The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector
Abstract
:1. Introduction
2. Theoretical Background and Hypotheses
2.1. Impact of Degree of Big Data Technology Transformation on Green Operations in Banking Sector
2.2. The Moderating Role of Green Credit
2.3. The Moderating Role of the Fund
2.4. The Moderating Role of Bonds
3. Methodology
3.1. Sample Selection and Data Sources
3.2. Definition of Variables
3.2.1. Dependent Variable
3.2.2. Independent Variable
3.2.3. Moderating Variable
Green Credit Ratio
3.2.4. Control Variable
3.3. Research Model
4. Empirical Analysis Results
4.1. Descriptive Statistics
4.2. Correlation Analysis
4.3. Analysis of Empirical Results
5. Conclusions and Implications
5.1. Discussion
5.2. Conclusions
5.3. Limitations and Future Outlook
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Name | Non-Cash Payment Services (Billion Transactions) | Non-Cash Payment Services (Trillion Yuan) | Electronic Payment Services (Billion Transactions) | Electronic Payment Services (Trillion Yuan) |
---|---|---|---|---|
2015 | 943.22 | 3448.85 | 1052.34 | 2506.23 |
2016 | 1251.11 | 3687.24 | 1395.61 | 2494.45 |
2017 | 1608.78 | 3759.94 | 1525.8 | 2419.2 |
2018 | 2203.12 | 3768.67 | 1751.92 | 2539.7 |
2019 | 3310.19 | 3779.49 | 2233.88 | 2607.04 |
2020 | 2547.21 | 4013.01 | 2352.25 | 2711.81 |
2021 | 4395.06 | 4415.56 | 2749.69 | 2976.22 |
2022 | 4626.49 | 4805.77 | 2789.65 | 3110.13 |
Name | Variable Name | Abbreviation | Definition of Variable |
---|---|---|---|
Independent Variables | Degree of Big Data Technology Transformation | FTLF | Ln (keyword word frequency + 1) |
Dependent Variables | Green Operations | GES | Bloomberg Environmental Rating |
Moderating Variables | Green Credit Ratio | GCR | Ln (green credit balance/total loans + 1) |
Fund Market Share | MSF | Ln (fund issuance/total commercial bank fund issuance + 1) | |
Bond Market Share | BIR | Ln (bond issuance/total commercial bank bond issuance + 1) | |
control variable | Company Size | Size | Ln (book value of total assets at year-end) |
Gearing Ratio | Lev | Total liabilities at year-end/total assets at year-end | |
Company Age | AGE | Ln (year of observation − year of establishment + 1) | |
Company Performance | ROA | Net profit/total assets | |
Revenue Growth Rate | GRO | Revenue growth rate | |
Nature of the Largest Shareholder | SOE | 1 for state-owned enterprises, 0 for others | |
Shareholding Ratio of the Largest Shareholder | TOP1 | Shareholding ratio of the largest shareholder |
Variables | N | Mean | sd | Min | Max |
---|---|---|---|---|---|
GES | 235 | 3.082 | 0.884 | 0.0837 | 4.303 |
FTLF | 235 | 1.957 | 0.771 | 0 | 3.526 |
GCR | 235 | 0.0363 | 0.0372 | 0 | 0.171 |
MSF | 235 | 1.217 | 1.517 | 0 | 7.429 |
BIR | 235 | 0.00407 | 0.00587 | 0 | 0.0438 |
Size | 235 | 28.42 | 1.570 | 25.56 | 31.31 |
Lev | 235 | 0.924 | 0.00972 | 0.897 | 0.947 |
AGE | 235 | 3.253 | 0.452 | 2.079 | 4.710 |
ROA | 235 | 0.875 | 0.176 | 0.424 | 1.437 |
GRO | 235 | 0.0858 | 0.0864 | −0.156 | 0.428 |
TOP1 | 235 | 24.46 | 17.05 | 4.180 | 67.39 |
SOE | 235 | 0.630 | 0.484 | 0 | 1 |
GES | FTLF | GCR | MSF | BIR | Size | Lev | AGE | ROA | GRO | TOP1 | SOE | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
GES | 1 | |||||||||||
FTLF | 0.211 *** | 1 | ||||||||||
GCR | 0.152 ** | 0.0300 | 1 | |||||||||
MSF | 0.166 ** | 0.110 * | 0.126 * | 1 | ||||||||
BIR | 0.221 *** | 0.162 ** | 0.486 *** | 0.180 *** | 1 | |||||||
Size | 0.474 *** | 0.179 *** | 0.00700 | 0.0520 | 0.0410 | 1 | ||||||
Lev | −0.215 *** | 0.157 ** | −0.0830 | −0.190 *** | −0.0420 | −0.0640 | 1 | |||||
AGE | 0.309 *** | 0.116 * | 0.0100 | 0.140 ** | 0.0350 | 0.682 *** | −0.281 *** | 1 | ||||
