Uncovering the Mechanism of Elevating High-Tech Export Competitiveness in China’s Sustainable Economic Development: Force of Digital Economy
Abstract
1. Introduction
2. Mechanism Analysis
2.1. Direct Mechanism
2.2. Indirect Mechanism
2.2.1. Technological Innovation as a Mediator
2.2.2. Industrial Upgrading as a Mediator
3. Methodology and Data
3.1. Model Construction
3.2. Variable Selection
- (1)
- Dependent Variable: Export Competitiveness of High-Tech Products (Eci)
- (2)
- Core Explanatory Variable: Digital Economy Development Level (De)
- (3)
- Mediating Variables
- (4)
- Control Variables
3.3. Measurement Method
- 1.
- Standardization
- 2.
- Calculate the characteristic proportion :
- 3.
- Determine the entropy value for the j-th indicator:
- 4.
- Compute the information utility value for the j-th indicator:
- 5.
- Calculate the weight assigned to the j-th indicator:
- 6.
- Calculate the comprehensive evaluation index:
3.4. Data
4. Descriptive Analysis and Indicator Measurement
4.1. Descriptive Analysis
4.2. Measurement of Digital Economy Development Level
4.3. Measurement of Export Competitiveness
5. Empirical Study
5.1. Baseline Regression Analysis
5.2. Heterogeneity Analysis
5.3. Endogeneity Test
5.4. Robustness Test
5.5. Mediation Effect Test
5.6. Further Analysis
5.7. Discussion
6. Conclusions
6.1. Main Results
6.2. Theoretical Contributions
6.3. Managerial Implications
6.4. Limitations and Future Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Regional Analysis of Digital Economy Development Level Measurement
Appendix B. Analysis of Trade Competitiveness Index (Tci)
Year | CCT | LST | ET | CIMT | AT | OET | BT | MT | OT |
---|---|---|---|---|---|---|---|---|---|
2011 | 0.5762 | 0.0609 | −0.4238 | −0.6799 | −0.6105 | −0.2559 | −0.0406 | −0.1143 | −0.3106 |
2012 | 0.5479 | 0.0376 | −0.4027 | −0.5726 | −0.6902 | −0.1942 | −0.0099 | −0.1372 | −0.2524 |
2013 | 0.5502 | 0.0152 | −0.3435 | −0.5064 | −0.7104 | −0.1929 | −0.1208 | −0.0189 | −0.1750 |
2015 | 0.5815 | −0.0413 | −0.3788 | −0.4827 | −0.6536 | −0.1603 | −0.2422 | 0.1294 | −0.0519 |
2016 | 0.5842 | −0.0675 | −0.4190 | −0.4646 | −0.6262 | −0.1565 | −0.3367 | 0.2177 | −0.0564 |
2017 | 0.6032 | −0.0825 | −0.4409 | −0.5177 | −0.6576 | −0.1467 | −0.4295 | 0.2662 | −0.1652 |
2018 | 0.6048 | −0.0550 | −0.4327 | −0.5610 | −0.6312 | −0.1637 | −0.4524 | 0.2153 | −0.0002 |
2019 | 0.6026 | −0.0339 | −0.3980 | −0.5222 | −0.5873 | −0.1363 | −0.5223 | 0.1860 | 0.0292 |
2020 | 0.6005 | −0.0165 | −0.3683 | −0.4841 | −0.5037 | −0.1047 | −0.5651 | 0.1567 | 0.0590 |
2021 | 0.5827 | 0.0927 | −0.3368 | −0.4732 | −0.5214 | −0.0131 | 0.4514 | 0.2254 | 0.3458 |
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Variable Type | Variable Name | Sample Size | Mean | Standard Deviation | Minimum | Maximum | Median |
---|---|---|---|---|---|---|---|
