Why Choose This Topic: How to Choose a Feasible Research Topic
You may have a list of possible topics but still not know which one is narrow enough to complete, supported by enough data to analyse, and defensible before the committee. In that situation, the reason for choosing your topic should not be treated as a few opening sentences added out of obligation. It is the first test of your entire thesis: Is the problem real, which variables need to be measured, who will answer the questionnaire, and can you run the analysis in SPSS or SmartPLS?
A topic that sounds current but has no suitable respondents can leave you stuck in Chapter 3. A manageable topic with a clear model and data that you can collect within the remaining time is usually more likely to reach completion. This guide takes you from the reason for choosing the topic to the topic title, research variables, scales, data source, and sample size, so you can check each point against your current plan.
If you are writing the introduction, you can also read the guide to the opening section, then compare your reason for choosing the topic with the research objectives and research questions.
Criteria for choosing an undergraduate thesis topic
The research problem affects real people
Your reason for choosing the topic should begin with an observable problem in an industry, organisation, or specific group of people. For example, you might be interested in customers' continued use of e-wallets, employees' intention to leave, learners' acceptance of online learning platforms, or consumers' evaluation of delivery service quality.
You need to express the problem in one sentence that identifies both the population and the context: “The rate of continued use of digital banking applications remains unstable among young users in Ho Chi Minh City.” This is still an initial statement, not a research finding. The finding appears only after you collect and analyse the data.
The problem can be converted into measurable variables
A quantitative topic needs concepts that can be measured with items. “Sustainable development” is too broad if you have not specified its components. “Perceived usefulness, perceived risk, and intention to use digital banking services” has a clearer structure because each concept can be linked to a scale and a hypothesis.
Write down at least one dependent variable and three to five independent variables with a theoretical rationale. If your topic includes a mediator or moderator, add it only when you understand how to test it and still have enough time. A model with more variables does not automatically make a thesis stronger, but it does make the questionnaire longer and the explanation more complicated.
A student can collect the data
This criterion is often overlooked when students name a topic. You need to know where the respondents are, whether they match the target population, how many responses you can realistically obtain, and how much time remains for data collection. A topic requiring internal data from a bank, hospital, or government agency carries substantial risk if you do not already have access.
The checklist below helps you eliminate unsuitable topics before writing the proposal.
| Check criterion | Question to answer | Sign that you should keep the topic |
|---|---|---|
| Respondents | Who will answer the questionnaire | You can reach this group in practice |
| Research variables | Can the variables be measured with items | You have a reference scale and clear definitions |
| Context | Which location, industry, and period | The scope is narrow enough for data collection |
| Sample size | Can you obtain the required number of responses | You have an access channel and a questionnaire distribution plan |
| Analysis | Will you use SPSS or SmartPLS | The model fits the method you have chosen |
| Timeline | How many weeks remain | There is enough time to clean data, rerun analyses, and write Chapter 4 |
The title must define the scope
Your topic title should show the main relationship, population, and context. A practical structure is: “Factors affecting [dependent variable] among [population] in [context].” For example: “Factors affecting students' intention to continue using e-wallets in Ho Chi Minh City.”
Avoid putting too many locations, industries, and population groups into one title. You should also avoid adding “solutions for improving” when the study uses only a cross-sectional questionnaire. Survey data can show relationships or the level of influence within a model, but it is not enough to claim that a solution caused change over time.
Topic ideas by group
The topics below are frameworks for development, not titles to copy directly into a proposal. Each one must be adjusted to the respondent group, location, and scale you can find.
Technology use behaviour
Topic: Factors affecting young people's intention to use digital banking applications. Possible independent variables include perceived usefulness, perceived ease of use, trust, and perceived risk. The dependent variable is behavioral intention. Davis's TAM can provide the basis for perceived usefulness and perceived ease of use, with the source (Davis, 1989).
Topic: Factors affecting students' intention to continue using e-wallets. Possible independent variables include satisfaction, perceived usefulness, trust, and perceived security. The dependent variable is continuance intention. You need to specify that respondents have used an e-wallet at least once, rather than distributing the questionnaire to people who have never used one.
