Data Collection
Research Design
The goal of this study was to establish whether the investment in CSR strategies has an effect on financial outcomes for SME firms that are based in the United States operating in the manufacturing sector. The quantitative methodology was selected to address the identified research questions and consequently test the hypotheses.

The independent variables that were identified are management perception of CSR activities and the age of the firms. The social variables that are considered include workplace activities, community activities, as well as customer activities, and these are identified as the indicators of the social CSR. The environmental CSR is measured in terms of environmental activities. The independent variables are measured on the 5-point Likert-scale, which is how the questionnaire is analyzed. The dependent variable in this study is the financial outcomes of these firms. The variable is assessed as the perception of managers and owners on the extent of improvement of sales and profits in the 2019 financial year. The dependent variable is measured in terms of three performance indicators. The research takes place using a quantitative design when studying these variables. The research design that is identified has also reflected the limitation of database access, the analytical approach, and the response time (Martelli and Greener, 2018). There is the use of a questionnaire survey despite the challenges in terms of response bias that have been identified.

The researcher also considered using either the qualitative or mixed methods research designs. The review of the literature showed that qualitative studies are not the conventional Design for studying the two variables, and it was deemed as inappropriate (citation is needed),. There are various complexities that come with mixed methods, and this is why they cannot be applied in this study the better justification for rejection of mixed method is needed (citation is needed),. The quantitative Design is prevalent when it comes to approaching CSR-financial outcomes.

Population
The target population for this study was the management/owners of SME firms that are in the manufacturing sector. The operational definition of manufacturing SME firms is taken from the definition given by NAICS. The manufacturing sector that was used in the study included the firms which take part in the chemical, physical, or mechanical conversion of the raw materials into new products. The target population of the manufacturing sector was 2500 top executives and managers that are operating in the United States. The expected response rate based on several explored studies is 35%. This response rate has been established based on the metanalysis of these studies. The study makes use of an electronic version of a customized questionnaire made that was distributed to the managers and top executives that are part of the population.

Sample selection and Design
A power analysis was used in the sample selection. This is a strategy that was identified in similar CSR studies. These studies have also used questionnaire surveys to investigate these relationships. The power analysis was used in determining whether the study sample size was appropriate and would lead to a proper conclusion. The study used a confidence level of 95% which is conventional for several CSR studies (Taherdoost, 2016). The selection of the sample also considered the work of various researchers over time. One of the studies analyzed on sample size showed that the sample should be more than 50 as a rule of thumb. The understanding when it comes to approaching research is that when one has a larger sample size there will be fewer errors. The studies on the CSR and financial outcomes in the SME that were analyzed for this section had an average sample size from 55 to 193 with the average being 121.

Investigation of the research designs that have been used previously to study financial outcomes and CSR together with a consideration of the limitations that were identified, the data collection process that was identified was perceptual, and the instrument to be used is a questionnaire survey (Cho and Lee, 2019). The data that will be used in the study will be primary data from the questionnaires that will be distributed to the SMEs that are part of the sample. The survey will be emailed to the managers and owners of SMEs with the email contact information which will be obtained from the US Small Business Administration. The firms that will be selected are those on the SBA database on the manufacturing sector. The analysis of the primary data will be done using IBM SPSS Statistics version 25. The study will also involve the testing of the hypotheses. The testing of the 4 hypotheses will be done using ANOVA, t-test, and Chi-square options that are available on the analysis tool.

Data Collection Instrument
The instrument that was identified by the researcher was a questionnaire that was developed. The questionnaire survey has been designed to group, sort, and assess the extent of the use of CSR for SMEs and how it has affected the financial outcomes of these firms. The questionnaire survey has been peer-reviewed by fellow academics who are aware of the development process of surveys. There is also a pilot test to make sure that the instrument will be reliable and valid to measure the established variables. There is also testing of the average variance extracted (AVE) and composite reliability (CR) to further support the instrument (Basias and Pollalis, 2018). The AVE is used to measure the convergent validity. In this study, the best way to analyses the instrument is using the independent test of internal consistency using the CR while the convergent validity will be tested using the AVE approach.

Time series data will capture the measurement of the variables at one particular period of time. This study will acquire a snapshot of the relevant data on the variables through capturing of the data for the year 2019. The variables and responses for these firms will be based on 2019 and the results will be a reflection of CSR activities and financial outcomes in the year. Acquiring the time series data will be through the questionnaires and through the database of the Small Business Administration.

Ethical Issues
Ethics are the set of rules that can either be written or unwritten and they govern behavior towards self and others. The ethics in research offer a guide on how one is expected to behave during the process and why such behavior is encouraged. The research ethics govern how scientific and other forms of research that are performed by institutions such as universities should be carried out. Ethics are important because they are a guide to promoting the objectives of the research and expanding this body of knowledge. Researchers are able to be accountable for their actions when they live as per the ethical standards that have been established (Martelli and Greener, 2018). The use of ethics makes sure that the public can trust the findings of the research. Confidence is important in research particularly when looking at issues such as funding and support.

