paper title (use style: paper title) - er · web viewthis paper proposes and evaluates an...

An Investigation Into Factors Affecting Acceptance of eLearning Systems and Websites

Dhiya Al-Jumeily1 and Abir Hussain1

1Applied Computing Research Group, Liverpool John Moores University,

Liverpool, [email protected] , [email protected]

Abstract

This paper investigates the use of Technology Acceptance Model (TAM) to understand factors affecting acceptance of eLearning Systems and related resources by students and teachers. In particular, the impact of cultural factors on the adoption of eLearning methodologies is studied. eLearning facilitates extra channels of learning support and research has shown that, when utilized as part of an established teaching mechanism, can improve learning process. Despite this perceived usefulness, there is still a resistance to widespread adoption of eLearning and considerable difficulty in determining its correct use, and the correct measure of use as a successful learning (support) tool.

Keywords- Component, eLearning, Technology Acceptance Model, Cultural Impacts on Pedagogy;

1. Introduction

Computing technology is, in one form or another, now near-ubiquitous and pervades in all walks of life. Previously, computing users could be expected to have a degree of technical expertise in their chosen computing platform and a preparedness to invest time in learning to use a new software package or utility. However, the current computing scene requires that technology and interfaces to it support the increasing expectations, knowledge and skills of their particular users. This extends to eLearning and learning support, such that the software supports the learner experience, rather than create another learning outcome for the learner to fulfil. One of the most important principles in Human Computer Interaction is ‘Usability’ which is about designing systems so that they are easy to learn and safe, enjoyable and flexible to use. Recognising the importance of both Usefulness and Ease of Use of Technology, the TAM was proposed by (Davis 1989) as a model that can be used to predict and quantify the acceptance of new technology. This research has developed with an eye on the role of culture on the acceptance of a given technical solution – with a particular focus on the cultural factors in the Middle-East.This paper proposes and evaluates an adaptation of the TAM for eLearning, investigating relationships between culture or social factors and the acceptance of an eLearning system. A number of studies were carried out to compare cultures, investigating social and cultural differences affecting people’s acceptance of eLearning as a viable mechanism for learning and learning support.

TAM was selected as a suitable basis for an evaluation of eLearning acceptance; proposing that a person’s intention and willingness to use any given new technology depends on two main factors: Perceived Usefulness and Perceived Ease of Use. The main social and cultural factors considered and identified as inputs to the eLearning TAM are: Tangibility (perceived benefit of system use), Subjective Norm (peer or social pressure towards system use), Individualism vs. Collectivism, Power Distance (acceptance of an (un)fair distribution of power), and Uncertainty Avoidance (tendency towards familiar structures and systems).A quantitative research method was followed to allow capturing data for statistical analysis. An online questionnaire was used for this purpose in order to collect user response data to allow the comparison between two different cultures. Participant samples from the UK and the Middle-East (Oman) were targeted. Over 60 eLearners took part in the online questionnaire. The initial results showed, as suggested by the TAM, that there is a clear positive correlation between a perceived usefulness and Ease of Use of an eLearning system, and the willingness to use it. The small-scale study highlights perceived usefulness as the most important criterion for technology acceptance for eLearning websites. Statistical analysis shows a strong positive correlation between the cultural factor of subjective norm (i.e. the perceived social pressure for a given system) and the willingness to use it in both UK and Middle-East-based samples. Analysing cultural differences between the regions show a significant bias towards effects of subjective norm on the willingness to use a system; the Middle-East sample shows a far stronger correlation of social pressure, while the UK showed a stronger correlation between tangibility (i.e. observed benefits of system use) and the intention to use a system.The following section gives a brief primer of the traditional Arab culture and its acceptance of technological innovations.

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2. Technology and the Middle-East Culture

A number of studies have been undertaken on the application of Technology Acceptance Models for eBusiness or eHealth (e.g. Succi Walter, Z 1999, Khushman 2009), but there is a need for a similar application of Technology Acceptance Studies for eLearning. Furthermore, it has already been recognised that culture plays an important role in the acceptance of new technologies. In general new “eTechnologies” are developed in the western world, and as a result, these technologies are designed with the “home market” users in mind. Factors resulting from this can adversely affect the adoption of these technologies in developing countries, such as the Middle-East or Arab world. Fandy (Fandy 2000) identifies that many studies put great significance on a need for technology transfer to developing countries, but few studies actually investigate how cultural norms and other factors present in Arab countries could influence user acceptance and uptake of new technologies and systems. A number of studies have linked culture with attitudes towards website usage and acceptance, perceived usefulness, perceived Ease of Use, website literacy, and website usage (e.g. Watson et a1. 1997). According to Hofstede (Hofstede 1991), Arab cultures exhibit a high degree of uncertainty avoidance. In a high uncertainty avoidance culture, people will tend to consider a new system or method of working (such as computer technology) to be a risky or uncertain choice; as such, they will try to resist the changes in their life and work style and avoid the new system and uncertainty.Hill et al, (Hill, et al 1998) state that Arab cultures tend towards communication on a face-to-face basis, and therefore resist new technologies that move away from this traditional communication model. However, more recent studies, such as from Alkadi (Alkadi 2005) argue that even with a greater pervasion of new technologies, the cultures still exhibit resistance to replacing long-held traditions and values, and long-held social norms for interactions and community. Applying a formal TAM, Rose and Straub (Rose and Straub 1998) studied the main factors affecting technology adoption in five Arab countries. They indicate that an increase in perceived Ease of Use and perceived usefulness may increase the adoption of IT in the Arab world. However, Arab culture is collectivist and family-oriented such that the Internet and WWW can be seen as a possible intrusion and threat to family and social life. This paper investigates the use of a TAM to understand factors, extending to consider the wider cultural inputs outlined above that affect acceptance of eLearning Systems and Websites. The next section outlines the development of studies of, and methodologies for Technology Acceptance modelling and prediction.

