A Study on the Development of a Classification System for Industry-Academia-Research Cooperation Information

2.1 Previous Studies on Database Development and Integrated Information Networks

The development of databases and integrated information networks for the systematic management and utilization of industry-academia-research cooperation information has been extensively studied across various fields. These studies primarily focus on structuring information, enhancing usability, and providing policy support. Key studies are as follows:

Lee et al. (2015) proposed an optimal approach to database construction for groundwater data in Gyeonggi Province, including well locations, usage, water quality, groundwater levels, and pollution areas. They combined this data with spatial information to create maps, improving the utilization of dispersed data and enhancing regional resource management efficiency.

Gong et al. (2012) conducted a study on constructing a database for information about assistive devices for people with disabilities. By analyzing online data collection and sharing systems, they proposed a framework for providing reliable and comprehensive information, contributing to improved data reliability and accessibility.

Song (2006) investigated the development of a GIS-based integrated database for the Korean Land Information System (KLIS) to resolve data inconsistency and redundancy issues. This study emphasized the necessity of constructing a national network using high-speed broadband to enhance data management.

Kim and Park (2020) explored ways to improve public services using big data on marine information. Through online surveys conducted by government agencies, they identified concrete strategies to enhance service quality, accessibility, and user satisfaction.

The Ministry of Land, Infrastructure, and Transport (2020) highlighted the necessity of developing integrated information networks for solving urban problems through the establishment of a smart city platform. Their approach involved inter-ministerial cooperation to build a unified platform, improving administrative service efficiency.

2.2 Previous Studies on the Utilization of Industry-Academia-Research Cooperation Information Networks

Research on the utilization of industry-academia-research cooperation information networks has proposed various models to enhance the efficiency and usability of integrated information networks. Key studies include:

Park (2001) developed an operational framework for a radiation safety management integrated information system. This framework provided a systematic approach to managing disaster response, public inquiries, regulation, and safety information, contributing to industrial and technological advancement and national competitiveness.

Kim (2002) introduced a process-integrated information system (PIIS) for the LNG industry, focusing on integrating safety management, construction processes, and operations. The study emphasized improving flexibility and expertise in the LNG sector and proposed measures to enhance international competitiveness.

Choi et al. (2008) addressed the construction of integrated job information networks for South Korea. Their study suggested directions for providing essential information for students’ career planning and employment, based on surveys and demand analyses. They also identified gaps between the services provided and those actually utilized by students and proposed improvements to address these discrepancies.

These prior studies demonstrate the importance of systematic information management and utilization for maximizing resource efficiency and supporting policy and strategy development. Building upon this foundation, the present study aims to develop an integrated information management system and thematic classification framework for industry-academia-research cooperation. This effort seeks to establish a more efficient and systematic information network to support collaboration and innovation.

4.1 Investigation and Classification of Similar Cases

This study investigated and classified similar cases to derive practical insights for the construction and utilization of the Industry-Academia-Research Cooperation Information System (IARCIS). The cases were categorized into the following types:

• 1) Integrated information networks related to industry-academia cooperation,
• 2) Other relevant integrated information networks, and
• 3) Thematic information resource websites.

The selection criteria for these cases were as follows: The cases had to provide insights into potential utilization strategies for the IARCIS, be actively operated by reliable institutions through 2024, and demonstrate active user engagement.

Additionally, for other relevant integrated information networks, the focus was placed on employment and education-related platforms, as they are likely to attract the main stakeholders of the IARCIS, including corporations, government agencies, research institutions, and students.

4.2 Similar Cases in the Development of the Industry-Academia-Research Cooperation Information Classification System

In the process of developing a classification system for the Industry-Academia-Research Cooperation Information System (IARCIS), an investigation into existing studies and cases revealed that no direct examples of classification system construction for such a purpose currently exist. As a result, this study collected foundational data for classification system development by referencing studies and cases with strong relevance to the subject. These examples are broadly categorized into four groups, each offering significant insights into data management and systematic approaches within the context of industry-academia cooperation.

Table 1.

