Barriers to adopting technology for teaching and learning in Oman

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Computers & Education 53 (2009) 575–590

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Barriers to adopting technology for teaching and learning in Oman Said Al-Senaidi, Lin Lin *, Jim Poirot Department of Learning Technologies, University of North Texas, 3940 North Elm Street, Denton, TX 76207, USA

a r t i c l e

i n f o

Article history: Received 2 November 2008 Received in revised form 18 March 2009 Accepted 24 March 2009

Keywords: Country-specific developments Computer-mediated communication Distance education and telelearning Pedagogical issues Post-secondary education

a b s t r a c t This study investigates the perceived barriers to adopting information and communication technologies (ICT) in Omani higher education. One hundred faculty members from four different departments at the College of Applied Sciences in Oman participated in the study. The participants took a survey, which was developed based on the Western literature. Five factors were extracted from the survey: lack of equipment, lack of institutional support, disbelief of ICT benefits, lack of confidence, and lack of time. The findings showed that the faculty members perceived moderate degrees of barriers in applying ICT to their teaching practices. Group differences based on gender, academic rank, and academic field were generally not found except for the interaction effects on the barriers related to lack of equipment, disbelief of ICT benefits, and the overall mean. Male faculty members with less usage of ICT perceived more barriers regarding the lack of computing equipment, disbeliefs of ICT benefits, and the overall barrier than the female counterparts. It is recommended that the survey be further refined to include more subtle and culturally relevant items, larger sample sizes, and more heterogeneous samples to validate and extend the findings. Important implications of this study include a need to provide more institutional support, technical training, and personal time for faculty members to learn and upgrade their knowledge and skills in educational technologies. Published by Elsevier Ltd.

1. Introduction The growth of information and communication technologies (ICT) has dramatically reshaped the teaching and learning processes in higher education (Pulkkinen, 2007; Wood, 1995). ICT for education is more critical today than ever before since its growing power and capabilities are triggering a change in the delivery means of education (Pajo & Wallace, 2001). The higher education institutions around the globe have increasingly adopted ICT as tools for teaching, curriculum development, staff development, and student learning (Kumpulainen, 2007; Usluel, Asßkar, & Basß, 2008). Although ICT has the potential of improving educational methods and the quality of teaching and learning, the advantages of ICT are often under-realized (Surry & Farquhar, 1997). The adoption of ICT at universities is often poorly implemented and is based on unfounded optimism (Taylor, 1998). A large numbers of faculty members are still hesitant or reluctant to adopt technology for teaching tasks (Jacobson, 1998). Research has found serious obstacles to fully integrating technology into the teaching and learning processes in higher education (Becta, 2004). In addition, there are no universal solutions to the problems as the ICT adoption is not merely a technical issue. Instead, the rate of adoption is affected by factors such as innovation characteristics and various economic, sociological, organizational, and psychological variables (Straub, Keil, & Brenner, 1997). Straub et al. (1997) used the Technology Acceptance Model (TAM) in a cross-cultural study with participants from Japan, Switzerland, and the United States. They discovered that TAM exhibited fidelity for the US and Switzerland, but not in Japan, which suggested that the model may not predict technology use across all cultures Straub et al. (1997). In another intercultural study, Pelgrum (2001) reported that there was a substantial variation regarding the most significant barriers to ICT between teachers in different countries. Hence, research into ICT barriers in the Arabic culture is needed. The purpose of the study is to extend the existing research by exploring the barriers to adopting technology for teaching and learning in the Omani cultural context. It aims at identifying the barriers to adopting Abbreviations: ICT, information and communication technologies; TAM, technology acceptance model; DoI, diffusion of innovation; TRA, theory of reasoned action; BI, behavioral intention; SQU, Sultan Qaboos University; CAS, College of Applied Sciences. * Corresponding author. Address: 9529 Courtright Drive, Keller, TX 76248, USA. Tel.: +1 817 741 7365 (Home); +1 940 369 7572 (Office); fax: +1 940 565 4194. E-mail addresses: [email protected] (S. Al-Senaidi), [email protected], [email protected] (L. Lin), [email protected] (J. Poirot). 0360-1315/$ - see front matter Published by Elsevier Ltd. doi:10.1016/j.compedu.2009.03.015


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S. Al-Senaidi et al. / Computers & Education 53 (2009) 575–590

technology for Omani faculty members using a culturally relevant survey, describing the perceived obstacles by faculty members, and exploring the possible group differences based on gender, academic field, and academic rank regarding the use of ICT.

