The Investment Efficiency Of Private And Public Firms: Evidence From Korea

This study examines the investment efficiency of private and public firms in Korea. Prior studies suggest that the investment efficiency of firms can change according to the companies' agency problem caused by the existence of information asymmetry. Moreover, they argue that there is less information asymmetry in private firms than in public firms, because the major investors of private firms have access to the internal information of the companies. We extend these studies by comparing the investment efficiency of private and public firms using an extended audited financial dataset of Korean firms. Our results show that the investment efficiency of private firms is higher than that of public firms, because the agency problem of the former is lower than that of the latter. Additionally, private firms invest more efficiently in R&D and capital expenditures than public firms. Further, when we use alternative exogenous firm-specific proxies to measure the likelihood of over or under-investment, the results are substantially consistent with the main results. Finally, we re-test our hypotheses by including financial reporting quality proxies as control variables in the main regression model. These investigations further support our main results. Our study contributes to emerging literature on the difference between private and public firms by showing that the investment efficiency of the former is different from that of the latter. In addition, this study provides additional evidence on the agency problem that affects firms' investment decisions

The Effect Of Abnormal Pay Dispersion On Earnings Management

This study examines the effect of the abnormal pay dispersion on earnings management. Prior studies find that pay dispersion among top executives affect firm performance and executive turnover. We expect that abnormal pay dispersion among top executives affects financial reporting practice as well as firm performance and turnover and provide evidence of positive association between abnormal pay dispersion and earnings management. This result suggests that executives are more likely to be engaged in earnings management to increase their compensation when they feel unfairness from the relative level of compensation. This finding helps financial statement users interpret firm performance and anticipate future outcomes by implying that additional managerial incentives for financial reporting are derived from internal pay dispersion. Our finding that abnormal pay dispersion leads to higher agency costs should also be of interest to shareholders

Soft, curved electrode systems capable of integration on the auricle as a persistent brain–computer interface

Recent advances in electrodes for noninvasive recording of electroencephalograms expand opportunities collecting such data for diagnosis of neurological disorders and brain–computer interfaces. Existing technologies, however, cannot be used effectively in continuous, uninterrupted modes for more than a few days due to irritation and irreversible degradation in the electrical and mechanical properties of the skin interface. Here we introduce a soft, foldable collection of electrodes in open, fractal mesh geometries that can mount directly and chronically on the complex surface topology of the auricle and the mastoid, to provide high-fidelity and long-term capture of electroencephalograms in ways that avoid any significant thermal, electrical, or mechanical loading of the skin. Experimental and computational studies establish the fundamental aspects of the bending and stretching mechanics that enable this type of intimate integration on the highly irregular and textured surfaces of the auricle. Cell level tests and thermal imaging studies establish the biocompatibility and wearability of such systems, with examples of high-quality measurements over periods of 2 wk with devices that remain mounted throughout daily activities including vigorous exercise, swimming, sleeping, and bathing. Demonstrations include a text speller with a steady-state visually evoked potential-based brain–computer interface and elicitation of an event-related potential (P300 wave)

Early identification of emerging technologies: A machine learning approach using multiple patent indicators

Patent citation analysis is considered a useful tool for identifying emerging technologies. However, the outcomes of previous methods are likely to reveal no more than current key technologies, since they can only be performed at later stages of technology development due to the time required for patents to be cited (or fail to be cited). This study proposes a machine learning approach to identifying emerging technologies at early stages using multiple patent indicators that can be defined immediately after the relevant patents are issued. For this, first, a total of 18 input and 3 output indicators are extracted from the United States Patent and Trademark Office database. Second, a feed-forward multilayer neural network is employed to capture the complex nonlinear relationships between input and output indicators in a time period of interest. Finally, two quantitative indicators are developed to identify trends of a technology's emergingness over time. Based on this, we also provide the practical guidelines for implementation of the proposed approach. The case of pharmaceutical technology shows that our approach can facilitate responsive technology forecasting and planning

How Institutional and Ecological Forces Shape the Career Profiles of Organizational Leaders: An Analysis of US Law School Deans, 1894–2009

How do macro social forces shape the career profiles of organizational leaders? The aim of the article is to answer this question by examining how institutional and ecological forces have influenced the careers of law school deans in the US from the late 19th century to the present. Specifically, we focus on the coexistence of two social forces—professionalization and the diversity of an organizational population. On the one hand, we view professionalization as a converging institutional force that promotes homogeneity among leader career profiles. The diversity of an organizational population, on the other hand, is viewed as a diverging ecological force that increases heterogeneity among leader career profiles. We show how these two opposing forces have left different imprints on leader career profiles with a unique career data of 1396 deans in American law schools from 1894 to 2009. We utilize optimal matching analysis to assess the degree of similarity (or dissimilarity) among deans’ career sequences and test our hypotheses. This study contributes to our understanding of the link between macro social transformations and leader career profiles

