Carbon emissions are one of the major problems in the environment today globally. Emissions of carbon have been affecting the industries greatly. In this research, the stakeholder theory will be discussed with effect to the carbon emissions. There are certain theoretical as well as practical motivations to this concept of carbon emissions in accordance with the stakeholder theory. The research is mainly aimed at evaluating the changes in the emissions of carbon by the companies.
In view of dependence on carbon-based utilities, our atmosphere is changing quickly and it’s a remediable issue of the present corporate world. Organizations convey and discharge a huge measure of carbon in the earth in perspective of their radiation of taking care of plant abuse in pointless aggregate and a section of the ruinous fabricated blends utilized as a bit of storing up of the thing. Nowadays the organizations also end up being extremely aware of the need to make a move on environmental change.
The Stakeholder got a huge influence on the organization. According to Donaldsom and Preston (1995), “Stakeholder theory has been used to describe (a) the nature of the firm (Brenner and Cochran, 1991), (b) the way managers think about managing (Brenner and Molander, 1977), (c) how board members think about the interests of corporate con stridencies (Wang and Dewhirst, 1992), and (d) how some corporations are actually managed (Clarkson, 1995; Halal, 1990; Kreiner and Bhambri, 1991)”. There is not much disagreement on what kind of entity can be a stakeholder. Persons, groups, neighborhoods, organizations, institutions, societies, and even the natural environment are generally thought to qualify as actual or potential stakeholders (Mitchell and Agle, 1997). One investor’s advantage is to expand the benefits and lessen the useless exercises from the business in here and now, yet the primary focus of association ought to be to deal with the prerequisites and meet the desires for Stakeholder (George, 2003). The primary individuals who ring a bell while considering who holds the stakes at an association are the Shareholder and the government. Other than the undeniable, Stakeholder consolidates each one of those exercises and responsibilities affects association execution, like laborers, customers, and business accomplices (Bridoux and Stoelhorst, 2014).
Analyses have also shown that the associations in the USA transmit a massive proportion of carbon into the air. 23 – 24 % of the carbon Dioxide transmitted by the corporate firms wherever all through the world start with the USA associations. The USA uses oil-based goods to make essentialness and subsequently, the surge of carbon in the atmosphere by the USA firms are high (Jones and Wicks, 1999). This examination paper is thusly away to work up the capabilities in the rate changes in the arrival of carbon by the relationship in the context of USA as for the presentation of jolts that a few affiliations accommodate the association and some others don’t. The adjustments in the arrival of carbon dioxidehave been assessed in a rating scale from the year 2012 to 2013. The choice of the relationship in the spread of carbon dioxide is all things considered in the context of the impact the accessories play on the affiliations. Considering the choice of the accessories, the affiliations will pick whether to give influencing forces to the association or not.
H11: There is statistically significant difference that exists between the companies who provide incentive in 2012 and the companies who do not provide incentive in 2012 on percentage changes in carbon emissions. (Alternative)
H12: There is statistically significant difference that exists between the companies who provide incentive in 2013 and the companies who do not provide incentive in 2013 on percentage changes in carbon emissions. (Alternative)
H13: There is statistically significant difference that exists between the companies who provide incentive in 2012 and the companies who provide incentive in 2013 on percentage changes in carbon emissions. (Alternative)
H14: There is statistically significant difference that exists between the companies who do not provide incentive in 2013 and the companies who do not provide incentive in 2013 on percentage changes in carbon emissions. (Alternative)
H15: There is statistically significant difference that exists between the companies who provide incentive in 2012 and the companies who do not provide incentive in 2013 on percentage changes in carbon emissions. (Alternative)
H16: There is statistically significant difference that exists between the companies who do not provide incentive in 2012 and the companies who do not provide incentive in 2013 on percentage changes in carbon emissions. (Alternative)
Similarly, the trend for 2012 and 2013 are almost similar though there are marginal differences which means if the company doesn’t provide any incentive to the management than the company shows a very less effort in carbon emissions reduction.