ROA | −0.0910 | −0.117 * | −0.0490 | 0.0810 | 0.00200 | 0.202 *** | −0.111 * | 0.306 *** | 1 | |||
GRO | −0.155 ** | 0.0870 | −0.0190 | −0.0400 | −0.00700 | −0.218 *** | 0.245 *** | −0.184 *** | 0.202 *** | 1 | ||
TOP1 | 0.308 *** | 0.266 *** | 0.0920 | 0.0590 | 0.148 ** | 0.659 *** | −0.0240 | 0.648 *** | 0.0100 | −0.151 ** | 1 | |
SOE | 0.131 ** | 0.0250 | −0.0130 | 0.0390 | 0.0220 | 0.250 *** | −0.00600 | 0.137 ** | 0.141 ** | −0.0860 | 0.193 *** | 1 |
(1) | (2) | |
---|---|---|
Variables | GES | GES |
FTLF | 0.194 *** | 0.134 * |
(2.76) | (1.88) | |
Lev | −7.295 | |
(−1.02) | ||
AGE | −0.081 | |
(−0.47) | ||
Size | 0.374 *** | |
(6.41) | ||
ROA | −0.465 | |
(−1.34) | ||
GROWTH | 0.550 | |
(0.78) | ||
SOE | 0.014 | |
(0.14) | ||
TOP1 | −0.002 | |
(−0.51) | ||
Constant | 1.921 *** | −0.659 |
(8.36) | (−0.10) | |
City fixed | Yes | Yes |
Year fixed | Yes | Yes |
Observations | 235 | 235 |
R-squared | 0.311 | 0.440 |
F test | 0 | 0 |
r2_a | 0.277 | 0.393 |
F | 9.158 | 9.431 |
(1) | (2) | |
---|---|---|
GES | GES | |
FTLF | 0.134 * | |
(1.88) | ||
DTA | 0.529 * | |
(1.90) | ||
Lev | −7.295 | −7.376 |
(−1.02) | (−1.03) | |
AGE | −0.081 | −0.081 |
(−0.47) | (−0.47) | |
Size | 0.374 *** | 0.374 *** |
(6.41) | (6.41) | |
ROA | −0.465 | −0.465 |
(−1.34) | (−1.34) | |
GROWTH | 0.550 | 0.550 |
(0.78) | (0.78) | |
SOE | 0.014 | 0.015 |
(0.14) | (0.14) | |
TOP1 | −0.002 | −0.002 |
(−0.51) | (−0.52) | |
Constant | −0.659 | −0.585 |
(−0.10) | (−0.09) | |
City fixed | Yes | Yes |
Year fixed | Yes | Yes |
Observations | 235 | 235 |
R-squared | 0.440 | 0.440 |
F test | 0 | 0 |
r2_a | 0.393 | 0.394 |
F | 9.431 | 9.441 |
(1) | (2) | (3) | (4) | |
---|---|---|---|---|
Variables | GES | GES | GES | GES |
FTLF | 0.169 ** | 0.179 *** | 0.150 ** | 0.135 ** |
(2.52) | (2.69) | (2.22) | (2.04) | |
GCR | 1.975 * | |||
(1.93) | ||||
GCR x FTLF | 3.764 ** | |||
(2.08) | ||||
MSF | 0.001 | |||
(0.39) | ||||
MSF x FTLF | 0.063 * | |||
(1.92) | ||||
BIR | 0.003 | |||
(0.66) | ||||
BIR x FTLF | 0.353 *** | |||
(3.42) | ||||
Size | 0.308 *** | 0.289 *** | 0.314 *** | 0.317 *** |
(6.48) | (6.00) | (6.60) | (6.80) | |
Lev | −22.933 *** | −19.883 *** | −21.375 *** | −21.617 *** |
(−4.10) | (−3.54) | (−3.77) | (−3.93) | |
AGE | −0.067 | −0.050 | −0.087 | −0.011 |
(−0.38) | (−0.28) | (−0.49) | (−0.06) | |
ROA | −1.105 *** | −1.072 *** | −1.159 *** | −1.202 *** |
(−3.37) | (−3.32) | (−3.50) | (−3.70) | |
GRO | 0.437 | 0.389 | 0.437 | 0.433 |
(0.68) | (0.61) | (0.68) | (0.69) | |
TOP1 | −0.004 | −0.005 | −0.004 | −0.007 |
(−0.89) | (−1.09) | (−0.91) | (−1.51) | |
SOE | 0.078 | 0.057 | 0.071 | 0.077 |
(0.75) | (0.55) | (0.68) | (0.75) | |
Constant | 16.383 *** | 14.040 *** | 14.886 *** | 14.772 *** |
(3.11) | (2.68) | (2.79) | (2.84) | |
Observations | 235 | 235 | 235 | 235 |
R-squared | 0.329 | 0.355 | 0.340 | 0.363 |
r2_a | 0.31 | 0.33 | 0.31 | 0.33 |
F | 13.841 | 12.304 | 11.550 | 12.765 |
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© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
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Yun, J.; Jin, S. The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector. Systems 2024, 12, 135. https://doi.org/10.3390/systems12040135
Yun J, Jin S. The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector. Systems. 2024; 12(4):135. https://doi.org/10.3390/systems12040135
Chicago/Turabian StyleYun, Jiawen, and Shanyue Jin. 2024. "The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector" Systems 12, no. 4: 135. https://doi.org/10.3390/systems12040135
APA StyleYun, J., & Jin, S. (2024). The Degree of Big Data Technology Transformation and Green Operations in the Banking Sector. Systems, 12(4), 135. https://doi.org/10.3390/systems12040135