1 Introduction

One of the main challenges of education managers in the twenty-first century is coping with the constant development of technology. Embracing technology in higher education institutions is not a matter of choice. Technology has transformed or at least changed the landscape of several industries and the education sector is no exception. Continuous pressures from escalating costs, calls for higher quality of education, and rising students’ expectations drive the introduction of new technologies in the educational sector. In their book titled “Managing Technology in Higher Education”, Bates and Sangra (2011) reflect the struggle of universities to integrate information and communication technology (ICT) within their more traditional activities. The authors call for adopting a radical approach to universities’ technology management. One important component in this radical approach is the university educator whose role surpasses just adoption and usage. Additionally, new approaches in learning and teaching necessitate the introduction of educational technologies, which would be far less effective without well-informed users (Barnett 2003). Therefore, instructors need to be capable of capitalizing on educational technologies and be willing to design the learning environment in a way that is conducive to the effective learning process. Despite the increasing availability and development of new learning technologies, the application and deployment potential of Information and Communication Technology (ICT) in education is yet to be fully utilized. Technology adoption has been extensively examined using the technology acceptance model (TAM). While limited efforts have been directed towards explaining technology adoption through technology readiness (TR) framework, despite its relatively higher overall reliability as suggested by Parasuraman (2000). The advantage of TRI to apply a rigorously developed scale for data collected from a broader range of industries/companies enhances generalizability (Parasuraman et al. 2005). Thus, there is a need to examine technology readiness (TR) as a measurement tool in the education sector. This is an industry where TR has not been previously tested and where the dynamics for e-learning adoption is considered important for educators and managers’ decision-making.

The current empirical study is conducted in an educational institution setting, where faculty members are referred to as instructors. Data for the study is collected from one private university in Egypt and another private university in the United Arab Emirates (UAE). The main objective of the study is to examine the relationships between instructors’ technology readiness and their willingness to adopt e-learning technologies in their respective institutions.

The study addresses two main research questions:

  1. 1.

    What is the level of instructors’ technology readiness (TR), attitudes, human interaction preference, and behavioral intentions to adopt e-learning technologies in Egypt and UAE?

  2. 2.

    How does the effect of instructors’ technology readiness affect their attitude and behavioral intentions towards e-learning technologies?

2 Theoretical background

2.1 E-learning

According to Welsh et al. (2003) e-learning is defined as the delivery of information and instruction to particiapants using a computer network over the internet. Rosenberg and Rosenberg (2000) defines e-learning as the use of internet technologies as a channel to deliver solutions to learners. Armitage and O’leary (2003, p4) defines e-learning as “the use of digital technologies and media to deliver, support and enhance teaching, learning, assessment and evaluation”. Horton (2011) defined e-learning as distance education that uses computer-based technologies, information communication technologies (ICTs), and learning management systems Litteljohn and Pegler (2007) depict e-learning as an umbrella term that includes computer mediated learning, web learner and web based instruction. Holmes and Gardner (2006) stress that e-learning provides access to resources that promote learning on an anyplace anytime-basis. We see the various author present a diversity of aspects and definitions associated with e-learning. Those definotions agree on the key point, which is the use of Information technology as a core element of learning, while presenting an ever increasing diversity in the nature and scope of contribution for e-learning activities. As technologies evolve so too does the definition of e-learning. New technology brings along the continous search for effective methods for learning and teaching excellence. The current study adopts the definition developed by Litteljohn and Pegler (2007) due to its comprehensivness in conceptualizing e-learning as a process of learning and teaching using information technologies. The definition accomodates technology regardless the level of advancement, rather than limiting the definition to specific technologies existing at a point of time. It encompasses teaching and learning as a whole with all the related issues both current and potential.

2.1.1 Drivers for e-learning technologies

A multitude of drivers for e-learning strategy has been cited in the literature. Several reasons drive e-learning adoption including the potential for accessibility to learning at any time, in any place, the enhanced capability of tailoring learning to individual needs, and flexibility in meetings students’ busy schedules, while allowing higher quality and cost savings (Zhang and Nunamaker 2003; Rosenberg and Rosenberg 2000). Cost cutting as a driver for e-learning is a point challenged by Littlejohn and Pegler’s study (Litteljohn and Pegler 2007) where the researchers illustrate the difficulty of estimating e-learning costs and cost savings. Pepicello and Pepicello (2003) look at quality as a driver for e-learning adoption, and find a quantifiable improvement in quality of student administration with the use of e-learning technologies.

