Abstract

The COVID-19 pandemic has forced Indian engineering institutions (EIs) to bring their previous half-shut shades completely down. Attracting new admissions to EI campuses during the pandemic have become a ‘now or never’ situation for EIs. During crisis situations, EIs have struggled to return to their normal track. The pandemic has drastically changed students' behavior and family preferences due to mental stress and the emotional life associated with it. Consequently, it has become the need of hour to examine the choice characteristics influencing the selection of EIs during the COVID-19 pandemic.The purpose of this study is to critically examine institutional influence and pandemic influence that affects students’ choice about engineering institutions (EIs) during COVID-19 pandemic situation and consequently to study relationships between them. A quantitative research, conducted through a self-report survey composed by a closed-ended structured questionnaire was performed on the students who were recently enrolled in the EIs (academic year 2020–2021) belonging to North Maharashtra region of India during the pandemic.The findings of this study have revealed that institutional and pandemic influence have directed EI choice under the COVID-19 pandemic. It is also found that pandemic influence is positively affected by institutional influence. The study demonstrated that EIs can attract new enrollments by repositioning their institutional characteristics that regulate pandemic influence. The study can be a measuring tool for policy makers to attract new enrollments under pandemic situation.

Highlights

  • Worldwide, engineering education is viewed as a career of progressive growth that has the potential to shape human skills (Blom and Saeki, 2011), social and quality of life (Rojewski, 2002), economy of the country (Cebr, 2016) and overall development of the country (Downey and Lucena, 2005)

  • A literature review aligned with the objective of this study has enabled this study to implement quantitative methods due to their ability to frame hypotheses (Borrego et al, 2009), capabilities to operate on multivariate statistical data (Creswell and Creswell, 2017), ability to analyze relationships with definiteness (Creswell, 2012b), reliability (Steckler et al, 1992) and success in educational research (Tight, 2012)

  • The research Hypothesis of this study that states there is no significant relationship between institutional influence and pandemic influence under the COVID-19 pandemic situation is tested by knowing the relationship PI←II, which shows that this relationship is statistically significant in the positive direction (B 1⁄4 0.942, Composite reliability (CR) 1⁄4 16.434, p < 0.001)

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Summary

Introduction

Worldwide, engineering education is viewed as a career of progressive growth that has the potential to shape human skills (Blom and Saeki, 2011), social and quality of life (Rojewski, 2002), economy of the country (Cebr, 2016) and overall development of the country (Downey and Lucena, 2005). Engineering education has proven to be a key factor for the sustainable and profitable development of society. It encourages global competitiveness through engineering inventions for the benefit of society at large. As acknowledged by previous literature, is a subtle and complex phenomenon (Hossler et al, 1989a) that involves a multifaceted and inconsistent set of institutional influencing characteristics (Chapman, 1981; Obermeit, 2012). Engineering education is highly contrasted with respect to the multidimensional thoughts of students and institutional offers related to the quality of staff and teaching-learning, infrastructure and facilities, course value and delivery, and outcome benefits

Statement of the problem
Objective of study
Literature review
Institutional influence
Pandemic influence
Research gap and significance of study
Conceptual framework and hypothetical model
Research design
Scale design and data collection
Data analysis and statistical results
Statistical fitness of data
C10 C11 C12
Step I - scale reduction and component extraction by EFA
Step II – executing the measurement model through CFA and SEM
Model fitness and hypothesis validation
Statistical inference and discussions
Conclusion
Limitations and future research
Findings
Methods
Full Text
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