HI6006 Competitive Strategy Editing Service
Delivery in day(s): 4
In terms of the whether incentive was provided by the company, 52.6% (n = 66) of the chosen companies said to provide incentives for the management of climate strategic change issues, including the attainment of targets while 47.4% (n = 27) did not provide incentives.
Figure 5: Pie chart on whether the companies provided incentives or not
Table 4: Incentive offering based on companies
As can be seen, it is evident that there is no statistically significant association between type of company and offering incentives to motivate employees to conserve carbon emissions.
Descriptive were obtained from the various categories of the firms with regards to their industry and whether they offer incentives or not.
Table 5: Descriptive Statistics when providing incentives to management
From table 5, it can be seen that when an incentive is provided to the management the percentage change of gross global combined scope 1 and 2 emissions declines. However, this is true for the banking industry (from -3.81 to -10.68). In the trading and retail industry, the average percentage change of gross global combined scope 1 and 2 emissions increased (from -16.43 to 0.18 in the trading industry and from -0.10 to 8.47 in the retail industry).
On the other hand, it can be seen that the median of the banking, trading and retail industry are -9.2, -2.2 and -0.045. More evidence can also be seen based on the mode where the banking industry has no mode while the modes of the trading and retail industries are -5 and 0 respectively. Since the mean is not similar to the median, it can be concluded that the data is asymmetrical. With this in mind, it is seen that the banking industry is negatively skewed (median is greater than the mean) while the trading and retail industries are positively skewed (mean is greater than the median). Conversely, the kurtosis of the banking and the retail industry is positive kurtosis (distribution has heavier tails and a sharper peak) while the kurtosis of the trading industry is negative kurtosis (distribution has lighter tails and heavier peak). The three industries combined have a mean that is greater than the median. Thus the distribution is positively skewed at 6.62 and also has a positive kurtosis at 50.04.
Conversely, it can be seen that the median of the banking, trading and retail industry are -.43, -12.7 and 0. The modes for the three industries are 0 for the retailing industry and none for the banking and trading industries. Since the mean is not similar to the median, it can be concluded that the data is asymmetrical. The banking and the trading industries are negatively skewed while the retailing industry is positively skewed. Conversely, the banking and the retailing industry gave a positive kurtosis while the trading industry has a negative kurtosis. Similar to table 1 analysis, a negatively skewed distribution suggest that the median is greater than the meanwhile a positively skewed distribution suggest that the mean is greater than the median. Moreover, a positive kurtosis suggests that the distribution has heavier tails and a sharper peak while a negative kurtosis suggests that the distribution has a lighter tail and a heavier peak. The three industries combined have a median that is greater than the mean. Thus the distribution is negatively skewed at -0.74 and also has a positive kurtosis at 0.81.
When there are no incentives to the management, the banking and the trading industries have the largest range compared to when incentives are provided to the management. However, in the retailing industry, the opposite is true where the range is larger when there are incentives to the management compared to when there are no incentives to the management.
Table 6: Variance on incentives
From table 6 above it can be seen that the variance when an incentive is provided is 689.52 while an incentive is not provided it is 136.519. Thus, it can be said that the percentage change when there is an incentive to the management is very high compared to the percentage change when no incentive is provided to the management.
An ANOVA analysis was carried out to determine if there were any statistically significant differences between the means of the two groups of incentives.
Table 7: Incentives ANOVA
From table 7, it can be seen that the difference was not statistically significant. Thus, we can conclude that there was no statistically significant difference in the percentage change regardless of the presence or absence of incentives to the management.
Thus, there was a need to confirm whether the industries had varied changes in the percentages of emissionsof gross global combined scope 1 and 2 emissions declines.
Table 8: Industrial ANOVA
On the basis of industries, it is evident that there was a statistically significant difference in the percentage change of gross global combined scope 1 and 2 emissions decline.
Table 9: Multiple Comparisons (ANOVA)
Table 9 reveals that the percentage change in emissions was statistically significantly lower in the banking and the retailing industry (p = 0.0032) compared to the trading and the banking industry (p = 0.157).
In regards to the main focus of the study, determining if there is any reduction observed in the carbon emission for the firms providing incentives to their management, the aspect of incentives to the management was introduced as a covariate.
Table 10: Tests of Between-Subject Effects (ANCOVA)
It is evident from table 10 that there is a statistically significant difference (p = 0.033) in the percentage change ofgross global combined scope 1 and 2 emissions decline between the industries when adjusted for the presence or absence of incentives to the management.
Table 11: Pairwise comparison (ANCOVA)
However, the introduction of incentives to the management still shows that the percentage change in emissions was statistically significantly lower in the banking and the retailing industry (p = 0.035) compared to the retailing and the trading industry (p = 1.00).
A scatter plot was developed to check whether there was any relationship between the two categories (when there are incentives to the management and when there are no incentives to the management).