The company provides incentives for the management
Table 1: Descriptive statistics of 2012 and 2013 when company provides incentives to management.
|
2012 |
2013 |
|
|
Mean |
-10.03 |
-25.41 |
|
Median |
-8.50 |
-8.50 |
|
Mode |
-14 |
-1 |
|
Range |
67.80 |
488.33 |
|
Variance |
164.47 |
7731.80 |
|
Standard deviation |
12.82 |
87.93 |
|
Interquartile range |
9.75 |
10.48 |
|
quartile deviation |
4.88 |
5.24 |
|
Skewness |
-2.01 |
-5.41 |
|
Kurtosis |
6.72 |
29.45 |
From table 1 above, it is evident that the mean of carbon emissions when the company provides incentives for the management is -10.03 ± 12.82 in 2012 and -25.41 ± 87.93 in 2013. Thus, the mean of the level of carbon emissions is higher in 2012 compared to 2013 when the company provides incentives to the management. However, the median of the level of carbon emissions for both years is the same (both at -8.50). Since the median is greater than the mean, we conclude that the distribution is negatively skewed (Skewness of the level of carbon emissions is -2.01 and -5.41 in 2012 and 2013 respectively). Conversely, the mode the two distribution varies with -14 in 2012 and -1 in 2013. Thus, -14 level of percentages changes in carbon emissions occurs more in 2012 while -1 occurs more in 2013. The range of the level of carbon emissions in 2012 is 67.80 while in 2013 is 488.33. From this, it can be seen that in 2013, the level of dispersion of 488.33 is greater than the 2012 level of dispersion of 67.80. Factoring in the interquartile range, it can be seen that the level of dispersion in 2013 (10.48) is greater than the level of dispersion in 2012 (9.75). Since the interquartile range is not affected by outliers, it is evident that the interquartile range is a better measure of spread through the results remain the same. The quartile deviation in 2012 and 2013 are lower compared to the interquartile range. This is expected since the quartile deviation is robust than the interquartile range since it considers every variable in the dataset. Consequently, the quartile deviation is higher in 2013 (5.24) compared to 2012 (4.88). Thus, there is more spread in 2013 compared to 2012.
Consequently, the variance of the level of carbon emissions in 2013 (7731.80) is greater than 2012(164.47). Hence, the level of carbon emissions is more volatile in 2013 compared to the level of carbon emissions in 2012.
The kurtosis on the level of carbon emissions distribution is 6.72 in 2012 and 29.45 in 2013. Since the kurtosis is greater than 3, it is evident that the two datasets have heavier tails than a normal distribution.
The company does not provide incentives for the management
Table 2: Descriptive statistics of 2012 and 2013, when a company does not provide incentives to the management.
|
2012 |
2013 |
|
|
Mean |
-6.88 |
-14.23 |
|
Median |
-8.11 |
-10.46 |
|
Mode |
-13 |
-20 |
|
Range |
70.00 |
77.50 |
|
Variance |
147.55 |
234.06 |
|
Standard deviation |
12.15 |
15.30 |
|
Interquartile range |
11.73 |
12.16 |
|
quartile deviation |
5.87 |
6.08 |
|
Skewness |
-0.78 |
-2.78 |
|
Kurtosis |
4.09 |
9.99 |
From table 2, it is evident that the mean of carbon emissions when the company does not provide incentives for the management is -6.88 ± 12.15 in 2012 and -14.23 ± 15.30 in 2013. Hence, the mean of the level of carbon emissions is higher in 2012 compared to 2013. In this scenario, the company does not provide incentives to the management.
On the other hand, the median of the level of carbon emissions for both years is -8.11 and -10.46 in 2012 and 2013 respectively. The median is greater than the mean in 2013 while it is less in 2012. From this, it can be deduced that the distribution in 2013 is negatively skewed while the distribution in 2012 is positively skewed. However, the negativity if the skewness is proved in 2013 (-2.78) unlike in 2012 where the skewness was found to be negative (-0.78).
The mode of the two distribution varies from -13 in 2012 and -20 in 2013. Thus, -13 level of percentages changes in carbon emissions occurs more in 2012 unlike in 2013 where -20 occurs more. The range of the level of carbon emissions in 2012 is 70 while in 2013 is77.5. From this, it can be seen that in 2013, the level of dispersion of 77.5 is greater than the 2012 level of dispersion of 70. Factoring in the interquartile range, it can be seen that the level of dispersion in 2013 (12.16) is greater than the level of dispersion in 2012 (11.73). As expected, the quartile deviation in 2012 and 2013 are lower compared to the interquartile range. Consequently, the quartile deviation is higher in 2013 (6.08) compared to 2012 (5.87). Thus, there is more spread in 2013 compared to 2012.
