ABSTRACT
Nowadays, Electronic Commerce (EC) course is becoming one of the most important taught courses at all business schools due to increased businesses over the Internet network by utilizing all available technologies. However, the success of such courses cannot be measured by number of students who pass or fail but rather by how such courses influence and can change the daily life of these students. Previous literature stressed on the important role of the teacher in developing students’ skills and knowledge and transferring this expertise outside the classroom. This study explores the factors explain teachers' influence on learners' use of EC technology outside the classroom. A questionnaire (survey) was developed and distributed to the students enrolled in the “introduction to EC” course at the Jordan of University. Using structured equation modeling (SEM) a total of 545 valid questionnaires were retrieved and analyzed. The results of the study showed that teachers’ capacity support and behavior support are significant factors that established a facilitating condition which have a significant impact on students' computer self-efficacy. On the other hand, teachers’ affection support and computer self-efficacy are found to be significant factors that strengthened students’ perceived usefulness which supported the use of EC technology outside the classroom. As a result of increased students’ computer self-efficacy and perceived usefulness, the empirical analysis revealed positive significant effect on students’ use of EC technologies outside the classroom. This research presented a set of recommendations and polices that are very handful in developing successful EC courses that support the teachers’ role, leverage student knowledge that goes beyond the classroom settings.
Key words: Electronic commerce, student skills, teacher influence, technology.
Since the introduction of the Internet, electronic commerce (e-commerce or EC) grew vastly to dominate many aspects of how we buy and sell as both end customers and businesses. E-commerce is seen as the application of technology toward the automation of business transactions and workflow where money, information, services, and products are exchanged over the internet, networks, and other digital technologies (Whinston, 1997, Laudon and Laudon, 2004). In fact, e-commerce has become one of the most critical aspects of managerial strategy as organizations search for ways to compete more effectively in the global marketplace (Maqableh, 2010; Rezaee et al., 2005).
Recognizing the importance of e-commerce and to meet changing business environment and due to the lack of adequate course that tackle e-commerce, University of Jordan (UJ) has introduced an elective course for all university students run by Management Information Systems Department at the Business School. The course includes ten modules that cover: basic e-commerce concepts and models, e-commerce infrastructure, social commerce, e-marketing, e-payment methods and technologies, e-commerce security, mobile commerce, e-governments, e-business ethics and emerging e-commerce technologies (Maqableh, 2012; Masa’deh et al., 2013b). The lectures are normally three times a week, which are equal to 3 credit hours conducted in traditional class rooms not in labs. Thus the lecturers use classical teaching method while they strive to utilize case study and homework as a way to leverage the students’ practical experience.
In fact, e-commerce plays a very critical role in empowering young generation not only to enhance their educational skills inside the class rooms but more importantly to leverage and empower them in their daily lives. Hence it is very important for faculty members (teachers) not only to make sure that they introduce their students to e-commerce education in the class room but also to influence their students to utilize e-commerce services and technologies outside the class rooms. Furthermore, teachers represent important mediators for transferring the knowledge outside the classroom to real life practice (Davis, 2003; Masa’deh et al., 2013a; Katyal and Evers, 2004). However, the factors that influence students’ use of e-commerce outside the classroom can range from teachers' expectancies, peers' encouragement and support, encouragement, guidance and learning materials (Lai, 2015).
Yet, investigating the factors that define how teachers influence their students outside the classroom especially in an essential topic like e-commerce and in a developing country context can reveal very interesting results. Consequently, this work seeks to contribute to the literature by modeling teachers' influence on students' use of e-commerce outside the classroom in University of Jordan as a case that can be replicated to other developing countries.
THEORETICAL BACKGROUND AND HYPOTHESES DEVELOPMENT
Although the theoretical framework of this study was adopted from Lai (2015) (Figure 1), a number of significantly related studies that provide additional support for the theoretical foundation while seeking to explore the attributes that influence learners' use of e-Commerce technologies outside the classroom were reviewed. Here, two key aspects which are the focus of the model will be discussed, viz: the key theories of technology adoption and use and teachers role (affection, capacity and behavior) in supporting learners’ use of technology.
E-commerce has long been recognized as one of the most significant technologies that have received very little attention in higher education and academia. In fact, Rezaee et al. (2005) have explicitly stressed that e-commerce education has not received adequate coverage despite the high demand and interest in e-commerce education and the importance of integration e-commerce education where the exponential growth in e-commerce increases the demand for individuals possessing sufficient knowledge and experience in e-commerce. However, introducing an e-commerce course at the University level course may not enough to produce such qualified e-commerce individuals or meet the market demand. In fact, teachers and students have to work together to carry out the knowledge from the classroom to the daily life. Such approach in teaching e-commerce can help transform the learning experience and heavily influence the success of the learners' in their personal life as more business and work activities are based on e-commerce technologies.