3. Technology Acceptance

The study of technology acceptance is concerned with identifying and measuring factors that influence a group of users’ acceptance of a new or adapted technological system. Technology acceptance involves modelling the process a user (group) follows in deciding to use a system through to integrating it into their usage pattern. According to (Fishbein and Ajzeen 1975, Succi 1999, Davis 1989), the main behavioural change models and theories applicable to technology acceptance models are:

Theory of Reasoned Action Theory of Planned Behaviour Technology Acceptance Model

The Theory of Reasoned Action (Fishbein and Ajzeen 1975) proposes that the behaviour of people is affected by their beliefs and the beliefs of others, which are combined to describe an intention to perform a particular action. For example: the intention to use an Interaction Technology can be predicted by the individual’s attitude, combined with the perceived societal attitude towards that Technology. In short, if a person has a desire to use a technology, they are likely to use it! The Theory of Reasoned Action is seen as a general model, since it does not identify attitudes towards a particular behaviour (e.g. using an IT solution) that can then be used to predict the use of IT (Succi 1999, Davis 1989). Therefore, these parameters need to be identified and specified for each case. The theory of Reasoned Action was extended by (Ajzen 1985,1991) to describe the theory of Planned Behaviour, outlined in Figure 1. (Here Figure 1 shows different reference than (Ajzen 1985,1991) which is Miller (2005)). As shown in Figure 1, in the theory of planned behaviour, a Behavioural Intention concept leads to a person performing a particular behaviour. It is primarily dependent on three main factors:

Attitude: the person’s attitude towards a particular behaviour (e.g. to use a system). Subjective Norm: a person’s beliefs about others’ attitudes regarding and expectations of a particular behaviour. Perceived Behavioural Control: the person’s beliefs about factors affecting the ease of undertaking, or performance

expected of a specific behaviour.

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Figure 1. Theory of Planned Behaviour : Miller (2005)

These three factors are combined to affect an individual’s intention to behave in a certain way – such as in this study, towards the use of a particular system. This theory of planned behaviour was still considered to be difficult to use in predicting intentions and behaviours; in that it is difficult to identify and measure some of the main factors. However, it is still considered to be an improved theory on the Theory of Reasoned Action (Miller 2005).

3.1 A Technology Acceptance Model

The TAM was proposed by (Davis 1989), as a model that can be used to predict the acceptance of new technology; the intention to use it and therefore, the likelihood of use of the new technology. The TAM is used as a primary model for predicting technology acceptance in the field of Information System, because it uses factors that can be easily measured and can be specified adequately for application to new technology. The TAM shown in Figure 2 proposes that people’s attitudes towards a new Technology depend on two main factors: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Perceived usefulness quantifies how much people think that the new technology will be useful in helping them to perform tasks and achieve desirable goals or results. If people perceive a new technology to be useful then they will have a positive attitude towards it and are more likely to accept it. This in turn increases their intention to use it and therefore increases their likelihood of actually adopting the new technology.

Figure 2. TAM ( Davis 1989 )

Perceived Ease of Use describes how people perceive a technology to be simple to learn and use, interesting and attractive for them to use. This may include factors such as how clear it is to newcomers and how it will allow them to ‘do the job’ quickly and efficiently. If people perceive technology as easy to use, that will develop their attitude and intention to use it and adopt it. Both PU and PEOU are quantifiable variables that can be measured and affected by work in the Usability and Human Computer Interaction (HCI) fields. This helps to make TAM attractive to many researchers to further specify and improve the model. It has been subject to different investigations, such as (Lingyum and Dong 2008).

The TAM was extended to integrate variables from existing behavioural change models, including social factors considered to affect both PU and PEOU, by (Venkatesh and Davis 2000). This extended TAM is shown in Figure 3:

The Extended TAM includes the following social factors considered to influence behaviour change, specifically PU: Subjective Norm

Image Voluntariness and Experience Job Relevance, Output Quality, and Result Demonstrability.

