scholarly journals Graduate Students Report Strong Acceptance and Loyal Usage of Google Scholar

2012 ◽  
Vol 7 (4) ◽  
pp. 96 ◽  
Author(s):  
Lisa Shen
Keyword(s):  
Graduate Students ◽  
Technology Acceptance ◽  
Google Scholar ◽  
University Of Minnesota ◽  
Contributing Factors ◽  
Web Based ◽  
Tam Model ◽  
Acceptance Model ◽  
Strongly Agree ◽  
The University

Objective – To determine the frequency of graduate students’ Google Scholar usage, and the contributing factors to their adoption. The researchers also aimed to examine whether the Technology Acceptance Model (TAM) is applicable to graduate students’ acceptance of Google Scholar. Design – Web-based survey questionnaire. Setting – The survey was conducted over the internet through email invitations. Subjects – 1,114 graduate students enrolled at the Twin Cities campus of the University of Minnesota. Methods – 9,998 graduate students were invited via email to participate in a study about their perceptions of Google Scholar in the fall of 2009. A follow-up email and a raffle of two $25 gift certificates were used to provide participation incentive. The survey measurements, which consisted of 53 items in 15 questions, were based on modifications to the validated TAM using measurements adopted by other studies using the same instrument. Each item was scored using five-point scales ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). Because the TAM model is based on direct user experience, only responses from those who have used Google Scholar in the past were included in the data analysis. Main Results – The survey had a response rate of 11.4%, with 73% of the respondents reporting having used Google Scholar at least once before. However, only 45% of those who had used Google Scholar reported linking to full text articles through the customized library link “frequently or always.” On average, respondents found Google Scholar easy to use (M=4.09 out of 5) and access (M=3.86). They also perceived Google Scholar as a useful resource for their research (M=3.98), which enhanced their searching effectiveness (M=3.89). However, respondents were less enthusiastic when asked whether they often found what they were looking for using Google Scholar (M=3.33) or whether it had enough resources for their research (M=3.14). Nonetheless, most still felt they made the correct decision to use Google Scholar (M=3.94), even if their loyalty towards Google Scholar was limited (M=3.23). The researcher categorized survey measurements into 9 TAM-based variables and performed regression analysis (all with p

scholarly journals Technology Acceptance Model Perilaku Pengguna pada Sistem Enterprise Dompet Dhuafa

2021 ◽  
Vol 4 (1) ◽  
pp. 11-16
Author(s):  
Imam Ali Khumaidi ◽  
Rinda Hesti Kusumaningtyas
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Behavioral Intention ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Actual System ◽  
Web Based ◽  
Tam Model ◽  
Acceptance Model ◽  
Behavioral Intention To Use

Dompet Dhuafa Enterprise System (DESI) is a system created in 2015. Initially it was created to replace the Fundrising Administration System (SANDRA) because SANDRA is a desktop-based application. Nowadays DESI is only used for budgeting functions and “Tebar Hewan Kurban” (THK) its a sacrificial animal stocking functions. DESI which has daily functions such as budgeting and seasonal functions in THK. In its implementation, DESI which is a web-based system sometimes becomes difficult to access when the system has a problem. This certainly makes the budgeting function that is used in daily activities become not implemented. THK's function in DESI was also inseparable from problems, one of which was the presence of volunteers who did not directly enter the data of prospective victims into DESI during the THK implementation. New data entered into DESI on the first Tasyrik day, for example, will increase work in other divisions. It would be different if it had been done before the sacrificial feast. This study aims to determine the factors that influence the acceptance behavior of DESI users. In this quantitative study using the Technology Acceptance Model (TAM) model. Sampling in this study using purposive sampling and accidental sampling. This study uses PLS-SEM for data processing and uses the application of SmartPLS version 3. There are 6 variables examined in this study, namely perceived usefulness, perceived easy of use, format, attitude using, behavioral intention to use, and actual system usage. From 7 hypotheses proposed there is one hypothesis that is rejected, namely the path attitude toward using the behavioral intention to use.

