Namibian Teachers’ Behavioural Intentions on Students’ Mathematics Learning Using Their Own Devices

What drives people to accept automated vehicles? Findings from a field experiment

Introduction: The use of shared automated vehicles (SAVs) should lead to several societal and individual benefits, including reduced greenhouse gas emissions, reduced traffic, and improved mobility for persons who cannot safely drive themselves. We define SAVs as on-demand, fully automated vehicles in which passengers are paired with other riders traveling along a similar route. Previous research has shown that younger adults are more likely to report using conventional ridesharing services and are more accepting of new technologies including automated vehicles (AVs). However, older adults, particularly those who may be close to retiring from driving, stand to greatly benefit from SAV services. In order for SAVs to deliver on their aforementioned benefits, they must be viewed favorably and utilized. We sought to investigate how short educational and/or experiential videos might impact younger, middle-aged, and older adult respondents’ anticipated acceptance and attitudes toward SAVs. Knowing what types of introductory experiences improve different age groups’ perceptions of SAVs will be beneficial for tailoring campaigns aiming to promote SAV usage. Methods: We deployed an online survey using the platform Prolific for middle-aged and older respondents, and our departmental participant pool for younger adults, collecting 585 total responses that resulted in 448 valid responses. Respondents answered questions regarding their demographic attributes, their ridesharing history, preconceptions of technology, as well as their anticipated acceptance attitudes towards SAVs as measured by the dimensions of the Automated Vehicle User Perception Survey (AVUPS). After this, respondents were randomly assigned to an intervention condition where they either watched 1) an educational video about how SAVs work and their potential benefits, 2) an experiential video showing a AV navigating traffic, 3) both the experiential and educational videos, or 4) a control video explaining how ridesharing works. Anticipated acceptance attitudes towards SAVs were measured again after this intervention and difference scores calculated to investigate the effect of the intervention conditions. Prolific respondents were paid at a rate of $9.50/hour and younger adults received course credit. Results: Controlling for preconceptions of technology and ridesharing experience, a MANOVA was run on the difference scores of the dimensions of the AVUPS (intention to use, trust/reliability, perceived usefulness (PU), perceived ease of use (PEOU), safety, control/driving-efficacy, cost, authority, media, and social influence). Both older and middle-aged adults expressed significantly greater increases in PEOU and PU of SAVs than younger adults. We also observed an interaction between age and condition for both PU and PEOU. For PU, older adults’ difference scores were found to be significantly greater than younger adults’ for the control video condition. With PEOU, older adults’ difference scores were significantly greater than both younger adults’ for the control video condition, and middle-aged adults had greater difference scores for the educational-only video condition than younger or older adults. Discussion: The increases in PU observed for older adults in the control condition suggests that educating them on how to use currently available ridesharing services might transfer to and/or highlight the benefits that automated ridesharing might provide. The PEOU interactions also suggest that middle-aged adults might respond more positively than younger or older adults to an educational introduction to SAVs. Conclusion: The positive findings pertaining to PU and PEOU show that exposure to information related to SAVs has a positive impact on these attitudes. PU’s and PEOU’s positive relationship to behavioral intentions (BI) in the Technology Acceptance Model, coupled with the findings from this study, bode well for higher fidelity interventions seeking to inform and/or give individuals experience with SAVs. Providing information on how currently available ridesharing services work helped our older adult respondents recognize the potential usefulness of SAVs. Knowing that different age groups may respond better to educational versus experiential interventions, for example middle-aged adults in this study responding more positively to the educational video condition than younger or older adults, may be useful for targeted promotional campaigns.