Consequently, the variance of the level of carbon emissions in 2013 (234.06) is greater than 2012(147.55). Hence, the level of carbon emissions is more volatile in 2013 compared to the level of carbon emissions in 2012.
The kurtosis on the level of carbon emissions distribution is 4.09 in 2012 and 9.99 in 2013. Since the kurtosis is greater than 3, it is evident that the two datasets have heavier tails than a normal distribution.
Based on the descriptive analysis, it can be seen that carbon emissions have decreased in 2013 compared to 2012 regardless of whether there are incentives for the management or not. However, when there are incentives for the management, carbon emissions are less. It can be also seen from the above descriptive analysis, when the company provide incentive to the management the standard deviation in 2013 is much bigger compared to 2012 and that means the company take some efforts to reduce the carbon emissions where the standard deviation is almost similar in both 2012 and 2013 when the company does not receive any type of incentive. It goes same with the range as well where the range is almost same in both years when the company does not receive any incentive but it shows a big deviation when the company receives some incentives(67 in 2012 and 488 in 2013). The stakeholders’ theory claim that an organization must make a profit to benefit all the stakeholders. Thus, when a company is forced to reach its targets, corporate responsibilities are bound to be overlooked. Thus, more carbon emissions will be seen by the company in the USA.
The results of the descriptive statistical analysis only provides an exploratory idea about the impact of incentives being included into the company business strategy and offered to the management to influence the company to reduce its carbon emissions. Although the descriptive analysis reveals a general decrease in carbon emissions from 2012 to 2013 among the sample, regardless of the fact that a company has such an incentive plan in place or not, it cannot be asserted to be true for the population of companies that exist or not. The statistical validity of the conjecture that inclusion of such incentives to influence a core stakeholder group, that is the management, could go on to significantly impact and hence reduce the emission levels is hence still not established on the sole of the descriptive measures. In order to come to an evidence based conclusion regarding the impact of incentives, inferential analysis is then employed to approach the problem.
Inferential statistics is a tool that allows for the researcher to arrive at conclusions about the whole population on the basis of the given representative sample, with some degree of controlled error. The two errors associated with the methodology are type I error or the probability that the true null hypothesis is rejected. This is also equivalent to the level of significance of the test. Type II error or the probability that the false alternative will be accepted. Then naturally, decreasing one error would imply the increase in the other. The following methodology set the type I error as 0.05 and the type II error was minimized to arrive at the conclusion. This means that the null is rejected whenever the p-value is found to be less than 0.05, at 5% level of significance. The difference in the percentage change of the companies who give incentives between 2012 and 2013 was then tested for using the paired t-tests. The difference between the two groups of companies, namely, those who give incentives and those who do not, in terms of percentage emission in 2012 and in 2013 was tested for using two independent sample t-tests. The same was employed for the difference between the companies who offered incentives in 2012 and those which did not offer incentives in 2013 and those which did not offer incentives in 2012 and those which did offer incentives in 2013.
The first assumption for both paired t-tests and independent sample t-tests is that the data is free from outliers. This is primarily due to the fact that the test statistics are based on the mean which is highly susceptible to presence of outliers. Thus presence of extreme values in the data could potentially lead to erroneous results.The two sets of data to be compared for the paired t-tests must be on the same attribute but dependent on each other, such that they may be from the same individual or object in different time or scenarios, that is, should be observations on the same variable at different scenarios but related to the same unit. The assumption for independent t-tests however is that the data samples are on the same variable but drawn independently from each other. Again, another assumption is that the data samples are underlined by some normal distribution.
The data is therefore first explored for the presence of outliers. The following boxplots show the presence of values that lie beyond the Interquartile range and those that may be regarded as extremes. The observations numbered, 7, 11, 14 and 25 were identified as outliers and hence removed from the dataset before commencing with the tests for the six specified hypothesis.