Many factors control learners’ use of e-commerce technology or any other technology outside the classroom in the daily life. In fact, when it comes to technology use, a large number of previous studies rely on the grounded theories of technology adoption and use. Technology Acceptance Model (TAM) is one significant technology acceptance and usage models that was developed by Davis et al. (1989). TAM was the first to introduce two main concepts namely: “perceived usefulness” and “perceived ease-of-use” as the main factors that contribute to technology acceptance and use.
While TAM itself was an extension theory of reasoned action (TRA) (Ajzen and Fishbein, 1980), an important theory and extension to the TAM was later developed by Venkatesh et al. (2003) known as the unified theory of acceptance and use of technology (UTAUT) model. UTAUT defined four determinants of technology usage intention and use behavior, namely: 1) performance expectancy, 2) effort expectancy, 3) social influence, and 4) facilitating conditions.
TAM, UTAUT and many other variation, extensions and models were used and developed to explore and define the factors that influence teachers and learners use of technology in and outside the classroom. For instance, Hsu et al. (2009) used a modified Technology Acceptance Model (TAM) model to explore the factors that control business students learning and use of statistical software. Their results showed that computer attitude and statistical software self-efficacy have significant, positive effects on perceived usefulness, while perceived usefulness and perceived ease of use positively affect learners’ intentions to use statistical software. In a related study by Mohammadi (2015) in which he studied the impact of quality features, perceived ease of use, perceived usefulness on the intention to use e-learning technologies in Iran and found that intention to use, user satisfaction, system quality and information quality are key factors driving users use and satisfaction of e-learning. In another work by Mohammadyari and Singh (2015) using Unified Theory of Acceptance and Use of Technology (UTAUT), revealed a clear relation between an individual's level of digital literacy (defined as the ability to understand, analyze, assess, organize and evaluate information using digital technologies) and individuals’ performance and using of e-learning technologies.
In the last few years, many researchers have focused on how student put the education they receive in the class room into practice outside the classroom and explored the factors that control this process. For instance, Lai et al. (2012) in their study titled “What factors predict undergraduate students’ use of technology for learning? A case from Hong Kong”, found that compatibility of technology, learning styles, availability of encouragement and support from peers and teachers, and attitudes toward technology use were dominant predictors of students’ technology use for learning. However, their work revealed that perceived usefulness and ICT literacy skills had less predictive power that contribute to students’ technology use for learning.
Research has accumulate evidence that some key factors that influence students' use of technology inside and outside the class room include; the learning value and subject, influence from peers, parents and community scientific literacy, users interest access to ICT, students' background, school/home environment, computer self-efï¬cacy and individuals' past experience (Erdogdu and Erdogdu, 2015; Compeau and Higgins, 1995; Chan et al., 2015; Fauville et al., 2015; Bandura, 1977). In general, Kopcha (2012) defined three general “categories” of factors that can be either barriers or enablers for technology integration and use inside and outside the classroom namely: teacher-related behavior, technology use, and student-related behavior, yet as many researcher argue, teacher role remains the most single important factor that can heavily contribute to the successful use of technology by learners inside and outside the classroom. Yet, teachers’ role is dependent on many aspects that are related to them. In fact, teachers’ beliefs, skills, leadership and characteristics are just some of these critical aspects that affect learners’ use of technologies in and outside the classroom.
Ertmer et al. (2012) examined teacher beliefs and their effect on their technology integration and practices and found teachers’ beliefs and attitudes were perceived as having the biggest impact on student success while factors such as passion for technology, problem-solving mentality, and support, played a role in shaping their practices. In conclusion, the authors highlighted that the attitudes and beliefs toward technology, their knowledge and skills are either the key enablers or barriers for integration technology in the teaching and learning process.
Another related study that deals with teachers' beliefs and technology integration, Kim et al. (2013) found that teacher beliefs about the nature of knowledge and learning (epistemology), Teachers’ beliefs about effective ways of teaching (conceptions), and technology integration practices are important attributes when seeking to integrate technology practice. Another study entitled “Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration” by Mueller et al. (2008) found that teacher’s comfort with computers, beliefs of computers as an instructional tool, training, motivation, support, and teaching efficacy are primary factors that have positive influence on learners and teachers.
However, teachers' belief is not only enough, teachers' skills and the way of they present and deliver materials can be significant factors. In fact, Ottenbreit-Leftwich et al. (2010) showed that technology use is dependent on creating customized classroom materials, improving classroom management, enhancing student comprehension, with technology skills, and promoting student learning. Moreover, Hao and Lee (2015) in their study on integrating Web 2.0 technologies in the process of learning and teaching, found that teachers’ characteristics such as levels of Web 2.0 usage in instruction, gender, and discipline area explain the usage and integration stage of web 2.0 technology that varies from just informational to become knowledge and collaboration usage.
