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The influence of technology acceptance model (TAM) factors on students’ e-satisfaction and e-retention within the context of UAE e-learning
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Mohammad Ahmad Al-hawari and Samar Mouakket University of Sharjah, Sharjah, United Arab Emirates Abstract Purpose – The main purpose of this paper is to highlight the significance of TAM factors in the light of some external factors on students’ e-retention and the mediating role of e-satisfaction within United Arab Emirates (UAE) e-learning context. Design/methodology/approach – The relative importance of TAM factors was examined, as well as enjoyment and blackboard design on students’ e-satisfaction and e-retention. The survey was designed and administrated using face-to-face method. Data were collected from a convenient sample of students who use blackboard system. AMOS 6 was used to test for the hypothesized relationships. Findings – Perceived usefulness has a direct and positive relationship with students’ e-satisfaction and e-retention while perceived ease of use has only a direct relationship with students’ e-retention. Design features and enjoyment have only a significant relationship with students’ e-satisfaction without any direct relationship with students’ e-retention. Finally, students’ e-satisfaction has a direct relationship with students’ e-retention. Research limitations/implications – This research has only surveyed students from one university in UAE. Further testing of the proposed conceptual model across different industries and countries is needed to determine the generalisability and consistency of this study’s findings. Practical implications – The proposed model of students’ e-retention prediction has the potential to help UAE university managers to understand some of the factors influencing students’ behaviours and attitudes toward e-learning systems. This will lead to improving the education quality within the context of UAE. Originality/value – This paper is a significant trial in how TAM factors and other external factors might influence students’ e-satisfaction and e-retention within UAE e-learning context. Keywords Students, Customer satisfaction, E-learning, United Arab Emirates Paper type Research paper
Introduction The development of the internet has profoundly influenced education. Recently, universities in UAE are using online course management systems such as blackboard system to improve educational outcomes in a globalized and dynamic educational environment. Blackboard system can be defined as “an electronic learning environment in the form of an intranet which enables teachers and students to design education together” (Vrielink, 2006). The research literature indicates that online course management systems offer an innovative, convenient, and functional resource that has strong potential to meet today’s learners’ requirements (Vrielink, 2006), hence enhancing retention and satisfaction rates. In this paper, we explore the degree to which technology
Education, Business and Society: Contemporary Middle Eastern Issues Vol. 3 No. 4, 2010 pp. 299-314 q Emerald Group Publishing Limited 1753-7983 DOI 10.1108/17537981011089596
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is indeed meeting students’ and teachers’ needs, using technology acceptance model (TAM) factors, as well as some other external factors, rather than the frequently used predictors of satisfaction and retention such as responsiveness, customization, security, reliability, and accessibility. Using the TAM model to predict satisfaction and retention rates within UAE education system, rather than using the regular quality factors, will contribute toward further understanding of how to build quality education. Research into satisfaction and retention predictors related to UAE context is limited in the marketing literature. In the United Arab Emirates (UAE), there has been a great emphasis on utilizing e-learning to complement traditional methods of teaching in universities, but with very little empirical research that examines students’ attitudes toward the use of blackboard. Because students are considered the focal point for higher education institutions, examining their attitudes toward e-learning is essential to the success and progress of these institutions. Empirical research is thus required to examine the extent to which TAM factors, as well as some other external factors, enhance or diminish students relationship with blackboard system in UAE education context. This study attempts to develop a comprehensive model linking the factors of the TAM model as well as two other external factors to students’ satisfaction and retention within the use of blackboard system context. Our model, in particular, tries to understand the nature of relationships between the independent variables of TAM factors: perceived ease of use (PEOU), and perceived usefulness (PU) as well as two external factors; design features (DF), and enjoyment (ENJ) and the dependent variables of e-satisfaction and e-retention (Figure 1). The technology acceptance model The TAM model states that users’ positive perception of usefulness as well as ease of use toward any technology will lead to a positive attitude toward using that particular
PU
TAM factors
PEOU
E-satisfaction
E-retention
DF
ENJ
Figure 1. The conceptual model
Independent variables
External factors
