Can Digital Transformation Restrain Corporate ESG Greenwashing—A Test Based on Internal and External Joint Perspectives
Abstract
:1. Introduction
2. Theoretical Analysis and Research Hypothesis
2.1. Digital Transformation and ESG Greenwashing
2.2. Digital Transformation, Investor Attention, and ESG Greenwashing
2.3. Digital Transformation, Executives’ Environmental Cognition, and ESG Greenwashing
3. Research Design
3.1. Sample Selection and Data Source
3.2. Variable Definition
3.2.1. ESG Greenwashing
3.2.2. Digital Transformation
3.2.3. Executive Environmental Cognition
3.2.4. Investor Attention
3.2.5. Control Variables
Type of Variable | Variable Name | Variable Abbreviation | Variable Measurement | Reference | Data Source |
---|---|---|---|---|---|
Dependent Variable | ESG Greenwashing | GWS | The result calculated using the formula | Zhang [50] | Bloomberg, Huazheng [60,61] |
Independent Variable | Digital Transformation | Digital | The results obtained from Python processing of the company’s annual report | Yuan [53] | Company Annual Report |
Mechanism variable | Investor Attention | Lnbaidu | Add 1 to the network search index and take the logarithm | Ying [56], wang [57] | Baidu Index website [62] |
Moderating variable | Executive environmental cognition | EEC | Frequency of keywords related to executives’ environmental awareness in annual reports of listed companies | Xiao [49] | Company Annual Report |
Control variables | Company Size | Size | The natural logarithm of total assets | Zhai [58], lu [59] | CSMAR database [63] |
Return on Equity | ROE | Net Profit/Average Owner’s Equity Balance | |||
Current Ratio | Liquid | Current assets/current liabilities | |||
Financial Leverage | LEV | Total liabilities/total assets | |||
Fixed Asset Ratio | Fixed | Fixed assets/total assets | |||
Firm Age | FirmAge | ln(Current year − Year of establishment + 1) | |||
Ownership Concentration | Top1 | Number of shares held by the largest shareholder/total number of shares | |||
Whether Audited by the Big 4 | Big4 | The company has been audited by the four major accounting firms as 1, otherwise it is 0 | |||
Tobin’s Q ratio | TobinQ | Stock market value/total assets | |||
CEO Duality | Dual | The Chairman and General Manager are the same person, with a value of 1; otherwise, it is 0 |
3.3. Model Settings
4. Empirical Test and Result Analysis
4.1. Descriptive Statistic
4.2. Benchmark Regression and Moderation Test
4.3. Endogenous Problem Handling
4.3.1. Instrumental Variables Method
4.3.2. Heckman Two-Stage Model
4.3.3. Entropy Balancing Matching
4.3.4. Change Model Method
4.4. Robustness Test
4.4.1. Replace Variables
4.4.2. Lag Regression
4.4.3. Expand the Range of Tail Reduction
4.4.4. Changing Sample Range
4.5. Further Analysis
4.5.1. Mechanism Verification
4.5.2. Heterogeneity Testing and Analysis
