• Information Systems Review
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• v.25 no.4
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• pp.131-159
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• 2023

Recently, amidst the backdrop of the COVID-19 pandemic shifting towards an endemic phase, there has been a rise in discussions and debates about the future of the metaverse. Simultaneously, major metaverse platforms like Roblox have been launching services integrated with generative AI, and Apple's mixed reality hardware, Vision Pro, has been announced, creating new expectations for the metaverse. In this situation where the outlook for the metaverse is divided, it is crucial to diagnose the metaverse from an ecosystem perspective, examine its key ecological features, driving forces for development, and future possibilities for advancement. This study utilized Wang's (2021) Information Ecology Theory (IET) framework, which is representative of ecosystem research in the field of Information Systems (IS), to derive the Metaverse Industrial Ecosystem (MIE). The analysis revealed that the MIE consists of four main domains: Tech Landscape, Category Ecosystem, Metaverse Platform, and Product/Service Ecosystem. It was found that the MIE exhibits characteristics such as digital connectivity, the integration of real and virtual worlds, value creation capabilities, and value sharing (Web 3.0). Furthermore, the interactions among the domains within the MIE and the four characteristics of the ecosystem were identified as driving forces for the development of the MIE at an ecosystem level. Additionally, the development of the MIE at an ecosystem level was categorized into three distinct stages: Narrow Ecosystem, Expanded Ecosystem, and Everywhere Ecosystem. It is anticipated that future advancements in related technologies and industries, such as robotics, AI, and 6G, will promote the transition from the current Expanded Ecosystem level of the MIE to an Everywhere Ecosystem level, where the connection between the real and virtual worlds is pervasive. This study provides several implications. Firstly, it offers a foundational theory and analytical framework for ecosystem research, addressing a gap in previous metaverse studies. It also presents various research topics within the metaverse domain. Additionally, it establishes an academic foundation that integrates concept definition research and impact studies, which are key areas in metaverse research. Lastly, referring to the developmental stages and conditions proposed in this study, businesses and governments can explore future metaverse markets and related technologies. They can also consider diverse metaverse business strategies. These implications are expected to guide the exploration of the emerging metaverse market and facilitate the evaluation of various metaverse business strategies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.6 no.2
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• pp.107-117
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• 2004

If doubtlessly contributes much to agriculture and rural development. The roles can be summarized as; 1. to activate rural areas and to provide more comfortable and safe rural life with equivalent services to those in urban areas, facilitating distance education, tole-medicine, remote public services, remote entertainment etc. 2. To initiate new agricultural and rural business such as e-commerce, real estate business for satellite officies, rural tourism and virtual corporation of small-scale farms. 3. To support policy-making and evaluation on optimal farm production, disaster management, effective agro-environmental resource management etc., providing tools such as GIS. 4. To improve farm management and farming technologies by efficient farm management, risk management, effective information or knowledge transfer etc., realizing competitive and sustainable farming with safe products. 5. To provide systems and tools to secure food traceability and reliability that has been an emerging issue concerning farm products since serious contamination such as BSE and chicken flu was detected. 6. To take an important and key role for industrialization of farming or lam business enterprise, combining the above roles.

• Fiber Technology and Industry
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• v.2 no.4
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• pp.490-508
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• 1998

