Although patents have mainly been considered as the results of the invention process, of late their value as strategic assets have increasingly been emphasized. Consequently, the competition for patents among firms has intensified, and the number of patent disputes have been steadily increasing. Patent disputes, which cause enormous expense and resource utilization, increase uncertainty and have been considered as a threat or problem for the firms involved. Patent disputes are expected to have a significant impact on the decision making about subsequent innovation activities. This study attempts to analyze the effect of patent disputes on the subsequent innovative activities of the firms that are sued. After experiencing litigation as defendants, we examine their subsequent patenting strategies. According to the results of the study, firms who are experiencing patent litigation are more likely to achieve high-quality patents and cite recent technology when they apply for patents. Meanwhile, patent litigation experience has been shown to negatively affect the amount of subsequent patents applied. This study increases understanding by examining whether patent disputes, which have been mainly recognized negatively as obstacles, can be an opportunity that comes during a crisis.

Response surface methodology (RSM) empirically studies the relationship between a response variable and input variables in the product or process development phase. The ultimate goal of RSM is to find an optimal condition of the input variables that optimizes (maximizes or minimizes) the response variable. RSM can be seen as a knowledge management tool in terms of creating and utilizing data, information, and knowledge about a product production and service operations. In the field of product or process development, most real-world problems often involve a simultaneous consideration of multiple response variables. This is called a multiple response surface (MRS) problem. Various approaches have been proposed for MRS optimization, which can be classified into loss function approach, priority-based approach, desirability function approach, process capability approach, and probability-based approach. In particular, the loss function approach is divided into univariate and multivariate approaches at large. This paper focuses on the univariate approach. The univariate approach first obtains the mean square error (MSE) for individual response variables. Then, it aggregates the MSE's into a single objective function. It is common to employ the weighted sum or the Tchebycheff metric for aggregation. Finally, it finds an optimal condition of the input variables that minimizes the objective function. When aggregating, the relative weights on the MSE's should be taken into account. However, there are few studies on how to determine the weights systematically. In this study, we propose an interactive procedure to determine the weights through considering a decision maker's preference. The proposed method is illustrated by the 'colloidal gas aphrons' problem, which is a typical MRS problem. We also discuss the extension of the proposed method to the weighted MSE (WMSE).

This explorative study examines the difference in firm performance according to the adoption of the core technology of the Fourth industrial revolution, including artificial intelligence(AI), internet of things (IoT), cloud computing, and big data technology. Additionally, we investigate the importance of internal organizational structure exclusively responsible for information security. We analyze unique microdata offered by the Korea Information Society Development Institute to examine the impact of the adoption of the new technologies and the existence of organizational structure for information protection on firm performance, i.e., firm sales. By considering the core information technology as powerful knowledge assets, we argue that the adoption of such technology leads firms to have comparative advantage comparing to the competitors. Also, we emphasize the need to consider the organizational structure suitable for information security, which can become a structural asset of a firm.

Recently, in the global financial transaction field, global transactions using computer systems and server hubs between foreign exchanges beyond the one-dimensional offline transactions and two-dimensional online transactions have been actively conducted. In the previous research by Lee Hyung-wook and Lee Min-jae (2018), as the globalization has accelerated following the development of digital technology and the expansion of ubiquitous communication network, the role of companies, the attribute of economic value creation and economic structure are being reorganized. It is said that O2O (Online to Offline) transactions are increasing due to the development. As a result, a new financial transaction paradigm is emerging that solves the inconveniences of existing financial services and enhances speed and convenience. Considering the global network trend and the rapidly developing and evolving digital bank environment, the necessity of utilizing the business platform model is emerging. However, despite this necessity, there are very limited cases in which such an attempt has been applied in practice. Accordingly, this study seeks to explore the business platform of the new financial transaction system. Specifically, the case study systematically examines the actual implementation of a unique network connection model with Koscom's global investment bank, which is currently in charge of the domestic financial transaction system, and improves ICT innovation performance and process through this. I would like to suggest a solution. In particular, this study analyzed a variety of business model construction and use cases by pursuing a platform connection with digital banks, which has recently been increasingly in demand. Therefore, this study intends to pursue the original and long-term profitability of the company by utilizing ICT innovation and platform business model, and also analyzes the convenience and excellence of trading for institutional and individual investors using the platform of digital bank. The implications of this study are significant in that it explores and explores the actual cases of ICT innovation and additional digital bank platform-connected business models based on this, and suggests a unique and preemptive business strategy of the company in the future.

The majority of research on institutional theory suggests that the new institutional practices presented by national governments and expert groups is a legitimate demand by society, and that the organization is a passive actor that accepts it. However, individual organizations often perform so-called decoupling acts that run their organizations in their own way instead of following the way the system requires, despite the pressures of a strong institutional environment. In this study, the decoupling behavior of these organizations can be varied by the characteristics of the highest decision maker in the organization based on the upper-echelon theory(UET) even if there is no difference in pressure experienced by individual organizations, and their relationship is empirically analyzed among secondary educational institutions that are relatively strongly regulated by the government. According to the analysis of 192 high schools in Korea, the female principal, the younger the principal, and the higher the educational background, the more likely they are to engage in decoupling behavior that are different from the intent and content of government policies. Therefore, from the results of this study, meaningful theoretical and practical implications can be provided for researchers and managers in the field of knowledge management research.

