Predicting the Acceptance of Cloud-based Virtual Learning Environment the Roles of Self Determination and Channel Expansion Theory

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Predicting the acceptance of cloud-based virtual learning

environment: The roles of Self Determination and Channel

Expansion Theory

Teck-Soon Hew a, Sharifah Latifah Syed Abdul Kadir b,⇑

a Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur, Malaysiab Department of Operation and Management Information System, Faculty of Business and Accountancy, University of Malaya, Kuala Lumpur, Malaysia

a r t i c l e i n f o

Article history:

Received 16 November 2015

Received in revised form 11 January 2016

Accepted 29 January 2016

Available online 11 February 2016

Keywords:

Cloud-based VLE

Self Determination Theory

Channel Expansion Theory

VLE content design

VLE interactivity

a b s t r a c t

The emergence of the cloud computing technology has further enhanced the capabilities of

the cloud-based virtual learning environment (VLE) compared to the grid computing based

VLE as teaching resources can be accessed, saved, retrieved and shared on the cloud any

time any where without any limitation. Unlike existing VLE literature that examines

extrinsic motivation (e.g. TAM; UTAUT) from the perspective of the learners in the context

of the conventional grid-computing VLE; this study examine the intrinsic motivation from

the teachers’ perspective in the context of the cloud-based VLE. So far, the influences of Self

Determination Theory (i.e. relatedness, competence, autonomy) and Channel Expansion

Theory (i.e. media richness) have been over-looked. In this study, the roles of SDT, CET,

VLE content design and interactivity together with the trust-in-website, attitude toward

knowledge sharing and school support are being examined. A sample of 1064 respondents

was gathered using simple random sampling across the country and analyzed withPLS-SEM. The research model is able to predict intention to use with 65.96% variance

explained. SDT, CET, VLE content design, Attitude toward knowledge sharing,

trust-in-website, school support and education significantly effects intention to use VLE.

This study provides theoretical and practical implications while contributing to the VLE

literature.

2016 Elsevier Ltd. All rights reserved.

1. Introduction

The Malaysian government has launched the 1BestariNet (means 1SmartNet) project as a platform to transform teaching

and learning while establishing Malaysia as a model of excellence in Internet-enabled integrated learning. The project isaimed at delivering world class education to 5.5 million school children spread across 10,000 schools, over 329,847 square

kilometers with 500,000 teachers and 4.5 million parents using the sophisticated 4G connectivity to the Frog virtual learning

environment (VLE) delivered through FrogAsia via a joint venture between the Malaysian Ministry of Education (MoE) and

YTL Communications. The 1BestariNet project is the world’s first nation-wide deployment of school in the cloud. Fig. 1

depicts the components of the 1BestariNet.

VLE is ‘‘a web-based communications platform that allows students, without limitation of time and place, to access dif-

ferent learning tools, such as program information, course content, teacher assistance, discussion boards, document sharing

http://dx.doi.org/10.1016/j.tele.2016.01.004

0736-5853/ 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.

E-mail addresses: [email protected] (T.-S. Hew), [email protected] (S. L. S. A. Kadir).

Telematics and Informatics 33 (2016) 990–1013

Contents lists available at ScienceDirect

Telematics and Informatics

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e/ t e l e


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systems, and learning resources” (Van Raaij and Schepers, 2008, p. 839). The Frog VLE (Fig. 2) was meant to fostering an inno-

vative Malaysians generation who are empowered to acquire possession of their education and are ready to contend in a

worldwide knowledge-based economy.

Fig. 1. 1BestariNet components. Note: 1BRIS = 1BestariNet receiver integrated system.

Fig. 2. Login to the Frog VLE platform.

T.-S. Hew, Sharifah Latifah Syed Abdul Kadir/ Telematics and Informatics 33 (2016) 990–1013 991



Although with a long record of integrating technology into the schools however research findings reveal that teachers are

not maximizing the adoption of technology. For decade, MoE has spent the equivalent of 1 billion pound sterling (USD1.6bn)

on ICT in education initiatives. However, the usage of ICT in schools still falls short of expectation with one study finding that

nearly 80% of teachers spent not more than 1 h a week using ICT. Although teachers have been sent for courses to obtain skill

and knowledge in real use of computer and ICT, most have fallen back to conventional instruction (Kumar et al., 2008).

Hence, we should enhance the level of ICT usage in classrooms to alleviate the issue of under-optimization of ICT usage

in class room teaching. Besides that, majority of the prior researches focused primarily on the undergraduates or university

instructors contexts. Since the culture and environment of teaching and learning in universities and schools are different in

terms of subject syllabus, pedagogical approach, levels of instruction, assessment and evaluation, education administration,

structure of organization and etc., the studies’ findings may differ and inapplicable to the school context. Besides, all the

studies were aimed at identifying the underlying factors of VLE acceptance from the learners’ context and very few have

focused on teachers’ perspective on a nation wide scale.

Furthermore, the previous studies have focused mainly on the online or web based learning systems such as Moodle, e-

LMS, WBLS, Blackboard and etc. which use the conventional grid computing technology that do not provide the facilities of

unlimited storage space, information sharing and collaboration as well as access to teaching and learning resource material

in comparison to the facilities provided by the cloud computing infrastructure. Cloud computing offers ‘‘opportunity of flex-

ibility and adaptability to use the computing resources on-demand” (Ercan, 2010, p. 939). Besides, it also supports socially

oriented theories of learning and cooperative learning via collaborative instruction approaches (Thorsteinsson et al., 2010).

The implication of 24/7 accessibility and accessible-anywhere of the cloud-based Frog VLE provide a clear demarcation and

distinction between grid-based and cloud-based VLEs in terms of changed capabilities, functionalities and opportunities for

teachers and students.

Previously only TAM (Sánchez and Hueros, 2010), TAM2 (Van Raaij and Schepers, 2008), TAM-ISSM (Motaghian et al.,

2013) and UTAUT (Sumak et al., 2010) were used as the research models. These studies have mostly focused on the extrinsic

motivational or utilitarian factors instead of the intrinsic factors such as teachers’ self determination and motivation toward

using the VLE technology. There is scarcity in the studies on effects of self-motivation and self determination to use the VLE

without any external influence and interference. Furthermore, none of these studies have examined the influences media

richness. As the VLE consists of a media rich environment with lots of video, sound, animation, graphics and other multime-

dia elements, the effects of media richness are worth studying. Hence, it would be interesting to examine whether the self

determination and media richness play significant roles in influencing teachers to use the VLE system.

Moreover, the task specific characteristics of content design and interactivity of the VLE system have been omitted in the

previous studies. Since the VLE system provide various teaching and learning contents such as teaching community,

FrogStore online resources, teaching sites, assignment module, forums, quizzes, email and etc, the impacts of these content

design may need further investigation. Similarly, as the VLE involves interactivity between the teachers and the VLE system,

the effects of interactivity toward the intention to use and perceived instructional effectiveness warrant an investigation to

be carried out. As the cloud-based Frog VLE enables knowledge sharing of educational resources in the cloud, it would be

interesting to see whether knowledge sharing attitude has any effects on teachers’ behavioral intention. Finally, school

support is included to see whether there are any impacts on the adoption of the VLE.

2. Literature review

To evade from reinventing the wheel, we have conducted extensive literature review pertaining to the adoption of the

cloud-based VLE but to our best effort, there are no studies on this subject matter. However, we have managed to obtain

some VLE related studies that uses the conventional grid computing technology or web based VLE. The literature review

showed that focus has been given from the perspective of undergraduates or students and not from the teachers’ perspective.

These studies have deployed extrinsic motivational factors such as TAM, TAM2, ISSM, 3-TUM, ELSS or UTAUT. There are no

studies that examine the roles of SDT and CET on the use of VLE. Since SDT involves intrinsic motivational factors while CET

encompasses the media richness attribute of the VLE platform, a study on their impacts on behavioral intention will surely

contribute to the existing IS literature. Besides that there is hardly any attention given to the characteristics of VLE such as

content design, interactivity and attitude toward knowledge sharing. These special cloud-based VLE attributes may provide

vital insight on the behavioral intention of teachers. Due to the fact that the cloud-based Frog VLE is a relatively new context

that is enabled with cloud-based capabilities and the lack of focus on the influence of the internal self determination and

media richness on VLE acceptance, it would be useful to investigate the roles that SDT and CET can play in predicting the

adoption of VLE.

Based on the review, we found that there are several validity issues pertaining to the data analyses and findings. For

examples, except for Eom (2012), none of the studies have engaged English back-translation into local languages. This

may compromise the accuracy of the original meanings as cultural differences may give rise to variation in interpretations.

Except for Sun and Hsu (2012), no expert panels were engaged in validating the face validity of the instrument. Besides, the

content validity was not validated as there are no reports of content validity index. Since these studies have used a single

instrument for both independent and dependent variables and common method bias was not examined, CMB issues may

arise. Besides that, non-response bias is also ignored. The validity of the data analyses is also problematic as there were

992 T.-S. Hew, Sharifah Latifah Syed Abdul Kadir/ Telematics and Informatics 33 (2016) 990–1013

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no tests on multivariate assumptions (i.e. normality, linearity, multicollinearity and homoscedasticity). Finally, except for

Motaghian et al. (2013) who used cluster sampling, convenience sampling was engaged and this may reduce the accuracy

and generalization of the findings. A summary of the past VLE related studies is shown in Table 1.