2. Literature review 2.1. Theoretical models of ICT adoption With the ongoing development of ICT and the diverse fields it affects, various theoretical models have been proposed for a better understanding concerning its diffusion, adoption, acceptance, and usage (Davis, 1989; Rogers, 2003; Scurry, Ensminger, & Haab, 2005; Taylor and Todd, 1995; Venkatesh and Davis, 2000; Venkatesh, Morris, Davis, & Davis, 2003; Yi, Jakson, Park, & Probst, 2006). Among them, three theories have been influential: Davis and associates’ Technology Acceptance Model (TAM) (Davis, Bagozzi, & Warshaw, 1989), Rogers’ (1995, 2003) Diffusion of Innovation (DoI) theory, and Rieber and his associates’ five-step hierarchical model of technology diffusion (Hooper & Rieber, 1995; Rieber & Welliver, 1989). Davis et al. (1989) adapted Fishbein and Ajzen’s (1975) Theory of Reasoned Action (TRA) and developed the Technology Acceptance Model (TAM) to explain the behavioral intention and actual behavior of a person’s computer usage. According to TRA, a person’s specified behavior is determined by the person’s attitude and subjective norm. Behavioral intention (BI) is a prerequisite of the likelihood of performing a specific behavior (Ajzen & Fishbein, 1980). Hence, TAM postulates that a person’s computer usage is mainly affected by his or her BI. Furthermore, TAM proposes the causal links among ‘‘perceived usefulness” (U), ‘‘perceived ease of use” (EOU), attitude (A), and BI. According to TAM, A is a major determinant of BI (A–BI link), which is influenced by U and EOU (A = U + EOU). U has also been linked to BI (U–BI link). Besides, the TAM proposed that U and EOU are affected by various external variables (such as user characteristics and organizational factors). External variables are expected to influence BI by affecting beliefs (U and EOU) and attitudes (A) and then influencing actual behavior (Davis et al., 1989). For the past two decades, substantial empirical evidence has supported the TAM. In a meta-analysis study on TAM with 88 published studies, King and He (2006) concluded that the TAM is a valid and robust model. Rogers’ (1995, 2003) Diffusion of Innovation (DoI) Theory sets up another foundation for many studies related to technology adoption and diffusion. Rogers defined diffusion as ‘‘the process by which an innovation is communicated through certain channels over time among the members of a social system” (Rogers, 1995, p. 10) and an innovation as ‘‘an idea, practice or object that is perceived as new by the individual” (Rogers, 1995, p. 11). Roger’s diffusion theory involves four main elements: innovation, communication channels, time and the social system. Each of these elements influences the adoption or rejection of an innovation in a complicated, interdependent way. Rogers’ diffusion theory contains four major parts: diffusion process, adopter categories, perceived attributes, and rate of adoption. First, diffusion is a process that occurs over time and can be seen as having five distinct stages – knowledge, persuasion, decision, implementation, and confirmation. Second, members of a population vary greatly in their willingness to adopt a particular innovation. Individual characteristics can be used to divide the population into five categories – innovators, early adopters, early majority, late majority, and laggards. Third, people’s perception of an innovation influences their adoption decision. The five perceived attributes of innovation have been shown to have strong influence – trialability (the degree to which potential adopters can experiment with the new behavior), observability (the degree to which the results of an innovation are visible to others), relative advantage (the degree to which a new system is perceived as being better than the alternative it supersedes), complexity (the degree to which an innovation is perceived as difficult to understand and use), and compatibility (the similarity with previously adopted innovations). Finally, the rate of adoption indicates the relative speed with which members of a social system adopt an innovation. Innovations are diffused over time in a pattern that resembles a forward-leaning sshaped curve where time is in the X axis and the number of organizations adopting is on the Y axis (Rogers, 1995, 2003). The diffusion theories have been used to explain the phenomena of technology diffusion in higher education. For instance, many authors have used Roger’s concepts of adopter categories and rate of adoption to investigate the differences between early and late adopters, the perceived barriers and incentives to adoption of web-based education (WBE) innovations, and the rate of adoption of WBE innovations (Ebersole & Vorndam, 2002; Ferrarini & Poindexter, 2001; Jacobson, 1998). Jacobson (2000) used the five stages of the innovation decision process as a conceptual framework for the consideration of individual stories about adopting technology for teaching and learning. Other authors (Bronack and Riedl, 1998; Jones, 1999) have used the perceived characteristics of innovations to examine why some innovations work and others do not. Surry and Farquhar (1997) discuss how four of the most commonly examined diffusion theories have been used to build diffusion theories specific to instructional technology. Yi et al. (2006) reported relative advantage, complexity, observability, and image as the most important factors in predicting users’ intentions to make use of technology. In a study on the use of the Internet as an instructional tool in Brazil, Martins, Steil, and Todesco (2004) found that the two most significant predictors are trialability and observability. Surry and Gustafson (1994) concluded that compatibility, complexity, and relative advantage are important factors when introducing an innovation into instructional settings. Rieber and Welliver (1989) and Hooper and Rieber (1995) proposed a five-step model to describe the stages of growth associated with infusing a new technology into teaching and learning: familiarization, utilization, integration, reorientation, and evolution. In the familiarization phase, the teacher simply learns how to use the technology. At the utilization phase, the teacher uses technology in the classroom but has little understanding of, or commitment to, the technology as a pedagogical and learning tool. During the integration phase, the technology becomes an integral part of the course in terms of delivery, learning, management, or other aspect of the class. In the reorientation phase, the teacher uses the technology as a tool to facilitate the reconsideration of the purpose and function of the classroom. Finally, teachers who reach the evolution phase are able to continually modify the classroom structure and pedagogy to include evolving learning theory, technologies, and lessons learned from experience (Hooper & Rieber, 1995). According to Hooper and Rieber (1995), many teachers progress only to the integration phase and do not transform their philosophical orientation of how learning can occur in the classroom through technology. They further stated that each level on the hierarchy requires a different set of support services, funding, time, and administrative and student expectations. Mismatches in a teacher’s level of technology adoption with certain internal or external sources of potential barriers provided an almost certain failure to adopt a technology in the classroom. Researchers adopting the Rieber et al.’s model reported that the potential barriers affecting an individual’s technology adoption are often a combination of several factors - sociocultural factors such as economics and location (Bereiter, 1994), personological variables of



