This paper proposes relational capability as a main driver of constructing inter-organizational collaboration networks. Based on social network theory and relational view literature, three components of relational capability are constructed and implemented by an agent-based model. The components include organizational capability, structural capability, and trust between a partner and a focal firm. These three components are updated by two micro mechanisms: structural mechanism and relational mechanism. Structural mechanism is a feedback loop in which the relational capability increases structural capability and vice versa. Relational mechanism is a learning-by-doing process in which a focal firm experiences success or failure of collaboration and the experience increases or decreases cumulative trust in a partner firm. Result of agent-based simulation shows that a collaboration network emerges through interactions of firm's relational capabilities and the characteristics of emerged networks vary with the contribution of structural capability and trust to relational capability. Specifically, in case structural capability contributes more to relational capability, the average degree centrality and collaboration proportion increases as time passes and enters into an equilibrium state. In that case, almost every firms participated in the network collaborates each other so that the emerged network becomes highly cohesive. In case trust contributes more to relational capability, the results are reversed. In an equilibrium state, the balance of contribution between structural capability and trust makes an emerged network larger and maximizes average degree centrality of the network.

Recently, interest in research and development (R&D) investment decisions have increased among Korean domestic enterprises. However, existing R&D investment studies only focused on government R&D investment policies while only a few studies investigated firm level R&D investment. Prior literatures also overlooked the feedback loop between R&D investment and firm performance. Therefore, this paper identifies a system dynamics model for R&D investment decision making in domestic electronics firms. The conceptual model is derived from R&D investment-related theories found in bodies of literature on company performance, enterprise activity, and market maturity. This study investigates the dynamic feedback between R&D activities and sales using the system dynamics model. In other words, the system dynamics model is used to explain the change in the closed feedback circulation structure in R&D investment activities including technology development, production process, and marketing that subsequently result in sales increase and re-investment into R&D from the generated revenues. There are two major results. First, a similar ratio of investment on technology development and production process derives the higher company sales. Second, regardless of market maturity, marketing investment ratio positively affects sales and R&D budget growth. This study provides a system dynamics model to find the optimal ratio for R&D investment and suggests managerial strategic implications on electronic firm R&D investment decision making under market maturity condition.

We analyze the systemic risk based on the information flows using the variance decomposition, DebtRank methods, and the Industry Sector Indices during 2001. 01 to 2015. 08. Using the KOSPI stock market as our setting, we find that (i) the systemic risk calculated by information flows of variance decompositions method shows strong positive relations with the market volatility, (ii) the magnitude of systemic risk measured from the information flows network by DebtRank method increases after the subprime financial crisis.

Keyword indexed in multidisciplinary journals show trends about science and technology innovation. Nature and Science were selected as multidisciplinary journals for our analysis. In order to reduce the effect of plurality of keyword, stemming algorithm were implemented. After this process, we fitted growth curve of keyword (stem) following bass model, which is a well-known model in diffusion process. Bass model is useful for expressing growth pattern by assuming innovative and imitative activities in innovation spreading. In addition, we construct keyword co-occurrence network and calculate network measures such as centrality indices and local clustering coefficient. Based on network metrics and yearly frequency of keyword, time series analysis was conducted for obtaining statistical causality between these measures. For some cases, local clustering coefficient seems to Granger-cause yearly frequency of keyword. We expect that local clustering coefficient could be a supportive indicator of emerging science and technology.

As research management is growing in importance for research organizations, their disciplinary structures need to be interpreted. However, it is not only difficult but ambiguous to detect causal relations between subjects because diverse disciplines interacting with each other lead the development of organizational research. Therefore, this article summarizes the major concepts and results recently achieved in the related fields such as research management, bibliographic analysis, information theory, and networks to characterize organizational knowledge structures. Relevant analytical methods obtained from the literature can be applied to empirical situations. Predictive causal relations can be measured using an information theoretic indicator on a series of organizational research portfolios identified from bibliographic information. A network approach would be suitable to manage organizational research effort from a holistic view. Knowledge structures of the Government-funded Research Institutes in Korea are explored experimentally.