Sumak et al. (2010) used the UTAUT model which is an upgrade of the TAM model ( Lee et al., 2001; Motaghian et al.,

2013; Sánchez and Hueros, 2010) and TAM2 (Van Raaij and Schepers, 2008). The core constructs in these models are

perceived usefulness (eq. performance expectancy) and perceived ease-of-use (eq. effort expectancy). Although these models

do have their merits and are able to explain behavioral intention of the VLE however the important roles of intrinsic

motivation (e.g. self determination), media richness and task related constructs of the cloud-based VLE such as interactivity,

content design, trust-in-website and attitude toward knowledge sharing have been unintentionally overlooked. Similarly,

the 3-tier use model (Liaw, 2008), ELSS (Eom, 2012) and ISSM (Motaghian et al., 2013) have mainly focused on the informa-

tion, system and service quality, usage intention and users’ satisfaction. Not much has been done in examining the roles of

SDT, CET and VLE task related constructs. Hence, it is imperative to further extend the previous works in order to advance the

extant literature especially from the cloud-based VLE context.

3. Theoretical background

In the next section, we will elucidate the two theories underpinning this study.

3.1. Self Determination Theory (SDT)

SDT is a macro theory of motivation which expounds scientifically the dynamics of motivation, well-being and needsfrom the social perspective (Deci and Ryan, 1985). Self-determination is referred as ‘‘a quality of human functioning that

involves the experience of choice. It is the capacity to choose and have those choices. . .be the determinants of one’s actions”

(Deci and Ryan, 1985, p. 38). SDT is about the self determination at the back of the options which a person makes without

any external interference or influence and the fundamental psychological desires that are the foundation of their self-

motivation. SDT asserts that mankind has three universal and fundamental needs of relatedness (i.e. the sense of included

or affiliated with others), autonomy (i.e. the feeling of control and agency) and competency (i.e. the sense of capability with

activities and tasks). The relatedness is referred as the desire of feeling connected, to love, to be loved, to care and care for

while autonomy implies a yearning to act with desire to feel psychologically free. Deci and Ryan (2000) assert that compe-

tence is our craving to interact efficiently toward the surroundings, to experience a feeling of competence in achieving desire

results and to avert undesirable happenings. In this study, perceived relatedness is define as ‘‘the sense of identification or

connectedness an individual feels with other humans” where as perceived autonomy is ‘‘the degree of having control over an

individual’s own actions” (Yoon and Rolland, 2012, p. 1136). Likewise, perceived competence is ‘‘an individual’s belief that he

or she can perform a particular task or behavior effectively”. ( Yoon and Rolland, 2012, p. 1135).

Unlike the Social Cognitive Theory (Bandura, 1982) which theorized motivation as a colossal construct, SDT hypothesizes

motivation into 3 major groups of intrinsic motivation (i.e. doing an act as it is pleasurable, aesthetically pleasing and opti-

mally challenging), extrinsic motivation (i.e. performing a act as it brings about a independent result) and amotivation (i.e.

the condition of intention deficiency to act). The extrinsic motivation is divided into 4 kinds of introjected, identified, exter-

nal and integrated regulation (Fig. 3). Amotivation is the slightest self-determined motivation whereas intrinsic motivation

indicates the strongest self-determined motivation. SDT presumes that self-determined motivation (i.e. identified regulation

& intrinsic motivation) will bring about positive consequences and non-self-determined motivation (amotivation, external

and introjected regulation) will bring about negative consequences (Deci and Ryan, 1985). Hence, the three SDT constructs

are relevant to the features of VLE in terms of flexible learning (i.e. any time, any where), technical competencies, computer-

mediated instruction and social interaction.

3.2. Channel Expansion Theory (CET)

By integrating the core concepts of media richness and social influence theories, CET (Carlson and Zmud, 1999) extended

Media Richness Theory (MRT) further than its original dogmatic structure by examining how media richness perceptions

temporal expansion through an individual’s media richness perception via the acquirement of numerous experiences. Via

these experiences, individuals build related social information processing schema foundation which can encode/decode ‘rich’

media more effectively. Individual’s media richness perception is based on experiences with the topic, the media, a commu-

nication partner and the organizational context while media richness is an essential aspect of the choice and adoption of

media (Carlson and Zmud, 1999).

Knowledge-building experience of the organization’s members influences the media richness perception. The experience

with communication channel would allow individuals to learn the selections, attributes, applications and restrictions of the

channel. This would allow more efficient use of communication channel by adapting its application to the attributes of the

required function that would eventually lead to increase in perceived media richness by the individual. Experiences with a

communication partner that include interactions and mutual learning like language patterns and message construction will

allow the individual to use a richer language and to encode the messages leading to related cues specifically for him or her.


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Similarly, the experiences in knowledge-building of a topic may allow users to adopt specialized, common jargon and

facilitate easy communication. Lastly, the use of shared cultural references and symbols in an organizational context will

enable for a richer communication via the channel as a result from the raise in the modernization of the knowledge base.

Due to the fact that the cloud-based Frog VLE is equipped with a rich variety of media, the construct of perceived media rich-

ness (PMR) from CET is indeed relevant to the research context. In this research, PMR is referred as the level to which a tea-

cher believes that VLE is capable of carrying a wide range of media according to the criteria of capacity in immediate

feedback, personal focus, multiple cues and language variety (Dennis and Kinney, 1998; Fernandez et al., 2013).

4. Research model development

Drawing from SDT and CET, we proposed an integrated SDT–CET model for cloud-based VLE motivation. CET has been

studied in evolution of online discussion forum (Fernandez et al., 2013) and mobile instant messaging (Ogara et al., 2014)

but not in the context of VLE. On the other hand, SDT has been studied as the indirect antecedents of undergraduate online

learner motivation (Chen and Jang, 2010) and university college teachers’ e-learning continuance intention (Roca and Gagné,

2008); nevertheless its role as direct determinants of intention to use VLE from the context of school teachers’ remains unex-

plored. Similarly, interactivity (Chen et al., 2007), content design (Lee et al., 2009) and school support (Lai and Chen, 2011)

have been studied from the context of e-learning, e-CRM and teaching blogs respectively. The influence of these constructs

from the VLE context has not been studied. Therefore these theories and constructs are included in developing the theoret-

ical model. Table 2 illustrates the construct analysis.

The current study fits, builds on and extends the previous works which used TAM, UTAUT and ISSM in predicting behav-

ioral intention from the new context of cloud-based VLE by developing new causal relationships using SDT, CET theories and

VLE related constructs of interactivity, content design with trust-in-website and knowledge sharing attitude acting as the

mediator variables.

5. Hypothesis development

Perceived relatedness may be conceptualized as the sense of connectedness and identification a person feels with others.

According to SDT, individuals are more inclined to support their groups’ goals when they are connected to members of the

group. Once the persons are in an autonomy-supportive mode, they will possess a feeling of connectedness that will increase

their motivation (Yoon and Rolland, 2012). This sense of connectedness and identification will positively influence knowl-

edge sharing behaviors (Shen et al., 2010). The perception of togetherness will promote an individual’s activeness to share

knowledge and thus we suggest the following hypothesis:

Fig. 3. The self determination continuum. Note: PLOC = perceived locus of control. Kleih and Kübler (2013).


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Table 2

Construct analysis of past VLE related studies.

Relationship Author(s)

Sumak

et al.

(2010)

Van Raaij and

Schepers (2008)

Sánchez and

Hueros (2010)

Chou and

Liu (2005)⁄Eom

(2012)

Motaghian

et al. (2013)

Sun and

Hsu

(2013)⁄

Liaw

(2008)

Lee et al.

(2001)⁄Poon

et al.

(2004)⁄

PIIT? ANX

EE? AT ns

PE? AT +

PEOU? AT +

PU? AT +

SI? AT +

TS? AT ns

AT? BI ns

EE? BI ns

IQ ? BI ns

PE? BI ns

PEOU? BI +

PU? BI + +

SAT? BI +

SE? BI ns

SerQ ? BI ns

SI? BI +

SN? BI ns

SQ ? BI +

ILA? ELE +

MI? ELE +

SQ ? ELE +

ANX?

PEOU

IQ ? PEOU +

PIIT?

PEOU

+

SE? PEOU +

SerQ ?

PEOU

+

SN? PEOU +

SQ ? PEOU ns

TS? PEOU +

ILA?

PU +IQ ? PU +

MI? PU +

PEOU? PU + + ns

PIIT? PU ns

SE? PU ns +

SerQ ? PU ns

SN? PU + +

SQ ? PU ns +

TS? PU +

IQ ? SAT +

MI? SAT +

SE? SAT ns +

SML ? SAT ns

SQ ? SAT + +

SAT? SEF +

U? SEF nsAT?U +

BI?U + +

FC?U +

IQ ?U ns

PEOU?U ns +

PU?U + ns

SAT?U ns

SE?U +

SML ?U ns

SN?U ns

SQ ?U ns

Note: + indicates significant positive relationship; indicates significant negative relationship; ns = not significant; *indicate the study involves no causal

relationships, PIIT = personal innovativeness in IT; ANX = anxiety; PE = performance expectancy, EE = effort expectancy, SI = social influence, AT = attitude,

PU = perceived usefulness, PEOU = perceive ease of use, SN = subjective norm, SE = self efficacy, SQ = system quality, IQ = information quality; SerQ = service

quality; SE = self efficacy; SML = self managed learning; SAT = satisfaction; SEF = system effectiveness; TS = technical support; MI = multimedia instruction;ILA = interactive learning activities; ELE = e-learning effectiveness; U = use.




H1a. Perceived relatedness has positive significant influence on attitude toward knowledge sharing.

The want for relatedness for teachers who manage learners’ adoption of e-learning signifies the longing to get associated

with and supported by individuals in the social surroundings (Sørebø et al., 2009). Satisfying the desire for connectedness

and support within a social perspective will affect the extent of motivation (Deci and Ryan, 1985). The indirect effect of per-

ceived relatedness on BI has been verified by Sørebø et al. (2009) and Roca and Gagné (2008). According to SDT, people tend

to support their group’s objectives more when they are connected with other members. Deci and Ryan (2000) assert that

when actions are not innately enjoyable or appealing, the core rationale why they execute them is due to the appreciation

by significant others to whom they feel associated (i.e. colleagues, friends or family). Hence, we anticipate that the level of perceived relatedness will positively influence the extent of intention to use VLE among teachers. Thus, the following

hypothesis is constructed:

H1b. Perceived relatedness positively and significantly influences behavioral intention to use VLE.