Dependent Variable | Eci | 330 | 0.632 | 0.878 | 0.001 | 4.165 | 0.294 |
Core Explanatory Variable | De | 330 | 0.130 | 0.131 | 0.004 | 0.880 | 0.092 |
Control Variables | Edu | 330 | 17.748 | 1.229 | 13.98 | 22.7 | 17.79 |
Open | 330 | 0.265 | 0.291 | 0.008 | 1.548 | 0.142 | |
Fdi | 330 | 8.381 | 8.092 | 0.003 | 35.760 | 5.815 | |
Fin | 330 | 3.380 | 1.085 | 1.678 | 7.578 | 3.146 | |
Mediating Variables | Ti | 330 | 15.879 | 33.550 | 0.002 | 245.918 | 4.093 |
Upgra | 330 | 5.347 | 3.443 | 0.101 | 32.405 | 4.567 |
Primary Indicator (Weight) | Secondary Indicator (Weight) | Tertiary Indicators | Unit | Weight |
---|---|---|---|---|
Industrial Digitalization (0.4002) | Industry (0.1826) | Industrial changed value | CYN hundred million | 0.0330 |
Expenditure on technological transformation | CYN ten thousand | 0.0340 | ||
New product sales revenue from large industrial enterprises | CYN ten thousand | 0.0583 | ||
Full-time equivalent of R&D personnel in large industrial enterprises | Person-years | 0.0573 | ||
Agriculture (0.0679) | Changed value in agriculture, forestry, animal husbandry, and fisheries | CYN hundred million | 0.0246 | |
Number of rural broadband access users | Ten thousand households | 0.0433 | ||
Tertiary Industry (0.1497) | Changed value of tertiary industry | CYN hundred million | 0.0312 | |
Original insurance premium income | CYN hundred million | 0.0292 | ||
Express business revenue | CYN ten thousand | 0.0893 | ||
Digital Industrialization (0.3792) | Infrastructure (0.0936) | Internet broadband access ports | Ten thousand locations | 0.0268 |
Postal service outlets | Locations | 0.0284 | ||
Year-end total of mobile phone users | Ten thousand households | 0.0219 | ||
Length of long-distance optical fiber cables | Ten thousand kilometers | 0.0165 | ||
ICT Industry (0.2756) | Main business revenue of computer industry | CYN hundred million | 0.0895 | |
Telecom business volume | CYN hundred million | 0.0548 | ||
Software business revenue | CYN ten thousand | 0.0818 | ||
Urban employment in information transmission, software, and IT services | Ten thousand people | 0.0495 | ||
Digital Development Environment (0.2304) | Digital Talent Cultivation (0.0371) | Number of students in regular higher education | Ten thousand people | 0.0173 |
Local government education expenditure | CYN hundred million | 0.0198 | ||
Innovation Environment (0.1933) | Internal expenditure on R&D in high-tech industries | CYN ten thousand | 0.0815 | |
Number of domestic patent applications granted | Items | 0.0642 | ||
Local government spending on science and technology | CYN hundred million | 0.0476 |
Division | Region | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Mean Value |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
North China | Beijing | 0.0994 | 0.1101 | 0.1224 | 0.1365 | 0.1488 | 0.1627 | 0.1810 | 0.2019 | 0.2202 | 0.2467 | 0.2755 | 0.1732 |