Reference Materials for the Industry-Academia-Research Cooperation Information System Classification

First, the University-Industry Cooperation Activity Survey Report by the Ministry of Education (2022) systematically investigated and analyzed statistics on university-industry cooperation in Korea. This report established a scientific foundation for data-driven policies to promote industry-academia cooperation. By emphasizing the importance of data management and utilization, the report provided a direction for enhancing the effectiveness of industry-academia collaboration.

Second, Hye-Jin Jeong's (2021) Study on Enhancing the Effectiveness of University-Industry Cooperation Activity Surveys proposed a structured system for planning, designing, implementing, and providing feedback on surveys related to university-industry cooperation activities. This study presented specific strategies to improve the efficiency and effectiveness of the survey process, thereby laying the groundwork for achieving tangible outcomes in industry-academia cooperation.

Third, Hyuk-Jin Ko's (2021) Joint Data Platform Development Plan for Government-University-Industry suggested a plan for building a standardized platform to generate and distribute higher education financial support data. This research proposed a concrete data management model to strengthen partnerships between universities and industries while improving the rationality and efficiency of higher education financial management.

Fourth, Younghee Noh's (2022) Development of Expertise Indices for Industry-Academia Cooperation Experts and Service Design for Sanhak-Injaenet presented various service strategies utilizing the expert pool offered by Sanhak-Injaenet. This study contributed to expanding the potential of industry-academia cooperation by effectively leveraging expert networks and designing innovative services to activate the Sanhak-Injaenet platform.

Lastly, the National Research Foundation of Korea's (2013) Package Consulting Manual for Enhancing the Functions of Industry-Academia Cooperation Foundations proposed consulting solutions tailored to specialized models of industry-academia cooperation foundations through the organization of expert advisory panels. This manual has been widely used as a critical resource to strengthen the capacity of industry-academia cooperation foundations and support their systematic management and operations.

These cases provide valuable insights into the development of a classification system for the Industry-Academia-Research Cooperation Information System (IARCIS). They highlight practical approaches for systematically managing and utilizing data related to industry-academia cooperation. Building on these examples, this study aims to develop a thematic classification system for the IARCIS, contributing to maximizing the efficiency and impact of industry-academia cooperation.

5.1 Subjects of Analysis and Methodology for Developing the Thematic Classification System for the Industry-Academia-Research Cooperation Information System

5.2 IARCIS Classification System

2) Coding of the IARCIS Classification System

To enable systematic and logical management of the IARCIS, the classification system was coded hierarchically. This coding system enhances efficiency in information retrieval and management, creating a user-friendly data management system. The coding structure is based on the principles of the decimal classification system, following the hierarchical format below:

Table 2.

Joint Data Platform Development Plan for Government-University-Industry

5.3 Expert Opinions

This study collected feedback from experts with over 10 years of experience in the field of industry-academia-research cooperation to design a more systematic and practical classification system for the IARCIS. The expert feedback focused on verifying the system's suitability, usability, and validity. It included a review of the six main categories, their subcategories, and detailed items, along with suggestions for improvement.

5.4 Final Thematic Classification System for the Industry-Academia-Research Cooperation Information System

The final classification system for the IARCIS organizes information related to industry-academia cooperation into six main categories: organizations, infrastructure, workforce development, entrepreneurship and employment, research and development, and commercialization. Each category includes detailed subcategories, such as personnel, budget, networks, education, and R&D activities, which are further broken down into specific items.

The system employs a hierarchical coding structure based on the decimal system for efficient data management and retrieval. It provides a comprehensive framework to systematically organize information, support practical applications, and facilitate collaboration among diverse stakeholders in industry-academia cooperation. This classification system enhances resource utilization and strategic decision-making while ensuring clarity and usability.

Table 3.

Final Thematic Classification System for the IARCIS

6.1 Conclusion

This study was conducted based on the awareness that, despite the significant role of industry-academia-research cooperation (IARC) in driving national economic growth and innovation, there is a lack of systematic management and utilization of related information. Efficient information management is essential to maximize the outcomes of IARC, and this requires the development of a structured classification system and metadata. The necessity of this research is emphasized in the following aspects:

First, systematic classification of information facilitates easier database management and information retrieval, enhancing usability.

Second, a clear classification system allows IARC opportunities to be more easily identified and connected.

Third, structured data provision supports effective decision-making during policy and strategy formulation.

