💻 computer science

Push-Pull Determinants Among Bangladeshi Students Enrolled in NCR Private Universities: A Single-Destination Exploratory Study

This exploratory study of Bangladeshi students in India's National Capital Region identifies political instability in Bangladesh as the primary push factor and geographical proximity as the leading pull factor, revealing that visa accessibility and cultural familiarity form a composite accessibility advantage while infrastructure satisfaction significantly drives students' intent to recommend their institutions.

Original authors: Md Millat Hosen, Md Nazmus Sakib

Published 2026-06-23

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Md Millat Hosen, Md Nazmus Sakib

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine a group of students from Bangladesh standing at a crossroads, trying to decide where to go for their university education. This paper is like a map that explains why many of them chose a specific path: the private universities in India's National Capital Region (NCR), near New Delhi.

The researchers used a classic travel theory called "Push-Pull" to understand their choices. Think of it like this:

Push Factors are things that kick the student out of their home country (like a strong wind blowing them away).
Pull Factors are things that attract them to a new place (like a magnet or a delicious smell).

Here is what the study found, broken down into simple stories and analogies:

1. The "Kick" Out of Bangladesh (Push Factors)

The biggest reason students left Bangladesh wasn't just that they wanted better classes; it was that their home environment felt unstable.

The Main Push: The strongest "kick" came from political and administrative chaos. Imagine trying to study for a final exam while the lights keep flickering off, the schedule keeps changing, and the government is in turmoil. That uncertainty was the number one reason students wanted to leave.
Other Kicks: They also felt pushed out because there weren't enough seats in top public universities (like a crowded bus with no room left) and because Bangladesh lacked specific modern tech courses (like AI) that they craved.

2. The "Magnet" in India (Pull Factors)

Once they decided to leave, why India? Why not Australia or Germany?

The Strongest Magnet: The top reason was closeness. India is right next door, and the culture feels very familiar. It's like choosing to visit a neighbor's house instead of flying to another continent.
The Runner-Up Magnet: Visa accessibility was almost as important. Getting a visa to India felt like walking through an open door, whereas getting a visa to the West felt like trying to pick a very difficult lock.
The Surprise: Surprisingly, cost (tuition and living expenses) was important, but it wasn't the most important factor. The "ease of getting there" and "feeling at home" mattered more than saving a few dollars.

3. The "Travel Agents" (Advisory Networks)

Most students didn't plan this trip alone. About 73% of them used educational consultants (travel agents for students) to help them pick a university and a course.

However, these students weren't blindly trusting the agents. About 41% of them double-checked the agents' advice by looking at official university websites or talking to current students. It was a mix of "asking for directions" and "checking the map yourself."

4. The "Satisfaction Score"

After arriving in India, did the students like it?

The Connection: The study found a direct link between satisfaction and recommending the place. Think of it like a restaurant: if you enjoy the food and the service (satisfaction), you are much more likely to tell your friends to go there (recommendation).
The Reality Check: The students were generally okay with their experience (scoring just above "average"), but not overwhelmingly excited. The researchers suggest that if universities want more students to recommend them, they need to make sure the reality matches the promises made before the students arrived.

5. Three Types of Travelers (The Clusters)

The researchers used a computer program to group the students into three distinct "personality types" based on their answers:

The "Comprehensively Motivated" (39%): These students were pushed hard by problems in Bangladesh and pulled strongly by everything India offered. They were the most intense group.
The "Proximity-Led" (34%): These students weren't necessarily forced out by bad conditions in Bangladesh. They simply liked India because it was close, familiar, and easy to get into. They chose India because it felt like a natural, easy step.
The "Low-Salience" (27%): These students didn't have strong feelings about the push or pull factors measured in the survey. Their reasons for coming might be personal, like having family there, which the survey didn't ask about.

6. The Competition

When these students were deciding, they weren't just choosing between "Bangladesh" and "India." Many of them were also seriously considering Australia and Germany (tied for the top alternative).

The Takeaway: India isn't just a backup plan; it's a strong competitor to Western countries for these students, mostly because it's easier and faster to get to.

Summary

In short, this paper tells the story of Bangladeshi students who, faced with instability at home, chose India not just because it was cheap, but because it was close, familiar, and easy to enter. They relied on advisors to help them navigate the choice, and their willingness to recommend India to others depends heavily on whether their actual experience matches their expectations.

Note: The authors admit this study is a "first look" (exploratory) with a small group of students (56 people), so while the patterns are clear, they need to be tested with larger groups in the future to be absolutely certain.

Technical Summary: Push-Pull Determinants Among Bangladeshi Students Enrolled in NCR Private Universities

Problem Statement and Research Gap
While international student mobility from Bangladesh is a significant demographic phenomenon, the specific decision-making processes governing migration to India's National Capital Region (NCR) remain underexplored in academic literature. Existing push-pull research predominantly focuses on Western destinations, leaving a gap in understanding intra-regional South Asian flows. This study addresses three specific underexplored dimensions: (1) the relative salience of push versus pull factors for the Bangladesh-India dyad; (2) the role of educational advisory networks as information intermediaries; and (3) the relationship between post-enrollment satisfaction and recommendation intent. The paper posits that short-haul intra-regional mobility may be organized less by discrete, rank-ordered pull factors and more by a "composite accessibility advantage" where geographic nearness, cultural familiarity, and visa feasibility reinforce one another.