Service quality and customer behaviour
Topic: The effect of service quality on customer satisfaction at convenience stores. The independent variables may be based on SERVQUAL components such as reliability, responsiveness, assurance, empathy, and tangibles. The dependent variable is satisfaction. The original SERVQUAL scale is presented in (Parasuraman et al., 1988).
Topic: Factors affecting customers' repurchase intention on e-commerce platforms. Possible independent variables include perceived value, trust, service quality, and satisfaction. The dependent variable is repurchase intention. You need to specify when the customer made the purchase so that the response does not rely entirely on distant memories.
Human resources and workplace behaviour
Topic: Factors affecting service employees' turnover intention. Possible independent variables include job satisfaction, organizational commitment, workload, and perceived organizational support. The dependent variable is turnover intention. This topic requires access to current employees, because people who have already left are no longer suitable for the initial sample criteria.
Topic: The effect of the work environment on employee engagement among office employees. Possible independent variables include supervisor support, coworker support, job autonomy, and workload. The dependent variable is employee engagement. You need to explain how employee engagement differs from job satisfaction, rather than treating the two concepts as one variable.
Education and learning behaviour
Topic: Factors affecting students' satisfaction with online learning. Possible independent variables include platform quality, lecturer support, perceived ease of use, and perceived usefulness. The dependent variable is satisfaction. Respondents must have studied online in the context you describe.
Topic: The effect of learning motivation and self-efficacy on students' academic performance. The independent variables are motivation and self-efficacy, while the dependent variable can be self-reported academic performance. If you use self-reported grades, state clearly how they are measured and discuss the data's limitations in the methodology chapter.
Suggested models and scales
The topic title is only the starting point. Before finalising it, draw the model with boxes and arrows. Every arrow should become a testable hypothesis, such as “Perceived usefulness has a positive effect on the behavioral intention of digital banking users.”
| Topic group | Suggested model or framework | Possible dependent variable | Reference source |
|---|---|---|---|
| Technology acceptance | TAM | Intention to use | (Davis, 1989) |
| Planned behaviour | TPB | Intention to perform the behaviour | (Ajzen, 1991) |
| Extended technology acceptance | UTAUT | Intention to use and use behaviour | (Venkatesh et al., 2003) |
| Digital consumer behaviour | UTAUT2 | Intention or use behaviour | (Venkatesh et al., 2012) |
| Service quality | SERVQUAL | Satisfaction or repurchase intention | (Parasuraman et al., 1988) |
Do not combine every variable from several models simply because they all appear relevant. Choose one main framework, then explain why you are adding variables in your context. For each scale, record the author, year, number of items, original context, and the way you adapted the wording. A reference scale has not yet been validated for your sample.
After data collection, the usual process includes checking the data, running Cronbach's Alpha, running EFA if required by the research design, and then testing the model. Cronbach's Alpha from 0.7 can be considered acceptable according to (Nunnally, 1978), while a corrected item-total correlation from 0.3 is stated in (Nunnally et al., 1994). These are sourced reference criteria, not reasons to remove items mechanically.
If you use SmartPLS, separate the measurement model from the structural model. Outer loading from 0.7, CR from 0.7, and AVE from 0.5 are commonly reported according to (Hair et al., 2022) and (Fornell and Larcker, 1981). HTMT below 0.85, or below 0.90 for closely related concepts, is proposed in (Henseler et al., 2015). CFI, TLI, and RMSEA belong to CB-SEM and should not be included in a SmartPLS analysis.
Where to obtain data and how large should the sample be
Write your data plan as soon as you choose the topic, before creating the questionnaire. State the unit of analysis, screening criteria, questionnaire distribution channel, collection period, and treatment of invalid responses. A valid response should complete the main sections, come from the correct respondent group, and show no sign of selecting the same response level for every item.
If you need to collect responses through an online questionnaire, fillform.info is DoThesis's own form tool for this step. Google Forms is a familiar alternative. Whichever tool you use, you still need to control the sample criteria yourself. Do not distribute a public questionnaire and then conclude that the sample represents the entire population.
