reality-based closed-loop 3D image (hereafter Virtualgraph). Then we try to see whether such image would increase evocativeness in a mobile commerce environment and whether higher telepresence of the visual image of a product can increase the purchase intention of that product. In order to find the above, we developed a model comprised of constructs containing telepresence, perceived value price, perceived food quality, and vividness of visual imagery questionnaire (VVIQ). We used Virtualgraph application to conduct an experiment, and then conducted an interview as well as a survey. As results of the experiment, survey and interview, we found the followings. First, users evoke imagination better with Virtualgraph than with still images. Second, increased evocativeness affects purchase intention if the perceived quality of fresh food product is satif actory. Third, increased evocativeness makes users value products higher and do even much higher when the perceived quality of fresh food product is good. From the interview, we could find that the experimental group had higher purchase intentions and perceived products as more expensive ones. Also, they perceived images of products clearer and more vivid than did the control group. We also discuss the strategic implications of using Virtualgraph in mobile shopping malls.

• Journal of Global Scholars of Marketing Science
• /
• v.17 no.2
• /
• pp.1-29
• /
• 2007

The flow theory becomes one of the most important frameworks in the internet research arena. Hoffman and Novak proposed a hierarchical flow model showing the antecedents and outcomes of flow and the relationship among these variables in the hyper-media computer circumstances (Hoffman and Novak 1996). This model was further tested after their initial research (Novak, Hoffman, and Yung 2000). At their paper, Hoffman and Novak explained that the balance of challenge and skill leads to flow which means the positive optimal state of mind (Hoffman and Novak 1996). An imbalance between challenge and skill, leads to negative states of mind like anxiety, boredom, apathy (Csikszentmihalyi and Csikszentmihalyi 1988). Almost all research on the flow 4-channel model have been focusingon flow, the positive state of mind (Ellis, Voelkl, and Morris 1994 Mathwick and Rigdon 2004). However, it also needs to examine the formation of the negative states of minds and their outcomes. Flow researchers explain play or playfulness as antecedents or the early state of flow. However, play has been regarded as a distinct concept from flow in the flow literatures (Hoffman and Novak 1996; Novak, Hoffman, and Yung 2000). Mathwick and Rigdon discovered the influences of challenge and skill on play; they also observed the influence of play on web-loyalty and brand loyalty (Mathwick and Rigdon 2004). Unfortunately, they did not go so far as to test the influences of play on state of mind. This study focuses on the relationships between state of mind in the flow 4-channel model and play. Early research has attempted to hypothetically explain state of mind in flow theory, but has not been tested except flow until now. Also the importance of play has been emphasized in the flow theory, but has not been tested in the flow 4-channel model context. This researcher attempts to analyze the relationships among state of mind, skill of play, challenge, state of mind and web loyalty. For this objective, I developed a measure for state of mind and defined the concept of play as a trait. Then, the influences of challenge and skill on the state of mind and play under on-line shopping conditions were tested. Also the influences of play on state of mind were tested and those of flow and play on web loyalty were highlighted. 294 undergraduate students participated in this research survey. They were asked to respond about their perceptions of challenge, skill, state of mind, play, and web-loyalty to on-line shopping mall. Respondents were restricted to students who bought products on-line in a month. In case of buying products at two or more on-line shopping malls, they asked to respond about the shopping mall where they bought the most important one. Construct validity, discriminant validity, and convergent validity were used to check the measurement validations. Also, Cronbach's alpha was used to check scale reliability. A series of exploratory factor analyses was conducted. This researcher conducted confirmatory factor analyses to assess the validity of measurements. All items loaded significantly on their respective constructs. Also, all reliabilities were greater than.70. Chi-square difference tests and goodness of fit tests supported discriminant and convergent validity. The results of clustering and ANOVA showed that high challenge and high skill leaded to flow, low challenge and high skill leaded to boredom, and low challenge and low skill leaded to apathy. But, it was different from my expectation that high challenge and low skill didnot lead to anxiety but leaded to apathy. The results also showed that high challenge and high skill, and high challenge and low skill leaded to the highest play. Low challenge leaded to low play. 4 Structural Equation Models were built by flow, anxiety, boredom, apathy for analyzing not only the impact of play on state of mind and web-loyalty, but also that of state of mind on web-loyalty. According the analyses results of these models, play impacted flow and web-loyalty positively, but impacted anxiety, boredom, and apathy negatively. Results also showed that flow impacted web-loyalty positively, but anxiety, boredom, and apathy impacted web-loyalty negatively. The interpretations and implications of the test results of the hypotheses are as follows. First, respondents belonging to different clusters based on challenge and skill level experienced different states of mind such as flow, anxiety, boredom, apathy. The low challenge and low skill group felt the highest anxiety and apathy. It could be interpreted that this group feeling high anxiety or fear, then avoided attempts to shop on-line. Second, it was found that higher challenge leads to higher levels of play. Test results show that the play level of the high challenge and low skill group (anxiety group) was higher than that of the high challenge and high skill group (flow group). However, this was not significant. Third, play positively impacted flow and negatively impacted boredom. The negative impacts on anxiety and apathy were not significant. This means that the combination of challenge and skill creates different results. Forth, play and flow positively impacted web-loyalty, but anxiety, boredom, apathy had negative impacts. The effect of play on web-loyalty was stronger in case of anxiety, boredom, apathy group than fl ow group. These results show that challenge and skill influences state of mind and play. Results also demonstrate how play and flow influence web-loyalty. It implies that state of mind and play should be the core marketing variables in internet marketing. The flow theory has been focusing on flow and on the positive outcomes of flow experiences. But, this research shows that lots of consumers experience the negative state of mind rather than flow state in the internet shopping circumstance. Results show that the negative state of mind leads to low or negative web-loyalty. Play can have an important role with the web-loyalty when consumers have the negative state of mind. Results of structural equation model analyses show that play influences web-loyalty positively, even though consumers may be in the negative state of mind. This research found the impacts of challenge and skill on state of mind in the flow 4-channel model, not only flow but also anxiety, boredom, apathy. Also, it highlighted the role of play in the flow 4-channel model context and impacts on web-loyalty. However, tests show a few different results from hypothetical expectations such as the highest anxiety level of apathy group and insignificant impacts of play on anxiety and apathy. Further research needs to replicate this research and/or to compare 3-channel model with 4-channel model.

