Mgt723 Research Project - Free Assessment Answers

Described how you arrived at the firms that remain in your sample

 

Described the characteristics of your sample: countries and/or industries represented by the sample. 

 

Considered the implications for the generalisability of your results.

 

Your data: 

 

Deleted the columns for the variables that you are not interested in

 

Collected data on your additional variable, i.e., added another column of data

 

Coded qualitative (string variables) that you want to analyse in SPSS

 

Considered how you will deal with missing data or #N/A cells (if you have any)

 

Checked the data for any anomalies, potentially corrupt data

The main aim of this research paper is to examine the impact of the organizational behavior related to climate change on the emission of carbon. To examine the impact the data was collected from 60 firms from three developing countries, namely India, South Africa, and Brazil. The focus for the research is on the developing countries as the emission of carbon is increasing in these countries with the economic growth taking the pace. So, the data from the developing countries has been extracted from the master data set. The selection of companies from each countries was based on the random selectiono(Doytch & Uctum, 2011; Millimet & Roy, 2011).

Extracted data was cleaned in excel and the missing values were addressed. Once the data was ready, it was exported to SPSS for the descriptive and inferential analysis.

The descriptive statistics results show that the average emission of Co2 for the selected firms is 130927619401. However the standard deviation is also very high which means that the emission of Co2 is different for different firms and there is high variation among them. The skewness is positive indicating that the data is positively skewed. Other results from the descriptive statistics are also shown in the table below.

Similarly the mean value of the change in emission for the firms shows that the average change is 9.5 % with standard deviation of 12.78. This indicates that variation is very high.

Statistics
		emission of Co2	emission change
N	Valid	53	60
	Missing	7	0
Mean		130927619401.9400	13.8333
Median		18596000000.0000	9.5000
Mode		1000.00a	.00
Std. Deviation		556257639927.78660	12.78833
Variance		309422561978031150000000.000	163.541
Skewness		6.729	.695
Std. Error of Skewness		.327	.309
Kurtosis		47.166	-.765
Std. Error of Kurtosis		.644	.608
Minimum		1000.00	.00
Maximum		3996010000000.00	44.00
Sum		6939163828302.82	830.00
Percentiles	25	1466798000.0000	2.4225
	50	18596000000.0000	9.5000
	75	53151950000.0000	24.4350

The results from the histograms are shown in below which has been shown in the figures below and the results shows that the emission of Co2 do not follow the normal distribution. Most of the firms have low emission whereas some of the firms have very high level of emission. .

The descriptive results for the categorical variables have been shown through the pie charts which are easy and effective way to present the categorical variables

Results shows that for 90 % of the firms the board has the highest level of direct responsibility for carbon emission, followed by the senior managers which comprises for 7 % of the firms.

Similarly 80 % of the firms provide incentives to their managers to reduce the emission of carbon.

When it comes to the risk management process 88 % of the firms in the data set integrate it with the other programs in the organization. Only 9 % of the firms have specific process for the emission.

95 % of the firms have integrated climate change into their business strategies. For developing countries it is very impressive results.

However the firms in the developing countries do not have internal prices for carbon. Only 27 % of the firms have the internal price for carbon.

66 % firms are directly engaged in influencing the public on climate change issues, whereas 5 % of firms do not have any program to influence the public.

Half yearly is the most used frequency for monitoring the carbon emission, whereas annually monitoring is done by 33 % of the firms in the data set.

Furthermore 59 % of the firms said that their products are low on carbon whereas 37 % of them said that their products are not low in terms of carbon emission. Rest of the firms were not sure about their products.

Chi-Square Tests
	Value	df	Asymp. Sig. (2-sided)
Pearson Chi-Square	106.000a	104	.427
Likelihood Ratio	38.125	104	1.000
Linear-by-Linear Association	.201	1	.654
N of Valid Cases	53
a. 159 cells (100.0%) have expected count less than 5. The minimum expected count is .02.

Results from the chi square test shows that there is no difference in the percentage change in the emission for firms which have different authority to take the direct responsibility for carbon emission. In other words the carbon emission is same for the firms which have board as the highest level of direct responsibility or some other managers as the highest level of direct responsibility. This is because the chi square value is insignificant.