Figure 6: Percentage change Scatterplot
From figure 6 above, it is evident that the percentage changeschange ofgross global combined scope 1 and 2 emissions decline in the two categories were positively but were weakly correlated (r = 0.34). The correlation, derived from the shown R2linear is 0.113.
The aim of this study was determining whether there was any reduction observed in the carbon emissions for the firms providing incentives to their management. With this regard, the null hypothesis stated that there was no relationship between industries providing incentives to their employees for carbon emission and reduction of CO2e in the environment. Consequently, the alternate hypothesis stated the opposite whether there is a positive relationship between industries incentives to their employees for carbon emission and reduction of CO2e in the international business environment.
From the descriptive statistics, it was seen that there was a greater change in the percentage change of carbon emission of CO2e in the environment (-3.25%) when the management was provided with incentives. On the other hand, when there were no incentives, the percentage change in the CO2e emissions in the environment was lower at -4.3%. Thus, the descriptive statistics suggest that when there are incentives to the management, firms among the various industries will opt to adjust their CO2e emissions more compared to when the incentives are not provided to the management.
It was seen that there was greater volatility when incentives were provided to the management. When incentives to the management were not made available, the volatility was lower. From the analysis of variance, it was observed that the difference in the percentage changes with regards to the presence or absence of incentives when the firms were aggregated regardless of the industry was not statistically significant. Consequently, when the analysis of variance was done on the basis of the industry, it was seen that the difference was statistically significant (p < 0.05). Hence proving the hypothesis that there is a difference between industries when providing incentives to their employees for carbon emission ad reduction of CO2e in the environment. However, the percentage change was statistically and significantly lower in the banking and retailing industry while it remained high in the banking industry. Nevertheless, the aspect of different industries had to be introduced resulting in the analysis of covariance (ANCOVA). The presence of incentives was used as the covariates. In this regard, it was found that there was the statistically significant difference in the percentage change of C02 emissions and reduction when the various industries are either given incentives or not. The percentage change was still statistically and significantly lower in the retailing and retailing industry while it remained high in the banking industry. Thus, this confirmed the hypothesis that there is a difference between industries which provide incentives to their employees for carbon emission and reduction of CO2e in the environment. But does this means that there is a relationship between industries providing incentives to their employees for carbon emission ad reduction of CO2e in the environment?
From the correlation analysis, it was seen that there was a positive relationship between industries providing incentives to their employees for carbon emission and reduction of CO2e in the environment. Thus, this was used in not accepting the null hypothesis and instead choose the alternate hypothesis.
The research has shown that the impact of incentives on the management on carbon emissions affects various industries in different ways. Ideally, there is a positive relationship between industries incentives to their employees for carbon emission and reduction of carbon in the environment (p < 0.05). The presence of incentives is seen to have the highest percentage change in the banking industry. Consequently, with the presence of incentives, it was seen that most of the firms were willing to reduce their carbon emission as observed from the high volatility of the percentage change in the carbon emission and reduction.
The study has shown that there was a positive relationship between industries which provide incentives to their employees for carbon emission and reduction of carbon in the environment. Such a result has also been documented in similar correlations. However, King and Lenox (2001) cast doubt on the causal claim which controls for the fixed characteristics and the strategic position for a firm. Nevertheless, the research supports the research by Dahlamann & Brammer (2013) which found out that firms who offer both monetary and non-monetary incentives upon their intensity in carbon emissions reduce the intensity of carbon emissions for energy-intensive firms. Though this is only evident if they assign the responsibility for climate change to an independent director. Most of the studies have shown that when employees are provided with incentives, there are more likely to give priority to the task at hand and give their highest level of commitment and devote more of their time to the task (Zhao et al., 2012). Employees are more likely to respond to incentives, especially monetary incentives by working harder or choose to self-select themselves into those performance jobs pay which best matches their own level of ability.
The difference in the various percentage changes on the industrial levels just goes to prove that carbon reduction is not vitally equally for all firms and across all industries (Zhao et al. 2010). In some certain industries, the reduction of emissions of carbon is a central economic issue. Firms in these industries emit a high amount of carbon emissions through their services, products, and operations. This is true especially in the trading industry where even the introduction of incentives has seen a minimal reduction in carbon emissions. On the other hand, in the banking and retailing industry, efficiency is key and with the introduction of incentives, the carbon emissions have been seen to reduce drastically.
The volatility of the percentage change in the reduction of carbon emissions and reduction can be attributed to the behavioral school which argues that incentives have a progressive effect on the motivation of employees through the provision of positive reinforcement (Kamenica, 2012). As a result, it leads to the increase in the frequency of behavioral rewards thereby leading to an enhanced performance. Thus, as more incentives are provided, various companies will be able to minimize their carbon footprint through minimization of wastage and enhancement of efficiency in the production line (Foxon 2013).