Universities commonly face several challenges in terms of budget cuts in education resulting in overburden teaching staff (Tucker and Gentry 2009), limitations on students’ services and facilities, significant reduction in research funding and scholarships, along with the probable implications of lost opportunities for development and in some cases regression of the educational quality level. At best, there are only budgetary constraints and therefore, e-learning could be a viable strategy for addressing the inevitable reality of limited resources. Use of e-learning technologies thus becomes an indispensable option, especially considering that students are increasingly technology savvy. There are also some controversial points regarding the specific drivers of e-learning however. Litteljohn and Pegler (2007) pose several questions to facilitate developing a stance regarding the key drivers of institutional adoption of e-learning. These questions revolve around the potential of e-learning to improve teaching and learning quality, widen students’ participation, cut cost, and meet students’ expectations and needs.

Factors motivating individuals’ e-learning adoption are somewhat different from organizational or institutional - level adoption. The perceived attributes theory introduced by Rogers (1995) develops a framework of five attributes that support the adoption rate of an innovation; including relative advantage of the adopted technology, compatibility with previous experience and values, complexity, trialability and observability of the results by potential adopters. Additionally, individuals’ self-efficacy driven by social learning theory (Bandura 1982) is also a consistent influence on adoption. According to Piccoli et al. (2001), technological efficacy among e-learning participants is higher than technology efficacy of participants in traditional classroom setting.

2.2 Technology readiness

Technology readiness (TR) refers to a condition or state in which the user or employee is prepared to try new technology. TR is conceptualized as role clarity, ability, and source of motivation for technology use (Meuter et al. 2005). Parasuraman (2000) has defined TR as “people’s propensity to embrace and use new technologies for achieving their goals in home life and at work”. The conceptualization of TR index is based on the premise that individuals hold favourable and unfavourable views simultaneously towards technology based products and services. Accordingly, the TR encompasses four contributing factors. The first two factors optimism and innovativeness, establish and support technology readiness. The other two factors namely discomfort and insecurity inhibit TR in the individual. The four factors work together to form the individual’s view of technology. Parasuraman (2000) defines Optimism as holding a positive belief that technology provides more control, flexibility, and efficiency. Innovativeness is defined as a person’s belief of being a technology pioneer whereas discomfort is an inhibitor of technology in which a person perceives lack of control over technology while insecurity is defined as being distrustful of technology to work properly.

A study conducted on learners’ satisfaction by Sawang et al. (2013) shows that adoption of e-learning technologies is an important measure of the implementation of e-learning and that low adoption rates tend to increase the cost of e-learning. Therefore, understanding and investigating adoption dynamics is important. Table 1 below summarizes definitions of constructs and then the conceptual framework is presented.

Table 1 Construct Operational definitions and measurement
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Based on the definitions presented for research variables in Table 1, the relationships among the study variables is to be developed and examined in the following section.

3 Conceptual framework and hypothesis

Three main theories are considered the foundation for the development of the conceptual framework of the current study namely Theory of Reasoned Action (TRA), Theory of Planned Behavior (TPB) and Self-efficacy theory. Ajzen and Fishbein (1980) introduced TRA as a theory of human behavior that scrutinizes the relation between human beliefs, attitude, and behavior. According to Conner and Armitage (1998), TPB explains attitude behavior relationships. Self-efficacy relates to ones’ beliefs about attaining desired goals in a particular domain (e.g. Bandura 1986; Muddox 2000). Figure 1 shows the research conceptual framework.

Fig. 1
figure 1

Conceptual framework

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The Theory of Reasoned Action (TRA), developed by Ajzen and Fishbein (1980), is a well-established theory in Social Psychology that predicts individuals’ willingness to carry out a specific behavior. According to the TRA, behaviors or at least individuals’ intention to perform those behaviors can be predicted if attitudes are present. Accordingly, it can be deduced that attitudes affect behavioral intentions

  1. H1.

    Instructors’ attitudes towards e-learning technologies influence behavioral intentions towards adopting e-learning technologies.

Patry and Pelletier (2001) define attitudes as “positive or negative feelings towards the relevant subject matter”. Mick and Fournier (1998) pointed out that, individuals who interact with technology often experience positive and negative feeling simultaneously. Based on TRA, behavioral beliefs are one of the variables that define attitudes and perceived consequences of behaviours shapes the person’s attitude toward the behaviour as suggested by Davis et al. (1989). Parasuraman (2000) describes technology readiness TR construct as a view of technology and state of mind that determines individuals’ predisposition to use technologies. Therefore, it can be hypothesized that TR affects attitudes. The direction of the relationship between TR dimensions and attitude varies between drivers and inhibitors. Lin and Chang (2011) suggest that optimism and innovativeness are drivers of TR, motivating customers to adopt and use technological products and form a positive attitude towards technology and thereby are expected to have a positive impact on instructors’ attitude while discomfort and insecurity are expected to have a negative influence. Discomfort and insecurity are inhibitors, making individuals averse to adopting and using technology. Lin and Chang (2011) believes that technology is too complex, instructions to use are unclear, and adequate technical support is unavailable. Therefore, inhibitors of TR are more likely to result in a negative attitude towards e-learning technologies. Therefore, a relationship can be hypothesized between TR and attitude.

