• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.101-114
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• 2023

In the wartime, aircraft carrying out a mission to strike the enemy deep in the depth are exposed to the risk of being shoot down. As a key combat force in mordern warfare, it takes a lot of time, effot and national budget to train military flight personnel who operate high-tech weapon systems. Therefore, this study studied the path problem of predicting the route of emergency escape from enemy territory to the target point to avoid obstacles, and through this, the possibility of safe recovery of emergency escape military flight personnel was increased. based problem, transforming the problem into a TSP, VRP, and Dijkstra algorithm, and approaching it with an optimization technique. However, if this problem is approached in a network problem, it is difficult to reflect the dynamic factors and uncertainties of the battlefield environment that military flight personnel in distress will face. So, MDP suitable for modeling dynamic environments was applied and studied. In addition, GIS was used to obtain topographic information data, and in the process of designing the reward structure of MDP, topographic information was reflected in more detail so that the model could be more realistic than previous studies. In this study, value iteration algorithms and deterministic methods were used to derive a path that allows the military flight personnel in distress to move to the shortest distance while making the most of the topographical advantages. In addition, it was intended to add the reality of the model by adding actual topographic information and obstacles that the military flight personnel in distress can meet in the process of escape and escape. Through this, it was possible to predict through which route the military flight personnel would escape and escape in the actual situation. The model presented in this study can be applied to various operational situations through redesign of the reward structure. In actual situations, decision support based on scientific techniques that reflect various factors in predicting the escape route of the military flight personnel in distress and conducting combat search and rescue operations will be possible.

• Asia Marketing Journal
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• v.13 no.3
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• pp.209-231
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• 2011

Internet is having strong impact on the consumer's decision making process. Information search has been done actively through internet today. The online reviews can be crucial information cue to evaluate the alternarive products. The online WOM(Word-Of-Mouth) effect depends on the characteristics of information sender, receiver, and WOM. This study is to examine the influence of the online word of mouth on the consumer purchase intention and the moderating role of product involvement, consumer regulatory focus and self-efficacy. Positive customer reviews on the products influence the purchase intention positively and negative customer reviews influence it negatively. Moderating role of involvement in the causal relation between the valence of online reviews and purchase intention is tested. In case of positive WOM, it is predicted that purchase intention for high involvement products is higher than that of low involvement. In case of negative WOM, purchase intention for high involvement product is lower than that of low involvement product. And this study invetigate the moderating role of regulatory focus. In case of positive WOM, it is predicted that promotion focus oriented consumers have higher purchase intention than prevention focus oriented consumers. In case of negative WOM, prediction is that prevention focus oriented consumers have lower purchase intention than promotion focus oriented consumers. Then we examine the moderating role of self efficacy in the causal relation between the valence of online reviews and purchase intention. In case of positive WOM, it is predicted that consumers with low self efficacy have higher purchase intention than consumers with high self efficacy. In case of negative WOM, it is predicted that consumers with low self efficacy have lower purchase intention than consumers with high self efficacy. Emprical results support our prediction and four hypotheses derived from our conceptual framework are all accepted. This study suggest that the level of product involvement, consumer regulatory focus and the level of self-efficacy influence the consumer responses of the valence of online reviews. Therefore marketers need to manage online reviews based on the level of product involvement, regulatory focus orientation and the level of self-efficacy of target consumers.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.143-163
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• 2016

