Gathering of Data

The study used secondary data on the prior performance of insurance companies as indicated by The Australian Prudential Regulation Authority (APRA) to ascertain the relationship between investment and profit level. Only data from the year 2000 onwards were included in the analysis, and the companies must have been in operation for over three years at the time of this study. The companies under study were drawn from four main sectors namely Agriculture, Mining, Automobile and Service sectors.

The inclusion criteria for the secondary data acquired by the researcher were that the data had to be related to profitability and investment and the company must be in operation in the 21^st century.  The collected data included the degree of investments and the corresponding profitability at a given time period. Table 1 below shows the detailed components of the received data. An introductory letter from the institution was used to get free access to the data.

Table 1 Table 1: Data Collection

Table 1: Data Collection

Name of the Data source Save Time On Research and Writing Hire a Pro to Write You a 100% Plagiarism-Free Paper. Get My Paper	Company	Data Details	Format of Data File	URL link	cost	Target Data Source
Data 1	The Australian Prudential Regulation Authority (APRA)	Investment projects, profit levels	txt	No	Free	Yes
Data 2	Malls	Employee Job motivation/satisfaction	txt	No	$1000	No
Data 3	Hospitals	Job motivation and revenue	txt	No	$2000	No

The accessed data was stored in appropriate file format as “raw data” and recorded as shown in the sample table 2 below:

Name of Data source	Collection Date	Storage location of file	Data file storage name	Format of saved Data file	No. of Data Records
Data 1	14/02/2017	My Library	APRA Survey.txt	txt	50
Data 2	02/03/207	My Library	Melbourne Mall Survey.xls	xls	20
Data 3	03/04/2017	Documents	The Royal Melbourne Hospital Survey.txt	txt	20

Table 2 Table 2: Data Storage

Table 2: Data Storage

This section deals with the treatment of the raw data that was gathered based on different features to make the data meaningful and ready for analysis. Therefore, the areas included under this section are data pre-processing, choice of features or, experiment design, and application.

The collected raw data was cross-examined to in preparation for direct input of the proposed methodology. This is because not all the survey questions were answered in the same way and some responses were irrelevant and inconsequential to the analysis outcomes.

The researcher further deduced the gathered data after processing using features that were relevant to the research topic. The data’s dimensionality was reduced to further simplify it. The resulting data set was recorded in the sample table 3 as illustrated below. 

Date	Name of Data source	Objective of  Pre-processing	Method of Pre-processing	Data Records (Original)	Data Records (results)	Original Features	No. Result Features	Name of New Data File
20/02/2017	Data 1	Reduce the data volume without affecting results	Data Reduction	50	40	3	3	Final_APRASurvey.txt
08/03/207	Data 2	Remove incomplete data	Data Cleaning	20	18	6	6	Final_Melbourne Mall Survey.xls
08/04/2017	Data 3	Adding new features inferred by the current attributes	Data transformation	20	18	6	8	Final_The Royal Melbourne Hospital Survey.txt

Table 3 Table 3: Selection and Reduction of Data.

Table 3: Selection and Reduction of Data.

The section discusses the implementation of the proposed methodology in experimenting out the research. It outlines the various steps that guided the completion of the study. This section highlights the data collection methods used and the techniques and procedures used for gathering and analysing data.

The study utilised a descriptive design to collect quantitative data demonstrating the association between investment and profitability of an organization and labour motivation with profitability. Christensen et al. (2011) asserts that descriptive statistics helps in the connection between the study design and analysis. Therefore, this research adopted the mixed research method, where both qualitative and quantitative methods of data collection were used (Creswell, & Creswell, 2017) with an objective of providing solution to the problem statement. Data relating to investment and profitability of the various Australian companies that were affected by globalisation were considered. The Australian Prudential Regulation Authority (APRA) provided the access to the data after the submission of an introductory letter from the University.

Sources of Data

The target population this survey comprised of all the companies that were affected by globalisation in Australia and were still in operation in the 21st Century. According to the Australian Prudential Regulation Authority (APRA), 78% of Australian companies that were affected by globalisation and only half of them were still in operation in the 21st Century.

A simple random sampling method was adopted. 20 corporations were selected for the survey from the existing population provided by APRA. A representative sample is that which ensures that all subjects in the population study have an equal chance of selection (Saunders, 2011).  Therefore, all the companies that fitted the selection criteria had an equal chance of being selected. The study was conducted for a period of five months.

The survey used secondary data which was retrieved from the listed companies affected by globalisation by APRA. The nature of data was the nature of investments and corresponding profit levels for specific years.