Furthermore, Hung and Chou (2015) in their study on students and teachers behavior in blended and online learning environments found that course designer and organizer, discussion facilitator, social supporter, technology facilitator, and assessment designer are key factors in both environment and were similar across the blended learning and online learning. Although, students exhibited the greatest weight for the course designer and organizer dimension, followed by the technology facilitator and discussion facilitator dimension, in the online learning environments discussion facilitator dimension was more critical. Yet, in any case using technology tools will always be rewarding. In fact, a study by Chuang, et al. (2015) explored teachers' technology integration practice and its relation to their technological pedagogical content knowledge (TPCK) and found that teachers' technology integration practice with ICT tools is linked directly to TPCK scores and more importantly they revealed the link between social media and self-assessed TPCK.
However, the teaching and learning process is changing. Universities role is shifting dramatically from their traditional teaching and learning delivery models to an online version enabled by Web 2.0 technologies and led by groups and communities characterized by increase knowledge sharing and self-learning (Kulakli and Mahony, 2014). Hence, some may argue that teachers' role and influence may be diminishing. Up-to-date many of the previous studies revealed the contradictory. In fact, although learning using Web 2.0 technologies have dramatically broaden the classroom environment allowing more learners’ participation and increasing creative behavior while transforming education research and practice (Greenhow et al., 2009) and creating greater autonomy in students' learning, teachers' leadership and guidance still have an important impact on students' engagement and learning experience in and outside the classroom (Katyal and Evers, 2004).
Deepwell and Malik (2008) investigated how students utilize learning technology for self-directed learning, where they examined; student expectations of the technology, lecturers’ engagement and technology support of education process and revealed that academic guidance, effective technology use, and lecturer role are significant factors for students’ self-directed learning in and out-side the class rooms.
In summary, whatever the teachers’ role entail from behavioral, capacity and affection support, their role remain the one of the most profound aspects that promote and support students’ inside and most importantly outside classroom (Lai, 2015).
Based on previous literature review and the work of Lai (2015), Figure 2 demonstrates the research’s conceptual framework and the hypothesized relationships between the adopted constructs.
H1: Affection Support will have a positive effect on Perceived Usefulness.
H2: Capacity Support will have a positive effect on Facilitation Conditions.
H3: Behavior Support will have a positive effect on Facilitation Conditions.
H4: Perceived Usefulness will have a positive effect on Technology Use.
H5: Facilitation Conditions will have a positive effect on Computer Self-efficacy.
H6: Computer Self-efficacy will have a positive effect on Technology Use.
H7: Computer Self-efficacy will have a positive effect on Perceived Usefulness.
This research uses structural equation modeling (SEM) approach based on AMOS 20.0 to study the relationships and to test the hypotheses between the observed and latent constructs in the proposed research model. SEM is a statistical methodology that uses a confirmatory (that is, hypothesis-testing) approach to the analysis of a structural theory, bearing in mind certain phenomena. Normally, this theory embodies ‘causal’ processes that make observations on multiple variables (Bentler, 1990). Furthermore, the structural equation modeling process consisted of two components: validating the measurement model and fitting the structural model. While the former is accomplished through exploratory factor analysis, the latter was accomplished by path analysis with latent variables (Kline, 2005). Using a two-step approach assures that only the constructs retained from the survey that have good measures (validity and reliability) will be used in the structural model (Hair et al., 2010).
The basis for data collection and analysis is a field study in which respondents answered all items on a five point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Based on the theoretical framework of this study adopted from Lai (2015), research elements provided a valued source for data gathering and measurement as their reliability and validity have been verified through previous research and peer reviews. Table 1 shows the measured constructs and the items measuring each construct.
Sample and procedure
Empirical data for this study were collected through computer-based survey in Jordan. Specifically, survey questionnaire was used to gather data for hypotheses testing from University of Jordan. Before implementing the survey, the instrument was reviewed by four lecturers who are specialized in the Management Information Systems (MIS) discipline in order to identify problems with wording, content, and question ambiguity.
The population of this study consists of all students from Business School at the University of Jordan located in Jordan, which counts are more than 6000 according to the university’s registration unit. The students from Jordan University Business School were selected as sample using simple random sampling method (that is, probabilistic sampling) by which the elements do not have a known or predetermined chance of being selected as subjects. The sample size of this study was determined based on the rules of thumb for using SEM within AMOS 20.0 in order to obtain reliable and valid results. Kline (2010) suggested that a sample of 200 or larger is suitable for a complicated path model. Furthermore, taking into account the complexity of the model which considers the number of constructs and variables within the model and after eliminating the incomplete responses surveys (24), our sample size (545) meets the recommended guidelines of Kline (2010), Krejcie and Morgan (1970) and Pallant (2005). The demographic data of the respondents are reported in Table 2.
As shown in Table 2, the demographic profile of the respondents for this study revealed that the sample consisted of more females; most of them between 17 and less than 23 years old, in their second and third academic years, and most of them use different types of IT more than 3 h.