Taxi, one of the passenger services, is an essential transportation system in our society. In spite of the importance of taxi service, deterioration of the service quality is continuously reported. In particular, taxi service failures due to traffic accidents have emerged as the most serious social problem which may incur a considerable amount of social costs. In this study, we focus on analyzing the causes of service quality deterioration-traffic accidents of both corporately owned and privately owned taxies in Seoul. The research objective is to find out the determinants of service quality deterioration of Taxi industry and to suggest policies for improving the taxi service in Seoul. We collected the archival data from KTSA (Korea Transportation Safety Authority) and ODP (Open Data Portal), which span from 2005 to 2014. Hierarchical regression analyses and a set of mediation tests are conducted. The root factors inducing traffic accidents of taxies are divided into competitive, and profitability factors. The statistical results indicate that the accident ratio per taxi is not only affected by competitive/profitability factors such as number of active taxi drivers, number of bus passengers, and taxi fare, but also mediated by the traffic violation ratio. To alleviate the current problems with taxi service in Seoul, we conclude that the number of taxies be reduced and the taxi fare be increased. Finally, we suggest several policies to improve the endemic taxi industry problems, over-supply and the low profitability by enhancing the linkage between service quality and profitability.

Diffusion of innovation is the process in which an innovation is communicated through certain channels over time among the members of a social system. The literatures have emphasized the importance of interpersonal network influences on individuals in convincing them to adopt innovations and thereby promoting its diffusion. In particular, the behavior of opinion leaders who lead in influencing others' opinion is important in determining the rate of adoption of innovation in a system. Centrality has been recognized as a good indicator that quantifies a node's influences on others in a given network. However, recent studies have questioned its relevance on various different types of diffusion processes. In this regard, this study aims at examining the effect of a node exhibiting high centrality on expediting diffusion of innovations. In particular, we considered the situation where two innovations compete with each other to be adopted by potential adopters who are personally connected with each other. In order to analyze this competitive diffusion process, we developed a simulation model and conducted regression analyses on the outcomes of the simulations performed. The results suggest that the effect of a node with high centrality can be substantially reduced depending upon the type of a network structure or the adoption thresholds of potential adopters in a network.

Through the event study methodology and the case study on the Company T and its subsidiaries, this study analyzes the effect of credit rating downgrade in the Korean stock market. Our empirical results cast some doubts on whether credit rating agencies made adequate credit rating adjustments on the Chaebol companies, and suggest that little information was provided to the bond market investors. This study provides some policy implications by recommending that regulators encourage credit rating agencies to provide more accurate and appropriate information to market participants.

Incorporation of individual learning and forgetting behaviors within worker-task assignment models produces a mixed integer nonlinear program (MINLP) problem, which is difficult to solve as a NP hard due to its nonlinearity in the objective function. Previous studies commonly assume homogeneity among workers in workforce scheduling that takes account of learning and forgetting characteristics. This paper expands previous researches by considering heterogeneous individual learning/forgetting, and investigates the impact of worker heterogeneity in initial expertise, steady-state productivity, learning and forgetting on system performance to assist manager's decision-making in worker-task assignments without tackling complex MINLP models. In order to understand the performance implications of workforce heterogeneity, this paper examines analytically how heterogeneity in each of the four parameters of the exponential learning and forgetting (L/F) model affects system performance in three cases : consecutive assignments with no break, n breaks of s-length each, and total b break-periods occurred over T periods. The study presents the direction of change in worker performance under different assignment schedules as the variance in initial expertise, steady-state productivity, learning or forgetting increases. Thus, it implies whether having more heterogenous workforce in terms of each of four parameters in the L/F model is desired or not in different schedules from the perspective of system productivity measurement.