5. Research Conclusion and Discussion
5.1. Conclusion and Comparison
5.2. Marginal Contribution
5.3. Managerial Implications
5.4. Research Limitations and Future Prospects
Author Contributions
Funding
Institutional Review Board Statement
Data Availability Statement
Conflicts of Interest
References
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Category | Main Characteristic Terms |
---|---|
Artificial Intelligence | Artificial Intelligence (AI), Business Intelligence, Image Understanding, Deep Learning, Investment Decision Support System, Intelligent Data Analysis, Machine Learning, Intelligent Robotics, Semantic Search, Face Recognition, Speech Recognition, Biometric Technology, Natural Language Processing, Identity Verification, AI Technology, Autonomous Driving |
Big Data | Big Data, Data Mining, Text Mining, Data Visualization, Heterogeneous Data, Credit Scoring, Augmented Reality, Mixed Reality, Virtual Reality |
Cloud Computing | Cloud Computing, Stream Computing, Graph Computing, In-Memory Computing, Multi-Party Secure Computing, Brain-Inspired Computing, Green Computing, Cognitive Computing, Converged Architecture, Billion-Scale Concurrency, EB-Level Storage, Internet of Things, Cyber-Physical System |
Application of Digital Technology | Mobile Internet, Industrial Internet, Mobile Connectivity, Internet Healthcare, Electronic Commerce, Mobile Payment, Third-Party Payment, Near Field Communication Payment (NPC Payment), Smart Energy, Business-to-Business (B2B), Business-to-Consumer (B2C), Customer-to-Business (C2B), Online-to-Offline (O2O), Network Connectivity, Smart Wearables, Smart Agriculture, Intelligent Transportation, Smart Healthcare, Smart Customer Service, Smart Home, Blockchain Energy Grid, Smart Investment Advisory, Smart Cultural Tourism, Smart Environmental Protection, Unmanned Retail, Internet Finance, Digital Finance, Fintech, Financial Technology, Quantitative Finance, Open Banking |
Blockchain | Blockchain, Digital Currency, Distributed Computing, Privacy-Enhancing Technologies, Smart Financial Contracts |
Variables | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|
N | Mean | SD | Min | Max | |
GWS | 7489 | −0.492 | 1.236 | −3.344 | 2.846 |
Digital | 7489 | 0.865 | 0.913 | 0.0374 | 4.826 |
Lnbaidu | 7489 | 13.031 | 0.731 | 11.449 | 15.137 |
EEC | 7489 | 4.010 | 4.830 | 0 | 21 |
Size | 7489 | 23.35 | 1.269 | 19.630 | 26.452 |
Lev | 7489 | 0.480 | 0.190 | 0.0349 | 0.908 |
ROE | 7489 | 0.0869 | 0.120 | −0.926 | 0.407 |
Liquid | 7489 | 1.854 | 1.843 | 0.268 | 25.080 |
FIXED | 7489 | 0.239 | 0.176 | 0.00164 | 0.719 |
Dual | 7489 | 0.194 | 0.395 | 0 | 1 |
Top1 | 7489 | 37.633 | 15.856 | 8.087 | 75.779 |
TobinQ | 7489 | 1.930 | 1.402 | 0.802 | 15.607 |
FirmAge | 7489 | 2.972 | 0.310 | 1.609 | 3.611 |
Big4 | 7489 | 0.144 | 0.351 | 0 | 1 |
Variables | (1) | (2) | (3) |