Do Mobile Technologies Have a Place in Universities?

2016 ◽  
pp. 25-52 ◽  
Author(s):  
José Carlos Sánchez-Prieto ◽  
Susana Olmos-Migueláñez ◽  
Francisco José García-Peñalvo
Keyword(s):  
Higher Education ◽  
Literature Review ◽  
Technology Acceptance ◽  
Mobile Technology ◽  
Education Level ◽  
The State ◽  
Mobile Technologies ◽  
Tam Model ◽  
Acceptance Model ◽  
The University

The present chapter provides an analysis of the possible applications of the TAM (Technology Acceptance Model) for the study of the mobile technology acceptance process at the higher education level, through a literature review on the state of the art. With this aim in mind, the chapter will start by presenting the theoretical principles of the TAM model and its evolution. After that, the authors will describe the state of the research on the topic, first within the field of education in general, to focus then on the university level, and finally on mobile learning specifically. Throughout the analysis, the most significant studies will be highlighted as an example. Lastly, the authors will provide some brief conclusions and proposals for future lines of research derived from the consulted literature.

scholarly journals Grey literature: use, creation, and citing habits of faculty researchers across disciplines (version 2; 20191107)

2019 ◽  
Author(s):  
Kristen Cooper ◽  
Wanda Marsolek ◽  
Shannon Farrell ◽  
Amy Riegelman ◽  
Julia Kelly
Keyword(s):  
Graduate Students ◽  
Grey Literature ◽  
Google Scholar ◽  
Twin Cities ◽  
Collection Development ◽  
University Of Minnesota ◽  
Electronic Survey ◽  
Different Types ◽  
The University ◽  
Conference Papers

INTRODUCTION Grey literature is ephemeral and the level to which it is created, used, and cited by faculty, graduate students, and other researchers is not well understood. METHODS This electronic survey was distributed to a sample (57%) of the faculty across a wide variety of disciplines with the only criteria based on tenure and tenured track faculty at the University of Minnesota - Twin Cities, a large R1 institution. RESULTS Faculty across the disciplines both use and create grey literature for several reasons, such as the more rapid publication process. DISCUSSION Many faculty in a wide variety of disciplines are using and creating grey literature. The survey illustrates the different types of grey literature that are being used and why. Other topics such as how faculty are finding grey literature (via Google Scholar and professional contacts), if they are citing it and what types they create (e.g., conference papers, preprints, technical reports) are also discussed. CONCLUSION As a result of this survey librarians can provide support for faculty who use and create grey literature in all disciplines and advocate for and promote grey literature to faculty. With more scholars participating in systematic reviews, librarians will need to be more cognizant of where and how grey literature is discoverable. Implications for Practice1.Findings confirm that grey literature is being used and created in multiple disciplines, not just the sciences.2.Librarians can impact faculty’s discovery of grey literature outside the existing avenues of Google Scholar and professional contacts.3.Faculty are using grey literature to stay current on research which is a reason academic libraries should consider increasing their awareness and collection development of grey literature.

Why Do Physicians Reject the Electronic Health Record: A Theoretical Perspective? (Preprint)

2018 ◽  
Author(s):  
Weam Alfayez ◽  
Arwa Alumran ◽  
Dr Saja A. Al-Rayes
Keyword(s):  
Information Technology ◽  
Electronic Health Record ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Health Record ◽  
Record System ◽  
Factors Influencing ◽  
Tam Model ◽  
Electronic Health ◽  
Acceptance Model