The Frog VLE is a cloud-based instructional platform which supports collaborative instruction methods (Thorsteinsson

et al., 2010). For instance, teachers may collaborate through the Community and Forum applications to share their instruc-

tional experiences and creative ideas. Since ‘‘personal attitudes toward behavior are significant predictors of intention to

engage in that behavior therefore an individual’s behavioral intention to share knowledge may be determined by the attitude

toward knowledge sharing” (Chow and Chan, 2008, p. 460). It may be hypothesized that when the level of teachers’ attitude

toward knowledge sharing is high, their behavioral intention will also increase. Hence we may theorize that:

H1c. Attitude toward knowledge sharing positively and significantly influences behavioral intention to use VLE.

Gagné and Deci (2005) assert that autonomy influences the level of intrinsic motivation since it drives integration and

internalization of extrinsic motivation that may bring about authentic intrinsic motivation. Sørebø et al. (2009) and Roca

and Gagné (2008) found that there is indirect effect of perceived autonomy on BI. Gagné et al. (2000) discovered that man-

agement autonomy support directly influences acceptance of organizational changes. A need of autonomy signifies a yearn-

ing to self-regulate teachers’ engagement in using VLE (Sørebø et al., 2009). It is expected that autonomy will influence the

degree of intrinsic motivation as it drives integration and internalization of extrinsic motivation that will bring about

authentic intrinsic motivation (Gagné and Deci, 2005). It is therefore theorized that when teachers perceive that they have

the autonomy in using VLE, their intention to use VLE will also increase. Based on these justifications, we would like to

hypothesize that:

H2. Perceived autonomy has positive significant influence on intention to use VLE.

Sørebø et al. (2009) opined that instructors will feel more qualified to manage learners’ adoption of e-learning if the needfor e-learning competence is fulfilled. Perceived competence has been found to have significant indirect impact on BI (Roca

and Gagné, 2008). It is presumed that the degree of perceived competence will influence the teachers’ level of confirmation

(Sørebø et al., 2009). We expect that VLE competence will make teachers more effective in using VLE and boost teachers’

behavioral intention. Thus, the hypothesis is theorized as follows:

H3a. Perceived competence has positive and significant influence on behavioral intention to use VLE.

Perceived competency in Frog VLE includes teachers’ abilities to use the VLE platform to perform various instructional

activities. These competencies will enable them to understand the operations and contents of the VLE system. It is with this

understanding comes the confidence and trust to use the system. We anticipate a teacher with low perceived competency to

be less convinced and trust the VLE compared to a teacher that have high degree of perceived competency. Hence, we come

up with the following hypothesis:

H3b. Perceived competency positively and significantly influences trust in website.

Hsu et al. (2011) suggested that online trust may be categorized into 2 categories based on the nature of trust namely,

system trust and interpersonally trust. System trust is ‘‘the belief resulting from the reliability and reliance of an information

system while interpersonal trust refers to the belief resulting from the feeling of secure for other parties in the social

exchange” (Hsu et al., 2014, p. 237). It is believed that the more the teachers perceive that the VLE system is trustable,

the more likely the intention for them to use the system. Thus, the hypothesis is formulated as follows:

H3c. Trust in website has positive significant influence on behavioral intention.

School support is defined as the level to which a teacher perceives that schools are dedicated to successful execution and

adoption of VLE (Venkatesh and Bala, 2008). Previous studies have shown that management support is crucial in the accep-

tance of technology innovation assistance as the needed resources can be obtained effortlessly ( Huang et al., 2009). If top

management is supportive of information systems (ISs), more resources are expected to be provided to support and develop


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the ISs (Yap, 1989) and thus boosting the facilitating conditions for the ISs (Thong et al., 1996). Hence, it is theorized that

when the management of the school strongly supports teachers to use VLE, this will increase the level of facilitation for

teachers to use the VLE system. Thus, the below hypothesis is theorized:

H4. School support positively and significantly influences behavioral intention to use VLE.

Dennis and Kinney (1998) opine that richer media may allow users to communicate faster and to better comprehend

equivocal or ambiguous messages and thus would result in better performance. Saeed et al. (2010) assert that PMR has direct

impact on attitude and attitude has direct impact on intention to use blog/podcast. (Saeed et al., 2008) on the other hand

found that PMR influences perceived usefulness while perceived usefulness directly affect on intention to use second life.

Saeed and Sinnappan (2010) have validated the effect of PMR on BI of blog, podcast and second life. Since media richness

is referred as the capacity of carrying information of a medium or its capability to facilitate understanding and shared mean-

ing (Daft and Lengel, 1983), it is presumed that media richness positively influences individual’s intention to use a technol-

ogy. Therefore, it is theorized that when users perceived VLE to be rich with variety of media such as video, audio, text,

graphic, animation and etc, the intention to use VLE will be intensified. The multi-media environment tends to attract learn-

ers attention more easily compared to the conventional chalk-and-talk instructional method. Thus, teachers may perceived

that using a media rich platform may provide a more conducive and interesting environment for learning. Hence, we would

like to posit the hypothesis as follows:

H5a. PMR has positive significant influence on behavioral intention to use VLE.

The Frog VLE comes with a media rich interface that allows for easy interaction between the teachers and the Frog OS.

Teachers will be able to capture pictures, sound, videos, animations and etc from the cloud and use various media channels

to disseminate the knowledge to the students. A study has found that PMR has positive influence on interactivity of online

discussion forum (Balaji and Chakrabarti, 2010). Therefore, we postulate that in the context of the cloud-based VLE, when the

level of media richness is increased, there will also be equivalent increase in the level of interactivity. Therefore, we would

posit the hypothesis as follows:

H5b. PMR has positive significant influence on VLE interactivity.

Users are more likely to perceive that adopting e-learning services is easy when e-learning services are equipped with

abundant contents tailored to fulfill their needs (Lee et al., 2009). The indirect effects of content design on BI have been ver-

ified by Lee et al. (2009). Hong et al. (2002) also found that screen design has positive indirect effect on BI of digital libraries.

It is hypothesized that when teachers perceive the content design is appropriate and comprehensive, the intention to use VLE

will be higher. Teachers may be reluctant to use VLE if the system is unable to provide comprehensive and adequate contentin accordance with the national curriculum. Therefore, the hypothesis is theorized as follows:

H6a. VLE content design positively and significantly influences behavioral intention.

To enhance the VLE content design, more pictures, graphics, photos, sound, videos, animations and other means of

instructional media will be included in the creation of departmental sites in the Frog VLE. Giving the media richness of these

contents, we forecast that the degree of PMR will also be increased accordingly. Hence, the following hypothesis is

formulated:

H6b. VLE content design has positive and significant influence on PMR.

5.1. Control variables

Since VLE involves some IT and computer skills, teachers will specialization related to these skills may have higher inten-

tion to use VLE compared to other non-IT or non-computer specialized teachers. Thus, it is hypothesized that specialization

will have some confounding effect on BI. Therefore, we put forward the following hypothesis:

H7. Specialization has positive significant influence on behavioral intention to use VLE.

User experience was found to have a positive effect on system usage (DeLone, 1988). Thus, it is assumed that teaching

experience will have some confounding effect on BI. Hence, the hypothesis is posited as follows:

H8. Teaching experience has positive significant influence on behavioral intention to use VLE.

Individual differences are assumed to be most pertinent to both human-computer interaction studies (Dillon and Watson,

1996) and IS success (Harrison and Rainer, 1992). Leong et al. (2011) found that there is indirect effect of academic qualifi-


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cation on BI. Hence, it is theorized that educational level will have some confounding effect on BI. Hence, the following

hypothesis is theorized:

H9. Educational level has positive significant influence on behavioral intention to use VLE.

Hence, we would like to propose a SDT–CET model in predicting the behavioral intention of Frog VLE as in Fig. 4.

6. Research methodology

To examine the research model, we have engaged survey method to gather the data and then used Structural Equation

Modeling (SEM) with Partial Least Square (PLS) to corroborate the hypotheses.

6.1. Measurement and instrument development

The development and validation of the research instrument have been reported in a separated paper. The steps involved

in this process are illustrated in Fig. 5. Every item was measured using 7-point Likert scale as it is able to provide greater

dispersion while avoiding neutral responses. The responses ranges from (1) strongly disagree to (7) strongly agree. The list

of items and their sources are shown in Appendix A. The Malay version of the survey instrument can be obtained from the

authors upon request.

6.2. Sample and data collection procedures

As of 31 December 2014, the population of teachers is 419,820. The list of 351 champion schools is used as the sampling

frame with the unit of analysis being the Malaysian primary and secondary school teacher. The list of champion schools may

be obtained from the authors upon request. Champion schools are selected schools which were given training on Frog VLE by

FrogAsia. Simple random sampling is used to select 50 schools across the country after approvals were obtained from MoE,

state and district education departments as well as headmasters and principals. Questionnaires were posted to these schools

using registered self-addressed envelops. In total 1720 questionnaires were posted and 1240 were returned. Due to incom-

pleteness and double entries, only 1075 were usable samples, hence the response rate is 72.1%.


VLE attribute constructs Control Variables

VLE

Behavioral Intention

PerceivedRelatedness

Perceived

Autonomy

Perceived

Competency

Perceived MediaRichness

School Support

Attitude toward

Knowledge

Sharing

Trust inWebsite

Specialization Teaching

Experience

Education

LevelVLE Content

DesignVLE

Interactivity


H1a

H1b

H2

H3a

H3b

H4

H5a

H5b

H1c

H3c

H6a

H6b

H7 H8 H9

Fig. 4. Research model.