Anticipating technology-driven industry convergence: evidence from large-scale patent analysis

Industry convergence has been the subject of many prior studies, yet most have focused on certain domains based on ex post evaluation. This study presents a systematic approach to anticipating technology-driven industry convergence using large-scale patent analysis covering all technology fields. Our approach includes patent co-classification analysis with the concordance between patent classes and industrial sectors to measure technological relations between industries; centrality and brokerage analysis to identify the specific roles of technology fields in industry convergence; and finally link prediction analysis to anticipate technology-driven industry convergence. A case study with the patents issued by the United States Patent and Trademark Office from 1976 to 2014 confirms that our approach provides a holistic and forward-looking perspective on technology-driven industry convergence

Concentric diversification based on technological capabilities: Link analysis of products and technologies

This research responds to the needs for concentric diversification by focusing on how firms can find new business opportunities based on their technological capabilities. We propose a systematic approach to identifying potential areas for concentric diversification at a product level via link analysis of products and technologies. For this, first, text mining is utilised to construct an integrated patent-product database from the US patent and trademark database. Second, association rule mining is employed to construct a product ecology network using directed technological relationships between products. Third, a link prediction analysis is conducted to identify potential areas for concentric diversification at a product level. Finally, three quantitative indicators are developed to assess the characteristics of the areas identified. Our case study employs a total of 850,676 patents and 328,288 products in the integrated patent-product database from 2010 to 2014 and shows that the proposed approach enables a wide-ranging search for potential areas for concentric diversification and the quick assessment of their characteristics, with statistically significant results. We believe that the proposed approach will be useful as a complementary tool for decision making for small and medium-sized high-tech companies that are considering entering new business areas, but which have little domain knowledge.clos

Identifying the Policy Direction of National R&D Programs Based on Data Envelopment Analysis and Diversity Index Approach

The Korean government has been continuously conducting diverse national R&D programs to discover new growth engines. The Republic of Korea is one of the countries with the largest investment in national R&D, but its efficiency was relatively low. In response, this study established a framework to identify the characteristics and direction of outstanding R&D programs. In this study, the performance of the R&D programs was identified in the sub-program unit. The efficiency of the national R&D program was analyzed using the data envelopment analysis model through the outputs of the national R&D programs such as papers and patents. However, patent and paper output would take time to be realized. Therefore, this study also calculated the diversity index of R&D programs to identify their potential expected performance. This study applied the suggested framework in the electric vehicle fields, which is one of the core growth engines of South Korea. A list of outstanding programs was identified from the National Institute of Science and Technology Information (NTIS) data. Additionally, this study also discovered the main technology areas and their current issues of outstanding and brand-new R&D programs. These results could contribute to suggesting the policy direction to conduct high-performance national R&D programs

Stochastic technology life cycle analysis using multiple patent indicators

Technology life cycle analysis plays a crucial role in setting up investment-related strategies. The dominant approach to technology life cycle analysis utilizes curve fitting techniques to observe technological performance over time. However, doubts have been expressed about the accuracy and reliability of this method, due to its use of single indicators and the necessity of making assumptions about pre-determined growth curves. As a remedy, we propose a stochastic technology life cycle analysis that uses multiple patent indicators to examine a technology's progression through its life cycle. We define and extract seven time-series patent indicators from the United States Patent and Trademark Office database, and employ a hidden Markov model-which is an unsupervised machine learning technique based on a doubly stochastic process-to estimate the probability of a technology being at a certain stage of its life cycle. Based on this model, this paper also investigates patterns of technology life cycles, future prospects of a technology's progression, and characteristics of patent indicators between technology life cycle stages. The systematic process and quantitative outcomes the proposed approach offers can facilitate responsive and objective technology life cycle analysis. A case of molecular amplification diagnosis technology is presented.clos

Valuation of University-Originated Technologies: A Predictive Analytics Approach

Experts have difficulty assessing the economic value of university-originated technologies due to the high level of uncertainty associated with the commercialization of early stage and basic technologies. This article proposes a random forest approach to the valuation of university-originated technologies that integrates monetary value and patent value models for technology valuation. First, a technological characteristics-value matrix was constructed after defining a total of 23 indicators from the U.S. Patent and Trademark and Scopus databases and extracting the value of university-originated technologies from technology transaction databases. Second, a random forest model, an ensemble machine learning model based on a multitude of decision trees, was employed to assess the economic value of university-originated technologies. Finally, the performance of our approach was assessed using quantitative metrics. A case study of the technologies registered in the Office of Technology Licensing of Stanford University confirms, with statistically significant outcomes, that our method is valuable as a complementary tool for the valuation of university-originated technologies