The demographics of Internet users are the most basic and important sources for target marketing or personalized advertisements on the digital marketing channels which include email, mobile, and social media. However, it gradually has become difficult to collect the demographics of Internet users because their activities are anonymous in many cases. Although the marketing department is able to get the demographics using online or offline surveys, these approaches are very expensive, long processes, and likely to include false statements. Clickstream data is the recording an Internet user leaves behind while visiting websites. As the user clicks anywhere in the webpage, the activity is logged in semi-structured website log files. Such data allows us to see what pages users visited, how long they stayed there, how often they visited, when they usually visited, which site they prefer, what keywords they used to find the site, whether they purchased any, and so forth. For such a reason, some researchers tried to guess the demographics of Internet users by using their clickstream data. They derived various independent variables likely to be correlated to the demographics. The variables include search keyword, frequency and intensity for time, day and month, variety of websites visited, text information for web pages visited, etc. The demographic attributes to predict are also diverse according to the paper, and cover gender, age, job, location, income, education, marital status, presence of children. A variety of data mining methods, such as LSA, SVM, decision tree, neural network, logistic regression, and k-nearest neighbors, were used for prediction model building. However, this research has not yet identified which data mining method is appropriate to predict each demographic variable. Moreover, it is required to review independent variables studied so far and combine them as needed, and evaluate them for building the best prediction model. The objective of this study is to choose clickstream attributes mostly likely to be correlated to the demographics from the results of previous research, and then to identify which data mining method is fitting to predict each demographic attribute. Among the demographic attributes, this paper focus on predicting gender, age, marital status, residence, and job. And from the results of previous research, 64 clickstream attributes are applied to predict the demographic attributes. The overall process of predictive model building is compose of 4 steps. In the first step, we create user profiles which include 64 clickstream attributes and 5 demographic attributes. The second step performs the dimension reduction of clickstream variables to solve the curse of dimensionality and overfitting problem. We utilize three approaches which are based on decision tree, PCA, and cluster analysis. We build alternative predictive models for each demographic variable in the third step. SVM, neural network, and logistic regression are used for modeling. The last step evaluates the alternative models in view of model accuracy and selects the best model. For the experiments, we used clickstream data which represents 5 demographics and 16,962,705 online activities for 5,000 Internet users. IBM SPSS Modeler 17.0 was used for our prediction process, and the 5-fold cross validation was conducted to enhance the reliability of our experiments. As the experimental results, we can verify that there are a specific data mining method well-suited for each demographic variable. For example, age prediction is best performed when using the decision tree based dimension reduction and neural network whereas the prediction of gender and marital status is the most accurate by applying SVM without dimension reduction. We conclude that the online behaviors of the Internet users, captured from the clickstream data analysis, could be well used to predict their demographics, thereby being utilized to the digital marketing.

• Asia pacific journal of information systems
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• v.24 no.2
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• pp.211-232
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• 2014

Recently, there is a growing interest toward social commerce using SNS(Social Networking Service), and the size of its market is also expanding due to popularization of smart phones, tablet PCs and other smart devices. Accordingly, various studies have been attempted but it is shown that most of the previous studies have been conducted from perspectives of the users. The purpose of this study is to derive user-centered CSF(Critical Success Factor) of social commerce from the previous studies and analyze the CSF perception gap between social commerce service providers and users. The CSF perception gap between two groups shows that there is a difference between ideal images the service providers hope for and the actual image the service users have on social commerce companies. This study provides effective improvement directions for social commerce companies by presenting current business problems and its solution plans. For this, This study selected Korea's representative social commerce business Ticket Monster, which is dominant in sales and staff size together with its excellent funding power through M&A by stock exchange with the US social commerce business Living Social with Amazon.com as a shareholder in August, 2011, as a target group of social commerce service provider. we have gathered questionnaires from both service providers and the users from October 22, 2012 until October 31, 2012 to conduct an empirical analysis. We surveyed 160 service providers of Ticket Monster We also surveyed 160 social commerce users who have experienced in using Ticket Monster service. Out of 320 surveys, 20 questionaries which were unfit or undependable were discarded. Consequently the remaining 300(service provider 150, user 150)were used for this empirical study. The statistics were analyzed using SPSS 12.0. Implications of the empirical analysis result of this study are as follows: First of all, There are order differences in the importance of social commerce CSF between two groups. While service providers regard Price Economic as the most important CSF influencing purchasing intention, the users regard 'Trust' as the most important CSF influencing purchasing intention. This means that the service providers have to utilize the unique strong point of social commerce which make the customers be trusted rathe than just focusing on selling product at a discounted price. It means that service Providers need to enhance effective communication skills by using SNS and play a vital role as a trusted adviser who provides curation services and explains the value of products through information filtering. Also, they need to pay attention to preventing consumer damages from deceptive and false advertising. service providers have to create the detailed reward system in case of a consumer damages caused by above problems. It can make strong ties with customers. Second, both service providers and users tend to consider that social commerce CSF influencing purchasing intention are Price Economic, Utility, Trust, and Word of Mouth Effect. Accordingly, it can be learned that users are expecting the benefit from the aspect of prices and economy when using social commerce, and service providers should be able to suggest the individualized discount benefit through diverse methods using social network service. Looking into it from the aspect of usefulness, service providers are required to get users to be cognizant of time-saving, efficiency, and convenience when they are using social commerce. Therefore, it is necessary to increase the usefulness of social commerce through the introduction of a new management strategy, such as intensification of search engine of the Website, facilitation in payment through shopping basket, and package distribution. Trust, as mentioned before, is the most important variable in consumers' mind, so it should definitely be managed for sustainable management. If the trust in social commerce should fall due to consumers' damage case due to false and puffery advertising forgeries, it could have a negative influence on the image of the social commerce industry in general. Instead of advertising with famous celebrities and using a bombastic amount of money on marketing expenses, the social commerce industry should be able to use the word of mouth effect between users by making use of the social network service, the major marketing method of initial social commerce. The word of mouth effect occurring from consumers' spontaneous self-marketer's duty performance can bring not only reduction effect in advertising cost to a service provider but it can also prepare the basis of discounted price suggestion to consumers; in this context, the word of mouth effect should be managed as the CSF of social commerce. Third, Trade safety was not derived as one of the CSF. Recently, with e-commerce like social commerce and Internet shopping increasing in a variety of methods, the importance of trade safety on the Internet also increases, but in this study result, trade safety wasn't evaluated as CSF of social commerce by both groups. This study judges that it's because both service provider groups and user group are perceiving that there is a reliable PG(Payment Gateway) which acts for e-payment of Internet transaction. Accordingly, it is understood that both two groups feel that social commerce can have a corporate identity by website and differentiation in products and services in sales, but don't feel a big difference by business in case of e-payment system. In other words, trade safety should be perceived as natural, basic universal service. Fourth, it's necessary that service providers should intensify the communication with users by making use of social network service which is the major marketing method of social commerce and should be able to use the word of mouth effect between users. The word of mouth effect occurring from consumers' spontaneous self- marketer's duty performance can bring not only reduction effect in advertising cost to a service provider but it can also prepare the basis of discounted price suggestion to consumers. in this context, it is judged that the word of mouth effect should be managed as CSF of social commerce. In this paper, the characteristics of social commerce are limited as five independent variables, however, if an additional study is proceeded with more various independent variables, more in-depth study results will be derived. In addition, this research targets social commerce service providers and the users, however, in the consideration of the fact that social commerce is a two-sided market, drawing CSF through an analysis of perception gap between social commerce service providers and its advertisement clients would be worth to be dealt with in a follow-up study.

