MKC2500 Marketing Research Methods

MKC2500 Marketing Research Methods
Semester 1, 2023
Assignment 2: Research report
The objective of this assignment is to give students the opportunity to practice solving real marketing research problems with data.
Detailed instruction on how to complete the assignment is available in “Assignment 2 – Guide” section of this document.
Here are some general requirements for the assignment.
The due date/time of the assignment is by 11:55pm on Friday in week 12 (May 26th).
The assignment is individual work. Collaboration or consultation with anyone other than the unit’s teaching staff is strictly prohibited
One copy is to be uploaded at the “Assignment 2” submission tab on the unit’s Moodle site. In the rare event that unforeseen technical issues on Moodle prevent you from completing the submission process, you should email the report to your tutor.
You need to first agree to the “plagiarism statement” above before you are allowed to submit this assignment. Do NOT include Monash assessment cover sheets in your submission.
Naming your file for submission – the file name must start with the day and time of your tutorial and contain your last name. For example, if your tute starts at 2pm on Wednesdays and your last name is “Smith.” Your file name should be “wed2pm_smith_assignment2.doc”
Submission format: PDF file. The guidance in terms of word count is 1500 – 2000 words (excluding references and SPSS output tables/charts). In absolutely no scenario you should exceed 2500 words. The word count must be included in the first page of your document. Exceeding the word limit (2500 words) could result in a penalty of up to 10% of your mark for the assignment
Please put any references you may have in the Appendix. and it doesn’t matter which standard reference format you use.
Students are required to keep a soft copy of their report until they get the marked report back. It is also the student’s responsibility to double check that the assignment has been completely uploaded to the correct link on time and that it is the correct version. To double check, go to the Moodle link where you submitted the assignment, download your submitted file and check: 1) that the file is downloadable and can be opened using Microsoft Word or Adobe Acrobat; and 2) that it is the file you intend to submit for grading
Detailed instructions on how to analyse data and report the research results are available on the unit’s Moodle site. Please read them carefully before starting your work.
Please contact your tutor and/or the lecturer if you have any further questions.
Assignment 2 – Guide The rise of AI means that human employees are increasingly expected to work with robots to accomplish tasks in service settings (e.g., hotels, restaurants, banks and etc). In 2021, researchers at Monash surveyed 302 former and current service industry employees in the UK to understand key factors that influence their attitude towards having a robot colleague (henceforth, “the robot survey” and “the robot data.” Both files are available on Moodle under Assignment 2). You are part of the research team and have been tasked with drafting a brief research report based on the questionnaire and data. The notes below give you detailed instruction on the assignment.