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the teacher such as age, gender, attitude, and beliefs (Bradley & Russell, 1997; European Commission, 2003), exposure to, and adoption of emerging technologies within the practice of teaching such as levels of technology acceptance and adoption (Anderson, 1993; Hooper and Rieber, 1995; Rieber and Welliver, 1989). Obviously, there is no single, unified, universally accepted theory of adoption and diffusion. Each theoretical model addresses different aspects of the diffusion process or a different type of innovation or organization (Scurry, Ensminger, & Haab, 2005). 2.2. Empirical evidences of barriers to ICT diffusion In addition to theoretical exploration, a rich body of empirical research has focused on barriers to ICT adoption. For instance, Rogers (1999) interviewed 28 college and university teachers in Minnesota and found that the four top barriers are funds not specified for technology-related needs, lack of sharing best practices across system, need of technical support staff, and need of release time and time for training faculty and staff. Chizmar and Williams (2001) reported that three major barriers to adoption of instructional technology for the majority of faculty at Illinois State University are lack of institutional support, lack of financial support, and lack of time to learn new technologies. By investigating a sample of 125 faculty members in the College of Sciences and Humanities at Ball State University, Butler and Sellbom (2002) found that major factors affecting ICT adoption are technology reliability, learning to use new technologies, uncertainty about its worth, and lack of institutional support. After reviewing studies on barriers to technology adoption at the international level for teachers across the education levels, Becta (2004) drew the following conclusions: (a) A very significant determinant of teachers’ levels of engagement in ICT is their level of confidence in using the technology. Teachers who have little or no confidence in using computers in their work will try to avoid them altogether. (b) Levels of access to ICT are significant in determining levels of use of ICT by teachers. (c) Inappropriate training styles result in low levels of ICT use by teachers. (d) Teachers are sometimes unable to make full use of technology because they lack the time needed to fully prepare and research materials for lessons. (e) Technical faults with ICT equipment are likely to lead to lower levels of ICT use by teachers. (f) Resistance to change is a factor which prevents the full integration of ICT in the classroom. (g) Teachers who do not realize the advantages of using technology in their teaching are less likely to make use of ICT. (h) There is little evidence that supports the view that age affects levels of teachers’ ICT use. (h) There is some evidence to suggest that teachers’ gender has an effect on the degree to which they use ICT, with male teachers making more use of ICT than female teachers, and with female teachers reporting greater levels of computer anxiety than male teachers. Several authors classified barriers into two types: the external or first-order barriers, which relate to the limited resources, lack of time, lack of technical support, and technical problem, and the internal or second-order barriers, which relate to the teachers’ attitudes to ICT such as lack of confidence, resistance to change and negative attitudes, and no perception of benefits (Ertmer, 1999; Snoeyink and Ertmer, 2001). Another way of grouping the barriers is to consider whether they relate to the individual (i.e., teacher level barriers) such as lack of time, lack of access to quality computing resources, lack of effective training and technical problems, or to the institution (i.e., school level barriers) including lack of time, lack of confidence, resistance to change and negative attitudes, and no perception of benefits (Veen, 1993). The lack of time could fall under either category as teacher’s lack of time may be due to the systems put in place by the school, making it therefore a school level barrier. The lack of time may also be caused by the teacher’s own organization and preferences, which makes it a teacher level barrier. Understanding the extent to which these barriers affect individuals and institutions can help decide how they are to be tackled (Becta, 2004). It has been argued that there are close relationships between many of the identified barriers to ICT use. Any factors influencing one barrier are likely to influence several other barriers. For example, teacher confidence is directly affected by levels of personal access to ICT, levels of available technical support and the amount and type of training available, all of which can be seen as barriers to ICT themselves (Ertmer, 1999). 2.3. Higher education in Oman and educational technology in the Omani context The Sultanate of Oman is situated in the south-eastern part of the Arabian Peninsula. There are more than 20 universities and colleges in both public and private sectors. These institutions increasingly rely on information and communication technologies to develop their students’ skills and organizational infrastructures. The Ministry of Higher Education has converted six colleges of education to the College of Applied Sciences (CAS) in effect for the 2005/2006 academic year. The six colleges/departments – Nizwa, Ibri, Sur, Sohar, and Rosraq – offer 5 year academic programs, including a one-year foundation course, followed by four-year Bachelor’s degree courses in information technology, international business administration, design or communications. A total of 2010 students have been admitted to the foundation courses for the current academic year. At present a total of 7876 male and female students are studying at these colleges (Omani Ministry of Information, 2006). Omani’s utilization of ICT in higher education has proceeded rapidly in the past decade (Al Musawi & Abdelraheem, 2004). For instance, when Sultan Qaboos University (SQU) began to implement e-learning using WebCT in 2001, there were only 8 online courses and 981 users. By the end of autumn 2002, 40 courses were offered to different colleges at SQU with 3001 students. This indicates a rapid adoption of WebCT utilization within the university (Al Musawi & Abdelraheem, 2004). Furthermore, faculty and students at SQU seemed to be in favor of the new technology (Akinyemi, 2003). When CAS began to implement e-learning using Blackboard in 2007, there were only 15 online courses and 581 users. By the end of fall 2008, 30 courses will have been offered to different departments at CAS with 890 students. This indicates a rapid adoption of Blackboard utilization within the colleges and universities. Today Omani university and college students can easily navigate the Internet and search for resources. Studies on e-learning carried out in Oman show that e-learning is needed and the web-assisted instruction is equally effective as face-to-face instruction in students’ achievements (Osman and Ahmed, 2003). Some


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researchers have predicted that e-learning at SQU and higher education in Oman in general is promising regardless of challenges in technical, logistical and human factors (Al Musawi & Abdelraheem, 2004). However, educational technology in Omani higher education is characterized by the underutilization of advanced technology and unsatisfactory staff skills to fulfill the required level (Al Khawaldi, 2000). Studies show that teachers are, in many instances, short of the required preparation time to apply the new educational innovations (Abdelraheem & Al Musawi, 2003). In addition, many teachers do not use, and sometimes resist the use of technology. Possible explanations for such resistance are: poorly designed software, technophobia, doubt that technology improves learning outcomes, and fear of redundancy. Research also explains that new technologies require new skills; and that Omani higher education institutions are falling behind in professional development (Al Musawi, 2007).

