• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.111-127
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• 2023

This study is a study on user perception of ChatGPT use. The goal of this study is to analyze the relationship between user policy expectations and user innovativeness on ChatGPT technology acceptance and intention to use using variables of TRA (Theory of Reasoned Action). The impact of policy expectations and user innovativeness on the intention to use by mediating usefulness and hedonic motivation, and the impact of subjective norms on the usefulness and intention to use were analyzed by dividing them into the MZ generation and the non-MZ generation. It was verified whether there was a moderating effect on the effect of age differences on usefulness by interacting with policy expectations. An online survey was conducted on 300 ChatGPT users using PLS (Partial Least Square) structural equations and SPSS Package, and statistical analysis was performed using PLS and SPSS. According to the analysis results, it was confirmed that the higher the initial user's innovativeness, the higher the intention to use ChatGPT. In addition, the moderating effect analysis comparing the differences between the MZ generation and the non-MZ generation showed that policy expectations had a negative effect on the usefulness of ChatGPT use.

• 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.

• Journal of Korea Port Economic Association
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• v.30 no.4
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• pp.69-89
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• 2014

The business performance of port industry is steadily getting worse due to international environmental regulation. The port industry should be prepared according to ambient condition change. IMO(International Maritime Organization) is tightening up environmental regulation of vessel and maritime industry field. ECA(Emission Control Area), starting with the Baltic, has initialized and has been expanded. Korea must strengthen the control of vessel in accordance with IMO's restriction, if Korea is designated as emission control area. These situations cause the expansion of LNG-fuelled ships. Add to the larger trend of ships, Korean government should be done a preemptive action against LNG bunkering industry. This study proposes the concept of floating offshore LNG bunkering system and is conducted its economic feasibility evaluation based on empirical analysis. We examine the theoretical foundation and basic information via "A Planning Study on the Engineering Development of Floating Offshore LNG Bunkering Terminal" in 2013 and we evaluate the business potential by using the report above mentioned. The results of this study are as follows. The values of B/C analysis are between 0.679 and 2.516 depending on market share and R&D contributiveness. In case of 10.9%(market share), if market share are 50% and 60%, the value of B/C analysis are 0.697 and 0.837 respectively. Except in two cases, all remaining values are over 1.0. Moreover, the research is conducted sensitivity analysis to remove the project uncertainty. In order to maintain economical validity, a project manager have to establish business strategies which are not to cause increase of expense and sustain market share and R&D contributiveness in the scenario with normal levels.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.167-194
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• 2019

This research starts from the four basic concepts of incentive incompatibility, limited information, myopia and decision variable which are confronted when making decisions in keyword bidding. In order to make these concept concrete, four framework approaches are designed as follows; Strategic approach for the incentive incompatibility, Statistical approach for the limited information, Alternative optimization for myopia, and New model approach for decision variable. The purpose of this research is to propose the statistical optimization model in constructing the portfolio of Sponsored Search Advertising (SSA) in the Sponsor's perspective through empirical tests which can be used in portfolio decision making. Previous research up to date formulates the CTR estimation model using CPC, Rank, Impression, CVR, etc., individually or collectively as the independent variables. However, many of the variables are not controllable in keyword bidding. Only CPC and Rank can be used as decision variables in the bidding system. Classical SSA model is designed on the basic assumption that the CPC is the decision variable and CTR is the response variable. However, this classical model has so many huddles in the estimation of CTR. The main problem is the uncertainty between CPC and Rank. In keyword bid, CPC is continuously fluctuating even at the same Rank. This uncertainty usually raises questions about the credibility of CTR, along with the practical management problems. Sponsors make decisions in keyword bids under the limited information, and the strategic portfolio approach based on statistical models is necessary. In order to solve the problem in Classical SSA model, the New SSA model frame is designed on the basic assumption that Rank is the decision variable. Rank is proposed as the best decision variable in predicting the CTR in many papers. Further, most of the search engine platforms provide the options and algorithms to make it possible to bid with Rank. Sponsors can participate in the keyword bidding with Rank. Therefore, this paper tries to test the validity of this new SSA model and the applicability to construct the optimal portfolio in keyword bidding. Research process is as follows; In order to perform the optimization analysis in constructing the keyword portfolio under the New SSA model, this study proposes the criteria for categorizing the keywords, selects the representing keywords for each category, shows the non-linearity relationship, screens the scenarios for CTR and CPC estimation, selects the best fit model through Goodness-of-Fit (GOF) test, formulates the optimization models, confirms the Spillover effects, and suggests the modified optimization model reflecting Spillover and some strategic recommendations. Tests of Optimization models using these CTR/CPC estimation models are empirically performed with the objective functions of (1) maximizing CTR (CTR optimization model) and of (2) maximizing expected profit reflecting CVR (namely, CVR optimization model). Both of the CTR and CVR optimization test result show that the suggested SSA model confirms the significant improvements and this model is valid in constructing the keyword portfolio using the CTR/CPC estimation models suggested in this study. However, one critical problem is found in the CVR optimization model. Important keywords are excluded from the keyword portfolio due to the myopia of the immediate low profit at present. In order to solve this problem, Markov Chain analysis is carried out and the concept of Core Transit Keyword (CTK) and Expected Opportunity Profit (EOP) are introduced. The Revised CVR Optimization model is proposed and is tested and shows validity in constructing the portfolio. Strategic guidelines and insights are as follows; Brand keywords are usually dominant in almost every aspects of CTR, CVR, the expected profit, etc. Now, it is found that the Generic keywords are the CTK and have the spillover potentials which might increase consumers awareness and lead them to Brand keyword. That's why the Generic keyword should be focused in the keyword bidding. The contribution of the thesis is to propose the novel SSA model based on Rank as decision variable, to propose to manage the keyword portfolio by categories according to the characteristics of keywords, to propose the statistical modelling and managing based on the Rank in constructing the keyword portfolio, and to perform empirical tests and propose a new strategic guidelines to focus on the CTK and to propose the modified CVR optimization objective function reflecting the spillover effect in stead of the previous expected profit models.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.29-56
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• 2021

