The Relationship Between User Perception and User Behaviour in Interactive Information Retrieval Evaluation

Zhuang, Mengdie; Toms, Elaine G.; Demartini, Gianluca

doi:10.1007/978-3-319-30671-1_22

Cited by 10 publications

(5 citation statements)

References 22 publications

(39 reference statements)

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“…Another interesting study, which was found to be aligned with our present research outcome, also proved that enjoyment was more significant than perceived ease of use and perceived usefulness when predicting technology acceptance among a group of teachers (Chen et al., 2012; Teo and Noyes, 2011). Lastly, the obtained result for user perception also found to be contradicted studies that demonstrated a positive and moderate relationship between user perception and user behavior (Zhuang et al., 2016). The rejection of H3, H4 and H5 may signify a shift in thinking paradigm among adolescents and young adults who are adept and savvy with digital technologies (Gan and Balakrishnan, 2017, 2018).…”

Section: Discussioncontrasting

confidence: 55%

“…Lau (2011) investigated user perceptions for the adoption of Web 2.0 tools among hospital-based nurses and concluded that the identified behavioural perceptions are crucial to understand the adoption intention of Web 2.0 for knowledge sharing, learning, social interaction, and the production of collective intelligence. User perception is often associated with measures of perceived usability (Zhuang et al., 2016).…”

Section: Introductionmentioning

confidence: 99%

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Usability factors predicting continuance of intention to use cloud e-learning application

Wang

Ling

Lau

et al. 2019

Heliyon

142

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In this ever-progressive digital era, conventional e-learning methods have become inadequate to handle the requirements of upgraded learning processes especially in the higher education. E-learning adopting Cloud computing is able to transform e-learning into a flexible, shareable, content-reusable, and scalable learning methodology. Despite plentiful Cloud e-learning frameworks have been proposed across literature, limited researches have been conducted to study the usability factors predicting continuance intention to use Cloud e-learning applications. In this study, five usability factors namely Computer Self Efficacy (CSE), Enjoyment (E), Perceived Ease of Use (PEU), Perceived Usefulness (PU), and User Perception (UP) have been identified for factor analysis. All the five independent variables were hypothesized to be positively associated to a dependent variable namely Continuance Intention (CI). A survey was conducted on 170 IT students in one of the private universities in Malaysia. The students were given one trimester to experience the usability of Cloud e-Learning application. As an instrument to analyse the usability factors towards continuance intention of the application, a questionnaire consisting thirty questions was formulated and used. The collected data were analysed using SMARTPLS 3.0. The results obtained from this study observed that computer self-efficacy and enjoyment as intrinsic motivations significantly predict continuance intention, while perceived ease of use, perceived usefulness and user perception were insignificant. This outcome implies that computer self-efficacy and enjoyment significantly affect the willingness of students to continue using Cloud e-learning application in their studies. The discussions and implications of this study are vital for researchers and practitioners of educational technologies in higher education.

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Section: Discussioncontrasting

confidence: 55%

Section: Introductionmentioning

confidence: 99%

Usability factors predicting continuance of intention to use cloud e-learning application

Wang

Ling

Lau

et al. 2019

Heliyon

142

View full text Add to dashboard Cite

show abstract

“…The UTAUT model focuses on user perception of the technology [91], including perceived usefulness and ease of use, perceived enjoyment [99], and satisfaction [16]. [103]. Studies discuss user perception in terms of theories of "technology acceptance" [43].…”

Section: Students' Use and Perception Of Online Learningmentioning

confidence: 99%

Online Learning and COVID-19 in Higher Education: The Value of IT Models in Assessing Students’ Satisfaction

Azizan

Lee

Crosling

et al. 2022

Int. J. Emerg. Technol. Learn.

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This study of the COVID-19 online learning experiences of students in a Singaporean higher education institution (HEI) assesses the students’ ‘fit’ (ie, their satisfaction) with online learning, as gauged through the Information Technology (IT) models of Task-Technology Fit (TTF) and Unified Theory of Acceptance and Use of Technology (UTAUT). While current studies have found that curriculum, instructor and learner factors have impacted on students and online learning during COVID-19, it has been observed that there is insufficient understanding of factors that predict students’ online learning satisfaction [24]. This study thus contributes where there are few, if any studies that assess students’ online learning satisfaction through technological models. Furthermore, the study’s finding are that the students’ perception of, general attitude to, and intention to use the online learning shaped their satisfaction, thus pointing to the value of such technological models in understanding student learning at a deeper level. Students’ experiential comments from the study also form recommendations for effective online teaching practice which apply to Singapore, and broadly for higher education (HE) online learning design. Significantly, the information technology (IT) models are shown as valuable in assessing HE students’ online learning satisfaction.

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“…Moreover, after analysing the processed tweets, it is apparent that Twitter is now being used as a medium to express commuters’ grievances, with a preference for an instantaneous reaction rather than perception from the commuters’ end. However, user perception, defined as the degree of perceived usability of the service provided (Zhuang et al, 2016 ), would assist in analysing the perceived service quality of the Delhi metro. This will be accomplished through efforts by the DMRC and the Delhi metro’s social media management agency to advance the use of Twitter and nudge commuters to share their experiences with the service offered by making Twitter a forum for commuter interaction or engagement.…”

Section: Discussionmentioning

confidence: 99%

Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India

Agrawal

Kuriakose

2022

Comput.Urban Sci.

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Owing to the onset of the new media age, the idea of e-public participation has proven to be a great complement to the limitations of the conventional public participation approach. In this respect, location-based social networks (LBSN) data can prove to be a game shift in this digital era to offer an insight into the commuter perception of service delivery. The paper aims to investigate the potential of using Twitter data to assess commuters’ perceptions of the Delhi metro, India, by presenting a comprehensive methodology for extracting, processing, and interpreting the data. The study extracts Twitter data from the official handle of the Delhi metro, performs semantic and sentiment analysis to comprehend commuters’ concerns and assesses commuters’ sentiments on the predicted concerns. The paper outlines that the current depth of Twitter data is more inclined to instantaneous responses to grievances encountered. Moreover, the analysis presents that for the data extraction period, the topics ‘Ride Safety’ and ‘Crowding’ have the lowest scores, while ‘Personnel Attitude’ and ‘Customer Interface’ have the highest scores. Further, the paper highlights insights gleaned from Twitter data in addition to the aspects included in the conventional satisfaction survey. The paper concludes by outlining the opportunities and limitations of LBSN analytics for effective public transportation decision-making in India.

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The Relationship Between User Perception and User Behaviour in Interactive Information Retrieval Evaluation

Cited by 10 publications

References 22 publications

Usability factors predicting continuance of intention to use cloud e-learning application

Usability factors predicting continuance of intention to use cloud e-learning application

Online Learning and COVID-19 in Higher Education: The Value of IT Models in Assessing Students’ Satisfaction

Implications of a Twitter data-centred methodology for assessing commuters’ perceptions of the Delhi metro in India

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