One of the latest fields of research in the area of output devices is tactual display devices [13,31]. These tactual or haptic devices allow the user to receive haptic feedback output from a variety of sources. This allows the user to actually feel virtual objects and manipulate them by touch. This is an emerging technology and will be instrumental in enhancing the realism of wearable augmented environments for certain applications. Tactual displays have previously been used for scientific visualization in virtual environments by chemists and engineers to improve perception and understanding of force fields and of world models populated with the impenetrable. In addition to tactual displays, the use of wearable audio displays that allow sound to be spatialized are being developed. With wearable computers, designers will soon be able to pair spatialized sound to virtual representations of objects when appropriate to make the wearable computer experience even more realistic to the user. Furthermore, as the number and complexity of wearable computing applications continues to grow, there will be increasing needs for systems that are faster, lighter, and have higher resolution displays. Better networking technology will also need to be developed to allow all users of wearable computers to have high bandwidth connections for real time information gathering and collaboration. In addition to the technology advances that make users need to wear computers in everyday life, there is also the desire to have users want to wear their computers. In order to do this, wearable computing needs to be unobtrusive and socially acceptable. By making wearables smaller and lighter, or actually embedding them in clothing, users can conceal them easily and wear them comfortably. The military is currently working on the development of the Personal Information Carrier (PIC) or digital dog tag. The PIC is a small electronic storage device containing medical information about the wearer. While old military dog tags contained only 5 lines of information, the digital tags may contain volumes of multi-media information including medical history, X-rays, and cardiograms. Using hand held devices in the field, medics would be able to call this information up in real time for better treatment. A fully functional transmittable device is still years off, but this technology once developed in the military, could be adapted tp civilian users and provide ant information, medical or otherwise, in a portable, not obstructive, and fashionable way. Another future device that could increase safety and well being of its users is the nose on-a-chip developed by the Oak Ridge National Lab in Tennessee. This tiny digital silicon chip about the size of a dime, is capable of 'smelling' natural gas leaks in stoves, heaters, and other appliances. It can also detect dangerous levels of carbon monoxide. This device can also be configured to notify the fire department when a leak is detected. This nose chip should be commercially available within 2 years, and is inexpensive, requires low power, and is very sensitive. Along with gas detection capabilities, this device may someday also be configured to detect smoke and other harmful gases. By embedding this chip into workers uniforms, name tags, etc., this could be a lifesaving computational accessory. In addition to the future safety technology soon to be available as accessories are devices that are for entertainment and security. The LCI computer group is developing a Smartpen, that electronically verifies a user's signature. With the increase in credit card use and the rise in forgeries, is the need for commercial industries to constantly verify signatures. This Smartpen writes like a normal pen but uses sensors to detect the motion of the pen as the user signs their name to authenticate the signature. This computational accessory should be available in 1999, and would bring increased peace of mind to consumers and vendors alike. In the entertainment domain, Panasonic is creating the first portable hand-held DVD player. This device weight less than 3 pounds and has a screen about 6' across. The color LCD has the same 16:9 aspect ratio of a cinema screen and supports a high resolution of 280,000 pixels and stereo sound. The player can play standard DVD movies and has a hour battery life for mobile use. To summarize, in this paper we presented concepts related to the design and use of wearable computers with extensions to smart spaces. For some time, researchers in telerobotics have used computer graphics to enhance remote scenes. Recent advances in augmented reality displays make it possible to enhance the user's local environment with 'information'. As shown in this paper, there are many application areas for this technology such as medicine, manufacturing, training, and recreation. Wearable computers allow a much closer association of information with the user. By embedding sensors in the wearable to allow it to see what the user sees, hear what the user hears, sense the user's physical state, and analyze what the user is typing, an intelligent agent may be able to analyze what the user is doing and try to predict the resources he will need next or in the near future. Using this information, the agent may download files, reserve communications bandwidth, post reminders, or automatically send updates to colleagues to help facilitate the user's daily interactions. This intelligent wearable computer would be able to act as a personal assistant, who is always around, knows the user's personal preferences and tastes, and tries to streamline interactions with the rest of the world.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.157-172
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• 2023

