Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning

Agudo-Peregrina, Ángel F.; Hernández-García, Ángel; Pascual-Miguel, Félix

doi:10.1016/j.chb.2013.10.035

Cited by 242 publications

(169 citation statements)

References 43 publications

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“…The first step was to test the reliability and convergent validity of the survey items [43]. As illustrated in Table 3, every single value of the factors loading in the proposed model was satisfactory and exceeded the recommended minimum measurement (0.7) [44][45][46]. The resulting values indicated that our model was an appropriate explanation for the dimensionality of all the factors.…”

Section: Results Analysismentioning

confidence: 99%

A Behavioral Model of Managerial Perspectives Regarding Technology Acceptance in Building Energy Management Systems

Chin

Lin

2016

Sustainability

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Abstract:The Building Energy Management System (BEMS), a well-known system that has been implemented in some energy corporations, has become attractive to many companies seeking to better monitor their energy consumption efficiency. This study investigated the external factors that influence acceptance of the BEMS from managerial perspectives. An extended model based on the Technology Acceptance Model (TAM) was created to evaluate the implementation of the BEMS in the manufacturing industries. A structural equation modeling (SEM) approach was used to analyze the model by adopting compatibility, features, technology complexity, and perceived risk as the external variables, and integrating the five dimensions of perceived ease of use, perceived usefulness, attitude, user satisfaction, and behavioral intention. The analysis results indicated that the external factors positively influenced users' behavioral intention to use the BEMS through expected satisfaction, perceived ease of use, and perceived usefulness. Suggestions for BEMS developers are provided as well.

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Section: Results Analysismentioning

confidence: 99%

A Behavioral Model of Managerial Perspectives Regarding Technology Acceptance in Building Energy Management Systems

Chin

Lin

2016

Sustainability

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“…Reference [31] stated that job relevance is a cognitive process that could affects the perceived usefulness of technology. Previous studies have shown job relevance [24], [31], [33], [35] and relevance for learning [36] have positively significant influence on perceived usefulness. Hence, it is hypothesized that:…”

Section: B Relevance For Analysing (Ra) and Perceived Usefulness (Pu)mentioning

confidence: 99%

“…Job relevance means the individual's perception that technology is applicable to users' jobs [24]. Reference [36] examined the job relevance construct towards e-learning systems, but called it 'relevance for learning' to explain the use of such technology for learning and performing learning-related tasks.…”

Section: B Relevance For Analysing (Ra) and Perceived Usefulness (Pu)mentioning

confidence: 99%

“…Following [36], this study has renamed the job relevance construct as 'relevance for analysing' since the DM technology's main feature is to analysing large amounts of data [37] in order to get informed decisions-instead of guesswork decisions [38]. Reference [31] stated that job relevance is a cognitive process that could affects the perceived usefulness of technology.…”

Section: B Relevance For Analysing (Ra) and Perceived Usefulness (Pu)mentioning

confidence: 99%

“…Studies by [28]- [36], have successfully used and extended Technology Acceptance Model (TAM) for predicting and explaining individual technology acceptance based on the internal psychological beliefs of the individual user. TAM proposes two important determinants to analyse what causes user to accept or reject new technology: perceived usefulness (PU) and perceived ease of use (PEOU) [29]- [32].…”

Section: Studies Of Data Mining Technology and The Adoption Of Tammentioning

confidence: 99%

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The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

