| Course | GB730 Applied Business Research Design and Methodology |
|---|---|
| Unit | Unit 4 |
| Paper type | Design justification |
| Length | About 1,090 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | MBA |
| Updated | October 2026 |
Free sample paper for GB730 Unit 4
Three Rivals and a Winner: A Unit 4 Design Justification for a Study of Revenue Management Overrides
[Student Name]
Purdue University Global
GB730: Applied Business Research Design and Methodology
Unit 4 Design Justification
[Instructor Name]
[Date]
The hotel company and its circumstances are composite and illustrative; the methods sources cited are real.
The Chain the Design Must Serve
The problem is that senior revenue leaders lack evidence on how managers' overrides of system-recommended rates relate to occupancy and average daily rate, and on why managers say they override. The purpose is to examine that relationship and then explore managers' reasons so that their accounts can help explain it. The three research questions, settled in Unit 3, ask about the adjusted association, about managers' described reasoning and about how the accounts help explain the association. Any design that fails to answer one of them would break the chain the earlier units built.
Three Tests the Design Must Pass
Any design must pass three tests drawn from that chain. It must be able to estimate the association between overrides and each outcome while accounting for measured demand. It must be able to produce managers' own accounts of their reasoning. And it must connect the two, so that the accounts explain the patterns rather than sitting beside them.
Rival 1: A Quasi-Experiment Comparing Hotels
The case for it. A quasi-experiment would strengthen causal inference. If some hotels had adopted the revenue management system earlier than others, or if some had restricted overrides while others had not, the study could compare changes in outcomes across those groups, using a difference-in-differences design that controls for factors common to all hotels (Shadish et al., 2002).
Why it falls short. The company installed the system at all 22 hotels in the same month in 2022, and override rights were the same at every hotel from the first day. There is no group of hotels without the system and no group with restricted overrides to serve as a comparison. A quasi-experiment needs variation in treatment that the setting does not provide.
Rival 2: A Qualitative Case Study
The case for it. A case study of how revenue managers make decisions would capture the reasoning the records cannot, and it would allow a rich account of the information managers use, how they interpret the system's recommendations and how their decisions differ across hotels (Yin, 2018). It would also be less dependent on the company's data systems, whose reason codes are mostly blank.
Why it falls short. A case study could explain decisions but could not tell revenue leaders whether overrides are associated with better or worse outcomes, which is the first half of the problem. Leadership's disagreement is about outcomes. A design that cannot speak to them leaves the problem unaddressed.
Rival 3: A Survey of Revenue Managers
The case for it. A survey could reach every revenue manager quickly, measure attitudes toward the system with validated scales, and ask how often and why managers override.
Why it falls short. The company has only about 30 revenue managers, too few for the statistical analysis a survey would need to relate attitudes to outcomes. More important, managers' reports of how often they override would be less accurate than the system's own logs, which record every override. A survey would measure perceptions of behavior when the behavior itself is already recorded.
The Chosen Design: Explanatory Sequential Mixed Methods
An explanatory sequential design runs the numbers first and then uses interviews built to account for what the numbers show (Creswell & Plano Clark, 2018). Here the quantitative phase uses the system's logs and the property management system's records covering three years and about 24,000 hotel-nights, to estimate how overrides relate to rooms sold and to the rate guests paid, holding constant the demand signals the records capture: bookings on hand two weeks ahead, the day of the week, events in each city and what nearby competitors charged. The qualitative phase interviews about fifteen revenue managers about how they decide whether to override.
The design passes all three tests. The quantitative phase estimates the adjusted association. The qualitative phase produces managers' accounts. And the connection between them, described next, makes the accounts explain the patterns.
Why Records Rather Than Self-Report
The design's quantitative phase relies on records rather than asking managers about their decisions. That choice follows from what the company's systems capture. The revenue management system logs every recommended rate and every rate actually published, with a time stamp and the user who made the change, so every override can be identified without asking anyone. The property management system records rooms sold and the rate paid for every night at every hotel. Records of this kind measure behavior directly and avoid the recall errors and social desirability that self-reports introduce, especially when the person reporting knows a colleague in the revenue department is asking.
The Connecting Step
The point where the phases connect is the selection of interviewees. After the quantitative phase, each manager's overrides will be characterized by how they associate with outcomes: some managers' overrides will associate with higher rates without lower occupancy, others with lower occupancy, and others with little difference. Interviewees will be chosen from each of these groups, so that the interviews can ask whether managers in different groups describe different reasoning. Interview questions will also be adjusted to probe specific patterns, such as overrides concentrated on weekends or around events.
The interviews are not chosen before the numbers arrive; the numbers decide who is interviewed and what they are asked.
