Investigating IT Faculty Resistance to Learning Management System Adoption Using Latent Variables in an Acceptance Technology Model.
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ABSTRACT:
SUBMITTER: Bousbahi F
PROVIDER: S-EPMC4605370 | biostudies-other | 2015
REPOSITORIES: biostudies-other
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Project description:Background: It is critical to understand the factors that could affect the acceptance of the coronavirus disease 2019 (COVID-19) vaccine in the community. The aim of this study was to determine factors that could possibly affect the acceptance of Indonesian citizens of COVID-19 vaccination using a Technology Acceptance Model (TAM), a model how users come to accept and use a technology. Methods: An online survey was conducted between the first and fifth of November, 2020. Participants were asked to respond to questions on acceptance, perceived usefulness, perceived ease of use, perceived religiosity towards, and amount of information about COVID-19. This study used the Technology Acceptance Model (TAM) as the framework to decide factors that affect vaccine acceptance. Structural Equation Model was employed to assess the correlation between all explanatory variables and vaccine acceptance. Mann-Whitney test and Kruskal-Wallis rank were employed to assess demographic factors associated with acceptance. Results: In total, 311 responses were included for analysis. Our TAM model suggested that high perceived usefulness significantly increased COVID-19 vaccine acceptance and high perceived ease of use significantly increased the perceived usefulness. Perceived religiosity did not substantially affect vaccine acceptance. The amount of information on COVID-19 also did not significantly affect vaccine acceptance. Our data suggested that vaccine acceptance was associated with age, type of occupation, marital status and monthly income to some degree. Conclusion: Since perceived usefulness affects vaccine acceptance, the government should focus on the usefulness of the vaccine when promoting the COVID-19 vaccine to Indonesian citizens. In addition, since perceived ease of use significantly affects users' acceptance to COVID-19 vaccine, the easier to acquire the vaccine in the community, the higher chance that the citizens are willing to be vaccinated.
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Project description:Mounting evidence of the efficacy of active learning has prompted educators to consider adoption of these practices in college-level classrooms. One tenet of active learning is that most, if not all, students have the ability to learn. Instructors' perspectives on learning, however, may or may not be aligned with this. One belief held by some educators is that intelligence is fixed, that is, some students are more intelligent and have a higher ability to learn than others. Instructors with a fixed mindset may not be convinced that their investment in developing active-learning materials will be as fruitful as the education evidence suggests, because these instructors may not believe that most students can grow in their learning. Here, we explored the relationship between fixed mindsets and the adoption of active-learning strategies. We found that instructors with higher fixed mindsets were less persuaded that active-learning strategies were a good idea and less likely to implement the teaching practices. Our research suggests that development initiatives should explicitly address educators' lay theories of intelligence ( fixed or growth mindset) to support successful implementation of active learning.
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Project description:Understanding travelers' acceptance of Advanced Traveler Information Systems (ATIS) is crucial to the implementation of Intelligent Transportation Systems (ITS) capable of mitigating traffic congestion and improving network performance. This paper adopted an extended Technology Acceptance Model (TAM) to predict and explain road users' intention to use Variable Message Sign (VMS) information. In addition to the traditional parsimonious TAM constructs (perceived usefulness, perceived ease of use and behavioral intention), the model examined the effects of attitude towards route diversion, familiarity with road network and information quality on road users' acceptance of VMS. 762 drivers were interviewed and the obtained data were processed using Structural Equation Modeling. The results showed that travelers' attitude towards route diversion had a positive effect on perceived usefulness and intention to use VMS. Information quality had a positive direct effect on perceived usefulness, perceived ease of use and attitude towards route diversion. Familiarity with the network had a positive effect on attitude towards route diversion and a negative effect on the perceived usefulness of VMS information. Perceived ease of use significantly and positively affected perceived usefulness and intention to use VMS. Perceived usefulness also had a positive effect on intention. Several academic and practical implications were also discussed.