Fourth, it provides foundational data for performance evaluation and analysis, enabling systematic assessments of IARC activities.

Fifth, organizing and sharing collaboration cases and experiences contributes to the dissemination and growth of IARC.

The purpose of this study is to develop a classification system and metadata to systematically manage and enhance access to IARC information. Through this, the study aims to maximize the consistency and utility of IARC information and contribute to policy formulation and performance evaluation. Specifically, the objectives of this study are, first, to develop a thematic classification system for IARC information; second, to design metadata for systematic information management; and third, to build an IARC information repository based on the developed framework.

To achieve these objectives, this study employed the following methodologies: literature review, case studies, expert feedback (FGI), and comprehensive analysis. The literature review provided the theoretical foundation for the classification system, while case studies analyzed domestic and international examples for benchmarking. In the expert feedback process, professionals with over 10 years of experience in the IARC field were invited to validate the suitability and practicality of the classification system and metadata. These processes enhanced the applicability and usability of the classification system.

The final proposed classification system comprehensively covers the key elements of IARC and consists of the following six main categories:

First, IARC Organizations, including university headquarters, IARC centers, and university-affiliated research institutes. Second, IARC Infrastructure, addressing physical and institutional resources such as budget, space, equipment, systems, and networks. Third, Workforce Development, divided into general education and industry-academia-based education, focusing on practical training. Fourth, Entrepreneurship and Employment, including entrepreneurship education and career-related activities, supporting startups and career development. Fifth, Research and Development, encompassing R&D activities and outcomes, covering both national and private R&D achievements. Sixth, Commercialization, including technology transfer and enterprise support activities, bridging research outcomes to commercialization.

This study contributes to IARC information management and utilization in the following ways:

First, it provides a framework for comprehensively understanding and managing the diverse aspects of IARC. Second, the classification system and metadata maximize systematic information management and usability. Third, it provides reliable data for effective decision-making in policy formulation and performance evaluation. Fourth, by systematically organizing collaboration cases and experiences, it promotes the dissemination and development of IARC.

The results of this study are expected to enhance the usability of IARC information and contribute to the development of IARC ecosystems at both national and regional levels. Continuous improvement and expansion of this classification system and repository are encouraged to further increase the value of IARC.

6.2 Recommendations for Future Research

To ensure the sustainable operation and reliability of the IARC information repository, it is essential to establish data governance policies. Specifically, legal governance related to information management must be established to guarantee data security, privacy, and ethical use. It is important to implement institutional measures to prevent legal and ethical issues in the processes of data collection, storage, sharing, and deletion.

Legal governance should clearly define procedures for information collection, management, sharing, and protection, and provide a framework that clarifies the responsibilities and authorities of participating organizations. This system will enhance transparency and reliability in data management and support the sustainability of the IARC ecosystem.

Key components of legal governance include the following:

First, data protection and privacy management. The IARC information repository must handle sensitive personal and corporate data in compliance with data protection laws and related regulations. To ensure the security of information management, robust data protection policies must be established and thoroughly implemented. Second, clarification of access and usage rights. The data access and usage rights of participating organizations must be clearly defined, and strong security measures must be implemented to prevent unauthorized access. This will clarify accountability during the data usage process. Third, establishment of data quality management systems. Standardized procedures must be introduced to maintain the accuracy and consistency of collected data. A data quality management system must be established to enhance the reliability of data utilization. Fourth, responsibility sharing and dispute resolution mechanisms. Responsibilities in the data lifecycle, including provision, management, and usage, must be clearly defined. A structured mechanism for resolving disputes that may arise must also be established, contributing to mutual trust among participating organizations and improving cooperation efficiency. Fifth, strengthening legal compliance and audit procedures. The repository must comply thoroughly with relevant laws and regulations during its operation, and regular audits and evaluations must be conducted to identify and address operational and legal issues. This will ensure the sustainability and reliability of the information management system.

Data governance policies play a pivotal role in reinforcing the operational stability and trustworthiness of the IARC information repository. These policies ensure clarity and transparency in information management, strengthen collaboration among participating organizations, and maximize the effectiveness of IARC. Future research should focus on designing and implementing detailed data governance policies, thereby contributing to the development of practical and sustainable IARC ecosystems.