Studies in Science of Science ›› 2026, Vol. 44 ›› Issue (2): 397-409.
• Frontiers and Opinions • Previous Articles Next Articles
LIU Yu-mei1, CHEN Ling-zhi2, SUN Yu-tao1,3(
)
Received:2024-11-13
Revised:2025-01-03
Online:2026-02-15
Published:2026-02-15
Contact:
SUN Yu-tao
通讯作者:
孙玉涛
作者简介:柳玉梅(1995-),女,博士研究生。基金资助:CLC Number:
LIU Yu-mei, CHEN Ling-zhi, SUN Yu-tao. Beyond performance feedback: Dual-field imitation pressure and R&D investment[J]. Studies in Science of Science, 2026, 44(2): 397-409.
柳玉梅, 陈灵芝, 孙玉涛. 超越绩效反馈:双重场域模仿压力与研发投资[J]. 科学学研究, 2026, 44(2): 397-409.
| 变量类型 | 变量 | 变量名称 | 变量含义及测量 |
|---|---|---|---|
| 被解释变量 | RD | 焦点企业研发投资 | 企业研发投入占销售收入的比值 |
| 解释变量 | RD_D | 双重场域模仿压力 | 同区域-行业企业第t-1年研发强度均值 |
| 调节变量 | Diff | 区域场域与行业场域模仿压力冲突 | 同行业企业和同区域企业第t-1年研发强度均值之差的绝对值 |
| 控制变量 | Size | 企业规模 | 企业第t-1年员工数量的自然对数 |
| Age | 企业年龄 | 企业建立年份到第t-1年的年数 | |
| SOE | 企业所有权 | 虚拟变量,若所有权性质为国有企业取1;否则取0 | |
| Tobin'Q | 企业托宾Q值 | 企业第t-1年市场价值/企业期末总资产 | |
| Lev | 资产负债率 | 企业第t-1年总负债/总资产 | |
| Revenue | 主营业务收入 | 企业第t-1年主营业务收入总额的对数 | |
| Revenue_I | 行业主营业务收入 | 第t-1年同行业企业主营业务收入均值的对数 | |
| Income_R | 地区人均生产总值 | 第t-1年地区生产总值除以年平均常住人口 |
Table 1 Definition and measurement of variables
| 变量类型 | 变量 | 变量名称 | 变量含义及测量 |
|---|---|---|---|
| 被解释变量 | RD | 焦点企业研发投资 | 企业研发投入占销售收入的比值 |
| 解释变量 | RD_D | 双重场域模仿压力 | 同区域-行业企业第t-1年研发强度均值 |
| 调节变量 | Diff | 区域场域与行业场域模仿压力冲突 | 同行业企业和同区域企业第t-1年研发强度均值之差的绝对值 |
| 控制变量 | Size | 企业规模 | 企业第t-1年员工数量的自然对数 |
| Age | 企业年龄 | 企业建立年份到第t-1年的年数 | |
| SOE | 企业所有权 | 虚拟变量,若所有权性质为国有企业取1;否则取0 | |
| Tobin'Q | 企业托宾Q值 | 企业第t-1年市场价值/企业期末总资产 | |
| Lev | 资产负债率 | 企业第t-1年总负债/总资产 | |
| Revenue | 主营业务收入 | 企业第t-1年主营业务收入总额的对数 | |
| Revenue_I | 行业主营业务收入 | 第t-1年同行业企业主营业务收入均值的对数 | |
| Income_R | 地区人均生产总值 | 第t-1年地区生产总值除以年平均常住人口 |
| 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 |
|---|---|---|---|---|---|---|
| RD_R | 0.150 (0.086) | 0.157** (0.078) | 0.104 (0.086) | |||
| RD_I | 0.404*** (0.083) | 0.430*** (0.086) | 0.352*** (0.082) | |||
| RD_D | 0.267*** (0.105) | 0.381*** (0.109) | 0.246*** (0.116) | 0.388*** (0.111) | ||
| RD_D*Diff | 0.291** (0.152) | |||||
| Diff | 0.167*** (0.070) | |||||
| Size | 0.464** (0.175) | 0.395** (0.17) | 0.403** (0.17) | 0.392** (0.17) | 1.491*** (0.244) | |
| Age | 0.177*** (0.013) | 0.165*** (0.013) | 0.165*** (0.013) | 0.150*** (0.013) | 0.206*** (0.027) | |
| SOE | 0.246 (0.189) | 0.269 (0.189) | 0.259 (0.189) | 0.266 (0.189) | 0.224 (0.202) | |
