• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.155-169
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• 2014

With the rapid advances in technologies, organizations are more likely to depend on information systems in their decision-making processes. Business Intelligence (BI) systems, in particular, have become a mainstay in dealing with complex problems in an organization, partly because a variety of advanced computational methods from statistics, machine learning, and artificial intelligence can be applied to solve business problems such as demand forecasting. In addition to the ability to analyze past and present trends, these predictive analytics capabilities provide huge value to an organization's ability to respond to change in markets, business risks, and customer trends. While the performance effects of BI system use in organization settings have been studied, it has been little discussed on the use of predictive analytics technologies embedded in BI systems for forecasting tasks. Thus, this study aims to find important factors that can help to take advantage of the benefits of advanced technologies of a BI system. More generally, a BI system can be viewed as an advisor, defined as the one that formulates judgments or recommends alternatives and communicates these to the person in the role of the judge, and the information generated by the BI system as advice that a decision maker (judge) can follow. Thus, we refer to the findings from the advice-giving and advice-taking literature, focusing on the role of explanations of the system in users' advice taking. It has been shown that advice discounting could occur when an advisor's reasoning or evidence justifying the advisor's decision is not available. However, the majority of current BI systems merely provide a number, which may influence decision makers in accepting the advice and inferring the quality of advice. We in this study explore the following key factors that can influence users' advice taking within the setting of a BI system: explanations on how the box-office grosses are predicted, types of advisor, i.e., system (data mining technique) or human-based business advice mechanisms such as prediction markets (aggregated human advice) and human advisors (individual human expert advice), users' evaluations of the provided advice, and individual differences in decision-makers. Each subject performs the following four tasks, by going through a series of display screens on the computer. First, given the information of the given movie such as director and genre, the subjects are asked to predict the opening weekend box office of the movie. Second, in light of the information generated by an advisor, the subjects are asked to adjust their original predictions, if they desire to do so. Third, they are asked to evaluate the value of the given information (e.g., perceived usefulness, trust, satisfaction). Lastly, a short survey is conducted to identify individual differences that may affect advice-taking. The results from the experiment show that subjects are more likely to follow system-generated advice than human advice when the advice is provided with an explanation. When the subjects as system users think the information provided by the system is useful, they are also more likely to take the advice. In addition, individual differences affect advice-taking. The subjects with more expertise on advisors or that tend to agree with others adjust their predictions, following the advice. On the other hand, the subjects with more knowledge on movies are less affected by the advice and their final decisions are close to their original predictions. The advances in predictive analytics of a BI system demonstrate a great potential to support increasingly complex business decisions. This study shows how the designs of a BI system can play a role in influencing users' acceptance of the system-generated advice, and the findings provide valuable insights on how to leverage the advanced predictive analytics of the BI system in an organization's forecasting practices.

• The Korean Society of Law and Medicine
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• v.17 no.1
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• pp.107-135
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• 2016

Medical tour can be said to be a new high added-value tour industry of 21st century. The development of varied and distinguished medical tour products by each country will further vitalize the medical tour industry. As the interest in such medical tour increases, it is necessary to analyze the demand and interests of tourists accurately and prepare medical tour products to be provided in order to develop and promote medical tour products. The government considers the medical tour industry as an industry with high expected effects in job creation through promotion of experts in global healthcare industry and national economy development through high added-value creation, and has expanded aid policies in medical tour field with improvement of medical tour immigration system, one-stop service system for medical tourists, and medical tour labor force promotion system. Nevertheless, there are disputes between foreign patients and medical tour inviting businesses, along with medical accident disputes between foreign patients and medical staff and disputes with those working in the tourism industry. This article reviews the types of disputes occurring around the inviting businesses related to medical tours and tried to review the resolutions. Through this, it was found that medical tour inviting businesses have the responsibility to connect the mediated benefits and risks and also the responsibility to process the tasks. Thus, in case dispute occurs due to passive actions from establishing agency agreement to active mediation results, it is difficult to escape the liabilities. Also, in a medical tour agency contract, the inviting business must be aware that it bears the responsibility to explain and advise the details on benefits and risks to foreign patients. The "Guide to arbitration system for resolution of medical disputes with foreign patients" by Korea Health Industry Development Institute Act presents a method to resolve disputes according to the [laws on medical accident damage relief and medical dispute arbitration] in case a dispute due to medical accidents occurs to foreign patients when the foreign patients prepare diagnosis agreement, Whether such method is sufficient to protect foreign patients, however, is thought to require discussions from more diverse perspectives. In order to vitalize medical tourism, the development of diverse products is also important, but the countermeasures against related disputes should also be prepared. Such is expected to contribute to a greater advancement based on trust of foreign medical tourists alongside excellent medical technologies.