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Behaviour

Perceived Behavioural

Control

Subjective Norm

Attitude

Behavioural Intention

Actual Use

Behavioural IntentionAttitude

Perceived Ease of use

Perceived Usefulness

External Variables

Figure 3. Extended TAM Model ( TAM2, Venkatesh and Davis 2000)

The extended TAM examines some of the External Variables outlined in the original TAM, and places a much greater emphasis on the input of social factors on a potential user’s perception of a system’s usefulness. Subjective measures such as societal norms and image may be as much a product of a target market’s culture as it is a measure of the technological system itself. The next section examines cultural factors relevant to technology use and acceptance.

4. Cultural factors and Theories

Cultural factors in different societies and countries have been studied for a long time (Del Galdo 1996, Hofstede 1980, Hall 1982, Tropennaars 1993). It is noted that as culture affects behavioural change in general, it also plays a role in the use of new technology. It is accepted that culture may be defined in various ways, but for the scope of this paper, culture is considered to describe:

Common behavioural traits and way of life Community or group interaction with group members and/or environment Common influence of historical and/or non-tangible factors such as traditions, religion, history and language.

Hofstede (Hofstede, G. 1991) proposed Four Cultural Dimensions that identify, define and compare different national cultures. These dimensions are described in the following subsections.

4.1 Individualism / Collectivism

This dimension describes, in a spectrum, how individuals are concerned with themselves and others in their group. In a high-individualistic society, individuals are primarily concerned with their own success. Decisions are influenced primarily by personal consequences, and little consideration is given to loyalty or effects on other individuals, except very close ones such as families. Individualistic society values the freedom, personal (or alone!) time and encourages own-initiative action, self-help, competition, change and free enterprise. Individualistic society tends to communicate in low-context communication forms, where communicants are not required to have shared context or understanding to grasp a given message; the message is explicit in itself (Stengers et al, 2005; and Kang and Araujo, 2006). Conversely, a society with a high level of collectivism has strong bonds between its people who would typically engage in greater social interaction. Social interaction would extend to shared welfare and care, and consensus-based decision making, or empathetic decision making – where the impact of a decision on others is considered. This kind of society puts more importance on tradition, religion, history and accepted consensus. Communication is generally high-context; communicants are often required to have a shared unspoken understanding based on experience in order to grasp the content of a message; a full meaning is implied given the circumstances.

4.2 Power Distance

This dimension describes the way a given society manages and tolerates social inequalities. Societies with High Power Distance usually have very high leadership powers with central control and many layers in the hierarchy or organisation in society. People are influenced highly by leader(s), managers, or parents (Hofstede 1991, and Marcus and Gould, 2000). This usually extends to a large difference in salary and status between people at the different levels of society. In societies with a Low Power Distance people there is less central control and hierarchical layers in organisations. People work closely with their leaders, managers or

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TAMResult

Demonstrability

Output Quality

Job Relevance

Image

Subjective norm

Experience Voluntariness

Usage behaviour

Intention to Use



parents and have some informal relationships with them. Generally, there is lower inequality; less difference in salary or status between people from different social levels.

4.3 Uncertainty Avoidance

This dimension measures how much a culture can tolerate an uncertain situation, or take risks – engaging in actions that can have unpredictable outcomes (Nakata and Sivakumar 1996). Hofstede (Hofstede 1991) shows different cultures have different levels of certainty avoidance; such that people in cultures with low uncertainty avoidance are more willing to explore unknown situations or approaches – and embrace new systems and ways of working. However, people in high uncertainty avoidance cultures place importance on tried, tested and accepted methods; preferring to stick to what they are familiar with, avoiding risks and engaging in new uncertain situations.

4.4 Masculinity / Femininity

This dimension is related to a cultural definition of gender roles (Hofstede 1991). Masculine cultures tend to promote competitions, performance and merit-based success. In such cultures it is important to be strong and ambitious; hard work and high rewards. Aggression is considered an acceptable solution to conflict or competition. Equally, there are clear gender roles with little-to-no overlap (Kang and Araujo, 2006). However, cultures with low masculinity or high femininity, exhibit opposite tendencies. There tends to be greater overlap between gender roles – collaborations and negotiations are promoted as the way to work; economic reward takes second place to the protection of people and the environment. Feminine cultures place a great importance on relationships and trust rather than performance; nepotism may trump merit in a business setting. Hofstede’s cultural dimensions have been widely used to study cultural factors and difference; enabling study and data analysis to be conducted to quantify and compare these dimensions across different cultures. Hofstede conducted an extension study which compared these four dimensions across many countries and cultures. Table 1 below shows, according to Hofstede’s study, the score of UK and Arab countries on various cultural dimensions. This shows how different UK and Arab cultures can be. UK culture is high on individualism and low on Power distance and uncertainty avoidance, while Arab culture is low on individualism and high on power distance and uncertainty avoidance. The next section gives a brief overview of the proposed model evaluated in this paper and how it integrates the factors described in this and the previous section.

