This database system integrates cybersecurity recruitment post information released by A-share listed companies and their affiliated enterprises (including subsidiaries and associated companies), covering three major recruitment platforms (BCE sources).
The data is constructed through a dual screening mechanism:

• Topic Identification: Extracts cybersecurity-related positions from full recruitment data of BCE sources based on 200+ cybersecurity keywords (such as “Information Security,” “Network Security,” “Data Security,” “Firewall,” etc.);
• Corporate Affiliation: Conducts fuzzy matching between the hiring company names and the parent listed companies as well as their associated enterprise lists, ensuring comprehensive mapping of corporate hierarchical relationships (e.g., subsidiaries/associated companies).

The database contains over 40 fields, including stock code(证券代码), affiliated company relationships(关联公司关系), job function description(岗位职能描述), skill tags(技能标签), salary information(薪资待遇), and geographic distribution(地域分布) (detailed in the form structure). With a time span covering 2014–2024, it provides structured micro-data support for studying companies' investments in human capital for cybersecurity.

Data Features:

• Corporate Hierarchy Transparency: Maps equity relationships (e.g., "subsidiaries/associated companies") between listed companies and recruiting entities, revealing cybersecurity resource allocation strategies in conglomerates and bridging the gap of corporate organizational structure in traditional data.
• Multi-dimensional Coverage:
   - Space Dimension: Job locations pinpointed to province, city, and district levels;
   - Time Dimension: Release dates specified to day-level precision, supporting longitudinal trend analysis;
   - Functional Dimension: Covers full-chain positions across technical (penetration testing), managerial (Security Operations Director), and compliance (Technical Compliance Engineer(等保工程师)) roles.
• Complementary Affiliated Fields: Cross-validation between "Recruiting Company Name(招聘公司名称)" and "Affiliated Company Name(关联公司名称)", supplemented by "Fuzzy-matched Name(模糊匹配名称)" to improve corporate identification error tolerance and ensure entity affiliation accuracy.
• Dynamic Quality Control: The "Tag(标签)" field marks matching quality (e.g., "1-Position Match," "2-Skill Tag Match"), enabling researchers to filter high-confidence samples as needed and reduce noise interference.

Potential Application Scenarios:

• Academic Research: Correlate skill requirements with firms' patents/R&D investments to validate technology implementation paths; analyze regional cybersecurity talent clustering effects.
• Business Services: Benchmark competitors' recruitment strategies; leverage sudden high-salary offers as an indicator for security incident response needs, serving as an early risk warning signal.

  The CnOpenData A-share Listed Companies Cybersecurity Recruitment Data systematically integrates a panoramic view of cybersecurity human capital among A-share listed companies for the first time. Leveraging strengths such as corporate-level transparency and multi-dimensional coverage, it fills the micro-data gap in cybersecurity economics research. It provides high-granularity evidence support for academia to investigate corporate digital security investment logic and for industries to optimize talent strategies, contributing to multi-dimensional analysis of China's cybersecurity capacity building.

Database Usage Guide

Logic for Applying CnOpenData A-share Listed Companies Cybersecurity Online Recruitment Data:
https://mp.weixin.qq.com/s/BZrJhKNdekKKp24wJSM01Q

Data Scale

Time Range

2014–2024 (Updateable as needed)

Field Display

Sample Data

A-share Listed Companies Cybersecurity Recruitment Data—B Source

A-share Listed Companies Cybersecurity Recruitment Data—C Source

A-share Listed Companies Cybersecurity Recruitment Data—E Source

Related Literature

• Kunpeng Sun, Ting Luo, Xing Xiao, 2021: “Talent Policies, R&D Personnel Recruitment, and Corporate Innovation,” Economic Research, No. 8.

Data Update Frequency

Annual Updates