Tianjin | 0.0355 | 0.0433 | 0.0488 | 0.0518 | 0.0569 | 0.0582 | 0.0574 | 0.0630 | 0.0676 | 0.0759 | 0.0808 | 0.0581 | |
Hebei | 0.0796 | 0.0802 | 0.1006 | 0.1062 | 0.1138 | 0.1283 | 0.1423 | 0.1612 | 0.1841 | 0.2046 | 0.1988 | 0.1371 | |
Shanxi | 0.0400 | 0.0458 | 0.0503 | 0.0492 | 0.0527 | 0.0534 | 0.0627 | 0.0688 | 0.0771 | 0.0880 | 0.0960 | 0.0622 | |
Inner Mongolia | 0.0363 | 0.0385 | 0.0428 | 0.0452 | 0.0485 | 0.0514 | 0.0552 | 0.0596 | 0.0646 | 0.0700 | 0.0684 | 0.0528 | |
Northeast China | Liaoning | 0.0784 | 0.0843 | 0.0956 | 0.1009 | 0.0992 | 0.0960 | 0.1006 | 0.1088 | 0.1136 | 0.1215 | 0.1223 | 0.1090 |
Jilin | 0.0339 | 0.0372 | 0.0387 | 0.0445 | 0.0448 | 0.0486 | 0.0523 | 0.0540 | 0.0754 | 0.0619 | 0.0608 | 0.0502 | |
Heilongjiang | 0.0447 | 0.0493 | 0.0534 | 0.0565 | 0.0588 | 0.0626 | 0.0694 | 0.0727 | 0.0797 | 0.0816 | 0.0777 | 0.0642 | |
East China | Shanghai | 0.0944 | 0.1042 | 0.1182 | 0.1320 | 0.1413 | 0.1628 | 0.1871 | 0.1958 | 0.2215 | 0.2432 | 0.2643 | 0.1695 |
Jiangsu | 0.2514 | 0.2932 | 0.3228 | 0.3443 | 0.3784 | 0.4073 | 0.4367 | 0.4682 | 0.5126 | 0.5765 | 0.6160 | 0.4189 | |
Zhejiang | 0.1436 | 0.1663 | 0.1915 | 0.2037 | 0.2383 | 0.2528 | 0.2900 | 0.3409 | 0.3843 | 0.4207 | 0.4438 | 0.2796 | |
Anhui | 0.0681 | 0.0755 | 0.0846 | 0.0924 | 0.1088 | 0.1231 | 0.1415 | 0.1668 | 0.1904 | 0.2149 | 0.2279 | 0.1358 | |
Fujian | 0.0712 | 0.0826 | 0.0932 | 0.1020 | 0.1139 | 0.1272 | 0.1439 | 0.1669 | 0.1830 | 0.1861 | 0.1994 | 0.1336 | |
Jiangxi | 0.0403 | 0.0454 | 0.0537 | 0.0600 | 0.0697 | 0.0781 | 0.0921 | 0.1085 | 0.1281 | 0.1465 | 0.1511 | 0.0885 | |
Shandong | 0.1601 | 0.1821 | 0.2073 | 0.2174 | 0.2428 | 0.2606 | 0.2827 | 0.3003 | 0.3206 | 0.3545 | 0.3946 | 0.2657 | |
Central China | Henan | 0.0948 | 0.1059 | 0.1240 | 0.1330 | 0.1490 | 0.1649 | 0.1850 | 0.2129 | 0.2363 | 0.2653 | 0.2690 | 0.1764 |
Hubei | 0.0730 | 0.0846 | 0.0962 | 0.1072 | 0.1208 | 0.1345 | 0.1515 | 0.1744 | 0.1981 | 0.2095 | 0.2297 | 0.1436 | |
Hunan | 0.0802 | 0.0918 | 0.1038 | 0.1033 | 0.1149 | 0.1227 | 0.1384 | 0.1517 | 0.1751 | 0.1961 | 0.1997 | 0.1344 | |
South China | Guangdong | 0.2756 | 0.3161 | 0.3662 | 0.3868 | 0.4457 | 0.5062 | 0.5780 | 0.6878 | 0.7764 | 0.8514 | 0.8799 | 0.5518 |
Guangxi | 0.0445 | 0.0510 | 0.0554 | 0.0577 | 0.0654 | 0.0718 | 0.0813 | 0.0968 | 0.1190 | 0.1343 | 0.1274 | 0.0822 | |
Hainan | 0.0059 | 0.0075 | 0.0093 | 0.0099 | 0.0120 | 0.0139 | 0.0159 | 0.0190 | 0.0229 | 0.0255 | 0.0258 | 0.0152 | |
Southwest China | Chongqing | 0.0367 | 0.0428 | 0.0511 | 0.0570 | 0.0659 | 0.0756 | 0.0856 | 0.0938 | 0.1082 | 0.1220 | 0.1278 | 0.0788 |
Sichuan | 0.0961 | 0.1057 | 0.1244 | 0.1361 | 0.1535 | 0.1700 | 0.1949 | 0.2233 | 0.2586 | 0.2931 | 0.2965 | 0.1866 | |
Guizhou | 0.0266 | 0.0325 | 0.0362 | 0.0412 | 0.0479 | 0.0534 | 0.0620 | 0.0735 | 0.0863 | 0.0968 | 0.0912 | 0.0589 | |