Recently, various types of products have been launched with the rapid growth of the e-commerce market. As a result, many users face information overload problems, which is time-consuming in the purchasing decision-making process. Therefore, the importance of a personalized recommendation service that can provide customized products and services to users is emerging. For example, global companies such as Netflix, Amazon, and Google have introduced personalized recommendation services to support users' purchasing decisions. Accordingly, the user's information search cost can reduce which can positively affect the company's sales increase. The existing personalized recommendation service research applied Collaborative Filtering (CF) technique predicts user preference mainly use quantified information. However, the recommendation performance may have decreased if only use quantitative information. To improve the problems of such existing studies, many studies using reviews to enhance recommendation performance. However, reviews contain factors that hinder purchasing decisions, such as advertising content, false comments, meaningless or irrelevant content. When providing recommendation service uses a review that includes these factors can lead to decrease recommendation performance. Therefore, we proposed a novel recommendation methodology through CNN-based review usefulness score prediction to improve these problems. The results show that the proposed methodology has better prediction performance than the recommendation method considering all existing preference ratings. In addition, the results suggest that can enhance the performance of traditional CF when the information on review usefulness reflects in the personalized recommendation service.

• Management & Information Systems Review
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• v.38 no.1
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• pp.201-223
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• 2019

The purpose of this study is to verify empirically the causal relationship between service quality, ease of use, customer satisfaction, and repurchase intention of mobile shopping mall. And this study is to investigate the ease of use and customer satisfaction mediating effect of between service quality and repurchase intention. Therefore, 323 university students in Jeonnam area were surveyed and the structural equation model was derived based on previous research. Service quality of mobile shopping mall make a significant effect on using easiness, purchasing satisfaction and repurchase intention. However, among service quality of mobile shopping mall, service scape like mobile interface and site design made a positive effect on purchasing satisfaction, but did not any effect on repurchase intention. In other words, service quality factors that make positive effects on customer's pleasant using and repurchase intention make a positive effect on repurchase intention when providing and using the service customer wants faithfully rather than external part of the site and mutually influencing attitude or behavior well. The implications suggested by this study are as follows. First, service quality of mobile shopping mall makes a significant effect on repurchase intention, so it's necessary to improve CS service system so as to treat customers' inquiries or inconveniences actively during mobile shopping and return and refund of defective products quickly and conveniently. And, in addition to the finally used factors in analysis process, benefits using customers' grade by number of purchases, such as various events, coupons, reserve, etc. and active contents marketing strategies providing more various pleasures and values of shopping are necessary. Second, satisfaction of mobile shopping mall makes a positive effect on repurchase intention, so visiting of site and repurchasing of product are continuously done as customers' satisfaction on shopping mall is increasing. Therefore, shopping mall site requires differentiation of contents, exact plan and practice of service, marketing, etc. so that customers can feel more satisfaction. This study is significant as it systematically analyzed concepts of components that service quality of mobile shopping mall makes an effect on using easiness, purchasing satisfaction, and repurchase intention, verified the relations, systematized it by theoretical structure, and widened the understanding of effects making an effect on repurchase intention.