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

• Journal of Intelligence and Information Systems
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• v.19 no.2
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• pp.1-20
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• 2013

Nowadays, social network is a huge communication platform for providing people to connect with one another and to bring users together to share common interests, experiences, and their daily activities. Users spend hours per day in maintaining personal information and interacting with other people via posting, commenting, messaging, games, social events, and applications. Due to the growth of user's distributed information in social network, there is a great potential to utilize the social data to enhance the quality of recommender system. There are some researches focusing on social network analysis that investigate how social network can be used in recommendation domain. Among these researches, we are interested in taking advantages of the interaction between a user and others in social network that can be determined and known as social relationship. Furthermore, mostly user's decisions before purchasing some products depend on suggestion of people who have either the same preferences or closer relationship. For this reason, we believe that user's relationship in social network can provide an effective way to increase the quality in prediction user's interests of recommender system. Therefore, social relationship between users encountered from social network is a common factor to improve the way of predicting user's preferences in the conventional approach. Recommender system is dramatically increasing in popularity and currently being used by many e-commerce sites such as Amazon.com, Last.fm, eBay.com, etc. Collaborative filtering (CF) method is one of the essential and powerful techniques in recommender system for suggesting the appropriate items to user by learning user's preferences. CF method focuses on user data and generates automatic prediction about user's interests by gathering information from users who share similar background and preferences. Specifically, the intension of CF method is to find users who have similar preferences and to suggest target user items that were mostly preferred by those nearest neighbor users. There are two basic units that need to be considered by CF method, the user and the item. Each user needs to provide his rating value on items i.e. movies, products, books, etc to indicate their interests on those items. In addition, CF uses the user-rating matrix to find a group of users who have similar rating with target user. Then, it predicts unknown rating value for items that target user has not rated. Currently, CF has been successfully implemented in both information filtering and e-commerce applications. However, it remains some important challenges such as cold start, data sparsity, and scalability reflected on quality and accuracy of prediction. In order to overcome these challenges, many researchers have proposed various kinds of CF method such as hybrid CF, trust-based CF, social network-based CF, etc. In the purpose of improving the recommendation performance and prediction accuracy of standard CF, in this paper we propose a method which integrates traditional CF technique with social relationship between users discovered from user's behavior in social network i.e. Facebook. We identify user's relationship from behavior of user such as posts and comments interacted with friends in Facebook. We believe that social relationship implicitly inferred from user's behavior can be likely applied to compensate the limitation of conventional approach. Therefore, we extract posts and comments of each user by using Facebook Graph API and calculate feature score among each term to obtain feature vector for computing similarity of user. Then, we combine the result with similarity value computed using traditional CF technique. Finally, our system provides a list of recommended items according to neighbor users who have the biggest total similarity value to the target user. In order to verify and evaluate our proposed method we have performed an experiment on data collected from our Movies Rating System. Prediction accuracy evaluation is conducted to demonstrate how much our algorithm gives the correctness of recommendation to user in terms of MAE. Then, the evaluation of performance is made to show the effectiveness of our method in terms of precision, recall, and F1-measure. Evaluation on coverage is also included in our experiment to see the ability of generating recommendation. The experimental results show that our proposed method outperform and more accurate in suggesting items to users with better performance. The effectiveness of user's behavior in social network particularly shows the significant improvement by up to 6% on recommendation accuracy. Moreover, experiment of recommendation performance shows that incorporating social relationship observed from user's behavior into CF is beneficial and useful to generate recommendation with 7% improvement of performance compared with benchmark methods. Finally, we confirm that interaction between users in social network is able to enhance the accuracy and give better recommendation in conventional approach.