3. Methodology 3.1. Research questions Although there are some studies on the general ICT instructional uses in Oman (e.g., Abdelraheem & Al Musawi, 2003; Abu Jaber & Osman, 1996; Al Khawaldi, 2000; Al Musawi, 2002; Al Musawi & Akinyemi, 2002; Al Musawi & Abdelraheem, 2004; Al Rawahy, 2001, Bialo & Soloman, 1997; Boyd, 1997; Kook, 1997), investigations into barriers to adopting technology for faculty members in Omani higher education remain scarce (Akinyemi & Al Musawi, 2002). Thus, this study focuses on the barriers to adopting technology in teaching and learning for the College of Applied Sciences faculty members. Three questions guide the study: Are the general findings on barriers to adopting technology mainly from the Western culture applicable to the Arabic culture? What are the perceived barriers to adopting ICT by CAS faculty members? Of the perceived barriers to ICT adoption, are there any group differences in terms of gender, academic rank, and academic field?

3.2. Research participants One hundred faculty members (51 males) from four different departments at CAS participated in the study. Most of them are junior faculty members including 44 assistant professors and 43 lecturers. More than half of the participants are in the age range of 30–44 year-old. Thirty-five percent of the faculty are 45 year-old and above. The information about their actual ICT usage was also collected. The demographic information of these teachers is presented in Table 1. Whereas the numbers of participants on gender and academic fields were approximately equal in each group, technology usage and academic rank had imbalanced group sizes, which may pose a challenge to the subsequent statistical analysis on group differences due to the small cell sizes. Thus, these two variables were recoded as shown in Table 1 to obtain relatively larger cell sizes.

Table 1 Demographic variables of the participants (N = 100). Demographic variables

Frequency

Percentage (%)

Gender 1 = male 2 = female

51 49

51 49

Age 1 = under 30 years old 2 = 30–44 years old 3 = 45 years old and above

12 53 35

12 53 35

Technology usage 1 = once or more a day 2 = once a week 3 = twice a month 4 = once a month 5 = never

1 26 33 34 6

1 26 33 34 6

Teaching field 1 = information technology 2 = communication 3 = business 4 = English language

24 29 22 25

24 29 22 25

Career rank 1 = professor 2 = associate professor 3 = assistant professor 4 = lecturer

3 10 44 43

3 10 44 43

Technology usage recoded 1 = light user (i.e., twice a month and less) 2 = heavy user (i.e., once a week and more)

73 27

73 27

Career rank recoded 1 = tenure track (i.e., professors) 2 = lecture

57 43

57 43



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3.3. The survey development As there are no standardized measurement instruments on barriers to adopting technology available, an 18-item instrument ‘‘Barriers to Adopting Technology for Teaching and Learning in Oman” was constructed for the study (see Appendix A). These items were primarily drawn from the Western literature related to barriers to adopting technology. The major categories of barriers which were identified and intended to be tackled on in this study include: lack of teacher confidence (Bosley & Moon, 2003, Bradley & Russell, 1997; Fabry & Higgs, 1997; Larner & Timberlake, 1995); resistant to change and negative attitude (Cuban, Kirkpatrick, & Peck, 2001; Ertmer, 1999; Mumtaz, 2000; Snoeyink and Ertmer, 2001; Veen, 1993), no perceived benefits (Cox, Preston, & Cox, 1999; Mumtaz, 2000; Snoeyink & Ertmer, 2001; Yuen & Ma, 2002); lack of time (Cuban, 1999; Cuban et al., 2001; Ebersole and Vorndam, 2002; Fabry and Higgs, 1997; Jacobson, 1998; Preston, Cox, & Cox, 2000; Snoeyink and Ertmer, 2001); lack of training (Kirkwood, Van Der Kuyl, Parton, & Grant, 2000; Preston et al., 2000; Simpson, Payne, Munro, Hughes, & Lynch; 1999; Veen, 1993; Wild, 1996); lack of access to computing resources (Bosley and Moon, 2003; Fabry and Higgs, 1997; Guha, 2000; Mumtaz, 2000; Pelgrum, 2001; Preston et al., 2000); and lack of institutional support (Butler and Sellbom, 2002; Cuban, 1999; Snoeyink and Ertmer, 2001). The survey was validated for facial validity by a panel of American and Arabic experts in computing education and pilot-tested with several faculty members at CAS. After the survey was finalized, it was delivered to all of the faculty members at four departments including information technology, international business administration, design or communications, and English language. The faculty members were asked to indicate to what extent they agree or disagree with each of the statements on a Likert rating scale with five options (1 = strongly disagree, 2 = disagree, 3 = neutral, 4 = agree, and 5 = strongly agree).