• Journal of Technology Innovation
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• v.20 no.2
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• pp.75-107
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• 2012

Previous researches on technological innovation have several limitations such as lack of general mechanism for technological innovation(inputs, throughputs and outputs of technological innovation), large company oriented studies, and ignoring importance of technology management capabilities. So, this study suggested a new model using resource-based theory and system theory, and empirically applied that to SMEs. Structural equation model analysis by using 223 SMEs in Daegu region provided a support for most of hypotheses. Research results showed that all of factors on technological innovation were significantly and positively related with each other: inputs(R&D leadership, innovation strategy, R&D investment, R&D human resource management, external network), throughputs(portfolio management, project management, technology commercialization) and output(technological innovation). In case of technological innovation inputs, R&D leadership influenced on innovation strategy positively and significantly. And R&D leadership and innovation strategy had positive and significant effects on R&D investment, R&D human resource management and external network. R&D human resource management and external network exerted positive and significant influences on technological innovation throughputs such as portfolio management and project management. But R&D investment did not significant impacts on technological innovation throughputs. Among technological innovation throughputs, both portfolio management and project management had positive and significant effect on technology commercialization. In addition, technology commercialization acted positively and significantly technological innovation output. This study suggests necessary of efforts to implement innovation strategy and manage R&D human resource effectively based on CEO's innovativeness and entrepreneurship. Also, if SMEs want to develop technology and commercialize it, they have to cooperate with external technology resources and informations. Research results revealed that proper level of R&D investment, internal and external communication, information sharing, and learning and cooperative culture were very important for improvement of technological innovation performance in SMEs. Especially, this research suggested that if SMEs manage technological innovation process effectively based on resource-based and system approaches, then they can overcome their resource limitations and gain high technological innovation performance. Also, useful policy support for technological innovation of central or regional government by this research model is important factor for SMEs' technological innovation performance.

• Management & Information Systems Review
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• v.37 no.3
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• pp.83-113
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• 2018

The purpose of this study to empirically examine a smart work investment and job performance by change resistance. Firstly, There investigates mediating role of the communication between the smart work investment and the job performance. Secondly, It will identify the job productivity differences through a level of organizational change resistance that reduced smart work investment. The smart work is to provide the flexibility of time and location and is a working method to improve a work productivity of organization members. The introduction of smart work means the adoption of new organizational culture, institution and technology and requires a novel change of a custom and pattern on existing organization culture and institution because of transformation form of communication and collaboration. The method of this study adopts a structural equation model to test a mediating effect of communication and a moderating effect of change resistance level. This model confirms whether smart work investments provide a positive impact on communication and organizational productivity. In addition, I will classify a change resistance level of smart work by cluster analysis and then check a critical path difference of job productivity between each group. As a result, The organizational IT, institution and culture on the smart work investment appeared to important influencers in communication and also had a direct influence of individual performance. Also, The three independent variables of smart work investment have an indirect influence of individual and organizational performance through communication mediating variables. However, the organizational IT and institution as independent variables do not provide direct influence of organization performance. Nevertheless, two independent variables of organizational IT and institution have an indirect influence the organization performance through communication mediating variables. As a result of confirming a productivity of three groups on organization resistance, there was a difference the individual and organizational performance among groups. The low-level group of organizational resistance showed high coefficient value of performance compared to other groups. The group analysis implications, The smart work investment appeared significantly to revise the institution first, build culture secondly and advanced technology lastly. The theoretical implication from this study contributes an extension of social science theory through socio-technical systems, institution, culture, change resistance and job performance based on smart work. The practical implications explain the smart work success in step-by-step investment rather than radical investment as level management of change resistance. In future research, the smart work performance between private and public firms will analyze a difference of the organizational culture, institution, technology and performance.

