Kathmandu University grades on a CGPA system with a C+ minimum in every course and no graduation while carrying an F, which tells you something about how the dissertation is assessed: consistency matters, and a weak analysis chapter is not offset by a strong literature review.
KU cohorts also tend to include working professionals, which changes what is feasible. Employer access is often your strongest asset, and topics that exploit it finish more reliably than topics that depend on cold outreach.
Each topic below names its data source, a method and an honest feasibility rating. Confirm scope with your supervisor and treat your programme's own dissertation guideline as binding.
What we help with
Topic feasibility — An honest read on whether the data exists and whether the question fits your available research window.
Proposal and literature review — A problem statement and review that establish a genuine gap rather than summarising everything published.
Methodology design — Sampling, instrument and analysis plan built to answer your stated objectives specifically.
Analysis and defence preparation — Statistical analysis, interpretation, and preparation for the questions examiners reliably ask.
Working with KU's grading requirements
Kathmandu University's evaluation scheme requires a C+ or above in every course and a minimum CGPA to graduate, with no graduation while carrying an F. For dissertation work the practical implication is that a partial submission is genuinely costly — there is no averaging out a weak final chapter against strong coursework.
That argues for choosing a topic you can definitely finish over one that is more ambitious but might not land. A well-executed study of a narrow question consistently marks better than a partially completed study of an important one.
Build in a checkpoint at the halfway mark where you honestly assess whether the data is arriving as planned. If it is not, changing scope at the midpoint is recoverable; discovering it at submission is not.
Using professional access without compromising the study
Many KU MBA students work while studying, and studying your own organisation is both legitimate and practical. What it requires is explicit handling. State the relationship in your methodology, describe what access you had that an outside researcher would not, and address how that might have shaped responses.
Where colleagues are respondents, anonymity is harder to guarantee than the consent form implies. Say how you protected it — aggregated reporting, no department-level breakdowns in small units, no verbatim quotes that identify a role.
If your employer wants to review the dissertation before submission, agree the terms early. A review for commercial sensitivity is normal; a review that could suppress unfavourable findings is a problem you should raise with your supervisor before you collect anything.
Finance and capital markets
Secondary-data topics are the safest route through a compressed research window, and KU examiners are comfortable with quantitative work.
Market efficiency of the Nepal Stock Exchange
A foundational question that remains genuinely open for a small, thinly traded market like NEPSE.
Data
NEPSE daily index and individual stock price series over ten years or more.
Method
Runs test, autocorrelation and variance ratio tests for weak-form efficiency.
Feasibility
Moderate
Bank-specific and macroeconomic determinants of profitability in Nepal
Combines bank data with macro series, giving the analysis more depth than a bank-only model.
Data
Bank annual reports plus NRB macroeconomic indicators — inflation, GDP growth, policy rate.
Method
Panel regression with bank-specific and macro predictors; fixed effects.
Feasibility
Straightforward
Risk management practice in Nepali financial institutions
Regulatory requirements give you a documented benchmark to compare actual practice against.
Data
NRB directives as the benchmark; survey of risk officers plus disclosure analysis.
Method
Gap analysis of practice against regulatory requirement; descriptive scoring.
Feasibility
Moderate
Determinants of foreign direct investment inflow to Nepal
Policy-relevant and answerable with published macro data, suiting students who prefer econometrics to fieldwork.
Data
Department of Industry FDI approvals, NRB balance of payments, macro indicators.
Method
Time series regression or ARDL with cointegration testing.
Feasibility
Demanding
Behavioural biases among Nepali retail investors
A small retail-dominated market is where behavioural effects show most clearly.
Data
Primary survey of active investors, reachable through broker offices and investor groups.
Method
Bias scale measurement; regression against trading frequency and portfolio outcomes.
Feasibility
Moderate
Impact of financial technology on traditional banking revenue in Nepal
Live commercial question with both published data and willing interviewees.
Data
Bank annual reports for fee and commission income; interviews with digital banking heads.
Method
Trend analysis of revenue composition with qualitative explanation from interviews.
Feasibility
Moderate
Marketing and consumer behaviour
KU's Kathmandu Valley base makes urban consumer research practical. Use a sampling frame you can document rather than convenience sampling you cannot defend.
Customer experience and loyalty in Nepali e-commerce
The sector is maturing fast enough that loyalty, not acquisition, is becoming the real question.
Data
Primary survey of online shoppers; platform review data as supplementary evidence.
Method
Customer experience dimensions regressed on loyalty intention; mediation analysis.
Feasibility
Moderate
Brand positioning of Nepali versus international banks
Both compete for the same urban customer, which makes the positioning contrast measurable.
Data
Primary survey with attribute rating across banks.
Method
Perceptual mapping via multidimensional scaling or correspondence analysis.
Feasibility
Demanding
Determinants of consumer trust in Nepali online marketplaces
Trust is the stated barrier to e-commerce growth in Nepal, so the finding has practical weight.
Data
Primary survey covering buyers and non-buyers.
Method
Trust construct measurement; SEM or multiple regression with purchase intention.
Feasibility
Moderate
Effect of loyalty programmes on customer retention in Nepali retail
Programmes are now widespread enough to compare across chains.
Data
Customer survey plus programme data from a cooperating retailer.
Method
Comparison of retention between members and non-members; logistic regression.
Feasibility
Moderate
Health consciousness and food purchase behaviour among urban Nepali consumers
A visible shift in Valley consumption that has not been documented academically.
Data
Primary survey with health consciousness and purchase behaviour scales.
Method
Regression of purchase behaviour on health consciousness with demographic moderators.
Feasibility
Straightforward
Word-of-mouth and brand choice in Nepali service markets
Word of mouth carries unusual weight in Nepali consumer decisions, which makes it worth measuring rather than assuming.
Data
Primary survey across selected service categories.
Method
Regression of brand choice on word-of-mouth exposure and source credibility.
Feasibility
Straightforward
Strategy, leadership and organisation
Professional cohorts make these viable. Secure organisational access in writing before the proposal is submitted.
Strategic agility in Nepali organisations during disruption
Recent disruptions give respondents concrete events to describe rather than abstractions.
Data
Interviews with senior managers plus employee survey across several organisations.
How does KU's grading affect dissertation planning?
KU requires a minimum grade in every course and does not graduate students carrying an F, so a weak dissertation is not offset by strong coursework. The practical consequence is to favour a topic you can certainly complete over a more ambitious one you might not.
Can I study my own employer?
Yes, and it is common in professional cohorts. Declare the relationship in your methodology, explain what access it gave you, and be specific about how you protected colleague anonymity — aggregated reporting rather than department-level breakdowns in small teams.
Should I choose qualitative or quantitative?
Choose what your question needs, not what you find easier. If you are describing how something happens, qualitative is right; if you are testing whether a relationship holds, quantitative is. Mixed methods is stronger but roughly doubles the work, so commit to it only if your timeline genuinely allows.
How current do my references need to be?
Your theoretical foundations can be older, but the empirical literature should be recent enough to show the gap still exists. A review resting mainly on pre-2015 work invites the question of what has been published since, and that is an uncomfortable question to face at defence.
What do you help with?
Topic feasibility, proposal structure, literature review organisation, methodology and sample size, data analysis, and draft review. We do not write the dissertation or collect your data — see our academic integrity policy for the boundary.
Is your service confidential?
100%. Your identity and academic work stay completely private and are never shared with your college or anyone else. We use secure, encrypted communication.
How do I get a quote?
Message us on WhatsApp at +977 9768768340 with your assignment brief and deadline, and we'll reply with a transparent quote — usually within a couple of hours.