BACKGROUND Many theories/ models adopted from behavioral sciences literature or developed within the field of information technologies could help in understanding the technology acceptance, usage, and effective adoption. OBJECTIVE The main aim of this paper is to review the different theories/ models that can help in understanding information technology/system acceptance and use, and to choose the most appropriate theoretical framework that could be applied to understand the factors influencing physicians’ use of the Electronic Health Record system (EHR) at King Fahd Military Medical Complex (KFMMC) in Dhahran city, Saudi Arabia. METHODS The theories/ models were reviewed using scientific databases. The inclusion criteria were if the theories/ models used to explain individual behaviors toward accepting and using of information technology including the once conducted within the healthcare. RESULTS The review showed that there were five theories/ models were used within information technology studies to understand the technology acceptance and used. There were Theory of Reasoned Action, Theory of Planned Behaviour, Innovation Diffusion Theory, Unified theory of acceptance and use of technology, and Technology Acceptance Model. Each has different explanatory power of technology use. The most appropriate theoretical framework to understand the reason behind physician use of the EHR at KFMMC would be the Technology Acceptance Model (TAM). TAM model could explain up to 75% of the variation in the behavioral intention (acceptance), and up to 62% of the variation in the actual use. It is the gold standard for assessing the usage of health technologies and systems. In fact, the TAM model is one of the core models used to explore the physician’s perceptions of the Electronic Health Record system adoption. CONCLUSIONS This review showed that there are different theories available in the literature can be used to justify the reason behind electronic health record acceptance. TAM is one of the effective, simplest models used to understand the factors influencing physicians to use the EHR-system. Further studies need to apply the TAM model to check its ability in explaining the reason behind EHR within different hospitals in Saudi Arabia

scholarly journals Facilitators and barriers to the utilization of the ACT SMART Implementation Toolkit in community-based organizations: a qualitative study

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Aksheya Sridhar ◽  
Amy Drahota ◽  
Kiersten Walsworth
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Autism Spectrum ◽  
Ease Of Use ◽  
Science Literature ◽  
Community Based ◽  
Web Based ◽  
Community Based Organizations ◽  
Facilitators And Barriers ◽  
Acceptance Model

Abstract Background Evidence-based practices (EBPs) have been shown to improve behavioral and mental health outcomes for children diagnosed with autism spectrum disorder (ASD). Research suggests that the use of these practices in community-based organizations is varied; however, the utilization of implementation guides may bridge the gap between research and practice. The Autism Community Toolkit: Systems to Measure and Adopt Research-Based Treatments (ACT SMART) Implementation Toolkit is a web-based implementation toolkit developed to guide organization-based implementation teams through EBP identification, adoption, implementation, and sustainment in ASD community-based organizations. Methods This study examined the facilitators and barriers (collectively termed “determinants”) to the utilization of this toolkit, based on the perspectives of implementation teams at six ASD community-based organizations. Two independent coders utilized the adapted EPIS framework and the Technology Acceptance Model 3 to guide qualitative thematic analyses of semi-structured interviews with implementation teams. Results Salient facilitators (e.g., facilitation teams, facilitation meetings, phase-specific activities) and barriers (e.g., website issues, perceived lack of ease of use of the website, perceived lack of resources, inner context factors) were identified, highlighting key determinants to the utilization of this toolkit. Additionally, frequent determinants and determinants that differed across adapted EPIS phases of the toolkit were noted. Finally, analyses highlighted two themes: (a) Inner Context Determinants to use of the toolkit (e.g., funding) and (b) Innovation Determinants (e.g., all website-related factors), indicating an interaction between the two models utilized to guide study analyses. Conclusions Findings highlighted several factors that facilitated the utilization of this implementation guide. Additionally, findings identified key areas for improvement for future iterations of the ACT SMART Implementation Toolkit. Importantly, these results may inform the development, refinement, and utilization of implementation guides with the aim of increasing the uptake of EBPs in community-based organizations providing services to children with ASD and their families. Finally, these findings contribute to the implementation science literature by illustrating the joint use of the EPIS framework and Technology Acceptance Model 3 to evaluate the implementation of a web-based toolkit within community-based organizations.

Using TAM to Explore Vocational Students’ Willingness to Adopt a Web-Based BIM Cost Estimating System

2014 ◽  
Vol 1079-1080 ◽  
pp. 1098-1102 ◽  
Author(s):  
Yun Wu Wu ◽  
I Ting Hsu ◽  
Hsin Yi Lin
Keyword(s):  
Cost Analysis ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Vocational School ◽  
Vocational Students ◽  
Cost Estimating ◽  
Web Based ◽  
Willingness To Adopt ◽  
Analysis System ◽  
Acceptance Model

In this study, the technology acceptance model (TAM) is used to analyze from a vocational school to usethe Web-based BIM cost analysis system developed in this study.