Conjecture 1 testing whether the companies who give incentives have higher reduction in carbon emission than those who do not give incentives in the year 2012 was analyzed by means of the independent samples t-test. The mean change in emission for the companies with incentives in 2012 is -7.478 and that for the companies without incentives is -6.258. The following tables give the results, having assumed 5% level of significance. It is seen tha the mean difference is -1.22 and the 95% confidence is between -7.17 and 4.735. The p-value is greater than 0.05.
|
Group Statistics |
|||||
|
Incentives |
N |
Mean |
Std. Deviation |
Std. Error Mean |
|
|
Y2012 |
1 |
26 |
-7.4788 |
8.71238 |
1.70864 |
|
0 |
26 |
-6.2585 |
12.35772 |
2.42355 |
|
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 |
|||||||||
|
Y2012 |
Equal variances assumed |
.859 |
.358 |
-.412 |
50 |
.682 |
-1.22038 |
2.96530 |
-7.17637 |
4.73560 |
|
Equal variances not assumed |
-.412 |
44.929 |
.683 |
-1.22038 |
2.96530 |
-7.19308 |
4.75231 |
Conjecture 2 testing whether the companies who give incentives have higher reduction in carbon emission than those who do not give incentives in the year 2013 was analyzed by means of the independent samples t-test. The mean change in emission for the companies with incentives in 2013 is -9.2027 and that for the companies without incentives is -11.952. The following tables give the results, having assumed 5% level of significance. It is seen that the mean difference is 2.75 and the 95% confidence is between -2.36 and 7.86. The p-value is greater than 0.05.
|
Group Statistics |
|||||
|
Incentives |
N |
Mean |
Std. Deviation |
Std. Error Mean |
|
|
Y2013 |
1 |
26 |
-9.2027 |
8.09431 |
1.58742 |
|
0 |
26 |
-11.9527 |
10.15206 |
1.99098 |
|
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 |
|||||||||
|
Y2013 |
Equal variances assumed |
1.446 |
.235 |
1.080 |
50 |
.285 |
2.75000 |
2.54636 |
-2.36451 |
7.86451 |
|
Equal variances not assumed |
1.080 |
47.637 |
.286 |
2.75000 |
2.54636 |
-2.37080 |
7.87080 |
Then testing for conjecture 3, the mean percentage reduction in carbon emission for companies who give out incentives for the year 2012 was found to be -8.07 and the same for those who do not give incentives is -9.88. The mean difference was found to be 1.8076 which lies in the 95% confidence interval -3.36 and 6.98. The p-value was found to be 0.478 which is greater than 0.05. Therefore the paired samples test comparing the performance of companies who give incentives in terms of carbon emissions for the years 2012 and 2013, although identified some reduction in percentage emission, however found no evidence to suggest statistically significant decrease in the carbon emission levels.
|
Paired Samples Statistics |
|||||
|
Mean |
N |
Std. Deviation |
Std. Error Mean |
||
|
Pair 1 |
incentives_2012 |
-8.0769 |
26 |
9.50336 |
1.86376 |
|
incentives_2013 |
-9.8846 |
26 |
8.35381 |
1.63832 |
|
Paired Samples Test |
|||||||||
|
Paired Differences |
t |
df |
Sig. (2-tailed) |
||||||
|
Mean |
Std. Deviation |
Std. Error Mean |
95% Confidence Interval of the Difference |
||||||
|
Lower |
Upper |
||||||||
|
Pair 1 |
incentives_2012 – incentives_2013 |
1.80769 |
12.80943 |
2.51214 |
-3.36615 |
6.98153 |
.720 |
25 |
.478 |
Testing for conjecture 4, the mean percentage reduction in carbon emission for companies who do not give out incentives for the year 2012 was found to be -5.2692 and the same for those who do not give incentives is -12.00. The mean difference was found to be 6.73 which lies in the 95% confidence interval 1.16 and 12.30. The p-value was found to be 0.020 which is less than 0.05. Therefore the paired samples test comparing the performance of companies who do not give incentives in terms of carbon emissions for the years 2012 and 2013, although identified some reduction in percentage emission, however found no evidence to suggest statistically significant decrease in the carbon emission levels.