---|---|---|---|
GWS | GWS | GWS | |
Digital | −0.068 *** | −0.105 *** | −0.151 *** |
(−4.07) | (−4.76) | (−6.09) | |
EEC*DIG | −0.019 *** | ||
(−3.56) | |||
EEC | −0.022 *** | ||
(−6.59) | |||
Size | 0.058 *** | 0.067 *** | |
(3.56) | (4.13) | ||
ROE | 0.311 ** | 0.346 *** | |
(2.51) | (2.80) | ||
Lev | −0.169 | −0.156 | |
(−1.48) | (−1.37) | ||
Liquid | 0.020 * | 0.020 * | |
(1.89) | (1.84) | ||
FIXED | −0.202 * | −0.126 | |
(−1.95) | (−1.20) | ||
Dual | 0.214 *** | 0.210 *** | |
(5.92) | (5.84) | ||
FirmAge | −0.073 | −0.063 | |
(−1.35) | (−1.16) | ||
TobinQ | 0.043 *** | 0.035 *** | |
(3.71) | (3.01) | ||
Big4 | 0.584 *** | 0.565 *** | |
(12.74) | (12.34) | ||
Top1 | −0.001 | −0.000 | |
(−0.79) | (−0.49) | ||
Constant | −0.433 *** | −1.723 *** | −1.907 *** |
(−22.01) | (−4.25) | (−4.72) | |
Ind/year | YES | YES | YES |
Observations | 7489 | 7489 | 7489 |
R-squared | 0.003 | 0.061 | 0.068 |
Variables | (1) | (2) |
---|---|---|
The First Stage | The Second Stage | |
Dig | GWS | |
Digital_IV | 2.181 *** (15.72) | |
Digital | −0.234 *** (−3.03) | |
Controls | Yes | Yes |
Kleibergen-Paap rk LM | 195.86 *** | 195.86 *** |
Kleibergen-Paap Wald rk F | 247.00 {16.38} | 247.00 {16.38} |
Year | Yes | Yes |
Industry | Yes | Yes |
Observations | 5914 | 5914 |
Variables | (1) First-Stage | (2) Second-Stage |
---|---|---|
Dummy | GWS | |
Digital | −0.105 *** | |
(−4.79) | ||
IV1 | 0.794 *** | |
(11.95) | ||
IMR | 0.464 *** | |
(4.56) | ||
Controls | YES | YES |
Constant | −4.628 *** | −7.013 *** |
(−84.75) | (−5.53) | |
Ind | Yes | Yes |
year | Yes | Yes |
Observations | 32, 441 | 7489 |
R-squared | 0.338 | 0.064 |
Variables | Experimental Group | Weighted Precontrol Group | Weighted Control Group | ||||||
---|---|---|---|---|---|---|---|---|---|
Mean | Variance | Skewness | Mean | Variance | Skewness | Mean | Variance | Skewness | |
Size | 23.3331 | 1.4335 | 0.3320 | 23.3766 | 1.7848 | 0.1983 | 23.3333 | 1.4338 | 0.3319 |
ROE | 0.0981 | 0.0150 | −2.2271 | 0.0758 | 0.0134 | −1.9256 | 0.0981 | 0.0150 | −2.2270 |
Lev | 0.4662 | 0.0317 | −0.1249 | 0.4944 | 0.0405 | −0.1989 | 0.4662 | 0.0317 | −0.1251 |
Liquid | 1.9301 | 2.9895 | 5.1834 | 1.7771 | 3.7945 | 4.6230 | 1.9298 | 2.9896 | 5.1839 |
FIXED | 0.2000 | 0.0221 | 0.9917 | 0.2790 | 0.0367 | 0.4763 | 0.2001 | 0.0221 | 0.9921 |
Dual | 0.2465 | 0.1858 | 1.1766 | 0.1413 | 0.1214 | 2.0596 | 0.2464 | 0.1857 | 1.1770 |
FirmAge | 2.9932 | 0.0962 | −0.8815 | 2.9502 | 0.0953 | −1.0829 | 2.9932 | 0.0962 | −0.8815 |
TobinQ | 2.1257 | 2.4193 | 2.7723 | 1.7351 | 1.4356 | 3.3269 | 2.1254 | 2.4187 | 2.7727 |
Big4 | 0.1351 | 0.1169 | 2.1348 | 0.1533 | 0.1298 | 1.9245 | 0.1351 | 0.1169 | 2.1347 |
Top1 | 35.7465 | 256.2942 | 0.4131 | 39.5213 | 238.8450 | 0.1572 | 35.7493 | 256.3281 | 0.4128 |
VARIABLES | (1) | (2) | (3) |
---|---|---|---|
Benchmark Regression | Before Entropy Equilibrium | After Entropy Equilibrium | |