Methodology
The study employs a cross-sectional quantitative survey design grounded in Lee's (1966) push-pull theoretical framework.

Sample: Data was collected from Bangladeshi students enrolled in private universities in the NCR (Sharda University, Noida International University, and Galgotias University). The initial dataset contained 63 responses; after quality filtering (removing straight-lining, logical contradictions, and extreme bifurcation), the final analytical sample was $n = 56$ .
Instrument: A structured online questionnaire (Google Forms) with five sections covering demographics, push factors (4 items), pull factors (4 items), advisory networks, and post-enrollment outcomes (satisfaction and recommendation intent). Items utilized a 5-point Likert scale.
Analytical Techniques:
- Descriptive Statistics: Means and standard deviations were calculated for all factors.
- Clustering: K-means clustering ( $k=3$ ) was applied to the eight push-pull Likert scores to identify student decision profiles. Cluster validity was assessed via the elbow method, silhouette coefficients, and one-way ANOVA.
- Regression: Binary logistic regression was used to predict high recommendation intent (outcome: $E2 \geq 4$ ). Due to the modest sample size and events-per-variable (EPV) ratio, a reduced five-predictor model was prioritized over a full ten-predictor model to mitigate estimation uncertainty.
- Software: Analysis was conducted using Python 3.11 (pandas, numpy, scipy, scikit-learn).

Key Results

Push Factors: Political and administrative disruptions to Bangladesh's academic calendar emerged as the leading push factor ( $M = 3.73$ ), followed by the absence of specialized technology programs ( $M = 3.27$ ). Competitive exam pressure and seat capacity were less salient.
Pull Factors: Geographical and cultural proximity ( $M = 3.80$ ) and visa accessibility ( $M = 3.73$ ) were the strongest pull factors. Statistical analysis revealed no significant mean difference between these two items ( $p = .598$ ), suggesting they function as a composite construct. Tuition affordability and cost of living followed, though all pull factors exceeded the scale midpoint.
Alternative Destinations: Australia and Germany were the most frequently considered alternatives (33.9% each), tied with the proportion of students who considered India exclusively.
Advisory Networks: Advisory influence was reported by 73.2% of respondents (broad threshold) and 66.1% (stricter threshold). The primary service need was university and course selection (48.2%).
Satisfaction and Recommendation: Infrastructure satisfaction ( $M = 3.11$ ) and recommendation intent ( $M = 3.29$ ) were moderately and significantly correlated (Pearson $r = .497$ ; Spearman $\rho = .535$ ). Logistic regression identified infrastructure satisfaction as the strongest predictor of high recommendation intent (Odds Ratio = 2.54, 95% CI [1.09, 5.89]).
Clustering: K-means analysis identified three distinct decision-profile clusters:
1. Comprehensively Motivated (39.3%): High scores across all push and pull dimensions.
2. Proximity-Led Enrollers (33.9%): Low academic competition push but high pull from proximity and visa accessibility.
3. Low-Salience Enrollers (26.8%): Moderate-to-low scores across all measured dimensions.
  ANOVA confirmed significant differences ( $p < .01$ ) across clusters for key variables including visa accessibility, political disruption, and cultural proximity.

Key Contributions and Significance
The paper offers four principal contributions to the literature on international student mobility:

Composite Accessibility Advantage: The study refines the conventional push-pull model by demonstrating that for short-haul intra-regional corridors (Bangladesh-India), geographic nearness, cultural familiarity, and visa accessibility operate as a combined decision structure rather than separable attributes. This "composite accessibility advantage" is more salient than cost or program availability alone.
Political Disruption as a Primary Driver: The research identifies political and administrative instability in Bangladesh as the dominant push factor, a finding linked to the 2024 political transition in the country. This highlights structural unreliability in the academic calendar as a more urgent motivator than curricular gaps.
Satisfaction-Loyalty Alignment: The study confirms that satisfaction and recommendation intent rise and fall together in this context. Infrastructure satisfaction is the strongest predictor of word-of-mouth recommendation, suggesting that closing the gap between pre-admission counseling and actual student experience is the highest-leverage intervention for institutions.
Typological Segmentation: The identification of three statistically validated decision-profile clusters provides a foundation for differentiated institutional communication strategies, distinguishing between students driven by comprehensive constraints, those driven by proximity, and those whose motivations lie outside the measured variables.

Limitations and Modesty of Claims
The authors explicitly frame these findings as exploratory. The study acknowledges several limitations:

Sample Size: The $n=56$ dataset is below the conventional threshold for stable logistic regression with multiple predictors, necessitating a reduced model and caution in interpreting effect sizes.
Single-Destination Design: As the study only surveyed students who chose India, the findings regarding pull factors are conditional on that choice and cannot be generalized to the entire population of Bangladeshi students considering international options.
Sampling Bias: The use of researcher-affiliated distribution channels (WhatsApp groups linked to the lead author's former internship) and convenience sampling may introduce self-selection bias.
Methodological Assumptions: The use of K-means on ordinal Likert data and the lack of independent back-translation for the survey instrument are noted as constraints.

Consequently, the paper does not claim definitive causal relationships but rather proposes a preliminary typology and a refined theoretical understanding of intra-regional mobility that requires validation through larger, multi-destination, and longitudinal studies.