The recent Fourth Industrial Revolution is accelerating changes due to digital transformation. According to this trend, the existing start-up paradigm is changing, and new business models based on new technologies and creative ideas are emerging. In addition, the diversity of mentoring relationships and environments such as online mentoring, reverse mentoring, group mentoring, and multiple mentoring is also increasing. However, most mentors in their 50s and 60s, who are mainly active in the start-up field, have been able to help mentees a lot based on their own experience and expertise, but they are having difficulty responding to the changing environment due to a lack of understanding and experience of new technologies and environments. To cope with these changes well, mentors must constantly study, acquire and apply the latest technologies to improve their understanding of new technologies and the environment. In addition, it is necessary to have an understanding and respect for the diversity of mentoring relationships and environments, and to maximize the effectiveness of mentoring by actively utilizing them. Therefore, mentors should recognize that they directly affect the growth and development of mentees, constantly acquire new knowledge and skills to maintain and develop expertise, and actively deliver their knowledge and experiences to mentees. Therefore, in this study, was tried to empirically analyze the relationship between mentoring's influence on mentor's job satisfaction through mentor's personal learning and self-efficacy. The results of the empirical analysis were as follows. Among the functions of mentoring, career function and role modeling were found to have a positive effect on both personal learning and self-efficacy, which are parameters, and job satisfaction, which is a dependent variable. On the other hand, psychological and social functions have a positive effect on personal learning, but they do not have an effect on self-efficacy and job satisfaction. In addition, as a result of analyzing the mediating effect, all mediating effects were confirmed for career functions, and only the mediating effect of self-efficacy was confirmed for role modeling. Through this study, mentoring is an important factor in promoting job satisfaction, personal learning and self-efficacy, and this study can be said to be academically and practically meaningful in that it confirmed personal learning and self-efficacy as factors that increase mentor's job satisfaction, and the focus of mentoring research was shifted from mentee to mentor to study the impact of mentoring on mentors.

• Journal of Distribution Research
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• v.15 no.4
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• pp.1-19
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• 2010

As having the movement of developing private brand (PB) goods, domestic big retailers are facing up with new problems. Thus, it is required studies of PB products, and how consumers recognize PB products as a consideration commodity set. Also, it is worthy in order that it gives us the important meaning on the marketing strategy with focusing on evaluating the differences between customers buying PB grocery goods with respect to demographic characteristics and purchasing behaviors. PB has some advantages for customers and retailers. However, according to AC Nielson's report (2005), Asian and emerging market has 1/5 sales relatively to Western countries. But we can assume that the emerging market has the most potential growth through this result. As a result from several other studies, it becomes necessary to not only increase the rate of selling composition of PB product temporarily, but also analyze the characteristics of customers using big retailers and segmenting customer groups to make PB product as a consideration commodity set for them. In addition, it is needed to have a variety of acts of marketing. From studies related to PB, there is a prejudice - cheap products have low quality - but, evaluation by customers who have used those products shows neutral stand, and there is a study representing that it is the most important to accumulate the belief between the retailers selling PB products and consumers using those for the accurate evaluation and intention on purchasing. Also, by the result from analyzing the characteristics of customers buying PB products, we could assume that higher income and higher education level, more preference on PB products. Especially, according to TNS's research, the primary targets of PB product are 30's who seeks value for money and planned spending habits, and 40's who have teenager children, and are interested in encouraging themselves. This paper used Probit model to analyze the characteristics of consumers. This model helps us to analyze with the variables representing the demographic characteristics of consumers (gender, age, educational level, occupation, income level, living area), and variables related to purchasing behavior (visiting frequency on big retailers, the average amount that they pay for goods in there, and check-up which brand made those goods). The method we used in this study is by man to man interview and survey on-line with the rate of 89% and 11% in Seoul and Gyunggi Province, respectively, for about one month from the beginning of February, 2008. As a result of this, under the assumption that people buy PB products more as long as they go shopping more, it was not meaningful for target groups which we pointed out as frequently visiting customers to be. Although, we have expected women buy more PB products than men do, gender doesn't mean anything for the result. And, it has inferred that married people buy more PB goods than singles do. It was also meaningless with variables related to occupation. Because housewives are often exposed to any kind of supermarket than workers are, we could not get any relatives. Moreover, we couldn't proof that younger generation prefer big retailers more than older people who 50~60's. Education levels doesn't affect on the purchase of PB product as well. Related to living area, the result is statistically not similar as we expected whether living in Seoul or not. It shows there is no relationship with the preference on retail brands and PB products, and it is similar with the study researched by TNS(2008) that customers tend to buy PB product impulsively no matter which brand it is and where they are even though their shopping place is the big market where customers are often using. Variables on which we had meaningful results are income level and living place. That is, customers who have 3,000,000~6,000,000 WON every month on average are more willing to buy PB products than other customers whose income is over 6,000,000 WON, and residents not living in Seoul prefer PB goods than those who are living in Seoul. To explain more about what we got, if there is only one condition about customer's visiting frequency on big retails, we could come up with this result that more exposed to PB products, more purchasing frequency. Consequently, it brings the important insight that large retailers have to prepare something to make customers visit them often to increase selling rate of PB products. To demonstrate the result of analyzing more, what is more efficient variables are demographically including marital status, income level, and residential area to buy items that affect the PB products and could include the frequency of visiting large markets by the purchase habits. Specifically, then, married couples rather than singles, middle-income customers than high-income customers, and local residents not living in Seoul than customers in Seoul are more likely to purchase PB goods. In addition, as long as a customer visits two times more, then the purchasing rate of PB products is to increase over 5.3%. Therefore, it seems that retailers are better to make a shopping place as fun and comfortable places. With overwhelming the idea that PB products are just cheap, one-time purchase goods, it is needed to increase the loyalty on those goods like NB products, try to make PB products as a consideration products set, and occur to sustainable sales. Especially, as suggested by this paper, it seems like it strongly needs to identify the characteristics of customers who prefer PB, to segment those customers, and to select the main target, and to do positioning with well-planned marketing strategies. Then, it is able to give us a meaningful point on marketing strategy by developing the field of PB study, identifying the difference of life style and shopping habits of customers.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.141-156
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• 2013

