• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.309-323
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• 2023

As the role of online reviews has become increasingly crucial, numerous studies have been conducted to utilize helpful reviews. Helpful reviews, perceived by customers, have been verified in various research studies to be influenced by factors such as ratings, review length, review content, and so on. The determination of a review's helpfulness is generally based on the number of 'helpful' votes from consumers, with more 'helpful' votes considered to have a more significant impact on consumers' purchasing decisions. However, recently written reviews that have not been exposed to many customers may have relatively few 'helpful' votes and may lack 'helpful' votes altogether due to a lack of participation. Therefore, rather than relying on the number of 'helpful' votes to assess the helpfulness of reviews, we aim to classify them based on review content. In addition, the text of the review emerges as the most influential factor in review helpfulness. This study employs text mining techniques, including topic modeling and sentiment analysis, to analyze the diverse impacts of content and emotions embedded in the review text. In this study, we propose a review helpfulness prediction model based on review content, utilizing movie reviews from IMDb, a global movie information site. We construct a review helpfulness prediction model by using an explainable Graph Neural Network (GNN), while addressing the interpretability limitations of the machine learning model. The explainable graph neural network is expected to provide more reliable information about helpful or non-helpful reviews as it can identify connections between reviews.

• Journal of Korea Water Resources Association
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• v.56 no.spc1
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• pp.1007-1014
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• 2023

Two-dimensional flow analysis, a fundamental component of hydrodynamics, plays a pivotal role in numerically simulating fluid behavior in rivers and waterways. This modeling approach heavily relies on parameters such as eddy viscosity and roughness coefficient to accurately represent flow characteristics. Therefore, combination of appropriate parameters is very important to accurately simulate flow characteristics. In this study, an automation algorithm was developed and applied to find the optimal combination of parameters. Previously, when applying a two-dimensional flow analysis model, former researchers usually depend on the empirical approach, which causes many difficulties in finding optimal variable values. Using the experimental data, we tracked errors according to the combination of various parameters and applied the algorithm that can determine the optimal combination of parameters with the Python language. The automation algorithm can easily determine the most accurate combination by comparing the flow velocity error values among the two-dimensional flow analysis results among the combinations of 121 (11×11) parameters. In the perspective of utilizing automation algorithm, there is an expected high utility in promptly and straightforwardly determining the optimal combination of parameters with the smallest error.

• Journal of Industrial Convergence
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• v.21 no.12
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• pp.11-23
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• 2023

This study aims to investigate the relationship between service recovery justice, residual emotions, and customer behavior. It empirically verifies that low justice in service recovery affects residual emotions and, in turn, has an impact on customers' negative behaviors. Furthermore, this research distinguishes customer-brand relationship quality into emotional relationship quality and cognitive relationship quality and seeks to validate that the type of relationship quality may influence the extent to which the justice of recovery processes affects residual emotions. Data was collected through surveys, and hypotheses were tested using structural equation modeling. The research findings indicate that among the dimensions of service recovery justice, procedural justice and interactional justice significantly influence residual emotions. Moreover, residual emotions have a significant impact on both the intention to revisit and the intention to engage in negative word-of-mouth. In addition, the impact of distributive justice and procedural justice on residual emotions was found to be higher for cognitive relationship quality than emotional relationship quality, and the impact of interactional justice on residual emotions was found to be higher for emotional relationship quality than cognitive relationship quality.

• Journal of Service Research and Studies
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• v.14 no.2
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• pp.158-177
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• 2024

This paper proposes and tests a theoretical model of the relational link between a novel form of customer-perceived fairness for a reward design (distributive peer justice climate) and C2C helping intention via community identification and online C2C interaction (friend-, neighboring customer-, audience-interaction) qualities in a collective consumption context (MMORPG). To test hypotheses, we amassed survey data within a collective consumption context (massively multiplayer online role-playing games, MMORPGs). We used structural equation modeling in analyzing the survey data. The results reveal that user-perceived distributive peer justice climate for a reward design enhances their C2C helping intention via community identification and C2C interactions in MMORPG contexts. Collective consumption-type service managers should focus on promoting the user-perceived distributive peer justice climate for their reward system to enhance users' present C2C co-creation experience (community identification, C2C interaction) and future C2C co-creation behavior (helping intention). By adopting an intra-unit level distributive justice concept (customer-perceived distributive peer justice climate) to a reward design in a collective consumption context (MMORPGs), this study informed collective consumption-type service managers of the importance of its management.