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Project description:ObjectiveTo examine the factors that could influence the decision of healthcare professionals to use a telemonitoring system.Materials and methodsA questionnaire, based on the Technology Acceptance Model (TAM), was developed. A panel of experts in technology assessment evaluated the face and content validity of the instrument. Two hundred and thirty-four questionnaires were distributed among nurses and doctors of the cardiology, pulmonology, and internal medicine departments of a tertiary hospital. Cronbach alpha was calculated to measure the internal consistency of the questionnaire items. Construct validity was evaluated using interitem correlation analysis. Logistic regression analysis was performed to test the theoretical model. Adjusted odds ratios (ORs) and their 95% confidence intervals (CIs) were computed.ResultsA response rate of 39.7% was achieved. With the exception of one theoretical construct (Habit) that corresponds to behaviors that become automatized, Cronbach alpha values were acceptably high for the remaining constructs. Theoretical variables were well correlated with each other and with the dependent variable. The original TAM was good at predicting telemonitoring usage intention, Perceived Usefulness being the only significant predictor (OR: 5.28, 95% CI: 2.12-13.11). The model was still significant and more powerful when the other theoretical variables were added. However, the only significant predictor in the modified model was Facilitators (OR: 4.96, 95% CI: 1.59-15.55).ConclusionThe TAM is a good predictive model of healthcare professionals' intention to use telemonitoring. However, the perception of facilitators is the most important variable to consider for increasing doctors' and nurses' intention to use the new technology.
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Project description:BackgroundIn the collaborative business environment, blockchain coupled with smart contract removes the reliance on a central system and offers data integrity which is crucial when the transacting parties rely on the shared data. The acceptance of such blockchain-based systems is necessary for the continued use of the services. Despite many extensive studies evaluating the performance of blockchain-based systems, few have focused on users' acceptance of real-life applications.ObjectiveThe main objective of this research is to evaluate the user acceptance of a real-life blockchain-based system (BBS) by observing various latent variables affecting the development of users' attitudes and intention to use the system. It also aims to uncover the dimensions and role of trust, security and privacy alongside the primary Technology Acceptance Model (TAM)-based predictors and their causal relationship with the users' behavior to adopt such BBS.MethodsWe tested the augmented TAM with Trust Model on a BBS that comprises two subsystems: a Shopping Cart System (SCS), a system oriented towards end-users and a Data Sharing System (DSS), a system oriented towards system administrators. We set research questions and hypotheses, and conducted online surveys by requesting each participant to respond to the questionnaire after using the respective system. The main study comprises two separate sub-studies: the first study was performed on SCS and the second on DSS. Furthermore, each study data comprises initial pre-test and post-test data scores. We analyzed the research model with partial least square structural equation modelling.ResultsThe empirical study validates our research model and supports most of the research hypotheses. Based on our findings, we deduce that TAM-based predictors and trust constructs cannot be applied uniformly to BBS. Depending on the specifics of the BBS, the relationships between perceived trust antecedents and attitudes towards the system might change. For SCS, trust is the strongest determinant of attitudes towards system, while DSS has perceived privacy as the strongest determinant of attitudes towards system. Quality of system shows the strongest total effect on intention to use SCS, while perceived usefulness has the strongest total effect on intention to use DSS. Trust has a positive significant effect on users' attitudes towards both BSS, while security does not have any significant effect on users' attitudes toward BBS. In SCS, privacy positively affects trust, but security has no significant effect on trust, whereas, in DSS, both privacy and security have significant effects on trust. In both BBS, trust has a moderating effect on privacy that correlates with attitudes towards BBS, whereas security does not have any mediating role between privacy and attitudes towards BBS. Hence, we recommend that while developing BBS, particular attention should be paid to increasing user trust and perceived privacy.
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Project description:Learning causal networks from large-scale genomic data remains challenging in absence of time series or controlled perturbation experiments. We report an information- theoretic method which learns a large class of causal or non-causal graphical models from purely observational data, while including the effects of unobserved latent variables, commonly found in many genomic datasets. Starting from a complete graph, the method iteratively removes dispensable edges, by uncovering significant information contributions from indirect paths, and assesses edge-specific confidences from randomization of available data. The remaining edges are then oriented based on the signature of causality in observational data. The approach and associated algorithm, miic, outperform earlier methods on a broad range of benchmark networks. Causal network reconstructions are presented at different biological size and time scales, from gene regulation in single cells to whole genome duplication in tumor development as well as long term evolution of vertebrates. Miic is publicly available at https://github.com/miicTeam/MIIC.
