The integrated social cognitive theory with the TAM model: The impact of M-learning in King Saud University art education

Abstract

Technology in higher education now includes a substantial amount of mobile learning (M-learning). M-learning also enables students to use the internet and technology for research, teamwork, and idea sharing. Additionally, in order to use M-learning systems, both students and teachers must accept M-learning. However, not enough research has been done in Saudi Arabia to determine how satisfied students are with their real use of mobile learning for educational purposes. As a result, the current study intends to investigate students’ behavioral intentions to utilize mobile learning, their happiness with the technology, as well as their impressions of how they actually use mobile learning systems. Therefore, this study aimed to develop a new model by integrating social cognition theory and the technology acceptance model to better understand the elements that influence the adoption of mobile learning in higher education (TAM). The majority of the information was gathered through a survey, with 412 university students’ randomly assigned questionnaires. The data analysis tools utilized were SPSS and Smart-PLS3.3.3. The studies proposed research model could, according to the study’s findings, account for 52.5% of the variation in how mobile learning systems were actually used. This information is crucial for understanding how social and educational technology factors affect the actual use of mobile learning systems. With only two hypotheses being rejected, this study created a new model that supported 16 of them. The findings indicated a beneficial relationship between 10 social and educational technology elements. The findings also indicated a favorable impact on students’ behavioral intentions to use and student happiness, which favorably impacts the actual use of M-learning in higher education. In order to improve students’ academic performance via mobile learning, social cognitive theory and the TAM model are combined as a consequence of the study’s empirical results. Therefore, we encourage students to collaborate with their colleagues at higher education institutions and use M-learning in their classrooms.

Acceptance of Mobile Learning Technology by Teachers: Influencing Mobile Self-Efficacy and 21st-Century Skills-Based Training

“…UTAUT has gained popularity due to its comprehensive nature and ability to predict technology adoption in different contexts [30,31]. Several studies have validated the effectiveness of UTAUT in predicting the adoption of various technologies, such as mobile banking, social media [7,27,38], and online shopping [39]. Additionally, UTAUT has been used to compare technology adoption between different cultures and countries.…”

“…We have studied various technology acceptance models to understand the factors influencing technology adoption by teachers. Various studies [26][27][28][29][30] have focused on examining the acceptance of technology in education, particularly in response to the COVID-19 pandemic. Each study used different models and variables to investigate various aspects of technology adoption, including students' actual use of e-learning platforms [26,30], the impact of mobile learning on art education, the use of personal learning environments for self-regulated learning, and the intention of art teachers and lecturers to continue using blackboards.…”

Consumers' behavioral intention toward online shopping in the post‐COVID‐19 period

“…UTAUT's scope has broadened to scrutinize the bedrock factors affecting the intention to use information systems and technology applications (Alkhwaldi et al, 2022; Eneizan et al, 2022). It has found utility in explaining the adoption and usage of various technologies such as online learning management systems, green banking, intelligent elevators, digital health, e‐government, advanced driver assistance systems, engineering e‐laboratories, and AI‐based intelligent products (Almogren & Aljammaz, 2022; Bouteraa et al, 2023; Edo et al, 2023; Rahman et al, 2017; Sohn & Kwon, 2020). In the consumer realm, UTAUT has been widely used to study technology adoption (Alkhowaiter, 2022; Bouteraa et al, 2023; Escobar‐Rodríguez & Carvajal‐Trujillo, 2014; Vishwakarma et al, 2020).…”

The role of interoperability and inter-side benefits on merchants' e-wallet adoption: the case of Peruvian nanostores

“…SCT is proposed as an overarching theory to understand the relationship between the identified variables. In recent years, this theory has been applied to measuring technology benefits in diverse contexts (Almogren and Aljammaz, 2022; Alvarez-Risco et al. , 2022; Li et al.…”

“…SCT is proposed as an overarching theory to understand the relationship between the identified variables. In recent years, this theory has been applied to measuring technology benefits in diverse contexts (Almogren and Aljammaz, 2022;Alvarez-Risco et al, 2022;Li et al, 2022). SCT postulates that human behavior is shaped by the interplay of personal, environmental, and behavioral factors (Bandura, 1984(Bandura, , 1997.…”