| Tobin'Q | -0.024 (0.017) | -0.023 (0.017) | -0.023 (0.017) | -0.022 (0.017) | -0.020*** (0.018) | |
| Lev | -0.016*** (0.003) | -0.016*** (0.003) | -0.016*** (0.003) | -0.016*** (0.003) | -0.017*** (0.003) | |
| Revenue | -0.779*** (0.098) | -0.772*** (0.097) | -0.782*** (0.108) | -0.776*** (0.108) | -0.759*** (0.107) | |
| Revenue_I | -0.06 (0.104) | -0.002 (0.103) | -0.04 (0.122) | -0.007 (0.122) | -0.027 (0.120) | |
| Income_R | 0.014 (0.032) | 0.027 (0.033) | 0.035 (0.037) | 0.03 (0.036) | 0.029 (0.037) | |
| cons | 2.827*** (0.504) | 2.243*** (0.504) | 2.210*** (0.503) | 2.249*** (0.502) | 2.158*** (0.504) | 2.158*** (0.504) |
| Id FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| No. of obs. | 19636 | 21789 | 21789 | 19636 | 19636 | 19636 |
| Adj. R2 | 0.11 | 0.12 | 0.15 | 0.16 | 0.296 | 0.09 |
| F/ Wald chi-square | 25.56 | 30.76 | 31.23 | 31.14 | 28.34 | 22.03 |
Table 3 Regression results
| 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 |
|---|---|---|---|---|---|---|
| RD_R | 0.150 (0.086) | 0.157** (0.078) | 0.104 (0.086) | |||
| RD_I | 0.404*** (0.083) | 0.430*** (0.086) | 0.352*** (0.082) | |||
| RD_D | 0.267*** (0.105) | 0.381*** (0.109) | 0.246*** (0.116) | 0.388*** (0.111) | ||
| RD_D*Diff | 0.291** (0.152) | |||||
| Diff | 0.167*** (0.070) | |||||
| Size | 0.464** (0.175) | 0.395** (0.17) | 0.403** (0.17) | 0.392** (0.17) | 1.491*** (0.244) | |
| Age | 0.177*** (0.013) | 0.165*** (0.013) | 0.165*** (0.013) | 0.150*** (0.013) | 0.206*** (0.027) | |
| SOE | 0.246 (0.189) | 0.269 (0.189) | 0.259 (0.189) | 0.266 (0.189) | 0.224 (0.202) | |
| Tobin'Q | -0.024 (0.017) | -0.023 (0.017) | -0.023 (0.017) | -0.022 (0.017) | -0.020*** (0.018) | |
| Lev | -0.016*** (0.003) | -0.016*** (0.003) | -0.016*** (0.003) | -0.016*** (0.003) | -0.017*** (0.003) | |
| Revenue | -0.779*** (0.098) | -0.772*** (0.097) | -0.782*** (0.108) | -0.776*** (0.108) | -0.759*** (0.107) | |
| Revenue_I | -0.06 (0.104) | -0.002 (0.103) | -0.04 (0.122) | -0.007 (0.122) | -0.027 (0.120) | |
| Income_R | 0.014 (0.032) | 0.027 (0.033) | 0.035 (0.037) | 0.03 (0.036) | 0.029 (0.037) | |
| cons | 2.827*** (0.504) | 2.243*** (0.504) | 2.210*** (0.503) | 2.249*** (0.502) | 2.158*** (0.504) | 2.158*** (0.504) |
| Id FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| No. of obs. | 19636 | 21789 | 21789 | 19636 | 19636 | 19636 |
| Adj. R2 | 0.11 | 0.12 | 0.15 | 0.16 | 0.296 | 0.09 |