  1. H2.

    Instructors’ technology readiness influences instructors’ attitude towards e-learning technology

Peattie (2001) explains that an attitude-behaviour gap usually exists with a mismatch between individuals’ revealed preferences and their actual behaviours. The attitude-behaviour disconnect can be investigated using behavioural reasoning theory (BRT), introduced by Westaby (2005), which investigates the reasons for and against individuals’ behaviours. Uncovering individuals’ reasoning in choosing to perform or not perform certain behaviours can explain potential gaps between attitude and behaviour. Individuals’ inclination for human interaction proves to have a major influence on technology adoption decisions based on Curran and Meuter (2005). Consequently, the human interaction construct, would be a viable influence on technology adoption, and affect the relationship between instructors’ attitudes and behavioural intention towards e-learning technology use.

  1. H3.

    Human interaction has a significant influence on behavioral intentions towards e-learning technologies adoption

According to Ajzen (2002) perceived control over performance of a behavior can account for significant variance in intentions and actions. Accordingly, the greater instructors’ belief that they have higher technological readiness, the better their perception of behavioral control, which in turn leads to higher behavioural intentions. In their study Yi et al. (2006) show that innovativeness positively affects individuals’ behavioural intentions to adopt technology. Lin and Hsieh (2006) find that TR is positively related to behavioural intentions towards using self-service technologies.

  1. H4.

    Instructors’ technology readiness influences instructors’ behavioral intentions towards e-learning technologies.

4 Research methodology

4.1 Research setting

Data for this study comes from two private universities, which are used as the research. Unlike public universities, private universities receive a full-fee paying students which enhances their ability to invest in education technology. While, public universities are constrained by the level of spending on education at the national level, which in many cases restricts development and introduction of modern education technology. In seeking their program validation and accreditation from international accrediting bodies, private universities are more aligned to global standards of education and hence, more inclined to adopt more modern practices and tools such as e-learning technologies as compared to public institutions.

This research uses a cross sectional survey of faculty is conducted in one university in Egypt and another in U.A.E. The number of students’ enrolled in the U.A.E for 2014/2015 was 870 students and around 1550 students at the private university in Egypt. Both universities invest in e-learning technologies and have similar learning technology platforms, using the Blackboard learn, inter and intra communication platforms, and database management systems. However, the usage rates are unsatisfactory from the management perspective of the selected universities. Such managerial perception of e-learning utilization is aligned with research findings that illustrate the existence of barriers to e-learning utilization (e.g. Leem and Lim 2007). Accordingly, the two sites selected are considered relevant to the current study. Both institutions have small numbers of faculty, with 51 total faculty members in Egypt and 46 in the UAE.

The resulting sample of completed responses contains 31 responses from the private university in Egypt and 35 from the U.A.E. This constitutes a response rate of respectively 61% and 76%, which is very good. Rosce (1975) proposes that sample sizes larger than 30 and less than 500 are appropriate for most research. A non-probability judgemental sample is used in which the criterion for selection is to have spent at least one year within the university under study. The selection criterion is to ensure that participants have sufficient interaction and experience based on which they can form a perception and an attitude regarding e-learning technologies, hence provide a meaningful and informative response to the study.

4.2 Data collection

To address the current research questions, a questionnaire is developed and directed to instructors to measure study variables. To ensure content validity of the questionnaire, academic experts have reviewed and evaluated the questionnaire items before dissemination. Expert panel suggested eliminating few statements seen as redundant. Items in the questionnaire are adapted from relevant previous studies to ensure content validity. A pilot test of 10 questionnaires was first conducted to ensure that the scale items are clear and content is relevant and well adapted to the educational context. Some changes to the wordings and some statements are further adjusted, to fit the study in hand. The questionnaire is divided into two main sections in which Section 1 is composed of 28 items of five-point Likert scale statements bounded by “strongly disagree to strongly agree”. Section 2 includes nominal scales to obtain demographic information of the sample units in terms of academic title, age, gender, nationality, and income. Some statements are reversed to minimize biases, and to encourage respondents handle the questionnaire statements attentively. Data collection is self-administered and questionnaires are sent by email, filled, and returned back.

4.3 Data analysis

The analysis activities including activities associated with, validation of the constructs and hypotheses tests are conducted using the SPSS suite (ver.21). SmartPLS version 3.0 software package is used for PLS structural Equation modelling.