The present study examined the relationship among behavioural intention (BI) to adopt online learning, perceived usefulness (PU), perceived ease of use (PEU), self-regulated online learning (SR) and online learning self-efficacy (SE). A total of 900 university students with online learning experience from many provinces of China took part in the study. Structural equation modelling (SEM) was used to analyse the data accepted. The results indicate that PU has a significant positive effect on BI; SR has a significant positive effect on PEU, PU and BI. SE has a significant positive effect on PEU, PU and BI. In addition, SE and SR have significant indirect effects on BI through the mediation of PU. The outcomes have tangible theoretical and practical implications. They not only replicates previous research and provides possible space for further expansion of TAM, but also provide us with an opportunity to reflect on and actively take practical measures to improve BI. These efforts include teachers, parents and other educators trying to promote students’ academic achievements, self-efficacy and self-regulation in the process of online learning. The former is the most concerning issue, while the latter two are the source of students’ motivation. Furthermore, educators should make appropriate use of the role of digital technology in online learning and be careful not to exaggerate the value of digital technology, let alone equate it with online learning.

The purpose of this study is to determine the impact of information quality (IQ) and computer self-efficacy (CSE) on perceived ease of use (PEOU) and perceived usefulness (PU), which were hypothesized to impact behavioral intention (BI) to use E-learning. Based on the Technology Acceptance Model, this study was conducted among undergraduate students of the College of Administrative and Financial Sciences, Cihan University-Erbil. The students participated in E-learning that supported the traditional teaching method via Moodle platform during COVID-19 pandemic. 209 valid questionnaires were collected and analyzed using Structural Equation Modelling to examine seven proposed hypotheses. This study found that CSE had a positive and significant impact on PEOU but an insignificant impact on PU. Moreover, IQ positively and significantly impacted PU and PEOU. PEOU and PU significantly impacted BI to use E-learning among undergraduate students. PEOU had a significant impact on PU in the context of the education system. The implication of this study is universities and administrators of E-learning programs must focus on CSE, IQ, PEOU and PU that play a significant role in increasing E-learning adoption by students, and consequently influence the success of the educational process and productivity in the time of COVID-19 outbreak.KeywordsE-LearningComputer self-efficacyBehavioral intentionInformation qualityTAM

This paper examines mathematics teachers’ level of acceptance and intention to use the Augmented Reality Geometry Tutorial System (ARGTS), a mobile Augmented Reality (AR) application developed to enhance students’ 3D geometric thinking skills. ARGTS was shared with mathematics teachers, who were then surveyed using the Technology Acceptance Model (TAM) to understand their acceptance of the technology. We also examined the external variables of Anxiety, Social Norms and Satisfaction. The effect of the teacher’s gender, degree of graduate status and number of years of teaching experience on the subscales of the TAM model were examined. We found that the Perceived Ease of Use (PEU) had a direct effect on the Perceived Usefulness (PU) in accordance with the Technology Acceptance Model (TAM). Both variables together affect Satisfaction (SF), however PEU had no direct effect on Attitude (AT). In addition, while Social Norms (SN) had a direct effect on PU and PEU, there was no direct effect on Behavioural Intention (BI). Anxiety (ANX) had a direct effect on PEU, but no effect on PU and SF. While there was a direct effect of SF on PEU, no direct effect was found on BI. We explain how the results of this study could help improve the understanding of AR acceptance by teachers and provide important guidelines for AR researchers, developers and practitioners.

This study examined Chinese undergraduate medical students’ acceptance and adoption of intelligent tutoring systems (ITSs) using the general extended technology acceptance model for e-learning via a Likert-scale questionnaire. Specifically, it examined the relations between the five antecedents and the four core components in the model (i.e., perceived usefulness (PU), perceived ease of use (PEOU), behavioural intention and actual use of ITSs). The results of PLS-SEM showed that perceived enjoyment was the most influential antecedent as it significantly impacted both PU and PEOU. Both self-efficacy and prior experience only significantly contributed to PEOU but not PU. Both PU and PEOU significantly and positively predicted behavioural intention, which in turn had a significant and positive path to actual use. The results provide some practical implications to teachers as to how to encourage Chinese undergraduate medical students’ adoption of ITSs: by integrating some gamification elements into the learning activities in ITSs to foster students’ enjoyable feelings or familiarising students with using ITSs so that they can quickly adapt to learning through them. This could be achieved by providing guidance in using ITSs via videos, websites or booklets, or at the beginning of the course, inviting senior students to share their perceived advantages and usefulness of using ITSs. Implications for practice or policy: Because of the importance of perceived enjoyment, teachers may integrate some gamification into the learning activities in ITSs to nurture students’ enjoyable feelings. To enable students to quickly accommodate learning via ITSs, institutions may provide students with information on common features of ITSs or how to navigate a specific ITS. Teachers may explicitly explain how learning objectives can be better achieved through using an ITSs so that students will appreciate its usefulness.