GWS | GWS | GWS | |
Digital | −0.105 *** | ||
(−4.76) | |||
Treatdig | −0.078 ** | −0.190 *** | |
(−2.42) | (−3.48) | ||
Size | 0.058 *** | 0.054 *** | 0.082 *** |
(3.56) | (3.34) | (3.07) | |
ROE | 0.311 ** | 0.318 ** | 0.378 |
(2.51) | (2.57) | (1.59) | |
Lev | −0.169 | −0.182 | −0.237 |
(−1.48) | (−1.59) | (−1.37) | |
Liquid | 0.020 * | 0.021 ** | 0.003 |
(1.89) | (1.98) | (0.18) | |
FIXED | −0.202 * | −0.119 | −0.547 *** |
(−1.95) | (−1.16) | (−3.05) | |
Dual | 0.214 *** | 0.214 *** | 0.187 *** |
(5.92) | (5.93) | (2.83) | |
FirmAge | −0.073 | −0.061 | −0.015 |
(−1.35) | (−1.13) | (−0.20) | |
TobinQ | 0.043 *** | 0.043 *** | 0.048 *** |
(3.71) | (3.75) | (3.23) | |
Big4 | 0.584 *** | 0.593 *** | 0.589 *** |
(12.74) | (12.93) | (8.67) | |
Top1 | −0.001 | −0.001 | 0.001 |
(−0.79) | (−0.55) | (0.97) | |
Ind/year | YES | YES | YES |
Constant | −1.723 *** | −1.722 *** | −2.121 *** |
(−4.25) | (−4.25) | (−3.44) | |
Observations | 7489 | 7489 | 7489 |
R-squared | 0.061 | 0.059 | 0.077 |
VARIABLES | (1) | (2) | (3) |
---|---|---|---|
GWS | GWS | GWS | |
Digital | −0.108 *** | −0.113 *** | −0.110 *** |
(−4.86) | (−4.95) | (−4.81) | |
Controls | YES | YES | YES |
Constant | −1.495 *** | −1.489 *** | −1.395 *** |
(−3.55) | (−3.04) | (−2.92) | |
Ind | YES | NO | YES |
Year | YES | NO | NO |
Province | YES | YES | NO |
Ind×year | NO | YES | NO |
Province×year | NO | NO | YES |
Observations | 7489 | 7489 | 7489 |
R-squared | 0.081 | 0.088 | 0.104 |
Variables | (1) | (2) | (3) | (4) | (5) |
---|---|---|---|---|---|
Replace Independent Variables | Lag Regression | Expand the Range of Tail Reduction | |||
GWS | GWS | GWS | GWS | GWS | |
DIG | −0.100 *** | ||||
(−4.33) | |||||
L2.Digital | −0.170 *** | ||||
(−5.71) | |||||
L.Digital | −0.147 *** | ||||
(−5.50) | |||||
DigCompre | −0.126 *** | ||||
(−7.65) | |||||
DCG_w | −0.032 ** | ||||
(−2.40) | |||||
Controls | YES | YES | YES | YES | YES |
Constant | −1.793 *** | −1.603 *** | −2.629 *** | −3.560 *** | −1.673 *** |
(−4.42) | (−3.97) | (−5.75) | (−7.00) | (−4.58) | |
Ind/year | Yes | Yes | Yes | Yes | Yes |
Observations | 7489 | 7489 | 5934 | 4930 | 7489 |
R-squared | 0.059 | 0.066 | 0.071 | 0.077 | 0.059 |
Variables | (1) | (2) | (3) |
---|---|---|---|
Excluding COVID-19 Pandemic Years | Excluding Directly Administered Municipalities | Including Years After 2015 | |
GWS | GWS | GWS | |
Digital | −0.101 *** | −0.069 *** | −0.124 *** |
(−3.54) | (−2.76) | (−5.22) | |
Controls | YES | YES | YES |
Constant | −0.996 ** | −1.257 *** | −2.961 *** |
(−2.10) | (−2.83) | (−6.23) | |
Ind | Yes | Yes | Yes |
year | Yes | Yes | Yes |
Observations | 5592 | 5716 | 5742 |
R-squared | 0.053 | 0.048 | 0.071 |
VARIABLES | (1) | (2) | (3) |
---|---|---|---|
GWS | lnbaidu | GWS | |
lnbaidu | −0.104 *** | ||
(−4.18) | |||