Yunnan | 0.0358 | 0.0406 | 0.0467 | 0.0493 | 0.0555 | 0.0606 | 0.0701 | 0.0864 | 0.1033 | 0.1187 | 0.1075 | 0.0704 | |
Northwest China | Shaanxi | 0.0500 | 0.0563 | 0.0563 | 0.0631 | 0.0693 | 0.0766 | 0.0868 | 0.0958 | 0.1090 | 0.1252 | 0.1368 | 0.0917 |
Gansu | 0.0196 | 0.0248 | 0.0281 | 0.0298 | 0.0329 | 0.0352 | 0.0406 | 0.0477 | 0.0562 | 0.0621 | 0.0592 | 0.0397 | |
Qinghai | 0.0043 | 0.0059 | 0.0064 | 0.0077 | 0.0088 | 0.0096 | 0.0110 | 0.0129 | 0.0148 | 0.0168 | 0.0142 | 0.0102 | |
Ningxia | 0.0050 | 0.0050 | 0.0063 | 0.0087 | 0.0083 | 0.0093 | 0.0122 | 0.0162 | 0.0177 | 0.0194 | 0.0197 | 0.0116 | |
Xinjiang | 0.0247 | 0.0278 | 0.0312 | 0.0337 | 0.0391 | 0.0406 | 0.0448 | 0.0520 | 0.0616 | 0.0676 | 0.0658 | 0.0444 |
Variable | (1) LnEci | (2) LnEci | (3) LnEci | (4) LnEci | (5) LnEci |
---|---|---|---|---|---|
LnDe | 0.728 *** (9.33) | 0.640 *** (7.71) | 0.836 *** (10.17) | 0.835 *** (10.11) | 0.656 *** (5.85) |
LnEdu | 2.031 *** (2.82) | 1.471 ** (2.18) | 1.479 ** (2.14) | 1.640 ** (2.38) | |
LnOpen | 0.813 *** (6.92) | 0.813 *** (6.86) | 0.814 *** (6.92) | ||
LnFdi | 0.003 (0.05) | −0.003 (−0.05) | |||
LnFin | 0.744 ** (2.35) | ||||
cons | 0.348 * (1.79) | −5.705 *** (−2.64) | −2.160 (−1.04) | −2.195 (−1.01) | −3.951 * (−1.73) |
Year | Yes | Yes | Yes | Yes | Yes |
Province | Yes | Yes | Yes | Yes | Yes |
N | 330 | 330 | 330 | 330 | 330 |
R2 | 0.226 | 0.246 | 0.350 | 0.350 | 0.362 |
Eastern Region | Central Region | Western Region | |
---|---|---|---|
Variable | LnEci | LnEci | LnEci |
LnDe | 0.026 | 0.694 *** | 1.060 *** |
(0.24) | (2.89) | (4.13) | |
LnEdu | −0.375 | −0.002 | 1.437 |
(−0.69) | (−0.00) | (0.89) | |
LnOpen | 0.637 *** | 0.709 *** | 0.756 *** |
(4.14) | (3.27) | (4.48) | |
LnFdi | −0.029 | −0.200 ** | 0.192 ** |
(−0.57) | (−2.31) | (2.06) | |
LnFin | −0.369 | 1.458 *** | 1.822 ** |
(−1.29) | (2.88) | (2.29) | |
Cons | 1.769 | 0.837 | −3.770 |
(1.05) | (0.23) | (−0.65) | |
N | 121 | 88 | 121 |
R2 | 0.310 | 0.666 | 0.525 |
Province | Yes | Yes | Yes |
Year | Yes | Yes | Yes |
Variable | (1) First Stage | (2) Second Stage |
---|---|---|
L.De | 0.976 *** (177.93) | |
LnDe | 0.471 *** (5.77) | |
LnEdu | −0.042 (−0.78) | 4.258 *** (5.51) |
LnOpen | 0.018 *** (3.10) | 0.838 *** (9.98) |
LnFdi | 0.001 (0.19) | 0.132 ** (2.54) |
LnFin | −0.045 *** (−2.94) | 0.267 (1.21) |
Cons | 0.245 (1.51) | −11.790 *** (−5.01) |
N | 300 | 330 |
R2 | 0.996 | 0.686 |
Variable | (1) LnRca | (2) LnRca | (3) LnRca | (4) LnRca | (5) LnRca |
---|---|---|---|---|---|
LnDe | 0.728 *** (9.50) | 0.600 *** (7.43) | 0.563 *** (6.56) | 0.572 *** (6.62) | 0.440 *** (3.68) |
LnEdu | 2.836 *** (4.12) | 2.902 *** (4.20) | 2.734 *** (3.85) | 2.864 *** (4.02) | |
LnOpen | −0.146 (−1.26) | −0.130 (−1.11) | −0.139 (−1.19) | ||
LnFdi | −0.057 (−1.02) | −0.061 (−1.10) | |||
LnFin | 0.530 (1.57) | ||||