Mediating variables
Dependent variable
Notes: PU – perceived usefulness; DF – design features; ENJ – enjoyment; PEOU – perceived ease of use
technology which will lead later to the actual system use. Legris et al. (2003) concluded after an intensive literature review that TAM can be considered as a very powerful tool, but it has to be integrated into a broader one which has to involve variables related both to human and social changes. Yi and Hwang (2003), for example, added ENJ, learning goal orientation, and application of self-efficacy as external variables to the model. In this paper, we add ENJ as well as blackboard DF (BDF) as external factors as they are widely used in the literature. Since the introduction of the TAM model by Davis (1989), it has been generally used for predicting acceptance, adoption, and use of information systems (Halawi and McCarthy, 2007). However, in this research, we are going to use TAM model for a different purpose; rather than predicting the acceptance and use of information systems, we will investigate how TAM factors mainly might contribute toward increasing the rate of students’ satisfaction and retention. There is no comprehensive investigation in the literature examining how the students’ perception of TAM factors, as well as the external factors of DF and ENJ, will influence students’ e-satisfaction, which in turn will lead to higher rate of e-retention. Students’ e-retention in this paper measures the extent that students are not only continuously using blackboard but also psychologically attached to it (Al-hawari, 2006). In the following sections, an intensive literature investigation of the proposed model relationships will be introduced. The relationship between the proposed independent factors and e-satisfaction Service quality is linked to users’ satisfaction in the information systems and marketing fields (Yen and Lu, 2008). Satisfaction in the field of marketing is defined generally as the feelings or judgments of the customer toward products or services after they have been used (Jamal and Naser, 2003). Customer or client satisfaction is considered a key to success in today’s highly competitive environment. The importance of satisfaction as a factor in strategy development for customer and market-oriented firms cannot be underestimated (Kohli and Jaworski, 1990) and, not surprisingly, organizations have been increasingly active in conducting satisfaction surveys (Danaher and Haddrell, 1996). Such research into satisfaction has been influenced dramatically by the variety of measurement scales used in satisfaction instruments tests (Devlin et al., 1993). E-satisfaction is a significant importance in online services-related literature, since this satisfaction influences users decision to continue using the distribution channel or not (Lin and Sun, 2009). Szymanski and Hise (2000) viewed e-satisfaction as the users’ judgment of their online overall experience over a period of time. E-satisfaction, in this paper, measures the degree in which users are both satisfied/dissatisfied and pleased/displeased with blackboard services. Bansal et al. (2004) reviewed many studies that investigated the antecedents of e-satisfaction; they found that most of the variables that contributed to higher level of e-satisfaction are either related to the web site or to the perceived value of the web site. This classification of variables that generate e-satisfaction is obviously related to service quality factors rather than TAM factors. In the literature, there is an intensive investigation of the relationship between e-service quality different factors such as reliability, responsiveness, personalisation, security, trust, interactivity, accessibility, and e-satisfaction. Most of those studies found a positive association between e-quality factors and e-satisfaction. However, to the authors’ knowledge, there is only one study that tried to link TAM factors to e-satisfaction and e-loyalty conducted within online shopping context by Lin and Sun (2009). They found
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a positive and significant relationship between TAM factors and e-satisfaction as well as e-loyalty. However, in their study, they did not specify the different TAM factors influence on e-satisfaction and e-loyalty; instead they aggregate all factors into one construct called “technology acceptance factor”. Accordingly, they did not provide an accurate picture on which or how each TAM factor might influence e-satisfaction as well as e-loyalty. In this study, we proposed the two factors of TAM (PEOU); and PU, as well as another two external factors (BDF and ENJ) as independent variables influencing E-satisfaction and e-retention. Despite the use of TAM factor in different studies as quality factors, the literature lacks a focused discussion of the importance of TAM factors as powerful predictors of students’ e-satisfaction and e-retention within e-learning context. PEOU is defined as “the degree to which a person believes that using a particular system would be a free effort” (Davis, 1989). Different studies have used the construct PEOU as antecedent of e-satisfaction (Barnes and Vidgen, 2000; Jeong and Lambert, 2001; Madu and Madu, 2002; Zeithaml et al., 2001). In an e-learning context, students that perceive the system to be easy to use, develop better attitudes toward e-learning (Saade´ and Kira, 2009). PU – is defined as “the degree to