Validating the TAM model in Haryana state of India: During COVID-19

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
komal Goyal ◽  
Ashutosh Nigam ◽  
Neha Goyal ◽  
Manisha Goyal
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Learning System ◽  
Perceived Ease Of Use ◽  
Tam Model ◽  
E Learning ◽  
Continuous Usage Intention ◽  
Acceptance Model

Purpose -Response to the sudden pandemic, COVID-19 drastic shift of work practices requires the implementation of an e-learning system, but their adoption requires a thorough understanding of the user acceptance process. Our key effort is to incorporate a framework, “internet experience” to determine the existing Technology Acceptance Model (TAM) applicability in describing students’ s e-learning decisions in Haryana state. Design/methodology- The researcher has administered a questionnaire based on TAM’s constructs and collected data from UG and PG students of Haryana state of India. Findings – The results revealed that perceived usefulness (PU) has a significant positive relationship with continuous usage intention (CUI) whilst, perceived ease of use (PEOU), computer self-efficacy (CSE) and internet experience (IE) have not. Attitude towards E-learning system (ATELS) has found no relevance in this model. Originality/value Majority research has been done using the TAM model in a developed nation. Empirical study has not been done in Haryana state so far.

Cognitive Mapping Decision Support for the Design of Web-Based Learning Environments

2010 ◽  
Vol 5 (3) ◽  
pp. 36-53 ◽  
Author(s):  
Raafat George Saadé
Keyword(s):  
Online Learning ◽  
Technology Acceptance ◽  
Cognitive Mapping ◽  
Education Student ◽  
Research Model ◽  
Student Beliefs ◽  
Implementation Framework ◽  
Web Based ◽  
E Learning ◽  
Acceptance Model

There is much interest in the study of online learning in higher education. Student beliefs towards online learning may influence intentions and ultimately performance. Many studies have flaws in their research design and fall short in providing useful insight for decision making. In this regard, the need for developing practical e-learning implementation framework(s) is crucial. The main objective of this study is to employ the cognitive map technique to causal relationships among belief factors, and investigating various impact chains via simulations. The authors identify optimal e-learning design and implementation, while a partial least squares approach was performed to validate the proposed research model anchored in the Technology Acceptance Model. A survey was carried out and data were collected from 102 respondents. The proposed research model was tested and subsequent cognitive mapping simulations were performed. This study provides designers, instructors and decision makers an approach by which they can identify relevant factors for design, implementation and maintenance.

The Role of Internet Self-Efficacy in Acceptance of Web-Based Electronic Medical Records

2011 ◽  
pp. 54-76 ◽  
Author(s):  
Qingxiong Ma ◽  
Liping Liu
Keyword(s):  
Technology Acceptance ◽  
Self Efficacy ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Perceived Ease Of Use ◽  
Record System ◽  
Web Based ◽  
Medical Record System ◽  
Acceptance Model

The technology acceptance model (TAM) stipulates that both perceived ease of use (PEOU) and perceived usefulness (PU) directly influence the end user’s behavioral intention (BI) to accept a technology. Studies have found that self-efficacy is an important determinant of PEOU. However, there has been no research examining the relationship between self-efficacy and BI. The studies on the effect of self-efficacy on PU are also rare, and findings are inconsistent. In this study, we incorporate Internet self-efficacy (ISE) into the TAM as an antecedent to PU, PEOU, and BI. We conducted a controlled experiment involving a Web-based medical record system and 86 healthcare subjects. We analyzed both direct and indirect effects of ISE on PEOU, PU, and BI using hierarchical regressions. We found that ISE explained 48% of the variation in PEOU. We also found that ISE and PEOU together explained 50% of the variation in PU, and the full model explained 80% of the variance in BI.

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