Cons | 1.157 *** (6.04) | −7.308 *** (−3.54) | −7.851 *** (−3.72) | −7.117 *** (−3.19) | −8.438 *** (−3.55) |
Year | Yes | Yes | Yes | Yes | Yes |
Province | Yes | Yes | Yes | Yes | Yes |
N | 330 | 330 | 330 | 330 | 330 |
R2 | 0.232 | 0.273 | 0.277 | 0.279 | 0.285 |
(1) | (2) | (3) | (4) | (5) | |
---|---|---|---|---|---|
Variable | LnEci | LnEci | LnEci | LnEci | LnEci |
LnDe | 0.515 *** | 0.429 *** | 0.493 *** | 0.472 *** | 0.363 *** |
(3.57) | (3.08) | (3.62) | (3.41) | (2.85) | |
LnEdu | 3.728 *** | 3.882 *** | 4.030 *** | 3.070 *** | |
(5.16) | (5.52) | (5.57) | (4.56) | ||
LnOpen | 0.521 *** | 0.508 *** | 0.681 *** | ||
(4.35) | (4.20) | (6.04) | |||
LnFdi | 0.054 | 0.019 | |||
(0.87) | (0.34) | ||||
LnFin | 1.874 *** | ||||
(7.65) | |||||
Cons | −0.618 *** | −11.472 *** | −10.878 *** | −11.552 *** | −10.736 *** |
(−2.60) | (−5.42) | (−5.28) | (−5.24) | (−5.32) | |
N | 330 | 330 | 330 | 330 | 330 |
R2 | 0.041 | 0.119 | 0.172 | 0.174 | 0.311 |
Province | Yes | Yes | Yes | Yes | Yes |
Year | Yes | Yes | Yes | Yes | Yes |
Variable | (1) LnTi | (2) LnEci | (3) LnUpgra | (4) LnEci |
---|---|---|---|---|
LnDe | 1.357 *** (10.53) | 0.494 *** (3.64) | 0.815 *** (7.63) | 0.463 *** (3.70) |
LnTi | 0.106 ** (2.04) | |||
LnUpgra | 0.216 *** (3.47) | |||
LnEdu | 0.648 (0.85) | 1.863 *** (2.70) | 1.935 *** (3.04) | 1.514** (2.19) |
LnOpen | −0.214 * (−1.71) | 0.849 *** (7.51) | −0.151 (−1.45) | 0.859 *** (7.71) |
LnFdi | −0.198 *** (−3.29) | 0.058 (1.05) | −0.041 (−0.83) | 0.045 (0.85) |
LnFin | 0.719 ** (1.99) | 0.627 * (1.91) | 0.705 ** (2.35) | 0.551 * (1.70) |
Cons | 6.819 *** (2.67) | −5.632 ** (−2.42) | −3.016 (−1.42) | −4.255 * (−1.87) |
Year | Yes | Yes | Yes | Yes |
Province | Yes | Yes | Yes | Yes |
N | 330 | 330 | 330 | 330 |
R2 | 0.591 | 0.367 | 0.498 | 0.383 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Variable | LnTi | LnEci | LnUpgra | LnEci |
LnDe | 0.390 ** | 0.289 ** | 0.327 *** | 0.268 ** |
(2.46) | (2.30) | (2.68) | (2.16) | |
LnTi | 0.191 *** | |||
(4.19) | ||||
LnUpgra | 0.290 *** | |||
(4.96) | ||||
LnEdu | 3.318 *** | 2.436 *** | 3.476 *** | 2.062 *** |
(3.96) | (3.62) | (5.38) | (3.03) | |
LnOpen | −0.556 *** | 0.787 *** | −0.348 *** | 0.782 *** |
(−3.97) | (6.99) | (−3.22) | (7.08) | |
LnFdi | −0.206 *** | 0.059 | −0.053 | 0.035 |
(−2.92) | (1.04) | (−0.98) | (0.63) | |
LnFin | 3.289 *** | 1.246 *** | 2.228 *** | 1.228 *** |
(10.81) | (4.42) | (9.49) | (4.56) | |
Cons | −7.172 *** | −9.366 *** | −11.025 *** | −7.539 *** |
(−2.86) | (−4.70) | (−5.70) | (−3.68) | |
Year | Yes | Yes | Yes | Yes |
Province | Yes | Yes | Yes | Yes |
N | 330 | 330 | 330 | 330 |
R2 | 0.449 | 0.350 | 0.413 | 0.364 |
Variable | Thresholds | Threshold Value | F Value | p Value | 10% Level | 5% Level | 1% Level |
---|---|---|---|---|---|---|---|
Digital Economy Development Level | Single Threshold | −3.107 | 55.81 | 0.013 | 30.370 | 35.389 | 56.081 |