The initial analysis looks at data validation, which generates descriptive statistics to assess the overall quality of data. The subsequent Cronbach Alpha test shows basic reliability (convergence) of the scales and sub scales used in the survey. These are measured separately to avoid inflated reliability values that may result from the larger number of items in the scale (Cronbach 1951; Field 2009). Both Chronbach alpha reliability measures are good. For the data from Egypt αEgy is 0.86 and for the U.A.E data αUAE of 0.89. Sekaran and Bougie (2010) point out that the closer the Cronbach alpha is to 1, the better the internal consistency/reliability of the construct measured by the indicators. At the same time, reliabilities that are less than 0.6 are considered poor. Those in the 0.7 range, acceptable, and those over 0.8 are considered good. All construct reliabilities measures exceed 0.6 as shown in Table 4 and are within the same range of reliability measures from other studies conducted in other industries (e.g. Parasuraman 2000; Jiun-Sheng and Hsieh 2012).

A follow-up Exploratory Factor analysis is used to evaluate the convergent and discriminant validity of dimensions of TR (from Fig. 1) and of the other constructs used in the PLS structural equation analyses. The correlation matrix of our items shows that factor analysis would be appropriate for examining the structure of the scale since correlations between variables in each of the scales are greater than (.3). Principal components analysis (PCA) is used for examining the construct structure and reveals the presence of 6 components with Eigen values greater than 1. These explaining 37, 14, 12, 8, 6, and 5% of the variance respectively, with a cumulative total 83% of variance explained.

Four components from the EFA are retained to accommodate the theoretical approach of the scale of having two influencing and two hindering factors of technology adoption. Oblique rotation is used to evaluate the four components. The rotated components show a simple structure with strong loadings, with most variables are loading substantially on only one component (Thurstone 1947). The four-component solution explain a total of 71.9% of the variance with Components 1, 2, 3, and 4 contributing 36.7%, 14.4%, 12.3% 8.4% respectively.Footnote 1 The EFA confirms the four-factor structure originally developed by Parasuraman (2000). It also shows a good level of convergence and discriminant validity of the scales in the dataset, which allows us to test the hypotheses using Kendall’s tau correlations and operationalization of a partial least squares (PLS) structural equation model in order to test the relationships among variables.

We also checked the relationship among independent variables for presence of multi-collinearity through examination of variance-inflated factors (VIF). Pallant (2007) points out that the cut-off point for the existence of multicollinearity among independent variables is 10. All VIF values for independent variables are less than 1.5, which is well below the cut off point.

After data cleaning, preparation and checking for hypothesis testing suitability, sample characteristics and descriptive statistics are presented before hypothesis testing. Relative mean difference and Mann Whitney U non-parametric test are used to compare the mean scores between Egyptian and Emirati instructors in terms of their technology readiness, attitude behavioural intentions, and preference to human interaction in the following section.

4.3.1 Sample characteristics

The sample composition is described in terms of instructors’ academic title, age, gender, and nationality. Table 2 represents the profile of participants.

Table 2 Sample Characteristics
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The sample in Egypt is composed of 54% teaching assistants and 27% assistant professors where 92% of the sample is less than 45 years old. The sample includes 34% males and 66% females. All instructors working at the university in Egypt are Egyptian nationals. The majority of respondents in U.A.E sample are assistant professors constituting (74%) of the sample group and 83% of respondents are less than 45 years old. The sample has slightly more male faculty (61%) as compared to females (39%) and at the same time, the majority of the respondents (83%) are less than 45 years old. Nationalities are almost uniformly represented so that Indians represent (31%), Pakistanis (20%) and Egyptians (20%). These three nationalities represent the majority of faculty in the university and therefore their presence in the sample is ensured so that no specific national culture would affect the results of respondents’ technology readiness (e.g. Parasuraman et al. 2004).

4.3.2 Descriptive statistics

Table 3 below presents the overall descriptive statistics of the variables in the survey.

Table 3 Descriptive Statistics
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Based on descriptive statistics in Table 3, instructors in both Egypt and U.A.E tend to have an overall positive technology readiness (TR) (\( \overline{\mathrm{x}} \) EGY = 3.35) and U.A.E (\( \overline{\mathrm{x}} \) UAE = 3.42). Instructors in Egypt and U.A.E also show a strong positive attitude towards e-learning technologies (\( \overline{\mathrm{x}} \) EGY = 3.94, \( \overline{\mathrm{x}} \) UAE = 4.22), however a slightly lower score for behavioral intention (\( \overline{\mathrm{x}} \) EGY = 3.90, \( \overline{\mathrm{x}} \) UAE = 4.14) is obtained. Results show a preference for face to face interaction with students, instead of mediated computer-based exchanges, with a score of (\( \overline{\mathrm{x}} \) EGY = 3.86, \( \overline{\mathrm{x}} \) UAE = 4.09).

