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1 MKF2121 Marketing Research Methods Semester 2, 2020 Assignment 2: Marketing Research Analysis 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. 1. The due date/time of the assignment is by 11:59pm on Sunday after week 14 (Nov 22). 2. The assignment is individual work. Collaboration or consultation with anyone other than the unit’s teaching staff is strictly prohibited 3. 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. 4. You need to first agree to the "plagiarism statement" above before you are allowed to submit this assignment. Do NOT include cover sheets in your submission. 5. Submission format: PDF file. Maximum number of words is 2000(±10%, and excluding words and numbers that are part of your SPSS tables or references). The word count must be included in the first page of your document. Exceeding the word count could result in a penalty of up to 10% of your mark for the assignment 6. Please put any references you may have in the Appendix. 7. 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 Adobe Acrobat; and 2) that it is the file you intend to submit for grading 8. Please contact your tutor and/or the lecturer if you have any further questions. 2 Assignment 2 - Guide A marketing research analysis report communicates the result of a study to clients. The report is usually commissioned by the manager at the end of the Market Research Process to solve the Management Decision Problem (MDP) and inform future decisions. Suppose that you have been commissioned by the American telecom company IBM to draft a brief research report based on the questionnaire and data of Case 3.2 “IBM: A Top Provider of Computer Hardware, Software, and Services” (henceforth, “the IBM survey” and “the IBM data.”). Please read the IBM case and survey from the textbook (Pages 766-773) and understand the MDP. You will be using the IBM dataset to solve the MRP and report to the manager. Note that the survey collected includes information for the entire IT industry. This includes both IBM and its major competitors – Microsoft, Compaq/HP etc. You may choose to focus on the industry, or any one vendor in particular. Below is the framework of the research report – 1. Definition of the research problem (a) Define the market research problem (MRP) Based on the questionnaire provided, 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 a customer attitude, belief or behavioural variable that is measured by the IBM 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 IBM survey. In approaching this task, you should 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 IBM make use of the information to improve their decision-making? (b) Provide a brief justification for your MRP Provide a brief explanation on how this research can help your client (i.e., why it is important to understand the particular customer attitude or behaviour in this context). This should be consistent with section 3 below where you discuss the managerial implication of your findings. This could also include references from a background literature review. 2. Research approach and hypotheses Come up with at least 5 comparative or relational research questions (RQ). Note that you need to develop your own original RQs (i.e., you cannot the RQs used in mini-tasks or class examples). Collectively, your RQs should cover ALL the components of your marketing research 3 problem proposed in the previous section. Note that because these RQs need to be “answerable” by the IBM dataset, they should only involve variables measured in the IBM survey. This requires you to be very familiar with each question in the questionnaire. You can depict the research approach in the form of an Analytical Model. This section also shows development of research approach and hypotheses, specification of information needed and a data analysis plan. For each RQ, • 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 or cluster analysis 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 1. T‐tests (one sample/independent samples/paired samples) can be used to test for differences between means of subgroups/variables. 2. Chi‐square test of association can be used to test the (non-linear) association between two categorically scaled variables. 3. Chi-square test of proportions can be used to test for sample representativeness. 4. 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. 4 5. Correlation analysis measures the degree to which there is a linear association between two interval or ratio scaled variables. 6. Multiple regression can be used to explain the variation in dependent variables (outcome or effect variables) using other metric variables as independent variables (predictors), and/or test for differences across groups (using dummy variables). 7. Cluster analysis to identify key market segments 3. Key Descriptive/Summary Statistics First, briefly describe the data based on the information you have. For EACH of your variables, provide appropriate descriptive statistics. These could include frequency bar-charts/histograms or descriptive tables. • You only need to produce a bar chart/histogram for each variable (i.e., the SPSS frequency table is NOT required) • You can also choose descriptive tables for metric 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 and does not count towards your word count You may also consider the following questions as you describe the data. Read the case for more details. a) What is the target population? b) What is the sample size? Is the sample representative of the population? c) 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) 4. Statistical Analysis Results You are required to report the results of your statistical tests. This includes both the SPSS output as well as the interpretation of the test/regression/segmentation analyses. • Clearly state the outcome of each statistical analysis and what it means in plain English. The outcome of each statistical analyses should be part of the main text. • Please provide all SPSS statistical output from which you have derived your results— including any steps in data cleaning and recoding. Failure to comply could result in 30% reduction in your final mark for this assignment. • Make sure that the output tables are properly labelled and clearly indicate which analyses it tests. Examples for including SPSS output 5 Table 1. Comparing the difference between 2007 and 2011 revenue (H1) Paired Samples Test Paired Differences t df Sig. (2-tailed) Mean Std. Deviation Std. Error Mean 95% Confidence Interval of the Difference Lower Upper Pair 1 Firm revenue in 2011 – Firm revenue in 2007 .53 .77 .10 .32 .74 5.15 60 .001 5. Discussions of the managerial implication of your main findings Discuss