THE UNIVERSITY OF NEW SOUTH WALES
SCHOOL OF INFORMATION SYSTEMS AND TECHNOLOGY MANAGEMENT
TERM 1 2023
COMM1190: DATA, INSIGHTS AND DECISIONS
FINAL EXAMINATION
1. Writing Time: 3 Hours.
2. Reading and Submission Allowance Time: 1 Hour.
3. This is an Online Open-Book Exam, your responses must be your original
work. You must attempt this Final Exam by yourself without any help from
others. Thus, you have NOT worked, collaborated or colluded with any other
persons in formulating your responses. The work that you are submitting for
your Final Exam is your OWN work.
4. Release date/time (via Moodle): 6th, April 1:00pm (Australian Eastern Time
Zone)
5. Submission date/time (Via Turnitin): 6th, April 5:00pm (Australian Eastern
Time Zone)
6. Failure to upload the exam by the submission time will result in a penalty of
15% of the available marks per hour of lateness.
7. This Examination Paper has 8 pages, including the cover page.
8. Total number of Questions: 3 Questions.
9. Answer all 3 Questions.
10. Total marks available: 100 marks. This examination is worth 50% of the total
marks for the course.
11. Questions are not of equal value. Marks available for question sub-parts are
shown on this examination paper.
12. Some questions have word limits as indicated on the question. These word
limits must be adhered to. Text in excess of the specified word limit(s) may not
be considered in the marking process.
13. Answers to questions are to be written in the exam answer sheet template
provided. Please ensure that you provide all details required on the cover
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sheet of your Final Exam answer sheet.
14. Failure to submit exam answers with the correct exam answer sheet will result
in 10% penalty of your overall exam marks.
15. Students are reminded of UNSW’s rules regarding Academic Integrity and
Plagiarism. Plagiarism is a serious breach of ethics at UNSW and is not taken
lightly. For details, see Examples of plagiarism.
For this assessment task, you may use standard editing and referencing
software, but not generative AI.
You are permitted to use the full capabilities of the standard software to
answer the question (e.g., Microsoft Word’s standard editor, Grammarly, etc.).
If the use of generative AI such as ChatGPT is detected, it will be
regarded as serious academic misconduct and subject to the standard
penalties, which may include 00FL, suspension and exclusion.
16. This Final Exam is an open book/open web, and further information is
available “Here”.
• You are permitted to refer to your course notes, any materials provided by
the course convenor or lecturer, books, journal articles, or tutorial
materials.
• It is sufficient to use in-text citations that include the following information:
the name of the author or authors; the year of publication; the page
number (where the information/idea can be located on a particular page
when directly quoted), For example, (McConville, 2011, p.188).
• You are required to cite your sources and attribute direct quotes
appropriately when using external sources (other than your course
materials).
• When citing Internet sources, please use the following format:
website/page title and date.
• If you provide in-text citations, you MUST provide a Reference List. The
Reference list will NOT BE counted towards your word limit.
17. Students are advised to read the Final Exam paper thoroughly before
commencing.
18. The Lecturer-in-Charge (LiC) / Exam Referee will be available online (via
Moodle) after the Open-book Exam paper is released for a period of one hour.
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QUESTION 1 40 MARKS
In the past, the state branches of a large charity were responsible for soliciting
donations. The Head Office of the charity has decided to change the system and to
administer the process centrally. As part of the transition to the new system, they
recognised there were some differences across states, but now they need to decide
on a single process.
The basic approach used by most state branches was to keep a register of past
donors and then, once a year, send them a letter (or email) thanking them for their
past support while at the same time asking whether they would donate again. One
key variant of this was where some states organised sponsorships from a major
supermarket chain and, in addition to the letter, included a $50 voucher to the
sponsoring supermarket for anyone who subsequently donated more than $200.
Because of this precedent, one state, which previously had not provided incentives,
conducted a field experiment to explore the possibility. They took a random sample
of over 400 previous donors and then randomly allocated them into two groups. One
group was offered a $50 voucher for anyone donating more than $200 while the
other group received the usual letter of thanks and request to donate again.
The Head Office now needs to decide whether to include the incentive or not in their
yearly requests to previous donors. As part of the decision-making process, the
charity employed the firm AA Analytics to provide evidence on the effectiveness of
offering the voucher incentive using the available data sources. They were given
access to observational data drawn from the charity’s donor database containing
information on the amount donated in the last year as well as very limited socio-
demographic information, but it does include whether or not they resided in a state
where the vouchers were offered. Access was also given to the experimental data,
which included a more extensive set of socio-demographic information collected as
part of the experiment. Table 1 provides sample means, separated according to
whether they received a voucher or not, for both the observational and experimental
data sets.