4. Results 4.1. The factorial structure of the survey In order to find the possible factorial structure of the survey, a series of factor analyses were performed. First, the 100 observations for the 18 items were submitted to principal component analysis with Promax, an oblique rotation strategy. The purpose of this step was to decide whether a high order factor analysis would be required. The results showed the correlations among the six extracted factors were all below .270, below the usual cutoff standard of .40 (Thompson, 2004). These low correlation coefficients from the oblique rotation indicated that an orthogonal rotation was appropriate for this study and no high order analysis was required. Next, the same set of data was submitted to principal component analysis with Varimax as the orthogonal oration method. Six factors were suggested from the K1 method (i.e., Eigenvalue greater than 1). As the criterion of Eigenvalue 1 was not generally reliable, parallel analysis was performed 10 times to get a stable average Eigenvalue for each factor. The comparisons of Eigenvalues between the K1 Varimax method and the parallel analysis technique suggested a five-factor structure, explaining 53.61% of the total variance. However, item 11 had weak correlations with all of the five factors, less than the usual cutoff criterion of .40 (Hair, Black, Babib, Anderson, & Tatham, 2006). In the next run without item 11, item 10 had the same problem of weak correlations as did item 11 previously. After item 10 was deleted, all of the remaining 16 items had a significant loading on only one factor. The Scree plot (see Appendix B) also indicated that there were five factors for the 16-item survey. Using the cutoff value of .45 for structure coefficients as the criterion (Hair et al., 2006), three items (2, 3, and 14) had substantial coefficients with factor value one as in Table 2. The descriptions of these items related to the concern of hardware and software. Thus, this factor was named as lack of computing equipment. Three items (8, 9, and 15) heavily loaded on factor two which were about the institution’s training, administrative, and financial support. Hence, it was named as lack of institutional support. Items 5, 6, 12, and 13 loaded on factor three. These items were all regarding a teacher’s disbelief of the value of ICT. So this was called disbelief of ICT benefits. Three items (1, 16, and 17) loaded on factor four, which was about fear or deficiency of technical skills. Consequently, factor four was called lack of confidence in ICT. The remaining three items (4, 7, and 18) loaded on factor five – lack of time. Table 2 shows these five factors explained 57.4% of the total variance, with a similar contribution from each factor. 4.2. The psychometric properties of the survey Table 3 shows that the internal consistency reliability coefficients in the Cronbach alpha for the entire survey and the five factors were .73, .71, .63, .57, .54, and .53, respectively. Although somewhat low, especially for the individual factors, these reliability coefficients were acceptable for an exploratory study (DeVellis, 1991). The low alpha coefficients for the factors could be attributed to the fact that fewer items were bundled in each factor as the internal consistency reliability tended to be larger when more times were included in a factor (Crocker and Algina, 2006). The convergent and discriminate validity were examined through the inter-factor correlations. Table 4 shows most of the correlations among the five factors were significantly correlated with one another in a positive way, implying that they all targeted on a common construct. Thus, the convergent constructive validity was supported. On the other hand, these correlation coefficients were less than .30. In other words, the shared variance between any two factors was less than 9%, suggesting the discriminate constructive validity was supported as well. In summary, the above psychometric results on reliability and validity suggested that the survey was acceptable and valid in the present sample.


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Table 2 Factor pattern/structure matrix for 16 items on the barriers to adopting technology scale rotated to the varimax criterion (N = 100). Ma

Items

2. Faculty lack of access to essential hardware. 3. Faculty lack of access to essential software 14. Software is inappropriate for meeting students’ needs. 8. Faculty’s university cannot provide convenient time for training. 9. Lack of administrative support for the adopting technology into teaching and learning. 15. Lack of adequate financial support to develop technology- based activities. 5. Faculty thinks technology is unreliable. 6. Faculty lack of time to adopt technology 12. Faculty perception that classroom management is more difficult when using technology. 13. Faculty lack of interest in using technology in teaching and learning. 1. Faculty lack of basic technology skills for adapting technology in teaching and learning 16. Faculty fear of using technology. 17. Students fear of using technology. 7. Students require too much time for email them (Email, Blackboard email, etc). 18. Faculty lack of time for implementation of course delivery Lack of technical support for technology. Trace % Variance Total% variance: 57.4% Mean h2

3.02 3.02 2.72 3.66 3.69 3.60 2.70 3.26 2.98 2.82 2.82 2.67 2.93 3.53 3.02 3.81

SD

.97 1.07 1.02 1.05 .94 1.07 .91 1.02 .96 1.14 .94 1.05 1.10 .96 1.06 .98

Factorb F1

F2

F3

F4

F5

h2

.81 .82 .67 .08 .06 .03 .21 .12 .05 .05 .38 .24 .16 .11 .06 .03 2.08 13%

.01 .07 .12 .71 .74 .69 .04 .37 .09 .30 .34 .09 .15 .13 .05 .13 1.96 12%

.22 .09 .21 .27 .11 .07 .73 .52 .66 .58 .14 .06 .10 .04 .13 .05 1.83 11%

.11 .01 .00 .01 .14 .14 .19 .07 .25 .16 .50 .73 .76 .18 .15 .24 1.66 10%

.01 .13 .11 .18 .06 .10 .10 .19 .05 .01 .10 .23 .06 .73 .65 .69 1.62 10%

.71 .70 .52 .62 .59 .50 .23 .46 .52 .45 .54 .65 .65 .59 .46 .56

.57

a

5 point Likert scale with 1 = strongly disagree and 5 = strongly agree. Factor I, lack of equipment. Factor II, lack of institutional support. Factor III, disbelief of ICT benefits. Factor IV, lack of confidence. Factor V, lack of time. Items are sorted by factors. Structure coefficient greater than .45 are underlined. Percent variance is post-rotated. As there are just 16 items, ‘‘% Variance” is Trace divided by 16 times 10. b

Table 3 M, SD, and Cronbach alphas for barriers to adopting technology (N = 100).

Overall Scale Factor 1 (lack of equipment) – 3 items Factor 2 (lack of support) – 3 items Factor 3 (disbelief of the ICT benefits) – 4 items Factor 4 (lack of confidence) – 3items Factor 5 (lack of time) – 3 items

M

SD

Alpha

3.14 2.92 3.65 2.94 2.81 3.45

.45 .81 .77 .67 .75 .72

.73 .71 .63 .57 .54 .53

Table 4 The inter-factor correlation matrix (N = 100).

F1 F2 F3 F4 F5

F1

F2

F3

F4

F5

– .03 .28** .22* .20*

– .30** .13 .26**

– .20* .27**

– .23*

–

Note: p < .05. p < .01. F1, lack of equipment; F2, lack of support; F3, disbelief of the ICT benefits; F4, lack of confidence; F5, lack of time.