Dyslexia research in China is crucial because of limited awareness, lack of tools, and insufficient support. These issues seriously hinder students' academic success. To address this gap, this study examines how history-related short videos on social media platforms influence the learning behavior of students with dyslexia. This study introduces dyslexia as a moderating variable within the Technology Acceptance Model (TAM) framework for the first time, addressing a significant gap in understanding how learning differences influence technology adoption in educational contexts. History poses special challenges for learners with dyslexia because it requires extensive reading and complex texts. Short videos may offer a more accessible and engaging alternative. A cross-sectional study was conducted among 400 university students in Hebei province, China using a self-administered questionnaire. Several scales were used in the survey: Quality of Content (CQ), Perceived Ease of Use (PEOU), Perceived Usefulness (PU), Behavioral Intention (BI), Dyslexia Scale, and Learning Behavior (LB). Descriptive statistics and structural equation modeling (SEM) analyses were performed. A P value of <0.05 was considered significant. The results show positive correlations among key variables. Content quality was positively correlated with perceived usefulness (r=0.369), perceived ease of use (r=0.329), and behavioral intention (r=0.203), all p<0.01. Ease of use is also associated with usefulness (r=0.323), and usefulness with intention (r=0.249). Learning intention further predicts behavior (r=0.359). However, the direct link between ease of use and behavioral intention is not significant (p=0.17). Dyslexia significantly moderates the relationship between PEOU and PU (p<0.001). However, it does not significantly affect the relationships between (1) PEOU and BI or (2) PU and BI, though these effects approach marginal significance. Students with dyslexia show different levels of learning intention depending on content quality, ease of use, and usefulness. Dyslexia significantly moderates some of these relationships. The findings suggest that history-related short videos can be effective educational tools, especially when they emphasize usability and usefulness.

The use of Information Technology (IT) in the banking sector has seen a phenomenal growth in India. For its effective use and implementation, employees' acceptance and the consequent use of IT are important. Davis' Technology Acceptance Model (TAM) (1989) explains users' acceptance of electronic systems. By applying the extended TAM approach to this study, the authors aim to uncover various factors that affect the attitude and acceptance of bank employees towards CBS. The TAM model identifies perceived usefulness (PU), and perceived ease of use (PEU) as impacting attitude towards IT and behavioural intention. The model is extended by identifying social influence, computer self-efficacy, and technological facility as factors affecting perceived usefulness and perceived ease of use. The target respondents for this study were bank employees from the public and private sectors banks (State Bank of India, Union Bank, Bank of India, ICICI Bank, and Axis Bank) situated in the National Capital Territory (NCT) of Delhi. Measurement items applied in this study were an adaptation of previously validated measures or were developed on the basis of a literature review. Responses were analysed using the 7-point Likert Scale that ranged from (1) ‘strongly disagree’ to (7) ‘strongly agree’. To test the reliability of the measures, a pilot study was conducted with 20 randomly chosen employees and modified scales. Structural equation modelling was used to analyse the data. Overall, the findings suggest that extended TAM variables positively influence the intention and usage behaviour through PU and PEU. That is, social influence enhances perceived usefulness; and computer self-efficacy and technology facility influence the perceived ease of use. Again, perceived ease of use significantly influences perceived usefulness which in turn has significant effect on behavioural intention and usage behaviour. Social influence of superiors was the prominent determinant of perceived usefulness of the CBS technology. Hence the role of managers as effective communicators is heightened especially in conveying the benefits of using IT and in turn achieving organizational goals.

