Exploring Cloud Computing Adoption: COVID Era in Academic Institutions

Published In

Technological Forecasting and Social Change

Document Type

Citation

Publication Date

8-1-2023

Abstract

Cloud computing (CC) is a revolution that can provide information technology (IT) as a service. CC offers infrastructure, platform, and software services, as demand peaks and surges. This paper aims to investigate how prospective adopters behave when external factors such as “Coronavirus Pandemic- COVID-19” impact their technology adoption decision-making. The study also explores how a prospective adopter behaves i.e., if his/her intention to adopt any new innovation increases in presence of stronger disruptive factors (COVID-19). This research empirically examines if the intent to adopt secured (online) services impacts actual CC adoption (CCA) in pre-COVID-19 and during COVID-19 eras. It also provides an idea of how prospective adopters behave when they face disruptions caused by the pandemic situation, and how the holistic relation is reflected in terms of its influence on academic performance. This study has used Technology Acceptance Model (TAM) with sequential mediation effect of intent to adopt secured online services and CCA on Academic Performance (AP) using a sample of 867 students from 25 different Indian universities in Tier 1 and Tier 2 cities. Using AMOS, a structural equation modelling was conducted to test the research model. The results highlight that there is a significant difference between the influence of perceived usefulness (PU) as well as perceived ease of use (PEOU) on CCA due to COVID-19. The results also provide empirical evidence of gender moderating the relationship of PU as well as PEOU with CCA. This is the first study that provides comparative results from pre-COVID and post-COVID era, this work provides a reference point to practitioners and academicians, especially when evaluating factors before making a final decision regarding any emerging technology's adoption.

Rights

© The Author(s)

DOI

10.1016/j.techfore.2023.122613

Persistent Identifier

https://archives.pdx.edu/ds/psu/40624

Publisher

Elsevier

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