Descriptive statistics
Several statistical methods take account of outliers (that is, cases with values well over or well under the majority of other cases), since the latter might affect the validity and reliability of the data (Pallant, 2005). Outliers were examined by using the box-plot method to determine them, and then compared the original mean with the 5% trimmed mean, to identify whether the outlier scores have a lot of impact on the mean. However, after careful examinations, no noticeable outliers were found from the 545 valid cases. As a result, it was decided to proceed to further examination using the 545 valid dataset. All the 26 items were tested for their means, standard deviations, skewness, and kurtosis.
The descriptive statistics presented below in Table 3 indicate a positive disposition towards the items. While the standard deviation (SD) values ranged from 0.74902 to 0.99540, these values indicate a narrow spread around the mean. Also, the mean values of all items were greater than the midpoint (2.5) and ranged from 3.7394 (BS1) to 4.2000 (TU3).
However, after careful assessment by using skewness and kurtosis, the data were found to be normally distributed. Indeed, skewness and kurtosis were normally distributed since all of the values were inside the adequate ranges for normality (that is, -1.0 to +1.0) for skewness, and less than 10 for kurtosis (Kline, 2010). Furthermore, the ordering of the items in terms of their means values, and their ranks based on three ranges (that is, 1 – 2.33 low; 2.34 – 3.67 medium; and 3.68 – 5 high) are provided.
Table 4 shows different types of goodness of fit indices in assessing this study initial specified model. It demonstrates that the research constructs fits the data according to the absolute, incremental, and parsimonious model fit measures, comprising chi-square per degree of freedom ratio (x²/df), Incremental Fit Index (IFI), Tucker- Lewis Index (TLI), Comparative Fit Index (CFI), and Root Mean Square Error of Approximation (RMSEA). The researchers examined the standardized regression weights for the research’s indicators and found that all indicators had a high loading towards the latent variables. Moreover, since all of these items meet the minimum recommended value of factor loadings of 0.50; and RMSEA less than 0.10 (Newkirk and Lederer, 2006), they were all included for further analysis. Therefore, the measurement model showed a better fit to the data (as shown in Table 3). For instance, x²/df was 2.911, the IFI = 0.94, TLI = 0.93, CFI = 0.94; and RMSEA 0.059 indicated better fit to the data considering all loading items.
Measurement model
Confirmatory factor analysis (CFA) was conducted to check the properties of the instrument items. Indeed, prior to analyzing the structural model, a CFA using AMOS 20.0 was conducted to first consider the measurement model fit and then assess the reliability, convergent validity and discriminant validity of the constructs (Arbuckle, 2009). The outcomes of the measurement model are presented in Table 5, which encapsulates the standardized factor loadings, measures of reliabilities and validity for the final measurement model.
Unidimensionality
Unidimensionality is the extent to which the study indicators deviation from their latent variable. An examination of the unidimensionality of the research constructs is essential and is an important prerequisite for establishing construct reliability and validity analysis (Chou et al., 2007). Moreover, in line with Byrne (2001), this research assessed unidimensionality using the factor loading of items of their respective constructs. Table 5 shows solid evidence for the unidimensionality of all the constructs that were specified in the measurement model. All loadings were above 0.50 which is the criterion value recommended by Newkirk and Lederer (2006). These loadings confirmed that 26 items were loaded satisfactory on their constructs.
Reliability
Reliability analysis is related to the assessment of the degree of consistency between multiple measurements of a variable, and could be measured by Cronbach alpha coefficient and composite reliability (Hair et al., 1998). Some scholars (e.g. Bagozzi and Yi, 1988) suggested that the values of all indicators or dimensional scales should be above the recommended value of 0.60. Table 5 indicates that all Cronbach-α values for the seven variables exceeded the recommended value of 0.60 (Bagozzi and Yi, 1988) demonstrating that the instrument is reliable. Furthermore, as shown in Table 5, composite reliability values ranged from 0.75 to 0.93, and were all greater than the recommended value of more than 0.60 (Bagozzi and Yi, 1988) or greater than 0.70 as suggested by Holmes-Smith (2001). Consequently, according to the above two tests, all the research constructs in this study are considered reliable.
As shown in Table 5, since the measurement model has a good fit; convergent validity and discriminant validity can now be assessed in order to evaluate if the psychometric properties of the measurement model are adequate.
Content, convergent, and discriminant validity
Although reliability is considered as a necessary condition of the test of goodness of the measure used in research, it is not sufficient (Creswell, 2009; Sekaran, 2003; Sekaran and Bougie, 2013), thus validity is another condition used to measure the goodness of a measure. Validity refers to which an instrument measures is expected to measure or what the researcher wishes to measure (Blumberg et al., 2005).
Indeed, the items selected to measure the seven variables were validated and reused from previous researches. Therefore, the researchers relied upon in the validity of the scale that was a pre-used scale that was developed from other researchers. In addition, the questionnaire items were reviewed by four instructors of the Business Faculty at University of Jordan. The feedback from the chosen group for the pre-test contributed to enhanced content validity of the instrument to confirm that the knowledge presented in the content of each question was relevant to the studied topic.