The aim of this research is to suggest a unified model that integrates the task fit technology (TTF) model, technology acceptance model (TAM), IoT concerns, and social incentive to examine behavioural intentions to use the Internet of Things (IoT) applications in the healthcare context. A sample of 257 respondents in Jordan who are potential users of IoT participated in this research. Structural equation modelling (SEM) applied via AMOS 25.0 package is carried out to examine the study hypotheses. The findings illustrate that the theoretical model which integrated the TTF model for utility/advantage and TAM for the technology acceptance offers a better comprehensive insight of users’ behaviour relevant to the study setting: (1) perceived usefulness (PU) and attitude (ATT) are significant to the behavioural intentions to use IoT; (2) PU has a critical mediation impact on the relationship between: perceived ease of use (PEoU), the TTF construct, security and privacy (SP), and social influences (SI) and behavioural intentions; (3) PEoU, TTF, and SI are revealed to play critical roles to predict behavioural intentions; (4) ITF, TTF, and SP affect the PEoU; (5) unexpectedly, PEoU and SI have no significant impact on ATT, and ITF and SP do not influence PU. The findings have theoretical and practical implications for IoT researchers, developers, and policymakers.KeywordsIoT acceptanceHealthcareJordanTAMTTFIoT concernsSocial influenceBehavioural intention

Extending the technology acceptance model and empirically testing the conceptualised consumer goods acceptance model

The technology acceptance model (TAM) is a popular measure of user adoption and acceptance of technology. The pharmaceutical marketing industry has largely incorporated technology-based applications to enhance operational efficiency, effectiveness, and client engagement in the past decade. No study has explored user acceptance by pharmaceutical executives in the context of technology's impact on performance. The study aims to explore the relationship between perceived ease of use (PEOU), perceived usefulness (PU), and behavioral intention (BIU) in the context of the perceived impact of technology on performance (TechIMP). Hypotheses were tested using factor-based structural equation modeling. A random sample of 282 marketing executives was drawn from pharmaceutical companies in Nigeria using an online questionnaire. The developed model provided acceptable measures of fit and validity. Significant positive relationships exist between PEOU, PU, and BIU, explaining 58% of the variance in TechIMP. PEOU had a stronger impact on BIU compared to PU. BIU was a significant link between PEOU and PU to TechIMP. Multigroup analysis showed key differences between male and female executives. The study adds to the existing literature by extending TAM to include TechIMP. Managers should enhance positive user perception and acceptance by engaging in simulated training before introducing new technology and ensuring flexibility of technology use.

The use of a learning management system (LMS) is believed to be significant for students’ academic performance, but students’ attitudes regarding its use are divided. Therefore, the purpose of this study is to apply a modified version of the technology acceptance model to determine the motivators for University of Professional Studies Accra students’ behavioural intentions (BIs) and actual use of LMSs using a convenient sampling technique to elicit data from first-year students in the faculty of information technology and communication studies. The investigation included a total of 188 samples. The study used structured equation modelling partial least squares to analyse the data. Specifically, the study employed the importance-performance map analysis to establish the factors that are important to students’ actual use of an LMS. The study’s findings suggest that REL and output quality significantly impact the perceived usefulness (PU). Also, perceived enjoyment and subjective norm exert a significant impact on the perceived ease of use (PEOU). Additionally, PEOU influences the PU, which in turn influences the BI. Furthermore, the findings reveal a strong link between BI and the actual use of the LMS. Finally, the study suggests that students’ BI to use the LMS is the most important factor for students’ actual use. PU is also an important determinant factor of students’ actual use. Following the study’s conclusion that BI and PU are important for students’ actual use of an LMS, higher education administrators must develop rules that increase the students’ PU of LMSs, while also ensuring that current measures that awaken students’ BIs are maintained or improved so that students can use the LMS for academic work.