Double Threshold | −3.107 | 55.81 | 0.013 | 30.370 | 38.347 | 59.409 | |
−4.616 | 30.90 | 0.060 | 26.585 | 33.407 | 41.730 |
Variable Name | Variable Interval | Regression Coefficient | Standard Error | T Value |
---|---|---|---|---|
Digital Economy Development Level | <−4.616 | 0.786 *** | 0.202 | 3.89 |
[−4.616,−2.663] | 0.972 *** | 0.229 | 4.24 | |
>−2.663 | 0.750 *** | 0.193 | 3.88 |
Empirical Stage | Objective | Methodology | Key Findings |
---|---|---|---|
Baseline Regression | Estimate direct effect of digital economy on high-tech export performance | Fixed-effects panel regression | Digital economy significantly boosts export competitiveness (p < 0.01); supports H1. |
Heterogeneity Analysis | Explore regional differences | Stratified regression by region | Stronger effects in central and western regions; highlights inclusivity potential of digital transformation. |
Endogeneity Test | Address potential reverse causality | 2SLS with lagged explanatory variable | Effect remains significant, confirming robustness and causality of the digital economy’s impact. |
Robustness Tests | Test stability of results | Variable substitution (dependent and explanatory) | Findings hold under alternate metrics (RCA, digital finance index), reaffirming reliability. |
Mediation Effect Analysis | Examine indirect pathways via innovation and upgrading | Stepwise regression + robustness checks | Technological innovation and industrial upgrading partially mediate the relationship; supports H2 and H3. |
Threshold Effect Analysis | Identify nonlinear effects | Hansen’s threshold panel model | Double threshold effect found; impact follows an inverted U-shape—optimal at intermediate digital maturity. |
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Hu, G.; Zhang, X.; Yang, J.; Sun, W.; Zhu, T. Uncovering the Mechanism of Elevating High-Tech Export Competitiveness in China’s Sustainable Economic Development: Force of Digital Economy. Sustainability 2025, 17, 3667. https://doi.org/10.3390/su17083667
Hu G, Zhang X, Yang J, Sun W, Zhu T. Uncovering the Mechanism of Elevating High-Tech Export Competitiveness in China’s Sustainable Economic Development: Force of Digital Economy. Sustainability. 2025; 17(8):3667. https://doi.org/10.3390/su17083667
Chicago/Turabian StyleHu, Genhua, Xuejian Zhang, Jing Yang, Wenda Sun, and Tingting Zhu. 2025. "Uncovering the Mechanism of Elevating High-Tech Export Competitiveness in China’s Sustainable Economic Development: Force of Digital Economy" Sustainability 17, no. 8: 3667. https://doi.org/10.3390/su17083667
APA StyleHu, G., Zhang, X., Yang, J., Sun, W., & Zhu, T. (2025). Uncovering the Mechanism of Elevating High-Tech Export Competitiveness in China’s Sustainable Economic Development: Force of Digital Economy. Sustainability, 17(8), 3667. https://doi.org/10.3390/su17083667