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Effectiveness and Market Friendly Activation of Restricted Stock Units (RSUs) in the Early-Stage Startup Ecosystem: A Focus Group Interview (FGI) Approach (초기창업생태계를 위한 양도제한조건부주식(RSU)의 시장친화적인 활성화 방안: 전문가 포커스그룹인터뷰(이하 FGI)중심으로)

  • Hwangbo, Yun;Yang, Youngseok
    • Asia-Pacific Journal of Business Venturing and Entrepreneurship
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    • v.19 no.4
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    • pp.1-12
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    • 2024
  • This paper aims to examine the effectiveness of Restricted Stock Units (RSUs) in attracting and retaining top talent for startups and venture companies in the context of their implementation in July 2024. The study investigates whether RSUs align with their original intent and identifies additional measures to enhance their effectiveness. Additionally, the paper explores strategies to actively adopt and revitalize RSUs in the business field from the perspectives of experts representing key market participants within the early-stage startup ecosystem in Korea. The study employs a three-pronged approach. First, a pre-study examines how RSUs overcome the limitations of existing stock compensation schemes, the benefits they offer, and the key conditions for ensuring market-friendly effectiveness. Second, experts involved in the RSU bill's early stages identify five issues that need to be addressed to ensure the bill's market-friendly effectiveness: RSU vesting conditions, RSU vesting targets, RSU vesting scope, RSU vesting timing, and RSU vesting-related tax benefits. Third, the study conducts an FGI with experts representing key market players in the early-stage startup ecosystem to examine the effectiveness and activation measures of the proposed RSU scheme, RSU adoption within the early-stage startup ecosystem minimizing conflict of interests with existing shareholders such as venture capital investor. Finally, experts emphasize the importance of clearly defining and communicating RSU benefits to businesses for effective RSU activation. This study's significance lies in its derivation of various insights from FGI research on the effective adoption and activation of RSUs within the early-stage startup ecosystem. Moreover, it is expected to provide a methodology for gauging opinion-gathering procedures for new bills introduced to foster startup and venture company growth.

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A Study on Industries's Leading at the Stock Market in Korea - Gradual Diffusion of Information and Cross-Asset Return Predictability- (산업의 주식시장 선행성에 관한 실증분석 - 자산간 수익률 예측 가능성 -)

  • Kim Jong-Kwon
    • Proceedings of the Safety Management and Science Conference
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    • 2004.11a
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    • pp.355-380
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    • 2004
  • I test the hypothesis that the gradual diffusion of information across asset markets leads to cross-asset return predictability in Korea. Using thirty-six industry portfolios and the broad market index as our test assets, I establish several key results. First, a number of industries such as semiconductor, electronics, metal, and petroleum lead the stock market by up to one month. In contrast, the market, which is widely followed, only leads a few industries. Importantly, an industry's ability to lead the market is correlated with its propensity to forecast various indicators of economic activity such as industrial production growth. Consistent with our hypothesis, these findings indicate that the market reacts with a delay to information in industry returns about its fundamentals because information diffuses only gradually across asset markets. Traditional theories of asset pricing assume that investors have unlimited information-processing capacity. However, this assumption does not hold for many traders, even the most sophisticated ones. Many economists recognize that investors are better characterized as being only boundedly rational(see Shiller(2000), Sims(2201)). Even from casual observation, few traders can pay attention to all sources of information much less understand their impact on the prices of assets that they trade. Indeed, a large literature in psychology documents the extent to which even attention is a precious cognitive resource(see, eg., Kahneman(1973), Nisbett and Ross(1980), Fiske and Taylor(1991)). A number of papers have explored the implications of limited information- processing capacity for asset prices. I will review this literature in Section II. For instance, Merton(1987) develops a static model of multiple stocks in which investors only have information about a limited number of stocks and only trade those that they have information about. Related models of limited market participation include brennan(1975) and Allen and Gale(1994). As a result, stocks that are less recognized by investors have a smaller investor base(neglected stocks) and trade at a greater discount because of limited risk sharing. More recently, Hong and Stein(1999) develop a dynamic model of a single asset in which information gradually diffuses across the investment public and investors are unable to perform the rational expectations trick of extracting information from prices. Hong and Stein(1999). My hypothesis is that the gradual diffusion of information across asset markets leads to cross-asset return predictability. This hypothesis relies on two key assumptions. The first is that valuable information that originates in one asset reaches investors in other markets only with a lag, i.e. news travels slowly across markets. The second assumption is that because of limited information-processing capacity, many (though not necessarily all) investors may not pay attention or be able to extract the information from the asset prices of markets that they do not participate in. These two assumptions taken together leads to cross-asset return predictability. My hypothesis would appear to be a very plausible one for a few reasons. To begin with, as pointed out by Merton(1987) and the subsequent literature on segmented markets and limited market participation, few investors trade all assets. Put another way, limited participation is a pervasive feature of financial markets. Indeed, even among equity money managers, there is specialization along industries such as sector or market timing funds. Some reasons for this limited market participation include tax, regulatory or liquidity constraints. More plausibly, investors have to specialize because they have their hands full trying to understand the markets that they do participate in

