• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.235-247
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• 2019

In order to achieve the results that are appropriate for the purpose of the enterprise, it is important to comprehensively understand the behaviors within the roles of the members of the organization and actions outside the roles. However, there have been relatively few studies on corporate social responsibility (CSR) among the existing studies that have been conducted to date. In particular, organizational citizenship behavior, a voluntary commitment by organizational members, is perceived as a very effective way of enhancing corporate performance, but studies on organizational citizenship behavior based on corporate social responsibility have rarely been conducted. In recent years, domestic companies have recognized social responsibility as an activity rather than an additional activity. Therefore, it is very meaningful to look at the organizational performance by examining the factors that make up this social responsibility from the perspective of the company. It is considered a task. In order to demonstrate this, this study collected 303 data from a business incubator center operated by universities and public institutions nationwide and used a total of 303 samples. As a result of the verification, the wages received by the members of the organization did not affect the organizational commitment, and the working hours and the working environment affected the organizational commitment. In addition, corporate social responsibility has an effect on organizational trust in the marketing side. Organizational commitment and organizational trust were studied to positively affect organizational citizenship behavior. In addition, this study divides the degree of perception of social responsibility of the organizational members into the high recognition group and the low recognition group and analyzed whether there is a difference in the level of organizational citizenship behavior according to the employment type (regular and irregular workers) The results of this study are as follows.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.14 no.2
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• pp.61-72
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• 2019

Since the financial crisis in 1997, large scale unemployment and poverty have become serious, but there has been a surge in public and social job creation projects. However, with the limitations of low-wage and short-term jobs, the need for long-term, high quality jobs gradually began to garner attention. In recent years, social enterprises have grown both quantitatively and qualitatively and interest in social enterprises has increased; more specifically, scholars are interested in the determinants of success and failure of social enterprises in the academic field. In this study, we examined the effects of social enterprise characteristics on financial and social performance. In particular, we empirically analyzed social enterprises registered in the Korea Social Enterprise Agency. The financial performance of the social enterprise was measured using the net income ratio, operating income ratio, and the return on asset. The social performance of the social enterprise was measured by the total number of workers and the employment rate of vulnerable social groups. The characteristics of the social enterprise included CEO characteristics (gender, age, experience in operating the social enterprise), firm size, and the elapsed time of authentication. The results of the empirical analysis are as follows. First, as a result of analysis for the effect on financial performance, we found that the financial performance has a statistically significant, positive relationship with firm size, organizational form, government subsidies, and capital adequacy ratio. And we found that the social performance has a statistically significant, negative relationship with CEO age and credit debt dependence. Second, as a result of analysis for the effect on social performance, we found that the total number of workers had a significant, positive relationship with CEO gender and CEO age, as well as firm size, government subsidies; whereas the total number of workers had a significant, negative relationship with certification type and industry dummy. Comparatively, the employment rate of the vulnerable social groups had a significant, positive relationship with CEO gender and certification type, but there was no statistically significant relationship with the government subsidies or firm size.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.2
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• pp.33-51
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• 2022

This study analyzes newly listed companies on KOSDAQ from 2011 to 2020 for both firms having experience in attracting venture investment before listing (VI) and those without having experience in attracting venture investment (NVI) by examining differences between two groups (VI and NVI) with respect to both the level of listing performance and that of firm performance (growth) after the listing. This paper conducts descriptive statistics, mean difference, and multiple regression analysis. Independent variables for regression models include VC investment, firm age at the time of listing, firm type, firm location, firm size, the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company. Throughout this paper, results suggest that listing performance and post-listed growth are better for VI than NVI. VC investment shows a negative effect on the listing period and a positive effect on the sales growth rate. Also, the amount of VC investment has negative effects on the listing period and positive effects on the market capitalization at the time of IPO and on sales growth among growth indicators. Our evidence also implies a significantly positive effect on growth after listing for firms which belong to R&D specialized industries. In addition, it is statistically significant for several years that the firm age has a positive effect on the market capitalization growth rate. This shows that market seems to put the utmost importance on a long-term stability of management capability. Finally, among the VC characteristics such as the age of VC, the level of expertise of VC, and the level of fitness of VC with investment company, we point out that a higher market capitalization tends to be observed at the time of IPO when the level of expertise of anchor VC is high. Our paper differs from prior research in that we reexamine the venture ecosystem under the outbreak of coronavirus disease 2019 which stimulates the degradation of the business environment. In addition, we introduce more effective variables such as VC investment amount when examining the effect of firm type. It enables us to indirectly evaluate the validity of technology exception policy. Although our findings suggest that related policies such as the technology special listing system or the injection of funds into the venture ecosystem are still helpful, those related systems should be updated in a more timely fashion in order to support growth power of firms due to the rapid technological development. Furthermore, industry specialization is essential to achieve regional development, and the growth of the recovery market is also urgent.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.2
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• pp.215-235
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• 2024