Text is the most widely used means of exchanging or expressing knowledge and information in the real world. Recently, researches on structuring unstructured text data for text analysis have been actively performed. One of the most representative document embedding method (i.e. doc2Vec) generates a single vector for each document using the whole corpus included in the document. This causes a limitation that the document vector is affected by not only core words but also other miscellaneous words. Additionally, the traditional document embedding algorithms map each document into only one vector. Therefore, it is not easy to represent a complex document with interdisciplinary subjects into a single vector properly by the traditional approach. In this paper, we introduce a multi-vector document embedding method to overcome these limitations of the traditional document embedding methods. After introducing the previous study on multi-vector document embedding, we visually analyze the effects of the multi-vector document embedding method. Firstly, the new method vectorizes the document using only predefined keywords instead of the entire words. Secondly, the new method decomposes various subjects included in the document and generates multiple vectors for each document. The experiments for about three thousands of academic papers revealed that the single vector-based traditional approach cannot properly map complex documents because of interference among subjects in each vector. With the multi-vector based method, we ascertained that the information and knowledge in complex documents can be represented more accurately by eliminating the interference among subjects.

In the studies for the recommender systems which solve the information overload problem of users, the use of transactional data has been continuously tried. Especially, because the firms can easily obtain transactional data along with the development of IoT technologies, transaction-based recommender systems are recently used in various areas. However, the use of transactional data has limitations that it is hard to reflect domain knowledge and they do not directly show user preferences for individual items. Therefore, in this study, we propose a method applying the word embedding in the transaction-based recommender system to reflect preference differences among users and domain knowledge. Our approach is based on SAR, which shows high performance in the recommender systems, and we improved its components by using FastText, one of the word embedding techniques. Experimental results show that the reflection of domain knowledge and preference difference has a significant effect on the performance of recommender systems. Therefore, we expect our study to contribute to the improvement of the transaction-based recommender systems and to suggest the expansion of data used in the recommender system.

This study analyzes not only the direct impact of R&D investment on corporate growth for 578 private firms in 2007-2016, but also whether corporate innovation activities play a role as a mediating parameter between R&D investment and corporate growth. For this purpose, we classify companies into IT and non-IT companies and measure the mediating effect by dividing innovation activities into the number of registered patents, applied patents, and sum of them. In addition, this study is based on both the systemGMM which is considered to be effective in solving the endogenous problems caused by the cross-sectional analysis in previous studies and ML-SEM which is a new method recently, and then compares two results. According to the empirical results, innovation activities has a role as partly mediating parameter on sales growth in non-IT companies. On the other hands, in IT companies, the increase in R&D investment leads to a decrease in sales of the company, and the increase in innovation activities increases the sales of the company. However, it was confirmed that IT companies also had positive effects by adjusting the lag of the R&D. In other words, this suggests that securing patents is more important than R&D investment for direct sales growth of IT companies. It is also evidence that immediate introduction of technology is necessary to respond to the speed of technological change since the cycle time of technologies of the IT field is relatively shorter compared to that of other fields.

The purpose of this study is to identify the antecedents that impact on the individual knowledge sharing based on motivational perspectives. Specifically, the authors investigate the roles of general self-efficacy and popularity on knowledge sharing drawn on costly signaling theory and trait activation theory. Data from 227 pairs of employees and coworkers working in companies located in South Korea supported the model. General self-efficacy was positively related to knowledge sharing and popularity was positively associated to knowledge sharing. Also, by taking an interactional approach, our study demonstrated the moderating effect of popularity on the relationship between general self-efficacy and knowledge sharing. The positive relationship between general self-efficacy and knowledge sharing is significant when popularity is low, but not significant when popularity is high. Our findings suggest that the various motivation types including intrinsic and extrinsic motivations for knowledge sharing of individuals exist simultaneously and these motivations have an interactive effect on knowledge sharing.

The development of platform service based on the information and communication technology has revolutionized patterns of commercial transactions, driving the growth of global economy. Furthermore, the radical advancement of artificial intelligence(AI) presents the huge potential to innovate almost all the industrial and economic activities. Given these technological developments, the goal of this paper is to investigate AI's impact on the platform service innovation as well as its influence on the business performance. For the goal, this paper presents the review of the types of service innovation, the nature of platform services, and technological characteristics of leading AI technologies, such as chatbot and recommendation system. As an empirical study, this paper performs a multiple case study of Kakao Group which is the leading mobile platform service with the most advanced AI in Korea. To understand the role and effect of AI on Kakao platform service, this study investigated three cases, including chatbot agent of Kakao Bank, Smart Call service of Kakao Taxi, and music recommendation system of Kakao Mellon. The analysis results of the case study show that AI initiated innovations in platform service concepts, service delivery, and customer interface, all of which lead to a significant decrease in the transaction costs and the personalization of services. Finally, for the successful development of AI, this research emphasizes the significance of the accumulation of customer and operational data, the AI human capital, and the design of R&D organization.

The smart speaker includes an AI assistant function in the existing portable speaker, which enables a person to give various commands using a voice and provides various offline services associated with control of a connected device. The speed of domestic distribution is also increasing, and the functions and linked services available through smart speakers are expanding to shopping and food orders. Through text mining-based customer review analysis, there have been many proposals for identifying the impact on customer attitudes, sentiment analysis, and product evaluation of product functions and attributes. Emotional investigation has been performed by extracting words corresponding to characteristics or features from product reviews and analyzing the impact on assessment. After obtaining the topic from the review, the effect on the evaluation was analyzed. And the market competition of similar products was visualized. Also, a study was conducted to analyze the reviews of smart speaker users through text mining and to identify the main attributes, emotional sensitivity analysis, and the effects of artificial intelligence attributes on product satisfaction. The purpose of this study is to collect blog posts about the user's experiences of smart speakers released in Korea and to analyze the attitudes of customers according to their attributes. Through this, customers' attitudes can be identified and visualized by each smart speaker product, and the positioning map of the product was derived based on customer recognition of smart speaker products by collecting the information identified by each property.