which a person believes that using a particular system would enhance his or her job performance” (Davis, 1989, pp. 330-1). Usefulness has also been used by different researchers as predictors of e-satisfaction within online context such as Yang et al. (2003). The PEOU and usefulness of e-learning among learners are important factors that affect the effectiveness of e-learning (Lim et al., 2007). Nusair and Kandampully (2008) defined ENJ (playfulness) as the devises that attract the attention of the online system users with enjoyable inputs, it might include features such as animation, music, video, and other multimedia effects. They further argued that ENJ is essential in attracting, satisfying, and retaining users. A study by Yi and Hwang (2003) investigated the actual use of blackboard by university students. Their findings indicated the important role of ENJ as a positive influence on the decision of students to use blackboard and subsequently on actual use. Goetz et al. (2006) showed that ENJ has a clear linkage to learning behaviour, such as self-regulated learning and creative problem solving. In the current study, BDF refers to the content layout and content updating, as well as user-friendliness (Cristobal et al., 2007). Within the offline context of face-to-face banking, Greenland and McGoldrick (2005) found a direct relationship between the style and design of bank branch environment and favourable customer reactions, including satisfaction. DF have also been studied widely in the e-commerce as well as marketing literature (Aladwani and Palvia, 2002). There are several research projects measuring the impact of DF on satisfaction and other intentional aspects of behaviour (Siomkos et al., 2006; Tractinsky et al., 2006). Cyr et al. (2006) investigated how DF could influence customers’ loyalty within mobile industry context. They found that DF have a significant indirect relationship with loyalty through usefulness, ease of use, as well as ENJ. Based on the above discussion, we proposed the following four hypotheses: H1. PU has a positive impact on students’ e-satisfaction. H2. BDF has appositive impact on students’ e-satisfaction. H3. Enjoyment has a positive impact on students’ e-satisfaction. H4. PEOU has a positive impact on students’ e-satisfaction.
E-satisfaction – e-retention relationship Retention has often been seen as synonymous with loyalty (Al-Hawari, 2006; Al-Hawari and Ward, 2004). Further, there seems to be a consensus among academics and practitioners that retention and loyalty are very similar (Maloles, 1997). For this reason, this study treats them as equivalent constructs, and the term “retention” is used in this research. Retention is difficult to define. In general, there are three distinctive approaches to measuring retention; behavioural measures, attitudinal measurement, and composite measurement (Bowen and Chen, 2001). In a service context, retention is frequently defined as observed behaviour (Liljander and Strandvik, 1994). However, the behavioural models that used repeat purchase as the only measurement of customer retention have been criticised for their lack of conceptual basis; since this measurement may not have indicated an attachment to a particular brand but may instead reflect mere habit (Day, 1969) and may not yield a comprehensive insight into the underlying reason for retention (Bloemer and Kasper, 1995). Consequently, retention has also been approached as an attitudinal construct (Hallowell, 1996) to reflect the emotional and psychological attachment inherent in retention (Bowen and Chen, 2001). This was demonstrated, for example, by the willingness of the customer to recommend a service provider to other consumers (Zeithaml et al., 1996). However, using the attitudinal measure only has also been criticised in the literature (Dick and Basu, 1994). The third approach has combined the behavioural, attitudinal, and cognitive aspect of retention (Bloemer et al., 1998). The involvement of a psychological/attitudinal construct with repeat purchases has been shown to be important in achieving absolute retention (Oliver, 1999). In this regard, retention has frequently been operationalised as the first thing that came to mind when making a purchase decision. Thus, e-retention can be defined in this paper as the degree to which users exhibit repeat behaviour to the blackboard system, and possess a positive attitudinal and cognitive disposition. Satisfaction has traditionally been regarded as a determinant of retention. The more satisfied the users were, the easier it was to get attached to blackboard system. The link between satisfaction and retention has been acknowledged in the literature. Many studies have investigated the relationship between satisfaction and retention rates in different industries (Ranaweera and Prabhu, 2003). Generally, positive satisfaction has been found to influence retention (Nguyen and LeBlanc, 1998). Some studies found that repurchase intentions were positively influenced by satisfaction across product categories, and that customers were more likely to be retained as satisfaction increased (Anderson and Sullivan, 1993). Favourable attitudes usually lead to satisfaction which is generally considered as a major driver to retention also in online settings (Ribbink et al., 2004). E-retention is considered as an outcome of e-satisfaction within e-services context as well (Sahadev and Purani, 2008). Accordingly, a fifth hypothesis was formed as follows: H5. Students’ e-satisfaction has a positive influence on students’ e-retention. The relationship between the proposed independent factors and e-retention The literature has sustained different views on the way that the four proposed independent factors in this paper could influence retention. Some authors have indicated indirect influence only through satisfaction (Caruana, 2002), while others argued for a direct effect (Ranaweera and Neely, 2003). As discussed previously, the general concept