Furthermore, as convergent validity test is necessary in the measurement model to determine if the indicators in a scale load together on a single construct; discriminant validity test is another main one to verify if the items developed to measure different constructs are actually evaluating those constructs (Gefen et al., 2000). As shown in Table 5, all items were significant and had loadings more than 0.50 on their underlying constructs.
Moreover, the standard errors for the items ranged from 0.050 to 0.087 and all the item loadings were more than twice their standard errors.
Discriminant validity was considered using several tests. First, it could be examined in the measurement model by investigating the shared average variance extracted (AVE) by the latent constructs. The correlations among the research constructs could be used to assess discriminant validity by examining if there were any extreme large correlations among them which would imply that the model has a problem of discriminant validity. If the AVE for each construct exceeds the square correlation between that construct and any other constructs then discriminant validity is occurred (Fronell and Larcker, 1981).
As shown in Table 5, this study showed that the AVEs of all the constructs were above the suggested level of 0.50, implying that all the constructs that ranged from 0.50 to 0.76 were responsible for more than 50% of the variance in their respected measurement items, which met the recommendation that AVE values should be at least 0.50 for each construct (Bagozzi and Yi, 1988; Holmes-Smith, 2001).
Furthermore, as shown in Table 6, discriminant validity was confirmed as the AVE values were more than the squared correlations for each set of constructs. Thus, the measures significantly discriminate between the constructs.
Structural model and hypotheses testing
In order to examine the structural model it is essential to investigate the statistical significance of the standardized regression weights (that is, t-value) of the research hypotheses (that is, the path estimations) at 0.05 level (Table 7); and the coefficient of determination (R²) for the research endogenous variables as well.
The coefficient of determination for Facilitation Conditions, Perceived Usefulness, Computer Self-Efficacy, and Technology Use were 0.17, 0.37, 0.52, and 0.46 respectively, which indicates that the model does account for the variation of the proposed model. Nevertheless, the significant yet limited predictive power R² of our model indicates high potential to better understanding the relationships among the research variables through incorporating additional variables and exploring factors that could impact the endogenous variables.
The results of this study provided empirical support for all research hypotheses. However, the level of support and influence for each hypothesis display some diversity in the model.
H2, and H3 capacity support and behavior support suggests teachers support and use of EC technology play a significant role in equipping the students with the necessary capability and knowledge required to use EC inside and outside the classrooms. In another word, those teachers sharing EC resources such as useful websites and tools along with EC tips and strategies will grow students EC and competence and facilitate their use of EC inside and outside the classrooms. As well, teacher’s who actually use EC technology in their classes and involve their students in these activities or give them as assignments in these courses are more likely to increase students' competence, capabilities and likelihood to use EC technologies outside the classrooms. In fact, previous literature provides numerous researches to support this argument. For instance, Kopcha (2012), Ertmer et al. (2012), Ottenbreit-Leftwich et al. (2010) and many others showed the importance role of teachers’ inside the classroom in supporting the use of technology and how their behavior can facilitate and support students use of technology as they will be more familiar and capable of using technology. Moreover, Deepwell and Malik (2008) as well as Lai (2015) reported that teachers recommendation and guidance on how to use the resources affects their students' self-directed use of technology while increasing their perceived usefulness of such resources and improving their know-how and experiences of technologies in and out of classrooms.
As a result of increased capacity support and behavior support that create a facilitating support through increase student competence, capabilities and likelihood to use EC technologies, H5 showed that these facilitation conditions have a positive effect on computer self-efficacy. This indicates that students will have greater self-confidence in their abilities and capabilities to select and use of proper electronic commerce technologies while enjoying this experience. The same was reported by Chang and Tung (2008), Compeau and Higgins (1995) as well as in the work of Hsu et al. (2009). It is expected that students’ confidence will very much increase in their ability to select and use proper technologies effectively if they are given the right practical education and guidance especially in age characterized by overwhelming diversity in technologies available at hand (Levy, 2009). Hence it is no wonder that facilitating conditions are established as moderator that influence students skills, confidence and hence their computer self-efficacy which will later influence the use and adoption of technology (Yousafzai et al., 2007).
The results of H1 and H7 revealed a significant positive impact of affection support and computer self-efficacy on perceived usefulness. In fact, the results indicate that teachers’ encouragement and actual use of EC technologies in this EC course in addition to students’ confidence in selecting and using suitable EC technologies that fit their needs will leverage students’ perceived usefulness of these technologies. This means that students’ believe that such EC course improved their knowledge, experience and interest while expanding their EC resources and chances to use EC technologies inside and outside the classroom. A number of researchers have revealed the importance of both teachers encouragement and students' confidence and in their study on students’ perceived usefulness for using learning such technologies inside and outside the classroom (Deepwell and Malik, 2008; Yousafzai et al., 2007). Ertmer et al. (2012), Lai (2015), Katyal and Evers (2004) and many others suggested a positive effect for affection support and computer self-efficacy on technology use and adoption by building up perceived usefulness.
