-716915-9779000ANNUAL REPORT

-1434488750500-7315206310660

-716915-9779000ANNUAL REPORT

2018

SRISHTI MAHIMA HARRISON | 20644857

SRISHTI MAHIMA HARRISON | 20644857

Submitted To:

Dr. Md. Manirujjaman

Submitted To:

Dr. Md. Manirujjaman

MKT5AMR(BU-2) - APPLIED MARKET RESEARCH

(ASSESSMENT 3)

25th October 2022

MKT5AMR(BU-2) - APPLIED MARKET RESEARCH

(ASSESSMENT 3)

25th October 2022

centercenter10348856-328710-795020-16510000TABLE OF CONTENTS TOC h z t "SPSS TITLE,1,SPSS HEADING,2" INTRODUCTION TO RESULTS PAGEREF _Toc117634540 h 5 SAMPLE CHARACTERISTICS PAGEREF _Toc117634541 h 6 RELIABILITY TEST PAGEREF _Toc117634542 h 7INDEPENDENT SAMPLE T-TEST PAGEREF _Toc117634543 h 8ONE WAY ANOVA PAGEREF _Toc117634544 h 9CHI-SQUARE PAGEREF _Toc117634545 h 10CORRELATION COEFFICIENT PAGEREF _Toc117634546 h 11REGRESSION PAGEREF _Toc117634547 h 13CONCLUSION PAGEREF _Toc117634548 h 15 RECOMMENDATIONS PAGEREF _Toc117634549 h 16REFERENCES PAGEREF _Toc117634550 h 17APPENDICES PAGEREF _Toc117634551 h 19

-795020-16510000Introduction to ResultsEntrepreneurship is the process of acting on opportunities and ideas to create value for other people Bacigalupo,2021). More recently, entrepreneurship has emerged as one of the most dynamic fields (Audretsch, 2012).

Ethical relationship marketing is the systematic study that focuses on moral values applied to corporate marketing decisions, activities, sales, and satisfying & retain customers. At the outset, it is very important to exemplify an area of ethics in relationship marketing and its theoretical underpinnings (Kushwaha, Singh, Tyagi, & Singh, 2020).

The descriptive analysis, confirmatory factors analysis, and path model with help of SPSS Amos confirm a theoretical framework for ethical relationship marketing. The framework will bind sellers and buyers in an ethical relationship that will have a longer and benefit-able life. It will also help marketers to establish an ethical selling policy and implement the same in an ethical way to satisfy the needs and desires of the customers (Kushwaha, Singh, Tyagi, & Singh, 2020).

Marketing researchers, companies and business schools need to be able to use statistical procedures correctly and accurately interpret the outputs, yet generally, these people are scared off by the statistics behind the different analyses procedures, thus they often rely on external sources to come up with profound answers to the proposed research questions (Charry, Coussement, & Heuvinck, 2016).

-795020-16510000 Sample CharacteristicsWhy did we choose sample characteristics? We use numerical data to categorise, rank and measure numerical data (Enago Academy, 2022). One of the limitations of using central tendency to accurately describe the sample characteristics is that sometimes statistics that are quantitatively not a good representation of the underlying facts can be produced when one naively applies the arithmetic mean to describe the central tendency of empirical data (Ferreira & Levy 2021).

The sample characteristics obtained will use demographic data characteristics (View Appendix 1). Most of the respondents are from age 20-29 as shown in (Appendix 3) are 1,140. The total number of respondents is 1697. (Appendix 4) shows that most of the respondents are female students (898). Most of the respondents are unemployed students (765) (View Appendix 7) also are Bundoora campus has the most respondents at 1305 while the Bendigo campus has 392 students, respondents.853 students are studying business majors while 839 are in other faculties (View Appendix 8).

-795020-16510000 Reliability TestThe Reliability Test is utilised for a variety of approaches for evaluating and juxtaposing the composite system and producing capacity, as well as future digital computer programmes (Billinton et. al, 1989). Examining the idea of test reliability in relation to the perceived, category, and item-specific characteristics, as well as the consistency of results in all these aspects through the test, to test (Cronbach, 1947).