There are several ethical issues that must be considered in this study. The first one which is important to consider is the respect for intellectual property. It is important to make sure that there is permission from the SMEs to use their data in the study. The SMEs have patents and copyrights that need to be respected. There is a lot of information about companies that should eb respected even though it will be provided under informed consent. It is important to obtained informed consent as well as permission when some sensitive data will be used. The second ethical issue that needs to be considered is legality. There are regulations and laws under the SBA on the use of the data on their website and for use of business data (Cho and Lee, 2019). It is critical to also respect the regulations that guide research. The third ethical issue that needs to be considered is confidentiality. Any information that is provided to the researcher under confidence should not be shared with anyone else. It is important to follow the set guidelines to protect some sensitive information such as the financial outcomes and specifics of programs that SMEs are taking. The final ethical issue that should be considered is “do no harm.” (Martelli and Greener, 2018). It is important to respect the human subjects who are taking part in the study through filling in questionnaires. There should be maximization of the benefits to the SMEs and the other stakeholders in the study.

What biases might you bring to the research and how will you address that biases?

Analysis of Stationary Data
The data will be collected using survey questionnaires and it is primary data and needs to be analyzed. The collected primary data will be analyzed using the SPSS Statistics software by IBM. The software will help in making sense of the primary data and establishing patterns from what is obtained in the questionnaires. The hypotheses which were identified earlier will be tested using ANOVA, Chi-square, and the t-test that are available on the SPSS package (Basias and Pollalis, 2018). These tests will help make sense of the data and establish if there is correlation between the variables that were identified earlier.

Data analysis using the methods presented above had been applied in 65 out of the 80 studies that had been analyzed for this purpose. The SPSS software was popular among the works of several authors whereas the options for testing were present in the majority of these studies looking at SMEs CSR and their financial outcomes (Lin et al., 2019). There are several advantages of using SPSS and the options available for hypothesis testing. One of the advantages is that data can be imported from various sources and it can be arranged as a database. The SPSS has a great user interface compared to other software that can be used for analysis. It is also quicker to learn and easy to use for researchers who have more than basics of statistics (Fuller et al., 2016). There are, however, some disadvantages of using the software to test hypothesis and look at the various options to study the data. It is expensive to purchase depending on the functionality that one wants (Lin et al., 2019). Some researchers have also pointed out that it has a limited functionality.

The following is an example of this form of data analysis:

Citation and references for above snapshot is needed and necessary!!!

One of the core parts of research is the validation of the findings and conclusion. The validity of the finds and conclusions refers to the degree to which these findings are correct and reasonable when it comes to the null hypothesis. The null hypothesis typically states that there is no relationship between the given variables. The validity of the finds and the conclusion deals mainly with the absence or presence of a relationship between any two variables (Ain et al., 2019). The findings and conclusions for this study will be validated through consulting with faculty where some members can assist in determining whether the conclusions and findings are related to the hypotheses that were presented in the study.

Please mention a better method to validate the data!!!

Testing Hypothesis
Research questions
1. How are the financial outcomes of SMEs once they participate in social CSR compared to lack of investment?

1. How are the financial outcomes of SMEs when they participate in environmental corporate social responsibility?

1. How are the monetary outcomes of SMEs when they participate in both forms of CSR (social and environmental)?

1. How do the financial outcomes of older Small and Medium-sized Enterprises (SMEs) compare to younger SMEs when they are all participating in both environmental and social CSR?

H01: The monetary outcomes of SMEs do not improve when they invest in social CSR.

H02: The monetary outcomes of SMEs do not improve when they invest in environmental CSR.

H03: The financial outcomes of SMEs do not improve when they participate in both environmental and social CSR.

H04: The financial outcomes of older SME firms compared to younger SMEs does not improve when they are all investing in both environmental and social CSR.

Hypothesis Testing
The hypothesis testing can either be done using the frequentist approach or the Bayesian approach. The frequentist method of hypothesis testing makes predictions on the hypothesis that is in the study using the data that has been collected using the data collection method. The Bayesian approach, on the other hand, is an accumulative approach in the analysis. The hypothesis testing needs to take into account the past knowledge of similar experiments and they are encoded into a prior (Lin et al., 2019). The prior will then be combined with the data that has been collected when doing the hypothesis testing. The frequentist approach will be used because it is the one that is used when the hypothesis can be tested without assigning of a probability. The frequentist approach will also be used because the data aims to only use the data that has been collected using the questionnaire surveys.

There are similar hypotheses that have been tested by various authors. However, the authors have mainly concentrated on the larger corporations whose information is available from various rating websites. The hypothesis on financial outcomes and CSR have been studied severally and there have been varying results on the topic (Miko?ajek-Gocejna, 2016). However, there is no study that has specifically looked at how CSR affects the financial outcomes of the firms. At the same time, there is no study that has compared the effects between the younger and older firms.

Please attach some references and illustrations from the literature that tested the Hypothesis before

The figure below shows a worked-out example of hypothesis testing using previously published data:

Citation and references for above snapshot is needed and necessary!!!

