Research on Technology Opportunity Identification by Integrating Technology Attribute Analysis and Competitive Environment Scanning —— A Case Study of Industrial Robots

Authors

• Daobin GAO, The Institute of Science of Science and S&T Management & WISE Lab, Dalian University of Technology, Dalian 116024

Keywords

Technical attribute analysis, competitive environment scanning, technology opportunity identification, patent information, industrial robot

Abstract

[Purpose/significance] Placing technology opportunity identification in the perspective of technology attribute analysis and competitive environment scanning provides intelligence support for enterprises to select technology opportunities and formulate technology competitive strategies in complex and changing market competition environments.

[Method/process] Using patents as the data source, first, the BERTopic model is used to explore potential technical topics, Secondly, starting from the technical attributes, and from both global and local perspectives, we propose indicators of technical importance and technical attention, and use their cross screening to select candidate sets of technical opportunities, Finally, a market competition intensity index that takes into account market monopoly and market selectivity is proposed, along with a subject competition intensity index that includes the number and distance of competing entities.A two-dimensional technology opportunity competition environment scanning strategic framework is further constructed to identify candidate technology opportunities as medium and low-risk technology opportunities.Take the domestic industrial robot field as an example to conduct empirical research.

[Result/conclusion] Based on the technology opportunity identification method presented in this article, a total of 14 candidate technology opportunities in the domestic industrial robot field were screened. Ultimately, 9 potential medium risk technology opportunities and 4 potential low-risk technology opportunities were identified, and the effectiveness of this method was further validated by combining enterprise product information.

