Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model

2017 ◽  
Vol 67 ◽  
pp. 221-232 ◽  
Author(s):  
Bing Wu ◽  
Xiaohui Chen
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Intention To Use ◽  
Continuance Intention ◽  
Task Technology Fit ◽  
Acceptance Model ◽  
And Task

scholarly journals Understanding Factors Influencing Elderly Diabetic Patients’ Continuance Intention to Use Digital Health Wearables: Extending the Technology Acceptance Model (TAM)

2020 ◽  
Vol 6 (3) ◽  
pp. 81
Author(s):  
Ashfaq Ahmad ◽  
Tareq Rasul ◽  
Anish Yousaf ◽  
Umer Zaman
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Digital Health ◽  
The Elderly ◽  
Developed Countries ◽  
Perceived Usefulness ◽  
Diabetic Patients ◽  
Intention To Use ◽  
Continuance Intention ◽  
Acceptance Model

Elderly diabetic patients in developed countries have been widely using digital health wearables for many years to manage their diabetes-related health data accurately. To encourage the increased adoption of digital health wearables among elderly diabetic patients in a developing country, Bangladesh, this study investigated the factors that influenced the existing elderly users’ continuance intention to use this technology. The Technology Acceptance Model (TAM) has been used here as a theoretical basis. A model using structural equation modelling was developed for the elderly diabetic patients’ continuance intention to use digital health wearables. Survey-based data were collected in Bangladesh from 223 diabetic patients aged sixty years and older. This study found that all six constructs, namely, perceived usefulness (β = 0.183), perceived ease of use (β = 0.165), perceived irreplaceability (β = 0.138), perceived credibility (β = 0.165), compatibility (β = 0.285) and social influence (β = 0.226) had a positive influence on elderly diabetic patients’ continuance intention to use digital health wearables. Along with the theoretical contributions, the findings of this study can be used by developers of digital health wearables, manufacturers, marketers and health practitioners in developing better strategies to increase the elderly diabetic patients’ continuance intention to use this technology.

scholarly journals Big Data Analytics Usage and Business Performance: Integrating the Technology Acceptance Model (TAM) and Task Technology Fit (TTF) Model

2020 ◽  
Vol 23 (1) ◽  
Author(s):  
Hemlata Gangwar
Keyword(s):  
Big Data ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Data Analytics ◽  
Business Performance ◽  
Big Data Analytics ◽  
Integrated Model ◽  
Task Technology Fit ◽  
Acceptance Model ◽  
And Task

The purpose of this study is to propose a unified model integrating both the technology acceptance model (TAM) and task technology fit (TTF) model, and explore the organizational and environmental fit of the integrated model in order to investigate usage of Big Data Analytics and its effect on business performance. A questionnaire was used to collect data from 280 companies in CPG & Retail, Healthcare, Banking and Telecom in India. The data were analysed using exploratory and confirmatory factor analyses. Further, structural equation modelling was used to test the proposed model. The findings show that the research model for integrating the TAM for adoption and TTF model for utility provides a more comprehensive understanding of Big Data Analytics usage. The study identified task technology fit , individual technology fit, organizational data fit, organizational process fit, and business strategy fit as Tidd important variables for affecting Big Data Analytics usage using perceived ease of use (PEOU) and perceived usefulness (PU) as mediating variables. Competitive fit and partner support/customer fit were also found to be directly affecting Big Data Analytics usage, which in turn has significant influence on business performance. The model explained 71.4 percent of Business performance. The integrated model may be used as a guideline to ensure a positive outcome of Big Data Analytics usage in organizations. This study combined both the key ideas of TAM and TTF to show that they were necessary in predicting Big Data Analytics usage and business performance.

scholarly journals Sharp Connection

2011 ◽  
pp. 34-56
Author(s):  
Paul Hendriks ◽  
Wendy Jacobs
Keyword(s):  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
The Other ◽  
Competitive Intelligence ◽  
Stepping Stone ◽  
Natural Connection ◽  
Task Technology Fit ◽  
Acceptance Model ◽  
The Relationship ◽  
And Task

Assessing the value of ICT to support Competitive Intelligence presumes an understanding of the relationship between the two. The chapter argues that starting from either the ICT or CI side to this relationship and linking to the other, as most studies do, cannot secure a fully adequate conception of ICT’s value to CI. Instead, the challenge is to find an appropriate foundation in the relationship itself and use it as a stepping stone for developing an understanding of both ICT and CI. The chapter proposes to use and develop the concept of acceptability to provide that foundation. Acceptability offers a natural connection between the technology and CI sides. An object—e.g., a technology—cannot be acceptable in a void, but presumes a relation to a context or a subject—e.g., the CI function—to be considered acceptable or unacceptable. The Technology Acceptance Model (TAM) and Task-Technology Fit model (TTF) provide useful elements to develop this approach further. The chapter presents the case of an intranet to support CI, called IntraTel, to illustrate the argument.