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7. Data analysis

In accordance with Anderson and Gerbing (1988), a 2-stage analysis was use in the data analysis with the first stage for

testing measurement model and the subsequent stage to examine the structural causal relationships. SmartPLS 3.0 (Ringle

et al., 2015) was deployed based on the following justifications. First, the aim of the research is to maximize the variance

explain in predicting the behavioral intention and not estimation of model fitness. Secondly, the study is exploratory in

its nature emphasizing on theory building (i.e. SDT–CET) rather than confirmation of an established theory. Lastly, the lack

of theoretical foundation, non-normality of data distribution and the high complexity of the research model with large num-

ber of variables further support the need to use the variance-based SEM compared to the covariance-based SEM.

7.1. Common method bias (CMB)

Since a single instrument was used to gather the independent and dependent variables, the issue of CMB may arise.

Therefore, we have carried out Harman’s single factor analysis and discovered that the percentage of variance explainedby a single factor is less than 50%. Hence we concluded that there is no issue of CMB.

7.2. Assessment of multivariate assumptions

The p < 0.05 of one-sample Kolmogorov–Smirnov test of normality indicated non-normality. The p > 0.05 of the devia-

tion from linearity indicated that there are linear relationships between most of the IVs and DVs. For those that do not

conform to this requirement, p < 0.05 of Ordinary Least Square further confirmed existence of linearity. Pearson’s

r < 0.90 (Tan et al., 2014) and VIF < 10 with tolerance >0.10 (Sim et al., 2014) indicated no issue of multicollinearity.

Homoscedasticity was validated using scatter plots of DVs and their regression standardized residuals. Based on Maha-

lanobis distance, 11 outliers were detected leading to the sample size of 1064 which is above the rule of thumb of 10 times

the maximum arrows pointing to an endogenous construct or 10 time the maximum number of indicators of the most

complicated construct.

Fig. 5. Steps in development and validation of the VLE instrument.


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7.3. Results

7.3.1. Measurement model

Table 3 shows that all average variance extracted (AVE) exceed 0.50 indicating adequate convergent validity while all CR

and alpha values are above 0.70 implying good construct reliability (Hair et al., 2012). Construct reliability is confirmed as all

CR are greater than AVE (Table 4). In terms of indicator reliability, the square values of the indicators loadings are greater

than 0.70 indicating adequate indicator reliability (Wong et al., 2015). Similarly, all square roots of AVE are larger than their

relevant correlation coefficients while the Fornell-Larcker’s (1991) ratio is less than one; validating the discriminant validity

of the constructs. This is further confirmed as all AVE are greater than MSV and ASV and all items load strongly to the rel-

evant construct and weakly to irrelevant constructs (Appendix B). By using the new Hetero-Trait Mono-Trait Ratio (HTMT)

criterion of discriminant validity for variance-based SEM, Table 5 shows that the discriminant validity is also confirmed as all

HTMT ratio is below 0.90 (Henseler et al., 2015). The confidence interval for the HTMT inference test (Appendix C) shows that

Table 4

Discriminant validity: Fornell-Larcker’s criterion.

AT BI PA PC PMR PR SS TW VCD VI AVE CR MSV ASV

AT 0.966 0.932 0.986 0.418 0.255BI 0.532 0.967 0.935 0.977 0.471 0.404

PA 0.483 0.683 0.874 0.765 0.907 0.587 0.419

PC 0.425 0.670 0.766 0.871 0.758 0.904 0.587 0.400

PMR 0.436 0.675 0.736 0.736 0.896 0.803 0.966 0.684 0.438

PR 0.647 0.430 0.412 0.389 0.377 0.886 0.785 0.936 0.418 0.202

SS 0.600 0.676 0.587 0.552 0.578 0.499 0.940 0.883 0.968 0.457 0.336

TW 0.549 0.679 0.629 0.608 0.636 0.441 0.587 0.966 0.934 0.977 0.461 0.359

VCD 0.441 0.686 0.744 0.720 0.827 0.436 0.599 0.628 0.907 0.823 0.965 0.684 0.442

VI 0.364 0.636 0.692 0.708 0.804 0.347 0.521 0.605 0.787 0.911 0.831 0.952 0.647 0.393

FLR 0.449 0.482 0.647 0.774 0.851 0.533 0.517 0.494 0.831 0.779

Note: Diagonal element shows the square root of the AVE; PR = perceived relatedness, PA = perceived autonomy, PC = perceived competence, PMR = per-

ceivedmedia richness, VCD= VLE content design, VI = VLE interactivity, SS = school support, AT = attitude towardknowledge sharing, TW = trust in website,

BI = behavioral intention; FLR = Fornell-Larcker’s ratio.

Table 3

Convergent validity and construct reliability.

AVE Composite reliability (CR) R2 Cronbach’s alpha

AT 0.9323 0.9857 0.4185 0.9819

BI 0.9346 0.9772 0.6596 0.9650

PA 0.7646 0.9069 0.0000 0.8461

PC 0.7582 0.9036 0.0000 0.8415

PMR 0.8033 0.9662 0.6838 0.9592

PR 0.7850 0.9359 0.0000 0.9088

SS 0.8833 0.9680 0.0000 0.9558

TW 0.9341 0.9770 0.3698 0.9647

VCD 0.8225 0.9653 0.0000 0.9568

VI 0.8307 0.9515 0.6470 0.9319

Note: PR = perceived relatedness, PA = perceived autonomy, PC = perceived competence, PMR = perceived media richness, VCD = VLE content design,

VI = VLE interactivity, SS = school support, AT = attitude toward knowledge sharing, TW = trust in website, BI = behavioral intention.

Table 5

Discriminant validity: Hetero-Trait Mono-Trait Ratio (HTMT) criterion.

AT BI PA PC PMR PR SS TW VCD VI

AT

BI 0.560

PA 0.499 0.705

PC 0.510 0.702 0.898

PMR 0.462 0.690 0.799 0.767

PR 0.706 0.511 0.492 0.493 0.439

SS 0.555 0.615 0.552 0.517 0.540 0.525

TW 0.527 0.674 0.617 0.595 0.611 0.489 0.515

VCD 0.493 0.673 0.813 0.751 0.816 0.477 0.565 0.598

VI 0.388 0.672 0.755 0.733 0.833 0.393 0.494 0.597 0.805

Note: PR = perceived relatedness, PA = perceived autonomy, PC = perceived competence, PMR = perceived media richness, VCD = VLE content design,VI = VLE interactivity, SS = school support, AT = attitude toward knowledge sharing, TW = trust in website, BI = behavioral intention.


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there are no confidence intervals which contain the value 1 implying that all constructs are empirically distinct (Henseler

et al., 2015). Unidimensionality was also validated as all items load strongly and significantly to their respective constructs

(Appendix D). Standardized Root Mean Square Residual (SRMR) of less than 0.08 is considered as a good model fit criterion

(Hair et al., 2014; Hu and Bentler, 1998). The SRMR for composite model in this study is 0.033.

7.3.2. Structural model

We then examined the structural model with bootstrapping technique in SmartPLS using 5000 samples with no sign

changes at a two-tailed 0.05 significance level. The model is capable of explaining 65.96% of the variance in BI, 68.38% in

PMR, 64.70% in VI, 41.85% in AT and 36.98% in TW. Fig. 6 shows the PLS path coefficients’ significance and the R2 of the

endogenous variables. All R2 exceeded 10% implying adequate explanatory power. The model also shows education level

is the only control variable that has significant confounding effect on BI.

7.3.3. Hypothesis testing

The significance of the causal relationships is evaluated based on the T -statistics of 1.960 ( p < 0.05), 2.56 ( p < 0.01) and

3.29 ( p < 0.001). Table 6 illustrates the beta coefficient together with the T -statistics. The results showed that 65.96% of

the variance in BI is explained with the strongest predictor being SS (b = 0.254, t = 7.507) followed by TW (b = 0.214,

t = 6.789), PC (b = 0.145, t = 3.981), VCD (b = 0.128, t = 2.922), PA (b = 0.107, t = 2.819), PMR (b = 0.079, t = 2.106), AT

(b = 0.075, t = 2.410) and the control variable of ED (b = 0.033, t = 1.971). Hence, hypothesis H1c, H2, H3a, H3c, H4, H5a,

H6a and H9 are supported. In contrary, PR (b = 0.026, t = 1.299), SPL (b = 0.013, t = 0.990) and TEX (b = 0.028,

t = 1.704) are not supported. Therefore, H1b, H7 and H8 are not supported. Furthermore, 41.85% of variance in AT is

accounted for by PR (b = 0.647, t = 27.679), 36.98% of variance in TW is explained by PC (b = 0.608, t = 23.406), 64.70% vari-

ance in VI is predicted by PMR (b = 0.804, t = 56.720) while 68.38% of variance in PMR is accounted for by VCD (b = 0.827,

t = 68.817). Thus, hypothesis H1a, H3b, H5b and H6b are supported.

7.3.4. Mediating effects

The mediating effects PC and VCD were assessed using Baron and Kenny’s (1986) technique as well as Sobel’s test for sig-

nificant mediation effect (Table 7). The mediator of PR was ignored as the relationship of PR ? BI is insignificant. We also

VLE Related Constructs


Control Variables

VLE

Behavioral Intention

PerceivedRelatedness

PerceivedAutonomy

Perceived

Competency

Perceived Media

Richness

School Support

Trust inWebsite

Specialization TeachingExperience

EducationLevel

VLE Content

DesignVLE

Interactivity


0.647***

-0.026 (ns)

0.107**

0.145***

0.608***

0.254***

0.079*

0.804***

0.075*

0.214***

0.128*

0.827***

-0.013 (ns) -0.028 (ns 0.033*

R2 = 0.6596

R2 = 0.4185

R2 = 0.6838

R2 = 0.3698

R2 = 0.6470

Attitude towardKnowledge

Sharing

Fig. 6. PLS SEM path analysis. Note: Dotted line represent insignificant path, ns = not significant; * p < 0.05; ** p < 0.01; *** p < 0.001.