In fact, it is practically challenging to read a study article without encountering an acknowledgement of the variable(s) reliability. Nevertheless, a significant number of authors offer credible evidence which is unfavourably deceptive and at finestunenlightening. (Morrow, & Jackson, 1993) (as referred to in Appendix 9).

Cronbach's Alpha will be applied in our analysis to gauge internal reliability. We are motivated to express our thoughts with students who perceive themselves as entrepreneurs. For the reliability test, all of the section "I" questions are used.

The intention of the reliability test is to inform the client based on the questionnaire if students are motivated to be entrepreneurs. The hypothesis developed in this section is as follows:

Alternative Hypothesis: Students are motivated in being entrepreneurs.

Null Hypothesis: Students are not motivated in being entrepreneurs.

Age, gender, and campus location are the variables evaluated in this scenario.

Based on the data analysis (View Appendix 11) the Cronbach Alpha is .876 which denotes threshold for internal reliability has been exceeded as the acceptable Cronbach alpha is .76. We also correlate items in section I (View Appendix 12) and found a high co-relationship between creating ones own company and choosing freely a career.

-795020-16510000Independent Sample T-TestWe use an independent sample T-test when we have two independent variables (Hitti and Khan, 2022). To ensure that any differences in the reaction are caused by the therapy (or absence of treatment) and not by other variables, the volunteers for this test should ideally be randomly assigned to two groups (IBM,2022).

The assumptions are that the data should be unrelated, random samples from normal distributions with the same population variance for the equal-variance t-test. The findings for the unequal-variance t-test ought to be discrete, and systematically different from normal distributions. The two-sample t-test can withstand variances from normality with incredible ease. When evaluating distributions graphically, ensure there are no outliers and that they are homogeneous (IBM,2022).

The hypothesis derived from this section are as follows:

Null hypothesis 1: Business majors cannot do the administrative and bureaucratic work to create their own businesses.

Null hypothesis 2: Non-business majors can do the administrative and bureaucratic work to create their own businesses.

Alternative hypothesis 1: Business majors can do the administrative and bureaucratic work to create their own businesses.

Alternative hypothesis 2: Non-business majors can do the administrative and bureaucratic work to create their own businesses.

Independent Variable: 4 (Business or Non-Business)

Dependent Variable: F3 (Administrative and bureaucratic work to create your own business)

From the analysis (View Appendix 17 & 18), we see the t value is 11.669, df is 1688 and the one-sided significance shows 0.01. Thus, by examining the P-value in the independent sample T-test table T >0.05 at 11.669. Henceforth, we retain the null hypothesis and conclude that the two means are the same but not statistically significantly different.

ONE WAY ANOVAAn analysis of variance test called a one-way ANOVA is used to evaluate whether there is a statistically significant difference between the means of three or more groups. In reality, the test makes use of variances to assist decide whether or not the population means are equal (Watts,2022).

The client seeks to understand in this section the influence of family on students' decision to pursue a career as an entrepreneur based on gender.

Independent Variable: Item 1 (Gender)

Dependent Variable: Section D (How gender can impact people to start their own businesses).

The hypothesis developed from the section is as follows:

Null Hypothesis: Gender does not impact the decision to pursue a career as an entrepreneur.

Alternative Hypothesis: Gender does impact the decision to pursue a career as an entrepreneur.

Each of the following variables' significance level is greater than 0.05. the decision rule is therefore to accept the null hypothesis, i.e., Gender does not impact the decision to pursue a career as an entrepreneur. (Appendix 19), shows the F value as 3.140, df as 1 and P-value at 0.77. The level of significance of the F value is greater than 0.05 thus we will not reject the null hypothesis. Gender, therefore, impacts the decision to start a company.

CHI-SQUAREThe Chi-Square test for categorical variables is applied to determine if two categorical variables are independent of one another or to judge how well a sample resembles the dispersion of a data set (goodness of fit) (Frankie, Ho & Christie, 2012).

The Dependent Variable used is Item 2 Age.

Independent Variable: I1 - Creating a new company (being an entrepreneur).

