ScholarFlow | 26-Step Ph.D. Research Framework Generator

ScholarFlow 26-Step AI Guide

Complete Ph.D. companion with Automated Data Analytics, SEM Modeling, and Word Document Export.

Research Configuration

Select Generation Mode:

Option 1: Topic Only Provide a topic. AI auto-generates objectives, hypotheses, and models.

Option 2: Topic + Objectives + Hypotheses Provide your own core inputs. AI builds the methodology around them.

Main Research Topic (Step 1) *

26-Step Framework Ready

PHASE 1: Research Foundation (Steps 1–5)

🔶 STEP 1: Research Area & Topic

Topic Selected:

🔶 STEP 2: Research Problem Identification

Problem Statement: Despite advancements, there remains a critical gap in understanding how specifically influences .

🔶 STEP 3: Literature Review Strategy

Sources: Scopus, Google Scholar.
Identify existing structural models and theoretical gaps.

🔶 STEP 4: Define Objectives

🔶 STEP 5: Develop Hypotheses

PHASE 2: Framework & Variables (Steps 6–7)

🔶 STEP 6: Identify Variables

Independent Variable (IV):
Dependent Variable (DV):
Mediator Variable: System Adaptability / Intermediary Factor
Moderator Variable: Demographic/Contextual Variance

🔶 STEP 7: Conceptual Framework & SEM Path Diagram

The following generic research model outlines the direct influence, mediating paths, and moderating effects governing the study:

PHASE 3: Methodology & Data Collection (Steps 8–13)

🔶 STEP 8: Research Methodology Design

Descriptive & Causal Research. Quantitative Deductive Approach.

🔶 STEP 9: Sampling Design

Stratified Random Sampling. Size: 300+ (SEM 10-times rule).

🔶 STEP 10: Data Collection Methods

Primary: Google Forms/LMS. Secondary: Journals.

🔶 STEP 11: Questionnaire Design

Sections: Demographics, Variables. 5-point Likert scale.

🔶 STEP 12 & 13: Pilot Study & Final Collection

Initial 30-sample test for reliability, followed by full deployment and data cleansing.

PHASE 4: Data Analysis Flow & Results (Steps 14–16)

🔶 STEP 14: Data Analysis Flow Diagram

The following flowchart delineates the exact sequential statistical pipeline from raw data to interpretation:

flowchart TD A([Data Collection]) --> B[Data Cleaning] B --> C[Descriptive Statistics] C --> D[Reliability Test
Cronbach Alpha] D --> E[Validity Test
AVE, CR] E --> F[Correlation Analysis] F --> G[Regression Analysis] G --> H{SEM / Path Analysis} H --> I[Hypothesis Testing] I --> J([Interpretation]) style A fill:#e2e8f0,stroke:#64748b style J fill:#ecfdf5,stroke:#10b981 style H fill:#eff6ff,stroke:#3b82f6,stroke-width:2px

🔶 STEP 15 & 16: Statistical Techniques & Interpretation

Upload your collected survey data to automate these steps, generate all quantitative/qualitative charts, and interpret the hypotheses.

Execute Automated Data Analysis

Upload Dataset (.csv) & Generate Dashboard

Comprehensive AI Analytics Dashboard

Auto-Generated Visualizations for Steps 15 & 16

A. Quantitative Statistical Visualizations

1. Frequency Distribution (Bar)

2. Trend Analysis (Line)

3. Correlation Analysis (Scatter)

4. Data Normality (Histogram)

5. Demographics (Pie)

6. Multi-Variable Impact (Bubble)

7. Data Spread & Outliers (Box-and-Whisker)

Min (Q0)

Max (Q4)

Median

Visual representation of quartile distribution, showing minimal outliers.

8. Correlation Matrix (Heat Map / 2D Density)

	IV	DV	Med	Mod
IV	1.00	0.75	0.42	0.21
DV	0.75	1.00	0.55	0.15
Med	0.42	0.55	1.00	0.68
Mod	0.21	0.15	0.68	1.00

B. Structural & Strategic Framework Visualizations

9. Root Cause (Fishbone / Ishikawa)

Method

Material

Manpower

Machine

10. Strategic Posture (SWOT Analysis)

Strengths:
High Validity
Robust SEM Model

Weaknesses:
Cross-sectional bias

Opportunities:
Longitudinal testing

Threats:
Demographic shifts

11. Construct Overlap (Venn Diagram)

Theory

Data

Application

12. Spatial Distribution (Geographical Map)

13. Thematic Structure (Mind Map)

mindmap root((Core Thesis)) Quantitative SEM Qualitative Cases

14. Stakeholder Roles (Organizational Chart)

graph TD A[Principal Investigator] --> B[Data Analysts] A --> C[Field Researchers]

15. Model Infrastructure (System Architecture)

graph LR DB[(Raw Data)] --> API[Processing Engine] API --> SEM{SmartPLS/SEM}

16. Data Collection Flow (DFD)

flowchart LR User((Respondent)) -->|Survey| Form[Collection Tool] Form --> Store[(Database)]

17. Project Implementation Timeline (Gantt Chart)

gantt dateFormat YYYY-MM-DD title Research Timeline Execution section Data Collection :active, des1, 2023-01-01, 30d Analysis : des2, after des1, 20d section Writing Final Draft : des3, after des2, 15d

PHASE 5: Discussion & Conclusion (Steps 17–21)

🔶 STEP 17: Discussion

Compare results with previous studies and justify the SEM path validations.

🔶 STEP 18: Findings & Conclusion

Summarize key insights and theoretical contributions to the domain.

🔶 STEP 19: Recommendations

Suggest practical implications for industry/academia.

🔶 STEP 20: Limitations

Acknowledge sample size limits and cross-sectional constraints.

🔶 STEP 21: Future Scope

Propose longitudinal studies based on the current findings.

PHASE 6: Thesis Writing & Defense (Steps 22–26)

🔶 STEP 22: Thesis Writing Structure

Intro, Lit Review, Methodology, Analysis, Discussion, Conclusion.

🔶 STEP 23: Plagiarism Check

Use Turnitin (Maintain < 10%).

🔶 STEP 24: Paper Publication

Target Scopus / UGC Care journals.

🔶 STEP 25: Thesis Submission

Final formatting to university guidelines.

🔶 STEP 26: Viva Voce (Final Defense)

Prepare PPT highlighting Problem, Methodology, and SEM Results.

data