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calculated the variance accounted for (VAF) of the mediators (Table 8). The result indicated that there are significant partial

mediation effects of TW and PMR. Table 9 illustrates in detail the direct, indirect and total effects of mediators of PC and VCD

toward BI.

7.3.5. Predictive power and effect sizes

Predictive power and effect size were assessed using blindfolding with omission distance of 9. Cohen (2013) opined that

the Q 2 of 0.02, 0.15 and 0.35 denote small, medium or large predictive relevance while the effect size f 2 and q2 are evaluated

as 0.02 for small, 0.15 for medium and 0.35 for large. Table 10 shows that all endogenous variables have very high predictive

relevance power.

Effect size was obtained using blindfolding with omission distance of 9 units. Since single predictor will lead to total effect

size, the effect size f 2 and q2 were not calculated. Table 11 indicates that effect sizes for all the predictors of BI which are

Table 6

Path coefficients.

Hypothesis PLS path Original

sample (O)

Sample

mean (M)

Standard

deviation (STDEV)

Standard

error (STERR)

T statistics

(|O/STERR|)

p Values

(two-tailed)

Remark

H1a PR ? AT 0.6469 0.6475 0.0234 0.0234 27.6789 0.000⁄⁄⁄ Supported

H1b PR ? BI 0.0260 0.0295 0.0200 0.0200 1.2985 0.194 Not Supported

H1c AT ? BI 0.0748 0.0746 0.0310 0.0310 2.4097 0.016⁄ Supported

H2 PA? BI 0.1065 0.1072 0.0378 0.0378 2.8193 0.005⁄⁄ Supported

H3a PC? BI 0.1451 0.1486 0.0365 0.0365 3.9812 0.000⁄⁄⁄

SupportedH3b PC? TW 0.6081 0.6087 0.0260 0.0260 23.4056 0.000⁄⁄⁄ Supported

H3c TW ? BI 0.2140 0.2123 0.0315 0.0315 6.7855 0.000⁄⁄⁄ Supported

H4 SS? BI 0.2542 0.2585 0.0339 0.0339 7.5069 0.000⁄⁄⁄ Supported

H5a PMR ? BI 0.0785 0.0790 0.0373 0.0373 2.1056 0.035⁄ Supported

H5b PMR ? VI 0.8043 0.8046 0.0142 0.0142 56.7202 0.000⁄⁄⁄ Supported

H6a VCD? BI 0.1275 0.1218 0.0437 0.0437 2.9218 0.004⁄⁄ Supported

H6b VCD? PMR 0.8269 0.8271 0.0120 0.0120 68.8168 0.000⁄⁄⁄ Supported

H7 SPL ? BI 0.0129 0.0183 0.0131 0.0131 0.9901 0.322 Not Supported

H8 TEX? BI 0.0282 0.0278 0.0165 0.0165 1.7037 0.088 Not Supported

H9 ED? BI 0.0330 0.0336 0.0168 0.0168 1.9710 0.049⁄ Supported

Note: * p < 0.05; ** p < 0.01; *** p < 0.001; PR = perceived relatedness, PA = perceived autonomy, PC = perceived competence, PMR = perceived media richness,

VCD= VLE content design, VI = VLE interactivity, SS = school support, AT = attitude toward knowledge sharing, TW = trust in website, BI = behavioral

intention; SPL = specialization; TEX = teaching experience; ED = education level.

Table 8Sobel’s test for significance of mediation.

Variables IV-M-DV SEM path Path

coefficient

Standard

error

Sobel’s test

statistics (T -value)

p-Value

(two-tailed)

Mediation

effect

VAF (%)

PC-TW-BI PC-TW 0.6081a 0.0260 6.5240 0.0000⁄⁄⁄ Supported 47.3

TW-BI 0.2140b 0.0315

PC-BI 0:1451c 0 0.0365

VCD-PMR-BI VCD-PMR 0.8269a 0.0120 2.1036 0.0354⁄ Supported 33.7

PMR-BI 0.0785b 0.0373

VCD-BI 0:1275c 0 0.0437

Note: * p < 0.05; *** p < 0.001; IV = independent variable; M = mediator variable; DV = dependent variable; PC = perceived competency; TW = trust in website;

VCD= VLE content design; PMR = perceived media richness; BI = behavioral intention; VAF = variance accounted for VAF¼ ababþc 0

n o; Source: http://

www.danielsoper.com/statcalc3/calc.aspx?id=31.

Table 7

Baron and Kenny’s mediation test.

Non mediated model Mediated model (trust in website as mediator) Mediation effect

Behavioral Intention Behavioral Intention

Perceived competency 0.6696⁄⁄⁄ 0.4072⁄⁄⁄ Partial mediation

Non mediated model Mediated model (perceived media richness as mediator)

Behavioral intention Behavioral intention

VCD content design 0.6863⁄⁄⁄ 0.4022⁄⁄⁄ Partial mediation

Note: *** p < 0.001.


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corresponding to the order of importance based on the strength of the beta coefficients of the path analysis in Section 7.3.3 of

hypothesis testing.

8. Discussion

This research investigated the roles of SDT and CET in predicting the BI to use VLE with 65.96% variance explained. In inte-

grated SDT–CET research model which is enhanced with the inclusion of the VLE related constructs of VCD and VI as well as

AT, TW and SS has been statistically corroborated and empirically validated. The results have revealed several interesting

findings. First of all, the direct effects of PA and PC were empirically validated for the first time. This has provided an evidence

to support the role of SDT in affecting teachers’ motivation from the context of cloud-based VLE. It showed that teachers’

intention to use the VLE is stronger in an autonomy-supportive surrounding and when they have fulfilled their needs for

competency. Secondly, PC also has direct effect on TW which directly influences BI. This showed that teachers’ attitude

toward knowledge sharing is greatly influenced by their need for competence and on the same time their motivation to

use the VLE is dependent on their knowledge sharing attitude. This further theorizing that when teachers are able to fulfill

their needs for competency in operating the cloud-based VLE, they will have higher tendency and propensity in sharing their

knowledge with their colleagues. At the same time, teachers will gain higher motivation to use the VLE for classroom instruc-

tional. Furthermore, the direct effect of PC on BI also further advances the findings of indirect effect by Sørebø et al. (2009)

and Roca and Gagné (2008). This has provided new evidence and support for the new direct relationship between PC and BI

especially from the context of cloud-based VLE.

Table 9

Total, direct and indirect effects.

Variables IV-M-DV Direct effect Indirect effect Total effect T -statistics p Values (two-tailed)

PC-TW-BI 0.1451 0.1302 0.2753 6.9946 0.000⁄⁄⁄

VCD-PMR-BI 0.1275 0.0650 0.1925 5.4717 0.000⁄⁄⁄

Note: *** p < 0.001; PC = perceived competency; TW= trust in website; VCD = VLE content design; PMR= perceived media richness; BI = behavioral Intention.

Table 10

Predictive relevance of endogenous variables.

Endogenous variable R2 Q 2 (Stone-Geisser’s)

AT 0.4185 0.8790

TW 0.3698 0.3444

PMR 0.6838 0.5469

VI 0.6470 0.5349

BI 0.6596 0.6149

Note: AT = attitude toward knowledge sharing, TW = trust in website, PMR = perceived media

richness, VI = VLE interactivity, BI = behavioral intention.

Table 11

Effect size.

Predictor variable Dependent variable Path coefficient f 2 q2

PR AT 0.6469 NA NA

PC TW 0.6081 NA NA

VCD PMR 0.8269 NA NA

PMR VI 0.8043 NA NA

AT BI 0.0748⁄ 0.0073 0.0151

PR 0.0260 0.0012 0.0008

PA 0.1065⁄⁄ 0.0103 0.0080

PC 0.1451⁄⁄⁄ 0.0209 0.0166

TW 0.2140⁄⁄⁄ 0.0608 0.0499

SS 0.2542⁄⁄⁄ 0.0873 0.0730

PMR 0.0785⁄ 0.0047 0.0039

VCD 0.1275

⁄

0.0123 0.0096SPL 0.0129 0.0006 0.0005

TEX 0.0282 0.0021 0.0021

ED 0.0336⁄ 0.0029 0.0031

Note: * p < 0.05; ** p < 0.01; *** p < 0.001; NA = not applicable to single predictor; PR = perceived relatedness, PA = perceived autonomy, PC = perceived com-

petence, PMR = perceived media richness, VCD = VLE content design, VI = VLE interactivity, SS = school support, AT = attitude toward knowledge sharing,

TW = trust in website, BI = behavioral intention; SPL = specialization; TEX = teaching experience; ED = education level.


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More interestingly, although PR was found to have no significant influence on BI, it has significant influence on AT which

directly affects BI. This further supports the theory that when teachers are able to fulfill their needs for relatedness, it may

motivate them to share their knowledge with their colleagues. This is consistent with Shen et al. (2010) however the finding

has provided new evidence and support from the context of cloud-based VLE rather than the existing virtual community

context. It shows that the closer the relatedness of teachers with their colleagues, the stronger the propensity for them to

engage themselves in knowledge sharing activities.

