Adoption and continued usage of mobile learning of virtual platforms in Iraqi higher education an unstable environment

Abstract

This study investigates the implementation and continued utilization of mobile learning (M-learning) in Iraqi universities, considering the challenging circumstances of an unstable environment. The research expands upon the UTAUT2 and ECM models. The main objective is to tackle the difficulties and possibilities in Iraq's higher education institutions (HEIs) caused by geopolitical instability and comprehend their influence on student acceptance, satisfaction, and ongoing M-learning usage. The study expands on the increasing significance of mobile learning, particularly in higher education institutions (HEIs). It acknowledges the distinct difficulties encountered by institutions in Iraq due to the region's instability. The study identifies deficiencies in current models and suggests expansions by introducing the variable "Civil Conflicts" to consider the unstable environment. The study seeks to enhance comprehension of M-learning acceptance in conflict-affected regions and offer insights for enhancing M-learning initiatives in Iraqi higher education institutions. To accomplish its goals, this study utilizes a quantitative survey to gather data from 399 students in five universities in Iraq. PLS-SEM is employed to analyze quantitative data and assess the extended UTAUT2 and ECM models. The study's results are anticipated to enhance the understanding of M-learning adoption and ongoing usage in conflict-affected regions, specifically in the context of Iraqi higher education institutions. The study's results can guide improving the efficiency of M-learning programs in Iraqi higher education institutions and provide valuable knowledge on supporting education in regions marked by instability. Researchers' findings can assist educators and policymakers in making well-informed choices to promote the continuity and excellence of education, especially in regions affected by conflict. Researchers can expand upon this study by conducting further investigations into the implementation and utilization of M-learning in volatile environments and assessing the efficacy of the suggested model enhancements.