The client seeks to know if age influences the desire to create a new company. The hypothesis generated for this section is as below:

Null Hypothesis: Age does not influence the desire of creating a new company

Alternative Hypothesis: Age does influence the desire of creating a new company

The Chi-square test conducted (View Appendix 22) shows the Pearson Chi-square value at 44.298, df at 24 and Asymptomatic significance at 0.007. The significance level is always at <0.05, therefore in this case age is a significant factor affecting the decision to create a new company hence we make the informed decision to reject the null hypothesis.

-795020-16510000CORRELATION COEFFICIENTThe statistic called the correlation coefficient, sometimes known as the r coefficient, is used to assess how strong or significant a link is (Taylor,1990).

Factors analysed in this section include the following:

Dependent Variable: Section B: Items B4, B5, B6, B7

Independent Variable: Gender

Using the specified variables, the eight hypotheses were formulated as follows:

Null Hypothesis 1: Gender does not impact the desire of being creative and innovative.

Null Hypothesis 2: Gender does not impact the desire of obtaining high incomes.

Null Hypothesis 3: Gender does not impact the desire of taking calculated risks.

Null Hypothesis 4: Gender does not impact the desire of being my own boss (independence).

Alternative Hypothesis 1: Gender does impact the desire of being creative and innovative.

Alternative Hypothesis 2: Gender does impact the desire of obtaining high incomes.

Alternative Hypothesis 3: Gender does impact the desire of taking calculated risks.

Alternative Hypothesis 4: Gender does impact the desire of being my own boss (independence).

From the correlation coefficient table (See Appendix 23), we can see various factors that are significant. The correlations are weak in the significance range as they all fall above 0.05. We, therefore, make the decision to reject the null hypothesis as gender explains the desire to be entrepreneurs.

(Appendix 23)

-795020-16510000REGRESSIONThe statistical technique of theregression model (analysis)is applied to analysethe relationship between different variables (Sykes, 1993). Evaluation of residuals, statistical significance, specification inaccuracy, multiple linear regression, normalised coefficients, and dummy variables are amongst the most advanced aspects that regression provides an in-depth exploration of (Lewis, 1980).

We can include numerous independent variables in an analysis by using multiple linear regression. This serves two purposes. First and foremost, it usually always provides a more comprehensive descriptionof the dependent variable because few occurrences result from a single source. Secondly, by eliminating the potentialof distorted impacts caused by the other independent variables, the impact of a given independent variable is substantially more certain (Lewis, 1980).

Estimating the parameters, drawing conclusions (using hypothesis tests and confidence intervals), as well as determining different types (intensity) of the error are all part of the descriptive statistics. Analyses of logical inconsistencies withinthedataset, poor model evaluation, and some other breaches of the fundamental assumptions supporting the inference techniques must also be conducted (Sa, Freund, & Wilson, 2006).

The variables selected are:

Dependent Variable: 3.3 I am unemployed

Independent Variable: Gender, Age and Campus Location

We seek to analyse if the current work situation based on demographic factors affects students' inclination to become entrepreneurs. Two hypotheses developed include:

Null Hypothesis: There is no relationship between students inclination to become entrepreneurs based on demographic factors and current work situation

Alternative Hypothesis: There is a relationship between students inclination to become entrepreneurs based on demographic factors and current work situation

We use the R square value to determine if the independent variables have an impact on the current work situation. The variance tables show stepwise multiple regression. (Appendix 31) shows an R2 of.088 thus the regression model is reliable as it exceeds 0.33. This tells us that demographic influences affect the current work situation of respondents.

(Appendix 31)

-795020-16510000CONCLUSIONFrom the report, we see that students who come from a business background have displayed an interest in entrepreneurship more than those that come from a non-business background. The student's age is seen to have significance based on the intentions of students to pursue entrepreneurship with those of the Age 20-29 cohort being more aggressive in pursuit. Cronbach Alpha was used to test the reliability of the questionnaire and a T-test was also used to test the motivation of students to become entrepreneurs. Gender was also found to be a significant factor in the family setup when it came to opening a new company. Subsequent studies can be used to assess how different sections of the current work situation affects students' decision to pursue entrepreneurship.