TABLE 1: HOFSTEDE DIMENSIONS: UK AND ARAB COUNTRIES

Country Power Distance Individualism Masculinity Uncertainty

AvoidanceUnited Kingdom 35 89 66 35

Arab World 80 38 52 68

5. Methodology

The work in this paper further extends the TAM (Davis 1989, and Venkatesh and Davis 2000), to create a proposed TAM for eLearning. The proposed TAM includes cultural and social factors relevant to eLearning and related technology. It also takes input from a recently proposed TAM for eBusiness from Khushman (Khushman 2009), specifically the concept of measures to quantify the quality of a given “eSystem”. As it can be seen in Figure 4, the proposed eLearning TAM has two main parts. The first part, inherited from the Davis model (Davis 1989), is based on the two factors of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). The second part concurs with the Ventakesh and Davis Extended TAM (Venkatesh and Davis 2000) in identifying that PU and PEOU are also influenced by outside factors.

Figure 4. the eLearning TAM including cultural and social factors

In the eLearning TAM, the authors integrate inputs measuring System Quality and Cultural factors. The factors shown in Figure 4 can be further classified and defined as follows:

eLearning System and Website Quality

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Hofstede Cultural factors

Social and Cultural Factors

eLearning Systems and Websites Quality

Intention to Use



eLearning System quality eLearning Information quality eLearning websites and System Attractiveness

Cultural factors Tangibility Subjective Norm

Hofstede’s “Cultural Dimensions” factors Power Distance Collectivism Uncertainty Avoidance

These combined input factors have been used within this investigation as existing literature and this research’s own findings noted that they appear the more relevant factors to eLearning. This is particularly notable with the cultural factors; Tangibility and the Subjective Norm were found to be particularly influential on a potential user’s perception. Trust is clearly a significant cultural factor, but is in the proposed system, considered as an extension of a subjective norm and more relevant for assessments of business, security or similar system applications. Several factors have been included from Hofstede’s Cultural Dimensions as discussed in Section 4. Results presented in the next section shows the UK and Middle-East region differing in Hofstede scores for Power Distance, Collectivism and Uncertainty avoidance – these specific factors have been included in the proposed model to investigate the differing effects of culture on acceptance of eLearning technology.

5.1Assessing the model’s variable selection

For the purpose of this study, quantitative data-capture-based research has been undertaken to allow statistical analysis. This analysis is intended to measure the influence and importance of the different factors in eLearning Technology Acceptance model. The data capture method selected was to gather data using an Online Questionnaire. The questionnaire is set out to produce quantitative data on the following key model variables:

eLearning system and website Quality eLearning information quality eLearning system attractiveness Cultural factors Tangibility Subjective Norm influence The three selected Hofstede cultural dimensions: Power Distance, Collectivism and Uncertainty Avoidance User-perceived Ease of Use (PEOU) User intention to use eLearning Systems

The online questionnaire allows easy data capture from participants around the world – specifically in the UK and the Middle-East - and has been created in the Questback online questionnaire creator and hosting service. While Questback captures the data in a proprietary form, response sets can be exported into a range of formats for analysis in statistical analysis tools such as SPSS. In order to create a suitable sample set, approximately equally-sized groups were required from the UK and the Middle-East. The online questionnaire was distributed electronically to students and teaching staff in Liverpool (UK) and a similarly-selected group in Oman (Middle-East). While the questionnaire collected identity data, this was automatically obfuscated by the Questback system, and was used only to provide information about the subject’s cultural base. One significant potential bias present in the sample set occurs as a result of many samples already being engaged (either as student or staff) in the Higher Education profession. Additionally, given the online nature of the questionnaire, it is assumed that the sample user has at least a user-level degree of familiarity with web technology and similar “eSystems”.

5.2 Questionnaire Design

The question bank used in the online questionnaire is designed to capture data for the identified eLearning Technology acceptance variables, as well as the cultural profile of the participants. The acceptance variables were targeted with 34 questions, while the remaining questions explored the user profile. A total of 41 questions gave an estimated participant completion time of under 10 minutes, to encourage people to finish the questionnaire. Several questions retargeted the same core variable, but the question delivery order was randomised in an attempt to minimise repeat answer bias. Answer choices were presented as a 5-point Likert scale (Strongly Disagree through to Strongly Agree).

5.3 Results, Discussion and Evaluation

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This section presents details of the main results that were obtained from, the questionnaires data. Different statistical analysis was carried out using the SPSS statistical Software.

5.3.1 Participant Profile AnalysisThe tables below shows the results of the questions answered for the section on Participants profile for the UK and Middle-East samples. In total 61 people took part in the online questionnaires, 29 participants from UK and 32 participants from Middle-East. Details description of the sample participated in the experiments are described in Table [2-8].

TABLE 2: GENDER

UK Sample Middle-East sample

Alternatives Percent Value Percent Value

1 Female 48,0 % 14 25,0 % 8

2 Male 51,7 % 15 75,0 % 24

Total 29 32

TABLE 3: AGE GROUP



1. less than 20 year 20,6 % 6 0.0 % 0

2. 20-29 year 48,3 % 14 9,4 % 3

3. 30-39 years 10,3% 3 50,0% 16

4. 40-49 years 13,7% 4 25,0% 8

5. 50 years or older 6,9% 2 15,6% 5

Total 29 32

TABLE 4: WHAT IS YOUR EDUCATIONAL BACKGROUND


Alternatives Percent Value Percent Value1. High School Certificate 31,0 % 9 3.1 % 1

2. Bachelor degree 20,7 % 6 18,8 % 6

3. Postgraduate degree 10,3% 3 65,6% 21

4. Other 38,0% 11 12,0% 4

Total 29 32

TABLE 5: FOR HOW LONG YOU HAVE BEEN USING THE INTERNET?