Finally the results form H4 and H6 as many previous literature showed clearly that perceived usefulness and computer self-efficacy have a positive effect on EC technology use inside and outside the classroom. The results indicate that the course increased students’ knowledge, experience and interest in using EC while increasing their confidence.
Consequently, this influence students EC technology use outside the classroom and in real life to progress, widen their opportunities and engages in new activities or experiences. As a matter of fact, perceived usefulness has long been considered a key factor that influences technology adoption since the introduction of the TAM Models (Davis et al., 1989; Davis, 2003; Venkatesh et al., 2003). At the same time many previous studies such as Yousafzai et al. (2007) revealed that computer self-efficacy can heavily affect the adoption and use of technology.
In particular, the work of Moss and Azevedo (2009) exposed a clear effect of computer self-efficacy on learning and use of technology in the learning environments. Hence, it is very important for students get the motivation, confidence and skills needed to utilize EC technologies outside the classrooms.
RECOMMENDATIONS AND CONCLUSIONS
Based on previous discussion and research results, the following recommendations can be considered:
1) To boost affection support, teachers need to use different motive tools to encourage their students to use EC technologies outside the classrooms. These include: extra curriculum activities, EC website use, homework’s, extra grades, group discussions, group projects and EC case study analysis.
2) To build up Facilitation Conditions (FC) through capacity and behavior support, teachers have to use teaching by example approach and keep updating their students with the latest and useful EC technologies and tools. Hence, classrooms need to be equipped with computers or laptops along with internet connections so the teachers can illustrate and show their students EC in action. At the same time, teachers need to keep up-to-date information on latest EC technologies while developing their skills and accumulating EC resources. Most importantly, teachers are required to engage their students in these activities so they develop their skills, knowledge and practical experience in utilizing EC technologies.
3) In order to keep the perceived usefulness of EC technologies especially outside the classroom, such courses need to have a combination of interesting and useful EC resources and illustration. This will help both the teachers and students to take what they learn to the next level. Moreover, if such courses are associated with practical labs or at least pre-defined mandatory lab visits, they will definitely enhance students experience and knowledge while improving their learning process and outcomes.
4) Teachers play a significant role in developing students' self-esteem and confidence to us and utilize EC technologies not only inside classroom but also outside the classroom. Hence, teachers need to understand that their role exceeds transferring knowledge, expertise and skills, but they have to keep in mind that they have to work on the moral and psychology of their students in ways that increase their self-confidence in using such technologies especially in eastern cultures such as Jordan were the culture of using EC technologies is still uncommon and not supported.
5) Finally, in order for students use to EC technologies outside the classrooms, they need to find them useful, helpful and full opportunities for them to progress. Therefore, the class settings, materials, and most importantly the teachers have to be structured around its utility for the students and community. Thus, a systematic review and evaluation not for the student utility but for the course materials and teachers as well need to be in place. Additionally, a regular update of at least course materials and resources is needed to keep the course useful, interesting and practical particularly in this age of rapid technology developments.
In summary, this research explored the factors and role of teachers in encouraging and developing students’ skills and knowledge to use EC technologies outside the classroom. The results of the study revealed the important role of teachers in leveraging students' capacity and developing their positive behavior to use EC technologies through the advancement of their computer self-efficacy. At the same time, the results showed a significant impact for teachers’ encouragement and support in increasing students' confidence and perceived usefulness of EC technologies and tools outside the classroom. Hence, developing successful EC courses that expand the walls of the classrooms require proper teachers' development and support, increased students' role and updated practical materials that are useful for the students’ in their education, daily life and future plans.
LIMITATIONS AND FUTURE WORK
This study has some limitations that need to be addressed in future studies. First and for most, since the model is very holistic and general other factors that account for country specific and cultural aspects should be tested. Moreover, as such courses are taught by different teachers, teachers’ characteristics and teaching styles need to be considered as well. Hence, an extended model can be developed to account for these aspects and later compared with this study model and results.
Another limitation of this study is the study time line and sample; future work should be extended to cover a time series and new groups of students to validate and confirm this study results while reducing any bias in survey research. Altogether, these are some of the challenges that represent noteworthy future work that may lead to interesting findings.
The authors have not declared any conflict of interests.
REFERENCES
|
Ajzen I, Fishbein M (1980). Understanding attitudes and predicting social behavior, Englewood Cliffs, NJ: Prentice-Hall. pp. 1-278
|
|
|
|
Arbuckle J (2009). Amos 18 user's guide. Armonk, New York, USA: SPSS Incorporated.
|
|
|
|
|
Bagozzi R, Yi Y (1988). On the evaluation of structural evaluation models. J. Acad. Mark. Sci. 16(1):74-94.