|
Paired Samples Statistics |
|||||
|
Mean |
N |
Std. Deviation |
Std. Error Mean |
||
|
Pair 1 |
no_incentive_2012 |
-5.2692 |
26 |
9.48918 |
1.86098 |
|
no_incentive_2013 |
-12.0000 |
26 |
10.24500 |
2.00921 |
|
Paired Samples Test |
|||||||||
|
Paired Differences |
t |
df |
Sig. (2-tailed) |
||||||
|
Mean |
Std. Deviation |
Std. Error Mean |
95% Confidence Interval of the Difference |
||||||
|
Lower |
Upper |
||||||||
|
Pair 1 |
no_incentive_2012 – no_incentive_2013 |
6.73077 |
13.79147 |
2.70473 |
1.16027 |
12.30126 |
2.489 |
25 |
.020 |
Conjecture 5 testing whether the companies who give incentives in 2012 have higher reduction in carbon emission than those who do not give incentives in the year 2013 was analyzed by means of the independent samples t-test. The mean change in emission for the companies with incentives in 2012 is -7.478 and that for the companies without incentives is –11.95. The following tables give the results, having assumed 5% level of significance. It is seen that the mean difference is 4.473 and the 95% confidence is between -7.958 and 9.7435. The p-value is greater than 0.05.
|
Group Statistics |
|||||
|
incentive2012_noincentive2013 |
N |
Mean |
Std. Deviation |
Std. Error Mean |
|
|
Percentage_change1 |
1 |
26 |
-7.4788 |
8.71238 |
1.70864 |
|
2 |
26 |
-11.9527 |
10.15206 |
1.99098 |
|
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 |
|||||||||
|
Percentage_change1 |
Equal variances assumed |
.986 |
.325 |
1.705 |
50 |
.094 |
4.47385 |
2.62363 |
-.79588 |
9.74357 |
|
Equal variances not assumed |
1.705 |
48.874 |
.095 |
4.47385 |
2.62363 |
-.79889 |
9.74658 |
Conjecture 5 testing whether the companies who do not give incentives in 2012 have higher reduction in carbon emission than those who give incentives in the year 2013 was analyzed by means of the independent samples t-test. The mean change in emission for the companies without incentives in 2012 is -9.202 and that for the companies with incentives in 2013 is –6.25. The following tables give the results, having assumed 5% level of significance. It is seen that the mean difference is -2.944 and the 95% confidence is between -8.76 and 2.87 having assumed equal variance. The p-value is greater than 0.05.
|
Group Statistics |
|||||
|
incentive2012_noincentive2013 |
N |
Mean |
Std. Deviation |
Std. Error Mean |
|
|
Percentage_change2 |
1 |
26 |
-9.2027 |
8.09431 |
1.58742 |
|
2 |
26 |
-6.2585 |
12.35772 |
2.42355 |
|
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 |
|||||||||
|
Percentage_change2 |
Equal variances assumed |
1.158 |
.287 |
-1.016 |
50 |
.314 |
-2.94423 |
2.89715 |
-8.76334 |
2.87488 |
|
Equal variances not assumed |
-1.016 |
43.117 |
.315 |
-2.94423 |
2.89715 |
-8.78644 |
2.89798 |
The results of the set of t-tests by assessing the p-values for the tests and comparing them with the level of significance 0.05. The null condition for the Hypothesis 1 for comparison between companies in 2012 who give and do not give incentives could not be rejected at 5% level since the p-value was greater than 0.05. The null for Hypothesis 2 was however rejected at 5% level implying that there is statistically significant difference between the two groups in 2013. The results show that companies who give incentives have greater reduction in emission 2013. The null of Hypothesis 3, 4, 5 and 6 were all failed to be rejected at 5% implying no significant difference in incentive giving and incentive not giving companies in 2013 and 2012 and no significant change in mean levels of emissions for either groups.
Stakeholder theory talks about the role of the various stakeholder groups in influencing business decision of corporations and organizations. As per the literature explored, stakeholders are individuals who have some kind of direct or indirect relationship with the organization, such that their actions are either aligned with the interest of the organization or vice versa. The may be categorized into groups of varying degrees of influence. These people would hence act in accordance to the wellbeing of the organization and vice versa. Thus it is expected that their position on certain issues could very well influence the position and mission, vision and business strategy of the organization itself.Stakeholder thus have been a key point of interest and contention in management studies and those involved in environmental awareness campaigns have also the approach of stakeholders to combat industrial policies which lead to eco-logical stress and environmental harm.