Wook¹,

Yusof²,

Nazri³

2015

IJFCC

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Abstract-Accepting the emergence of data mining technology by students is crucial to the successful implementation of the technology in education institutions. Although previous studies have empirically show the result of acceptance or adoption of data mining technology in numerous fields, however, they are focused at organisational-level. Hence, there is a need to explain what are the determinants could influence the acceptance of data mining technology at individual-level since they are the most affected by the technology. Therefore, this study adapts selected constructs in the Technology Acceptance Model 3 (TAM3) to conceptualise the research problem, namely in terms of perceived usefulness, perceived ease of use, relevance for analysing, anxiety of educational data mining technology, self-efficacy and facilitating conditions. To examine the model, this study surveyed 158 students from four public Institutions of Higher Learning in Malaysia. Pearson product-moment correlation coefficient is utilised to analyses the relationship between the constructs. The findings have revealed that most of the constructs have a high level of correlation.Index Terms-Acceptance, data mining, educational data mining, institutions of higher learning (IHLs), technology acceptance model 3 (TAM3). I. INTRODUCTIONData mining (DM) technology has become new paradigms to enhance the scope, quality, efficiency and achievements of educational system [1]. In particular, users increasingly benefit from the technology that provides special data interpretation and processing techniques [2]. With such capabilities, DM is now considered one of promising technologies for education [3] which commonly known as educational data mining (EDM). Earlier educational system faced a number of challenges; lack of significant knowledge for evaluating, planning, monitoring and marketing [4]. Hence, EDM has gained significant interest as suitable and realistic next-generation computer-based educational systems (CBES), which offers a potential solution to these challenges [5].In spite of the rapidly growing popularity of DM in the educational environment, most of studies have emphasize more on the technical aspect of DM application Manuscript received October 10, 2014; revised January 13, 2015. Muslihah Wook is with the Faculty of Defence Science and Technology, National Defence University of Malaysia, Sungai Besi Camp, 57000 Kuala Lumpur, Malaysia (e-mail: muslihah@upnm.edu.my).Zawiyah M. Yusof and Mohd Zakree Ahmad Nazri are with Faculty of Information Science and Technology, National University of Malaysia, 43600 Bangi, Selangor, Malaysia (e-mail: zmy@ftsm.ukm.my; mzan@ftsm.ukm.my).algorithms-ignoring the users' perception of the technology [6]- [8]. Moreover, there has been a lack of study analysing how users use the technology [9]. Therefore, it is important to identify user behaviour prior to implement the EDM technology, as this could help minimising underutilisation or eventual abandonment. II. EDUCATIONAL DATA MINING (EDM)EDM has been defined as "t...

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The Factors of the Psychotherapists' Attitude Towards Technology: Combining Attitudes Towards Technology and Technology Readiness Models

Koch,

Graczykowska,

Szumiał

et al. 2024

Clin Psychology and Psychoth

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ObjectivesThe study aimed to explore psychotherapists' attitudes towards new technologies. Understanding the factors influencing their acceptance of technologies is essential to improving the accessibility, quality, and effectiveness of psychotherapeutic care in the digital age. Based on previous research from different fields (e.g., e‐government and mobile applications), we integrated the Unified Theory of Acceptance and Use of Technology (UTAUT‐T) with the Technology Readiness Index (TRI). UTAUT‐T focuses on one's perception of technology, while TRI emphasizes individual characteristics. Therefore, we regard psychotherapists' attitudes towards digital solutions by analysing factors related to technology and users (psychotherapists).MethodsWe conducted an online questionnaire study involving 391 psychotherapists aged 25–66 (M = 40.90; SD = 7.79). Participants completed self‐report questionnaires: UTAUT‐T, TRI 2.0, and a survey measuring technology usage at work.ResultsThe model explained 30.2% of the variance in actual technology usage and 57.6% in behavioural intention. Both technology‐related and therapist‐related factors affect technology use. Some factors directly influence use behaviour (optimism, ease of use, convenience), while others are mediated by behavioural intention (pressure from others, professional support, therapy quality expectations).ConclusionsTechnology‐related and therapist‐related factors influence psychotherapists' use of technology. The study revealed that optimism plays a crucial role in addition to behavioural intention. This insight is vital for implementing new technologies in mental health services.

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Behavioral intention, use behavior and the acceptance of electronic learning systems: Differences between higher education and lifelong learning

Cited by 242 publications

References 43 publications

A Behavioral Model of Managerial Perspectives Regarding Technology Acceptance in Building Energy Management Systems

A Behavioral Model of Managerial Perspectives Regarding Technology Acceptance in Building Energy Management Systems

The Acceptance of Educational Data Mining Technology among Students in Public Institutions of Higher Learning in Malaysia

The Factors of the Psychotherapists' Attitude Towards Technology: Combining Attitudes Towards Technology and Technology Readiness Models

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