Using Partial Least Squares (PLS) in Predicting Behavioral Intention for Telehealth Use among Filipino Elderly

Diño, Michael Joseph S.; Guzman, Allan B. de

doi:10.1080/03601277.2014.917236

Cited by 49 publications

(39 citation statements)

References 84 publications

(102 reference statements)

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“…After performing a partial least squares-based multi-group analysis this study concluded that there was no moderating effect between GT and BI. This finding is congruent with research done by Arenas-Gaitan et al (2015), Diño and de Guzman (2015), and Krishnapillai and Ying (2017). The results of this study confirm that the effects of SI, HM, PV, and HT are similar in pathway and size for male and female learners.…”

Section: Discussionsupporting

confidence: 93%

“…This result is consistent with the findings of Teo and Noyes (2012), Jambulingam (2013), andArenas-Gaitan et al (2015). Infrastructure support to use mobile learning becomes unnecessary because younger generation are equipped with skills to utilise new technology (Diño & de Guzman, 2015). Furthermore, FCs effect may be captured by EE (Venkatesh et al, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…The results of this study confirm that the effects of SI, HM, PV, and HT are similar in pathway and size for male and female learners. Since the digital gap between male and female is reducing (Faqih & Jaradat, 2015), this could be one of the reasons that technology systems have become more manageable by both genders (Diño & de Guzman, 2015). The gender gap of technology acceptance is narrowed through technology proficiency enhancement courses and improved information and communication technology access (Lee, Park, & Hwang, 2015).…”

Section: Discussionmentioning

confidence: 99%

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Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia

Moorthy

Yee

T’ing

et al. 2019

AJET

107

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Mobile learning has become a common experience in higher education and in the professional workforce. However, the readiness of accounting students to engage in such learning appears to be weaker than in other disciplines. Therefore, this study set out to identify the factors affecting accounting students’ behavioural intention (BI) to accept mobile learning. The participants of this study were 358 accounting students of public universities in Malaysia. The study was anchored in the unified theory of acceptance and use of technology 2 (UTAUT2) (Venkatesh, Thong & Xu, 2012), which has been employed by researchers in various research areas such as mobile payments, e-learning, mobile banking, and online shopping. The study revealed that habits have the most influence on accounting students’ intention to adopt mobile learning through an investigation of technology acceptance in the domain of mobile learning. From the perspective of universities, the study posits that consistent usage of mobile learning could be encouraged through processes to nurture students’ habits when using mobile learning system as a tool to complete tasks. Findings provide a reference for the future UTAUT2 and mobile learning related studies.

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Section: Discussionsupporting

confidence: 93%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia

Moorthy

Yee

T’ing

et al. 2019

AJET

107

View full text Add to dashboard Cite

show abstract

“…Social influence can be defined as the importance an individual accords to the opinions of other regarding his/her use of a new system. Studies in the literature emphasize that performance expectancy (Venkatesh et al, 2003;Al-Gahtani et al, 2007;Taiwo & Downe, 2013;Kaba & Touré, 2014), effort expectancy (Venkatesh et al, 2003;Chiu & Wang, 2008;Diño & de Guzman, 2015), and social influence (Venkatesh et al, 2003;Taiwo & Downe, 2013) are important factors in predicting behavioral intention. The hypotheses proposed in this study concerning the factors that affect behavioral can be listed as follows:…”

Section: Literature Reviewmentioning

confidence: 99%

The acceptance and use of a virtual learning environment in higher education: an empirical study in Turkey, and the UK

Kurt

Tıngöy

2017

Int J Educ Technol High Educ

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This study evaluated the acceptance and use of a virtual learning environment in higher education by using the unified theory of acceptance and use of technology (UTAUT) model. Study data were collected by means of a questionnaire form, completed by 1032 students receiving undergraduate education in Turkey and the United Kingdom, who currently use similar virtual learning environments. The role of performance expectancy, effort expectancy, social influence and facilitating conditions were evaluated and tested for both countries. The study results demonstrated that the behavioral intention and use behavior regarding the utilization of a virtual learning environment in higher education differed between the two countries, and that the level of impact of the factors that shape behavioral intention and use behavior also differed from one factor to another.Keywords: Unified theory of acceptance and use of technology (UTAUT), Technology adoption, Virtual learning environment (VLE), Turkey, United Kingdom Significance statementWhile the fast progress in technology has been continuing, the transfer of improved technologies into different application fields has become a current issue. In parallel with the accelerated technological innovations, the utilization of technology in educational processes has also increased. Hence, the studies focusing on the acceptance and utilization of these technologies particularly by students have come into prominence. The key motivation of the present study, which investigates the different models dealing with the acceptance and utilization of information systems in the literature, is to determine the student acceptance and utilization of a virtual learning system based on a pre-tested model. Building on similar virtual learning systems in two public universities-one is in Turkey and the other is in the UK-this research aims to reveal the students' intentions to utilize the system and also determine similarities and differences in their behaviours in using the system.

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“…These studies have led to the development of conceptual models and frameworks to understand the relationship of these variables with the adoption behavior [20]. One such model is the UTAUT Model (as shown in Figure 1) coined by [21] which explained that performance expectancy [22]- [24], effort expectancy [25], [26], social influence [27] and facilitating conditions [28] play an important role in predicting behavioral intention to use a particular technology [29]- [31]. With an increase in the values of the four constructs, there is a simultaneous increase in the behavioral intention to use the technology which determines the users' acceptance or rejection of the technology.…”

Section: Literature Reviewmentioning

confidence: 99%

Transformation of Academic Ecology through Information Communication Technology Adoption

Dosaya¹,

Shukla²,

Nirban³

2020

IJIET

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The role of technology in evolving and uplifting the lifestyle of populations worldwide has been enormous since the advent of 21st Century. Education when combined with technology, escalates the whole process of growth and development by making the user more and more independent in managing complex tasks in real time with less effort. This is an integrated process involving many factors to interplay. Acceptance of technology in the field of education still remains a challenge. There exist discrepancies in access and acceptance of technology among users in the academic realm, especially in the developing countries. This study attempts to gauge such discrepancies and the factors that lead to them by digging into the attitudes that urge people to accept or reject the Learning Management System (LMS) -a widely used technological intervention in the teaching learning process. The study was conducted in an engineering institute in India and data was collected from both the students and the teachers. The factors identified by the Unified Theory of Acceptance and Use of Technology (UTAUT) Model were used to design tools for data collection. Interviews were also conducted to substantiate the quantitative findings. The results indicated that acceptance of LMS was less in women as compared to their male counterparts both among the teachers and the students. The dependency on technology is influenced by factors such as the extent to which the user considers it easy to operate and, social influence from colleagues and seniors. Thus, it becomes clear that Effort Expectancy and Social Influence play an important role in the acceptance or rejection of available technology aides in education among women.Index Terms-Learning management system, UTAUT model, learning management system, gender, effort expectancy, social influence.

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Using Partial Least Squares (PLS) in Predicting Behavioral Intention for Telehealth Use among Filipino Elderly

Cited by 49 publications

References 84 publications

Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia

Habit and hedonic motivation are the strongest influences in mobile learning behaviours among higher education students in Malaysia

The acceptance and use of a virtual learning environment in higher education: an empirical study in Turkey, and the UK

Transformation of Academic Ecology through Information Communication Technology Adoption

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