The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model

Information & Management 42 (2005) 317–327

The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model Raafat Saade´*, Bouchaib Bahli Department of Decision Sciences and Management Information Systems, John Molson School of Business, Concordia University, 1455 De Maisonneuve Blvd., Montre´al, Que´., Canada H3G 1M8 Accepted 31 December 2003 Available online 8 April 2004

Abstract Internet-based learning systems are being used in many universities and firms but their adoption requires a solid understanding of the user acceptance processes. Our effort used an extended version of the technology acceptance model (TAM), including cognitive absorption, in a formal empirical study to explain the acceptance of such systems. It was intended to provide insight for improving the assessment of on-line learning systems and for enhancing the underlying system itself. The work involved the examination of the proposed model variables for Internet-based learning systems acceptance. Using an on-line learning system as the target technology, assessment of the psychometric properties of the scales proved acceptable and confirmatory factor analysis supported the proposed model structure. A partial-least-squares structural modeling approach was used to evaluate the explanatory power and causal links of the model. Overall, the results provided support for the model as explaining acceptance of an on-line learning system and for cognitive absorption as a variable that influences TAM variables. # 2004 Elsevier B.V. All rights reserved. Keywords: Online learning; IT acceptance and adoption; Internet; Cognitive absorption; TAM; Perceptions

1. Introduction In the past decade, the interest in using the Internet and World Wide Web in the classroom as part of the learning environment increased drastically. The value of online learning has become widely recognized and accepted. Recent developments have put pressures on businesses and academic institutions to integrate online course material into their environments. * Corresponding author. Tel.: þ1-514-848-2424x2988; fax: þ1-514-848-2824. E-mail addresses: [email protected], [email protected] (R. Saade´).

The pressures involve: developing enhanced learning environments, creating online courses, absorbing cost reductions, increasing revenues (with more students per course), and improving course quality. Methods of effectively implementing online material has not, however, been well understood and few studies assess user acceptance of Internet-based learning systems (ILS). In an attempt to study and explain individual’s attitudes and behaviors in using ILSs, we found several theoretical models that have been proposed, including innovation diffusion theory, the theory of reasoned action, the theory of planned behavior, and the technology acceptance model (TAM). All generally agree

0378-7206/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.im.2003.12.013

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that an individual’s beliefs and perceptions of IT have a significant influence on their usage. In this study, an extended version of TAM [17] which includes the concept of cognitive absorption (CA) was used. In this, CA (defined as a state of deep involvement with the ILS) is an antecedent to perceived ease of use and perceived usefulness. This construct involves three dimensions: temporal dissociation, focused immersion, and heightened enjoyment. Our primary objective was to examine the influence of the three major cognitive absorption dimensions of the TAM constructs: perceived usefulness, perceived ease of use, and the behavioral intention to use ILSs. A secondary objective was to confirm the nature of the relationships between them in the Internet-based learning context.

2. Theoretical background 2.1. The technology acceptance model Studying the acceptance and use of IT has been the focus of many studies in IS research [1,3– 5,9,23,47,52,53]. More particularly, efforts have been in the building of theories that can predict the determinant factors of IT acceptance [6,39,40,44,50,54]. TAM developed by [15] became a widely used model in some types of IS research. It was adapted from the Theory of Reasoned Action (TRA) [20] and identified the relationships between perceived ease of use, perceived usefulness, attitudes, and behavioral intentions towards a target system [16]. Perceived usefulness (PU) was defined as the degree to which a person believes that using a particular system could enhance his or her job performance: it is the extent to which an individual believes that using the system enhances his/her performance. Perceived ease of use (PEU) can be described as the degree to which a person believes that using a particular system is free of effort. Previous research has demonstrated that individuals are more likely to use a new technology if they perceive that it is easy to use.

individual’s experience while using the technology [51]. The holistic approach focuses on capturing constructs such as the individual’s level of enjoyment while interacting with the technology and how time is perceived during the session. Such constructs were found to be significant predictors to outcomes related to technology use and acceptance. Holistic experiences can be described with the concept of cognitive absorption (CA), which can be best seen by its manifestation in the level of user involvement with Internet and video games [2]. Previous work suggested that there are positive outcomes from this type of engagement [25]. These may include more positive attitudes towards target behavior and greater exploratory use of the technology. The CA variables represent one form or another of intrinsic motivation, where ‘‘a behavior is performed for itself, in order to experience pleasure and satisfaction inherent in the activity’’ [19,55]. The theoretical basis derives from three closely inter-related streams. First, the trait of absorption describes an individuals’ state of deep attention, where he/she is totally absorbed with the event being experienced. Some have the propensity to experience this state more than others. Absorption was defined by [49] to be an individual’s trait involving a high propensity to engage in events with total attention, where the object of attention consumes all the individual’s resources. Second, the theory of flow developed by [14] describes ‘‘the state in which people are so involved in an activity that nothing else seems to matter.’’ This is characterized by the individual being engulfed with a sense of intense concentration, a feeling of being in total control of what he/she is doing, a loss of consciousness and an experience of time loss. Flow is an important element of understanding human-technology interactions as it explains the important antecedent of attitudes towards technologies. Third, the concept of engagement relates to playfulness: it encompasses the dimensions of intrinsic interest, curiosity, and attention focus without necessarily the feeling of being in control. 2.3. The Internet, learning and TAM