• Journal of Distribution Research
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• v.17 no.2
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• pp.29-62
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• 2012

As distribution environment is changing rapidly and competition is more intensive in the channel of distribution, the importance of retailer image and retailer equity is increasing as a different competitive advantages. Also, consumers are not functionally oriented and that their behavior is significantly affected by the symbols such as retailer image which identify retailer in the market place. That is, consumers do not choose products or retailers for their material utilities but consume the symbolic meaning of those products or retailers as expressed in their self images. The concept of self-image congruence has been utilized by marketers and researchers as an aid in better understanding how consumers identify themselves with the brands they buy and the retailer they patronize. Although self-image congruity theory has been tested across many product categories, the theory has not been tested extensively in the retailing. Therefore, this study attempts to investigate the impact of self image congruence between retailer image and self image of consumer on retailer equity such as retailer awareness, retailer association, perceived retailer quality, and retailer loyalty. The purpose of this study is to find out whether retailer-self image congruence can be a new antecedent of retailer equity. In addition, this study tries to examine how four-dimensional retailer equity constructs (retailer awareness, retailer association, perceived retailer quality, and retailer loyalty) affect customers' repatronage intention. For this study, data were gathered by survey and analyzed by structural equation modeling. The sample size in the present study was 254. The reliability of the all seven dimensions was estimated with Cronbach's alpha, composite reliability values and average variance extracted values. We determined whether the measurement model supports the convergent validity and discriminant validity by Exploratory factor analysis and Confirmatory Factor Analysis. For each pair of constructs, the square root of the average variance extracted values exceeded their correlations, thus supporting the discriminant validity of the constructs. Hypotheses were tested using the AMOS 18.0. As expected, the image congruence hypotheses were supported. The greater the degree of congruence between retailer image and self-image, the more favorable were consumers' retailer evaluations. The all two retailer-self image congruence (actual self-image congruence and ideal self-image congruence) affected customer based retailer equity. This result means that retailer-self image congruence is important cue for customers to estimate retailer equity. In other words, consumers are often more likely to prefer products and retail stores that have images similar to their own self-image. Especially, it appeared that effect for the ideal self-image congruence was consistently larger than the actual self-image congruence on the retailer equity. The results mean that consumers prefer or search for stores that have images compatible with consumer's perception of ideal-self. In addition, this study revealed that customers' estimations toward customer based retailer equity affected the repatronage intention. The results showed that all four dimensions (retailer awareness, retailer association, perceived retailer quality, and retailer loyalty) had positive effect on the repatronage intention. That is, management and investment to improve image congruence between retailer and consumers' self make customers' positive evaluation of retailer equity, and then the positive customer based retailer equity can enhance the repatonage intention. And to conclude, retailer's image management is an important part of successful retailer performance management, and the retailer-self image congruence is an important antecedent of retailer equity. Therefore, it is more important to develop and improve retailer's image similar to consumers' image. Given the pressure to provide increased image congruence, it is not surprising that retailers have made significant investments in enhancing the fit between retailer image and self image of consumer. The enhancing such self-image congruence may allow marketers to target customers who may be influenced by image appeals in advertising.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.107-129
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• 2022

It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.