According to (Fig. 2), instructors at the university in the U.A.E tend to have a slightly higher TR mean values than TR mean values of their counterparts in Egypt (RMD = 2%), attitude (RMD = 7%), behavioural intentions (RMD = 6%), and preference for human interaction (RMD = 6%). A further investigation of TR dimensions is presented in Fig. 3 below:

Fig. 2
figure 2

Descriptive statistics of study variables

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Fig. 3
figure 3

Descriptive statistics of TR dimensions

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Based on (Fig. 3), both instructors in Egypt and U.A.E tend to have their highest TRI score in Optimism (\( \overline{\mathrm{x}} \) EGY = 4.41, \( \overline{\mathrm{x}} \) UAE = 4.46) meaning that they tend to hold a strong positive view of technology with a slight difference in RMD of only 1% higher in UAE. Instructors’ innovation is higher in UAE (\( \overline{\mathrm{x}} \) EGY = 2.94, \( \overline{\mathrm{x}} \) UAE = 3.19) with RMD of 8%. Discomfort is slightly lower in Egypt than in U.A.E (\( \overline{\mathrm{x}} \) EGY = 2.97, \( \overline{\mathrm{x}} \) UAE = 3.12) with 5% RMD. Insecurity is 5% lower in the U.A.E as compared to Egypt. Overall, it can be deduced that there is no notable difference between instructors’ technology readiness, attitudes, behavioral intentions, and human interaction preference in Egypt and UAE. Mann-Whitney U non-parametric test is used to compare the means values of the two groups of instructors and check significance of results.

Mann-Whitney test shows that probability values – p-values are greater than 0.05 therefore, there is no statistically significant difference between Egyptian and Emirati instructors in terms of technology readiness, attitude, intentional behavior and human interaction.

The results show that instructors’ TR in Egypt did not significantly differ from their counterparts in UAE where U = 482, z = −.774, p ˃.05. Similarly, in terms of attitudes and behavioural intentions towards technology, p-values less than 0.05 show there is no difference between the two groups of instructors. However, for human interaction (HI) p-value is greater than .05, which shows a significant difference in human interaction (HI) preference. The mean rank values (Table 4), indicate that instructors in UAE has higher preference to HI = 39.3 compared to instructors preference to HI interaction in Egypt = 26.95. Based on the above discussion, there is no significant difference between instructors’ in Egypt and UAE in terms of TR, attitudes and behavioural intentions while HI preference is higher for instructors in UAE compared to instructors in Egypt.

Table 4 Mann-Whitney Ranks and Test Statistics
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4.3.3 PLS analysis

The subsequent analysis is operationalized using a covariance-based PLS Structural Equation Modelling (SEM) technique using SmartPLS version 3.0 software package. Recent review of published IS research using a PLS covariance analysis shows that most researchers choose to adopt this approach because of a small sample size and possible issues with normality of the variables in the data (Ringle et al. 2012). The choice for PLS use in this case necessitated by the relatively small size of the institutions and hence the limited population of instructors in the small sample.

The Convergent validity of the latent constructs in the model is assessed by the item loadings generated by the PLS analysis. Table 4 (below) shows that the indicator loadings are all well above 0.5, with the exception of the TRI construct. This is not an issue, because Technology Readiness as an Index of various cognitive perceptual processes, namely optimism, innovation, discomfort, and insecurity (Fig. 1) is operationalised as a formative construct. These perceptual processes are not indicative of a singular construct but instead reflect various cumulative influences, which build the TR as an index. Construct reliability is provided through the overall item loadings presented in Table 5 below.

Table 5 Item Loadings in the PLS model
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The overall reliability measure for the latent constructs in the model exceeds 0.6 for both samples and is within the same range of reliability measures from studies conducted in other industries (e.g. Parasuraman 2000; Jiun-Sheng and Hsieh 2012). Thus, the overall internal consistency reliability of the measures used in this study is relatively good (Table 6 below).

Table 6 Construct Reliability and Convergent Validity
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Finally, the Discriminant validity measures as generated by the PLS SEM show that while the constructs are well defined, they are also consistently different from each other with inter construct correlations well below the 0.7 threshold. Construct correlations from .15 to .55 also indicate the likelihood of significant relationships (Table 7 below).

Table 7 Construct Reliability and Convergent Validity
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4.3.4 Exploratory hypothesis testing

The hypotheses test is done to explore the likely relationship of instructors’ technology readiness, their attitude and behavioral intentions towards adopting e-learning technologies.