As artificial intelligence (AI) technology is recognized as a key technology that will determine future national competitiveness, competition for AI technology and industry promotion policies in major countries is intensifying. This study aims to present implications for domestic policy making by analyzing the policies of major countries on the start-up of AI companies, which are the basis of the AI industry ecosystem. The top four countries and the EU for the number of new investment attraction companies in the 2023 AI Index announced by the HAI Research Institute at Stanford University in the United States were selected, The United States enacted the National AI Initiative Act (NAIIA) in 2021. Through this law, The US Government is promoting continued leadership in the United States in AI R&D, developing reliable AI systems in the public and private sectors, building an AI system ecosystem across society, and strengthening DB management and access to AI policies conducted by all federal agencies. In the 14th Five-Year (2021-2025) Plan and 2035 Long-term Goals held in 2021, China has specified AI as the first of the seven strategic high-tech technologies, and is developing policies aimed at becoming the No. 1 AI global powerhouse by 2030. The UK is investing in innovative R&D companies through the 'Future Fund Breakthrough' in 2021, and is expanding related investments by preparing national strategies to leap forward as AI leaders, such as the implementation plan of the national AI strategy in 2022. Israel is supporting technology investment in start-up companies centered on the Innovation Agency, and the Innovation Agency is leading mid- to long-term investments of 2 to 15 years and regulatory reforms for new technologies. The EU is strengthening its digital innovation hub network and creating the InvestEU (European Strategic Investment Fund) and AI investment fund to support the use of AI by SMEs. This study aims to contribute to analyzing the policies of major foreign countries in making AI company start-up policies and providing a basis for Korea's strategy search. The limitations of the study are the limitations of the countries to be analyzed and the failure to attempt comparative analysis of the policy environments of the countries under the same conditions.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.1
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• pp.119-133
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• 2024

As a result of significant investments by the government in promoting public technology commercialization and fostering a venture startup ecosystem, there have been quantitative achievements, such as the registration of over 1,600 Innopolis Start-ups since 2006, generating a total revenue of 1.1 trillion won as of 2021. However, these achievements have been overshadowed by critical qualitative challenges, including a continuous decline in average revenue per Innopolis Start-up. This led to a focus on whether managers' technological and market orientations affect business performance. This study aims to provide insights into improving the qualitative growth of Innopolis Start-ups by analyzing the effects of technological and market orientations on business performance, as well as the moderating effects of adjusting marketing and research and development (R&D) costs on this relationship. Through prior research and empirical analysis, this study derives three main findings. First, technological excellence and innovation significantly influence the business performance of Innopolis Start-ups, while technological intensity does not. Second, customer orientation and competitive orientation significantly impact business performance, whereas entry barriers as a single factor do not. Third, adjusting marketing and R&D costs, as controlled variables obtained through general situations, has no direct impact on other variables. However, it interacts with entry barriers, influencing financial business performance, with R&D costs exhibiting a negative buffering effect and marketing costs showing a positive enhancing effect. This study confirms that both technological and market orientations directly influence the business performance of Innopolis Start-ups, thus being crucial factors affecting their growth. Moreover, it establishes that investments in marketing and R&D play significant roles in alleviating initial entry barriers and enhancing financial performance. Consequently, it underscores the importance of reinforcing technological and market orientations tailored to the characteristics of Innopolis Start-ups. Additionally, it proposes five theoretical contributions: strengthening institutional support systems for technology commercialization and innovation, improving qualitative evaluation criteria during the selection process of Innopolis Start-ups, conducting comprehensive analyses of technological and market aspects during startup selection, enhancing support for marketing education and consulting for smooth market entry, and supporting expenditure strategies and milestone setting tailored to the industrial characteristics of individual Innopolis Start-ups.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.19 no.3
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• pp.53-68
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• 2024

Generative AI is receiving a lot of attention around the world, and ways to effectively utilize it in the business environment are being explored. In particular, since the public release of the ChatGPT service, which applies the GPT-3.5 model, a large language model developed by OpenAI, it has attracted more attention and has had a significant impact on the entire industry. This study focuses on the emergence of Generative AI, especially ChatGPT, which applies OpenAI's GPT-3.5 model, to investigate its impact on the startup industry and compare the changes that occurred before and after its emergence. This study aims to shed light on the actual application and impact of generative AI in the business environment by examining in detail how generative AI is being used in the startup industry and analyzing the impact of ChatGPT's emergence on the industry. To this end, we collected company information of generative AI-related startups that appeared before and after the ChatGPT announcement and analyzed changes in industry, business content, and investment information. Through keyword analysis, topic modeling, and network analysis, we identified trends in the startup industry and how the introduction of generative AI has revolutionized the startup industry. As a result of the study, we found that the number of startups related to Generative AI has increased since the emergence of ChatGPT, and in particular, the total and average amount of funding for Generative AI-related startups has increased significantly. We also found that various industries are attempting to apply Generative AI technology, and the development of services and products such as enterprise applications and SaaS using Generative AI has been actively promoted, influencing the emergence of new business models. The findings of this study confirm the impact of Generative AI on the startup industry and contribute to our understanding of how the emergence of this innovative new technology can change the business ecosystem.