Factors Influencing Student Information Technology Adoption

VanDerSchaaf, Hans; Daim, Tuğrul U.; Başoğlu, Nuri

doi:10.1109/tem.2021.3053966

Cited by 39 publications

(13 citation statements)

References 84 publications

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“…For example, the research of Li and Zhao [ 39 ] reflects that effort expectancy is expected to have a significant positive impact on the intention of continuing to use MOOC. VanDerSchaaf et al [ 40 ] research also shows that effort expectancy and social influence are the key influences on college students’ intention to use information technology to obtain university services. However, the situation of this study is not without precedent.…”

Section: Discussionmentioning

confidence: 99%

“…Therefore, Venkatesh et al [ 38 ] put forward the Unified Theory of Acceptance and Use of Technology (UTAUT) model, which integrates eight models, namely the Theory of Reasoned Action (TRA), Technology Acceptance Model (TAM), Motivational Model (MM), Theory of Planned Behavior (TPB), Combined TAM, TPB (C-TAM-TPB), Model of PC Utilization (MPCU), Innovation Diffusion Theory(IDT), and Social Cognitive Theory (SCT). Venkatesh tested all the above models and integrated them to form a more refined model, UTAUT, which was widely used by later researchers [ 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Literature Reviewmentioning

confidence: 99%

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Building Information Modeling Learning Behavior of AEC Undergraduate Students in China

Peng

et al. 2022

Behavioral Sciences

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With the popularization and application of Building Information Modeling (BIM), the demand for BIM technical talents in the construction industry is increasing. Exploring college students’ BIM technical learning behavior is of great practical significance to improve education activities. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this research adds learning attitude variables to construct a theoretical model of influencing factors of college students’ BIM technology learning behavior. Chinese undergraduate students were asked to complete online questionnaires through peer-to-peer contact with sample colleges and universities. Finally, 1090 valid questionnaires were obtained. The students were sampled from research-oriented, applied research-oriented, application-oriented, and private research-oriented universities in seven regions of China: northeast, north, east, south, central, northwest, and southwest. The structural equation model was used to analyze the sampling data. The results indicate that college students’ BIM learning attitude, performance expectations, and social influence positively and directly impact their learning intention, which indirectly impacts their learning behavior. At the same time, promoting factors and learning intention demonstrate a significant positive and direct impact on learning behavior. Therefore, the following suggestions have been put forward to enhance college students’ learning motivation for BIM technology: increase the popularization of BIM technology in colleges and universities and improve the operation level of full-time BIM teachers. The latter enables colleges and universities to continuously and stably export qualified BIM technical talents to society and the market, resulting in a continuous industry development cycle.

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Section: Discussionmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Building Information Modeling Learning Behavior of AEC Undergraduate Students in China

Peng

et al. 2022

Behavioral Sciences

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show abstract

“…Finally, information technology innovations enhance service delivery, transforming universities to serve students' needs. This is facilitated by the unified theory of acceptance and use of technology and assists in developing management plans for graduating with high-standard degrees [163].…”

Section: Smart Governancementioning

confidence: 99%

Unpacking Smart Campus Assessment: Developing a Framework via Narrative Literature Review

Polin,

Yigitcanlar,

Washington

et al. 2024

Sustainability

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The emergence of the smart campus approach for university campuses addresses the digital transformation needs of higher education institutions, driven by the increasing capability and popularity of digital technologies like artificial intelligence and the internet of things. While existing research has laid a foundation for conceptualizing smart campuses and developing implementation frameworks, a significant gap remains in understanding how to assess a university campus’s ‘smartness’ effectively. The lack of a robust assessment framework makes it challenging to gauge the effectiveness of smart campus initiatives and identify areas for improvement. This study aims to bridge this research gap by identifying key indicators for evaluating the ‘smartness’ of university campuses. Using a narrative literature review method, the study comprehensively reviews the recent literature on smart campuses, organizational management, and societal applications, focusing on identifying pertinent indicators. By incorporating insights from different domains, the study presents a holistic understanding of the indicators necessary for assessing the ‘smartness’ of university campuses through the proposed smart campus assessment framework. The framework and the insights generated inform researchers and decision-makers in assessing and monitoring the effectiveness of smart campuses.

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“…For example, students with a high degree of digital literacy might view digital learning resources as being particularly easy to use (Sayaf et al, 2022). Likewise, students whose peers speak highly of certain resources might see more value in utilizing those resources themselves due to social influence (VanDerSchaaf et al, 2021). However, these studies tend to revolve around assessments of student adoption and engagement decisions.…”

Section: Student Resource Adoption and Self-direction In Blended Lear...mentioning

confidence: 99%

Engineering student experience and self-direction in implementations of blended learning: a cross-institutional analysis

et al. 2023

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Background Much of researchers’ efforts to foster wider implementation of educational innovations in STEM has focused on understanding and facilitating the implementation efforts of faculty. However, student engagement in blended learning and other innovations relies heavily on students’ self-directed learning behaviors, implying that students are likely key actors in the implementation process. This paper explores the ways in which engineering students at multiple institutions experience the self-directed selection and implementation of blended learning resources in the context of their own studies. To accomplish this, it adopts a research perspective informed by Actor-Network Theory, allowing students themselves to be perceived as individual actors and implementors rather than a population that is implemented upon. Results A thematic analysis was conducted in two parts. First, analysis identified sets of themes unique to the student experience at four participant institutions. Then, a second round of analysis identified and explored a subset of key actors represented in students’ reported experiences across all institutions. The findings show clear similarities and differences in students’ experiences of blended learning across the four institutions, with many themes echoing or building upon the results of prior research. Distinct institutional traits, the actions of the instructors, the components of the blended learning environment, and the unique needs and preferences of the students themselves all helped to shape students’ self-directed learning experiences. Students’ engagement decisions and subsequent implementations of blended learning resulted in personally appropriate, perhaps even idiosyncratic, forms of engagement with their innovative learning opportunities. Conclusion The institutional implementation of blended learning, and perhaps other educational innovations, relies in part on the self-directed decision-making of individual students. This suggests that instructors too hold an additional responsibility: to act as facilitators of their students’ implementation processes and as catalysts for growth and change in students’ learning behaviors. Developing a greater understanding of students’ implementation behaviors could inform the future implementation efforts of faculty and better empower students to succeed in the innovative classroom.

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Factors Influencing Student Information Technology Adoption

Cited by 39 publications

References 84 publications

Building Information Modeling Learning Behavior of AEC Undergraduate Students in China

Building Information Modeling Learning Behavior of AEC Undergraduate Students in China

Unpacking Smart Campus Assessment: Developing a Framework via Narrative Literature Review

Engineering student experience and self-direction in implementations of blended learning: a cross-institutional analysis

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