Stakeholder Theory in Canada and Germany Companies

This study compares carbon emissions for Canada and Germany companies and investigates the difference in average intensity emissions for companies that offer incentives towards management of emissions and those that do not. The study also explores the risk management procedures with regard to climate change risks and opportunities for the various companies. Data was collected from 166 firms in Canada and Germany and analyzed using descriptive and inferential statistics.

MGT723 Research Project
Semester 1 2018
Assessment Task 2: Data Collection
Title: - Stakeholder Theory in Canada and Germany
Companies

Introduction:
Carbon emissions is a serious issue globally and many countries are struggling to see how they
can contain this menace that has threatened the well-being of the climate. It has resulted to
unaccounted amount of pollution in some countries that it becomes very difficult to stay
comfortably. Activities by humans such as deforestation, consuming of non-renewable energy
sources, farming practices (e.g., the utilization of compost, and raising domesticated animals),
modern procedures, refrigeration, and the utilization of a few customer items — result in the
outflow of ozone harming substances (Chunbo & Stern, 2008).
One technique to empower and perhaps direct organizations to decrease the ozone depleting
substance outflows coming about because of their exercises, is through the deliberate or
compulsory detailing of organizations' ozone depleting substance discharges (Smith, 2009).
The aim of this study was to identify and compare the carbon emission for two countries: Canada
and Germany. The study also sought to find out whether there is significant difference in the
average intensity emissions for companies that offer incentives towards management of
emissions and those that do not.

Literature Review – Summary
Environmental change is at present a theme of incredible enthusiasm to organizations, financial
specialists, policy makers, and scholastics. In view of the part of ozone harming substances like
carbon outflows in causing worldwide warming and the huge outcomes that environmental
change may have on the planet, a plenty of activities trying to decrease the carbon outflows of
both open and private associations have developed around the world (Bonner, Hastie, &
Sprinkle, 2000). In like manner, numerous partnerships are giving motivating forces to their
workers outfitted towards diminishing carbon emanations coming about because of the
company's activities (Bansal & Roth, 2000).
The scholarly writing incorporates numerous examinations that have investigated the adequacy
of money related versus nonmonetary motivating forces, in fields as differing as financial
matters, psychology research and many more. Inside financial aspects, organization hypothesis
contemplates the impact of money related motivating forces on individual performance,
contending that fiscal motivations are utilized so as to adjust the key goals with those of the
agent's. In this way, the ideal contract is basically an exchange off between chance sharing
between the representative and the firm and motivating force arrangements, gave that the quality
of the money related motivating forces ought to diminish in extent to the commotion of the
execution measures upon which the agreement is based (Aguilera, Rupp, Williams, & Ganapathi,
2007).
.

Conceptual Model:
The conceptual model for this study is shown below;
Dependent Variable Independent Variables
Carbon emission
Intensity
 Country
 Offer of incentives
Hypotheses:
The following hypothesis were tested for this study;
 There is significant association between country and the highest level of direct
responsibility for climate change within the organization.
 There is no significant correlation between intensity and percentage change in intensity
for the various countries.
 There is no significant difference in intensity for the two countries (Canada and
Germany)
Data collection
Data was collected from Canada and Germany firms. The sample size for the selected firms in
the two countries is 166 firms and the included firms were randomly selected into the sample.
Data was processed and cleaned in Statistical Package for Social Sciences (SPSS). The missing
data were coded -999 and 999. Only useful variables were included in the dataset, the non-useful
variables were deleted from the dataset. A separate data file (excel file) has been attached
together with this report.

Data analysis (descriptive)
Before embarking on inferential statistics, we ran descriptive statistics to understand the
distribution of the dataset. We considered the numerical (scale) variables which in this case are
the Carbon emissions (intensity) and the percentage change in the carbon emissions for the year
2014.
Statistics
Carbon
emissions
(Intensity)
Percentage
change in
carbon
N Valid 166 166
Missing 0 0
Mean 218.0407 4.0558
Median .0001 .0000
Mode .00 .00
Std. Deviation 2574.81428 39.75537
Variance 6629668.573 1580.489
Skewness 12.833 6.094
Std. Error of Skewness .188 .188
Kurtosis 165.093 54.602
Std. Error of Kurtosis .375 .375
Range 33148.00 486.24
Minimum .00 -99.24
Maximum 33148.00 387.00
Percentiles
25 .0000 -4.8500
50 .0001 .0000
75 .0011 4.9250
The descriptive results in the above table shows that the average intensity is 218.04 while the
average percentage change in the carbon emission intensity is 4.06. Results also indicate that the
maximum intensity is 33148 with a minimum of 0. For the percentage change in intensity from
the previous year, we observed that the maximum percentage change in the intensity was 387%
with minimum percentage change being -4.85%. The skewness value indicates that the dataset