Sample size depends on the method, number of variables, and access to respondents. For regression research, the rule of n at or above 50 + 8m is stated in (Tabachnick et al., 2013), where m is the number of independent variables. For research with many items, the rule of 5 to 10 observations per item is discussed in (Hair et al., 2010). Calculate the sample size both ways, then choose the more cautious plan with a clear rationale.
For example, a questionnaire has 25 items. The rule of 5 to 10 observations per item gives a range of 125 to 250 responses. If the model has 6 independent variables, the formula 50 + 8m gives a minimum of 98 observations. This range is only a planning basis. It does not guarantee good data if the respondents are unsuitable or many responses are invalid.
When writing the methodology chapter, report the planned sample size, actual sample size, and number of responses retained after cleaning. If the two figures differ, explain the reason through the response-exclusion process. Do not alter the number simply to make it match the original plan.
Topics you should avoid
Topics without data access
Avoid topics that require customer lists, personnel records, internal revenue, or medical records if you do not have written permission or an agreed contact person. The promise that you “will be able to obtain the data” is not a data collection plan. If the data cannot be obtained, the model and scales you prepared will not help you run the analysis.
Topics with overly broad variables
Phrases such as “improving business performance,” “comprehensive sustainable development,” or “digital transformation in enterprises” need to be narrowed into a measurable outcome. If you cannot identify who will answer and what the dependent variable is, do not finalise the title yet.
Topics that require a strong causal claim
A one-time survey is suitable for testing relationships within a model. Be careful with titles that claim a policy “causes” an outcome or “measures impact” when the design contains no before-and-after data. A manageable title helps you explain the results in a way that matches the actual data.
Common mistakes when finalising a topic
The first mistake is choosing the subject first and looking for respondents later. Reverse the order: identify an accessible group, estimate how many responses you can collect, and then finalise the context.
The second mistake is taking a model from the internet and keeping every variable. Read the definition of each concept and check for overlap between variables. If perceived usefulness, perceived value, and satisfaction are used together, you must explain the boundaries between them.
The third mistake is writing the reason for choosing the topic with broad statements that do not connect to the model. A strong rationale should connect four points: the real-world problem, the affected population, the gap or need for testing in a specific context, and the value of the results.
The fourth mistake is setting research objectives that do not match the title. If the title concerns repurchase intention but the objective concerns general satisfaction, the model will become misaligned. You can also compare your plan with the guide to research questions to ensure that every question has corresponding variables and analysis.
The final mistake is writing a vague reason for choosing the topic, such as “the topic has theoretical and practical significance.” This sentence has value only when you specify which theory is being tested, which population is surveyed, and which decision the results may support. A few short, specific paragraphs will be more convincing than a full page of general statements.
Frequently asked questions
What should a research topic justification include?
Present the real-world problem, the context and affected population, the reason for studying it within the selected scope, and the quantitative approach. The final part can connect the rationale to the research objectives, research questions, and proposed model.
How do I identify a suitable research topic?
Check four factors at the same time: you understand the context, suitable respondents are accessible, reference scales are available, and you have enough time to clean the data, run the analysis, and write the results. If one factor is missing, narrow the topic before writing the proposal.
How many independent variables should a research topic have?
There is no fixed number that applies to every study. For an undergraduate thesis, three to five independent variables are often easier to control than a model with too many variables, but the final number must be based on the theory, scales, and planned sample size.
How should I name a research topic correctly?
The title should show the main relationship, dependent variable, population, and context. You can use the structure “Factors affecting [dependent variable] among [population] in [context],” then remove unnecessary elements so the title remains short and precise.
Should I choose a topic that many people have already studied?
You can choose it if you identify a clear context, population, or model that needs testing. A popular topic usually gives you an advantage because reference scales and literature are easier to find, but you still need to avoid copying the title, questionnaire, and hypotheses without adapting them to your study.
Does the reason for choosing a topic need citations?
Yes, when you make claims about a model, scale, theory, or methodological criterion. The description of the real-world problem should also have a source if you use specific statistics. When you do not have a verified source, describe the research scope without adding a number.
How to write this in your thesis
Open your current planning file, mark each topic against the six criteria above, keep one model with measurable variables, and prepare a provisional data table before rewriting the reason for choosing the topic. If you need to run the analysis on your .sav or .csv file, see M4 DoThesis data analysis.