Research Process & Problem Formulation: Steps, Types, Tools & Real Cases

TAKEAWAYS:

• Problem formulation is the foundation of research: it defines the what, why, and how of a study, turning vague questions into testable hypotheses.
• The research process follows a cyclical model (problem → design → data → analysis → reporting) but requires iterative refinement at each stage.
• Types of research problems (exploratory, descriptive, explanatory, predictive) dictate the methodology—e.g., exploratory for Daraz’s new delivery route testing vs. explanatory for Nabil Bank’s loan default analysis.
• Problem formulation tools (SWOT, PESTEL, 5W1H) help narrow scope—e.g., NTC’s internet outage problem could be framed as "Why do 60% of Pokhara users report slow speeds during peak hours?" (not "Is NTC’s service bad?").
• Real-world applications: WhatsApp’s end-to-end encryption problem was framed as "How to balance security with user accessibility?" (exploratory → experimental design).
• Exam focus: Expect case-based questions (e.g., "Formulate a research problem for Pathao’s driver attrition") and process diagrams (e.g., "Draw the research cycle for a NEPSE stock analysis").

1. The Research Process: A Cyclical Framework

Business research is not linear—it’s a dynamic cycle where each step informs the next. The core stages are:

graph LR
    A["Problem Identification"] --> B["Review of Literature"]
    B --> C["Formulation of Objectives"]
    C --> D["Research Design"]
    D --> E["Data Collection"]
    E --> F["Data Analysis"]
    F --> G["Interpretation & Reporting"]
    G -->|"Feedback"| A

Key Insight:

• Iteration is critical. For example, when Khalti investigated why 30% of transactions failed, initial data showed "server latency"—but deeper analysis revealed "low battery warnings on user devices" (a problem not in the original scope).
• Real-world trace: NTC’s fiber-optic expansion
  • Problem: Rural areas had 80% lower internet speeds than urban centers.
  • Process:
    1. Identify: "Why is bandwidth limited in Chitwan?"
    2. Literature: Reviewed global case studies (e.g., India’s BSNL rural rollout).
    3. Objective: "Measure the impact of terrain vs. infrastructure on speed in 5 districts."
    4. Design: Mixed-methods (surveys + technical logs).
    5. Findings: "70% of delays were due to backhaul bottlenecks, not terrain."

2. Problem Formulation: Turning Vague Questions into Researchable Issues

A well-formulated problem must be:

• Specific (not "How to improve eSewa?" but "Why do 15% of eSewa payments fail during Dashain?").
• Measurable (use quantifiable terms like "reduce by 20%").
• Feasible (avoid "How to eliminate corruption in Nepal?"—too broad).
• Relevant (ties to business goals, e.g., "How does Daraz’s return policy affect customer loyalty?").

Tools for Problem Formulation

Worked Example: Kathmandu Traffic Congestion

• Vague Problem: "Traffic in Kathmandu is bad."
• Refined Problem: "What are the top 3 factors contributing to the 40% increase in travel time on Ring Road during peak hours (7–9 AM), and how do they interact?"
  • Tools Used: PESTEL ("Lack of public transport" = Economic), 5W1H ("Who" = commuters vs. delivery vehicles).

3. Types of Research Problems

The nature of the problem determines the research design. Compare:

Real-world link:

• Google’s "Why do searches drop after algorithm updates?" → Explanatory research led to RankBrain (a machine-learning system to interpret queries).
• Nabil Bank’s "Why do SME loans default at 22%?" → Descriptive (data analysis) + Explanatory (interviews with defaulters).

4. Formulating Research Objectives

Objectives must be SMART:

• Specific: "Analyze" (not "Study").
• Measurable: "Reduce by 15%".
• Achievable: "Survey 500 users" (not "All Nepalis").
• Relevant: "Ties to eSewa’s fraud reduction goal."
• Time-bound: "Within 3 months."

Example: Pathao’s Driver Retention

• Problem: "Why do 40% of Pathao drivers quit within 6 months?"
• Objectives:
  1. To identify the top 3 reasons for driver attrition in Kathmandu (Descriptive).
  2. To determine if low earnings correlate with high quit rates (Explanatory).
  3. To propose a retention strategy based on findings (Predictive).

