MGMT 8525M Week 11 Research Paper Example

Course	MGMT 8525M Big Data Decision Making and Management
Week	Week 11
Paper type	Research paper
Length	About 860 words, 6 pages
Format	APA 7 student paper
School	Walden University
Program	PhD in Management
Updated	October 2026

Free sample paper for MGMT 8525M Week 11

Did Ride-Hailing Entry Reduce Alcohol-Related Crashes? A Research Paper Sorting the Published Studies by Design, Tracing Their Disagreement to Comparison Choices Rather Than Data Volume and Setting Out a Design for a City Licensing Decision

Student Name

Walden University

MGMT 8525M: Big Data Decision Making and Management

Instructor Name

Month Day, Year

What this page is doingThe title states the question, the paper's explanation for why studies disagree and the decision the evidence is meant to serve.

Abstract

This paper asks whether the entry of ride-hailing services reduced alcohol-related motor vehicle crashes in American cities, a question relevant to a mid-sized city council deciding whether to relax licensing caps on ride-hailing drivers. Published studies disagree, reporting reductions, no effect and increases. Sorting them by design shows that the disagreement traces mainly to differences in outcome measures, comparison groups and the handling of staggered entry dates, not to differences in data volume. The paper proposes a design for the council's question and sets out which conclusions the evidence supports and which it leaves open.

What this page is doingThe abstract states the question, the finding about the literature and the paper's limits in one paragraph.

The Question and the Decision

The council of Harmon City, a composite city of about 420,000 residents, caps ride-hailing driver permits at 1,500. Advocates argue that lifting the cap would reduce drunk driving. The decision needs an estimate of how much, if at all, ride-hailing availability reduces alcohol-related crashes, and how confident the council can be in that estimate.

The Literature, Sorted by Design

[after]

Each row was checked against the study's own abstract and methods section.

Table 1

Published Studies on Ride-Hailing and Crashes, by Design and Finding

Study	Setting and data	Design	Outcome	Finding \|
Greenwood and Wattal (2017)	California, 2009 to 2014	Difference in differences by city entry date	Alcohol-related fatalities	Reduction after the low-cost service entered \|
Brazil and Kirk (2016)	100 largest metropolitan areas, 2005 to 2014	Count models with staggered entry	Total and drunk-driving fatalities	No association \|
Dills and Mulholland (2018)	United States counties	Panel models with county and time effects	Fatal crashes and arrests	Reductions in fatal crashes reported \|
Morrison et al. (2018)	Four cities where service launched, stopped and resumed	Interrupted time series	Alcohol-involved injury crashes	Reductions in some cities, not others \|
Barrios et al. (2023)	United States cities	Staggered difference in differences	All fatal accidents	About a 3 percent increase \|

Why the Studies Disagree

Four design differences account for most of the disagreement. First, outcomes differ. Greenwood and Wattal (2017) and Morrison et al. (2018) studied alcohol-related crashes, the outcome most directly tied to the drunk-driving hypothesis, while Barrios et al. (2023) studied all fatal accidents. If ride-hailing reduces drunk driving but increases total vehicle miles, both findings can be true. Second, settings differ. Greenwood and Wattal studied California only, and their reduction appeared after the low-cost service arrived, not the premium one. Brazil and Kirk (2016) studied the largest metropolitan areas nationally. Third, measures of treatment differ: most studies code entry as a date, though actual availability grew gradually over months. Fourth, the handling of staggered entry differs. Goodman-Bacon (2021) showed that standard two-way fixed effects estimates with staggered treatment timing are weighted averages of many two-group comparisons, some of which use already-treated units as controls, and can be biased when effects change over time.

The studies with the largest datasets are not the ones that agree. The national studies cover more cities and more crashes, but they are not therefore more credible, because their comparisons may mix early and late entrants in ways the design does not handle.

What this page is doingThe reconciliation section attributes the disagreement to specific design choices, so the reader can see why size does not settle it.

A Design for Harmon City's Question

The council's question is narrower than the national one: what happens to alcohol-involved crashes in one city when driver supply rises. A design suited to it would follow the logic of difference in differences, as Angrist and Pischke (2009) set out: compare the change in alcohol-involved crashes during late-night weekend hours, when ride-hailing demand peaks, with the change during weekday daytime hours, before and after a change in permit numbers. Other cities that raised caps at different times could serve as additional comparisons, using an estimator robust to staggered timing.

Results From the Literature, as Intervals

The most directly comparable estimate comes from Morrison et al. (2018), who reported that in Portland the resumption of service was associated with a 61.8 percent reduction in the alcohol-involved crash rate, with a 95 percent confidence interval from 38.7 to 86.4 percent, while other cities in the same study showed no comparable change. Such a wide interval, and the variation across cities, is the honest summary of what is known.

Validity, by Type

Shadish et al. (2002) distinguished four kinds of validity. Statistical conclusion validity is threatened by the small number of alcohol-involved crashes in a single city, which will make intervals wide. Internal validity is threatened by events coinciding with permit changes, such as enforcement campaigns. Construct validity is threatened by crash reports that classify alcohol involvement inconsistently. External validity is limited, because effects found in California or Portland may not transfer to a city with different nightlife geography and transit.

