投早、投小、投巧？——合成生物研发投入策略研究

张兮; 肖展鹏; 魏馨; 朱智明; 张特

doi:10.12211/2096-8280.2025-071

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投早、投小、投巧？——合成生物研发投入策略研究

研究论文 | 更新时间：2026-05-22

- 投早、投小、投巧？——合成生物研发投入策略研究
- Invest early， less， and smartly—a study of synthetic biology R&D investment strategies
- 合成生物学 2026年7卷第2期页码：453-473
- 作者机构：
  
  1.天津大学管理与经济学部，天津 300072
  2.天津大学合成生物技术全国重点实验室，天津 300072
  3.北京工业大学经济与管理学院，北京 100124
- 作者简介：
  
  张兮（1982—），男，管理学博士，教授。研究方向为合成生物学风险管理，人工智能交叉研究，数据流通风险管理等。 E-mail：jackyzhang@tju.edu.cn
  魏馨（1996—），女，管理学博士，讲师。研究方向为数字化知识管理，合成生物学风险管理，人工智能交叉研究。 E-mail：weixin96@bjut.edu.cn
- 基金信息：
  
  国家自然科学基金(72442029);教育部哲学社会科学研究重大课题攻关项目(24JZD026);京津冀自然科学基金合作专项(25JJJJC0026);国家自然科学基金青年项目C类(72502226)
- DOI：10.12211/2096-8280.2025-071
  中图分类号： F272.5
- 收稿：2025-07-01，
  
  修回：2026-01-05，
  
  纸质出版：2026-04-30
- 稿件说明：
移动端阅览
张兮，肖展鹏，魏馨，朱智明，张特. 投早、投小、投巧？——合成生物研发投入策略研究［J］. 合成生物学， 2026， 7（2）： 453-473

ZHANG Xi， XIAO Zhanpeng， WEI Xin， ZHU Zhiming， ZHANG Te. Invest early， less， and smartly—a study of synthetic biology R&D investment strategies［J］. Synthetic Biology Journal， 2026， 7（2）： 453-473
张兮，肖展鹏，魏馨，朱智明，张特. 投早、投小、投巧？——合成生物研发投入策略研究［J］. 合成生物学， 2026， 7（2）： 453-473 DOI： 10.12211/2096-8280.2025-071.

ZHANG Xi， XIAO Zhanpeng， WEI Xin， ZHU Zhiming， ZHANG Te. Invest early， less， and smartly—a study of synthetic biology R&D investment strategies［J］. Synthetic Biology Journal， 2026， 7（2）： 453-473 DOI： 10.12211/2096-8280.2025-071.

摘要

未来产业是指由新兴技术创新推动、具有高成长潜力和战略价值的新型产业，其核心在于通过技术突破引领经济社会的高质量发展。作为未来产业的重要组成部分，合成生物技术以其高度的交叉学科特性突破了传统技术瓶颈，并通过“设计-构建-验证-学习”的流程推动技术创新。与此同时，合成生物技术研发具有高度不确定性，企业在投入时机、规模和策略选择上面临重大挑战。本文基于资源基础观、技术创新理论和信息技术生产率悖论，收集750家美国生物科技上市公司2019—2021年的财务数据与专利文本，利用LDA主题建模与余弦相似度量化研发投入，并通过回归分析检验其对企业运营绩效的影响。研究结果表明：合成生物研发投入与企业绩效呈显著倒U型关系；企业资产规模和流动性能够缓和该关系，且当企业规模超过一定阈值后，倒U型关系转变为正U型。由此提出三种策略启示：一是“投早投小”，在早期阶段分散投资多个小规模企业，以获取更高的边际绩效；二是“投巧”，在企业成长过程中进行精准整合，提升资源配置效率；三是针对大规模企业，应适度加大研发投入，实现由倒U型向正U型的转化。本文首次在实证层面用定量的方法刻画了合成生物技术研发投入对企业绩效的作用机制，并揭示了最优投入模式，不仅丰富了合成生物技术投入-产出关系的理论理解，也为生物科技企业提供了可操作的战略启示。

Abstract

Future industries denote emerging sectors propelled by technological innovations

which are characterized by substantial growth potential and strategic significance

and play a crucial role in facilitating high quality economic and social development. As a vital constituent of future industries

synthetic biology is characterized by its highly interdisciplinary nature. It utilizes the Design-Build-Test-Learn (DBTL) cycle to surmount traditional technological bottlenecks and foster innovations

but uncertainties intrinsic to synthetic biology research and development (R&D) present significant challenges for firms in ascertaining the time

scale

and strategy for investment. Based on the resource-based view

technological innovation theory

and the IT productivity paradox

this study explores the influence of R&D investment strategies for synthetic biology on firms’ operational performance. With financial data and patent text information of 750 U.S. biotechnology firms from 2019 to 2021

this study utilizes the Latent Dirichlet Allocation (LDA) topic-modeling approach and cosine similarity to quantify R&D inputs. Subsequently

regression analysis is applied to investigate the influence of synthetic biology R&D investment on firms’ operational performance

as well as the moderating effect of asset scale and liquidity. The findings indicate that: (1) There exists a significant inverted U-shaped relationship between the R&D investment in synthetic biology and firm performance; (2) Asset size and liquidity exert a positive moderating effect on this relationship; (3) Once the firm scale surpasses a specific threshold

the inverted U-shaped relationship transforms into a U-shaped one. Based on these findings

three strategic perspectives are put forward: making early and small scale investments to diversify risks and enhance marginal returns; making intelligent investments to optimize resource allocation during the growth of firms; for large scale firms

appropriately increasing R&D input to realize a positive transformation of the investment-performance curve. This study is among the pioneering studies to quantitatively capture mechanism through which R & D investment in synthetic biology affects firm performance. It identifies the optimal investment models

enriches the theoretical comprehension of the input-output relationship in synthetic biotechnology

and offers practical strategic guidances for biotechnology firms.

关键词

Keywords

references

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