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of retention has measured using three different approaches; behavioural, attitudinal, and composite. The use of either behavioural approach or altitudinal approach has been criticised in the marketing literature as introduced previously. It can be clearly concluded from most authors who used TAM model in their studies that they linked different TAM factors either directly or indirectly with, first, attitude toward using technology and, second, with actual system usage (Davis, 1993; Shih, 2004). Accordingly, we will be using the concept of retention measured using composite approach in order to reflect not only the actual usage of users but also how psychologically they are attached to the used system. There are very few studies that linked TAM factors to the general concept of retention. Among those that exist, Cyr et al. (2006) linked usefulness and ENJ directly to e-loyalty within mobile industry. They found that ENJ and usefulness has a positive and significant influence on e-loyalty. Another study conducted by Lin and Sun (2009) has also investigated the relationship between the composite factor of TAM (they did not separate it into different dimensions) and e-loyalty within online shopping context. They concluded an overall significant and positive relationship between the composite factor of technology acceptance and e-loyalty. They further indicated a direct and positive relationship between DF and e-loyalty. Accordingly, we formed the following hypotheses: H6. PU has a positive impact on students’ e- retention. H7. BDF has appositive impact on students’ e-retention. H8. Enjoyment has a positive impact on students’ e-retention. H9. PEOU has a positive impact on students’ e-retention. Methodology A quantitative study, involving the administration of a survey, was conducted in order to empirically measure and then test the relationship between variables. The survey instrument consisted of 31 items (as shown in Table I) which were identified through a comprehensive literature review of TAM model factors as well as two external factors, students’ e-satisfaction as well as students’ e-retention. Measuring independent factors Four factors were used, the two TAM factors; PEOU, and PU, as well as two external factors; DF, and ENJ (Davis, 1989, 1993; Shih, 2004; Cyr et al., 2006). Items were identified in relation to: (1) PEOU was extracted from various studies such as, Pikkarainen et al. (2004) and McKechnie et al. (2006). Six dominant items were selected from these studies. (2) PU items were drawn from many studies that used TAM model (Aboelmaged, 2010). This factor was represented by six items. (3) Enjoyment was generated predominately from a study conducted by Lee (2009) which focused on understanding the behavioural intention within online gaming context. Four distinct items were identified and adapted from this study. (4) System DF – five different items were mainly adopted from Lin and Sun (2009) study of the factors influencing satisfaction and loyalty within online shopping context.
Critical dimensions
Related items
PEOU
Learning blackboard is easy Doing things is easy Functions are understandable Flexible to interact with Easy to become skilful Black board overall is easy Blackboard improves my gradesa Useful in my course Saves my time Useful in my life Information provided is valuable Blackboard is informative Simple layout Clear design User friendly Easy to navigate Provide few clicksa Times passes quicklya Very pleasant Gives me pleasure Gives me enjoyment I am satisfied with services offered by blackboard DF Usefulnessa Enjoyment Ease of using blackboard I intend to continue using blackboard I recommend blackboard I encourage others using blackboard I say positive things about blackboard I will continue using blackboard even if I face problems
PU
BDF
ENJ
E-satisfaction
E-retention
Note: aItems deleted in the confirmatory factor analysis stage
Measuring users’ e-retention The main approaches to conceptualising retention were introduced. Recognition of a positive attitudinal and cognitive disposition underpinned the operationalisation of the concept. Consequently, five different items were extracted from Zeithaml et al. (1996), Ribbink et al. (2004) and Cyr et al. (2006). Measuring users e-satisfaction In the literature, there were two broad types of scales to measure customer satisfaction; single- and multi-items scales (Sureshchandar et al., 2002). A single-item scale cannot provide enough information on the construct, as it does not capture the complexity of satisfaction entirely (Danaher and Haddrell, 1996). However, recent studies emphasized the multi-faceted nature of customer satisfaction. Accordingly, five items have been identified to represent the different aspects of users’ e-satisfaction. These items have been adopted from Ribbink et al. (2004) and Sahadev and Purani (2008).