Correlation analysis

Correlations
		emission of Co2	highest leve of direct responsibilty	incentive for managers	Risk managment	climate change integrate into busines strategy	internal price on carbon	influence public policy on climate change
emission of Co2	Pearson Correlation	1	.062	-.101	.036	-.031	.140	-.003
	Sig. (2-tailed)		.658	.472	.800	.826	.317	.986
	N	53	53	53	53	53	53	53
highest leve of direct responsibilty	Pearson Correlation	.062	1	-.234	.416**	-.287*	.011	-.008
	Sig. (2-tailed)	.658		.072	.001	.026	.937	.955
	N	53	60	60	59	60	59	59
incentive for managers	Pearson Correlation	-.101	-.234	1	.199	.268*	.070	-.377**
	Sig. (2-tailed)	.472	.072		.130	.039	.597	.003
	N	53	60	60	59	60	59	59
Risk managment	Pearson Correlation	.036	.416**	.199	1	.035	-.033	-.214
	Sig. (2-tailed)	.800	.001	.130		.795	.804	.103
	N	53	59	59	59	59	59	59
climate change integrate into busines strategy	Pearson Correlation	-.031	-.287*	.268*	.035	1	.145	-.394**
	Sig. (2-tailed)	.826	.026	.039	.795		.273	.002
	N	53	60	60	59	60	59	59
internal price on carbon	Pearson Correlation	.140	.011	.070	-.033	.145	1	.111
	Sig. (2-tailed)	.317	.937	.597	.804	.273		.401
	N	53	59	59	59	59	59	59
influence public policy on climate change	Pearson Correlation	-.003	-.008	-.377**	-.214	-.394**	.111	1
	Sig. (2-tailed)	.986	.955	.003	.103	.002	.401
	N	53	59	59	59	59	59	59
**. Correlation is significant at the 0.01 level (2-tailed).
*. Correlation is significant at the 0.05 level (2-tailed).

Results from the correlation analysis suggest that the dependent variable is negatively related with the incentive for managers for carbon emission, influence public policy on climate change and climate change integrated into the business strategy. This was as expected, as the efforts by the firm to reduce the emission will lead to decrease in the carbon emission. With all other variables the correlation coefficient is greater than 0 or positive(Winn, Kirchgeorg, Griffiths, Linnenluecke, & Günther, 2011).

Regression analysis

The regression analysis is used in the current research so that effect of the explanatory variable can be examined on the response variable.

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	.200a	.040	-.085	579463298736.25620
a. Predictors: (Constant), influence public policy on climate change, internal price on carbon, Risk managment , incentive for managers , climate change integrate into busines strategy, highest leve of direct responsibilty

The R –squared show that the variation explained by the independent variables for the dependent variable is less than one percent. Rest of the variation is because of other factors not included in the data set(A. Saunders & Cornett, 2011; M. Saunders, Lewis, & Thornhill, 2009).

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	644198352071654000000000.000	6	107366392011942330000000.000	.320	.923b
	Residual	15445774870785970000000000.000	46	335777714582303700000000.000
	Total	16089973222857625000000000.000	52
a. Dependent Variable: emission of Co2
b. Predictors: (Constant), influence public policy on climate change, internal price on carbon, Risk managment , incentive for managers , climate change integrate into busines strategy, highest leve of direct responsibilty

The F statistic which measures the cumulative impact of the independent variables is also not significant as the p value is more than 0.05.

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	316765306677.125	1332900525594.956		.238	.813
	highest leve of direct responsibilty	22523120490.727	271515119723.888	.015	.083	.934
	incentive for managers	-190227710373.326	242814007299.436	-.130	-.783	.437
	Risk managment	57184981587.920	298476367093.034	.034	.192	.849
	climate change integrate into busines strategy	-156166188049.717	647055991109.176	-.039	-.241	.810
	internal price on carbon	111730261355.559	99528458555.748	.167	1.123	.267
	influence public policy on climate change	-66628498947.867	178556399225.969	-.063	-.373	.711
a. Dependent Variable: emission of Co2

In terms of regression coefficient all of the coefficient are positive indicating that all the independent variable have positive impact on the dependent variable. However the coefficients are not statistically significant(Guo, 2014; Orsato, 2017).

References

Doytch, N., & Uctum, M. (2011). Globalization and the Environmental Spillovers of sectoral FDI. New York.

Guo, Y. (2014). Climate Change Disclosure?: Determinants and impact. University of Hawai.

Millimet, D. L., & Roy, J. (2011). Three New Empirical Tests of the Pollution Haven Hypothesis When Environmental Regulation is Endogenous. Bonn.

Orsato, R. J. (2017). Organizational adaptation to climate change: learning to anticipate energy disruptions. International Journal of Climate Change Strategies and Management, 9(5), 645–665.

Saunders, A., & Cornett, M. M. (2011). Financial Institutions Management: A Risk Management Approach. The McGrawHillIrwin series in finance insurance and real estate Financial institutions and markets (Vol. 7th).

Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5th editio). Harlow: Pearson Education Limited.

Winn, M., Kirchgeorg, M., Griffiths, A., Linnenluecke, M. K., & Günther, E. (2011). Impacts from climate change on organizations: a conceptual foundation. Business Strategy and the Environment, 20(3), 157–173.