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Structural features and Diffusion Patterns of Gartner Hype Cycle for Artificial Intelligence using Social Network analysis (인공지능 기술에 관한 가트너 하이프사이클의 네트워크 집단구조 특성 및 확산패턴에 관한 연구)

  • Shin, Sunah;Kang, Juyoung
    • Journal of Intelligence and Information Systems
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    • v.28 no.1
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    • pp.107-129
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    • 2022
  • It is important to preempt new technology because the technology competition is getting much tougher. Stakeholders conduct exploration activities continuously for new technology preoccupancy at the right time. Gartner's Hype Cycle has significant implications for stakeholders. The Hype Cycle is a expectation graph for new technologies which is combining the technology life cycle (S-curve) with the Hype Level. Stakeholders such as R&D investor, CTO(Chef of Technology Officer) and technical personnel are very interested in Gartner's Hype Cycle for new technologies. Because high expectation for new technologies can bring opportunities to maintain investment by securing the legitimacy of R&D investment. However, contrary to the high interest of the industry, the preceding researches faced with limitations aspect of empirical method and source data(news, academic papers, search traffic, patent etc.). In this study, we focused on two research questions. The first research question was 'Is there a difference in the characteristics of the network structure at each stage of the hype cycle?'. To confirm the first research question, the structural characteristics of each stage were confirmed through the component cohesion size. The second research question is 'Is there a pattern of diffusion at each stage of the hype cycle?'. This research question was to be solved through centralization index and network density. The centralization index is a concept of variance, and a higher centralization index means that a small number of nodes are centered in the network. Concentration of a small number of nodes means a star network structure. In the network structure, the star network structure is a centralized structure and shows better diffusion performance than a decentralized network (circle structure). Because the nodes which are the center of information transfer can judge useful information and deliver it to other nodes the fastest. So we confirmed the out-degree centralization index and in-degree centralization index for each stage. For this purpose, we confirmed the structural features of the community and the expectation diffusion patterns using Social Network Serice(SNS) data in 'Gartner Hype Cycle for Artificial Intelligence, 2021'. Twitter data for 30 technologies (excluding four technologies) listed in 'Gartner Hype Cycle for Artificial Intelligence, 2021' were analyzed. Analysis was performed using R program (4.1.1 ver) and Cyram Netminer. From October 31, 2021 to November 9, 2021, 6,766 tweets were searched through the Twitter API, and converting the relationship user's tweet(Source) and user's retweets (Target). As a result, 4,124 edgelists were analyzed. As a reult of the study, we confirmed the structural features and diffusion patterns through analyze the component cohesion size and degree centralization and density. Through this study, we confirmed that the groups of each stage increased number of components as time passed and the density decreased. Also 'Innovation Trigger' which is a group interested in new technologies as a early adopter in the innovation diffusion theory had high out-degree centralization index and the others had higher in-degree centralization index than out-degree. It can be inferred that 'Innovation Trigger' group has the biggest influence, and the diffusion will gradually slow down from the subsequent groups. In this study, network analysis was conducted using social network service data unlike methods of the precedent researches. This is significant in that it provided an idea to expand the method of analysis when analyzing Gartner's hype cycle in the future. In addition, the fact that the innovation diffusion theory was applied to the Gartner's hype cycle's stage in artificial intelligence can be evaluated positively because the Gartner hype cycle has been repeatedly discussed as a theoretical weakness. Also it is expected that this study will provide a new perspective on decision-making on technology investment to stakeholdes.