Not at all 0,0 % 0 0,0 % 0

Less than 1 year 0,0 % 0 0,0 % 0

1-5 years 3,4% 1 3,1% 1

5-10 years 44,9% 13 12,5% 4

More than 10 years 51,7% 15 84,4% 27

Total 29 32

TABLE 6: HOW MANY TIMES DO YOU USE THE INTERNET DURING A WEEK?

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Not at all 0,0 % 0 0,0 % 0

Once per week 3,4 % 1 3,1 % 1

2-3 times per week 0,0% 0 9,4% 3

Once a day 13,8% 4 15,6% 5

Several times a day 82,8% 24 71,9% 23

Total 29 32

TABLE 7: HOW FREQUENTLY DO YOU VISIT WEBSITES ASSOCIATED WITH ELEARNING



Not at all 10,3 % 3 6,3 % 2Less than once a month 10,3 % 3 18,8 % 6

1 to 2 times a month 10,3% 3 34,4% 11

once a week 44,8% 13 18,8% 6

once a day 28,6% 8 21,9% 7

Total 29 32

TABLE 8. WHAT IS YOUR OCCUPATION



Student 75,9 % 22 3,1 % 1

Professional 13,8 % 4 50,0 % 16Other, please

specify 10,3% 3 46,9 15

Total 29 32

The general profile of participants were students, University staff and other professionals which is because the way the online questionnaire was targeting and distributed to gather data from people who need to use learning resources and learning material as part of their study or part of their job. Because of that the main age groups were between 20 and 39 years old, and the Educational background was usually either High School or University education. Most of the participants have been using the internet for very long time (5 to 10 years) and they use the internet once many times a day, which makes them quite familiar with the internet and frequent users of the internet. Most of the participant used eLearning websites between once a month and once a day, which means there are both frequent and occasional users of eLearning websites. At the same time, there are 16.6% of participants who do not visit eLearning websites, which is small but important percentage, and can help to get data about why some people don’t use or don’t want to use eLearning websites. Comparing between the samples from UK and Middle-East, we have noticed that there is some differences in the profile of the participants. One of the differences is that from the UK the Split of Male-to-Female participant was 51.7-48% while in Middle-East sample it was 75% Male and 25% Female. Also from the UK there were 20% of participants from the under 20 year age group, while from Middle-East all participants are in the age group of 20 to 49 years old. The older people in Middle-East sample explain the reason why more participants from Middle-East use the Internet for more than 10 years, compared to the UK sample.

5.3.2 Result of the Intention to Use eLearning based on Participant profile for the Full sampleFigure 5 shows that Gender does not affect intentions to use eLearning; therefore both male and female participants equally accept the use of eLearning. Figure 6 presents age group has very little effect on eLearning acceptance, with age between 30 to 39 years and over 50 years scoring slightly more on intention to use eLearning. Figure 7 shows that Educational background has little relation with eLearning acceptance, people with University education intend to use eLearning slightly more than others. Figure 8 shows that how much people use the internet has very little or no relation with eLearning acceptance.

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Figure 5. Gender vs Mean Intention to Use

Figure 6. Age Group vs Mean Intention to Use

Figure 7. Education Background vs Mean Intention to Use

Figure 8. Use of Internet vs Mean Intention to Use

5.3.3 Validation of the eLearning Technology Acceptance ModelFigure 9 shows that for combined UK and Mid-East sample:

There is a clear relationship between perceived Usefulness of eLearning, perceived Ease of Use of eLearning, and Intention to use eLearning.

Perceived Usefulness of eLearning increases with perceived Ease of Use of eLearning. Intention to use eLearning increases with the increase of Perceived Usefulness of eLearning increase, or perceived Ease of

Use of eLearning, or both. The strongest relationship is between Intention to use and Perceived Usefulness.

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This makes it possible to say the TAM model is valid for eLearning websites acceptance.

Figure 9. Scatter Plot analysis of Relationship between: Intention to Use eLearning, Perceived Usefulness of and Perceived Ease of Use of eLearning

5.3.4 Relationship between Intention to Use, Subjective Norm and Attractiveness

Figure 10 shows that in The UK sample there is relationship between Subject Norm and Intention to use eLearning; the UK people with high subjective norm show more intention to use eLearning In UK sample, Intention to use eLearning has also relationship with eLearning website attractiveness; people who prefer attractive eLearning website, also intend to use eLearning websites.

Figure 10. Scatter Plot analysis of Relationship between: Intention to Use eLearning, Subjective Norm and Attractiveness (UK Sample)

In the Middle-East sample (Figure 11), Intention to use eLearning has also relationship with eLearning website attractiveness; people who prefer attractive eLearning website, also intend to use eLearning websites. But subjective norm does not have strong relationship with Intention to use, instead it has relationship with attractiveness.