The non significance of the PR-BI association may be due to the self-independence propensity of the teachers in deciding

whether to use the Frog VLE. The feeling of getting connected with others is not imperative but more importantly the feeling

of being competent and having autonomy in using the Frog VLE means more for them in their decision making processes. The

study also showed that SS significantly influence BI. This indicates that school administrators’ support is a key driver for

teachers’ motivation and intention to use the VLE which is consistent with Huang et al. (2009). However, the finding has offer

new evidence and support from cloud-based VLE rather than the blog article recommendation context. It shows that the

more supportive the schools, the higher the tendency for teachers to use the VLE.

The direct effects of TW, AT and SS on BI were validated for the first time. These new findings have provided imperative

evidences and supports that teachers’ decision and intention to use VLE depends a lot on their perceptions on trust-in-

website, knowledge sharing attitude and support from their superiors. When teachers have trust in the Frog VLE website,

they will feel more secure to share their knowledge with their colleagues. This may create conducive environment for estab-

lishment of a learning organization with the strong supports from the school authorities.

From the CET perspective, PMR was found to have significant impact on BI and VI while being directly influenced by VCD.

The VCD–PMR is a newly established relationship. This further advances the work by Saeed et al. (2008, 2010) who found

indirect effect of PMR on BI in blog/podcast and second life context. It also extends the work by Balaji and Chakrabarti

(2010) who found indirect effects of PMR on VI from the context of online discussion forum. In addition, the study also

advances the findings by Lee et al. (2009) and Hong et al. (2002) who validated indirect effect of VCD on BI in the context

of digital libraries. These findings showed that when teachers are able to perceive the media richness of the VLE, it may moti-

vate them to use it in the classroom instruction and on the same time drive them to interact more with the VLE system. How-

ever, their media richness perception depends on their perception of the content design. Thus, it is important to ensure that

the content design can meet the teachers’ needs.

In terms of mediating effects, it was found that the relationship of PC-BI is partially mediated by TW whereas PMR par-

tially mediates the VCD-BI relationship. These findings have provided evidence and support on the mediating roles of TW

and PMR. The study showed that when teachers’ perception of trust-in-website increases, it may reduce the effect of per-

ceived competence on behavioral intention. Similarly, when teachers’ perception on media richness increases, it may also

reduce the impact of VCD on intention to use the VLE. Previously there have been no studies on the mediation effects of these

two mediators. Interestingly, we found no mediation effect of AT on the association between PR and BI. This indicates that

the influence of perceived relatedness is not affected by teachers’ attitude toward knowledge sharing. As long as the needs

for relatedness get stronger, teachers’ intention will be stronger too. In terms of control variables, only ED contributes to sig-

nificant influence on BI. SPL and TEX do not have any confounding effects on BI. These may be due to the sufficient training

and exposure given by the Ministry of Education through FrogAsia. Therefore, irrespective of the specialization and teaching

experience of the teachers, there are no differences in the intention to use the VLE system among these teachers.

9. Theoretical implications

The research has made a number of significant contributions. First of all, the newly developed SDT–CET integrated model

may contribute to the IS adoption literature in the sense that this is the first time ever direct effects of SDT and CET con-

structs were empirically validated. This advances the IS adoption literature as very less attention has been given to the role

of SDT and CET toward BI. Previously, much attention has been given to extrinsic motivation drivers such as TPB, TAM and

UTAUT. In this study, intrinsic motivational drivers from SDT were found to be strong predictors of BI. Previous studies have

main focused on investigating VLE from the learners or students perspective. Perhaps, this study may be the first to focus on

the perspective of teachers and from the context of cloud-based VLE.

Secondly, unlike previous studies of grid-based VLE, this study has theoretically contributed to the extant literature by

examining the effects of the unique VLE task-related constructs of content design and interactivity. The inclusion of these

VLE related constructs may provide more comprehensive and holistic theoretical understandings on how specific task-

related may influence teachers’ intention to use the cloud-based VLE. This has made the model more specific and relevant

to the context of the study in comparison to the existing TAM or UTAUT models.

Thirdly, several other new relationships were also established for the first time. These include the TW, AT, SS, PMR, VCD

and ED on BI as well as the PR-AT and PC-TW connections. These findings provide support for theorizing that when teachers

possess higher level of education, trust-in-website, willingness to share their knowledge and perceptions about the media

richness and content design of the VLE with the support from school administrators, their intention to use the VLE will also

be higher. The validations of these new links may further extend the extent of current IS literature. Thirdly, the study also

theoretically advances the VLE literature in validating the partial mediating effects of AT on PC-BI and PMR on the VCD-BI

relationships. The partial mediations indicate that PC and PMR by themselves are able to directly influence BI. Besides that,


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we also found no significant effects of PR, SPL and TEX on BI. These relationships have not been investigated in prior studies.

Next, with the introduction of the AT and TW constructs, the study has further enhanced the research model for the context

of the cloud-based VLE as the findings may provide new theories on the influences of teachers’ knowledge sharing attitude

and trust-in-website on their behavioral intention. This has further extended the existing VLE related body of knowledge.

Last but not least, the research model is able to predict BI, PMR, VI, AT and TW with 65.96%, 68.38%, 64.70%, 41.85% and

36.98% variance explained while validating the research model from the new context of a cloud-based VLE for the first time

using 1064 respondents randomly selected from 351 Frog champion schools across the country in comparison to the previ-

ous grid computing-based VLE related studies that mostly engaged convenience sampling. Therefore, the theoretical findings

from this study are able to provide a better generalization.

10. Practical implications

There are several important practical implications drawn from the research findings. First of all, MoE and FrogAsia should

put in more attention in encouraging connectedness and knowledge sharing among teachers. This may be done by having

more platform of interaction among teachers through teamwork activities and collaborations. More gatherings in the forms

of seminar, conference, workshop and in-house training can be conducted for teachers to build new connections especially in

using the Frog VLE. More promotion should be given in encouraging teachers to use the Community and Forum applications

so that teachers will be able to sharing the expertise, skills and knowledge. Secondly, more autonomy should also be given to

teachers so that they will have more freedom in using the VLE system. For example, teachers will be provided with Dongles

to access the Frog VLE from their homes or while traveling. This will allow them to build, upload and sharing instructional

resources anytime anywhere in the cloud. Not less important is the teachers’ competencies in using the VLE. More hand-ontrainings should be provided from time to time to update and refresh the skills and competencies required in the VLE. How-

ever, without adequate security and privacy protection in the VLE platform, teachers may distance themselves from using

the VLE. Hence, as the provider of the VLE, FrogAsia should always maintain the highest level of security and privacy pro-

tection in the VLE platform in order to build trust among teachers. It is only with trust in the hearts of the teachers that will

eventually lead to their intention to use the VLE.

Secondly, from the perspective of school administrators, strong support should be given to teachers in encouraging them

to use the platform. This may be done by recognizing the efforts of teachers who have use the VLE effectively and frequently.

Prizes or certificates may be given to these teachers or their success stories can be highlighted in the Frog VLE website and

school bulletin boards. Besides that, headmasters and principal may also consider the frequency of usage as one of the cri-

teria of annual appraisal.

Thirdly, FrogAsia should ensure that the VLE platform is always upgraded with media-rich functionalities. Whenever

there is breakthrough of new media technology, the latest development should be incorporated into the VLE. For example,

more media-rich apps can be provided in the FrogStore for teachers to download into their notebooks, smart-phones, tabletsor other upcoming mobile gadgets. In line with the media richness of the VLE, the content design of the VLE should also be

enhanced and enriched from time to time. This would enable teachers to save time and energy in preparing their lessons.

However, without interactivities from the teachers, a rich VLE content will be meaningless. Therefore, efforts should be

given in promoting the degree of interactivity among the teachers. This can be achieved by giving rewards in terms of recog-

nition to teachers who interact most with the VLE system. In line with these, FrogAsia may come up with apps in the plat-

form that measure the frequency of use by teachers and create a ‘hall of fame’ corner for the top 100 active users. This is

different from the common sense explanation that could be suggested without undertaking research. Beside that, other

rewards such as the yes credit (monetary), data rewards (MB storage) or frog credits (reward points) and etc can also be

given out.

Surprisingly and contrary to the expectations of current paradigm, perceived relatedness does not have significant influ-

ence on BI. Hence, we proposed that no new measures and actions are needed to promote teachers’ perception of related-

ness. MoE and FrogAsia may use the existing measures to maintain the level of perceived relatedness among teachers.

Equally unexpected are the non-significant effects of teachers’ specialization and teaching experience on BI. Therefore,MoE and FrogAsia need not consider these factors in their policy making and R&D processes. Hence a ‘one size for all’ strategy

may be used for teachers with different specializations and teaching experiences in order to increase their behavioral inten-

tion to use the cloud-based Frog VLE.

Last but not the least, since education level is a factor that influences BI, more encouragements should be given to non-

graduate teachers. For example, the number of scholarship recipients can be further increased to allow more non graduate

teachers to further their study to degree, master or Ph.D. level. Alternatively, more of them should be allowed to take full-

paid, half-paid or unpaid study leaves to upgrade themselves.

11. Limitations and future studies

The study is confined to the Malaysia geographical region. Thus the findings may not be pertinent to other geographical

regions. Therefore, future studies may be conducted in other geographical regions. Secondly, even though the research model

is capable of explaining 65.96% of the variance in BI, there may be other factors that can be included in the upcoming studies.


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Thirdly, a longitudinal study may be done to study the effect of time toward the intention to use the VLE. It will be also inter-

esting to examine the instructional effectiveness of the cloud-based VLE in future studies. Lastly, the effects of gender, age

and other demographic variables may be taken as moderators in upcoming studies to investigate whether the strengths of

the relationships are affected by these moderators.

12. Conclusions

This research has investigated the roles of SDT and CET from the context of the cloud-based Frog VLE in a nation-widesurvey. The findings have contributed significantly to the IS literature generally and VLE literature specifically. More impor-

tantly, scholars and practitioners will find this study useful in understanding the behavioral intention of teachers to use

cloud-based VLE which is different from the conventional grid-computing based or online VLE platform. In fact, to further

advance the literature, we have done a study on the instructional effectiveness of the VLE and will report the findings in

another separate paper.