• Journal of Intelligence and Information Systems
• /
• v.17 no.1
• /
• pp.127-138
• /
• 2011

The core service of most research portal sites is providing relevant research papers to various researchers that match their research interests. This kind of service may only be effective and easy to use when a user can provide correct and concrete information about a paper such as the title, authors, and keywords. However, unfortunately, most users of this service are not acquainted with concrete bibliographic information. It implies that most users inevitably experience repeated trial and error attempts of keyword-based search. Especially, retrieving a relevant research paper is more difficult when a user is novice in the research domain and does not know appropriate keywords. In this case, a user should perform iterative searches as follows : i) perform an initial search with an arbitrary keyword, ii) acquire related keywords from the retrieved papers, and iii) perform another search again with the acquired keywords. This usage pattern implies that the level of service quality and user satisfaction of a portal site are strongly affected by the level of keyword management and searching mechanism. To overcome this kind of inefficiency, some leading research portal sites adopt the association rule mining-based keyword recommendation service that is similar to the product recommendation of online shopping malls. However, keyword recommendation only based on association analysis has limitation that it can show only a simple and direct relationship between two keywords. In other words, the association analysis itself is unable to present the complex relationships among many keywords in some adjacent research areas. To overcome this limitation, we propose the hybrid approach for establishing association network among keywords used in research papers. The keyword association network can be established by the following phases : i) a set of keywords specified in a certain paper are regarded as co-purchased items, ii) perform association analysis for the keywords and extract frequent patterns of keywords that satisfy predefined thresholds of confidence, support, and lift, and iii) schematize the frequent keyword patterns as a network to show the core keywords of each research area and connecting keywords among two or more research areas. To estimate the practical application of our approach, we performed a simple experiment with 600 keywords. The keywords are extracted from 131 research papers published in five prominent Korean journals in 2009. In the experiment, we used the SAS Enterprise Miner for association analysis and the R software for social network analysis. As the final outcome, we presented a network diagram and a cluster dendrogram for the keyword association network. We summarized the results in Section 4 of this paper. The main contribution of our proposed approach can be found in the following aspects : i) the keyword network can provide an initial roadmap of a research area to researchers who are novice in the domain, ii) a researcher can grasp the distribution of many keywords neighboring to a certain keyword, and iii) researchers can get some idea for converging different research areas by observing connecting keywords in the keyword association network. Further studies should include the following. First, the current version of our approach does not implement a standard meta-dictionary. For practical use, homonyms, synonyms, and multilingual problems should be resolved with a standard meta-dictionary. Additionally, more clear guidelines for clustering research areas and defining core and connecting keywords should be provided. Finally, intensive experiments not only on Korean research papers but also on international papers should be performed in further studies.