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Figure 11. Scatter Plot analysis of Relationship between: Intention to Use eLearning, Subjective Norm and Attractiveness (Mid-East Sample)

5.3.5 Relationship between Hofstede’s cultural factors

Figures 12 and 13 show that in both UK and Middle-East samples the main relationship between Hofstede’s cultural factors is that there is a positive relationship between Collectivism and Power Distance. Figure 14 shows that there is some relationship but not a very strong one between eLearning website attractiveness and eLearning Information quality. There is also relationship but not a very strong one between eLearning website attractiveness and eLearning System quality. This means that people who want good eLearning Website quality and information, also want to see attractive eLearning websites.

Figure 12. Scatter Plot analysis of Relationship between: Power Distance, Collectivism and Uncertainty Avoidance (UK Sample)

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Figure 13. Scatter Plot analysis of Relationship between: Power Distance, Collectivism and Uncertainty Avoidance (Middle-East Sample)

Figure 14. Scatter Plot analysis of Relationship between: Attractiveness, Information quality and System quality

5.3.6 Reliability analysis for usefulness and tangibilityThis section shows some reliability results for factors for there were many questions asked in the questionnaire. Reliability of the 3 questions for usefulness: Using the measure of Cronbach's Alpha 0.377, reliability here is not very high, but using the measure of (Cronbach's Alpha if Item Deleted), it is better not to delete any of the 3 Usefulness questions.

5.3.7 Reliability analysis for usefulness and tangibilityThis section shows some reliability results for factors for there were many questions asked in the questionnaire, “Reliability of the 3 questions for usefulness”; Using the measure of Cronbach's Alpha 0.377, reliability here is not very high, but using the measure of (Cronbach's Alpha if Item Deleted), it is better not to delete any of the 3 Usefulness questions. Reliability of the 5 questions for Tangibility: Using the measure of Cronbach's Alpha 0.746, reliability here is good, and using the measure of (Cronbach's Alpha if Item Deleted), it is better not to delete any of the 5 Usefulness question, with the exception of question 1.

5.4 Comparison between UK and Middle-East samples

5.4.1 System and website quality and attractiveness:

The tables and Bar charts (Table 9,10 and 11) show the results of the questions answered for the section on eLearning Systems and Websites quality and attractiveness for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for question 9 shows that both samples very much prefer to have multiple ways of accessing the same information on an eLearning website, with Middle-East Sample preferring this option of FLEXIBILITY even more than UK sample. The results for question 36 shows that both samples very much prefer eLearning websites to have a consistent layout, with UK Sample preferring this option of CONSISTENCY even more than Middle-East sample. The results for question 23 show that

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both samples prefer eLearning website that enable them to customise the web pages that they use, with UK Middle-East clearly preferring this option of FLEXIBILITY more than UK sample.

TABLE 9. RESULTS OF QUESTION (9):

“I prefer to have multiple ways of accessing the same information on an eLearning website”UK Mean = 3.77, UK Median = 4

Middle-East Mean = 4.09, Middle-East Median = 4

UK Sample Middle-East sampleAlternatives Percent Value Percent Value

1. Strongly Disagree 0,0 % 0 6.3 % 2

2. Disagree 10,3 % 3 0,0 % 0

3. Neutral 24,1% 7 3,1% 1

4. Agree 31,0% 9 59,4% 19

5. Strongly Agree 24,5% 10 31,3% 10

Total 29 32


“I prefer eLearning websites to have a consistent layout”UK Mean = 4.16, UK Median = 4

Middle-East Mean = 3.91 , Middle-East Median = 4




2. Disagree 0,0 % 0 12,5 % 4

3. Neutral 6,9% 2 9,4% 3

4. Agree 58,6% 17 53,1% 17

5. Strongly Agree 34,5% 10 25,0% 8

Total 29 32


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“I prefer eLearning website that enable me to customise the web pages that I use. *UK Mean = 2.97, UK Median = 3





2. Disagree 17,2 % 5 3,1 % 1

3. Neutral 48,3% 14 25,0% 8

4. Agree 17,2% 5 46,9% 15

5. Strongly Agree 10,3% 3 25,0% 8

Total 29 32

5.4.2 Subjective norm:

The table and Bar chart (Table 12) show the result of the questions answered for the section on Subjective Norm for eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for question 13 shows most of Middle-East sample think that people who are important to them such as friends or parents expect them to use eLearning web-sites for learning, while most of the UK sample don’t agree or disagree on this point. This could mean that Middle-East participant is affected more by SUBJECTIVE NORMS compared to UK.


