• Journal of Venture Innovation
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• v.4 no.3
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• pp.17-30
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• 2021

Environmental deterministic perspectives and resource-based perspectives have different perceptions on the factors that determine corporate performance. While the environmental deterministic viewpoint sees the external environment as having a significant impact on corporate performance. On the other hand, the resource-compliant viewpoint believes that it is important to obtain the necessary resources through appropriate decision-making in order to overcome the uncertainty of the environment. Although the external environmental impact on corporate performance is important, the study is in the position that efforts within the company to cope with environmental uncertainty are necessary. This study identified the role that factors within the company play in the process of affecting the external environment of the company's performance. This study looked at whether the CEO's innovation direction plays an mediating role in the market environment favorability affecting sales growth rate. The data was collected using a survey method. We collected data from 138 small and medium-sized manufacturing companies in Gyeongin area. The collected data was analyzed using SPSS 22 packages. According to the analysis, market environment favorability positively affects sales growth rate, and the CEO's innovation direction plays a mediating role between market environment favorability and sales growth rate. The results of this study showed that depending on the market environment, the CEO's interest and willingness to innovate, present a vision for innovation, and institutionalize innovation activities increase management performance through innovation.

• Journal of Industrial Convergence
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• v.21 no.11
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• pp.13-27
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• 2023

This study aims to contribute to the reduction of the failure rate and social costs resulting from business failures by analyzing factors that affect the insolvency of social enterprises, as the role of social enterprises is increasing in our economy. The data used in this study were classified as normal and insolvent companies among social enterprises (including prospective social enterprises) that were established between 2009 and 2018 and received credit guarantees from credit guarantee institutions as of the end of June 2022. Among the collected data, 439 social enterprises with available financial information were targeted; 406 (92.5%) were normal enterprises, and 33 (7.5%) were insolvent enterprises. Through a literature review, eight non-financial factors commonly used for insolvency prediction were selected. The cross-analysis results showed that four of these factors were significant. Logistic regression analysis revealed that two variables, including corporate credit rating and the personal credit rating of the representative, were significant. Financial factors such as debt ratio, sales operating profit rate, and total asset turnover were used as control variables. The empirical analysis confirmed that the two independent variables maintained their influence even after controlling for financial factors. Given that government-led support and development policies have limitations, there is a need to shift policy direction so that various companies aspiring to create social value can enter the social enterprise sector through private and regional initiatives. This would enable the social economy to create an environment where local residents can collaborate to realize social value, and the government should actively support this.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.6
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• pp.873-881
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• 2023

The evolution of civil engineering technology, exemplified by recent milestones like the completion of the Gangnam Global Business Center (GBC), has fostered the construction of expansive civil and architectural structures both above and below the earth's surface. This surge in construction necessitates a commensurate advancement in research and technology pertaining to safety protocols applicable to these vast edifices. Such protocols encompass a spectrum of concerns, ranging from the preemptive mitigation of accidents to the effective management of exigencies such as fires. As the trajectory of construction endeavors continues unabated, encompassing both subterranean and elevated domains, a concomitant imperative emerges to refine the methodologies underpinning precise indoor positioning. To address this need, an innovative web-based simulator has been devised to emulate indoor positioning scenarios for rigorous testing. This research further entails the development of an indoor positioning data Application Programming Interface (API) fortified by Geographic Information System (GIS) spatial operation techniques. This API is anchored in the construction of intricate test data, centered on the spatial layout of building 13 at the Electronics and Telecommunications Research Institute (ETRI). Consequently, the study renders feasible the expeditious provisioning of diverse signal-based and image-based spatial information, pivotal for enhancing the navigational acumen of mobile devices. Path delineation, cellular signal mapping, landmark identification, and ancillary navigational aids are among the manifold datasets promptly furnished by the indoor positioning data API. In summation, this study engenders a crucial leap towards the fortification of safety protocols and navigational precision within the expansive confines of modern architectural wonders.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.6
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• pp.609-619
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• 2023

This study aimed to investigate the formation of the social perception of domestic maritime sports using text mining analysis of keywords and topics from domestic media coverage over the past 10 years related to representative maritime sports, including yachting, rowing, and canoeing. The results are as follows: First, term frequency (TF) and word cloud analyses identified the top keywords: "maritime," "competition," "experience," "tourism," "world," "yachting," "canoeing," "leisure," and "participation." Second, semantic network analysis revealed that yachting was correlated with terms like "maritime," "industry," "competition," "leisure," "tourism," "boat," "facilities," and "business"; rowing with terms like "competition" and "Chungju"; and canoeing with terms like "maritime," "competition," "experience," "leisure," and "tourism." Third, topic modeling analysis indicated that yachting, rowing, and canoeing are perceived as elite sports and maritime leisure sports. However, the perception of these sports has been demonstrated to have little impact on society, public opinion, and social transformation. In summary, when considering these results comprehensively, it can be concluded that yachting and canoeing have gradually shifted from being perceived as elite sports to essential elements of the maritime leisure industry. Contrariwise, rowing remains primarily associated with elite sports, and its popularization as a maritime leisure sport appears limited at this time.

• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.1-13
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• 2023

The widespread adoption of smartphones and advancements in internet technology have notably shifted content consumption habits toward video. This research aims to dissect the nature of videos posted on YouTube, the global video-sharing platform, to understand the characteristics of both produced and preferred content. For this study, dance was chosen as a specific subject from a variety of video categories. Data on YouTube videos associated with the term "dance" was compiled over three years, from 2019 to 2021. The investigation revealed a clear distinction between the types of dance videos frequently uploaded to YouTube and those that receive a high number of views. The empirical analysis of this study indicates a viewer preference for vlogs that provide insights into the daily lives of dance students, as well as for purpose-driven videos, such as those highlighting dance exam preparations or school dance events. Notably, the vlogs that attract the most attention are typically created by dance students at the college or secondary school level, rather than by professionals. Although the study was focused on dance, its methodologies can be applied to different subjects. These insights are expected to contribute to the development of a recommendation system that aids content creators in effectively targeting their productions.

• 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 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.

• Information Systems Review
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• v.23 no.4
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• pp.45-67
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• 2021

As the transaction volume of the C2C second-hand market is growing, the number of frauds, which intend to earn unfair gains by sending products different from specified ones or not sending them to buyers, is also increasing. This study explores the model that can identify frauds in the online C2C second-hand market by examining the postings for transactions. For this goal, this study collected 145,536 field data from actual C2C second-hand market. Then, the model is built with the characteristics from postings such as the topic and the linguistic characteristics of the product description, and the characteristics of products, postings, sellers, and transactions. The constructed model is then trained by the machine learning algorithm XGBoost. The final analysis results show that fraudulent postings have less information, which is also less specific, fewer nouns and images, a higher ratio of the number and white space, and a shorter length than genuine postings do. Also, while the genuine postings are focused on the product information for nouns, delivery information for verbs, and actions for adjectives, the fraudulent postings did not show those characteristics. This study shows that the various features can be extracted from postings written in C2C second-hand transactions and be used to construct an effective model for frauds. The proposed model can be also considered and applied for the other C2C platforms. Overall, the model proposed in this study can be expected to have positive effects on suppressing and preventing fraudulent behavior in online C2C markets.

• Information Systems Review
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• v.23 no.4
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• pp.103-125
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• 2021

Mobility services are rapidly developing along with information and communication technology (ICT) innovation. Recently, the on-demand mobility platform market is growing, and an environment is provided in which users can call services more conveniently and check the connection status in real time using smartphones. This study investigates the current status of users' perceptions and experiences of car-hailing mobility services such as KAKAO Taxi and UT Taxi, and it analyzes the factors affecting the intention to continuously use the car-hailing service, focusing on environmental and instrumental benefits and trust in driver and platform. In particular, this study examines whether the significance of the relationship between influencing factors and continuous use intention could vary depending on the degree of innovativeness and ICT utilization. The results of this study showed that perceived benefits (environmental benefits and convenience and economic instrumental benefits) and trust in driver had significant effects on increasing trust in platform. It was analyzed that the higher the trust in platform, the higher the intention to continuously use the car-hailing service. Furthermore, the influence of perceived environmental benefits and economic benefits on the trust in platform was different depending on the degree of individual innovativeness, and the influence of convenience and economic benefits on the trust in platform varied depending on the degree of ICT utilization. Referring to the results of this study, mobility service providers can better understand the current status of users' perceptions and trust for car-hailing services, and implement service promotion strategies suitable for individual characteristics.

• Information Systems Review
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• v.24 no.1
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• pp.105-123
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• 2022

Thanks to the remarkable success of Artificial Intelligence (A.I.) techniques, a new possibility for its application on the real-world problem has begun. One of the prominent applications is the bankruptcy prediction model as it is often used as a basic knowledge base for credit scoring models in the financial industry. As a result, there has been extensive research on how to improve the prediction accuracy of the model. However, despite its impressive performance, it is difficult to implement machine learning (ML)-based models due to its intrinsic trait of obscurity, especially when the field requires or values an explanation about the result obtained by the model. The financial domain is one of the areas where explanation matters to stakeholders such as domain experts and customers. In this paper, we propose a novel approach to incorporate financial domain knowledge into local rule generation to provide explanations for the bankruptcy prediction model at instance level. The result shows the proposed method successfully selects and classifies the extracted rules based on the feasibility and information they convey to the users.