● As the global economic environment is evolving amid the protracted COVID-19 pandemic, minimizing the pandemic’s impact on the sluggish economy and boosting economic sustainability will require yet another boom of corporate spinoffs in Korea. This was the case in the early 2000s, when many venture companies were created.
● A survey of 202 spinoff companies conducted by KIET found that most of the founders of those early startups were people in their early 40s who leveraged their past work experience, lacked access to private venture funds, and had limited knowledge of and low utilization of startup support programs.
- The average age of spinoff founders at the time of the launch of their ventures was 43.4 years old, with the majority of founders holding master’s or doctoral degrees (41.6 percent) and tapping into their corporate experience in technology- or research-related fields (58.4 percent) to pursue their ventures outside the incubator organization.
- Factors that affect founders’ decision to start a business include their past work experience (86.6 percent), education (three percent), upbringing (two percent), and vocational training (one percent).
- In the early days of spinoff enterprises, most funding was sourced from government support (35 percent), followed by bank loans (21.4 percent), financial support from the parent company (20.1 percent), and venture funds such as angel or venture capital (8.9 percent).
- Respondents rated their awareness of, utilization of, and satisfaction with corporate spinoff support programs with scores of 3.7, 3.8, and 3.7 points, respectively, on a scale of one to five.
- The survey also found that, when it comes to the operation and utilization of the government’s corporate spinoff support scheme, there is still a shortage of programs available for would-be founders and insufficient financial and tax benefits for incubator parent companies.
● Going forward, the promotion of corporate spinoffs and subsequent creation of quality jobs will require careful selection of and focus on the potential beneficiaries of startup support policies (promotion of technology/knowledge-based startups), promotion of positive perception of spinoffs (more mid-caps and SMEs incubating internal spinoffs instead of large corporations being the major incubator organizations), provision of systemized support programs, and improvement of the efficacy of corporate venture incubation programs.
The purpose of this study is to evaluate the national status of regional strategic industries by measuring the structural characteristics of regional industrial networks using the regional industry Atlas model, and to propose smart specialization strategies and policy tasks suitable for regional industrial development conditions.
The main contents of this study are organized as follows. First, we look at a literature review on research achievements that approach the principle of cluster formation from a network perspective. Second, the necessity of regional-specific smart specialization policies and new directions of regional industrial policies will be explored through derivation of achievements and implications for the implementation of Korean industrial cluster policies. Third, a survey on the characteristics of corporate transactions is conducted to confirm whether corporate transactions are a factor that forms an industrial cluster. The corporate transaction survey aims to investigate whether there is a preferrential attachment process in corporate transactions and whether the stability of corporate transactions contributes to the formation of local industry networks. Fourth, we present an industry Atlas model that extracts industrial networks from big data of inter-company transactions, and apply this model to measure the hierarchical structure and innovation capabilities of regional industrial networks in 14 metropolitan cities. Finally, a smart specialization strategy and regional industry policy tasks to reinforce regional economic innovation capabilities are presented using the results of regional industry hierarchy and innovation capabilities.
To summarize the results of the survey on the characteristics of business transactions, first, the selection of business partners is not made randomly when deciding on a transaction between companies, but it appears as a result of the preferrential attachment process, which is a result of optimization of business activities. And, if transaction costs are similar, the factors considered as the second best are continuous quality improvement, continuity of production, and proximity to customers as important transaction determinants. These survey results suggest that business relationships are an important factor in forming regional industrial clusters. Second, corporate transactions are continuous and stable. Once a transaction is made, corporate transactions tend to continue unless a specific reason for change occurs. In other words, it suggests that corporate transactions are continuous and that transaction stability exists. This suggests that the corporate transaction network is the foundation for forming and maintaining the regional industrial structure. Third, active business transactions act as a factor that induces regional innovation growth. According to the results of the survey, it is essential that companies make consistent efforts to improve their quality in order for them to survive in the transaction network. This means that corporate innovation efforts to maintain corporate transactions trigger regional innovation growth.
The regional industry Atlas model refers to an industrial structure analysis system to discover regional growth paths and regional industrial promotion policies based on regional business transactions, which are relational data. The industry Atlas model is used to visualize industrial networks by extracting business relations between industries from corporate transactions and to statistically analyze the characteristics of the network structure. In this study, using the concept of revealed relatedness proposed by Neffke et al.(2008), an industrial Atlas model is proposed, which is an industrial space mapping method. Industry Atlas is a network composed of nodes (industry) and links (industrial relatedness) with differentiated regional industrial spaces.
The industry Atlas model is used to measure the disassortativity and degree distribution hierarchical structure of local industry networks. The disassortativity of the industrial network structure indicates a tendency that the central node(industry) is connected with the peripheral node(industry). The disassortativity of the industrial network structure means the openness or innovation capability of the network structure.
And another characteristic that represents the differentiation of the network structure is the hierarchical structure of the degree distribution. The degree distribution of nodes represents the degree of concentration of each node (industry) in the network. This degree hierarchy is an index to evaluate whether there is an industry leadership of the industrial network.
In this study, by applying the industry Atlas model, we aim to present a smart specialization strategy that is proposed as a regional-specific industrial strategy by measuring industry leadership and innovation capacity for each regional industrial structure in 14 metropolitan cities. In addition, it is intended to discover and propose strategic industries to promote smart specialization strategies suitable for the characteristics of the regional industrial structure. Looking at the growth path of the region based on the structural characteristics of the manufacturing industry, first, it appears as Gwangju, Ulsan, Chungnam, Gyeongnam, and Sejong regions as the new industrial foundation regions. In addition, the regions requiring industrial structure transition strategy appear in Daegu and Gyeongbuk. Regions requiring industrial diversification strategy through increasing the related variety of industrial structure appear in Jeonnam, Daejeon, and Gangwon. Lastly, the regions requiring a strategy for modernization of regional major industries are Busan, Jeonbuk, Chungbuk, and Jeju.
The policies suitable for the smart specialization strategy type based on the characteristics of the regional manufacturing industry are classified as follows. The policy required for the creation of new industries and restructuring smart specialization types is a policy that reinforces the network structure’s disassortativity, centered on small and medium-sized venture companies based on the excellent cluster innovation capability of the region. In addition, the smart specialization policy, which requires industrial diversification and advancement of key industries, needs to promote related variety and high value-added policies of strategic industries, centering on regional leading companies.
Smart specialization policies for the creation of new industries and restructuring of regional industries are as follows: ① Strengthen the establishment of a network-based regional industry and technology ecosystem, ② Establish an innovative platform and a region-linked independent industrial ecosystem through expansion of industry relatedness, and ③ Advanced industrial complexes. It is an urban high-tech district that connects the city and neighboring cities. And, the policy tasks for industrial diversification and advancement of major industries smart specialization are as follows. ① structuring the industrial field (connecting future leading industries-main industries-related industries), and ② establishing a local industry monitoring system.
In conclusion, transaction data of individual companies located in the region can be used as relational data to analyze the regional industrial structure. Based on relational data, through the construction of an regional industry Atlas model that can analyze the industrial structure of the region, it will be possible to understand the industrial structure of the region through transactions between companies. In addition, since business-to-business transactions are sustainable and stable, the relationship between the business relationships once formed is highly likely to persist, and the relationship between the industrial structure can be grasped through business transaction data, and the network analysis model quantitatively analyzes the growth structure of local industry clusters. By doing so, it will be able to contribute to discovering local industry promotion policies.