• Journal of the Korean Society of Food Science and Nutrition
• /
• v.43 no.10
• /
• pp.1607-1613
• /
• 2014

The purpose of this study was to investigate intake of protein supplements by university students majoring in physical education. Intake experience rate, reasons for intake, purchasing place, effectiveness, satisfaction level, and side effects were analyzed using a questionnaire. Of 476 students, those who consumed protein supplements were 198 (41.6%). Male's intake experience rate was significantly higher than that of females, and members of health-related clubs also consumed more protein than non-members. The main purchasing place was internet shopping malls, and users obtained information from their friends or upperclassmen. The most frequently consumed protein supplement was 'WPH', and the most frequent reason for intake was 'building muscle or maintaining body shape'. For effectiveness, 'normal' was 49.0% and 'effectiveness' was 33.3%. For satisfaction, 'satisfaction' was 45.5% and 'normal' was 43.4%. The rate of side effects was 44.9%, and digestive issues such as diarrhea and indigestion were observed with high frequency. The results of this study show that education is needed for nutritional knowledge, adequate intake, and side effects of protein supplements.

• The Journal of Art Theory & Practice
• /
• no.13
• /
• pp.73-95
• /
• 2012

In terms of element and process, art today has already been fully systemized, yet tends to become even more systemized. All phases of creation and exhibition, appreciation and education, promotion and marketing are planned, adjusted, and decided within the order of a globalized, networked system. Each phase is executed, depending on the system of management and control and diverse means corresponding to the system. From the step of education, artists are guided to determine their styles and not be motivated by their desire to become star artists or running counter to mainstream tendency and fashion. In the process of planning an exhibition, the level of artist awareness is considered more significant than work quality. It is impossible to avoid such systems and institutions today. No one can escape or be freed from the influence of such system. This discussion addresses a serious distortion in the selection system as part of the system connotatively called "art museum system," especially to evaluate artistic achievement and aesthetic quality. Called "studio system" or "art star system," the system distinguishes successful minority from failed absolute majority and justifies the results, deciding discriminative compensations. The discussion begins from work by Hunter Jonakin and Dan Perjovschi. The key point of this discussion is not their art worlds but the shared truth referred by the two as the collusive "art market" and "art star system." Through works based on their experiences, the two artists refer to these systems which restrict and confine them. Jonakin's Jeff Koons Must Die! is avideo game conveying a critical comment on authoritative operation of the museum system and star system. In this work, participants, whether viewer or artist, are destined to lose: the game is unwinnable. Players take the role of a person locked in a museum where artist Jeff Koons' retrospective is held. The player can either look around and quietly observe the works, which causes a game-over, or he can blow the classical paintings to pieces and cause the artist Koons to come out and reprimand the player, also resulting in a game-over. Like Jonakin, Dan Perjovschi's some drawings also focuses on the status of the artist shrunken by the system. Most artists are ruined in a process of competition to survive within the museum system. As John Burger properly pointed out, out of the art systems today, public collections (art museums) and private collections have become "something unbearable." The system justifies the selection system of art stars and its frame of reference, disregarding the problem of producing numerable victims in its process. What should be underlined above all else is that the present selection system seriously shrinks art's creative function and its function of generating meaning. In this situation, art might fall to the level of entertainment, accessible to more people and compromising with popularity. This discussion is based on assumption and consciousness on the matter that this situation might cause catastrophic results for not only explicit victims of the system but also winners, or ones defined as winners. The system of art is probably possible only by desire or distortion stemmed from such desire. The system can be flourished only under the economic system of avarice: quantitatively expanding economy, abundant style, resort economy in Venice and Miami, and luxurious shopping malls with up-to-date facilities. The catastrophe here is ongoing, not a sudden emergence, and dynamic, leading the system itself to a devastating end.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.5
• /
• pp.29-39
• /
• 2018

Recently, In traditional markets, visitors are declining. The reason is the growth of large stores and Internet shopping malls. The government continues to support and policy to revitalize traditional markets. Government support has been focused on the selective attributes of traditional markets. However, the purchase intention of users in traditional markets is lowered. The reason is that it is in the hygiene of the traditional market. This study analyzed whether the optional attributes of traditional markets and service quality increase the intention of visit, In addition, the users of the traditional market analyzed the hygiene factor as an important factor in the intention of the visit. The results of the analysis is First, convenience, accessibility, transparency, attractiveness, and economic feasibility of selective attributes of traditional markets were analyzed to affect the intention to visit. Second, the merchant efficiency, the display efficiency, the product efficiency, and the transaction efficiency of the service quality of the traditional market influence on the visit intention. However, facility efficiency was not found to have any effect. Third, merchant hygiene factors, facility hygiene factors, and commodity hygiene factors were found to affect the intention to visit. These traditional market hygiene factors were analyzed to control the intention to visit. Therefore, it can be said that the hygiene factor of the traditional market plays a role in raising the intention of visiting the traditional market in activating the traditional market. The conclusion is that merchants and support groups should be prioritized in order to revitalize traditional markets. The importance of environmental hygiene is introduced and implications for research results are suggested.