Social media is a representative form of the Web 2.0 that shapes the change of a user's information behavior by allowing users to produce their own contents without any expert skills. In particular, as a new communication medium, it has a profound impact on the social change by enabling users to communicate with the masses and acquaintances their opinions and thoughts. Social media data plays a significant role in an emerging Big Data arena. A variety of research areas such as social network analysis, opinion mining, and so on, therefore, have paid attention to discover meaningful information from vast amounts of data buried in social media. Social media has recently become main foci to the field of Information Retrieval and Text Mining because not only it produces massive unstructured textual data in real-time but also it serves as an influential channel for opinion leading. But most of the previous studies have adopted broad-brush and limited approaches. These approaches have made it difficult to find and analyze new information. To overcome these limitations, we developed a real-time Twitter trend mining system to capture the trend in real-time processing big stream datasets of Twitter. The system offers the functions of term co-occurrence retrieval, visualization of Twitter users by query, similarity calculation between two users, topic modeling to keep track of changes of topical trend, and mention-based user network analysis. In addition, we conducted a case study on the 2012 Korean presidential election. We collected 1,737,969 tweets which contain candidates' name and election on Twitter in Korea (http://www.twitter.com/) for one month in 2012 (October 1 to October 31). The case study shows that the system provides useful information and detects the trend of society effectively. The system also retrieves the list of terms co-occurred by given query terms. We compare the results of term co-occurrence retrieval by giving influential candidates' name, 'Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn' as query terms. General terms which are related to presidential election such as 'Presidential Election', 'Proclamation in Support', Public opinion poll' appear frequently. Also the results show specific terms that differentiate each candidate's feature such as 'Park Jung Hee' and 'Yuk Young Su' from the query 'Guen Hae Park', 'a single candidacy agreement' and 'Time of voting extension' from the query 'Jae In Moon' and 'a single candidacy agreement' and 'down contract' from the query 'Chul Su Ahn'. Our system not only extracts 10 topics along with related terms but also shows topics' dynamic changes over time by employing the multinomial Latent Dirichlet Allocation technique. Each topic can show one of two types of patterns-Rising tendency and Falling tendencydepending on the change of the probability distribution. To determine the relationship between topic trends in Twitter and social issues in the real world, we compare topic trends with related news articles. We are able to identify that Twitter can track the issue faster than the other media, newspapers. The user network in Twitter is different from those of other social media because of distinctive characteristics of making relationships in Twitter. Twitter users can make their relationships by exchanging mentions. We visualize and analyze mention based networks of 136,754 users. We put three candidates' name as query terms-Geun Hae Park', 'Jae In Moon', and 'Chul Su Ahn'. The results show that Twitter users mention all candidates' name regardless of their political tendencies. This case study discloses that Twitter could be an effective tool to detect and predict dynamic changes of social issues, and mention-based user networks could show different aspects of user behavior as a unique network that is uniquely found in Twitter.