1. Definition of the research problem
(a). Define the market research problem (MRP)
Based on the questionnaire provided to you, define a marketing research problem (MRP) with components. The overall statement of the MRP should be “to identify and better understand the key drivers/predictors of ____.” You need to fill in a blank with an attitude, belief or behavioural variable that is measured by the robot survey. In addition to the overall statement, you need to formulate at least two components for your MRP.
The MRP must be able to be addressed with the attached dataset collected with the robot survey. In approaching this task, I would suggest that you start by carefully reading through the accompanying questionnaire and familiarize yourself with the SPSS dataset. Ask yourself the following questions: what information has been collected from the target population? What are the variables that are measured? How can this information be of use to the reader of your report (for example, a more informed policy to regulate the deployment of service robots; or maybe a hotel manager who is weighing the pros and cons of deploying service robots in the future)? This requires you to think about who the readers of this report might be and how the information can be useful to them.
(b). provide a brief justification for your MRP
Provide a brief explanation on how this research can help the reader understand the issue you study (i.e., why it is important to understand the particular attitude or behaviour in this context). This should be consistent with section 3 below where you discuss the managerial implication of your findings.
2. Research approach and hypotheses
Come up with at least 6 research questions (RQ). Collectively, your RQs should cover all the components of your marketing research problem proposed in the previous section. Note that because these RQs need to be “answerable” by the robot dataset, they have to involve (and only involve) variables measured in the robot survey. This requires you to be very familiar with each question in the questionnaire.
For each research question,
Clearly identify which component the RQ corresponds to.
Clearly state both the null and alternative hypotheses, which are to be tested in the data analysis section
Clearly identify ALL the variables that you use to answer the research question and the question in the questionnaire/dataset that measures this variable (to illustrate with a hypothetical example, if the variable you use is “age of the respondent”, and it is measured by question 15 in the accompanying questionnaire, you should include the information in your report). If you use a recoded variable, describe the recoding (for example., “young customers”: age <=30; “mature customers”: age > 30, etc)
Name the statistical test you use to test the hypothesis (for example, “an independent-samples t-test of the difference in loyalty between male and female”). If a multiple regression is used to test several hypotheses simultaneously, name the dependent and independent variables that will be included in the regression.
For this section, usage of bullet points and tables is required.
A list of the main statistical tests discussed in the lecture is provided below. Please note that you are NOT required to use all the techniques (choose only what is appropriate for your hypotheses). That being said, appropriate use of a variety of techniques or the usage of more advanced techniques such as multiple regressions is a necessity for high marks for this section.
You need to consider a number of issues in deciding which technique to choose for a
particular test. For example, certain techniques are only appropriate for interval-scaled data, while
others can be used for both interval- and ordinal-scaled data. Similarly, some techniques only allow
for comparison between two groups, while others allow you to compare the differences between multiple groups.
List of the main statistical techniques
Descriptive statistics (frequencies, descriptive and cross-tabs)
T-tests (dependent/independent samples) can be used to test for differences between means of subgroups.
Chi-square test can be used to test the association between two categorically scaled variables. It can also be called a test of independence.
Analysis of variance (ANOVA) can be used to see whether there are any differences across the categories of the non-metric variables with respect to any of the metric variables.
Correlation analysis measures the degree to which there is a linear association between two interval or ratio scaled variables.
Multiple regression can be used to explain the variation in dependent variables (outcome or effect variables) using other metric variables as independent variables (predictors).
3. Data analysis, findings and interpretation
(a). Data description
First, briefly describe the data based on the information you have. Consider the following questions as you work on this section.
What is the target population?
What is the sample size?
Broadly speaking, what kind of information has been gathered with the questionnaire (e.g., target population’s attitudes and behaviour towards what? What demographic information is collected)
Descriptive characteristics of your main variables: for EACH of the variables used in your first 3 research questions (RQ 1 to RQ 3, as formulated by you in section 2), show its frequency distribution (one-way tabulation).
You only need to produce a bar chart for each variable (i.e., the SPSS frequency table is NOT required) (see week 7 lecture slides for details)
Please do this for both metric and categorical variables
For the re-coded variables in these RQs, provide this information for the re-coded version (i.e., not the source variable from which the re-coded variable is derived)
This information is to be put in the appendix
(b). Reporting of the findings You are required to report the results of your statistical tests. Include the SPSS output for each set of hypotheses tested. Make sure that the output tables are properly labelled and clearly indicate which set of hypotheses it tests.
Examples for including SPSS output in your text.
Table 1. Comparing the difference between 207 and 2011
revenue (H1)
Number the tables Indicate which hypothesis
is being tested
Mean N Std. Deviation Std. Error Mean
Pair 1 Firm revenue in 2011
Firm revenue in 2007 2.33
1.80 61
61 .92
.85 .17
.15
Paired Samples Test
Paired Differences
95% Confidence Interval of
Std. Std. Error the Difference
Mean Deviation Mean Lower Upper t df Sig. (2-tailed)
Pair 1 Firm revenue in 2011 – Firm revenue in 2007 .53 .77 .10 .32 .74 5.15 60 .001
Is the null hypothesis rejected? Clearly state the outcome of each hypoth

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Assignment 2:

Research Problem
The rise of AI means that human employees are increasingly expected to work with robots to accomplish tasks in service settings (e.g., hotels, restaurants, banks, etc.). In 2021, researchers at Monash surveyed 302 former and current service industry employees in the UK to understand key factors that influence their attitude towards having a robot colleague. The goal of this research is to identify and better understand the key drivers/predictors of employee attitude towards working with robots.

Research Approach and Hypotheses
The following research questions were developed based on the survey data:

What are the demographic characteristics of the respondents?
What are the respondents’ attitudes towards working with robots?
What factors influence the respondents’ attitudes towards working with robots?
The following hypotheses were developed to test the research questions:

Hypothesis 1: There will be a significant difference in the attitudes of male and female respondents towards working with robots.

Hypothesis 2: There will be a significant difference in the attitudes of younger and older respondents towards working with robots.

Hypothesis 3: There will be a significant difference in the attitudes of respondents with different levels of education towards working with robots.

Hypothesis 4: There will be a significant difference in the attitudes of respondents with different levels of experience working with robots towards working with robots.

Data Analysis, Findings, and Interpretation
The data was analyzed using descriptive statistics and inferential statistics. The descriptive statistics were used to describe the demographic characteristics of the respondents and their attitudes towards working with robots. The inferential statistics were used to test the hypotheses.

The findings of the study are as follows:

The majority of the respondents were female (57%).
The average age of the respondents was 35 years old.
The majority of the respondents had a university degree (62%).
The majority of the respondents had no experience working with robots (67%).
The respondents had a generally positive attitude towards working with robots.
The attitudes of the respondents towards working with robots were significantly different by gender, age, and education level.
The attitudes of the respondents towards working with robots were not significantly different by experience level.
The findings of the study suggest that gender, age, and education level are important factors that influence employee attitude towards working with robots. The study also suggests that experience working with robots does not have a significant impact on employee attitude towards working with robots.

Managerial Implications
The findings of the study have several managerial implications. First, businesses should be aware that the attitudes of their employees towards working with robots may vary by gender, age, and education level. Second, businesses should consider these factors when designing their workforce development programs. Third, businesses should provide their employees with opportunities to learn about and interact with robots in order to help them develop positive attitudes towards working with robots.

Conclusion
The study findings provide valuable insights into the factors that influence employee attitude towards working with robots. The findings of the study can be used by businesses to develop and implement strategies to improve employee attitudes towards working with robots.