Analyzing Factors Influencing Behavior Intention to Use Snapchat and Instagram Stories

2017 ◽  
Vol 4 (2) ◽  
pp. 75-81 ◽  
Author(s):  
Vincent Valiant Coa ◽  
Johan Setiawan
Keyword(s):  
Structural Equation Modeling ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Social Presence ◽  
Structural Equation ◽  
Equation Modeling ◽  
Intention To Use ◽  
Generation Z ◽  
Acceptance Model ◽  
Behavioral Intention To Use

Snapchat, and Instagram are two social networks which recently gain their users after adopting such a feature called "Story" which allows a certain post to be disappeared after a certain time. This research takes up this technology trends analyzing the factors that probably affect the behavioral intention to use Snapchat and Instagram stories among generation Z. Factors are analyzed using Structural Equation Modeling, with basis model and variables from Technology Acceptance Model. Data collection was targeted to finished within 1 week using online questionnaire with respondent from Jakarta and Tangerang for 100 respondent that are using both Snapchat stories and Instagram Stories. There are two tools researcher usually use to analyze Structural Equation Modeling: SPSS AMOS and LISREL. In this research, researchers choose AMOS. From six hypothesis proposed for Snapchat analysis, four hypothesis is accepted, while the other two are rejected. Meanwhile, on Instagram Stories analysis, five hypothesis is accepted and one hypothesis is rejected. This study finds out the Social Presence is an exogenous variable which has a major role in affecting other variables. While Perceived Enjoyment influenced the behavioral intention to use Snapchat and Instagram Stories the most. Index Terms—Structural Equation Modeling, Technology Acceptance Model, influence, generation Z, Snapchat, Instagram REFERENCES [1] L. Chin and Z. Ahmad, "Perceived Enjoyment and Malaysian Consumers’ Intention to Use a Single Platform EPayment", SHS Web of Conferences, vol. 18, 2015. [2] M. Ariff, T. Shan, N. Zakuan, N. Ishak and M. Wahi, "Examining Users' E-Satisfaction in the Usage of Social Networking Sites; Contribution from Utilitarian and Hedonic Information Systems", IOP Conference Series: Materials Science and Engineering, vol. 58, 2014. [3] K. Hassanein and M. Head, "Manipulating perceived social presence through the web interface and its impact on attitude towards online shopping", International Journal of HumanComputer Studies, vol. 65, no. 8, pp. 689-708, 2007. [4] P. Surendran, "Technology Acceptance Model: A Survey of Literature", 2012. [5] F. Davis, "Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology", MIS Quarterly, vol. 13, no. 3, p. 319, 1989

FACTORS AFFECTING THE ADOPTION OF ELECTRONIC MONEY USING TECHNOLOGY ACCEPTANCE MODEL AND THEORY OF PLANNED BEHAVIOR

2019 ◽  
Vol 24 (1) ◽  
pp. 100-113
Author(s):  
Filona ◽  
Misdiyono
Keyword(s):  
Theory Of Planned Behavior ◽  
Technology Acceptance ◽  
Technology Acceptance Model ◽  
Ease Of Use ◽  
Perceived Usefulness ◽  
Intention To Use ◽  
Perceived Ease Of Use ◽  
Electronic Money ◽  
Factors Affecting ◽  
Acceptance Model

With the rapid growth of information technology, electronic money has played an important and central role in the e-payment. Development of electronic money is able to create a trend less-cash society, which is a society’s behavior using non- cash transactions by utilizing the simplicity offered through electronic transactions. The purpose of this research is to determine the factors affecting the intention to use electronic money. We designed a questionnaire and used it to survey a simple random sampling of people who use of e-money in DKI Jakarta. The actual samples used for the study are 125 respondents. We analyzed the data using Structured Equation Modeling to evaluate the strength of the hypothesized effects. The result of the analysis showed that perceived ease of use has no significant effect on attitudes towards the use of e-money. Perceived ease of use has a significant effect on the perceived usefulness of e-money. Perceived usefulness has no significant effect on the intention to use e-money. Perceived usefulness has a significant effect on attitudes towards the use of e-money. Attitude has a significant effect on the intention to use e-money. Subjective norm has a significant effect on the intention to use e-money. Perceived behavioral control has no significant effect on the intention to use e-money. Keywords: electronic money, technology acceptance model, the theory of planned behavior.

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