• Korean Business Review
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• v.14
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• pp.1-30
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• 2001

To prevent the VAT evasion and to form a sound VAT paying culture, we can draw the policy directions for government as follows: First, it is necessary to strengthen the tax supervision of small business and the administration of tax sources of cash-income industry. Second, the tax-deductible rate of the received tax invoices should be increased in a short-term base, and a simplified taxation system should be abolished in a long-term base. Third, in cases a trader has not received a tax invoice, an additional tax should be applied. Forth, to issue the tax invoice faster and conveniently, it is requested to introduce a new system which issues electronic card of registration when a businessman applies for his/her business registration. Fifth, to make tax standard transparent, it is desirable to punish the violator, relating to credit card regulations, stricter than present and to enforce the electronic bookkeeping. Sixth, for the reduction of noncompliance rate and creating a climate for autonomous, faithful tax return, it is necessary to expand and intensify tax investigation. And also it is necessary to make the level of penalty tax higher up and the level of criminal punishment less down, to keep up tax audit coverage. Seventh, a trader who is eligible for simplified taxation, whose tax base is under 12,000 thousand won, should not be required to pay the value added tax. But it is desirable to cut down them for the fairness of tax burden. Eighth, the effective date of the revised tax law should be fixed. Ninth, it is necessary to reinforce publicity and to educate on tax system and administration, for reducing tax evasion or tax avoidance and encouraging faithful tax return. Tenth, as the tendency of VAT evasion of distribution industry turns out to be the highest, it is requested not only to intensify tax administration on them but also to establish system and incentives, for introducing information system in distribution industry(introducing POS system, computerization of transaction record, establishing EDI between traders).

• Journal of Intelligence and Information Systems
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• v.27 no.4
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• pp.73-95
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• 2021

This study uses Node2vec graph embedding method and Light GBM link prediction to explore undeveloped export candidate countries in Korea's food and beverage industry. Node2vec is the method that improves the limit of the structural equivalence representation of the network, which is known to be relatively weak compared to the existing link prediction method based on the number of common neighbors of the network. Therefore, the method is known to show excellent performance in both community detection and structural equivalence of the network. The vector value obtained by embedding the network in this way operates under the condition of a constant length from an arbitrarily designated starting point node. Therefore, it has the advantage that it is easy to apply the sequence of nodes as an input value to the model for downstream tasks such as Logistic Regression, Support Vector Machine, and Random Forest. Based on these features of the Node2vec graph embedding method, this study applied the above method to the international trade information of the Korean food and beverage industry. Through this, we intend to contribute to creating the effect of extensive margin diversification in Korea in the global value chain relationship of the industry. The optimal predictive model derived from the results of this study recorded a precision of 0.95 and a recall of 0.79, and an F1 score of 0.86, showing excellent performance. This performance was shown to be superior to that of the binary classifier based on Logistic Regression set as the baseline model. In the baseline model, a precision of 0.95 and a recall of 0.73 were recorded, and an F1 score of 0.83 was recorded. In addition, the light GBM-based optimal prediction model derived from this study showed superior performance than the link prediction model of previous studies, which is set as a benchmarking model in this study. The predictive model of the previous study recorded only a recall rate of 0.75, but the proposed model of this study showed better performance which recall rate is 0.79. The difference in the performance of the prediction results between benchmarking model and this study model is due to the model learning strategy. In this study, groups were classified by the trade value scale, and prediction models were trained differently for these groups. Specific methods are (1) a method of randomly masking and learning a model for all trades without setting specific conditions for trade value, (2) arbitrarily masking a part of the trades with an average trade value or higher and using the model method, and (3) a method of arbitrarily masking some of the trades with the top 25% or higher trade value and learning the model. As a result of the experiment, it was confirmed that the performance of the model trained by randomly masking some of the trades with the above-average trade value in this method was the best and appeared stably. It was found that most of the results of potential export candidates for Korea derived through the above model appeared appropriate through additional investigation. Combining the above, this study could suggest the practical utility of the link prediction method applying Node2vec and Light GBM. In addition, useful implications could be derived for weight update strategies that can perform better link prediction while training the model. On the other hand, this study also has policy utility because it is applied to trade transactions that have not been performed much in the research related to link prediction based on graph embedding. The results of this study support a rapid response to changes in the global value chain such as the recent US-China trade conflict or Japan's export regulations, and I think that it has sufficient usefulness as a tool for policy decision-making.