| F/ Wald chi-square | 25.56 | 30.76 | 31.23 | 31.14 | 28.34 | 22.03 |
| 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 |
|---|---|---|---|---|---|---|
| RD_R | 0.264 (0.198) | 0.098 (0.081) | ||||
| RD_I | 0.823*** (0.164) | 0.356*** (0.081) | ||||
| RD_D | 2.804*** (0.148) | 0.346*** (0.108) | 0.323*** (0.113) | 0.249** (0.114) | 0.391*** (0.118) | |
| RD_D*Diff | 0.284* (0.151) | 0.285* (0.153) | ||||
| Diff | 0.117* (0.066) | 0.172** (0.070) | ||||
| IMR | -6.721*** (0.772) | |||||
| RD1 | 0.146*** (0.031) | 0.165*** (0.030) | ||||
| RD2 | 0.005 (0.034) | -0.006 (0.022) | ||||
| Market | -0.085*** (0.013) | |||||
| Supervisor | 0.012 (0.014) | |||||
| Bio4 | -0.061 (0.077) | |||||
| Report | 0.006*** (0.002) | |||||
| Control | Yes | Yes | Yes | Yes | Yes | Yes |
| Id FE | - | - | Yes | Yes | Yes | Yes |
| Year FE | - | - | Yes | Yes | Yes | Yes |
| cons | -0.330* (0.182) | 4.560*** (0.714) | 2.289*** (0.500) | 0.365 (0.768) | 1.553*** (0.506) | -3.067*** (0.763) |
| No. of obs. | 23203 | 23203 | 19636 | 19636 | 19636 | 19636 |
| Adj. R2 | - | - | 0.166 | 0.10 | 0.07 | 0.10 |
| F/ Wald chi-square | 1821.65 | 1821.65 | 23.28 | 20.55 | 22.53 | 21.54 |
Table 4 Robustness test results
| 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 | 模型6 |
|---|---|---|---|---|---|---|
| RD_R | 0.264 (0.198) | 0.098 (0.081) | ||||
| RD_I | 0.823*** (0.164) | 0.356*** (0.081) | ||||
| RD_D | 2.804*** (0.148) | 0.346*** (0.108) | 0.323*** (0.113) | 0.249** (0.114) | 0.391*** (0.118) | |
| RD_D*Diff | 0.284* (0.151) | 0.285* (0.153) | ||||
| Diff | 0.117* (0.066) | 0.172** (0.070) | ||||
| IMR | -6.721*** (0.772) | |||||
| RD1 | 0.146*** (0.031) | 0.165*** (0.030) | ||||
| RD2 | 0.005 (0.034) | -0.006 (0.022) | ||||
| Market | -0.085*** (0.013) | |||||
| Supervisor | 0.012 (0.014) | |||||
| Bio4 | -0.061 (0.077) | |||||
| Report | 0.006*** (0.002) | |||||
| Control | Yes | Yes | Yes | Yes | Yes | Yes |
| Id FE | - | - | Yes | Yes | Yes | Yes |
| Year FE | - | - | Yes | Yes | Yes | Yes |
| cons | -0.330* (0.182) | 4.560*** (0.714) | 2.289*** (0.500) | 0.365 (0.768) | 1.553*** (0.506) | -3.067*** (0.763) |
| No. of obs. | 23203 | 23203 | 19636 | 19636 | 19636 | 19636 |
| Adj. R2 | - | - | 0.166 | 0.10 | 0.07 | 0.10 |
| F/ Wald chi-square | 1821.65 | 1821.65 | 23.28 | 20.55 | 22.53 | 21.54 |
| 变量 | 区域场域 | 行业场域 | 双重场域 | |||
|---|---|---|---|---|---|---|
| 模型1高地位 | 模型2低地位 | 模型3高地位 | 模型4低地位 | 模型5高地位 | 模型6低地位 | |