4.3. Faculty member’s perception of barriers to adopting technology At the individual item level, Table 1 indicates that the means were around 3.00 with a standard deviation of around 1.00, in the range of 2.67–3.81. The five items in which the faculty members disagreed most (in order) were: item 16 – faculty fear of using technology, item 5 – faculty think technology is unreliable, item 14 – software is inappropriate for meeting students’ needs, item 13 – faculty lack of interest in using technology skills for adapting technology, and item 1 – faculty lack of basic technology skills. The four items in which the faculty members agreed most (in a sequence) were: item 18 – lack of technical support for technology, item 9 – lack of administrative support, item 8 – faculty’s university cannot provide convenient time for training, and item 15 – lack of adequate financial support. These results may suggest that: (1) the faculty members at CAS realized the values of ICT in teaching and learning; (2) they were interested in ICT; and (3) they already possessed the basic technical skills. However, they were dissatisfied with technical, administrative, training, and financial support. At the factor mean level, Table 3 indicates that three factor means were below the theoretical mean (i.e., 3.00): lack of computing equipment, lack of confidence, and disbelief of ICT benefits, whereas two factor means were above 3.00: lack of support and lack of time. In other words, the faculty members tended to disagree with the statements about barriers being from a lack of computing equipment, lack of confidence in their technical skills, and disbeliefs of ICT benefits. But they tended to agree that the major barriers to adopting ICT were lack of institutional support and lack of time. Collectively, the faculty members at CAS had a scale mean of 3.14, slightly above the theoretical mean. In other words, they perceived some barrier to adopting ICT in the teaching and learning processes.


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Table 5 further lists the means and standard deviations in each subgroup. Overall, these means were around the theoretical mean of 3.00 in each group. Although there seemed to be some differences among the groups, the mean differences were subjected to statistical testing. 4.4. The group differences Four grouping variables were considered in this study: gender, academic field, academic rank, and frequency of the ICT usage. One possible statistical analysis method to consider was a four-way ANOVA. However, as the sample size was limited, many cells would be left empty or with few instances. This was also true for the option of three-way ANOVAs. Even for two-way ANOVAs, only did the cell sizes work in three two-way ANOVAs without the variable of academic field meet the minimum cell size of 10 (Hair et al., 2006). Hence, two-way ANOVAs of gender by rank, gender by usage, and rank by usage on six means (i.e., five factor mean and one scale mean) were performed to examine these group differences. Before conducting the two-way ANOVAs, first, it was necessary to check the three assumptions for a two-way ANOVA: independent and random samples from the defined populations, normal distribution of the dependent variable, and homogeneity of variance (Hinkle, Wiersma, & Jurs, 2003). Although there was no way to justify if this sample was random from its population, just as in many other studies using convenient samples, the effect of the violation to the first assumption on the Type I error rate was minimal (Glass, Peckham, & Sanders, 1972). For the second assumption, as shown in Table 5, all factors except for the factor of lack of time were normally distributed. Therefore, to be consistent, no data transformations were conducted, even for factor five. In addition, a two-way ANOVA is generally robust as to the possible violation of this assumption (Hinkle et al., 2003). The last assumption of homogeneity of variance was met in all of the cases. In summary, the three prerequisites for a two-way ANOVA were basically met in this study. Table 6 shows that there were neither main nor interaction effects of gender and academic rank on all of the factor means and the scale mean. Also the practical significances of the main or interaction effects in g2 were trivial or small in all cases; less than 3% (Cohen, 1988). Male or female faculty members, whether they were in the tenure-track or worked as lecturers, had similar perceptions on various aspects of barriers to adopting technology. Similarly, neither main nor interaction effects of academic rank and technology usage were found on any of the six means. The practical significances were trivial as well (see Table 8). All faculty members in different academic ranks or with different frequencies of technology usage had similar views of barriers to adopting ICT in their practices. For the group differences by gender and technology usage, the interaction effect was found on the factors of lack of equipment, disbelief of ICT benefits, and on the scale mean: F(1, 96) = 4.81, p < .05; F(1, 96) = 4.54, p < .05, and F(1, 96) = 7.29, p < .01, respectively. In addition, a main effect of gender was found on the scale mean: F(1, 96) = 5.47, p < .05. However, the effect sizes in g2 were small for all of these significant effects (Cohen, 1988), between 4.5% and 7.1%. Appendix C presents the plots of the three significant interaction effects graphically. All of them are disordinal interactions. Hair et al. (2006) argued that a main effect should not be interpreted in a significant disordinal interaction as the group means on one variable were affected by different levels of the other variable. For this reason, the main effect of gender on the scale mean was not interpreted.

Table 5 Descriptive statistics by demographic variables (N = 100).

Gender (N = 100) Male (n = 51) Female (n = 49) Age (N = 100) 29 year-old and under (n = 12) 30–44 year-old (n = 53) 45 year-old and above (n = 35) Usage (N = 100) Frequent (n = 1) Often (n = 26) Sometimes (n = 34) Rarely (n = 33) Never (n = 6) Field (N = 100) Technology (n = 24) Communications (n = 29) Business (n = 22) English (n = 25) Rank (N = 100) Professor (n = 3) Associate prof (n = 10) Assistant prof (n = 44) Lecturer (n = 43) Usage recoded Heavy user (n = 27) Light user (n = 73) Rank recoded Tenure track (n = 57) Lecturer (n = 43)

F1

F2

F3

F4

F5

Total

2.92(.81) 2.96(.82) 2.88(.81) 2.92(.82) 3.17(.70) 2.90(.85) 2.87(.79) 2.92(.82) 3.33 2.86(.77) 2.94(.85) 2.97(.79) 2.72(1.10) 2.92(.81) 3.03(.86) 2.79(.80) 2.94(.61) 2.95(.95) 2.92(.81) 2.78(.19) 3.27(.51) 2.79(.90) 2.98(.78)