This study is a narrative review that aimed at exploring the benefits, risks, and policy controls of Bring Your Own Device (BYOD) for maximum and effective BYOD adoption. BYOD is a paradigm where employees use their personal devices such as Smartphones, tablets, laptops, etc., to access organizations’ enterprise resources such as email, file servers, and database contents and networks. Significant empirical evidence from literature revealed that 82% of organizations have a BYOD program, while 68% of organizations see a jump in productivity after switching to BYOD. Despite these important virtues, numerous investigations have shown that threats and attacks on organization data and resources: confidentiality, integrity, and availability are the greatest security concern in BYOD environment. In this study, the authors adopted a narrative review methodology. A search, using keywords, was conducted and fifty-five peer-reviewed journal papers within the last 5 years of this study were identified. These journals came from Pro Quest databases, ScienceDirect, Taylor and Francis, Crossref.com, Walden University collection of scholarly and peer-reviewed journals, and other related texts. Inclusion and exclusion criteria were applied, followed by a quality appraisal of the selected articles, before data were extracted. Results show that good BYOD policies, guidelines, and practices coupled with good controls and management may avert security breaches on employees' and organizations' devices and resources. Results also show that it is possible to use machine learning approaches to handle threats and attacks such as persistent threats, data leakage, and data theft, denial of service (DOS) attack, intrusion attack, and untrusted network in BYOD environment. Findings may positively impact and equip organizations and employees with proven information for successful security and risk management programs that may increase mutual trust and a healthy BYOD adoption environment.

This study examined the influence of second/foreign language (L2) writing selves, English academic writing learning experience (WLE), and education level (EDUL) on AI adoption by English‐as‐a‐foreign‐language learners in English academic writing. Perceived usefulness (PU), perceived ease of use (PEU), attitude towards use (ATU), behavioral intention (BI), the ideal and ought‐to L2 writing selves, and WLE were selected as latent variables. Hypotheses were established based on a review of relevant studies. Quantitative data were collected via an online questionnaire from 316 participants (166 undergraduates and 150 postgraduates) and analyzed using structural equation modeling. Our findings revealed that although L2 writing selves positively shaped their perceptions of AI, neither the ideal/ought‐to selves nor WLE directly predicted their intentions to use such tools. While the ideal self indirectly influenced ATU and BI through PEU and PU, the indirect effect of the ought‐to self was mediated by PU. Additionally, EDUL was found to positively moderate two pathways. The model explained 76.1% of the variance in BI. Through integrating the L2 motivational self system (L2MSS) and the technology acceptance model (TAM) in the English academic writing context, this study provided pedagogical suggestions to English academic writing teachers.

This study investigates the factors influencing the acceptance of e-training among faculty staff at Sebha University using the extended Technology Acceptance Model (TAM2). A total of 74 responses were collected via Google Forms concerning subjective norm, image, job relevance, output quality, result demonstrability, perceived ease of use (PEOU), perceived usefulness (PU), experience, voluntariness, and intention to use. Measurement and structural models were evaluated using Partial Least Squares Structural Equation Modelling (PLS-SEM) with SmartPLS 4. Reliability, internal consistency, and validity were confirmed, with Cronbach’s α ranging from 0.776 to 0.929 and AVE values from 0.688 to 0.876, indicating convergent validity. PEOU was found to predict PU, and both PEOU and PU predicted intention to use, albeit with weaker support than the model suggested (β = 0.441, p &lt; 0.001; β = 0.284, p = 0.046; β = 0.314, p = 0.033). No significant effects were identified for subjective norm, image, job relevance, output quality, or result demonstrability. Moderation analysis revealed that experience and voluntariness had no effect on the impact of subjective norm on PU or behavioural intention (all p &gt; 0.05; all f² &lt; 0.01). The model accounted for 66.5% of the variance in PU and 56.6% in intention, highlighting the importance of perceived ease of use and usefulness in the adoption of e-training.