Weaknesses the Design Keeps
The design does not solve the selection problem described in Unit 1. Adjusting for measured demand reduces it, but managers override when they know things the data do not record, and that unmeasured knowledge remains in the association. The design also depends on the candidate's access to the company's data and managers, which the site access plan must secure. And with about fifteen interviews, the qualitative phase can identify patterns of reasoning but not establish how common each is. These limits are stated so that the study's findings can be read for what they are.
Why This Design Is the Right Trade
Among the four designs considered, only the chosen one answers both parts of the problem with the data the setting actually provides. Its causal limits are real, but they are shared by every design available here, and it alone connects outcomes to reasons, which is what the company's leadership most needs to settle its disagreement.
References
Creswell, J. W., & Plano Clark, V. L. (2018). Designing and conducting mixed methods research (3rd ed.). SAGE.
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE.
Reading the GB 730 Unit 4 instructions
The fourth GB730 assignment asks you to justify your design by comparing it with alternatives a reasonable researcher might choose. Restate the problem, purpose and research questions so the design can be measured against them. Set out the tests any design must pass, drawn from that chain. Then present two or three rival designs, giving each its strongest case before explaining why it falls short in your setting. Base each rejection on a fact about the organization or the data, not on preference. Present the chosen design and show that it passes each test. Explain any important data choice, such as records over self-report. Describe how the phases connect if the design is mixed. Close by naming the weaknesses the chosen design keeps and why the trade is still the right one.
How this GB730 Unit 4 example is built
The justification restates the chain and turns it into three tests: estimate the association, capture managers' reasoning and connect the two. Each rival gets a two-part treatment, first its case and then its failure. The quasi-experiment falls on the same-month rollout, the case study on its silence about outcomes, and the survey on a small population and on logs that already record overrides. The chosen design is described with its roughly 24,000 hotel-nights and four demand controls. A section explains why system records beat self-report here. The connecting step, choosing interviewees by their quantitative pattern, gets its own section and the highlighted sentence. The paper ends by admitting that unmeasured knowledge stays in the association and that fifteen interviews cannot establish how common each pattern is.
GB730 Unit 4 rubric: what earns full points
The GB730 design justification is graded on fairness to the alternatives. Instructors give real credit only when each rival is presented at its strongest, then rejected with a fact from the setting. A chosen design that is described without testing it against the research questions loses marks even if it is a sensible design. For mixed methods, the rubric looks closely at the connecting step, since a design that runs two strands side by side is not explanatory sequential. Justifying the data source, especially records over self-report or the reverse, shows methodological awareness. Naming the weaknesses the design keeps is part of the grade, not an optional extra. Citations to Shadish and colleagues, Creswell and Plano Clark and Yin show where each design is defined.
GB730 Unit 4 help from the desk
GB730 design papers often present weak rivals that nobody would choose, which makes the winning design look strong without proving anything. Some reject alternatives for being hard or expensive rather than because the setting rules them out. A common gap in mixed designs is describing both phases without explaining how the first shapes the second. Students also tend to hide limitations to make the design look complete, which reads as overconfidence to a committee. Data choices, such as using logs instead of a survey, are frequently left unexplained. Give every rival its best argument, reject it with a fact, test your design against the questions, explain the connection and own its weaknesses. If you have a design in mind but no rivals argued yet, we can draft the comparison for you.
Get a GB730 Unit 4 example written to your instructions
Tell us which designs were considered, what the setting permits and the Unit 4 requirements your section set, rubric included. In 24-48h a custom justification argues for the favored design by giving each rival its best case, then naming the fact of the setting that rules it out and the weaknesses the chosen design keeps. Your first sample is unbilled. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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GB730 Unit 4 questions, answered
Is a mixed methods design harder to defend in GB730?
It requires more argument, because a reader will ask what the combination achieves that either strand alone could not. The answer must be specific: in an explanatory sequential design, the qualitative phase explains patterns the quantitative phase found. If the justification cannot say what integration adds, a single-method design is often the stronger choice.
How many alternatives should the justification consider?
Two or three serious ones is common. Include at least one from a different paradigm, so the choice between qualitative, quantitative and mixed is argued rather than assumed. An alternative that could plausibly have been chosen makes a stronger comparison than a design nobody would ever propose for the problem. Each should be rejected for a reason tied to your own setting.
Can feasibility decide the design?
It can decide between designs that would each answer the question, but it should not be the only reason. Saying a design was chosen because it was easy reads poorly. Saying the stronger design was impossible because the setting lacks a comparison group, and showing that fact, is legitimate reasoning a committee will accept.
Where can I find a free GB730 Unit 4 sample paper?
A complete GB730 Unit 4 design justification is on this page, weighing a quasi-experiment, a case study and a survey before choosing explanatory sequential mixed methods for a hotel override study.
Why choose records over a survey for the quantitative phase?
When an organization's systems already log the behavior being studied, records measure it directly. A survey would ask people to recall what the logs hold, adding recall error and the pull toward answers that look good.