Research Process & Problem Formulation: Steps, Models & Real-World Cases

TAKEAWAYS:

• Problem formulation is the foundation of research: a poorly defined problem leads to irrelevant or useless findings.
• Research objectives must be SMART (Specific, Measurable, Achievable, Relevant, Time-bound) to guide data collection and analysis.
• Research questions and hypotheses are tools to operationalize problems into testable statements (e.g., "Does WhatsApp’s end-to-end encryption reduce user trust in government surveillance?").
• Problem sources (e.g., gaps in literature, industry reports, or stakeholder complaints) determine the research’s urgency and scope.
• The research process is iterative: problems → objectives → questions → hypotheses → design → data → conclusions.
• Real-world applications span from NEPSE’s stock price forecasting to Pathao’s ride-demand prediction—all rely on well-formulated problems.

1. Understanding the Research Problem

A research problem is a gap in knowledge or an unresolved issue that requires systematic investigation. It arises when:

• Existing theories or data are incomplete (e.g., why do small businesses in Kathmandu fail despite government subsidies?).
• Practices lack evidence (e.g., does Daraz’s "Cash on Delivery" policy increase cart abandonment?).
• Stakeholders demand answers (e.g., why do Ncell customers complain about network drops during festivals?).

How to Identify a Research Problem

Use the "5Ws and 1H" framework:

graph TD
    A["Problem Identification"] --> B["Who is affected?"]
    A --> C["What is the issue?"]
    A --> D["Where does it occur?"]
    A --> E["When does it happen?"]
    A --> F["Why is it a problem?"]
    A --> G["How can it be solved?"]

Example:

• Problem: High customer complaints about NTC’s internet speed during peak hours.
• 5Ws:
  • Who: Urban users (Pokhara, Kathmandu).
  • What: Slow internet (ping > 200ms).
  • Where: Major cities with NTC towers.
  • When: 6–9 PM (work-from-home hours).
  • Why: Overloaded servers, poor maintenance.
  • How: Optimize server load or expand infrastructure.

2. Types of Research Problems

Problems can be classified based on their nature, scope, and source. Below is a comparison table:

Worked Example: Problem: High cart abandonment on Daraz during sales. Type: Explanatory (why do users leave?) + Prescriptive (how to reduce it?). Approach:

1. Data Collection: Survey 1,000 users who abandoned carts.
2. Findings:
   • 45% left due to unexpected shipping costs.
   • 30% faced payment failures (Khalti/Nepal Bank integration issues).
3. Solution: Offer "free shipping over $50" and improve Khalti API reliability.

3. Formulating Research Objectives

Objectives are specific goals that guide the research. They must be:

• SMART: Specific, Measurable, Achievable, Relevant, Time-bound.
• Linked to the problem: Each objective should address a part of the problem.

How to Write Research Objectives

Use action verbs and quantifiable targets:

mindmap
  root((Research Objectives))
    to["To identify"]
    to["To analyze"]
    to["To evaluate"]
    to["To compare"]
    to["To predict"]
    to["To recommend"]

Example for Ncell: Problem: High customer churn in rural areas. Objectives:

1. To identify the top 3 reasons for churn among rural Ncell users (measurable via surveys).
2. To analyze the correlation between network coverage and customer satisfaction (quantifiable via NPS scores).
3. To recommend a low-cost solution to improve coverage in 50% of underserved villages within 6 months.

4. Research Questions and Hypotheses

Research Questions (RQs)

• Purpose: Convert problems into testable questions.
• Format: Start with "How," "What," "Why," or "To what extent."
• Example for eSewa:
  • Problem: Low adoption of eSewa in rural Nepal.
  • RQ: To what extent does digital literacy influence eSewa usage among farmers in Kavrepalanchok?

Hypotheses

• Purpose: Predict relationships between variables (for explanatory research).
• Types:
  • Null Hypothesis (H₀): No relationship exists (default assumption).
  • Alternative Hypothesis (H₁): A relationship exists (what you test).
• Example for Daraz:
  • H₀: There is no significant difference in customer satisfaction between COD and digital payment users.
  • H₁: Customers who pay digitally report higher satisfaction than COD users.

Comparison Table:

5. The Research Process: From Problem to Design

The research process is cyclical and involves:

1. Problem Identification (from literature, industry reports, or stakeholder feedback).
2. Review of Literature (what is already known?).
3. Formulation of Objectives, Questions, and Hypotheses.
4. Choice of Research Design (qualitative, quantitative, or mixed).
5. Data Collection and Analysis.
6. Reporting Findings and Recommendations.

flowchart LR
    A["Problem Identification"] --> B["Literature Review"]
    B --> C["Formulate Objectives/Qs/Hypotheses"]
    C --> D{"Qualitative/Quantitative/Mixed?"}
    D --> E["Data Collection"]
    E --> F["Analysis"]
    F --> G["Reporting & Recommendations"]
    G -->|"Feedback Loop"| A

Real-World Trace: Nabil Bank’s Loan Default Study

1. Problem: Rising loan defaults in 2022 (12% vs. 8% in 2021).
2. Objective: To analyze the impact of income volatility on loan repayment rates.
3. Research Question: How does seasonal income fluctuation among farmers affect their ability to repay agricultural loans?
4. Hypothesis:
   • H₀: Income volatility has no significant impact on loan repayment.
   • H₁: Farmers with >20% income fluctuation are 3x more likely to default.
5. Design: Mixed-methods (surveys + financial records).
6. Outcome: Bank introduced flexible repayment plans for seasonal workers.