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Project description:Much empirical research that has shown that an individual's decision to adopt a new technology is the result of learning - both in personal experimentation as well as observing the experimentation of others. Yet even casual observation would suggest significant heterogeneity learning processes, manifesting itself in widely varying patterns of adoption over space and time. In this paper we explore this heterogeneity in the context of early adoption of hybrid rice in rural India. Using specially-designed experiments conducted as part of a primary survey in the field, we are able to identify which of four broad learning heuristics most accurately reflects individuals' information processing strategies. Linking these learning heuristics with observed use of rice hybrids, we demonstrate that pure Bayesian learning is well suited for the tinkering and marginal adjustments that would be required to learn about a technology like hybrid rice, but is also more cognitively taxing, requiring a longer memory and more complex updating processes. Consequently, only about 25 percent of the farmers in our sample can be characterized as pure Bayesian learners. Present-biased learning and relying on first impressions will likely hinder adoption of a technology like hybrid rice, even after controlling for access to credit and a rudimentary proxy for intelligence.
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Project description:I study how the misallocation of new technology to individuals who have low ex post returns to its use affects learning and adoption behavior. I focus on antimalarial treatment, which is frequently over-prescribed in many low-income country contexts where diagnostic tests are inaccessible. I show that misdiagnosis reduces average therapeutic effectiveness, because only a fraction of adopters actually have malaria, and slows the rate of social learning due to increased noise. I use data on adoption choices, the timing and duration of fever episodes, and individual blood slide confirmations of malarial status from a pilot study for a new malaria therapy in Tanzania to show that individuals whose reference groups experienced fewer misdiagnoses exhibited stronger learning effects and were more likely to adopt.
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Project description:BackgroundThe need for information exchange and integrated care has stimulated the development of interoperability solutions that bring together patient data across the health and care system to enable effective information sharing. Health Information Exchange (HIE) solutions have been shown to be effective in supporting patient care, however, user adoption often varies among users and care settings. This service evaluation aimed to measure user acceptance of HIE and explore barriers and facilitators to its wider uptake.MethodsA mixed-method study design was used. A questionnaire was developed using the Unified Theory of Acceptance and Use of Technology and administered to HIE users to assess technology acceptance. Pearson Chi2 tests were used to examine differences in acceptance between user groups and care settings. Web-based, semi-structured interviews were conducted drawing on the Normalisation Process Theory to explore barriers and facilitators to adoption. Interview data were analysed thematically using the Framework Approach.ResultsA total of 105 HIE users completed the survey and another 12 participated in the interviews. Significant differences were found in HIE acceptance between users groups and care settings, with high adopters demonstrating higher acceptance and social care users showing lower acceptance. Participants identified several drivers to adoption, including increased information accessibility, better care coordination, informed decision-making, improved patient care, reduced duplication of procedures, and time and cost savings. However, they also highlighted a number of barriers, such as lack of awareness about the solution and its value, suboptimal communication strategies, inadequate training and lack of resources for knowledge dissemination, absence of champions to support the implementation, lack of end-user involvement in the implementation and evaluation of HIE, unclear accountability and responsibility for the overall success of the programme, and patient confidentiality concerns.ConclusionsWorking to better engage stakeholders, considering the needs of users from different care settings, providing users with training resources and support to increase their knowledge and confidence in using the system, developing implementation strategies to seek user feedback and monitor performance, and using communication strategies to increase awareness of the product and its value, can help improve uptake and adoption of HIE.
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Project description:Blended learning (BL), a teaching method merging online and face-to-face learning, is lauded for its potential to enrich educational outcomes and tackle challenges entrenched in conventional teaching practices. In countries like Pakistan, where equitable access to quality professional development remains an obstacle, BL is a promising avenue to surmount training barriers. While BL adoption has evolved swiftly, research into its integration within teacher training remains limited. Notably, no comprehensive model exists describing the motivational factors influencing teachers' perceptions and intentions regarding the blended mode of teacher training. This study aims to identify the motivational elements that motivate schoolteachers in teacher training institutions in Pakistan to incorporate blended learning into their programs. The motivational factors identified in BL literature have been employed to craft a motivation model grounded in their causal relationship. This quantitative study examines the interplay between multiple motivational factors and their impact on BL adoption within teacher training and the BL environment. Surveying 350 schoolteachers (participants) from teacher training institutions, we employed Structural Equation Modeling (SEM) techniques with Smart PLS 4.0 for data analysis. Results reveal that extrinsic and intrinsic motivational factors significantly influence teachers' motivation to adopt BL for training. Notably, "overall training quality" and "educational environment" were non-influential. Overall, the findings underscore that considering a blend of extrinsic and intrinsic factors can wield a 65 % influence on BL adoption. The study's results provide practical guidance for educational leaders, curriculum designers, and faculty members aiming to cultivate a unified blended learning environment for teacher professional development. These insights also underscore the importance of incorporating essential motivational factors into forthcoming blended learning training programs.
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