Crossref
|
|
|
|
|
Bandura A (1977). Self-efficacy: towards a unifying theory of behavioral change. Psychol. Rev. 84:191-215.
Crossref
|
|
|
|
|
Bentler P (1990). Comparative fit indexes in structural models. Psychol. Bull. 107:238-246.
Crossref
|
|
|
|
|
Blumberg B, Cooper DR, Schindler PS (2005). Business research methods. Maidenhead, UK: McGraw-Hill. pp. 1-690.
|
|
|
|
|
Byrne B (2001). Structural equation modeling with AMOS: Basic concepts, applications, and programming. Mahwah, New Jersey, London: Lawrence Erlbaum Associates. pp. 1-416.
|
|
|
|
|
Chan NN, Walker C, Gleaves A (2015). An exploration of students' lived experiences of using smartphones in diverse learning contexts using a hermeneutic phenomenological approach. Comput. Educ. 82:96-106.
Crossref
|
|
|
|
|
Chang S, Tung F (2008). An empirical investigation of students' behavioral intentions to use the online learning course wLLAsites. Br. J. Educ. Technol., 39(1):71-83.
|
|
|
|
|
Chou T, Chang P, Cheng Y, Tasi C (2007). A path model linking organizational knowledge attributes, information processing capabilities, and perceived usability. Inform. Manag. 44:408-417.
Crossref
|
|
|
|
|
Chuang HH, Weng CY, Huang FC (2015). A structure equation model among factors of teachers' technology integration practice and their TPCK. Comput. Educ. 86:182-191.
Crossref
|
|
|
|
|
Compeau DR, Higgins CA (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quart. 19(2):189-211.
Crossref
|
|
|
|
|
Creswell J (2009). Research design: Qualitative, quantitative, and mixed methods approaches. 3rd Edn., Thousand Oaks: Sage Publications. pp. 1-269.
|
|
|
|
|
Davis FD, Bagozzi RP, Warshaw PR (1989). "User acceptance of computer technology: A comparison of two theoretical models." Manag. Sci. 35:982-1003.
Crossref
|
|
|
|
|
Davis HA (2003). Conceptualizing the role and influence of student-teacher relationships on children's social and cognitive development. Educ. Psychol. 38:207-234.
Crossref
|
|
|
|
|
Deepwell F, Malik S (2008). On campus, but out of class: an investigation into students' experiences of learning technologies in their self-directed study. Res. Learn. Technol. 16:1.
Crossref
|
|
|
|
|
Erdogdu F, Erdogdu E (2015). The impact of access to ICT, student background and school/home environment on academic success of students in Turkey: An international comparative analysis. Comput. Educ. 82:26-49.
Crossref
|
|
|
|
|
Ertmer PA, Ottenbreit-Leftwich AT, Sadik O, Sendurur E, Sendurur P (2012). Teacher beliefs and technology integration practices: A critical relationship. Comput. Educ. 59(2):423-435.
Crossref
|
|
|
|
|
Fauville G, Dupont S, von Thun S, Lundin J (2015). Can Facebook be used to increase scientific literacy? A case study of the Monterey Bay Aquarium Research Institute Facebook page and ocean literacy. Comput. Educ. 82:60-73.
Crossref
|
|
|
|
|
Fronell C, Larcker D (1981). Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res. 18: 39-50.
Crossref
|
|
|
|
|
Gefen D, Straub DW, Boudreau MC (2000). Structural equation modeling and regression: Guidelines for research practice. Commun. Assoc. Info. Syst. 4(7):1-70.
|
|
|
|
|
Greenhow C, Robelia B, Hughes JE (2009). Learning, teaching, and scholarship in a digital age web 2.0 and classroom research: What path should we take now? Educ. Res. 38(4):246-259.
Crossref
|
|
|
|
|
Hair J, Anderson R, Tatham R, Black W (1998). Multivariate data analysis. 5th Edn., New Jersey: Prentice-Hall International Inc. pp. 1-537
|
|
|
|
|
Hair J, Black W, Babin B, Anderson R, Tatham R (2010). Multivariate data analysis. 7th Edn. New Jersey: Prentice-Hall. pp. 1-816
|
|
|
|
|
Hao Y, Lee KS (2015). Teachers' concern about integrating Web 2.0 technologies and its relationship with teacher characteristics. Comput. Hum. Behav. 48:1-8.
Crossref
|
|
|
|
|
Holmes-Smith P (2001). Introduction to structural equation modeling using LISREL. Perth: ACSPRI Winter Training Program.
|
|
|
|
|
Hsu MK, Wang SW, Chiu KK (2009). Computer attitude, statistics anxiety and self-efficacy on statistical software adoption behavior: An empirical study of online MBA learners. Comput. Hum. Behav. 25(2):412-420.
Crossref
|
|
|
|
|
Hung ML, Chou C (2015). Students' perceptions of instructors' roles in blended and online learning environments: A comparative study. Comput. Educ. 81:315-325.
