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This paper examines the effect of fund manager replacement on investment performances of mutual funds. In managerial labor market of mutual fund industries with information asymmetry about the type and action of a fund manager, separating compensation may not be achievable due to imperfect evaluation of performances of fund managers. This paper extends contract theory to model the situations where a mutual fund offers pooling compensation contract to a fund manager based on his reputation. Under these environments, the fund manager has an economic incentive to acquire private benefit by manipulating performances and then to turn over to other mutual fund. Fund manager’s replacement is an aspect of adverse selection in the managerial labor market of fund industries. That is, a fund manager with low ability can select and manipulate unsuccessful investment portfolio generating loss to fund while he turns over to hide himself in the reputation under pooling contract mechanism. The empirical analysis of this paper provides the significant evidence that, differently from those of mutual funds of which managers stay in the same mutual funds, the fund performances drop after the fund managers turn over to other mutual funds. These empirical evidences support the theoretical prediction that the fund managers have incentive to manipulate short-term performances to maintain reputation for acquiring favorable compensation contracts.

ELS and ELW markets have been increasing sharply and become one of important financial markets in Korea since their inception. The purpose of this paper is to examine the impacts of issuance and maturity of ELS and ELW on both volume and volatility of underlying stocks in Korea. In this paper we follow Faff and Hiller (2005) and Antoniou and Holmes (1995) to examine the hedging effect hypothesis. The empirical results indicate that there is a significant effect of ELS issuance on the volume and volatility of underlying stocks, which is consistent with the hedging effect hypothesis. However, the empirical results of ELW issuance and maturity on those of underlying stocks do not support the hedging effect hypothesis but do support the complete market hypothesis.

This paper investigates a retrospective on the Journal of Derivatives and Quantitative Studies (JDQS) on its 30th anniversary based on bibliometric. JDQSs yearly publications, citations, impact factors, and centrality indices grew up in early 2010s, and diminished in 2020. Keyword network analysis reveals the JDQS's main keywords including behavioral finance, implied volatility, information asymmetry, price discovery, KOSPI200 futures, volatility, and KOSPI200 options. Citations of JDQS articles are mainly driven by article age, demeaned age squared, conference, nonacademic authors and language. In comparison between number of views and downloads for JDQS articles, we find that recent changes in publisher and editorial and publishing policies have increased visibility of JDQS.

This paper aims to shed light on the linkage between research and development (R&D) networks and public funding presented in a given period by using network-based evaluation tools as a means of exploring the relational dimension in public projects designed to foster technology R&D activities.

This research uses co-occurrence network analysis of relevant public projects to assess how technological associations might occur within the R&D activities of given publicly funded projects as well as conducts correlation analysis to understand the extent to which linkages of R&D activity in technology fields are related to public expenditure.

Core technology fields, regarded as eligible to receive continued public funding, are critical for enhancing competitiveness and sustainable growth at the nationally strategic technology level. Thus, the relationship between R&D and the level of government funding for these fields is generally perceived as strong. However, a few technology fields, which did not actively form specific network relationships with other technology fields, are considered to exceptionally drive the largest government support. This trend indicates that the government-funded R&D should be designed and managed not only to curb the inefficiencies existing in the current funding programs but also to achieve the appropriateness for further technology development.

Despite the comprehensive findings, this study has several limitations. First, it is difficult to control any confounding factors, such as the determinants and constraints of the government budget allocation and expenditure decisions over S&T areas, strategic frameworks for public investment and evolving policy landscapes in technology sectors, which lead to bias in the study results. Second, this study is based on a narrow, single-year data set of a specific field of projects supported by the Korean government’s R&D program. Therefore, the generalization of findings may be limited. The authors assumed that influences caused by confounding variables during the initial phase of the public funding schemes would not be significant, but they did not take into account possible factors that might arise coincident with the subsequent phase changes. As such, the issue of confounding variables needs to be carefully considered in research design to provide alternative explanations for the results that have been ruled out. The limitations of this study, therefore, could be overcome by comparing the outcome difference between subsidized and non-subsidized R&D projects or evaluating targeted funding schemes or tax incentives that support and promote various areas of R&D with sufficiently large, evidence-based data sets. Also, future research must identify and analyze the R&D activities concerning public support programs performed in other countries associated with strategic priorities to provide more profound insight into how they differ. Third, there are some drawbacks to using these principal investigators-provided classification codes, such as subjectivity, inaccuracy and non-representation. These limitations may be addressed by using content-based representations of the projects rather than using pre-defined codes. Finally, the role that government investment in R&D has played in developing new science and manufacturing technologies of materials and components through network relationships could be better examined using longitudinal analysis. Furthermore, the findings suggest the need for further research to integrate econometric models of performance outcomes such as input–output relations into the network analysis for analyzing the flow of resources and activities between R&D sectors in a national economy. Therefore, future research would be helpful in developing a methodological strategy that could analyze temporal trends in the identification of the effects of public funding on the performance of R&D activity and demand.

Public funding schemes and their intended R&D relationships still depend on a framework to generate the right circumstances for leading and promoting coordinated R&D activities while strengthening research capacity to enhance the competitiveness of technologies. Each technology field has a relatively important role in R&D development that should be effectively managed and supervised to accomplish its intended goals of R&D budgeting. Thus, when designing and managing R&D funding schemes and strategy-driven R&D relations, potential benefits and costs of using resources from each technology field should be defined and measured. In this regard, government-funded R&D activities should be designed to develop or accommodate a coordinated program evaluation, to be able to examine the extent to which public funding is achieving its objectives of fostering R&D networks, balancing the purpose of government funding against the needs of researchers and technology sectors. In this sense, the examination of public R&D relations provides a platform for discussion of relational network structures characterizing R&D activities, the strategic direction and priorities for budget allocation of the R&D projects. It also indicates the methodological basis for addressing the impact of public funding for R&D activities on the overall performance of technology fields.