| RD_I | -0.005 (0.063) | 0.432*** (0.136) | 0.418*** (0.102) | 0.387*** (0.130) | 0.619*** (0.190) | 0.131 (0.118) |
| P-value | 0.574*** | 0.292* | -0.418* | |||
| cons | Yes | Yes | Yes | Yes | Yes | Yes |
| control | Yes | Yes | Yes | Yes | Yes | Yes |
| Id FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| No. of obs. | 10319 | 11470 | 11286 | 10503 | 10177 | 9459 |
| R2 | 0.07 | 0.17 | 0.09 | 0.08 | 0.10 | 0.10 |
| F/ Wald chi-square | 10.33 | 20.96 | 12.87 | 18.47 | 12.69 | 17.47 |
Table 5 Regression results of the market status of firms
| 变量 | 区域场域 | 行业场域 | 双重场域 | |||
|---|---|---|---|---|---|---|
| 模型1高地位 | 模型2低地位 | 模型3高地位 | 模型4低地位 | 模型5高地位 | 模型6低地位 | |
| RD_I | -0.005 (0.063) | 0.432*** (0.136) | 0.418*** (0.102) | 0.387*** (0.130) | 0.619*** (0.190) | 0.131 (0.118) |
| P-value | 0.574*** | 0.292* | -0.418* | |||
| cons | Yes | Yes | Yes | Yes | Yes | Yes |
| control | Yes | Yes | Yes | Yes | Yes | Yes |
| Id FE | Yes | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes | Yes |
| No. of obs. | 10319 | 11470 | 11286 | 10503 | 10177 | 9459 |
| R2 | 0.07 | 0.17 | 0.09 | 0.08 | 0.10 | 0.10 |
| F/ Wald chi-square | 10.33 | 20.96 | 12.87 | 18.47 | 12.69 | 17.47 |
| 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 |
|---|---|---|---|---|---|
| RD_R | -0.039 (0.140) | -0.011 (0.145) | -0.079 (0.148) | ||
| RD_I | -0.044 (0.116) | 0.002 (0.114) | -0.100 (0.117) | ||
| RD_D | 0.286** (0.131) | 0.313** (0.126) | 0.321** (0.132) | ||
| control | Yes | Yes | Yes | Yes | Yes |
| Id FE | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes |
| No. of obs. | 19636 | 23203 | 23203 | 19636 | 19636 |
| R2 | 0.30 | 0.31 | 0.31 | 0.30 | 0.30 |
| F/ Wald chi-square | 4493.72 | 4652.84 | 4654.17 | 3885.35 | 3886.79 |
Table 6 Dual-field imitation pressure and firm R&D manipulation
| 变量 | 模型1 | 模型2 | 模型3 | 模型4 | 模型5 |
|---|---|---|---|---|---|
| RD_R | -0.039 (0.140) | -0.011 (0.145) | -0.079 (0.148) | ||
| RD_I | -0.044 (0.116) | 0.002 (0.114) | -0.100 (0.117) | ||
| RD_D | 0.286** (0.131) | 0.313** (0.126) | 0.321** (0.132) | ||
| control | Yes | Yes | Yes | Yes | Yes |
| Id FE | Yes | Yes | Yes | Yes | Yes |
| Year FE | Yes | Yes | Yes | Yes | Yes |
| No. of obs. | 19636 | 23203 | 23203 | 19636 | 19636 |
| R2 | 0.30 | 0.31 | 0.31 | 0.30 | 0.30 |
| F/ Wald chi-square | 4493.72 | 4652.84 | 4654.17 | 3885.35 | 3886.79 |
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