• Asia pacific journal of information systems
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• v.22 no.3
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• pp.75-97
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• 2012

Knowledge residing in the heads of employees has always been regarded as one of the most critical resources within a firm. However, many tries to facilitate knowledge transfer among employees has been unsuccessful because of the motivational and cognitive problems between the knowledge source and the recipient. Social capital, which is defined as "the sum of the actual and potential resources embedded within, available through, derived from the network of relationships possessed by an individual or social unit [Nahapiet and Ghoshal, 1998]," is suggested to resolve these motivational and cognitive problems of knowledge transfer. In Social capital theory, there are two research streams. One insists that social capital strengthens group solidarity and brings up cooperative behaviors among group members, such as voluntary help to colleagues. Therefore, social capital can motivate an expert to transfer his/her knowledge to a colleague in need without any direct reward. The other stream insists that social capital provides an access to various resources that the owner of social capital doesn't possess directly. In knowledge transfer context, an employee with social capital can access and learn much knowledge from his/her colleagues. Therefore, social capital provides benefits to both the knowledge source and the recipient in different ways. However, prior research on knowledge transfer and social capital is mostly limited to either of the research stream of social capital and covered only the knowledge source's or the knowledge recipient's perspective. Social network theory which focuses on the structural dimension of social capital provides clear explanation about the in-depth mechanisms of social capital's two different benefits. 'Strong tie' builds up identification, trust, and emotional attachment between the knowledge source and the recipient; therefore, it motivates the knowledge source to transfer his/her knowledge to the recipient. On the other hand, 'weak tie' easily expands to 'diverse' knowledge sources because it does not take much effort to manage. Therefore, the real value of 'weak tie' comes from the 'diverse network structure,' not the 'weak tie' itself. It implies that the two different perspectives on strength of ties can co-exist. For example, an extroverted employee can manage many 'strong' ties with 'various' colleagues. In this regards, the individual-level structure of one's relationships as well as the dyadic-level relationship should be considered together to provide a holistic view of social capital. In addition, interaction effect between individual-level characteristics and dyadic-level characteristics can be examined, too. Based on these arguments, this study has following research questions. (1) How does the social capital of the knowledge source and the recipient influence knowledge transfer respectively? (2) How does the strength of ties between the knowledge source and the recipient influence knowledge transfer? (3) How does the social capital of the knowledge source and the recipient influence the effect of the strength of ties between the knowledge source and the recipient on knowledge transfer? Based on Social capital theory and Social network theory, a multi-level research model is developed to consider both the individual-level social capital of the knowledge source and the recipient and the dyadic-level strength of relationship between the knowledge source and the recipient. 'Cross-classified random effect model,' one of the multi-level analysis methods, is adopted to analyze the survey responses from 337 R&D employees. The results of analysis provide several findings. First, among three dimensions of the knowledge source's social capital, network centrality (i.e., structural dimension) shows the significant direct effect on knowledge transfer. On the other hand, the knowledge recipient's network centrality is not influential. Instead, it strengthens the influence of the strength of ties between the knowledge source and the recipient on knowledge transfer. It means that the knowledge source's network centrality does not directly increase knowledge transfer. Instead, by providing access to various knowledge sources, the network centrality provides only the context where the strong tie between the knowledge source and the recipient leads to effective knowledge transfer. In short, network centrality has indirect effect on knowledge transfer from the knowledge recipient's perspective, while it has direct effect from the knowledge source's perspective. This is the most important contribution of this research. In addition, contrary to the research hypothesis, company tenure of the knowledge recipient negatively influences knowledge transfer. It means that experienced employees do not look for new knowledge and stick to their own knowledge. This is also an interesting result. One of the possible reasons is the hierarchical culture of Korea, such as a fear of losing face in front of subordinates. In a research methodology perspective, multi-level analysis adopted in this study seems to be very promising in management research area which has a multi-level data structure, such as employee-team-department-company. In addition, social network analysis is also a promising research approach with an exploding availability of online social network data.