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Table I. The measurement items
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Research design The study sample consisted of undergraduate university students at a medium size university in the UAE, which has adopted blackboard system as a teaching platform in all its courses in 2005. Students from different academic years, who have already used blackboard, were chosen as our research subjects, with the condition that they must have spent at least a semester at the University to ensure that they have a fair knowledge of the blackboard system. A paper-based questionnaire was administered to undergraduate students in different colleges by faculty members who agreed to participate in distributing it among their students in the class. The questionnaire consists of 31 items and was divided into seven parts. All parts of the questionnaire, except the first part (four items), which contained demographic data about the student (gender, academic year, college, and frequency of blackboard usage), were measured using a five-point Likert scale, ranging from 1 – strongly agree to 5 – strongly disagree, with the mid-point (3) representing the state of unsure or neutral. The subjects of this study consisted of a sample of 720 undergraduate university students from different colleges in the university. Out of the 720 distributed by the faculty members 550 questionnaires were returned to them and subsequently passed to the researchers. After cases with missing data were eliminated, the final sample consisted of 340 complete and usable questionnaires, yielding a response rate of 61.82 per cent. Measurement model Structured equation modelling was used to analyse the data and test the hypotheses. To assess the measurement model, four analyses were conducted (Al-Hawari, 2006; Al-Hawari and Ward, 2006). Unidimensionality was assessed first, prior to examining reliability and validity (Hair et al., 1995). In order to test for unidimensionality, confirmatory factor analysis (CFA) was conducted on measurement models for each of the constructs. In this study, the Comparative Fit Index (CFI) indices for all of the six constructs were above the 0.9 level which indicated evidence of unidimensionality. Second, squared multiple correlations (R 2) for each measurement item, composite reliability, and variance extracted for each factor were used in this study to test the construct reliability (Hair et al., 1995). The first run of the measurement model indicated that the R 2 for the majority of measurement items was greater than 0.5, which indicated a good reliability level (Holmes-Smith, 2001). Four items, however, were deleted as the R 2 values ranged from 0.1 to 0.3 which was less than 0.5 (shown with one asterisk at Table I). In the second run of testing, the measurement model R 2 values for all measurement items were greater than 0.5 or close (Table II). The values of composite reliability, variance extracted (Fornell and Larker, 1981) greatly exceeded the minimum acceptable values of 0.7 and 0.5, respectively, (Holmes-Smith, 2001), thereby indicating the reliability of measures and subsequently yielding very consistent results (Table II) (Zikmund, 2003). Evidence of convergent validity was gained as the measurement items represented their factors significantly; the critical ratio of every item exceeded the 1.96 value (Anderson and Gerbing, 1988) (Table II). To test for discriminant validity, the procedure described by Fornell and Larker (1981) was used. The analysis showed that the average variance extracted for each pair of variables was greater than the squared correlation for the same pair, indicating that each construct was distinct (Table III).