• Journal of the Korean Chemical Society
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• v.68 no.2
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• pp.107-116
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• 2024

The purpose of this study is to analyze changes in pre-service chemistry teachers' cognition of the nature of model in the evaluation and modification process of model using technology. Changes in cognition of the nature of model were analyzed focusing on the 'Abstraction' and 'Simplification' of the 'Representational aspect', 'Interpretation', 'Reasoning', 'Explanation' and 'Quantification' of the 'Explanatory aspect' that were deemed insufficient for pre-chemistry teachers in previous study. For this purpose, 19 third-year pre-service chemistry teachers at a teacher's college in Chungcheongbuk-do were asked to evaluate the model related to Boyle's law developed using technology, revise the model based on the evaluation results, and make a final evaluation. As a result of the study, it was confirmed that pre-service chemistry teachers' cognition of 'Simplification' of the 'Representational aspect' and 'Interpretation', 'Explanation', and 'Quantification' of the 'Explanatory aspect' changed positively through the evaluation and modification process of the model. Therefore, it was found that the evaluation and modification process of the model plays a key role in changing the cognition of the nature of model. However, there was little change in cognition of 'Abstraction' of the 'Representational aspect' and 'Reasoning' of the 'Explanatory aspect'. The cognition of these factors can be seen as more difficult to change than the cognition of other factors. To solve this problem, more sophisticated educational design for pre-service chemistry teachers is needed.

• Journal of the Korea Safety Management & Science
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• v.25 no.4
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• pp.45-56
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• 2023

This study attempted to provide implications by analyzing the impact of business Owner's safety commitment on industrial accidents and examining the mediating role of management supervisors' safety leadership and worker participation. Analysis was conducted on 2,067 manufacturing sites with 20 to 50 employees in the 10th Occupational Safety and Health Survey data. SPSS waw used to secure the reliability of the measurement variable. Hypothesis vertification was carried out after securing the suitability and validity of the structural model using AMOS. The direct impact of three latent variables on industrial accidents was confirmed: the business owner's safety commitment, the management supervisor's safety leadership, and the worker participation. The employer's safety will and the management supervisor's safety leadership do not directly affect industial accidents, but it has been verified that worker participation has a diret impact on industrial accident reduction. In addition, it has been confirmed that the safety leadership and worker participation of the management. Supervior have a complete mediating effect on the reduction of industrial accidents by mediating with the safety leadership of the management supervior and the participation of the workers. This study analyzed the impact on industrial accidents by dividing the stakeholders constituting the workplace into three classes: business owners, superviors, and workers, but the results suggest that employers and all workers inside the workplace may be organically linked to achieving the goal of reducing industrial accidents. Therefore, in order to establish an autonomous safety management system for safety and health at workerplaces, efforts are needed to reduce industrial accidents in their respective location by forming an organic community among internal stakeholders.

• Steel and Composite Structures
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• v.50 no.6
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• pp.705-720
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• 2024

Analyzing the collapse behavior of thin-walled steel structures holds significant importance in ensuring their safety and longevity. Geometric imperfections present on the surface of metal materials can diminish both the durability and mechanical integrity of steel shells. These imperfections, encompassing local geometric irregularities and deformations such as holes, cavities, notches, and cracks localized in specific regions of the shell surface, play a pivotal role in the assessment. They can induce stress concentration within the structure, thereby influencing its susceptibility to buckling. The intricate relationship between the buckling behavior of these structures and such imperfections is multifaceted, contingent upon a variety of factors. The buckling analysis of thin-walled steel shell structures, similar to other steel structures, commonly involves the determination of crucial material properties, including elastic modulus, shear modulus, tensile strength, and fracture toughness. An established method involves the emulation of distributed geometric imperfections, utilizing real test specimen data as a basis. This approach allows for the accurate representation and assessment of the diversity and distribution of imperfections encountered in real-world scenarios. Utilizing defect data obtained from actual test samples enhances the model's realism and applicability. The sizes and configurations of these defects are employed as inputs in the modeling process, aiding in the prediction of structural behavior. It's worth noting that there is a dearth of experimental studies addressing the influence of geometric defects on the buckling behavior of cylindrical steel shells. In this particular study, samples featuring geometric imperfections were subjected to experimental buckling tests. These same samples were also modeled using Finite Element Analysis (FEM), with results corroborating the experimental findings. Furthermore, the initial geometrical imperfections were measured using digital image correlation (DIC) techniques. In this way, the response of the test specimens can be estimated accurately by applying the initial imperfections to FE models. After validation of the test results with FEA, a numerical parametric study was conducted to develop more generalized design recommendations for the stainless-steel shell structures with the initial geometric imperfection. While the load-carrying capacity of samples with perfect surfaces was up to 140 kN, the load-carrying capacity of samples with 4 mm defects was around 130 kN. Likewise, while the load carrying capacity of samples with 10 mm defects was around 125 kN, the load carrying capacity of samples with 14 mm defects was measured around 120 kN.