• Korean Security Journal
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• no.13
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• pp.471-485
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• 2007

This study is performed to find out what type instructions are produced to players by coaches and what effects are resulted in cooperative spirit of the concerned group. Furthermore the study has its aims at advancing instructors' skills by using finding of it. The study used 'SPSS 11.0 FOR WINDOW - Statistical Package' to analyze the collected samples and dealt with data of 174 individuals. Statistical analysis of the research for hypothesis verification was about frequency, trust level, mutual relationship, variables, and T-verification. The meaningful level for any result was ranged within 95%(p< .05), 99%(p<.01). The finding are as follows. Effects on pleasure, one of elements of team spirits taken by instructor's training style are analyzed as follows. It was proved to be meaningful in relation with a series of activities like training, democratic, social, compensatory aspects and showed also considerable relation with power based behaviors. That says, players are found to enjoy high pleasure when social and bureaucratic behaviors of instructors are very energetic. In addition to that, training, democratic, and compensatory activities didn't show any meaningful effect. Team spirit was found to play a main role between instructor's behaviors and training, democratic, social rewarding activities. Democratic and social acts influence on team spirit. Looking into the detailed aspects, team spirit was resulted very high in the individuals with low democratic mind and was shown high group spirit by groups with high sociable activities. Teamworks was found to be affected by relation between instructor's acts and training, democratic, social and compensatory aspects and it showed meaningful relations with training, social, bureaucratic behaviors. Low degree of training and bureaucratic activities are found to prefer for power team spirit, and high social activities led a strong teamworks. Group binding spirit was influenced by training, democratic, social compensatory, bureaucratic behaviors and it showed to give effects on democratic, social, and bureaucratic activities of instructors. Low degree of democratic and bureaucratic behaviors are found to produce strong team spirit. In contrast with that, strong social activities was found to be motive of powerful team spirit. Value of team spirit was found to play a main role between instructor's behaviors and training, democratic, social, rewarding activities. It didn't show any meaningful effect on behavior of instructors.

• Journal of Food Hygiene and Safety
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• v.32 no.3
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• pp.179-186
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• 2017

For more efficient and proactive safety control of imported food, new trend in U.S. is emerging, which assesses the food safety control systems of exporting countries using Systems Recognition Assessment Tool and helps ensure safety of imported foods. This study examines trends in development and application of assessmemnt tool and country assessment reports in U.S. where an active discussion on this issue is in progress. The expert interviews were also conducted. U.S. Systems Recognition Assessment Tool was developed by FDA to recognize the potential value in leveraging the expertise of foreign food safety systems and help ensure safety of imported food. The tool is comprised of ten standards and provides an objective framework for determining the robustness of trading partners' overall food safety systems. Using its own tool, the U.S. FDA conducted a preliminary assessment of the food safety control systems of New Zealand and Canada. According to the U.S.-New Zealand and the U.S.-Canada assessment reports, the overall structure of the systems was similar between the countries. In summarizing the opinions of experts, such a trend in National Food Safety Control System Assessment may be utilized in the sanitary assessment and the control of imported food border inspection frequency before importing food. It would contribute to more effective distribution of national budget and increased public trust. Additionally, international collaboration as well as securing of qualified experts and sufficient budget appear to be crucial to further increase the utility of National Food Safety Control Systems Assessment. In conclusion, firstly, it is critically important for the competent authority of South Korea to proactively respond to international trend in National Food Safety Control System Assessment by identifying the details of its background, assessment purpose, core assessment elements, and assessment procedures. Secondly, it is necessary to identify and complement the weaknesses of Korea's food safety control system by reviewing it with U.S. Systems Recognition Assessment Tool. Thirdly, by adapting the assessment results from imported countries' food safety control systems to the imported food inspection intensity, the resources previously used in inspecting the imported food from accredited countries can be redistributed to inspecting the imported food from unaccredited countries, and it would contribute to more efficient imported food safety control. Fourthly, the competent authority of South Korea should also consider developing its own assessment tool designed to reflect the unique characteristics of its food safety control system and international guidelines.