Visual:

mindmap
  root((Pathao Driver Attrition Research))
    Objectives
      1. Identify Top 3 Reasons ["Survey 300 drivers"]
      2. Test Earnings vs. Quit Rate ["Correlation analysis"]
      3. Propose Retention Strategy ["Focus groups with ex-drivers"]
    Methods
      Primary: Interviews
      Secondary: Pathao’s internal data
    Expected Outcome: "10% reduction in attrition"

5. Common Pitfalls in Problem Formulation

In the Real World

1. eSewa’s Fraud Detection Problem

   • Idea Used: Explanatory research + SWOT analysis.
   • How: eSewa framed the problem as "What transaction patterns correlate with fraudulent activities?" (not "How to stop fraud?").
   • Outcome: Identified "repeated small transactions from new users" as a red flag → led to AI-based anomaly detection.

2. Daraz’s "Why Do 30% of Orders Get Delayed?"

   • Idea Used: Fishbone diagram + Descriptive statistics.
   • Process:
     • Root causes: "Logistics partner inefficiency" (60%), "Weather" (20%), "Last-mile delivery gaps" (15%).
     • Solution: Partnered with Nepal Post for rural deliveries (reduced delays by 25%).

3. Ncell’s "How to Reduce Churn in Tier-2 Cities?"

   • Idea Used: PESTEL (Economic: "Low disposable income") + Predictive modeling.
   • Findings: "Users churn when data costs exceed 15% of their monthly income."
   • Action: Introduced "Happy Hours" (discounted data at night).

6. Case Study: Nabil Bank’s Loan Default Analysis

Problem: "Why do 22% of SME loans default in Nepal?" Research Process:

1. Problem Formulation:
   • SWOT: Weakness = "Lack of collateral tracking"; External = "Economic downturns."
   • 5W1H:
     • Who: Borrowers in agriculture/retail.
     • What: Default rate.
     • When: Post-2020 pandemic.
2. Objectives:
   • Measure default rates by sector.
   • Identify top 3 risk factors.
3. Methods:
   • Primary: Interviews with 200 defaulters.
   • Secondary: Nabil Bank’s loan databases.
4. Findings:
   • Top 3 Causes:
     1. "Unforeseen cash flow drops" (45%).
     2. "High interest rates" (30%).
     3. "Poor financial literacy" (25%).
5. Solution:
   • Predictive model to flag high-risk loans.
   • Workshops on financial planning for borrowers.

Visual:

flowchart TD
    A["Problem: 22% SME Loan Defaults"] --> B["SWOT Analysis"]
    B --> C["5W1H Framework"]
    C --> D["Formulate Objectives"]
    D --> E["Primary Data: Interviews"]
    D --> F["Secondary Data: Bank Records"]
    E & F --> G["Identify Top 3 Causes"]
    G --> H["Develop Predictive Model"]
    H --> I["Implement Solutions"]

Exam Tip

1. Case-Based Questions (30–40% weight):

   • Do: "Formulate a research problem for [X company’s issue] using [tool]."
   • Example Answer:

     "Problem: Why do 25% of users abandon their carts on Daraz before checkout?" "Tools Used: Fishbone Diagram (revealed ‘hidden shipping costs’ as top cause) + 5W1H (targeted prime-time shoppers)."

   • Avoid: Vague problems like "How to improve Daraz?"—always quantify and scope.

2. Process Diagrams (20% weight):

   • Must-draw: Research cycle, problem formulation steps, or a tool (e.g., SWOT → objectives).
   • Example:

     flowchart LR
         A["Problem: NTC Outages"] --> B["PESTEL: Political Stability?"]
         B --> C["5W1H: Who=Rural Users"]
         C --> D["Objective: Measure outage duration by district"]

3. Tool Applications (20% weight):

   • Match tools to problems:
     • SWOT → Internal/external gaps (e.g., "Why is Himalayan Java’s export declining?").
     • PESTEL → Macro issues (e.g., "Why did NEPSE crash in 2023?").
     • Fishbone → Root causes (e.g., "Why do Pathao drivers quit?").

4. Short-Answer Tips:

   • Problem formulation: Always start with "The problem is [specific issue] as evidenced by [data]." Example:

     "The problem is that 35% of eSewa transactions fail during festivals, as shown in Q3 2023 logs."

   • Objectives: Use "To [verb] [specific action]" (e.g., "To measure the impact of weather on Daraz deliveries in 5 regions.").

5. Real-World Links (10% weight):

   • Expected: Tie answers to Nepali companies (e.g., "Like NTC’s outage analysis, [your answer] uses [tool] to...").
   • Example:

     "Similar to Khalti’s fraud detection, this research uses anomaly detection to identify [X]."

Final Checklist Before Submitting:

• Did I quantify the problem (e.g., "25% drop", "60% of users"?
• Did I link to a tool (SWOT, PESTEL, 5W1H)?
• Did I show the process (diagram or flowchart)?
• Did I use a real-world example (eSewa, Daraz, NTC)?