• Journal of Venture Innovation
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• v.7 no.1
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• pp.17-39
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• 2024

The purpose of this study was to analyze the effect of the government's R&D support and the use of stock options by venture companies on the innovation of venture companies, that is, innovation capabilities and innovation performance. An empirical analysis was conducted using the partial least squares structural equation modeling (PLS-SEM) method using the data from the detailed survey of venture companies conducted on domestic venture confirmation companies. As a result of the analysis, it was found that the benefit of government R&D support had a positive (+) effect on strengthening the innovation capabilities of venture companies, and R&D support also had a positive (+) effect on the innovation performance of venture companies. Next, it was found that the use of stock options by venture companies had a positive (+) effect on the reinforcement of the innovation capabilities of companies and a positive (+) effect on the innovation performance of venture companies. In addition, it was found that the innovation capabilities of venture companies significantly mediate between the government's R&D support and the use of stock options by venture companies and the innovation performance of companies. These analysis results show that the government's R&D support and the use of stock option systems can play a meaningful role in the innovation of venture companies, and also show that the innovation capabilities of venture companies have an important meaning in the process of innovation. Therefore, it is necessary to continue the stance of R&D support for ventures and at the same time to introduce multi-faceted policy measures to support corporate capacity building, and legal and institutional maintenance and policy support to revitalize the stock option system need to be continuously provided.

• The Journal of the Convergence on Culture Technology
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• v.10 no.3
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• pp.19-24
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• 2024

The future military combat environment is rapidly expanding the role and importance of artificial intelligence (AI) in defense, aligning with the current trends of declining military populations and evolving dynamics. Particularly, in the civilian sector, AI development has surged into new domains based on foundation models, such as OpenAI's Chat-GPT, categorized as Super-Giant AI or Hyperscale AI. The U.S. Department of Defense has organized Task Force Lima under the Chief Digital and AI Office (CDAO) to conduct research on the application of Large Language Models (LLM) and generative AI. Advanced military nations like China and Israel are also actively researching the integration of Super-Giant AI into their military capabilities. Consequently, there is a growing need for research within our military regarding the potential applications and fields of application for Super-Giant AI in weapon systems. In this paper, we compare the characteristics and pros and cons of specialized AI and Super-Giant AI (Foundation Models) and explore new application areas for Super-Giant AI in weapon systems. Anticipating future application areas and potential challenges, this research aims to provide insights into effectively integrating Super-Giant Artificial Intelligence into defense operations. It is expected to contribute to the development of military capabilities, policy formulation, and international security strategies in the era of advanced artificial intelligence.

• Journal of Korea Port Economic Association
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• v.39 no.4
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• pp.163-190
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• 2023

Recently, a framework crystallizing as Environmental, Social, and Governance(ESG) has been exerting significant influence not only on corporate investment and management philosophies but also on national policies. This ESG framework is becoming an essential requirement for all organizations. It has become an obligation at the corporate and national levels, particularly in the maritime, port, and logistics sectors. Anticipating that the adoption and utilization of the ESG framework will reach higher levels when it becomes a necessity, this study utilized data from international organizations such as the United Nations Conference on Trade and Development(UNCTAD), the World Bank, and the World Economic Forum to analyze the impact of the ESG framework on national economic performance through the maritime, port, and logistics sectors using Partial Least Squares Structural Equation Modeling(PLS-SEM). The analysis revealed that while the ESG framework did not have a direct impact on the national economy, it manifested substantial indirect effects through maritime, port, and logistics sectors. Therefore, in these sectors, the establishment of the ESG framework should be recognized not only as an expenditure and obligation but also as a crucial investment that positively influences the national economic performance. The study's findings are limited by the absence of data beyond 2019 due to the impact of COVID-19. Therefore, it is anticipated that more accurate current effects can be ascertained when newer data becomes available.