What the Evidence Licenses

The evidence licenses this claim: increased ride-hailing availability may reduce alcohol-involved crashes in some cities, particularly where late-night alternatives to driving are scarce. It does not license a prediction of a specific reduction for Harmon City, and it raises the possibility that total crashes could rise. The council could lift the cap gradually and measure, treating the change as an opportunity to learn.

References

Angrist, J. D., & Pischke, J.-S. (2009). Mostly harmless econometrics: An empiricist's companion. Princeton University Press.

Barrios, J. M., Hochberg, Y. V., & Yi, H. (2023). The cost of convenience: Ridehailing and traffic fatalities. Journal of Operations Management, 69(5), 823-855. https://doi.org/10.1002/joom.1221

Brazil, N., & Kirk, D. S. (2016). Uber and metropolitan traffic fatalities in the United States. American Journal of Epidemiology, 184(3), 192-198. https://doi.org/10.1093/aje/kww062

Dills, A. K., & Mulholland, S. E. (2018). Ride-sharing, fatal crashes, and crime. Southern Economic Journal, 84(4), 965-991. https://doi.org/10.1002/soej.12255

Goodman-Bacon, A. (2021). Difference-in-differences with variation in treatment timing. Journal of Econometrics, 225(2), 254-277. https://doi.org/10.1016/j.jeconom.2021.03.014

Greenwood, B. N., & Wattal, S. (2017). Show me the way to go home: An empirical investigation of ride-sharing and alcohol related motor vehicle fatalities. MIS Quarterly, 41(1), 163-187. https://doi.org/10.25300/MISQ/2017/41.1.08

Morrison, C. N., Jacoby, S. F., Dong, B., Delgado, M. K., & Wiebe, D. J. (2018). Ridesharing and motor vehicle crashes in 4 US cities: An interrupted time-series analysis. American Journal of Epidemiology, 187(2), 224-232. https://doi.org/10.1093/aje/kwx233

Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.

What the MGMT 8525M Week 11 instructions ask for

The final paper asks you to settle one question with evidence whose limits are examined openly. Choose a question tied to a decision where published studies disagree. State the question on the first page, describe the data sources, sort the literature by design and explain how a credible design would separate the effect from its confounders. Report results as intervals. Organize threats to validity by type: statistical conclusion, internal, construct and external. Include a section that explains why published studies disagree. Close by separating what the evidence supports from what you are proposing. Fifteen to twenty pages with an abstract and a substantial reference list is typical for this capstone paper.

How the MGMT 8525M Week 11 example is put together

The sample's abstract states the question, the finding about the literature and the paper's limits. A section ties the question to a council's permit cap. A table sorts five studies by setting, design, outcome and finding, each checked against its source. The reconciliation section explains how differences in outcome, setting, treatment timing and estimation account for the conflicting results, and notes that the largest datasets are not the most consistent. A design section proposes comparing late-night weekend crashes with weekday daytime crashes before and after permit changes. Results are reported as published intervals. A validity section assigns threats by type, and a final section states what the evidence licenses.

Where the marks sit in the MGMT 8525M Week 11 rubric

The reconciliation of conflicting studies carries the most weight. Papers that summarize studies one by one, or pick the study that supports a preferred answer, score low. Graders look for a question tied to a decision, a literature table organized by design, an explanation of disagreement rooted in specific design choices and a proposed design whose identifying assumption is stated. Results should be reported as intervals with their sources. Validity threats should be organized by type and assigned accurately. Keeping published support and the author's own proposal in separate sections is part of the grade. Extensive, accurate references and clear organization complete the marks. Treating the largest study as the most credible without examining its design is a common misstep.

Common MGMT 8525M Week 11 mistakes, and how to avoid them

Long final papers often lose focus. Write the question and the decision on the first page and refer back to them. Build the literature table before drafting the discussion, and read each study's methods section rather than its abstract alone. Look for design differences that explain conflicting results. State your own design's key assumption and how you would test it. Report intervals, not single numbers. Use the four validity types to organize threats. Keep the evidence and your proposal in separate sections. A tutor can review your literature table, and the ride-hailing paper above shows the complete structure. Start the literature table early, since it shapes everything else.

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Enclose the Week 11 guidelines, the rubric, and the question you plan to answer, plus any feedback from earlier weeks. A full research paper returns inside 24-48h, and a first request costs nothing. The sample builds on public datasets and published studies; any proprietary data your own paper draws on is yours to describe.

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MGMT 8525M Week 11 questions, answered

Where can I find a free MGMT 8525M Week 11 sample paper?

The ride-hailing and crashes research paper above is the complete Week 11 sample.

What question does the MGMT 8525M Week 11 paper ask?

Whether ride-hailing entry reduced alcohol-related crashes, to inform a city's decision on driver permit caps.

Why do published ride-hailing studies disagree?

They differ in outcomes measured, settings, how entry is coded and how staggered timing is handled, not mainly in data size.

What design does the paper propose for the city?

Comparing late-night weekend crashes with weekday daytime crashes before and after a permit change, with other cities as comparisons.

What does the evidence license?

That ride-hailing may reduce alcohol-involved crashes in some cities, but not a specific prediction for this one.