-795020-16510000 RecommendationsBased on the findings from the report, the following recommendations are suggested:

The universities can create policies and strategies for useful guidance on how to promote entrepreneurship during a period of post-crisis recovery from the effects of COVID-19 to enable students to create jobs after graduating.

The graduates can get graduate entrepreneurship support from the universities to encourage them to pursue the course.

Focussing on key marketing objectives to reduce costs and make the course more lucrative as well as develop campaigns to create more awareness regarding entrepreneurship and its benefits and relevance in society.

-795020-16510000REFERENCESAudretsch, D. (2012). Entrepreneurship research. Management decision.

Bacigalupo, M. (2021). Entrepreneurship as a competence. In World encyclopedia of entrepreneurship. Edward Elgar Publishing.

Billinton, R., Kumar, S., Chowdhury, N., Chu, K., Debnath, K., Goel, L., ... & Oteng-Adjei, J. (1989). A reliability test system for educational purposes-basic data.IEEE Transactions on Power Systems,4(3), 1238-1244.

Charry, K., Coussement, K., Demoulin, N., & Heuvinck, N. (2016). Marketing Research with IBM SPSS Statistics: A Practical Guide (1st ed.). Routledge. https://doi.org/10.4324/9781315525532Cronbach, L. J. (1947). Test reliability: It's meaning and determination.Psychometrika,12(1), 1-16.

Enago Academy. (2022). The importance of sampling methods in research design retrieved on 20th October 2022 available https://www.enago.com/academy/the-importance-of-sampling-methods-in-research-design/.

Ferreira, K. B., & Levy, S. (2021). Evaluating MPI resource usage summary statistics. Parallel Computing, 108, 102825.

Franke, T. M., Ho, T., & Christie, C. A. (2012). The Chi-Square Test: Often Used and More Often Misinterpreted. American Journal of Evaluation, 33(3), 448458.

Hitti, A., & Khan, S. R. (2022). Tutorial 18: Independent Samples t-test. Journal of Interdisciplinary Perspectives and Scholarship, 8(1), 18.

IBM Statistics. (2022). Independent-samples t-test accessed on 20th October 2022 available https://www.ibm.com/docs/en/spss-statistics/beta?topic=tests-independent-samples-t-testKushwaha, B., Singh, R. K., Tyagi, V., & Singh, V. (2020). Ethical Relationship Marketing in the Domain of Customer Relationship Marketing.Test Engineering and Management,83, 16573-16584

Lewis-Beck, M. S. (1980). Applied regression. SAGE Publications, Inc., https://dx.doi.org/10.4135/9781412983440.

Morrow Jr, J. R., & Jackson, A. W. (1993). How significant is your reliability?.Research quarterly for exercise and sport,64(3), 352-355.

Sa,P.,Freund,R.J.,Wilson,W.J.(2006).Regression Analysis.Netherlands:Elsevier Science.

Sykes, A. O. (1993). An introduction to regression analysis.

Taylor R. (1990). Interpretation of the Correlation Coefficient: A Basic Review. Journal of Diagnostic Medical Sonography.

Watts, V. (2022). 11.4 One-Way ANOVA and Hypothesis Tests for Three or More Population Means. Introduction to Statistics.

-795020-16510000APPENDICESAPPENDIX 1

APPENDIX 2

APPENDIX 3

APPENDIX 4

APPENDIX 5APPENDIX 6

APPENDIX 7

APPENDIX 8

APPENDIX 9

(Morrow, & Jackson, 1993)

APPENDIX 10

APPENDIX 11

APPENDIX 12

APPENDIX 13

APPENDIX 14

APPENDIX 15

APPENDIX 16

APPENDIX 17

APPENDIX 18

APPENDIX 19

APPENDIX 20

APPENDIX 21

APPENDIX 22

APPENDIX 23

APPENDIX 24

APPENDIX 25

APPENDIX 26

APPENDIX 27

APPENDIX 28

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APPENDIX 30

APPENDIX 31

APPENDIX 32

APPENDIX 33