Crossref
|
|
|
|
|
Katyal KR, Evers C (2004). Teacher leadership and autonomous student learning: Adjusting to the new realities. Int. J. Educ. Res. 41: 367-382.
Crossref
|
|
|
|
|
Kim C, Kim MK, Lee C, Spector JM, DeMeester K (2013). Teacher beliefs and technology integration. Teach. Teacher Educ. 29:76-85.
Crossref
|
|
|
|
|
Kline R (2005). Principles and practice of structural equation modeling. 2nd Edn., New York: The Guilford Press. pp. 1-366.
|
|
|
|
|
Kline R (2010). Principles and practice of structural equation modeling. The Guilford Press. pp. 1-427
|
|
|
|
|
Kopcha TJ (2012). Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development. Comput. Educ. 59(4):1109-1121.
Crossref
|
|
|
|
|
Krejcie R, Morgan D (1970). Determining sample size for research activities. Educ. Psychol. Meas. 30: 607-610.
|
|
|
|
|
Kulakli A, Mahony S (2014). Knowledge Creation and Sharing with Web 2.0 Tools for Teaching and Learning Roles in So-called University 2.0. Procedia-Social Behav. Sci. 150:648-657.
|
|
|
|
|
Lai C (2015). Modeling teachers' influence on learners' self-directed use of technology for language learning outside the classroom. Comput. Educ. 82:74-83.
Crossref
|
|
|
|
|
Lai C, Wang Q, Lei J (2012). What factors predict undergraduate students' use of technology for learning? A case from Hong Kong. Comput. Educ. 59(2):569-579.
Crossref
|
|
|
|
|
Levy M (2009). Technologies in use for second language learning. Mod. Lang. J. 93:769-782.
Crossref
|
|
|
|
|
Maqableh M (2010). Secure Hash Functions Based on Chaotic Maps for E-Commerce Application. Int. J. Inform. Technol. Manag. Inform. Syst. (IJITMIS), 1(1):12-19.
|
|
|
|
|
Maqableh M (2012). Analysis and design security primitives based on chaotic systems for ecommerce." Durham University, Durham, United Kingdom. pp. 1-220
|
|
|
|
|
Masa'deh R, Gharaibeh A, Maqableh M, Karajeh H (2013a). An Empirical Study of Antecedents and Outcomes of Knowledge Sharing Capability in Jordanian Telecommunication Firms: A Structural Equation Modelling Approach. Life Sci. J. 10(4): 2284-2296.
|
|
|
|
|
Masa'deh R, Shannak R, Maqableh M (2013b). A Structural Equation Modelling Approach for Determining Antecedents and Outcomes of Students' Attitude toward Mobile Commerce Adoption. Life Sci. J. 10(4): 2321-2333.
|
|
|
|
|
Mohammadi H (2015). Factors affecting the e-learning outcomes: An integration of TAM and IS success model. Telemat. Inform. 32(4): 701-719.
Crossref
|
|
|
|
|
Mohammadyari S, Singh H (2015). Understanding the effect of e-learning on individual performance: The role of digital literacy. Computers and Education, 82:11-25.
Crossref
|
|
|
|
|
Mueller J, Wood E, Willoughby T, Ross C, Specht J (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Comput. Educ. 51(4):1523-1537.
Crossref
|
|
|
|
|
Newkirk H, Lederer A (2006). The effectiveness of strategic information systems planning under environmental uncertainty. Inform. Manag. 43: 481-501.
Crossref
|
|
|
|
|
Ottenbreit-Leftwich AT, Glazewski KD, Newby TJ, Ertmer PA (2010). Teacher value beliefs associated with using technology: Addressing professional and student needs. Comput. Educ. 55(3):1321-1335.
Crossref
|
|
|
|
|
Pallant J (2005). SPSS survival manual: A step guide to data analysis using SPSS for Windows version 12. Chicago, Illinois: Open University Press.
|
|
|
|
|
Rezaee Z, Elam R, Cassidy JH (2005). Electronic-commerce education: Insights from academicians and practitioners. Adv. Account. 21:233-258.
Crossref
|
|
|
|
|
Sekaran U (2003). Research methods for business: A skill-building approach. 4th Edn., John Wiley and Sons, Inc.
|
|
|
|
|
Sekaran U, Bougie R (2013). Research methods for business: A skill-building approach. 6th Edn., John Wiley and Sons, Inc.
|
|
|
|
|
Venkatesh V, Morris MG, Davis GB, Davis FD (2003). User acceptance of information technology: Toward a unified view. MIS Quart. 27(3):425-478
|
|
|
|
|
Whinston AB (1997). Electronic commerce: A shift in paradigm, IEEE Internet Comput. 1(6):17-19.
Crossref
|
|
|
|
|
Yousafzai SY, Foxall GR, Pallister JG. (2007). Technology acceptance: A meta-analysis of the TAM: Part 1. J. Model. Manag. 2(3):251-280.
Crossref
|
|
