Panel Dataset to Assess Proactive Eco-Innovation in the Paradigm of Firm Financial Progression
Abstract
:1. Summary
2. Data Description
2.1. Descriptive Statistics
2.2. Unit Root Analysis
2.3. Data Diagnostics Tests
2.3.1. Outlier Identification
2.3.2. Test for Normality
2.3.3. Multicollinearity Test
2.3.4. Test for Heteroscedasticity
2.3.5. Test for Serial Correlation
2.3.6. Test for Endogeneity
3. Methods
4. User Notes
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Model | Explanation |
---|---|
Model 1 (Product eco-innovation) | TQ = β0 + β1prod_eii + ε |
Model 2 (Process eco-innovation) | TQ = β0 + β1proc_eii + ε |
Model 3 (Technology eco-innovation) | TQ = β0 + β1tech_eii + ε |
Model 4 (Organizational eco-innovation) | TQ = β0 + β1org_eii + ε |
Model 5 (Marketing eco-innovation) | TQ = β0 + β1mark_eii + ε |
Note. TQ = Tobin’s Q MVE = Firm’s Share Price × Common Shares outstanding PS = Liquidating Value of Preferred Stock DEBT = Value of Short-Term Liabilities β = (Beta) or Drift Element β1, 2…k = Slope Coefficients (Coefficient of independent variables) ε = Epsilon (Error Term) prod_ei = Product eco-innovation; proc_ei = Process eco-innovation; tech_ei = Technology eco-innovation; org_ei = Organizational eco-innovation; mark_ei = Marketing eco-innovation. |
Variable | Mean | Std. Dev. | Min | Max |
---|---|---|---|---|
Product eco-innovation | 1.4789 | 0.7736 | 0.43 | 3.00 |
Process eco-innovation | 1.5192 | 0.7014 | 0.64 | 3.00 |
Technology eco-innovation | 1.3905 | 0.7227 | 0.50 | 2.88 |
Organization eco-innovation | 1.3860 | 0.7584 | 0.33 | 3.00 |
Marketing eco-innovation | 1.2945 | 0.7995 | 0.00 | 3.00 |
Tobin’s Q | 1.1423 | 0.6052 | 0.30 | 3.47 |
Ho: Panels Are Stationery and Ha: Panels Comprise Unit Roots | ||
---|---|---|
Statistic | ||
product eco-innovation | z | 7.2292 |
p-value | 0.000 | |
process eco-innovation | z | 7.9354 |
p-value | 0.000 | |
technology eco-innovation | z | 7.1884 |
p-value | 0.000 | |
Organizational eco-innovation | z | 7.6754 |
p-value | 0.000 | |
Marketing eco-innovation | z | 6.9343 |
p-value | 0.000 | |
Tq (Tobin’s Q) | z | 7.6510 |
p-value | 0.000 |
Minimum | Maximum | Mean | Std. Deviation | |
---|---|---|---|---|
Cook’s distance for TQ (Tobin’s Q) | 0.000 | 0.084 | 0.007 | 0.013 |
Statistics of Skewness | Statistics of Kurtosis | |
---|---|---|
Product eco-innovation | 0.577 | −1.111 |
Process eco-innovation | 0.704 | −0.889 |
Technological eco-innovation | 0.750 | −0.864 |
Organizational eco-innovation | 0.593 | −1.090 |
Marketing eco-innovation | 0.410 | −0.774 |
Tobin’s Q | 1.648 | 2.090 |
Statistics (VIF) | Statistics (1/VIF) | |
---|---|---|
Model 1 (product eco-innovation) | 1.051 | 0.9515 |
Model 2 (process eco-innovation) | 1.035 | 0.9662 |
Model 3 (technological eco-innovation) | 1.069 | 0.9355 |
Model 4 (organizational eco-innovation) | 1.070 | 0.9346 |
Model 5 (marketing eco-innovation) | 1.046 | 0.9560 |
tq[Company,t] = Xb + u[Company] + e[Company,t] | ||
---|---|---|
Estimated Results: | ||
Var | sd = sqrt(Var) | |
tq | 0.366305 | 0.6052313 |
e | 0.0825608 | 0.287334 |
u | 0.2396043 | 0.4894939 |
tq[Company,t] = Xb + u[Company] + e[Company,t] | ||
---|---|---|
Estimated Results: | ||
Var | sd = sqrt(Var) | |
tq | 0.366305 | 0.6052313 |
e | 0.0820641 | 0.2864682 |
u | 0.2229383 | 0.4721634 |
tq[Company,t] = Xb + u[Company] + e[Company,t] | ||
---|---|---|
Estimated Results: | ||
Var | sd = sqrt(Var) | |
tq | 0.366305 | 0.6052313 |
e | 0.0830915 | 0.288256 |
u | 0.2348743 | 0.4846383 |
tq[Company,t] = Xb + u[Company] + e[Company,t] | ||
---|---|---|
Estimated Results: | ||
Var | sd = sqrt(Var) | |
tq | 0.366305 | 0.6052313 |
e | 0.080285 | 0.2833461 |
u | 0.2381306 | 0.4879863 |
tq[Company,t] = Xb + u[Company] + e[Company,t] | ||
---|---|---|
Estimated Results: | ||
Var | sd = sqrt(Var) | |
tq | 0.366305 | 0.6052313 |
e | 0.0830817 | 0.288239 |
u | 0.2342102 | 0.4839527 |
Statistics | |
---|---|
Model 1 (product eco-innovation) | 0.738 |
Model 2 (process eco-innovation) | 0.767 |
Model 3 (technological eco-innovation) | 0.736 |
Model 4 (organizational eco-innovation) | 0.703 |
Model 5 (marketing eco-innovation) | 0.773 |
Test Result | ||
---|---|---|
Statistics | p-Value | |
Model 1 (product eco-innovation) | 1.18 | 0.120 |
Model 2 (process eco-innovation) | 1.28 | 0.163 |
Model 3 (technological eco-innovation) | 2.52 | 0.340 |
Model 4 (organizational eco-innovation) | 0.33 | 0.062 |
Model 5 (marketing eco-innovation) | 2.59 | 0.370 |
Variable | Dimensions of Eco-Innovation | Instrument/Index | Content Analysis |
---|---|---|---|
Proactive Eco-innovation | Product | Seven indicators recommended in García-Granero et al. (2018). [30] | 0 = information not available, 1 = only brief information, 2 = Detailed information 3 = Detailed description with future strategies on innovation. |
Process | Eleven indicators recommended in García-Granero et al. (2018) [30] | ||
Technological | Eight indicators proposed in Arundel and Kemp (2009) [31] | ||
Organizational | Nine indicators proposed in García-Granero et al. (2018) [30] | ||
Marketing | Three indicators proposed in García-Granero et al. (2018). [30] | ||
Firm Financial Progression | Tobin’s Q |
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Toha, M.A.; Johl, S.K. Panel Dataset to Assess Proactive Eco-Innovation in the Paradigm of Firm Financial Progression. Data 2021, 6, 131. https://doi.org/10.3390/data6120131
Toha MA, Johl SK. Panel Dataset to Assess Proactive Eco-Innovation in the Paradigm of Firm Financial Progression. Data. 2021; 6(12):131. https://doi.org/10.3390/data6120131
Chicago/Turabian StyleToha, Md Abu, and Satirenjit Kaur Johl. 2021. "Panel Dataset to Assess Proactive Eco-Innovation in the Paradigm of Firm Financial Progression" Data 6, no. 12: 131. https://doi.org/10.3390/data6120131