• Journal of Global Scholars of Marketing Science
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• v.20 no.1
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• pp.4-14
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• 2010

Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. This study addresses the specific types of collaboration used in the fashion industry while also examining strategies that have been most successful for fashion companies and perceived benefits of collaboration from the customer perspective. In the present study we define fashion companies and brands as collaborators and their partners or stakeholders as collaboratees. We define collaboration as a cooperative relationship where more than two companies, brands or individuals provide customers with beneficial outcomes utilizing their own competitive advantages on an equal basis. Collaboration strategies entail information sharing and other varied forms of cooperation that are mutually beneficial to the company and stakeholder groups. Through collaboration, fashion companies have pursued both tangible differentiation, such as design and technology applications, and intangible differentiation such as emotional and psychological benefits to customers. As a result, collaboration within the fashion industry has become an important, value creating concept. This qualitative study utilized case studies and in-depth interview methodologies to examine customers' attitudes concerning collaboration in the fashion industry. A total of 173 collaboration cases were identified in Korean and international markets from 1998 through December 2008, focusing on fashion companies. Cases were collected from documented data including websites and industry data bases and top ranked portal search sites such as: Rankey.com; Naver, Daum, and Nate; and representative fashion information websites, Samsungdesignnet and Firstviewkorea. Cases were collected between November 2008 and February 2009. Cases were selected for the analysis where one or more partners were associated with the production of fashion products (excluding textile production), retail fashion products, or designer services. Additional collaboration case information was obtained from news articles, periodicals, internet portal sites and fashion information sites as conducted in prior studies (Jeong and Kim 2008; Park and Park 2004; Yoon 2005). In total, 173 cases were selected for analysis that clearly exhibited the benefits and outcomes of collaboration efforts and strategies between fashion companies and stakeholders. Findings show that the overall results show that for both partners (collaborator and collaboratee) participating in collaboration, that the major benefits are reduction of costs and risks by sharing resource such as design power, image, costs, technology and targets, and creation of synergy. Regarding types of collaboration outcomes, product/design was most important (55%), followed by promotion (21%), price (20%), and place (4%). This result shows that collaboration plays an important role in giving life to products and designs, particularly in the fashion industry which seeks for creative and newness. To be successful in collaboration efforts, results of the depth interviews in this study confirm that fashion companies should have a clear objective on why they are doing the collaboration. After setting the objective, they should select collaboratees that match their brand image and target market, make quality co-products that have definite concepts and differentiating factors, and also pay attention to increasing brand awareness. Based on depth interviews with customers, customer benefits were categorized into six factors: pursuit for individual character; pursuit for brand; pursuit for scarcity; pursuit for fashion; pursuit for economic efficiency; and pursuit for sociality. Customers also placed more importance on image, reputation, and trust of brands regarding the cases shown in the interviews. They also commented that strong branding should come first before other marketing strategies. However, success factors recognized by experts and customers in this study showed different results by subcategories. Thus, target customers and target market should be studied from various dimensions to develop appropriate strategies for successful collaboration.

• Asia pacific journal of information systems
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• v.24 no.3
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• pp.325-344
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• 2014