both for the intensity and percentage change in intensity from the previous year is positively
skewed (positive huge values). The standard deviation further shows that the dataset are widely
spread out from the mean with the standard deviation for the intensity being approximately 2575
and that of the percentage change from the previous year being 39.76. In terms of the mode, it is
clear that the most frequent values for both intensity and percentage change in intensity is 0.
Histograms
Histogram is one of the plots presented to further check on the distribution of the data.
Histogram helps to tell whether a given dataset follows a normal distribution or not. As can be
seen, two histograms are presented; one for the intensity and the other one for the percentage
change in intensity from the previous year. As can be seen, the plot below has a longer tail to the
right, this implies that the dataset (intensity) is skewed to the right (having longer tail to the
right). As such we can conclude that the dataset is not normally distributed or rather does not
come from a normally distributed population.

Another plot presented is that of the percentage change in intensity from the previous year which
just like the case of intensity plot above shows that the dataset is not normally distributed but
rather skewed to the right (having a longer tail to the right).
Frequencies
Among the selected companies, majority of them had the highest level of direct responsibility for
climate change within their organizations coming from the Board (79.4% (n = 131), this was
closely followed by the Senior Manager (14.5%, n = 24), other managers were 1.8% (n = 3)
while those who said no individual were 4.2% (n = 7).
Responsibility level
Frequency Percent Valid Percent Cumulative
Percent
Valid No individual 7 4.2 4.2 4.2
Other Manager 3 1.8 1.8 6.1
Senior Manager 24 14.5 14.5 20.6
Board 131 78.9 79.4 100.0

Total 165 99.4 100.0
Missing 999.00 1 .6
Total 166 100.0
Another question asked was whether the companies provide incentives for the management of
climate change issues, including the attainment of targets. Results showed that majority of
companies (66.5%, n = 109).
Provide incentives
Frequency Percent Valid Percent Cumulative
Percent
Valid
Yes 109 65.7 66.5 66.5
No 55 33.1 33.5 100.0
Total 164 98.8 100.0
Missing 999 2 1.2
Total 166 100.0
Majority of the companies (86%, n = 142) had integrated climate change in their business
strategies. Only 14% (n = 23) of the companies had not integrated climate change in their
business strategies.
Is climate change integrated into your business strategy?
Frequency Percent Valid Percent Cumulative
Percent
Valid
Yes 142 85.5 86.1 86.1
No 23 13.9 13.9 100.0
Total 165 99.4 100.0
Missing 999 1 .6
Total 166 100.0

We also sought to understand the risk management procedures with regard to climate change
risks and opportunities for the various companies. Results indicated that majority of the
companies (64%, n = 89) conducted their risk assessment procedures 6 months or more
frequently.
Frequency of monitoring
Frequency Percent Valid Percent Cumulative
Percent
Valid
Every 2 years 2 1.2 1.4 1.4
Annually 41 24.7 29.5 30.9
6 months or more frequently 89 53.6 64.0 95.0
Sporadically, not defined 7 4.2 5.0 100.0
Total 139 83.7 100.0
Missing 999.00 27 16.3
Total 166 100.0
29.5% (n = 41) of the companies said to perform the risk assessment annually while 1.4% (n = 2)
said to conduct the risk assessment after every 2 years.