6. Sources of Research Problems

Problems can emerge from multiple sources:

7. Common Pitfalls in Problem Formulation

Avoid these mistakes:

1. Too Broad: "Study the Nepali economy" → Fix: "Analyze the impact of fuel price hikes on transport costs in Kathmandu."
2. Too Narrow: "Study one Daraz seller’s success" → Fix: "Compare success factors of top 10% vs. bottom 10% Daraz sellers."
3. Lack of Feasibility: "Survey all 10M Nepali voters" → Fix: "Survey 500 voters in 5 districts."
4. Subjective Language: "Nepali customers are unhappy" → Fix: "30% of Ncell users rate service as ‘poor’ (NPS score < 30)."
5. Ignoring Stakeholders: Researching a problem without input from banks, NTC, or Daraz.

In the Real World

1. Khalti’s Payment Adoption Problem

   • Idea Used: Descriptive + Explanatory Research
   • How: Khalti identified that 60% of rural users abandoned transactions due to phone credit limits. They formulated:
     • Objective: Measure the impact of USSD vs. mobile app usage on completion rates.
     • Hypothesis: Users with <Rs. 20 credit are 40% less likely to complete payments via app.
   • Solution: Introduced credit-top-up reminders and low-credit payment options.

2. Daraz’s Logistics Bottleneck

   • Idea Used: Predictive Modeling
   • How: Daraz used historical sales data to predict peak delivery times (e.g., Dashain sales). They found:
     • Problem: 70% of delays occurred in Kathmandu and Pokhara due to traffic.
     • Research Question: How does traffic congestion correlate with on-time delivery rates?
     • Solution: Partnered with Pathao for dynamic route optimization.

3. NEPSE’s Stock Price Forecasting

   • Idea Used: Explanatory + Predictive Research
   • How: Investors use technical analysis (e.g., moving averages) and fundamental analysis (e.g., P/E ratios) to formulate:
     • Hypothesis: "Stocks with P/E < 10 will outperform the index by 15% in 6 months."
   • Real Example: During the 2023 COVID-19 recovery, NEPSE stocks with low debt-to-equity ratios were predicted to rise.

Exam Tip

How This Unit is Tested in PU Exams

1. Problem Formulation (30% of marks)

   • Expected: Given a scenario (e.g., "Ncell’s declining rural market share"), you must:
     • Identify the root problem (e.g., poor network in hills).
     • Write 2–3 SMART objectives.
     • Formulate 1 research question and 1 hypothesis.
   • Common Mistake: Writing vague objectives like "Study Ncell’s performance." Fix: "Increase rural network coverage by 20% in 1 year to reduce churn by 15%."

2. Research Process (25% of marks)

   • Expected: Draw a flowchart of the research process for a given problem (e.g., "Why do students fail in TU exams?").
   • Key Points to Include:
     • Problem → Literature review → Objectives → Design → Data → Analysis → Report.

3. Types of Problems (20% of marks)

   • Expected: Classify a given problem (e.g., "Impact of social media on youth spending") as exploratory/descriptive/explanatory.
   • Tip: Use the table above to match definitions.

4. Real-World Application (15% of marks)

   • Expected: Relate concepts to Nepali businesses (e.g., "How would Daraz use explanatory research?").
   • Tip: Always link to data (e.g., "Daraz’s 2023 report showed 30% of orders were abandoned due to X").

5. Avoiding Pitfalls (10% of marks)

   • Expected: Given a poorly written problem statement, correct it (e.g., change "Study WhatsApp" to "Analyze how end-to-end encryption affects user trust in WhatsApp Business").

Model Answer Structure for 10 Marks

Question: "Formulate research objectives and hypotheses for a study on ‘Why do small businesses in Pokhara fail within 2 years?’" Answer:

1. Problem: High failure rate (60%) of small businesses in Pokhara (source: Pokhara Metropolitan City report, 2023).
2. Objectives (SMART):
   • To identify the top 3 financial challenges faced by businesses in Ward 5, Pokhara (measurable via surveys).
   • To evaluate the correlation between loan interest rates (>12%) and business survival (quantifiable via financial records).
   • To recommend policy changes to reduce failure rates by 20% within 3 years.
3. Research Question:
   • To what extent does access to low-interest loans (<8%) improve the survival rate of small businesses in Pokhara?
4. Hypotheses:
   • H₀: Loan interest rates have no significant impact on business survival.
   • H₁: Businesses with loans at <8% interest have a 40% higher survival rate than those with >12% interest.
5. Design: Mixed-methods (surveys + financial data from Nabil Bank).

Visual Summary for Quick Revision:

mindmap
  root((Research Problem Formulation))
    Problem["1. Identify Problem"]
    Sources["Sources: Literature, Industry Reports, Stakeholders"]
    Objectives["2. Write SMART Objectives"]
    Questions["3. Formulate RQs (How/What/Why)"]
    Hypotheses["4. Test H₀ vs. H₁ (Explanatory Research)"]
    Design["5. Choose Design (Qual/Quant/Mixed)"]
    Pitfalls["Avoid: Too broad/narrow, subjective, infeasible"]