Digital | −0.105 *** | 0.073 *** | −0.097 *** |
(−4.76) | (7.41) | (−4.42) | |
Size | 0.058 *** | 0.350 *** | 0.094 *** |
(3.56) | (47.96) | (5.14) | |
ROE | 0.311 ** | −0.268 *** | 0.283 ** |
(2.51) | (−4.52) | (2.28) | |
Lev | −0.169 | −0.301 *** | −0.200 * |
(−1.48) | (−5.80) | (−1.75) | |
Liquid | 0.020 * | −0.012 *** | 0.019 * |
(1.89) | (−2.95) | (1.77) | |
FIXED | −0.202 * | −0.092 * | −0.211 ** |
(−1.95) | (−1.83) | (−2.04) | |
Dual | 0.214 *** | −0.078 *** | 0.206 *** |
(5.92) | (−4.56) | (5.70) | |
FirmAge | −0.073 | −0.013 | −0.074 |
(−1.35) | (−0.53) | (−1.38) | |
TobinQ | 0.043 *** | 0.113 *** | 0.054 *** |
(3.71) | (16.65) | (4.63) | |
Big4 | 0.584 *** | 0.112 *** | 0.596 *** |
(12.74) | (5.12) | (12.98) | |
Top1 | −0.001 | −0.008 *** | −0.002 |
(−0.79) | (−17.50) | (−1.60) | |
Constant | −1.723 *** | 5.592 *** | −1.142 *** |
(−4.25) | (30.67) | (−2.66) | |
Ind/year | YES | YES | YES |
Observations | 7489 | 7489 | 7489 |
R-squared | 0.061 | 0.446 | 0.063 |
VARIABLES | (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) |
---|---|---|---|---|---|---|---|---|---|
SOE | Non-SOE | Heavily Polluting | Non-Heavily Polluting | High Tech | Non-High-Tech | Eastern | Central | Western | |
GWS | GWS | GWS | GWS | GWS | GWS | GWS | GWS | GWS | |
Digital | −0.187 *** | −0.045 | 0.071 | −0.113 *** | −0.097 *** | −0.045 | −0.113 *** | −0.103 | −0.044 |
(−5.79) | (−1.53) | (1.09) | (−4.55) | (−3.64) | (−1.13) | (−4.54) | (−1.12) | (−0.74) | |
Controls | YES | YES | YES | YES | YES | YES | YES | YES | YES |
Constant | −3.633 *** | −0.169 | −2.326 *** | −1.201 ** | −1.654 *** | −1.996 *** | −2.509 *** | 0.350 | 0.519 |
(−6.17) | (−0.28) | (−3.76) | (−2.28) | (−2.97) | (−3.49) | (−4.84) | (0.38) | (0.54) | |
year | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Ind | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Observations | 4055 | 3434 | 2789 | 4700 | 3939 | 3550 | 5101 | 1083 | 1305 |
R-squared | 0.081 | 0.071 | 0.046 | 0.081 | 0.068 | 0.076 | 0.086 | 0.075 | 0.090 |
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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/).
Share and Cite
Xu, S.; Zhang, S.; Ren, Y.; Jiang, Q.; Wu, D. Can Digital Transformation Restrain Corporate ESG Greenwashing—A Test Based on Internal and External Joint Perspectives. Systems 2024, 12, 334. https://doi.org/10.3390/systems12090334
Xu S, Zhang S, Ren Y, Jiang Q, Wu D. Can Digital Transformation Restrain Corporate ESG Greenwashing—A Test Based on Internal and External Joint Perspectives. Systems. 2024; 12(9):334. https://doi.org/10.3390/systems12090334
Chicago/Turabian StyleXu, Shiwei, Siyuan Zhang, Yilei Ren, Qijun Jiang, and Dan Wu. 2024. "Can Digital Transformation Restrain Corporate ESG Greenwashing—A Test Based on Internal and External Joint Perspectives" Systems 12, no. 9: 334. https://doi.org/10.3390/systems12090334