How far into the future are risks considered?
Frequency Percent Valid Percent Cumulative
Percent
Valid
Up to 1 year 3 1.8 2.2 2.2
1-3 years 27 16.3 19.6 21.7
3-6 years 92 55.4 66.7 88.4
Unknown 16 9.6 11.6 100.0
Total 138 83.1 100.0
Missing 999.00 28 16.9
Total 166 100.0

Most of the companies (66.7%, n = 92) prepare their risk management procedures with regard to
climate change risks and opportunities for between 3-6 years into the future. 2.2% (n = 3) said to
prepare their risk management procedures with regard to climate change risks and opportunities
for up to 1 year into the future while 19.6% (n = 27) prepare for between 1-3 years.
Active
Frequency Percent Valid Percent Cumulative
Percent
Valid
No 63 38.0 38.2 38.2
Absolute 28 16.9 17.0 55.2
Absolute, intensity 21 12.7 12.7 67.9
Intensity 53 31.9 32.1 100.0
Total 165 99.4 100.0
Missing 999 1 .6
Total 166 100.0
85.3% (n = 139) of the companies said to have emissions reduction initiatives that were active
within the reporting year while the rest (14.7%, n = 24) did not have any programs in regard to
reduction initiatives.

Emission reductions
Frequency Percent Valid Percent Cumulative
Percent
Valid
Yes 139 83.7 85.3 85.3
No 24 14.5 14.7 100.0
Total 163 98.2 100.0
Missing 999 3 1.8
Total 166 100.0
Data analysis (inferential)
Inferential analysis helps to test a given hypothesis. In this study, the following inferential
analysis were used; Chi-Square, Correlation, t-test and ANOVA.
Chi-Square Test
We sought to investigate whether there is significant association between country and the highest
level of direct responsibility for climate change within the organization (Ryabko, Stognienko, &
Shokin, 2004). Results presented below shows that there is significant association between the
country where the country is based and the highest level of direct responsibility for climate
change within the organization ( χ2 ( 3 )=14.40 , p=0.002).
Responsibility level * country Cross tabulation
Count
country Total
Canada Germany
Responsibility level No individual 4 3 7
Other Manager 3 0 3

Senior Manager 22 2 24
Board 70 61 131
Total 99 66 165
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Exact Sig. (2-
sided)
Pearson Chi-Square 14.404a 3 .002 .001
Likelihood Ratio 17.779 3 .000 .001
Fisher's Exact Test 15.338 .001
N of Valid Cases 165
a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is
1.20.
Next, we looked at whether there is significant association between companies having emissions
reduction initiatives that were active within the reporting year and on how far into the future are
risks considered (Cohen, Cohen, West, & Aiken, 2002). As can be seen, results showed that
there is no significant association between the two variables.
How far into the future are risks considered? * Emission reductions Cross
tabulation
Count
Emission reductions Total
Yes No
How far into the future are
risks considered?
Up to 1 year 3 0 3
1-3 years 25 2 27
3-6 years 86 6 92
Unknown 11 4 15
Total 125 12 137
Chi-Square Tests
Value df Asymp. Sig.
(2-sided)
Exact Sig. (2-
sided)
Exact Sig. (1-
sided)
Point
Probability
Pearson Chi-Square 6.945a 3 .074 .074
Likelihood Ratio 5.342 3 .148 .187

Fisher's Exact Test 5.546 .108
Linear-by-Linear
Association
4.924b 1 .026 .034 .026 .014
N of Valid Cases 137
a. 4 cells (50.0%) have expected count less than 5. The minimum expected count is .26.
b. The standardized statistic is 2.219.
Correlation test
We performed a correlation test to determine the relationship that exists between intensity and
percentage change in intensity for the various countries (Székely & Bakirov, 2007). The results
are given below;
Correlations
Intensity-2014 % change-2014
Intensity-2014
Pearson Correlation 1 -.016
Sig. (2-tailed) .841
N 166 166
% change-2014
Pearson Correlation -.016 1
Sig. (2-tailed) .841
N 166 166
As can be seen, there is no significant correlation between the two variables at 5% level of
significance (r = -0.016, p = 0.841)
T-test
For this we sought to find out whether there is a significant difference in intensity for the two
countries (Canada and Germany). The following hypothesis was to be tested;
H0 : μC=μG
H A : μC ≠ μG
Tested at 5% level.
The results are given below;