“People who are important to me such as friends or parents expect me to use eLearning web-sites for my learning. *

UK Mean = 2.81, UK Median = 3Middle-East Mean = 3.63 , Middle-East Median = 4




2. Disagree 17,2 % 5 3,1 % 1

3. Neutral 51,7% 15 40,6% 13

4. Agree 13,8% 4 46,9% 15

5. Strongly Agree 6,9% 2 9,4% 3

Total 29 32

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5.4.3 Power distance:

The table and Bar chart (Table 13) show the result of the questions answered for the section on Power Distance for eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for question 14 shows most of Middle-East and UK samples DO NOT agree that the use of eLearning websites and Technologies should be limited to certain people such as managers, parents or highly educated people. While this is the view of the majority, there is a slightly bigger minority from Middle-East sample (15.7%) compared to UK sample (3.4 %) which either agree or strongly agree with this point. This could mean that Middle-East participants is affected slightly more by POWER DISTANCE compared to UK, majority of both samples generally don’t see this as an issue.


“The use of eLearning websites and Technologies should be limited to certain people such as managers, parents or highly educated people.*UK Mean = 1.71, UK Median = 2





2. Disagree 48,3 % 14 43,8 % 14

3. Neutral 6,9% 2 15,6% 5

4. Agree 0,0% 0 9,4% 3

5. Strongly Agree 3,4% 1 6,3% 2

Total 29 32

5.4.4 Uncertainty avoidance:

The table and Bar chart (Table 14) show the result of the questions answered for the section on Uncertainty Avoidance for eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for question 16 shows most of both Middle-East and UK samples prefer to search for and explore new eLearning websites,

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but the Middle-East sample clearly prefer explore new eLearning websites more than UK sample. This could mean that Middle-East participant has Less UNCERTAINTY AVOIDANCE compared to UK, for this situation.


“I prefer to search for and explore new eLearning websites.**UK Mean = 3.55, UK Median = 4



1. Strongly Disagree 0.0 % 0 3.2 % 1

2. Disagree 10.3 % 3 0.0 % 0

3. Neutral 31.0% 9 6.5% 2

4. Agree 41.4% 12 64.5% 20

5. Strongly Agree 17.2% 5 25.8% 8

Total 29 32

5.4.5 Tangibility:The tables and Bar charts (Table 15 and 16) show the result of the questions answered for the section on Tangibility for eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for questions 26 and 38 shows that the majority of both Middle-East and UK samples are either Neutral or prefer leaning using a tangible methods such as a book or news paper instead of electronic material. However it from both questions it is clear the UK sample prefer tangible moths more than the Middle-East samples, and actually more Middle-east percentage (minority) prefer the intangible methods (such as eLearning material) over tangible methods such as newspaper or printed material. This could mean that UK participants prefer more TANGIBILITY for learning compared to Middle-East sample.


“ I prefer to reading a book or a newspaper in the traditional way rather than reading the same book or Newspaper online”UK Mean = 3.65, UK Median = 4





2. Disagree 17.2 % 5 31.3 % 10

3. Neutral 20.7% 6 37.5% 12

4. Agree 31.0% 9 18.8% 6

5. Strongly Agree 31.0% 9 12.5% 4

Total 29 32

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“I prefer to learn by reading printed paper rather than reading online material on screen.**UK Mean = 3.58, UK Median = 4





2. Disagree 3.4 % 1 28.1 % 9

3. Neutral 41.4% 12 28.1% 9

4. Agree 31.0% 9 25.0% 8

5. Strongly Agree 24.1% 7 12.5% 4

Total 29 32

5.4.6 Collectivism

The tables and Bar charts (Table 17 and 18) show the result of the questions answered for the section on Collectivism for eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for questions 28 and 39 when considered together show that there is only some majority, but not a big majority that prefer to get help from friend or tutors when they use eLearning websites, with small minority that does not actually prefer to do that. For question 28 there the Middle-east sample shows slight more preference, while for question 39, the UK sample, shows more preference. This could mean that both UK and Middle-East participants prefer some COLLECTISM in a similar way (no much difference), and not very high COLLECTIVISM for both samples.


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“If I faced any problem while I am browsing an eLearning website, I will ask my friend or colleague for help.UK Mean = 3.52, UK Median = 4




1. Strongly Disagree 3.4% 1 0.0 % 0

2. Disagree 13.8% 4 9.4 % 3

3. Neutral 13.8% 4 18.8% 6

4. Agree 55.2% 16 56.3% 18

5. Strongly Agree 13.8% 4 15.6% 5

Total 29 32


“If I want to learn a new topic I prefer to first ask a friend or Tutor before using an eLearning website.UK Mean = 3.03, UK Median = 3





2. Disagree 27.6 % 8 51.6 % 16

3. Neutral 24.1% 7 22.6% 7

4. Agree 24.1% 7 9.7% 3

5. Strongly Agree 17.2% 5 9.7% 3

Total 29 32

5.4.7 Perceived usefulness:

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The table and Bar chart (Table 19) show the result of the question answered for the section on the Perceived Usefulness of eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples.