The subsequent hypotheses test was operationalized using the same covariance-based PLS Structural Equation Modelling (SEM) technique, which was used in the construct validation. The choice for PLS use in this case is also based on the relatively limited population of instructors in the selected universities and the relatively small sample size (despite the excellent response rate).

The SEM approach provides several benefits when doing exploratory hypotheses testing. Foremost is the ability to evaluate all of the relationships specified by the hypotheses using the available variance (or covariance) simultaneously. Unlike other GLM variance methodologies that only do singular IV= > DV relationship structure tests, SEM is able to test models that contain a sequence of relationships (a➔b➔c).

Table 8 (below) shows the overall estimates of coefficients from the PLS SEM hypotheses tests for each of the two locations included in this research study. The hypotheses are largely supported. The support for some of the hypotheses is mixed, because the estimated coefficients and R square calculations in the PLS SEM test show a strong influence between constructs in one of the sites while weak/poor support in the other.

Table 8 Summary of hypothesis testing results
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The differences in adjusted R square of the dependent constructs in the PLS -model for each of the two study sites show two different influence mechanism.

The adjusted R square for behavioral intention is relatively similar at both sites with 0.420 for Egypt and 0.468 for the UAE. H1 is clearly supported with Attitude showing a strong influence on Behavioral Intention with a strong coefficient estimate for both locations.

H2, which explores the influence of Technology Readiness on the attitudes of faculty for e-learning technology, shows partial support. The Adjusted R. square for the Attitudes shows a clearly strong TRI influence on attitudes in the sample from Egypt, but a weak influence in the data from the UAE.

H3, explores the influence of Human Interaction on behavioral intention, is also mixed. Here again we see an influence of Human Interaction on BI in the data from Egypt, but a relatively weak coefficient estimate in the data from the UAE.

Finally, H4 explores the way Technology readiness influences the Intention of Faculty to use e-learning technologies. The PLS SEM results show support for this hypothesis from both research sites, although the estimated coefficients for the two sites are strongly divergent. The coefficient estimate in the data from Egypt shows a negative influence of Technology Readiness on Behavioral Intention. The data from the UAE shows a positive influence of Technology Readiness on Behavioral intention.

The full models are shown by Figs. 4 and 5

Fig. 4
figure 4

Technology Readiness influence on BI in Egypt

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Fig. 5
figure 5

Technology Readiness influence on BI in the UAE

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5 Discussion and implications

The Technology Readiness scale used as a measurement tool has been examined in many contexts, but not in the education setting. Verhoef et al. (2009) have highlighted the need for examining the TR Index scale, and pinpointed a need for evaluative support for it. Use of the TR index in this study, can be considered a step towards developing such evaluative support where the scale reliability is tested within an educational setting. The current study explored the way this scale operates in the setting, showing a reasonable reliability level (αEGY = 0.86, αUAE = 0.89) slightly higher than the average reliabilities in other relevant studies which range between (α = 0.7 to α = 0.82) such as (Parasuraman 2000; Richey and Autry 2009; Rose and Fogarty 2010; Bachrach et al. 2006). Studies in education technology rely mainly on technology acceptance model (TAM) developed by Davis (1989) and management education by internet readiness (MEBIR) (e.g. Parnell and Carraher 2003, 2005; Mathews and Bhanugopan 2014; Lightner et al. 2009). Both TAM and MEBIR address technology adoption from a functional perspective, while overlooking the affective perspective. An educational context calls for human intensive interaction and therefore examining affective components of technology readiness is essential. Accordingly, a key implication for the current research is that examining TR in an educational setting is a step towards adding a viable tool to the education technology literature and can guide education managers towards measuring and tracking instructors’ TR, which allows for improvement of e-learning technology adoption.

The first research question in this study addressed the level of instructors’ Technology readiness, attitudes, human interactions, and behavioural intentions. Instructors show a moderate level of overall technology readiness in Egypt (\( \overline{\mathrm{x}} \) EGY = 3.35) and a slightly higher TR score in U.A.E (\( \overline{\mathrm{x}} \) UAE = 3.94) which is only 2% relative mean difference from Egypt. This result is further confirmed by the Mann Whitney test, which shows that there is no significant difference in instructors’ TR in Egypt and UAE. Other studies using this methodology indicate lower levels of technology readiness. For example, Rose and Fogarty (2010) study mature individuals’ propensity to use self-service banking technology and report a technology readiness level - mean value of (\( \overline{\mathrm{x}} \)= 2.94) which is 20% less than the average overall TR in the current study. This relatively wide difference could possibly be attributed to the differences in perceived risk in which potential negative experience and consequence in using e-learning technology is much lower than the potential negative consequences resulting from using self-service banking technology in which insecurity and discomfort are expected to be higher. The level of instructors’ TR shows that a considerable room for enhancing TR exists. The strategies to be employed for improving instructors’ TR at both universities partly depend on the scores of TR in the four dimensions of the construct and the way through which TR drives behavioral intentions at each university.