• The Korean Journal of Community Living Science
• /
• v.16 no.4
• /
• pp.47-57
• /
• 2005

To investigate the characteristics of edible flowers as a food material, we have examined the kinds, colors, sizes, fresh weights, pigments and shipping periods of edible flowers marketed on the cropping farms, selling agencies and Internet shopping malls from February through September, 2005. Thirty six kinds of edible flowers were marketed in Korea, and all but the chrysanthemum were introduced species. The characteristics of edible flowers were shown differently by the varieties following the same kinds of flowers. Those colors were yellow (twenty five kinds), red (twenty three), pink (twenty), white (eighteen), and orange (sixteen). Flower diameters were measured and showed that seven kinds of edible flowers were 1.0 to 2.0cm, fourteen 2.0 to 3.0cm, sixteen 3.0 to 4.0cm, eight 4.0 to 5.0cm, and nine over 5.0cm. Flower fresh weights were measured as follows: twenty one kinds of edible flowers were under 0.5g ($58.3\%$), eight were $0.6\∼1.0g(22.2\%$), and six were $1.1{\∼}1.5g(16.7\%$). The taste of edible flowers was often bitter (twenty one kinds), sweet and sour (seven), somewhat fragrant (six), fishy (three), and others (nine). The pigments of edible flowers were anthoxanthin (twenty seven kinds), flavonoid (twenty three), carotenoid (seventeen), and betanidin (four).

• Journal of Food Hygiene and Safety
• /
• v.27 no.4
• /
• pp.466-472
• /
• 2012

In this study, ribulose bisphosphate carboxylase (rbcL), RNApolymeraseC (rpoC1), intergenic spacer (psbA-trnH), and second internal transcribed spacer (ITS2) as identification markers for discrimination of P. mirifica in foods were selected. To be primer design, we obtained 719 bp, 520 bp, 348 bp, and 507 bp amplicon using universal primers from selected regions of P. mirifica. The regions of rbcL, rpoC1, and psbA-trnH were not proper for design primers because of high homology about P. mirifica, P. lobata, and B. superba. But, we had designed 4 pairs of oligonucleotide primers from ITS2 gene. Predicted amplicon from P. mirifica were obtained 137 bp and 216 bp using finally designed primers SFI12-miri-6F/SFI12-miri-7R and SFI12-miri-6F/SFI12-miri-8R, respectively. The species-specific primers distinguished P. mirifica from related species were able to apply food materials and processed foods. The developed PCR method would be applicable to food safety management for illegally distributed products in markets and internet shopping malls.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.16 no.4
• /
• pp.153-165
• /
• 2021

With the disappearance of boundaries between online and offline, the O2O(online to offline) platform service is rapidly growing. Unlike general products, freshness is an important decision-making factor for agri-food, and there are many limiting factors for growth as an open market among O2O platforms due to the characteristics of difficult refunds and exchanges compared to other items and new transaction methods. In order to overcome these obstacles, consumer innovation must be considered. The purpose of this study was to investigate the influence of O2O(online to offline) platform characteristics perception on agri-food repurchase intentions. And an empirical survey of the hypothesis is made that innovation will show a moderating effect between agri-food O2O platform characteristics and repurchase intention. And an empirical survey of the hypothesis is made that innovation will show a moderating effect between agri-food O2O platform characteristics and repurchase intention. For this purpose, Using a convenience sampling technique, an online survey was conducted through Google survey from April 1 to April 15, 2021. A total of final analysis data were collected from a total of 270 purchase experienced of agri-food O2O(online to offline) platform. The SPSS program was used for analysis, and multiple regression analysis was used for hypothesis verification. The results showed that Economic, Interaction, and Playfulness had a significant positive effect on agri-food repurchase intend. Also, Interactivity × innovation, playfulness × innovation were found to have a significant positive (+) effect on repurchase intention. The results of this study show that innovation reduces the burden on consumers for new systems and mobile transactions. The results of this study suggest that convenient interface design is important for activating O2O transactions of agri-food. In addition, education and support are needed to strengthen the IT competency of farmers. The results of this study will be able to contribute to the establishment of infrastructure for agri-food open market shopping malls. In future studies, the influence of the O2O platform type on the purchase intention should be studied continuously.

• 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.