“Using eLearning websites makes it easier to learn.*UK Mean = 3.39, UK Median = 4

Middle-East Mean = 4, Middle-East Median = 4




2. Disagree 7.1% 2 3.1 % 1

3. Neutral 28.6% 8 21.9% 7

4. Agree 46.4% 13 46.9% 15

5. Strongly Agree 14.3% 4 28.1% 9

Total 29 32

The results for question 30 show that both UK and Middle-East samples clearly perceive eLearning website as useful for Learning with only very small percentage perceiving eLearning websites as not useful or not making learning easier especially in the case of the Middle-east samples (only 3.1% that eLearning Website make learning easier). When comparing between the samples, we can notice that the Middle-East sample agrees more strongly that eLearning websites makes it easier to Learn. Therefore both samples perceive eLearning websites as useful, with more Middle-east participants perceiving eLearning Websites as Useful, compared to UK participants.

5.4.8 Perceived Ease of Use:

The tables and Bar charts (Table 20 and 21) show the result of the question answered for the section on the Perceived Ease of Use of eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for questions 18 and 31 shows that both UK and Middle-East samples clearly perceive eLearning website as Easy-to-Use with only very small percentage perceiving eLearning websites as not easy-to-use especially in the case of the Middle-east samples. When comparing between the samples, we can notice that the Middle-East samples agrees slightly more strongly that eLearning websites are easy to use. Therefore both samples perceive eLearning websites as Easy-to-Use, with slightly more Middle-East participants agreeing more strongly that eLearning Websites as are Easy to use, compared to UK participants.


“I think, it is easy to find information on the eLearning websites”

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UK Mean = 3.61, UK Median = 4Middle-East Mean = 3.84, Middle-East Median = 4




2. Disagree 3.4% 1 6.3 % 2

3. Neutral 24.1% 7 28.1% 9

4. Agree 69.0% 20 40.6% 13


Total 29 32


“I think that eLearning websites are easy to use.UK Mean = 3.48, UK Median = 4





2. Disagree 13.8% 4 3.3 % 1

3. Neutral 24.1% 7 26.7% 8

4. Agree 55.2% 16 50.0% 15


Total 29 32

5.4.9 Intention to use:The tables and Bar charts (Table 22 and 23) show the result of the question answered for the section on the Perceived Ease of Use of eLearning for the UK and Middle-East samples, for which there is some difference between the UK and Middle-East samples. The results for questions 32 and 41 show that both UK and Middle-East samples clearly Intend to Use and recommend with only very small percentage not intending to use or recommending eLearning websites (less than 4% in both cases). When comparing between the samples, it can be noticed that more Middle-East participants agree strongly with the intention to use and recommend eLearning websites. Therefore both samples very much intend to use and recommend eLearning websites with Middle-East

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participants agreeing more strongly with the Intention to Use and recommending eLearning Websites, compared to UK participants.


“I intend to use eLearning websites for my learning”UK Mean = 3.77, UK Median = 4

Middle-East Mean = 4, Middle-East Median = 4



2. Disagree 0.0% 0 3.1 % 1

3. Neutral 27.6% 8 18.8% 6

4. Agree 55.2% 16 53.1% 17

5. Strongly Agree 17.2% 6 25.0% 8

Total 29 32


“I recommend my friends to visit and use eLearning websites”UK Mean = 3.68, UK Median = 4





2. Disagree 3.4 % 1 0.0 % 0

3. Neutral 31.0% 9 9.4% 3

4. Agree 51.7% 15 43.8% 14

5. Strongly Agree 13.8% 4 46.9% 15

Total 29 32

6. Conclusion

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In total 61 people took part in the online questionnaire: 29 participants from UK and 32 participants from Middle-East. The general profile of participants were people who were mainly Students, University Staff and other professionals who need to use learning resources and learning material. The main age groups were between 20 and 39 years old, and the Educational background was usually either High- School or University education. Most of the participants have been using the internet for more than 5 years. In general participant had high intention to use eLearning and gender, age, educational background and internet experience made very little different to the level of intention to use and that a lack of perceived attractiveness and ease of use are the main obstacles that need to be overcome by a potential eLearning system. Regarding the eLearning TAM used the results showed the following:

There is a clear POSITIVE relationship between perceived Usefulness of eLearning, perceived Ease of Use of eLearning, and Intention to use eLearning.

Intention to use eLearning increase with both the increase of Perceived Usefulness of eLearning increase and perceived Ease of Use of eLearning.

The strongest relationship is between Intention to use and Perceived Usefulness

This makes it possible to say that the TAM is valid for eLearning websites acceptance. Regarding the Cultural Factors, the results showed the following:

There is a POSITIVE relationship between Subjective Norm and Intention to use eLearning; i.e. people with high subjective Norm show more intention to use eLearning.

Intention to use eLearning has also POSITIVE relationship with eLearning website attractiveness; people who prefer attractive eLearning website, also intend to use eLearning websites. Comparison between UK and Middle-East results show the following differences:

Surprisingly, UK sample shown more need for Tangibility compared to Middle-East. Overall, The Middle-East Samples shows slightly more Intention to Use eLearning compared to the UK sample.

Acknowledgements This research paper is made possible through the help and support from everyone at our Applied Computing Research Group. Thanks to Sam Crate for his help in formatting this paper.

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