Scores of TR dimensions in Egypt and UAE hold a similar pattern with a slight difference between the two countries. Optimism is the dimension where instructors score the highest in Egypt and UAE (\( \overline{\mathrm{x}} \) EGY = 4.41, \( \overline{\mathrm{x}} \) UAE = 4.46) reflecting instructors’ positive beliefs about the role of technology to support practical activities. Understanding of technology potential and growth of experience also has a positive impact. Other three TR dimensions have an average score in Egypt and UAE. Instructors hardly see themselves as technology pioneers reflected in their average score for innovativeness. This result is aligned with Jones (2004) research, which indicates that lack of confidence of teachers in using technology is affecting its utilization in teaching activities. Holding a moderate level of discomfort and insecurity is also an issue that calls for attention. Mossberg (1999) suggests that there is sufficient empirical evidence that reflects signs of growing consumer discomfort and lack of trust in technology. Accordingly, education managers need to focus their efforts on increasing instructors’ innovativeness and reducing discomfort and insecurity.

To increase instructors’ innovativeness, and thereby instructors’ TR, education managers need to design and implement relevant education technology training and technology leader positions which would support instructor training as part of formal professional development and certification. The level of instructors’ discomfort in using e-learning technologies is expected to decrease by providing timely and professional technical support whenever needed. Insecurity when using e-learning technologies can be attributed to intellectual property issues. For example, the content and study material authored by instructors’ which are uploaded on a learning platform are more liable to be publicly shared, used and even modified without the permission of the author as compared to traditional/ non electronic means. Education managers need to consider setting and enforcing a clear and well-communicated policy to protect instructors’ authorship and intellectual property rights for the material shared through e-learning technologies in addition to supporting IT efforts in increasing technical security and system encryption. Consequently, instructors’ insecurity is expected to decrease and thereby their technology readiness increases.

There is no significant differences between in TR, attitude, and behavioral intentions of instructors in U.A.E and in Egypt. This result can be attributed to the similarity of institutional characteristics, e-learning technologies employed, technology platforms, and size in terms of students and instructors number.

Instructors’ preference for human interaction is relatively high in Egypt and UAE, which has the potential of reducing instructors’ behavioral intentions for educational technology use. More specifically, instructors indicated that they feel more comfortable in face-to-face communication as opposed to activities through automated systems. Instructors find teaching to be more effective when carried out in face-to-face interaction with students. Preference to human interaction can also be partly explained by cultural values similar in both countries, where collectivism is relatively high as reflected by Hofstede (1983) cultural dimensions. Individualism scores for Egypt and the UAE are similarly low (roughly 25) reflecting a collectivistic culture. Hofstede asserts the influence of national culture on work related attitudes and values (Hofstede 1983). However, empirical evidence supports a higher preference for Human Interaction held by instructors in the UAE. The preference for face-to-face interaction can be attributed to cultural diversity in the UAE, were face-to-face interaction can support more effective communication in a more culturally (internationally) rich context.

Education managers need to recognize that in light of instructors’ preference to human interaction over e-learning technologies and automated systems, an overly automated environment may adversely affect instructors’ behavioural intentions towards e-learning technologies and even their overall teaching performance. Bagozzi (2007) highlighted the importance of considering social aspects in technology acceptance. Several strategies can be implements to deal with instructors’ human interaction preference: 1) Improve and reinforce instructors’ perception of the benefits and relative advantage using e-learning technologies. This strategy would possibly offset the effect of instructors’ preference for human interaction over e-learning technologies and enhancing instructors’ positive view of technology (optimism). 2) Given a higher preference to human interaction and having attitude as a key determinant to instructors’ behavioral intentions, education managers in both locations may shape and enhance instructors’ attitudes. This can be achieved through “humanizing” e-learning technologies and adding appealing features that involves human interaction via technology platforms. e.g. video lecture capturing, live chats option embedded into educational platforms, providing timely technical support, live streaming of relevant educational events, forums, and other interactive technologies.