Lobbying for incentives to be implemented to reduce carbon emissions needs to be pursued. There is a wealth of accumulated evidence which has shown how environmental performance influence financialperformance (Horváthová, 2010).However, there is still a gap on why firms choose to disclose and what the implications of their decisions are. Thus, what firms need to do in order to achieve a better environmental performance should be explored. Consequently, the incentives to the employees will have a positive impact on their performance through the increase of effort direction, intensity and duration (Hameed, Ramzan & Zubair, 2014). According to Nyberg et al., (2010), this is possible at the organizational level by attracting and retaining employees who have a high ability.
More efforts need to be put for a reduction in the emissions of not only carbon but also other toxic chemicals. Ambec et al. (2013) lobby for this since in their research they found out that a 10% reduction in emissions of toxic chemicals leads to an increase $34 million in the value of the market. Though most markets penalize firms for their carbon emissions, according to Ioannou and Serafeim (2012), some of the firms globally opt to avoid disclosing their decision on their emissions. Hence, according to Matsumura, Parkash & Vera-Munoz (2013), further, penalty needs to be imposed on the firms which opt not to disclose their emissions information.
Study limitations have to be identified so as to describe what the research instruments are not able to achieve. For this study, the important limitations had to be highlighted. Data used in the research was secondary in nature and thus was from online sources. The risk of using secondary data is that the data may have bias from the initial data collectors. Consequently, the data may not be presentative of the whole of the whole population. The sample size used in the study may play as a limitation to the project. In this research, the data used were from only three industries, the banking, retailing and trading industry. The three industries spanned only two countries, the United Kingdom and the United States. Consequently, 97 companies were used in the analysis. Thus, the results obtained may not be applicable to other industries or other countries across the globe.
A further research is recommended and needs to be carried out in order to improve this study. The future studies need to be inclusive of more industries and regions across the globe. The inclusion will ensure that the research can be used to across various industries and countries in the world. It is important to note that a more inclusive dataset will allow researchers to explore and shed new light on some of the most plausible mechanisms which are engrained with ambitious targets and incentive provisions which are linked with the degree of target completion. Future researchers can also seek to explore how different types of incentives affect firms with regards to the degree of carbon emission and reduction and how this effect the outcomes of the organization.
1. Ambec, S., Cohen, M.A., Elgie, S. and Lanoie, P., 2013. The Porter hypothesis at 20: can environmental regulation enhance innovation and competitiveness?. Review of environmental economics and policy, 7(1), pp.2-22.
2. Dahlmann, F. and Brammer, S., 2013, July. Corporate Governance vs. Corporate Environmental Governance: Complementary or Separate Drivers of Environmental Performance?. In Proceedings of the International Association for Business and Society (Vol. 24, pp. 153-162).
3. Foxon, T.J., 2013. Transition pathways for a UK low carbon electricity future. Energy Policy, 52, pp.10-24.
4. Hameed, A., Ramzan, M. and Zubair, H.M.K., 2014. Impact of compensation on employee performance (empirical evidence from banking sector of Pakistan). International Journal of Business and Social Science, 5(2).
5. Horváthová, E., 2010. Does environmental performance affect financial performance? A meta-analysis. Ecological Economics, 70(1), pp.52-59.
6. Ioannou, I. and Serafeim, G., 2010, August. THE IMPACT OF CORPORATE SOCIAL RESPONSIBILITY ON INVESTMENT RECOMMENDATIONS. In Academy of Management Proceedings (Vol. 2010, No. 1, pp. 1-6). Briarcliff Manor, NY 10510: Academy of Management.
7. Kamenica, E., 2012. Behavioral economics and psychology of incentives. Annu. Rev. Econ., 4(1), pp.427-452.
8. King, A. A. and Lenox, M. J., 2001, Does It Really Pay to Be Green? An Empirical Study of Firm Environmental and Financial management Performance: An Empirical Study of Firm Environmental and Financial Performance. Journal of Industrial Ecology, 5: 105–116.
9. Matsumura, E.M., Prakash, R. and Vera-Muñoz, S.C., 2013. Firm-value effects of carbon emissions and carbon disclosures. The Accounting Review, 89(2), pp.695-724.
10. Nyberg, A.J., Fulmer, I.S., Gerhart, B. and Carpenter, M.A., 2010. Agency theory revisited: CEO return and shareholder interest alignment. Academy of Management Journal, 53(5), pp.1029-1049.
11. Zhao, R., Neighbour, G., Han, J., McGuire, M. and Deutz, P., 2012. Using game theory to describe strategy selection for environmental risk and carbon emissions reduction in the green supply chain. Journal of Loss Prevention in the Process Industries, 25(6), pp.927-936.
12. Zhao, M., Tan, L., Zhang, W., Ji, M., Liu, Y. and Yu, L., 2010. Decomposing the influencing factors of industrial carbon emissions in Shanghai using the LMDI method. Energy, 35(6), pp.2505-2510.