The value of this work lies in a preliminary exploratory analysis that provides a high-level snapshot of the areas of metallurgy, polymers/chemistry/fibers and ceramics, funded by the Korean Government in 2016 to promote technological competitiveness by encouraging industries to maintain and expand their competencies.

The purpose of this study is to empirically examine the mediating effect of organizational communication in the relationships among transformational leadership, organizational justice and knowledge sharing within the higher education setting in South Korea.

A sample of 321 university employees, including 151 faculty members and 170 administrative staff members, from a representative private university, participated in the survey as part of a consulting project for university innovation. Structural equation modeling (SEM) was mainly used for data analysis to identify the structural relationship among the research variables.

The results of this study confirmed that transformational leadership positively influences organizational justice and knowledge sharing. However, the influence of organizational justice on knowledge sharing was not statistically significant. The mediating effect of organizational communication among those relationships to increase the members’ knowledge sharing behavior was statistically significant.

In this study, holistic aspects of the organization, including leadership, organizational culture and organizational strategy, were examined for encouraging employees’ knowledge sharing behavior while an organization undergoes innovational changes. More practically, this study suggested that organizational communication could be used as one of the critical strategies in the process of university innovation.

This study examined the human and social capital factors associated with higher post‐succession firm performance in family enterprises in Singapore. We also investigated the moderating influence of the board of directors in terms of its service role as stewards of the enterprise. We found that a successor’s industry experience and diversity of network ties were positively associated with firm performance and boards that focused their role as advisors to the successor enhanced post‐succession firm performance.

The purpose of this paper is to determine the key ingredients of Six Sigma in order to grasp and understand its essential characteristics and then identify suitable ingredients and complements in consideration of vision, strategies, capability, and circumstance of a company.

The study outlines the range of research in Korean enterprises, suppliers, and SMEs. Potential ingredients collected from previous studies about critical success factors, ingredients of Six Sigma and TQM, and criteria of quality awards (Malcolm Baldrige National Quality Award (MBNQA), European Foundation for Quality Management (EFQM) Award, Deming Prize, Korean National Quality Award) are classified into 11 categories based on Six Sigma expert opinions and affinity analysis. Also, questionnaires are surveyed from champions, master black belts and black belts in 90 Korean companies that have more than three years of experience in implementing Six Sigma. Statistical analysis with factor analysis and hypothesis testing has been done to select the key ingredients and to find the differences among the diverse types of companies.

By using factor analysis, three main factors are derived for each category respectively, and consequently 30 factors in 11 categories are concluded as the key ingredients of Six Sigma in Korean companies. There is a significant difference in importance of Six Sigma key ingredients according to company size, business type, and implementation phase.

A study may be conducted to identify Six Sigma success factors according to corporate characteristics from key ingredients identified in this study in future.

The paper investigates the key ingredients of Six Sigma based on a survey of diverse sizes, industries, and implementation phases of Korean companies. Compared with other studies conducted by empirical methods, the survey data are statistically analysed and the evaluation results are represented as quantitative indicators.

This study explores how recent Korean immigrant students experience learning social studies and how their unique social, cultural, and educational backgrounds as new immigrants shape their experiences in American schools. Based on survey and in-depth interviews with 43 Korean immigrant students in two urban and three suburban/rural areas, this mixed methods study examines Korean immigrant youths’ perceptions about the nature of history and social studies as well as their experiences of learning social studies in their everyday classroom contexts. Our data analysis demonstrates that Korean immigrant students face varying difficulties in constructing meaning in US history and engaging themselves in social studies learning, which results in a negative learning experience and subsequent disinterest in social studies. Researchers identified three major challenges that Korean immigrant youths experience in their social studies classrooms: (1) Lack of English proficiency, background knowledge, and American patriotism, (2) White, American-centered perspectives and marginalization of their country of origin, and (3) Teachers’ lack of care and disengaging pedagogies. The findings of this study provide implications for creating more meaningful and culturally relevant social studies learning for immigrant students.

This paper aims to focus on lane detection of unmanned mobile robots. For the mobile robot, it is undesirable to spend lots of time detecting the lane. So quickly detecting the lane in a complex environment such as poor illumination and shadows becomes a challenge.

A new learning framework based on an integration of extreme learning machine (ELM) and an inception structure named multiscale ELM is proposed, making full use of the advantages that ELM has faster convergence and convolutional neural network could extract local features in different scales. The proposed architecture is divided into two main components: self-taught feature extraction by ELM with the convolution layer and bottom-up information classification based on the feature constraint. To overcome the disadvantages of poor performance under complex conditions such as shadows and illumination, this paper mainly solves four problems: local features learning: replaced the fully connected layer, the convolutional layer is used to extract local features; feature extraction in different scales: the integration of ELM and inception structure improves the parameters learning speed, but it also achieves spatial interactivity in different scales; and the validity of the training database: a method how to find a training data set is proposed.

Experimental results on various data sets reveal that the proposed algorithm effectively improves performance under complex conditions. In the actual environment, experimental results tested by the robot platform named BIT-NAZA show that the proposed algorithm achieves better performance and reliability.

This research can provide a theoretical and engineering basis for lane detection on unmanned robots.

Discusses the 6th ITCRR, its breadth of textile and clothing research activity, plus the encouragement given to workers in this field and its related areas. States that, within the newer research areas under the microscope of the community involved, technical textiles focuses on new, ‘smart’ garments and the initiatives in this field in both the UK and the international community at large. Covers this subject at length.