Acknowledgements

This research is funded by the University of Malaya under research grant number PG037-2014B with the project entitled

‘‘Understanding the virtual learning environment”. Thanks to the Educational Research and Planning Division, Malaysian

Ministry of Education and State Education Departments for granting the permissions to conduct this research.

Appendix A. List of items and their sources

Construct and definition Items Source

Perceived relatedness (PR) PR1: I really like the people I work with Sørebø

et al.

(2009)

The degree of the desire to feel connected to others PR2: I get along with people at work

PR3_R: I pretty much keep to myself when I am at

work

PR4: I consider the people I work with to be my

friends

PR5: People at work care about me

PR6_R: There are not many people at work whom I

am close to

PR7_R: The people I work with do not seem to like

me much

PR8: People at work are pretty friendly toward me

Perceived autonomy (PA) PA1: I feel like I can make a lot of inputs in deciding

how I use VLE in my teaching profession

Sørebø

et al.

(2009)The degree of the desire to self-initiate and self-

regulate own behavior

PA2_R: I feel pressured at using VLE in my teaching

profession

PA3: I am free to express my ideas and opinions on

using VLE in my teaching profession

PA4_R: When I am using VLE, I have to do what I am

told

PA5: My feelings toward VLE are taken intoconsideration at work

PA6: I feel like I can pretty much use VLE as I want

to at work

PA7_R: There is not much opportunity for me to

decide for myself how to use VLE in my teaching

profession

Perceived competence (PC) PC1_R: I do not feel very competent when I use VLE

in my teaching profession

Sørebø

et al.

(2009)The degree of the desire to feel effective in attaining

valued outcomes

PC2: The other colleagues tell me I am good at using

VLE in my teaching profession

PC3: I have been able to learn interesting new skills

(continued on next page)


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Appendix A (continued)


in VLE through my profession

PC4: Most days I feel a sense of accomplishment

from working with VLE

PC5_R: As a teacher I do not get much of a chance to

show how capable I am in VLE

PC6_R: When I am using VLE I often do not feel very

capable

Perceived media richness (PMR) PMR1: The VLE features allow me to give and

receive timely feedback

Fernandez

et al.

(2013)The degree to which a teacher believes that VLE is

capable of carrying a wide variety of media based

on the criteria of capacity in immediate feedback,

personal focus, multiple cues and language

variety

PMR2: The VLE features allow me to tailor my

teaching to my own personal requirements

PMR3: The VLE features allow me to communicate a

variety of different cues (such as emotional tone,

attitude, or formality) in my teaching

PMR4: The VLE features allow me to use a rich andvaried language in my teaching

PMR5: I could easily explain concepts using the VLE

features

PMR6: The VLE features help me to communicate

quickly

PMR7: The VLE features help me to better

understand others

VLE content design (VCD) VCD1: The level of difficulty of the learning contents

is appropriate

Lee et al.

(2009)

The degree to which learning contents are designed

and developed to fit students’ needs

VCD2: The content of assignments is easy to

understand

VCD3: The amount of learning contents is

appropriate

VCD4: The delivery schedule of learning contents is

flexible

VCD5: VLE provides individualized learning

management

VCD6: VLE provides a variety of learning methods

VLE interactivity (VI) VI1: Interacting with VLE is like having a

conversation with a sociable, knowledgeable and

warm representative from my school

Chen et al.

(2007)

The degree of interaction that a teacher perceives as

having with the VLE system, and the extent to

which the VLE system is perceived to be

responsive to his/her needs

VI2: I feel as if VLE talked back to me while I was

navigating the VLE

VI3: I perceive the VLE to be sensitive to my needs

for information

VI4: My interaction level with the VLE was high

VI5: I don’t interact with the VLE much

School support (SS) SS1: My school i s committed to a vision of using VLE

in teaching

Lai and

Chen

(2011)The degree to which a teacher believes that his/her

school is committed to successful VLE

implementation and use

SS2: My school is committed to supporting my

efforts in using VLE for teaching

SS3: The school strongly encourages the use of VLE

for teaching


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Appendix A (continued)


SS4: My school will recognize my efforts in using

VLE for teaching

Attitude toward knowledge sharing (AT) AT1: Sharing of my knowledge with other teachers

is always good

Chow and

Chan

(2008)The degree of a teacher having positive feelingsabout sharing ideas and resources with those

with whom they have developed a close

relationship

AT2: Sharing of my knowledge with other teachersis always beneficial

AT3: Sharing of my knowledge with other teachers

is always an enjoyable experience


is always valuable to me


is always a wise move

Trust in website (TW) TW1: I think the VLE website is secure Hsu et al.

(2014)The degree of the belief resulting from the reliability

and reliance of the VLE website

TW2: I think the VLE website is reliable

TW3: I think the VLE website is trustworthy

Behavioral Intention (BI) BI1: I intend to use VLE in the coming months Venkatesh

et al.

(2003)

The degree to which a teacher has formulated

conscious plans to perform or not perform some

specified future behavior

BI2: I predict I would use VLE in the future

BI3: I plan to use VLE in the future

Appendix B. Cross loadings


AT1 0.9591 0.4997 0.4555 0.3896 0.4087 0.6159 0.5697 0.5240 0.4096 0.3275

AT2 0.9718 0.5144 0.4713 0.3999 0.4198 0.6308 0.5844 0.5414 0.4229 0.3465

AT3 0.9632 0.5264 0.4854 0.4447 0.4484 0.6299 0.5756 0.5475 0.4535 0.3851

AT4 0.9731 0.5151 0.4604 0.4137 0.4182 0.6347 0.5945 0.5168 0.4257 0.3489

AT5 0.9606 0.5142 0.4580 0.4016 0.4073 0.6115 0.5697 0.5190 0.4153 0.3487

BI1 0.5073 0.9538 0.6694 0.6544 0.6551 0.3961 0.6563 0.6479 0.6690 0.6244

BI2 0.5185 0.9775 0.6591 0.6510 0.6516 0.4233 0.6564 0.6657 0.6628 0.6166

BI3 0.5182 0.9689 0.6519 0.6363 0.6518 0.4262 0.6471 0.6557 0.6576 0.6029

PA1 0.4461 0.5768 0.8713 0.6506 0.6109 0.3860 0.5108 0.5319 0.6382 0.5716

PA3 0.4208 0.6053 0.8834 0.6471 0.6782 0.3541 0.5397 0.5530 0.6713 0.6276

PA6 0.4009 0.6083 0.8684 0.7109 0.6405 0.3408 0.4884 0.5635 0.6420 0.6140PC2 0.2584 0.4477 0.5878 0.7981 0.5327 0.2154 0.3572 0.4040 0.5150 0.5231

PC3 0.4572 0.6426 0.7019 0.8978 0.6908 0.4256 0.5632 0.5830 0.6841 0.6513

PC4 0.3670 0.6297 0.7010 0.9119 0.6801 0.3446 0.4942 0.5744 0.6601 0.6596

PMR1 0.3950 0.6048 0.6622 0.6569 0.8950 0.3488 0.5393 0.5710 0.7522 0.7370

PMR2 0.4089 0.6534 0.7049 0.7008 0.9007 0.3583 0.5424 0.5709 0.7777 0.7208

PMR3 0.3617 0.5789 0.6477 0.6323 0.8979 0.3064 0.4853 0.5707 0.7160 0.7344

PMR4 0.4318 0.6147 0.6640 0.6549 0.8906 0.3728 0.5326 0.5806 0.7396 0.6758

PMR5 0.4124 0.6200 0.6946 0.6812 0.9035 0.3549 0.5363 0.5740 0.7536 0.7112

PMR6 0.4068 0.6193 0.6612 0.6698 0.9117 0.3430 0.5256 0.5758 0.7586 0.7556

PMR7 0.3112 0.5399 0.5779 0.6199 0.8740 0.2762 0.4621 0.5475 0.6852 0.7105

PR2 0.6088 0.4582 0.4441 0.4277 0.4125 0.8726 0.4910 0.4514 0.4658 0.3965



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Appendix B (continued)


PR4 0.6071 0.3795 0.3613 0.3298 0.3126 0.9251 0.4720 0.3918 0.3777 0.2758

PR5 0.5341 0.3390 0.3427 0.3066 0.3268 0.8764 0.4081 0.3600 0.3598 0.2859

PR8 0.5315 0.3298 0.2944 0.3003 0.2705 0.8688 0.3844 0.3460 0.3264 0.2573

SS1 0.5490 0.6302 0.5343 0.5110 0.5273 0.4755 0.9510 0.5466 0.5573 0.4857

SS2 0.5681 0.6495 0.5598 0.5400 0.5640 0.4845 0.9521 0.5495 0.5819 0.5209

SS3 0.5856 0.6374 0.5357 0.5085 0.5252 0.4892 0.9491 0.5521 0.5584 0.4743

SS4 0.5503 0.6227 0.5758 0.5161 0.5571 0.4264 0.9063 0.5577 0.5541 0.4785

TW1 0.5288 0.6275 0.5904 0.5747 0.5853 0.4139 0.5439 0.9532 0.5756 0.5458

TW2 0.5252 0.6692 0.6169 0.5921 0.6352 0.4236 0.5791 0.9726 0.6280 0.6018

TW3 0.5372 0.6710 0.6151 0.5960 0.6228 0.4398 0.5773 0.9735 0.6148 0.6052

VCD1 0.3649 0.5983 0.6650 0.6326 0.7461 0.3611 0.5237 0.5660 0.8998 0.7321

VCD2 0.3776 0.6277 0.6810 0.6595 0.7611 0.3689 0.5610 0.5667 0.9222 0.7276

VCD3 0.3853 0.6348 0.6836 0.6790 0.7729 0.3791 0.5500 0.5905 0.9302 0.7540

VCD4 0.3735 0.6005 0.6483 0.6451 0.7410 0.3811 0.5112 0.5390 0.8938 0.7071

VCD5 0.4623 0.6353 0.6898 0.6471 0.7196 0.4443 0.5585 0.5594 0.8902 0.6493

VCD6 0.4348 0.6356 0.6812 0.6528 0.7580 0.4376 0.5550 0.5920 0.9046 0.7084

VI1 0.3770 0.6269 0.6486 0.6512 0.7451 0.3561 0.5088 0.5787 0.7630 0.9003

VI2 0.3112 0.5490 0.5905 0.6199 0.7114 0.3066 0.4363 0.5342 0.6876 0.9272

VI3 0.3421 0.5876 0.6497 0.6607 0.7593 0.3423 0.4996 0.5772 0.7430 0.9351

VI4 0.2942 0.5519 0.6313 0.6484 0.7144 0.2564 0.4533 0.5133 0.6702 0.8822

Appendix C. Confidence intervals for HTMT inference test

Original

sample (O)