Analysis and Forecasting of Time Series
The time series analysis of this data will take part using five distinct steps. The first one is one needs to visualize the time series. The second one is to stationarize the series. The third one involves the plotting of PACF/ACF charts and determination of the optimal parameters for the data that has been identified. The fourth step in the time series analysis will be building of the ARIMA model. The final step in the process will be making predictions using the time series analysis (Miko?ajek-Gocejna, 2016). The forecasting of the data is usually the last step and it provides a path of the variables going into the future. The predications are made after one develops the final ARIMA model so that one can establish where the future time points rare. The prediction takes place and one can now visualize the trends that are there and cross validate if this model is working the way that it should.

There are various studies that have also used these steps in doing the analysis as well as forecasting of the time series data. The authors used the methods to determine if their model can be able to forecast future points using the data that has been obtained from the study. There is a study which used the five steps of analysis and forecasting to show whether going into the future the large corporations will continue benefiting from their CSR activities in the long run (Miko?ajek-Gocejna, 2016). The findings and conclusions will be validated through viewing whether the forecasting presents a viable model. At the same time, there will be analysis of the results by a professional statistician and an academic from the relevant department in the school.

Time Table and Budget
WORK PLAN

TIME PERIOD

ACTIVITY

August to September 2020

September 2020

September to November 2020

December 2020

Proposal development

Proposal approval

Data collection

Data analysis

Project compiling

Budget

ITEM

COST IN US $

Data Collection

400.00

Transport Expenses

350.00

Stationary

100.00

Telephone

150.00

Hidden Costs

500.00

Total Expenses

1500.00

Conclusion
The study looks at the relationship between financial outcomes and CSR activities in the United States manufacturing sector. The focus of the study will be among the SME firms which operate in the country in the sector. There is limited research on the impact of CSR activities on the financial outcomes of various firms which are SMEs. The consequences of CSR investment are immediate when it comes to the SMEs compared to larger corporations. The study takes a quantitative approach to look at whether the leadership practices related to CSR investment in the manufacturing sector leads to an improved financial outcomes. There are four research questions that have been developed to explore the relationship between the financial outcomes and CSR. The tool that will be used for analysis is SPSS so as to achieve the given results.

References
Nollet, J., Filis, G., & Mitrokostas, E. (2016). Corporate social responsibility and financial outcomes: A non-linear and disaggregated approach. Economic Modelling, 52, 400-407.

Ruggiero, P., & Cupertino, S. (2018). CSR strategic approach, financial resources and corporate social performance: The mediating effect of innovation. Sustainability, 10(10), 3611.

Franco, S., Caroli, M. G., Cappa, F., & Del Chiappa, G. (2020). Are you good enough? CSR, quality management and corporate financial outcomes in the hospitality industry. International Journal of Hospitality Management, 88, 102395.

Cho, S. Y., & Lee, C. (2019). Managerial efficiency, corporate social performance, and corporate financial outcomes. Journal of Business Ethics, 1-20.

Lin, W. L., Law, S. H., Ho, J. A., & Sambasivan, M. (2019). The causality direction of the corporate social responsibility–Corporate financial outcomes Nexus: Application of Panel Vector Autoregression approach. The North American Journal of Economics and Finance, 48, 401-418.

Miko?ajek-Gocejna, M. (2016). The relationship between corporate social responsibility and corporate financial outcomes–Evidence from empirical studies. Comparative Economic Research, 19(4), 67-84.

Awaysheh, A., Heron, R. A., Perry, T., & Wilson, J. I. (2020). On the relation between corporate social responsibility and financial outcomes. Strategic Management Journal, 41(6), 965-987.

Feng, M., Wang, X., & Kreuze, J. G. (2017). Corporate social responsibility and firm financial outcomes. American Journal of Business.

Esteban-Sanchez, P., de la Cuesta-Gonzalez, M., & Paredes-Gazquez, J. D. (2017). Corporate social performance and its relation with corporate financial outcomes: International evidence in the banking industry. Journal of cleaner production, 162, 1102-1110.

Salehi, M., DashtBayaz, M. L., & Khorashadizadeh, S. (2018). Corporate social responsibility and future financial outcomes. EuroMed Journal of Business.

Su, R., Liu, C., & Teng, W. (2020). The heterogeneous effects of CSR dimensions on financial outcomes–a new approach for CSR measurement. Journal of Business Economics and Management, 21(4), 987-1009.

Meier, O., Naccache, P., & Schier, G. (2019). Exploring the Curvature of the Relationship Between HRM–CSR and Corporate Financial outcomes. Journal of Business Ethics, 1-17.

Taherdoost, H. (2016). Sampling methods in research methodology; how to choose a sampling technique for research. How to Choose a Sampling Technique for Research (April 10, 2016).

Basias, N., & Pollalis, Y. (2018). Quantitative and qualitative research in business & technology: Justifying a suitable research methodology. Review of Integrative Business and Economics Research, 7, 91-105.

Martelli, J., & Greener, S. (2018). An introduction to business research methods.

Fuller, C. M., Simmering, M. J., Atinc, G., Atinc, Y., & Babin, B. J. (2016). Common methods variance detection in business research. Journal of Business Research, 69(8), 3192-3198.

Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success–A systematic literature review. Decision Support Systems, 125, 113113.

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