3.65(.77) 3.74(.66) 3.56(.87) 3.65(.77) 3.83(.52) 3.67(.77) 3.77(.85) 3.65(.77) 3 3.81(.84) 3.73(.72) 3.44(.70) 3.72(1.06) 3.65(.77) 3.60(.79) 3.68(.76) 3.48(.81) 3.81(.75) 3.65(.77) 3.00(.58) 3.63(.51) 3.67(.85) 3.67(.76)

2.94(.67) 2.96(.65) 2.92(.69) 2.94(.67) 3.19(.53) 2.94(.69) 2.86(.67) 2.94(.67) 2.5 2.92(.70) 3.07(.71) 2.92(.61) 2.46(.49) 2.94(.67) 2.73(.57) 2.96(.61) 3.03(.64) 3.04(.82) 2.94(.67) 3.08(.63) 3.05(.61) 2.79(.62) 3.06(.72)

2.80(.74) 2.82(.69) 2.80(.81) 2.81(.74) 3.17(.58) 2.82(.71) 2.67(.82) 2.81(.74) 2.33 2.88(.72) 2.69(.73) 2.80(.82) 3.28(.44) 2.81(.74) 2.88(.65) 2.74(.70) 2.74(.76) 2.88(.89) 2.81(.74) 2.67(.67) 2.93(.68) 2.79(.79) 2.81(.74)

3.45(.72) 3.55(.65) 3.35(.78) 3.45(.72) 3.64(.68) 3.35(.81) 3.55(.62) 3.45(.72) 3 3.62(.54) 3.41(.62) 3.45(.90) 3.06(.85) 3.45(.72) 3.33(1.03) 3.34(.61) 3.42(.62) 3.72(.50) 3.45(.72) 3.89(.51) 3.20(.67) 3.42(.66) 3.52(.79)

3.14(.45) 3.19(.44) 3.09(.46) 3.14(.45) 3.39(.29) 3.12(.42) 3.08(.52) 3.14(.45) 2.81 3.20(.49) 3.16(.46) 3.11(.44) 3.01(.28) 3.14(.45) 3.09(.44) 3.09(.38) 3.12(.44) 3.27(.53) 3.14(.45) 3.08(.38) 3.21(.38) 3.07(.45) 3.20(.47)

2.88(.76) 2.94(.83)

3.78(.84) 3.60(.75)

2.91(.69) 2.95(.66)

2.86(.71) 2.79(.76)

3.59(.54) 3.40(.77)

3.19(.49) 3.12(.44)

2.87(.84) 2.98(.78)

3.63(.79) 3.67(.76)

2.85(.62) 3.06(.72)

2.81(.76) 2.81(.74)

3.40(.66) 3.52(.79)

3.10(.43) 3.20(.47)


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Table 6 ANOVA tables for the group difference on gender by academic rank. Factors

SS

df

MS

F

p

g2

Lack of equipment Gender Rank Gender rank Error Total

.34 .28 1.59 63.07 65.14

1 1 1 96 99

.34 .28 1.59 .66

.52 .43 2.41

ns ns ns

.005 .004 .025

Lack of institutional support Gender Rank Gender x Rank Error Total

..69 .48 .20 58.13 59.19

1 1 1 96 99

..69 .48 .20 .61

1.14 .08 .33

ns ns ns

.012 .001 .003

Disbelief of ICT benefits Gender Rank Gender x Rank Error Total

.09 1.01 1.13 41.93 44.14

1 1 1 96 99

.09 1.01 1.13 .44

.22 2.31 2.58

ns ns ns

.002 .023 .026

Lack of confidence Gender Rank Gender x Rank Error Total

.06 .00 .92 54.00 54.93

1 1 1 96 99

.06 .00 .92 .56

.10 .00 1.63

ns ns ns

.001 .000 .017

Lack of time Gender Rank Gender rank Error Total

.74 .34 .56 49.39 51.23

1 1 1 96 99

.74 .34 .56 .51

1.44 .67 1.09

ns ns ns

.015 .007 .011

Grand mean Gender Rank Gender rank Error Total

.30 .25 .21 19.34 20.04

1 1 1 96 99

.30 .25 .21 .20

1.49 1.23 1.04

ns ns ns

.015 .013 .011

Note: ns, not statistically significant at the .05 level.

For the disordinal interaction effects, they need to be further examined in post-hoc tests using simple effect testing (Maxwell & Delaney, 2004). For the interaction effect of gender by technology usage on lack of equipment, one simple effect was found to be significant. Male light users perceived more barriers than their female counterparts: t(25) = 2.44, p < .05. For the interaction effect on disbelief of technology benefits, however, no simple effect was found although the same simple effect as in the case of lack of equipment approached the .05 level significance marginally: t(25) = 1.94, p = .06. Male light users tended to disbelieve the ICT benefits more than the female light users did. Similar to the previous two cases, a simple effect of gender at the light usage level was found on the scale mean: t(25) = 3.02, p < .01. Male light users perceived more barriers to adopting technologies in teaching and learning than their female counterparts did.

