Title:
“Identifying Key Academic and Career-Related Challenges Faced by MBA Students: An Exploratory Factor and Cluster Analysis”
Authors:
Yash Deshmukh
Siddhi Zokande
Yuvraj Jain
Introduction
MBA students face multiple challenges during their academic journey, including academic pressure, skill gaps, and career uncertainty. These challenges impact their performance, confidence, and placement outcomes. Understanding the underlying factors behind these issues can help institutions design better support systems. This study uses Exploratory Factor Analysis (EFA) and Cluster Analysis to identify hidden variables and segment MBA students based on their problems.
Objectives
- To understand hidden variables by EFA (Exploratory Factor Analysis).
- To cluster the respondents based on identified challenges.
Problem Statement
MBA students face difficulties in balancing academics, developing industry-relevant skills, and preparing for placements. However, these issues may stem from underlying hidden factors such as stress, lack of confidence, or poor time management. The study aims to identify these latent variables and group students based on similar problem patterns.
Data Collection
- Method: Structured questionnaire
- Scale: 5-point Likert Scale
- Sample: 50 MBA students
- Sampling Technique: Convenience Sampling
Data Analysis
KMO and Bartlett’s Test
|
Measure |
Value |
|
Kaiser-Meyer-Olkin Measure of Sampling Adequacy |
.513 |
|
Bartlett’s Test of Sphericity |
Approx. Chi-Square: 268.850 |
|
df: 66 |
|
|
Sig.: .000 |
Interpretation:
· The KMO value is 0.513, which is slightly above 0.5.
This indicates that the sample is moderately adequate for factor analysis.
· Bartlett’s Test is significant (p = 0.000 < 0.05).
This means the variables are correlated enough to apply factor analysis.
Communalities
|
Variable |
Initial |
Extraction |
|
Manage Assignments |
1.000 |
.957 |
|
Workload Stress |
1.000 |
.664 |
|
Life Balance |
1.000 |
.891 |
|
Exam Pressure |
1.000 |
.148 |
|
Industry Exposure |
1.000 |
.212 |
|
Presentation Skills |
1.000 |
.981 |
|
Communication Skills |
1.000 |
.352 |
|
Theoretical Curriculum |
1.000 |
.801 |
|
Placement Worry |
1.000 |
.833 |
|
Specialization Confusion |
1.000 |
.510 |
|
Interview Nervousness |
1.000 |
.456 |
|
High Competition |
1.000 |
.248 |
|
Extraction Method: Principal Component Analysis. |
Interpretation:
- Extraction values above 0.50 are considered good.
- Variables like:
- Manage Assignments (0.957)
- Presentation Skills (0.981)
- Life Balance (0.891)
- Placement Worry (0.833)
are strongly explained by the extracted factors.
- High Competition (0.248) and Industry Exposure (0.212) are weakly explained.
Total Variance Explained
|
Component |
Initial Eigenvalues Total |
% of Variance |
Cumulative % |
Extraction Sums of Squared Loadings |
|
1 |
3.145 |
26.21 |
26.21 |
1.841 |
|
2 |
1.959 |
16.33 |
42.54 |
1.758 |
|
3 |
1.587 |
13.23 |
55.77 |
1.321 |
|
4 |
1.306 |
10.88 |
66.65 |
1.282 |
|
5 |
1.264 |
10.53 |
77.18 |
1.203 |
Interpretation:
- 5 factors have Eigenvalues greater than 1.
- These 5 factors explain 77.18% of total variance, which is very good (above 60% is acceptable).
Rotated Component Matrix
|
Variable |
Comp 1 |
Comp 2 |
Comp 3 |
Comp 4 |
Comp 5 |
|
Life Balance |
-.935 |
.034 |
-.003 |
-.117 |
.031 |
|
Presentation Skills |
-.982 |
.065 |
.012 |
-.052 |
-.105 |
|
Placement Worry |
-.869 |
.074 |
-.092 |
.248 |
-.055 |
|
Manage Assignments |
-.036 |
.976 |
.007 |
.060 |
.016 |
|
Theoretical Curriculum |
-.122 |
.877 |
.058 |
-.109 |
-.039 |
|
Workload Stress |
.071 |
.064 |
.809 |
.012 |
.016 |
|
High Competition |
.035 |
.055 |
.429 |
.236 |
.060 |
|
Communication Skills |
-.037 |
-.003 |
-.064 |
-.588 |
-.008 |
|
Interview Nervousness |
-.252 |
-.212 |
.191 |
.369 |
-.419 |
|
Specialization Confusion |
-.084 |
.037 |
-.075 |
-.022 |
-.704 |
|
Industry Exposure |
-.189 |
.186 |
.039 |
-.001 |
-.375 |
|
Extraction Method: PCA. Rotation Method: Varimax with Kaiser Normalization. a. Rotation converged in 6 iterations. |
Interpretation:
- Factor loadings above 0.50 are considered significant.
- Variables grouped under each component represent hidden underlying factors.
- The rotation converged in 6 iterations, indicating stable factor structure.
Cluster Analysis
|
Variable |
Cluster 1 |
Cluster 2 |
Cluster 3 |
|
Manage Assignments |
3.90 |
2.65 |
1.55 |
|
Workload Stress |
2.60 |
2.95 |
3.15 |
|
Life Balance |
2.10 |
4.15 |
1.65 |
|
Exam Pressure |
3.20 |
3.50 |
2.80 |
|
Presentation Skills |
1.80 |
4.20 |
1.60 |
|
Placement Worry |
2.10 |
4.45 |
1.95 |
|
Interview Nervousness |
2.60 |
4.00 |
3.60 |
|
High Competition |
4.40 |
4.40 |
4.60 |
|
Variable |
Cluster MS |
df |
Error MS |
df |
F |
Sig. |
|
Presentation Skills |
100.22 |
2 |
1.85 |
47 |
54.050 |
.000 |
|
Life Balance |
70.81 |
2 |
1.87 |
47 |
37.835 |
.000 |
|
Placement Worry |
71.04 |
2 |
1.96 |
47 |
36.244 |
.000 |
|
Manage Assignments |
25.10 |
2 |
1.47 |
47 |
17.051 |
.000 |
|
Theoretical Curriculum |
17.85 |
2 |
1.44 |
47 |
12.414 |
.000 |
|
Cluster |
N |
% |
|
1 |
10 |
20.0% |
|
2 |
20 |
40.0% |
|
3 |
20 |
40.0% |
|
Valid |
50 |
100.0% |
Cluster Interpretation
🔹 Cluster 1 (20%)
- High assignment stress
- Moderate placement concern
🔹 Cluster 2 (40%)
- High life balance struggle
- High placement worry
- High presentation skill concern
🔹 Cluster 3 (40%)
- High competition concern
- Moderate interview nervousness
Conclusion
· The study identifies the underlying factors affecting MBA students and segments them into meaningful clusters. Institutions can design targeted interventions such as stress management workshops, skill development programs, and placement mentoring sessions to address these specific groups.
