Intentions to Use Information Technologies

Thompson, Ron; Compeau, Deborah; Higgins, Chris; Lupton, Nathan

doi:10.4018/978-1-59904-295-4.ch006

Cited by 44 publications

(65 citation statements)

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“…However, the empirical research that examines such direct link has produced mixed results (Petter et al, 2008). Some authors have found a positive relationship between system quality (in its ease-of-use dimension) and usefulness (Rai, Lang, & Welker, 2002;Seddon & Kiew, 1997;Thompson, Compeau, & Higgins, 2006) whereas others have found the relationship between them is not significant (Chau & Hu, 2002;Wu & Wang, 2006). For example, Chau and Hu (2002), in their study on telemedicine systems, suggest that physicians are not likely to consider a technology to be useful simply because it is easy to use.…”

Section: Introductionmentioning

confidence: 94%

Automating and informating: roles to examine technology’s impact on performance

Bravo

Santana

Rodón

2016

Behaviour & Information Technology

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Information quality and system quality are considered key factors in determining the usefulness of systems. However, prior studies have yielded mixed results. This article holds that the system's assistance (the degree of the assistance provided by a system through its two roles: automating and informating) mediates in this relationship and may help to explain such mixed results. Furthermore, it holds that the system's level of intervention (the degree to which technology participates in carrying out the tasks) is another key factor in determining its usefulness, especially through the automating role. Data from 246 users filling different hierarchies and functional areas were collected. Results show that information quality and system's level of intervention explain usefulness through both roles. System quality explains usefulness through the informating role, but not through the automating role. The factors' differentiated effects through each role may help managers to establish more effective criteria and priorities in the different stages of an information system's life cycle.

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Section: Introductionmentioning

confidence: 94%

Automating and informating: roles to examine technology’s impact on performance

Bravo

Santana

Rodón

2016

Behaviour & Information Technology

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“…Individual's EUT is the individual's exposure to the technology as well as the skills and abilities that s/he gains through using a technology (Thompson et al, 2006). In technology use in education, the current level 5 Communications of the IBIMA of computer skills and extent of use of computing skills in teaching are important issues on instructors' acceptance (Sumner and Hostetler, 1999).…”

Section: Instructor Factorsmentioning

confidence: 99%

Instructors' Acceptance of Learning Management Systems: A Theoretical Framework

Al‐Busaidi¹,

Al-Shihi²

2010

CIBIMA

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In the knowledge age, the use of the information technology (IT) tools including learning management systems (LMS) has became an imperative. The adoption rate of LMS in academic and training institutions is very promising worldwide. Learning Management system includes several tools that provide academic and training institutions efficient and effective means to support distance education and supplement their traditional way of teaching. LMS also provide academic insinuations mechanisms and tools to store, manage, and share its academic resources and knowledge. Instructors' acceptance is essential for the deployment of LMS. The success of LMS in any institution starts by instructors' acceptance, which in turns initiates and promotes learners' utilization of LMS. Consequently, the objective of this paper is to develop a theoretical framework for evaluating instructors' acceptance of LMS based on the Technology Acceptance Model. This framework provides a comprehensive look of the critical factors that influence the instructors' perceived ease of use and perceived usefulness of LMS and consequently the actual use. These critical factors are related to the instructor, organization, and technology. Instructor factors include self efficacy, attitude toward LMS, experience, teaching style and personal innovativeness. Organization factors include motivators, technology alignment, organization support, technical support and training. Technology factors include system quality, information quality and service quality.

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“…A number of models have been extensively studies over the past three decades to examine factors that a direct effect on individuals' technology acceptance (Agarwal & Prasad, 1988;Morris & Dillon, 1997;Thompson, Compeau, & Higgins, 2006). The Technology Acceptance Model (TAM) proposed by Davis (1989) is the classical information systems (IS) model developed to explain computer-usage behavior and factors associated with acceptance of technology.…”

Section: Perceived Interestmentioning

confidence: 99%

A Proposed Framework to Understand the Intrinsic Motivation Factors on University Students’ Behavioral Intention to Use a Mobile Application for Learning

Shroff

Keyes

2017

JITE:Research

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Aim/Purpose: By integrating a motivational perspective into the Technology Acceptance Model, the goal of this study is to empirically test the causal relationship of intrinsic motivational factors on students’ behavioral intention to use (BIU) a mobile application for learning. Background: Although the Technology Acceptance Model is a significant model, it largely remains incomplete as it does not take into consideration the motivation factors and/or outside influences in the adoption of new technology. Methodology: A Mobile Application Motivation Instrument (MAMI) was developed from a comprehensive review of literature on intrinsic motivation and verified using a formalized card sorting procedure. Four intrinsic motivation scales were developed: perceived competence (COM), perceived challenge (CHA), perceived choice (CHO), and perceived interest (INT). Consequently, a scale to assess students’ behavioral intention (BIU) to use mobile applications was developed using existing scales from prior TAM instruments. Contribution: Incorporating the motivational factors into TAM may provide better explanation and prediction of student acceptance and usage of mobile applications. A potential contribution of this study is the development of a reliable and valid instrument that could be further used by a growing community of researchers, instructional designers, and instructors. Findings: Data were collected from 193 participants to test the causal relationship of perceived competence (COM), perceived challenge (CHA), perceived choice (CHO), and perceived interest (INT) on students’ behavioral intention to use (BIU) a mobile application, using a structural equation modeling approach. The structural path model indicated that perceived competence (COM), perceived challenge (CHA), perceived choice (CHO), and perceived interest (INT) had a significant influence on students’ behavioral intention to use (BIU) a mobile application for learning. Implications of this study are important for researchers and educational practitioners. Future Research: One environmental dimension, understudied but with likely implications for intrinsic motivation, is the social environment.

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Intentions to Use Information Technologies

Cited by 44 publications

References 0 publications

Automating and informating: roles to examine technology’s impact on performance

Automating and informating: roles to examine technology’s impact on performance

Instructors' Acceptance of Learning Management Systems: A Theoretical Framework

A Proposed Framework to Understand the Intrinsic Motivation Factors on University Students’ Behavioral Intention to Use a Mobile Application for Learning

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