5. Discussion This study explored the perceived barriers to adopting ICT by the faculty members at the College of Applied Sciences in Oman through the factorial structure analysis of a survey. The study further examined the possible differences in perceived obstacles in terms of gender, academic field, academic rank, and frequency of technology usage. Despite many empirical studies exploring the barriers to adopting technology (Becta, 2004), there were no standardized surveys available that were applicable to the present study. Some studies used a qualitative approach to explore the barriers (e.g., Ertmer, 1999; Guha, 2000, Veen, 1993), whereas other studies used a simple self-developed survey and described the findings only at the item level (e.g., Butler & Bellsom, 2002). In addition, it was suggested that the adoption of technology for university faculty members is culture-laden. Therefore, this study constructed a standardized survey to measure faculty’s perceived barriers to adopting technology in Omani higher education based on literature. The factor analysis demonstrated that there were five factors in the 16-item survey as designed: lack of computing equipment, lack of institutional support, disbelief of technology values and benefits, lack of personal confidence in technology, and lack of time. The fivefactor structure survey had acceptable internal consistency reliability and constructive validity. This may indicate the Western-originated constructs of barriers to adopting technology are applicable to the Arabic societies. However, the results of factor analysis did not support a higher order structure of barriers such as the classification of external and internal barriers or school-level and personal-level barriers as proposed by some researchers (Ertmer, 1999; Snoeyink & Ertmer, 2001; Veen, 1993). In addition, inconsistent


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S. Al-Senaidi et al. / Computers & Education 53 (2009) 575–590 Table 7 ANOVA tables for the group difference on academic rank by technology usage. Factors

SS

df

MS

F

p

g2

Lack of equipment Rank Usage Rank usage Error Total

.08 .12 .29 64.45 65.14

1 1 1 96 99

.08 .12 .29 .67

.11 .17 .43

ns ns ns

.001 .002 .004

Lack of institutional support Rank Usage Rank usage Error Total

.47 .730 1.46 57.11 59.19

1 1 1 96 99

.47 .730 1.46 .60

.79 1.23 2.45

ns ns ns

.008 .013 .025

Disbelief of ICT benefits Rank Usage Rank usage Error Total

.79 .07 .01 43.01 44.14

1 1 1 96 99

.79 .07 .01 .45

1.76 .16 .02

ns ns ns

.018 .002 .000

Lack of confidence Rank Usage Rank usage Error Total

.02 .11 .06 54.75 54.93

1 1 1 96 99

.02 .11 .06 .57

.03 .19 .10

ns ns ns

.000 .002 .001

Lack of time Rank Usage Rank usage Error Total

.10 .62 .12 50.12 51.23

1 1 1 96 99

.10 .62 .12 .52

.19 1.18 .22

ns ns ns

.002 .012 .002

Grand mean Rank Usage Rank usage Error Total

.19 .06 .00 19.73 20.04

1 1 1 96 99

.19 .06 ‘.00 .21

.93 .28 .00

ns ns ns

.010 .003 .000

Note: ns, not statistically significant at the .05 level.

with many studies claiming there were interwoven relationships among the barriers (Bradley & Russell, 1997; Cox et al., 1999; Cuban, 1999; Guha, 2000; Lee, 1997; Pelgrum, 2001; Pina & Harris, 1993; Ross, Hogaboam-Gray, & Hannay, 1999), this study found that the five factors had small correlations, suggesting that the major barriers were separate in the current sample. This may imply that the relationship of barriers in the Arabic culture is different from that in the Western culture, which should be further explored with more diverse samples in the Arabic societies. For the perceived barriers to adopting technology, the findings Table 7 showed that the faculty members at CAS overall reported a slight dissatisfaction with the possible barriers. The two areas that most prohibited faculty members from adopting technology were lack of institutional support and lack of time, whereas the other three factors (lack of equipment, disbelief of ICT benefits, and lack of confidence) were not major impediments. In other words, the CAS faculty members tended to believe the values and benefits of ICT, and to be satisfied with CAS’ computing equipment. They felt confident in computing technology as well. However, they perceived a lack of support from the university, especially the technical support. Lack of time seemed to be the major obstacle for the CAS faculty members to further adopting technology. To a great extent, the findings of the study – lack of support and lack of time as major hurdles in applying technology corroborate with the research results conducted in and outside Oman (Abu Jaber & Osman, 1996; Al Khawaldi, 2000; Al Musawi, 2002; Boyd, 1997, Bialo & Soloman, 1997; Kook, 1997). However, this study did not substantiate the other significant barriers repeatedly reported in Western literature including insufficiency or lack of ICT facilities (Beggs, 2000; Bussey et al., 2000; Braak, 2001; Butler & Sellbom, 2002), negative attitudes towards ICT (Cuban et al., 2001; Ertmer, 1999; Mumtaz, 2000; Snoeyink & Ertmer, 2001; Veen, 1993), and lack of teacher confidence (Bosley & Moon, 2003, Bradley & Russell, 1997; Fabry & Higgs, 1997; Larner & Timberlake, 1995). The possible explanation may be that (1) CAS, as the top one university in Oman, has satisfactory computing equipment (Omani Ministry of Information, 2006); (2) the CAS faculty members widely recognize the ICT values (Akinyemi, 2003); and (3) the CAS faculty members possess the basic skills for adopting educational technologies (Al Musawi & Abdelraheem, 2004). On group differences, findings from this study showed that gender, academic rank, academic field, and frequency of technology usage were not significant determinants of perceived barriers. However, interaction effects of gender and technology usage were found on lack of equipment, disbelief of ICT benefits, and the overall perception of barriers. In all three cases, male light technology users indicated more barriers than the female counterparts. It is unknown why male light users complained more than the female light user on these areas. Consistent with some findings from the Western culture (Becta, 2004), the present study did not find age effect relating to technology use or


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Table 8 ANOVA tables for the group difference on gender by technology usage. Factors

SS

df

MS

F

p

g2

Lack of equipment Gender Usage Gender usage Error Total

1.36 .00 3.10 61.2 65.14

1 1 1 96 99

1.36 .00 3.10 .64

2.12 .001 4.81

ns ns