The second research question addresses the relationship dynamics among the study variables at the two universities in Egypt and UAE. The attitude-behavior relationship in H1 is fully supported in Egypt and UAE (r 2= 0.68, r 2= 0.54) respectively. This result is consistent with theory of reasoned action (Ajzen and Fishbein 1980), which explains behavior as a consequence of attitude, and previous studies (e.g. Lin et al. 2011; Hennessy et al. 2012). Respondents’ Attitudes play a substantial influence on instructors’ behavioral intentions in both study locations, which implies that education managers need to consider the following strategies: 1) incorporating instructors’ requirements to gain their buy-in and to provide them with an overall positive experience. 2) developing training programs that focus on enhancing technological competencies and skills in dealing with e-learning technologies can reflect technology full potential and benefits hence develop a positive attitude. Beside the current research evidence, this strategy is further supported by previous studies. For example: Self-efficacy proved to be a strong predictor of attitudes and behavioural intentions (Broadhead-Fearn and White 2006). Dearing (1997) suggests that staff and students should receive appropriate training and support to enable them to realize the full potential of technology and enhance their technological abilities. Rogers (1995), suggests that the perception of potential adopters is the key for judging attitude and adoption of an innovation. 3) Nurture Work-related values of professionalism and enforce institutional policies so that adoption of e-learning technologies is not subject to attitudes. 4) Consider the integration of more interactive features into e-learning platforms.

TR- attitude relationship is partially supported. In Egypt, instructors’ TR has a strong influence on their attitude (r 2= 0.535) unlike instructors in UAE (r 2= 0.092). This can be explained by the complex nature of TR as a mega (2nd order formative) construct that is made up of two motivating factors, optimism and innovativeness, responsible for forming a positive attitude and two hindering/inhibiting factors, discomfort and insecurity, that are expected to negatively affect user attitude. Education managers are expected to address instructors’ TR in light of the specific institutional context since the interplay among TR dimensions may vary from a location to another with instructors’ perceptions, the nature of the e-learning technology to be adopted, and technology infrastructure available among other factors.

Human Interactions (HI) and behavioral intentions relationship is strongly supported in Egypt (r 2= 0.467) as apposed to the situation in the UAE (r 2= 0.063). Interestingly, HI has a positive relationship with behavioral intentions to adopt e-learning technologies in Egypt. Favoring face-to-face interaction entails instructors’ sociability and willingness to interact with students. Therefore, in this case, e-learning technologies can be seen as supplementary and complementary enablers for further interaction with students. Moreover, the face to face interaction may result into increased need for using e-learning technologies to answer further student enquires, or share video links for more explanation, or another ways of using e-learning technology that is triggered by face to face interactions. For UAE, instructors’ behavioral intentions are not affected by preference to HI. This can be attributed to the enforcement of institutional policies in the UAE and the fact that instructors working in the UAE are mainly professional expats who are committed to use e-learning technologies as deployed by the institution, irrespective of their preferences.

For the university in Egypt, more autonomy is provided to instructors, which partly explains the negative instructors’ TR- behavioral intentions relationship. The higher instructors’ TR in Egypt the more likely they are to recognize the potential of having negative consequences including technology failures, less protection of intellectual property, reduced face-to face interaction among other factors, limited technical support capabilities. This could be a reason why TR affects behavioral intentions through attitude. If instructors’ TR resulted in shaping a positive attitude then a positive behavioral intention is expected in Egypt. Therefore, education managers in Egypt need to develop and reinforce a positive attitude towards e-learning technologies since it is found to be a prerequisite to behavioral intentions.

In UAE, TR have a direct positive influence on behavioral intentions. The technology infrastructure, institutional policies and regulations, reduced autonomy and increased accountability forces a direct relationship that is less distorted by the factors mentioned at the university in Egypt.

6 Conclusion

Despite the similarity in the level of TR, attitudes, and behavioral intentions, the dynamics (mechanism) of the relationship among these variables are found to interact differently. Some strategies are expected to work well at both locations while others would be more relevant at one university than the other. Instructors’ TR is an important element of e-learning technology adoption that is commonly overlooked. Education managers are expected to incorporate prior knowledge of instructors TR and identify which element(s) have the largest effect on instructors’ behavioral intentions when setting technology related policies and plans. Such an understanding can help education managers predict instructors’ e-learning technology utilization, and develop most effective and relevant strategies for enhancing instructors’ usage.

7 Limitations and future research direction

The choice of non-probability judgemental sample limits the results within the frame and context of application. The current study did not support human interaction as a moderator that can partially explain the attitude – behaviour gap. According to Westaby (2005), drivers and inhibitors for technology adoption are context specific, therefore, there is an obvious research need for qualitative research to identify which most relevant constructs can potentially explain and close the attitude-behaviour gap in an educational setting.

The current study has a limited sample size due to a small population size and the domain in which the study conducted is relatively narrow. Consequently, findings are strictly limited to the domain in which the study is conducted and future research is required to re-examine and verify TRI in different kinds of universities with a larger sample within education.