Lecture note on “making recommendations” in an MBA dissertation report

Lecture note on “making recommendations” in an MBA dissertation report

 

In the context of doing my par-time four-month MBA dissertation project, I have a query about formulating recommendations in Chapter 6 (conclusions and recommendations) dissertation report, which is: how to formulate recommendations that are clearly supported by the dissertation report findings; that the recommendations are implementable; and that the recommendations are endorsed by the readers (e.g. managers in particular sector or managers for a particular client organization) as practical, useful, holistic and strategic. I feel that recommendations could not solely rely on the dissertation researching findings (e.g. Chapter 5 [discussions of findings]).

 

Yes — your instinct is right. In a strong MBA Chapter 6, recommendations should be grounded in findings, but not mechanically copied from Chapter 5; they should translate findings into managerial action, using judgment, theory, feasibility, and context.warwick.ac+1

Core principle

A useful way to think about it is:

• Chapter 5 answers: “What did the study find, and what does it mean?”
• Chapter 6 answers: “Given those meanings, what should managers do next, and why is that the best practical response?”

So recommendations are not just a repeat of findings; they are an evidence-informed decision proposal built from findings plus interpretation, literature, constraints, and managerial priorities.gradcoach+1

How to formulate them

Use a chain like this for each recommendation:

1.    Finding: state the key result briefly.

2.    Implication: explain the business problem/opportunity revealed.

3.    Recommendation: specify the action, owner, and scope.

4.    Justification: show why this is the best response, using evidence and relevant literature.

5.    Feasibility: mention resources, timeline, risks, or dependencies.

6.    Expected outcome: state what improvement should result.

This structure keeps the recommendation clearly linked to the report while making it managerial and actionable.premierdissertations+2

What makes it credible

To make recommendations implementable and more likely to be accepted by managers, they should be:

• Specific: say exactly what should be done.
• Prioritized: focus on the highest-impact actions first.
• Feasible: fit the organization’s resources, capability, and time.
• Strategic: connect to business goals, not just operational fixes.
• Context-sensitive: reflect the client organization or sector conditions.
• Measurable: include success indicators or outcomes where possible.

If a recommendation is theoretically attractive but unrealistic, it weakens Chapter 6. Better to offer a narrower, realistic action than a grand but unworkable one.gallowayresearch+1

How to go beyond Chapter 5

You are also correct that recommendations should not rely solely on the findings section. A strong Chapter 6 usually also draws on:

• Literature: to support why the recommendation makes sense beyond your sample.
• Theory: to explain the logic of the proposed action.
• Managerial judgment: to adapt the recommendation to real-world constraints.
• Assumptions and limitations: to signal where the recommendation is context-bound.
• Implementation considerations: resources, sequencing, risks, and accountability.

That combination makes the chapter feel practical, holistic, and strategic rather than merely descriptive.warwick.ac+2

Simple writing formula

You can use a sentence pattern like:

“Because the study found X, which suggests Y, the organization should do Z by [who/when], in order to achieve [outcome], subject to [constraint].”

Example:

“Because the study found that service inconsistency reduced customer trust, the firm should introduce a standardized frontline service checklist and monthly quality review, led by the operations manager, to improve service reliability within three months.”

That style is concise, evidence-linked, and implementable.

** a collection of lecture notes on research methods

Lecture note on statistical analysis methods: for MBA students

 Lecture note on statistical analysis methods: for MBA students

 

Highlight 4 main ideas of the following statistics analysis methods in the context of doing MBA dissertation projects and provide 2 application examples (that utilize questionnaire survey data) on each of these methods: 1. correlation analysis 2. Anova (single factor) 3. Chi-square test

Below is an MBA-dissertation-friendly guide to the three methods, focusing on what each method is for, how to think about it, and how to use questionnaire survey data in practice. I’ve kept the examples close to common dissertation topics such as consumer behavior, employee attitudes, and service quality.utwente+2

1) Correlation analysis (re: a video on linear regression)

Correlation analysis examines whether two quantitative variables move together, and in what direction. It is useful when your dissertation asks whether higher levels of one survey score are associated with higher or lower levels of another score.utwente

4 main ideas

• It measures the strength of association between two variables.
• It shows the direction of the relationship: positive, negative, or near zero.
• It does not prove causation, only co-movement.
• It works best when both variables are measured on a scale such as a Likert composite score, total score, or index created from questionnaire items.utwente

2 questionnaire-based application examples

• Customer satisfaction and repurchase intention. Use survey items to create a satisfaction score and a repurchase intention score, then test whether more satisfied respondents also report stronger intention to buy again.
• Work engagement and job performance self-rating. Use questionnaire scales for engagement and self-rated performance, then test whether respondents with higher engagement scores also report higher performance scores.

2) One-way ANOVA (re: a video on ANOVA)

One-way ANOVA tests whether the mean of a quantitative variable differs across three or more groups defined by a single categorical factor. In MBA dissertations, it is commonly used when you want to compare average survey scores across segments such as age groups, income groups, job levels, or customer types.utwente+1

4 main ideas

• It compares group means rather than individual responses.
• It uses one categorical independent variable with two or more levels.
• It is appropriate when the dependent variable is quantitative.
• If the overall test is significant, you usually follow up with post-hoc comparisons to see which groups differ.utwente+1

2 questionnaire-based application examples

• Service quality perceptions across age groups. Survey respondents rate service quality on a 1–5 scale, then compare mean ratings across age groups such as under 30, 30–45, and over 45.
• Purchase intention across income groups. Use a Likert-based purchase intention score and test whether the average intention differs among low-, middle-, and high-income respondents.

3) Chi-square test (re: a video on the chi-square test)

The chi-square test is used with categorical data to examine whether two variables are related or whether observed responses differ from expected patterns. For questionnaire surveys, it is especially useful when responses are recorded as categories such as yes/no, male/female, brand choice, or grouped levels like satisfied/neutral/dissatisfied.libraryguides.mcgill+2

4 main ideas

• It works with categorical variables, not scale means.
• It compares observed frequencies with expected frequencies.
• It can test independence between two survey variables.
• It is common for cross-tabulation analysis in business research questionnaires.libraryguides.mcgill+2

2 questionnaire-based application examples

• Preferred payment method by age group. Survey respondents choose payment method categories such as cash, card, or mobile wallet, and you test whether preference is associated with age group.
• Brand awareness by education level. Respondents indicate whether they know a brand or not, and you test whether awareness is independent of education level.

Quick way to choose

• Use correlation when both variables are numerical scores and you want association.
• Use one-way ANOVA when one categorical factor explains differences in a numerical survey score.
• Use chi-square when both variables are categorical or grouped into categories.

** a collection of lecture notes on research methods