What is a Conceptual Model?

 A Conceptual Model is a theoretical framework that explains relationships between different variables in a study. It is used to develop hypotheses and guide research without necessarily relying on direct empirical testing.

🔹 Purpose:
✅ Defines key variables and their relationships
✅ Helps in hypothesis development
✅ Provides a foundation for empirical research
✅ Used in conceptual research articles and literature reviews

📌 Steps to Develop a Conceptual Model

1️⃣ Identify the Research Problem

  • Define the main issue your research is addressing.
    📌 Example: "How does financial literacy impact FinTech adoption in rural India?"

2️⃣ Define Key Variables

  • Independent Variable (IV) – The factor that influences outcomes
  • Dependent Variable (DV) – The outcome being studied
  • Mediators & Moderators – Factors that explain or change the relationship

📌 Example:

  • IV: Financial Literacy
  • DV: FinTech Adoption
  • Moderator: Trust in Technology
  • Mediator: Perceived Ease of Use

3️⃣ Establish Relationships

  • Draw connections between variables
  • Use theoretical support (e.g., TAM, TPB, IDT)

📌 Example: If you are using TAM, you could propose that Financial Literacy → Perceived Ease of Use → FinTech Adoption.

4️⃣ Develop a Conceptual Framework Diagram

  • Use boxes and arrows to show relationships
  • Show direct and indirect effects

📌 Example Conceptual Model for FinTech Adoption:

scss
[Financial Literacy] → [Perceived Ease of Use] → [FinTech Adoption]  
                 ↘ [Trust in Technology] (Moderator) ↗

📌 Example Conceptual Models in Finance Research

🔹 1. Impact of Financial Literacy on Investment Behavior
✔ IV: Financial Literacy
✔ DV: Investment Decision-Making
✔ Moderator: Risk Tolerance
✔ Mediator: Financial Confidence

🔹 2. Adoption of Cryptocurrency in India
✔ IV: Perceived Risk, Government Regulations
✔ DV: Cryptocurrency Adoption
✔ Moderator: Trust in Digital Assets

🔹 3. Role of ESG Factors in Stock Market Performance
✔ IV: ESG Scores
✔ DV: Stock Returns
✔ Mediator: Investor Sentiment

📌 Tools to Design Conceptual Models

📌 Use these tools to create diagrams:
✅ Microsoft PowerPoint / Word
✅ Lucidchart (Online Diagram Tool)
✅ SmartDraw
✅ Canva

Comments

Post a Comment

Popular posts from this blog

Shodhganaga vs Shodhgangotri

  Shodhganga and Shodhgangotri are both initiatives by the INFLIBNET Centre (Information and Library Network Centre) in India. They aim to promote research and knowledge sharing in the academic community but serve different purposes. Here’s a comparison: Feature Shodhganga Shodhgangotri Purpose Repository for completed theses and dissertations. Repository for research proposals and synopses. Content Full-text doctoral theses and dissertations submitted by universities. Approved research proposals and synopses of doctoral students. Focus Stage of Research Final stage – after the research is completed. Initial stage – before the research begins. Target Audience Researchers looking for completed, peer-reviewed research work. Scholars looking for examples or ideas for research topics and methodologies. Utility ...
Read more

Two-Step System GMM (Generalized Method of Moments)

 The Two-Step System GMM is a dynamic panel data estimation technique designed to address: Endogeneity of explanatory variables Unobserved heterogeneity (fixed effects) Autocorrelation and heteroskedasticity It improves upon simpler estimators (like OLS or FE) and is particularly useful when the model includes lagged dependent variables . ⚙️ System GMM: The Basics Developed by Arellano & Bover (1995) and Blundell & Bond (1998) , System GMM combines: Difference GMM (Arellano & Bond, 1991): First-differences the model to remove fixed effects and uses lagged levels as instruments . Level GMM (Blundell & Bond) : Uses lagged differences as instruments for the level equation to improve efficiency. 🧮 The Dynamic Panel Model A typical dynamic panel data model is: y i t = α y i t − 1 + β X i t + μ i + ε i t y_{it} = \alpha y_{it-1} + \beta X_{it} + \mu_i + \varepsilon_{it} y i t ​ = α y i t − 1 ​ + β X i t ​ + μ i ​ + ε i t ​ Where: y...
Read more

50 interview questions with answers on Non-Parametric Tests

 Here are 50 interview questions with answers on Non-Parametric Tests in Research Methodology , ideal for academic, teaching, and research-based interviews.   Basic Conceptual Questions 1.       What is a non-parametric test? A non-parametric test is a statistical test that does not assume a specific distribution of the data, often used when assumptions of parametric tests are not met. 2.       When should you use a non-parametric test? When the data is ordinal, not normally distributed, or when sample sizes are small or variances are unequal. 3.       What is the major difference between parametric and non-parametric tests? Parametric tests assume a known distribution (e.g., normal), while non-parametric tests do not. 4.       Name some common non-parametric tests. Mann–Whitney U test, Wilcoxon signed-rank test, Kruskal–Wallis test, Friedman test, Chi-square tes...
Read more