The gathered data was reviewed for accuracy, completeness and consistency (as shown in Table 3). A descriptive tool will be used to analyse the data. A fixed effect regression model was utilised since it assists in examining the influence of variables that change with time (Cameron, & Trivedi, 2013). A similar model was utilised by Mong’o (2010) to examine the effect of profitability among Kenyan commercial banks. A fixed effect model is as illustrated below:

Yit = β0 + β1X1, it +…. + βkXk, it + uit

Where;

Yit represents the dependent variable with i = entity, t = time.

Xk, is independent variable

 βk is the independent variables’ coefficient

 uit the error term

The researcher tried to adopt the use of a representative sample, but this was partially impossible because of the diverse natures of the companies and economic sectors under which they operate.

The information provided was not verifiable by the researcher because the respective company respondents might have falsified the information submitted to APRA for their reasons

This section shows the expected outcomes from the data in the form of tables and regression equation. SPSS is to be used for the data analysis.

Table 4 Table 4: Descriptive features of the Net Profits, Investments and Labour Motivations for the period of 2000- 2005

Table 4: Descriptive features of the Net Profits, Investments and Labour Motivations for the period of 2000- 2005

	Minimum	Maximum	Mean	Std. Deviation
Net Profit	8,834,333.00	105,425,534	3,407,633.00	1.4
Investment Costs	55,453,900.00	35,564,555	1,257,776.00	6,257,656
Labour Motivation	–    6,579,393.00	169,049,343	–                  4,860,387.00	9,276,583

Table 5 Table 5: Regression Model Analysis

Table 5: Regression Model Analysis

R sq within = 0.00865

R sq between = 0.0021

R sq overall = 0.0356

Prob > F = 0.0083

rho = 0.43584848

Number of Observations = 20

Number of groups = 4

Storage of Data

Table 6 Table 6: Coefficients of Regression

Table 6: Coefficients of Regression

Net Profit	Coeff
Investments	0.063533
Labour Motivation	-1.8857443
Constant	6,758,474.00

From Table 6, the coefficients of the regressors show the variation in the net profit with every change in investment and labour motivation. This is proof that the variables have a significant impact on the net profit (dependent variable)

The regression equation can thus be shown as below:

Y = 6,758,474 + 0.063533Xit – 1.8857443Xit

Investment determines profit but is not dependent on it. Thus, investment and profit level are mutually exclusive events, when there is increase in investment, and then the profit is also expected to increase. However, it is not obvious that labour motivation will necessarily lead to profit increase because employees only offer effective service in a system that is already promising. The change in investment (independent variable) affects the profit level (dependent variable) in the same way whereas the change in investment doesn’t automatically lead to a corresponding change in labour motivation (dependent variables). Burke (2017) also asserts that any organizational change has a corresponding impact on its operations

The expected study findings indicate that not all changes in organizational structures affect the profitability of the firm. These findings were in agreement with those of Appelbaum et al. (2018). Investment changes were found to affect the firms’ profitability irrespective of the direction of investment change. Also, not all investments had a positive impact on the profit level of the company. Similarly Campello et al. (2011) also found that firms with limited access to financial resources preferred investments than savings even though were not able to meet their financial obligations as at and when they occurred. For instance, labour motivation during a crisis such as globalization was found to be inconsequential to the firm’s profitability whereas investment directly improved a firm’s profitability.

Methodology and Analysis of Findings

Gathering of Data

Sources of Data

Data collection

Data Storage

Experiment Design and Operation

Data Pre-processing

Dimension Reduction

Study Design

Research Design process

Population

Sample

Data Analysis

Limitations

Experiment Implementation Records

Study Findings

Experiment Results

The Expected Results

Summary of Expected Results

References: 

Appelbaum, S. H., Profka, E., Depta, A. M., & Petrynski, B. (2018). Impact of business model change on organizational success. Industrial and Commercial Training.

Burke, W. W. (2017). Organization change: Theory and practice. Sage Publications.

Cameron, A. C., & Trivedi, P. K. (2013). Regression analysis of count data (Vol. 53). Cambridge university press.

Campello, M., Giambona, E., Graham, J. R., & Harvey, C. R. (2011). Liquidity management and corporate investment during a financial crisis. The Review of Financial Studies, 24(6), 1944-1979

Christensen, L. B., Johnson, B., Turner, L. A., & Christensen, L. B. (2011). Research methods, design, and analysis.

Creswell, J. W., & Creswell, J. D. (2017). Research design: Qualitative, quantitative, and mixed methods approaches. Sage publications.

Mong’o, G. (2010). The relationship between cash-flows and profitability of commercial banks in Kenya. Unpublished MBA Project.

Saunders, M. N. (2011). Research methods for business students, 5/e. Pearson Education India.