First Page

47

Last Page

58

Submission Date

09-Oct-2024

Revision Date

13-Feb-2025

Acceptance Date

14-Feb-2025

Published Date

01-Jul-2025

Reference

[1] 高道斌, 吴红, 李志杰. 基于专利信息的竞争对手识别框架构建与实证研究[J]. 科技情报研究, 2024, 6 (3): 69-82.
[2] 高道斌, 陈悦, 韩盟, 等. 市场竞争属性映射下的企业技术机会识别框架构建[J]. 情报理论与实践, 2024, 47 (7): 115-125, 159.
[3] 吴洁, 刘子钰, 谢小东, 等. 竞争对手技术相似性与技术能力的企业技术机会识别[J]. 情报杂志, 2025, 44 (2): 92-100.
[4] WU Y W, JI Y J, GU F. Identifying firm-specific technology opportunities in a supply chain: Link prediction analysis in multilayer networks[J]. Expert Systems with Applications, 2023, 213: 119053.
[5] SEOL Y, LEE S, KIM C, et al. Towards firm-specific technology opportunities: A rule-based machine learning approach to technology portfolio analysis[J]. Journal of Informetrics, 2023, 17 (4): 101464.
[6] 王金凤, 陈慧源, 刘振锋, 等. 基于生成式拓扑映射和类比设计方法的技术机会识别[J]. 情报理论与实践, 2023, 46 (6): 127-135.
[7] REN H, ZHAO Y. Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks[J]. Technovation, 2020, 101: 102196．
[8] 吴柯烨, 孙建军, 谢紫悦. 基于专利文本挖掘的细粒度技术机会分析[J]. 情报学报, 2023, 42 (10): 1199-1212.
[9] 宋凯, 冉从敬. 基于主题挖掘与专利评估的技术机会识别研究: 以智慧农业为例[J]. 图书情报工作, 2023, 67 (3): 61-71.
[10] 高道斌, 李志杰, 吴红. “专利申请人/专利权人”主体视角下的技术机会识别研究[J]. 竞争情报, 2024, 20 (4): 18-29.
[11] 冉从敬, 李旺, 黄文俊. 高校高价值专利技术机会识别研究: 以“生成式人工智能”领域为例[J]. 信息资源管理学报, 2024, 14 (4): 103-116.
[12] 李子彪, 孙可远, 陈迪, 等. 基于科技差距评估GTM专利地图识别的技术机会[J]. 情报杂志, 2023, 42 (7): 147-153, 44.
[13] 许学国, 桂美增. 基于GTM逆向映射的技术创新机会识别: 以新能源汽车为例[J]. 情报理论与实践, 2021, 44 (6): 146-153, 198.
[14] 冯立杰, 刘可辉, 王金凤, 等. 基于知识网络与多维技术创新地图的技术机会识别路径研究与应用[J]. 数据分析与知识发现, 2023, 7 (8): 62-77.
[15] 岳俊举, 冯立杰, 冯奕程, 等. 基于多维技术创新地图与关联规则挖掘的技术机会识别方法研究[J]. 情报学报, 2017, 36 (8): 798-808.
[16] HONG S, LEE C. Effective indexes and classification algorithms for supervised link prediction approach to anticipating technology convergence: A comparative study[J]. IEEE Transactions on Engineering Management, 2023, 70 (4): 1430-1441.
[17] KIM T S, SOHN S Y. Machine-learning-based deep semantic analysis approach for forecasting new technology convergence[J]. Technological Forecasting and Social Change, 2020, 157: 120095.
[18] ZHU C, MOTOHASHI K. Identifying the technology convergence using patent text information: A graph convolutional networks (GCN) -based approach[J]. Technological Forecasting and Social Change, 2022, 176: 121477.
[19] 周潇, 许银彪, 史益. 基于深度学习与语义挖掘的技术创新组合识别与追踪[J]. 图书情报工作, 2022, 66 (10): 33-44.
[20] 吴柯烨, 孙建军, 张力, 等. 弱链接突变视角下的技术机会识别研究[J]. 图书情报工作, 2024, 68 (10): 81-96.
[21] 张彪, 陈云伟, 董坤. 基于链路预测与多维指标的企业技术机会发现方法研究[J]. 数据分析与知识发现, 2024, 8 (7): 137-148.
[22] 钟晨. 竞争环境扫描对企业竞争优势影响的概念模型研究[J]. 图书情报知识, 2013 (4): 59-65.
[23] 汪大锟, 化柏林. 基于BERTopic的新兴技术主题识别研究[J]. 科技情报研究, 2025, 7 (1): 131-140.
[24] 逯万辉. 科学文献主题建模方法及其效果评估研究[J]. 现代情报, 2024, 44 (4): 22-31.
[25] 吴红, 高道斌, 张彪. 专利视域下企业竞争对手识别的“T-M-C”模型研究[J]. 情报理论与实践, 2022, 45 (11): 136-142, 114.
[26] 龙小宁, 张美扬. 标准的力量: 来自中国标准必要专利的经验证据[J]. 管理世界, 2023, 39 (10): 149-168, 227, 169-173.
[27] 许鑫, 李倩, 衣春波, 等. 基于链接预测的技术机会识别研究: 以隐私保护领域专利数据为例[J]. 图书馆杂志, 2023, 42 (9): 103-112, 128, 136-137.
[28] 黄鲁成, 李晓宇, 李晋. 基于专利的ABOD-RFM技术机会识别方法研究[J]. 情报理论与实践, 2020, 43 (9): 144-149.
[29] 金可怡, 周立军, 杨静. 基于SAO结构的颠覆性技术关联机会发现路径研究[J]. 情报杂志, 2024, 43 (9): 84-91, 111.
[30] 李志杰, 吴红, 高道斌. 基于动态-静态专利指标的二维企业技术竞争力分析框架构建[J]. 竞争情报, 2023, 19 (5): 29-42.
[31] BEN-MENAHEM S M, KWEE Z, VOLBERDA H W, et al. Strategic renewal over time: The enabling role of potential absorptive capacity in aligning internal and external rates of change[J]. Long Range Planning, 2013, 46 (3): 216-235.
[32] 李宁娟. 环境扫描、战略更新与企业绩效研究: 基于竞争强度调节作用[J]. 科技创业月刊, 2022, 35 (8): 41-47.
[33] BO Q, ZOU B, GUO F. Environmental scanning perspective: The moderating effects of organizational risk and government subsidy in innovation search process[J]. Science and Public Policy, 2021, 48 (4): 531-540.
[34] YANG L, TAN J, XIA W, et al. Corporate performance, market-industry competition and enterprise environmental-protection investment[J]. Sustainability, 2022, 14 (9): 5459.
[35] FARH J L, HOFFMAN R C, HEGARTY W H. Assessing environmental scanning at the subunit level: A multitrait-multimethod analysis[J]. Decision sciences, 1984, 15 (2): 197-220.
[36] 曾莉, 蒋文蹊. 工业机器人全球专利分析[J]. 中国发明与专利, 2020, 17 (2): 73-79.
[37] 工业和信息化部. 中华人民共和国工业和信息化部公告[EB/OL]. (2024-12-27) [2025-01-19]. https://www.miit.gov.cn/jgsj/zbys/wjfb/art/2024/art_f8004d09b95d4b4f9008c5aa3809dea0.html.
[38] 遨博智能. iS系列协作机器人[EB/OL]. [2025-01-19]. https://www.aubo-robotics.cn/isproduct.
[39] 遨博智能. iH系列协作机器人[EB/OL]. [2025-01-19]. https://www.aubo-robotics.cn/ihproduct.
[40] 新松. 焊接自动化[EB/OL]. [2025-01-20]. https://www.siasun.com/article/21.html.
[41] 新松. 物流自动化[EB/OL]. [2025-01-20]. https://www.siasun.com/article/19.html.
[42] 新松. 新松工业机器人SR210A-210/2. 65[EB/OL]. [2025-01-20]. https://www.siasun.com/article/437.html.
[43] 博实. 自动装车系统[EB/OL]. [2025-01-23]. https://www.boshi.cn/product/63.html.
[44] 博实. RBD3-1100并联机器人[EB/OL]. [2025-01-23]. https://www.boshi.cn/product/3.html.

Digital Object Identifier (DOI)

10.19809/j.cnki.kjqbyj.2025.03.005

Recommended Citation

GAO, Daobin (2025) "Research on Technology Opportunity Identification by Integrating Technology Attribute Analysis and Competitive Environment Scanning —— A Case Study of Industrial Robots," Journal of Scientific Information Research: Vol. 7: Iss. 3, Article 5.
DOI: 10.19809/j.cnki.kjqbyj.2025.03.005
Available at: https://eng.kjqbyj.com/journal/vol7/iss3/5