Group Statistics
Country N Mean Std. Deviation Std. Error Mean
Intensity-2014 Canada 65 29.5208 178.19590 22.10248
Germany 44 756.7597 4996.77559 753.29226
Independent Samples Test
Levene's Test for
Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Difference
Std. Error
Difference
95% Confidence Interval of
the Difference
Lower Upper
Intensity-
2014
Equal variances
assumed
5.774 .018 -1.175 107 .243 -727.23893 618.97494 -1954 499.8
Equal variances
not assumed
-.965 43.074 .340 -727.23893 753.61645 -2247 792.5
An independent samples t-test was performed to compare the average emissions intensity for the
companies in Canada and those in Germany. Results showed that the average emissions by the
companies in Canada (M = 29.52, SD = 22.10, N = 65) was not significant different with the
average emissions by the companies in Germany (M = 756.76, SD = 4996.76, N = 44), t (107) =
-1.175, p > .05, two-tailed. The difference of 727.24 showed an insignificant difference.
Essentially results showed that intensity emissions by companies in Canada and in Germany
does not significantly differ.
A second t-test was performed to test the difference in the intensity for the companies that
offered incentives and those that did not. The hypothesis tested is as follows;
H0 : μY =μN

H A : μY ≠ μN
Tested at 5% level.
The results are given below;
Group Statistics
Provide incentives N Mean Std. Deviation Std. Error Mean
% change-2014 Yes 109 5.0337 45.53509 4.36147
No 55 2.2653 26.01115 3.50734
Independent Samples Test
Levene's Test for
Equality of
Variances
t-test for Equality of Means
F Sig. t df Sig. (2-
tailed)
Mean
Differen
ce
Std.
Error
Differen
ce
95% Confidence
Interval of the
Difference
Lower Upper
% change-
2014
Equal
variances
assumed
.624 .431 .417 162 .677 2.76840 6.63203 -10.32 15.86
Equal
variances not
assumed
.495 159.47 .622 2.76840 5.59677 -8.28 13.82
An independent samples t-test was performed to compare the average emissions for the
companies that offered incentives and those that did not offer incentives (Nikolić, Muresan,
Feng, & Singer, 2012). Results showed that the average emissions by the companies that offered
incentives (M = 5.03, SD = 45.54, N = 109) was not significant different with the average
emissions by the companies that did not offer incentives (M = 2.27, SD = 26.01, N = 55), t (162)
= 1.046, p > .05, two-tailed. The difference of 2.77 showed an insignificant difference.
Essentially results showed that offering incentives for the management of climate change issues,

including the attainment of targets did not significantly reduce amount of emissions at 5% level
of significance.
References
Aguilera, R. V., Rupp, D. E., Williams, C. A., & Ganapathi, J. (2007). Putting the S back in
corporate social responsibility: A multilevel theory of social change in organizations.
Academy of Management Review, 32, 836–863.

Bansal, P., & Roth, K. (2000). Why Companies Go Green: A Model of Ecological
Responsiveness. Academy of Management Journal, 43, 717-736.
Bonner, S. E., Hastie, G. B., & Sprinkle, S. M. (2000). A review of the effects of financial
incentives on performance by laboratory tasks: Implications for management accounting.
Journal of Management Accounting Research, 12(1), 19-64.
Chunbo, M., & Stern, D. (2008). Biomass and China's carbon emissions: A missing piece of
carbon decomposition. Energy Policy, 36(7), 2517-2526.
doi:10.1016/j.enpol.2008.03.013
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2002). Applied multiple regression/correlation
analysis for the behavioral sciences. Psychology Press, 34-41.
Fridleifsson, I. B., & Bertani, R. (2011). The possible role and contribution of geothermal energy
to the mitigation of climate change. 59-80.
Nikolić, D., Muresan, R. C., Feng, W., & Singer, W. (2012). Scaled correlation analysis: a better
way to compute a cross-correlogram. European Journal of Neuroscience, 1–21.
doi:10.1111/j.1460-9568.2011.07987
Ryabko, B. Y., Stognienko, V. S., & Shokin, Y. I. (2004). A new test for randomness and its
application to some cryptographic problems. Journal of Statistical Planning and
Inference, 123, 365–376. doi:10.1016/s0378-3758(03)00149-6
Smith, D. (2009). US sets the standard. Renewable Energy Focus, 10(4), 26–27.
doi:10.1016/s1755-0084(09)70147-4

Székely, G. J., & Bakirov, N. K. (2007). Measuring and testing independence by correlation of
distances. Annals of Statistics, 35(6), 2769–2794. doi:10.1214/009053607000000505