AA Analytics estimated several regression models using both sets of data. Table 2
provides the OLS estimates for regression models where the dependent variable is
the amount of the donation. For each data set there are results from two models: a
model that only includes the treatment variable (whether they received the voucher or
not), labelled M1 and M3 in the table; and a second model where available control
variables are included in addition to the treatment variable, labelled M2 and M4. Note
that the experimental data had an extended set of variables that were collected as
part of the experiment that was not available for use with the observational data.
These two tables represent the primary analyses that AA Analytics will use to inform
the decision of the Head Office on how their process of soliciting donations will
proceed in the future.
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Table 1: Sample means for key variables divided into treatment and control groups
Variables and definitions Observational data Experimental data
Treatment Control Treatment Control
Donation
Amount of donation in dollars
19.60 11.97 15.47 14.04
Voucher
= 1 if received a $50 voucher, =0 otherwise
1.00 0.00 1.00 0.00
Female
= 1 if donor is female, =0 otherwise
0.70 0.49 0.59 0.61
Income
Income in thousands of dollars
157.2 158.7
Age
Age of donor in years
50.7 50.4
Previous
= 1 if donated previously, =0 otherwise
0.29 0.32
Observations 1545 2723 216 213
Notes: (i) The observational data were extracted from the charity’s data base and the division into treatment and control
groups is done by matching people based on their state of residence. Apart from state of residence the only other socio-
demographic variable available in the observational data is the gender of the donor.
(ii) The experimental data were provided by one of the states who previously had not sent vouchers as incentives but
explored the possibility of doing so by conducting a field experiment where people were randomly assigned to treatment
and control groups and then sent a voucher or not depending on their assigned group. This data collection included an
additional survey that provided an extended set of socio-demographic characteristics that were available for analysis.
Table 2: Regression results for alternative models using both data sets
Variables Observational data Experimental data
M1 M2 M3 M4
Voucher 7.63
(5.77, 9.50)
5.05
(3.18, 6.93)
1.43
(-4.93, 7.80)
2.11
(-3.62, 7.85)
Female 11.77
(9.96, 13.59)
13.50
(7.04, 19.96)
Income 0.093
(0.071, 0.115)
Age 0.001
(-0.502,0.504)
Previous 9.18
(2.35, 16.00)
R-squared 0.015 0.051 0.000 0.199
Observations 4268 429
Notes: (i) This table reports regression coefficients estimated by ordinary least squares. The bracketed numbers ( , ) under
these estimates are the lower and upper limits of the 95% confidence interval.
(ii) For the M2 regression the observational data set does not include the extra variables that were available in the
experimental data and used in M4.
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Part 1.A [Max 300 words] 15 Marks
How do you interpret the estimated coefficients in M1 and M2? The estimated impact
of receiving a voucher seems very different in M1 and M2. Do you agree?
Irrespective of whether you agree or not, provide a possible explanation for the
difference. Do these results support the use of a voucher to increase donations?
Part 1.B [Max 100 words] 5 Marks
AA Analytics enquired about the design of the experiment and was told that
treatment and control groups were randomly assigned. Do the summary statistics in
Table 1 support this contention? Justify your answer.
Part 1.C [Max 200 words] 10 Marks
How do you interpret the estimated coefficients for Voucher in M3 and M4? Explain
the differences between the estimated coefficients for Voucher in M2 and M4.
Part 1.D [Max 200 words] 10 Marks
Ultimately the charity would like advice on whether to offer an incentive. Based solely
on the results that have been provided, what would be your preliminary advice on the
use of a voucher to increase donations? In addition to these results, what extra
information and/or analyses would be useful before making a final recommendation
to the charity’s Head Office?
QUESTION 2 20 MARKS
In the fast-paced world of technology, competition is fierce and innovation is key to
success. Despite being relatively new players in the tech industry, Pomme Co. and
Elan Inc. have made a name for themselves with their cutting-edge products and
unique approach to business. As a result of their promising growth and potential for
future success, both Pomme Co. and Elan Inc. have attracted a great deal of
attention from financial markets, with investors closely monitoring their stock prices
and performance.
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Part 2.A [Max 80 words] 4 Marks
Explain the message of this chart and the (ethical) persuasive techniques that the
chart uses to highlight this message.