PEOU Learning blackboard is easy Doing things is easy Understandable Flexible to interact with Easy to become skilful Blackboard overall is easy PU Useful in my course Saves my time Useful in my life Valuable Blackboard is informative BDF Simple layout Clear design User friendly Easy to navigate ENJ Very pleasant Gives me pleasure Gives me enjoyment E-satisfaction Enjoyment Ease of using blackboard I am satisfied with services offered by blackboard DF E-retention Continue using blackboard I recommend blackboard I encourage others I say positive things I will continue using even if I face problems
Variable name 0.653 0.612 0.648 0.692 0.594 0.653 0.453 0.469 0.508 0.466 0.704 0.413 0.523 0.627 0.486 0.692 0.901 0.548 0.448 0.490 0.400 0.421 0.497 0.613 0.672 0.530 0.469
0.673 0.685 0.713 0.683 0.839 0.643 0.723 0.792 0.697 0.832 0.949 0.740 0.669 0.700 0.623 0.649 0.705 0.783 0.820 0.728 0.685
R2
0.808 0.782 0.805 0.832 0.771 0.808
li
0.364 0.308 0.283 0.485 0.526
0.394 0.477 0.446 0.426
0.437 0.135 0.558
0.472 0.350 0.363 0.549
0.388 0.420 0.570 0.250 0.320
0.300 0.399 0.358 0.286 0.311 0.314
Variance estimates
12.582 13.024 13.506 12.125 11.458
9.956 10.549 9.597 9.940
17.584 18.797 15.142
10.684 10.691 11.362 10.364
11.542 11.614 11.131 13.365 12.670
17.548 15.791 16.401 17.130 15.522 13.041
Critical ratios
0.87
0.78
0.85
0.82
0.86
0.92
Composite reliability
0.56
0.50
0.65
0.54
0.57
0.66
Variance extracted
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Table II. Reliability test outcomes for each factor
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Finally, CFA was conducted to empirically investigate whether the proposed model reasonably fitted the data. The model x 2 is 705 (df ¼ 309, p ¼ 0.000). It should be noted that if the model chi-square significance is , 0.05; this indicates a problem with the model fitness by this criterion. However, the model chi-square criterion could be misleading as it is so conservative and very sensitive to sample size (Kline, 1998). Accordingly, researchers who use SEM believe that if they achieve a reasonable sample size . 200 and appropriate fit indicated by other fit tests such as CFI and root mean square error of approximation (RMESA), the significance of chi-square test can be disregarded and is not a reason by itself to modify the model (Byrne, 2001). In this research, the overall fit of the model was acceptable, with a chi-square x 2/df ratio of 2.28, RMSEA of 0.061, and the CFI of 0.918 (Byrne, 2001). Structural model The steps described in the last section reduced the data and resulted in a manageable number of valid and more reliable measurement items which were then used to evaluate the structural model in this section. The overall fit indices for the proposed structural model were x 2 ¼ 705 (df ¼ 309, p ¼ 0.000), x 2/df ratio of 2.28, a CFI of 0.918 and the RMSEA of 0.061 (Hair et al., 1995; Byrne, 2001). These values indicated that the model fits the data well. Having established the final structural equation model, it was possible to test the hypotheses developed for this study. These hypotheses can be tested by evaluating the path coefficients and the significance levels among the constructs in the model (Table IV). Analysing the results showed that PEOU was the only TAM factor that did not have a significant relationship with students’ e-satisfaction, rejecting H4, and accepting hypotheses H1, H2, and H3. PEOU as well as PU had a significant, direct, and positive relationship with students’ e-retention. Thus, H6 and H9 were accepted. On the other hand, BDF and users ENJ had no significant and direct relationship with students’ e-retention, which disproved H7 and H8. Finally, the analysis showed a significant and positive relationship between students’ e-satisfaction and students’ e-retention. Thus, H5 was supported. All of the students’ e-retention predictors explained 69 per cent of the construct indicating the importance of these factors in predicting the dependent variable. Students’ e-satisfaction variable has explained by 71 per cent by the independent factors. Research findings and implications The aim of the study was to highlight the influence of TAM factors as well as ENJ and DF on students’ e-satisfaction and e-retention in the context of e-learning system in UAE. PEOU
Table III. Discriminant validity test outcomes
PEOU PU BD ENJ E-SAT E-RET
0.359 0.554 0.058 0.378 0.387
PU
BD
ENJ
E-SAT
E-RET
0.615
0.600 0.555
0.645 0.610 0.595
0.580 0.535 0.520 0.575
0.61 0.565 0.550 0.605 0.530
0.259 0.033 0.375 0.551
0.097 0.518 0.279
0.295 0.092
0.453
Note: The upper level in italics represents the average extracted variance while the lower level represents the squared correlations for every pair
The relationships between variables
Standardised regression weights
PEOU ! e-sat PU ! e-sat BDF ! e-sat ENJ ! e-sat PEOU ! e-ret PU ! e-ret BDF ! e-ret ENJ ! e-ret e-sat – e-ret
Not significant 0.316 * * 0.440 * * 0.340 * * 0.192 * 0.548 * * Not significant Not significant 0.269 *
Note: Significance at *p , 0.05 and * *p , 0.01