It was noted that those in the upper management levels who are at the helm of decision making could be accounted for as key players in the endeavour to reduce industrial carbon emissions.Knopff et al. (2010), in their paper discussing the ways organization could be made to transition to a low carbon emission model, they identified introduction of incentive policies to incorporate newer technologies which lead to lower emissions for the management level. The literature reviewed for the purpose of research had isolated the industries in US as major contributors to global carbon emissions, especially due to their dealing sin oil related businesses and hence the study took those as representatives to study the phenomenon and effectiveness of incentive policies in reducing carbon emissions among them (Jones and Wicks, 1999). Keeping all this in mind, using the management employees as the key stakeholders and therefore tools of effecting environmentally conscious change in the organizations leading up to policy making that reduces carbon emissions, it is seen that for the most part the success in reducing carbon emission is slowly manifesting as the levels can be seen to decrease from 2012 to 2013. The impact of the management as stakeholders however was not found to be statistically significant in 2012. This however could be seen to have overturned in 2013 where companies with policies to incentivize the management for opting policies to reduce emissions was found to have greater percentage change in carbon emissions. The paper carried out six t-tests to explore the tentative relationship of the management as a stakeholder and the impact on emission output measured by percentage change in emissions.Even though the difference could not be found to be significant for the same group in subsequent years, it is seen that some impact can be identified.
Consequently the study finds that incentive policies have for the most part not been as successful so as to usher in drastic change in policy within the companies who have adopted them and even so have not been able to effect immediate significant change between 2012 and 2013. It is not clear whether the policies would have significant impact on a long term basis. However in 2013 some marginal change in the positive direction as far as reduction in emission is concerned indicates that some positive impact may be a possibility. However further in depth and persistent inquiry shall be required to come to any decisive conclusion regarding the effectiveness of incentivizing the stakeholder group of management level employees to curb carbon based emissions in the United States at least.
The sample used for the study is less than size 50. The sample again had outliers which led to the usable sample size to be 26. This is too small to amount to any robust result for the inferential test. The minimum sample size to ensure 95% confidence and 0.05 level of margin of error is about 250 to 300 which is much higher than the size considered. The t-test also has the assumption of normality which is never verified given the small size of the sample. The study therefore is limited in representation. The data also considered only 2 consecutive years to explore any significant change exists or not. Naturally it is not practical to assume that a company would be willing to effect such drastic change in policy, endangering its business process at one go. Thus the scope of the study ought to be more diversified both in terms of sample size as well as the time. Another limitation is that the study only focussed on companies which are based in the United States. However many multinational companies of significant impact in terms of carbon emissions are from outside the US. In fact Asia and South Asia is a significant player when it comes to carbon emissions, indicating that companies relating to and based in the regions should also be considered.
The study first of all should aim to increase more companies spread out across various sectors over a longer period of time. The data would then take the structure of a longitudinal data. The study could include more stakeholders such as the customers as well as the governments and their policies in their area of operations in addition to the management. The study would then be required to expand its scope to primary data analysis by collecting opinions via interviews and surveys of the various stakeholders. The study should also aim to take at least 5 years’ worth of readings and at least 300 companies and even greater number of observations from the consumers. The study should take companies which are not just limited to the USA but also from other locations, especially from industrially developing and active regions such as China and India.
Reference
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Brenner, S.N. and Cochran, P., 1991, July. The stakeholder theory of the firm: Implications for business and society theory and research. In Proceedings of the international association for business and society (Vol. 2, pp. 897-933).
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No matter how complex your assignment is, we can find the right professional for your specific task. Contact Essay is an essay writing company that hires only the smartest minds to help you with your projects. Our expertise allows us to provide students with high-quality academic writing, editing & proofreading services.
Free Features
Free revision policy
$10Free bibliography & reference
$8Free title page
$8Free formatting
$8How Our Essay Writing Service Works
First, you will need to complete an order form. It's not difficult but, in case there is anything you find not to be clear, you may always call us so that we can guide you through it. On the order form, you will need to include some basic information concerning your order: subject, topic, number of pages, etc. We also encourage our clients to upload any relevant information or sources that will help.
Complete the order form
Once we have all the information and instructions that we need, we select the most suitable writer for your assignment. While everything seems to be clear, the writer, who has complete knowledge of the subject, may need clarification from you. It is at that point that you would receive a call or email from us.
Writer’s assignment
As soon as the writer has finished, it will be delivered both to the website and to your email address so that you will not miss it. If your deadline is close at hand, we will place a call to you to make sure that you receive the paper on time.
Completing the order and download