Sample

mean (M)

Standard

deviation

(STDEV)

T statistics

(|O/STDEV|)

p Values 2.50% 97.50%

BI? AT 0.560 0.562 0.037 14.952 0.000 0.490 0.630PA?AT 0.499 0.497 0.039 12.824 0.000 0.411 0.567

PA? BI 0.705 0.704 0.035 20.018 0.000 0.626 0.768

PC? AT 0.510 0.509 0.041 12.434 0.000 0.433 0.588

PC? BI 0.702 0.702 0.038 18.488 0.000 0.621 0.769

PC? PA 0.898 0.896 0.028 31.974 0.000 0.842 0.949

PMR ?AT 0.462 0.459 0.038 12.022 0.000 0.385 0.534

PMR ? BI 0.690 0.689 0.027 25.167 0.000 0.635 0.735

PMR ? PA 0.799 0.798 0.023 35.350 0.000 0.753 0.837

PMR ? PC 0.767 0.767 0.025 31.073 0.000 0.722 0.819

PR ?AT 0.706 0.707 0.028 25.419 0.000 0.652 0.759

PR ? BI 0.511 0.513 0.039 13.023 0.000 0.433 0.585

PR ? PA 0.492 0.492 0.041 12.097 0.000 0.400 0.566

PR ? PC 0.493 0.494 0.042 11.861 0.000 0.411 0.577PR ? PMR 0.439 0.439 0.037 11.806 0.000 0.363 0.513

SS?AT 0.555 0.557 0.036 15.258 0.000 0.482 0.626

SS? BI 0.615 0.614 0.037 16.700 0.000 0.540 0.681

SS? PA 0.552 0.551 0.039 14.233 0.000 0.469 0.625

SS? PC 0.517 0.516 0.042 12.427 0.000 0.433 0.599

SS? PMR 0.540 0.537 0.036 14.996 0.000 0.467 0.606

SS? PR 0.525 0.527 0.040 13.163 0.000 0.449 0.601

TW?AT 0.527 0.528 0.041 12.746 0.000 0.443 0.609

TW? BI 0.674 0.673 0.033 20.596 0.000 0.609 0.736

TW? PA 0.617 0.617 0.040 15.589 0.000 0.535 0.690

TW? PC 0.595 0.596 0.044 13.453 0.000 0.503 0.673


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Appendix C (continued)

Original

sample (O)

Sample

mean (M)

Standard

deviation

(STDEV)

T statistics

(|O/STDEV|)

p Values 2.50% 97.50%

TW? PMR 0.611 0.611 0.033 18.724 0.000 0.545 0.669

TW? PR 0.489 0.491 0.043 11.473 0.000 0.401 0.570

TW? SS 0.515 0.514 0.039 13.275 0.000 0.425 0.585

VCD?AT 0.493 0.489 0.036 13.604 0.000 0.420 0.557

VCD? BI 0.673 0.672 0.032 21.175 0.000 0.609 0.729

VCD? PA 0.813 0.811 0.029 28.399 0.000 0.754 0.860

VCD? PC 0.751 0.749 0.031 24.029 0.000 0.685 0.811

VCD? PMR 0.816 0.813 0.021 38.026 0.000 0.769 0.851

VCD? PR 0.477 0.476 0.040 12.040 0.000 0.402 0.549

VCD? SS 0.565 0.563 0.037 15.156 0.000 0.485 0.631

VCD? TW 0.598 0.595 0.037 16.024 0.000 0.521 0.666

VI?AT 0.388 0.385 0.041 9.541 0.000 0.309 0.466

VI? BI 0.672 0.670 0.033 20.523 0.000 0.603 0.730

VI? PA 0.755 0.753 0.033 23.201 0.000 0.686 0.812

VI? PC 0.733 0.732 0.034 21.534 0.000 0.662 0.794

VI? PMR 0.833 0.831 0.020 40.705 0.000 0.789 0.867

VI? PR 0.393 0.394 0.041 9.627 0.000 0.311 0.474

VI? SS 0.494 0.489 0.041 12.004 0.000 0.410 0.571

VI? TW 0.597 0.596 0.035 17.287 0.000 0.529 0.664

VI?VCD 0.805 0.802 0.027 30.068 0.000 0.745 0.850

Note: PR = perceived relatedness, PA = perceived autonomy, PC = perceived competence, PMR = perceived media richness, VCD = VLE content design,

VI = VLE interactivity, SS = school support, AT = attitude toward knowledge sharing, TW = trust in website, BI = behavioral intention.

Appendix D. Unidimensionality analysis

Original sample

(O)

Sample mean

(M)

Standard deviation

(STDEV)

Standard error

(STERR)

T statistics (|O/

STERR|)

AT1 AT 0.9591 0.9592 0.0071 0.0071 135.8859

AT2 AT 0.9718 0.9717 0.0031 0.0031 312.1822

AT3 AT 0.9632 0.9632 0.0040 0.0040 242.5058

AT4 AT 0.9731 0.9729 0.0026 0.0026 372.0129

AT5 AT 0.9606 0.9604 0.0052 0.0052 183.7536

BI1 BI 0.9538 0.9539 0.0068 0.0068 140.4123

BI2 BI 0.9775 0.9777 0.0029 0.0029 340.2765

BI3 BI 0.9689 0.9690 0.0054 0.0054 180.0305

PA1 PA 0.8713 0.8706 0.0138 0.0138 62.9479

PA3 PA 0.8834 0.8832 0.0107 0.0107 82.3683

PA6 PA 0.8684 0.8684 0.0106 0.0106 81.9919PC2 PC 0.7981 0.7971 0.0210 0.0210 38.0060

PC3 PC 0.8978 0.8981 0.0074 0.0074 121.2815

PC4 PC 0.9119 0.9115 0.0069 0.0069 132.0080

PMR1 PMR 0.8950 0.8942 0.0087 0.0087 103.1478

PMR2 PMR 0.9007 0.8998 0.0078 0.0078 114.9893

PMR3 PMR 0.8979 0.8972 0.0082 0.0082 109.1851

PMR4 PMR 0.8906 0.8900 0.0115 0.0115 77.2900

PMR5 PMR 0.9035 0.9028 0.0076 0.0076 118.6838

PMR6 PMR 0.9117 0.9112 0.0061 0.0061 150.6774

PMR7 PMR 0.8740 0.8726 0.0107 0.0107 81.7355

PR2 PR 0.8726 0.8734 0.0115 0.0115 76.1459



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Appendix D (continued)

Original sample

(O)

Sample mean

(M)

Standard deviation

(STDEV)

Standard error

(STERR)

T statistics (|O/

STERR|)

PR4 PR 0.9251 0.9257 0.0063 0.0063 146.0666

PR5 PR 0.8764 0.8766 0.0125 0.0125 70.0041

PR8 PR 0.8688 0.8690 0.0134 0.0134 64.6017

SS1 SS 0.9510 0.9512 0.0044 0.0044 215.1947

SS2 SS 0.9521 0.9521 0.0053 0.0053 180.3103

SS3 SS 0.9491 0.9488 0.0054 0.0054 177.3802

SS4 SS 0.9063 0.9061 0.0099 0.0099 91.5978

TW1 TW 0.9532 0.9532 0.0076 0.0076 125.1029

TW2 TW 0.9726 0.9728 0.0039 0.0039 248.2204

TW3 TW 0.9735 0.9734 0.0036 0.0036 274.1178

VCD1 VCD 0.8998 0.9000 0.0079 0.0079 113.8131

VCD2 VCD 0.9222 0.9215 0.0081 0.0081 113.9101

VCD3 VCD 0.9302 0.9295 0.0067 0.0067 139.6101

VCD4 VCD 0.8938 0.8931 0.0119 0.0119 75.3971

VCD5 VCD 0.8902 0.8899 0.0130 0.0130 68.3075

VCD6 VCD 0.9046 0.9047 0.0081 0.0081 112.2875

VI1 VI 0.9003 0.9000 0.0083 0.0083 107.9352

VI2 VI 0.9272 0.9269 0.0079 0.0079 116.8107

VI3 VI 0.9351 0.9350 0.0066 0.0066 140.8492

VI4 VI 0.8822 0.8820 0.0094 0.0094 93.4067

Note: *** p < 0.001; PR = perceived relatedness, PA = perceived autonomy, PC = perceived competence, PMR = perceived media richness, VCD = VLE content

design, VI = VLE interactivity, SS = school support, AT = attitude toward knowledge sharing, TW = trust in website, BI = behavioral intention.

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Predicting the Acceptance of Cloud-based Virtual Learning Environment the Roles of Self Determination and Channel Expansion Theory