Technical support of Window XP ended in March, 8, 2014, and it makes OS(Operating System) users fall in a state of confusion. Sudden decision making of OS upgrade and replacement is not a simple problem. Firms need to change the long term capacity plan in enterprise IS management, but they are pressed for time and cost to complete it. Individuals can not help selecting the second best plan, because the following OSs of Window XP are below expectations in performances, new PC sales as the opportunities of OS upgrade decrease, and the potential risk of OS technical support ending had not announced to OS users at the point of purchase. Microsoft as the OS vendors had not presented precaution or remedy for this confusion. Rather, Microsoft announced that the technical support of the other following OSs of Wndow XP such as Window 7 would ended in two years. This conflict between OS vendor and OS users could not happen in one time, but could recur in recent future. Although studies on the ways of OS user protection policy would be needed to escape from this conflict, few prior studies had conducted this issue. This study had challenge to cautiously investigate in such OS user's reactions as the confirmation with OS user's expectation in the point of purchase, three types of justice perception on the treatment of OS vendor, psychological contract violation, satisfaction and the other betrayal behavioral intention in the case of Window XP technical support ending. By adopting the justice perception on this research, and by empirically validating the impact on OS user's reactions, I could suggest the direction of establishing OS user protection policy of OS vendor. Based on the expectation-confirmation theory, the theory of justice, literatures about psychological contract violation, and studies about consumer betrayal behaviors in the perspective of Herzberg(1968)'s dual factor theory, I developed the research model and hypothesis. Expectation-confirmation theory explain that consumers had expectation on the performance of product in the point of sale, and they could satisfied with their purchase behaviors, when the expectation could have confirmed in the point of consumption. The theory of justice in social exchange argues that treatee could be willing to accept the treatment by treater when the three types of justice as distributive, procedural, and interactional justice could be established in treatment. Literatures about psychological contract violation in human behaviors explains that contracter in a side could have the implied contract (also called 'psychological contract') which the contracter in the other side would sincerely execute the contract, and that they are willing to do vengeance behaviors when their contract had unfairly been broken. When the psychological contract of consumers had been broken, consumers feel distrust with the vendors and are willing to decrease such beneficial attitude and behavior as satisfaction, loyalty and repurchase intention. At the same time, consumers feel betrayal and are willing to increase such retributive attitude and behavior as negative word-of-mouth, complain to the vendors, complain to the third parties for consumer protection. We conducted a scenario survey in order to validate our research model at March, 2013, when is the point of news released firstly and when is the point of one year before the acture Window XP technical support ending. We collected the valid data from 238 voluntary participants who are the OS users but had not yet exposed the news of Window OSs technical support ending schedule. The subject had been allocated into two groups and one of two groups had been exposed this news. The data had been analyzed by the MANOVA and PLS. MANOVA results indicate that the OSs technical support ending could significantly decrease all three types of justice perception. PLS results indicated that it could significantly increase psychological contract violation and that this increased psychological contract violation could significantly reduce the trust and increase the perceived betrayal. Then, it could significantly reduce satisfaction, loyalty, and repurchase intention, and it also could significantly increase negative word-of-month intention, complain to the vendor intention, and complain to the third party intention. All hypothesis had been significantly approved. Consequently, OS users feel that the OSs technical support ending is not natural value added service ending, but the violation of the core OS purchase contract, that it could be the posteriori prohibition of OS user's OS usage right, and that it could induce the psychological contract violation of OS users. This study would contributions to introduce the psychological contract violation of the OS users from the OSs technical support ending in IS field, to introduce three types of justice as the antecedents of psychological contract violation, and to empirically validate the impact of psychological contract violation both on the beneficial and retributive behavioral intentions of OS users. For practice, the results of this study could contribute to make more comprehensive OS user protection policy and consumer relationship management practices of OS vendor.

• Knowledge Management Research
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• v.21 no.3
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• pp.45-65
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• 2020

It is desirable to invest in wine that increases its value, but wine investment itself is unfamiliar in Korea. Also, the process itself is unreasonable, and information is often forged, because pricing in the wine market is done by a small number of people. With the right solution, however, the wine market can be a desirable investment destination in that the longer one invests, the higher one can expect. Also, it is expected that the domestic wine consumption market will expand through the steady increase in domestic wine imports. This study presents the consortium block chain framework for revitalizing the wine market and enhancing transparency as the "right solution" of the nation's wine investment market. Blockchain governance can compensate for the shortcomings of the wine market because it guarantees desirable decision-making rights and accountability. Because the data stored in the block chain can be checked by consumers, it reduces the likelihood of counterfeit wine appearing and complements the process of unreasonably priced. In addition, digitization of assets resolves low cash liquidity and saves money and time throughout the supply chain through smart contracts, lowering entry barriers to wine investment. In particular, if the governance of the block chain is composed of 'chateau-distributor-investor' through consortium blockchains, it can create a desirable wine market. The production process is stored in the block chain to secure production costs, set a reasonable launch price, and efficiently operate the distribution system by storing the distribution process in the block chain, and forecast the amount of orders for futures trading. Finally, investors make rational decisions by viewing all of these data. The study presented a new perspective on alternative investment in that ownership can be treated like a share. We also look forward to the simplification of food import procedures and the formation of trust within the wine industry by presenting a framework for wine-owned sales. In future studies, we would like to expand the framework to study the areas to be applied.