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This paper proposed a conceptual model which was empirically validated by perceptual data collected from university students who are using the e-learning system blackboard. The results of the survey provided strong empirical support for six hypotheses of the nine hypothesized relationships between the constructs. Figure 2 shows the final model and highlights the significant relationships in bold. The findings of this paper confirm those found in the existing literature (Nusair and Kandampully, 2008; Aladwani and Palvia, 2002) and show that most of the independent factors have positively influenced students’ e-satisfaction except the PEOU factor. It is clear that e-learning contexts provides students the advantage of reduced time and effort while using different aspects of blackboard system, resulting in improved rates of satisfaction. In particular, DF of the blackboard system, positive perception of usefulness, and students’ positive and enjoyable experience of using blackboard all Independent factors
0.548
PU 0.316 DF
0.440 E-satisfaction 0.340
ENJ
PEOU
Independent variables
E-retention 0.26
0.192
Mediating variables
Dependent variable
Notes: PU – perceived usefulness; DF – design features; ENJ – enjoyment; PEOU – perceived ease of use Source: Developed for this research
Figure 2. Final model
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contributed to raising the level of students’ e-satisfaction. To build on these findings, instructors might provide their students useful information and material about the importance of blackboard, in order to generate a high level of students’ perception of the usefulness of the blackboard in their academic performance. They might also motivate their students to talk about the benefits of using blackboard system with others. Instructors could consider making the navigating experience using the blackboard system more enjoyable. Instructors might design interactive task enabling students to communicate directly with each other and with their instructors in order to develop a best solution scenario to any issue or problem. Use of humour, appealing graphics, video and audio, or 3-D virtual model could also assist in improving students’ enjoyable experience using blackboard system. Finally, students have to understand the simple and clear design of the blackboard layout, navigate easily, and perceive the friendly aspect of blackboard interface in order to gain a higher level of satisfaction. One interesting finding in this research is that the TAM factor PEOU did not contribute toward students’ e-satisfaction, but did contribute significantly to students’ e-retention. It seems that students’ feelings of exercising minimum or no effort in learning the use of blackboard system does not contribute toward their level of satisfaction, but it is essential for them to get attached to and continue using the system. One possible explanation of these results is that most of the university students already possess good skills in technology, enabling them to learn any new system quite easily. This might lead to the fact that students do not feel particularly appreciative when learning the features and the functions of a new system. However, ease of use is still valid in continuing using the system and getting psychologically attached to it. The other TAM factor, “PU”, has a very strong and direct relationship with students’ e-retention as well as a significant relationship with students’ e-satisfaction. This is reassuring as; ultimately, usefulness is surely one of the most important indicators of benefit for students and universities alike when considering the value of online course management systems like blackboard. Accordingly, Universities in general have to insure that students perceived blackboard as a system with great value and it is most useful for them in their education journey. The university has to ensure that all of the educational and internal marketing activities including positioning strategy are directed toward the idea that all students who use blackboard perceive it as a consistent source of good value. The two external factors BDF and ENJ factors did not have a direct relationship with students’ e-retention, though they had with e-satisfaction. One possible explanation of this result is that students might not appreciate the ENJ and the other BDF within the academic context since they are more focused on more important issues, such as the kind of information that the instructor delivers and the overall usefulness of the online learning management system in enhancing their academic performance. However, we should not degrade the importance of those two factors on e-retention as they still valid two factors in influencing e-retention via students’ e-satisfaction. E-satisfaction was also an important factor in influencing the level of students’ e-retention. This result was consistent with previous research (Ribbink et al., 2004). The findings in this research showed that students’ e-satisfaction mediates the effects of most independent factors except the ease of use on e-retention. E-retention can be considered as the key construct in students’ academic performance. Therefore, it follows that universities should emphasize and value factors such as DF and ENJ in order to increase e-satisfaction, which in turn will result in higher level of e-retention.
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[email protected] Samar Mouakket is currently an Assistant Professor at the MIS Department in the University of Sharjah, UAE. She received her PhD in 1996 in Management Information Systems from Sheffield University, the UK. Her research interests include systems analysis and user requirements determination during systems development, and the deployment of web-based business applications.
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