Part 2.B [Max 120 words] 6 Marks
Explain why this chart unfairly manipulates the viewer.
Part 2.C [Max 200 words] 10 Marks
Sketch TWO (2) alternative charts which more ethically support what happened.
Explain the choices made in each chart and persuasive techniques used to enhance
the message.
QUESTION 3 40 MARKS
Novaria is a country with 500,000 people located in the southern hemisphere, known
for its picturesque landscapes and thriving tourism industry. With abundant natural
wonders, such as pristine beaches, lush forests, and majestic mountains, Novaria
attracts millions of visitors annually. The tourism industry plays an important role in
the country's economy, providing jobs and generating revenue. The currency of
Novaria is the Novarian Dollar (NOD).
The tax office, called the Novarian Revenue Agency (NRA), collects taxes and
enforces tax laws in Novaria. The NRA uses a computerised system called the
Taxpayer Risk Assessment System (TRAS) to select tax returns for audit. The TRAS
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assigns a score to each tax return based on various factors, such as the income
reported, the number of deductions claimed, and the taxpayer's compliance history.
The scores range from 0-100. A higher score indicates that the return is more likely
to have a discrepancy between the true dollar amount owed to the NRA and the
claimed amount owed to the NRA. A sufficiently high score triggers an audit by an
NRA agent.
Part 3/A [Max 100 words] 5 Marks
Imagine that you are a data analyst working for the NRA. In the previous five years,
the score that triggered whether an NRA agent audits an individual is if their TRAS
score is 65 or higher. How would you evaluate the advantages and disadvantages of
the following scenarios: i) Raising the TRAS threshold score by 15 points to 80
points. ii) Lowering the TRAS threshold score by 15 points to 50 points.
Part 3.B [Max 300 words] 15 Marks
Despite the thriving tourism industry in Novaria, there is a large wealth divide
between the owners of hotels, restaurants, casinos, and tour companies and the
majority of citizens who are employed at these establishments. This could potentially
impact the tax system, as the income reported by these establishments could vary
greatly, and the TRAS may need to consider this when selecting tax returns for audit.
The following is the % breakdown of the population by earnings (90% of households
are classified as lower-income households and 10% of the population is classified as
higher-income households):
There are concerns that lower-income taxpayers are more likely to make errors on
their tax returns due to limited access to resources to help them prepare their returns
accurately. Also, low-income taxpayers may be more likely to claim certain tax credits
or deductions, such as the Earned Income Tax Credit, which are complex to
calculate and verify. Thus, there is a higher likelihood of claiming these credits or
deductions could make them more likely targets for audits.
A recent study by economists at the University of Novaria provided evidence that
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% of Households in Each Income Bracket (NOD)
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TRAS was more likely to audit the poorest households in Novaria. The figures show
that the lowest-income wage earners, defined as those earning under 300 NOD were
audited at a rate of 42 per 1,000 returns in 2022. For everyone else, the rate was 8
per 1,000 returns.
The head of the NRA, Taylor, has asked you to lead a team to devise a new model -
TRAS II - for determining audits. The key constraint is that a maximum of 20,000
people can be audited annually. Your team of researchers presented the following
potential approaches to present to Taylor.
• Approach 1: Adjust the original model so that 1) lower-income and higher-
income groups are modelled separately, and 2) the threshold for higher-
income groups remains at 70, but the threshold for lower-income groups is
raised to 80 before triggering an audit.
• Approach 2: Adjust the model so that 90% of audits are based on lower-
income and 10% of audits are based on higher-income households.
• Approach 3: Adjust the model so that the probability of correctly identifying an
individual who has committed a fraudulent return is equal in both groups.
Taylor wants you to evaluate the alternative approaches by comparing and
contrasting the objectives using an ethical decision framework.
i. Which approach is more likely to produce the most good and do the least
harm?
ii. Which approach is more likely to better treat people equally or proportionally?
iii. Which approach best serves the community as a whole and not just some
members?
Part 3.C [Max 100 words] 5 Marks
Based on your evidence of the advantages and disadvantages of each method,
decide which objective should underpin the new model. When justifying your
recommendation, explain how the decision reflects your ethical values.
Part 3.D [Max 300 words] 15 Marks
After listening to your proposals and recommendations, Taylor has become
increasingly interested in Ethical Decision Making. To learn more about different
ethical approaches, Taylor has asked you to explain the utilitarian approach, the
common-good approach, and the deontological approach so Taylor can clearly
differentiate between each approach. To support your explanations, you must
provide examples based on decisions in scenarios from one of the workshops.
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