Thematic Analysis: An Overview

 Thematic Analysis is a qualitative research method used to identify, analyze, and report patterns (themes) within data. It provides a flexible and accessible approach to interpreting meaning from textual or narrative data—especially useful in finance for exploring perceptions, policies, and communication.

 Definition

“Thematic analysis is a method for systematically identifying, organizing, and offering insight into patterns of meaning (themes) across a dataset.”
(Braun & Clarke, 2006)

 Purpose of Thematic Analysis

·         To uncover common themes or patterns in qualitative data.

·         To interpret how these themes relate to the research questions.

·         To allow researchers to understand underlying meanings, attitudes, and perceptions.

 When to Use Thematic Analysis in Finance Research

·         Analyzing interviews with financial experts, investors, or clients

·         Exploring sentiments in annual reports, earnings call transcripts, or policy documents

·         Studying stakeholder views on financial inclusion, fintech adoption, ESG compliance, etc.

 Steps in Conducting Thematic Analysis

(Based on Braun & Clarke’s Six-Phase Framework)

Step

Description

Example in Finance

1. Familiarization

Read and re-read data, note initial ideas

Reading interviews with CFOs about risk management

2. Coding

Generate codes for interesting features

Code statements like "risk culture", "hedging strategy", "compliance pressure"

3. Generating Themes

Group codes into broader themes

Codes like “compliance pressure” and “regulatory fatigue” → Theme: Regulatory Burden

4. Reviewing Themes

Check if themes work across the data

Does Regulatory Burden appear consistently across interviews?

5. Defining and Naming Themes

Refine and describe the themes

Theme: Risk Communication – how companies articulate risk

6. Writing the Report

Present data with quotes and interpretation

Discuss themes with support from quotes and connect to literature

 Key Concepts in Thematic Analysis

Concept

Description

Code

A label given to a piece of data (e.g., "financial literacy")

Theme

A broader pattern across coded data (e.g., Barriers to Financial Inclusion)

Data Set

The full range of collected data (e.g., 20 interviews, 10 reports)

Data Item

One piece of data (e.g., one interview or article)

Extract

A direct quote or text that supports a theme

 Tools Used

·         Manual coding (pen and paper or Excel)

·         Software:

o    NVivo

o    MAXQDA

o    Atlas.ti

o    Dedoose

 Applications in Finance Research

Research Area

Thematic Application

Behavioral Finance

Identify themes in investor behavior (e.g., "fear of loss", "herd mentality")

Fintech Adoption

Analyze barriers to digital banking adoption (e.g., "trust issues", "tech literacy")

Financial Literacy

Explore how different demographic groups perceive budgeting and investing

Sustainable Finance

Thematize corporate responses to ESG obligations

Banking Services

Understand customer satisfaction and complaint themes

 Advantages

·         Rich, detailed interpretation of data

·         Flexible and adaptable to various data types

·         Uncovers both explicit and implicit meanings

Limitations

·         Subject to researcher bias in theme identification

·         Time-consuming and labor-intensive

·         Requires careful documentation for reliability

 Example Research Topic (Finance)

Title: "Understanding Retail Investors’ Perceptions of Mutual Funds in Tier-II Indian Cities: A Thematic Analysis"

·         Data: In-depth interviews with 25 retail investors

·         Themes Identified:

1.      Low Financial Awareness

2.      Trust in Traditional Products

3.      Influence of Peers and Agents

4.      Risk Aversion and Lack of Clarity

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