2.2. Cognitive absorption Although TAM’s emphasis is on the behavior of an individual towards using IT, little is known on the

Instruction over the Internet is perceived by many to be a significant breakthrough in teaching and learning [18,34,36–38]. Internet technology facilitates the

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exchange of information and expertise and provides opportunities for learners in remote and disadvantaged locations [29,58]. The Internet allows educators to provide learners with new and innovative virtual environments in an attempt to stimulate and enhance their learning process [7]. In addition, Internet or web technologies are important because they support manipulation of information, facilitate/enhance communications among instructors and learners and provide tools to encourage creativity and initiative [13,43]. Originally, web-based learning problems were technology related and included issues of access, connection, Internet familiarity, and lack of independent learning [11]. As technology advanced, the problems shifted towards the learners’ side. They felt isolated and de-motivated. ‘‘Students are still working to come to grips with a new and difficult way of learning. They exemplify the concern by asking for more incentive, more time, more structure, and more guidance.’’ [28]. Studies of learners’ satisfaction are typically limited to one-dimensional post-training perceptions of learners [35]. Acceptance and satisfaction is multidimensional and includes a wide variety of critical variables; perceptions, beliefs, attitudes, learners’ characteristics, and level of involvement with the online course material [8]. If Internet-based learning environments are to benefit students then it is important from the student’s perspective that they are not seen as overly complex. The introduction of Internet-based learning environments may hinder the learning process if the technology is perceived as being complex and not

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useful to enhanced performance, and thus a distraction to learning.

3. Research model and hypotheses Understanding the variables that cause students to hold certain beliefs about an ILS would be of substantial value to students, institutions, businesses and instructors alike. It would also provide valuable insight for the successful design, implementation, and deployment of ILSs. The research model used here is shown in Fig. 1. This captures cognitive absorption as an underlying determinant of PU and PEU for ILS. The two belief constructs in turn influence the intention to use it. In our work, TAM described the relationships between the students’ IUC, PU and PEU. PU was defined as the extent to which a student believed that using the ILS would enhance his/her performance in the course, while PEU was defined as the extent at which the student believed that using the ILS was free from cognitive effort. Studies using TAM have suggested that perceived ease of use influences perceived usefulness [56]. In our study, we also investigated this relationship. We hypothesized that students who perceived the system easier to use would also perceive it be more useful as suggested by the finding from previous work [22,24,42,48,57]. It could be expected that students would believe in a use-performance relationship with the ILS, believing

Fig. 1. The research model.

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that if they use the ILS, their performance in the course would improve. We hypothesize: H1. Perceived usefulness will have a strong positive effect on the intention to use the ILS. Perceived ease of use is a student’s assessment that his/her interaction with the ILS will be relatively free of cognitive burden. They do not need to spend significant time and effort to operate it. PEU represents an intrinsically motivating component of the student-ILS interaction. Therefore, we hypothesize: H2. Perceived ease of use will have a positive effect on the intention to use the ILS. Perceived ease of use was found to influence intention to use directly and indirectly via perceived usefulness. These two causal pathways are supported by the notion that lower cognitive burden frees up students’ attentional resources, thereby allowing the student to focus on other learning matters. Therefore: H3. Perceived ease of use will have a positive effect on the perceived usefulness of the ILS. Other’s work suggests that individual behavior toward new IT is shaped by the holistic experiences with it: these addressed and captured holistic experiences with technology as external variables [27,30– 33,59]. Motivated by the interest in understanding the influence of holistic experiences on behavior towards the ILS, we applied the conceptual construct of CA as an external variable to the TAM. CA is defined here as a state of deep involvement with the ILS. This task was the content that needed to be learned. It had been argued that it can be a significant learning outcome in technology-mediated courses: by introducing an element of play into the system, intrinsic motivation may be produced. Cognitive engagement has been found to be significant in describing technology used in distance learning. CA was also expected to exhibit a positive influence through its dimensions. If students experience temporal dissociation, they lose track of time and seem to have completed a task in a shorter time than they actually did. Such focused immersion suggests that when student’s attentional resources are focused on a

task, the level of cognitive burden is reduced, resulting in the amplification of perceived ease of use. Finally, enjoyable activities are less taxing. We hypothesize: H4a. Cognitive absorption while using the ILS has a positive effect on the perceived ease of use of the ILS. CA has a salient influence on perceived usefulness and perceived ease of use. The state in which an individual is intrinsically motivated will heighten perceptions of a lower cognitive burden. The relationship between CA and perceived usefulness derives from the self-perception theory, which argues that individuals will seek to rationalize their actions and reduce cognitive dissonance. We therefore hypothesize: H4b. Cognitive absorption while using the ILS has a positive effect on the perceived usefulness of the ILS. Control and curiosity were dropped from the five CA dimensions because of the ILS’s design and the setup of the experiment. Students were asked to perform a specific task which was short and simple. They were guided by the ILS and were given only two choices, to go ahead to complete the task or stop the session. This ILS guidance removed student control. Curiosity was also absent since the students’ on-line activities were very limited and exploration was not possible. The high level of system guidance and repetitiveness also removed the element of curiosity.

4. Research methodology 4.1. Measures Items (presented in Table 1) used to measure the constructs were adopted from prior research work. The items were validated in a pilot study and some wording was changed to account for the context of using an Internet-based learning system. All items were measured using a five-point Likert-type scale with anchors from ‘‘Strongly disagree’’ to ‘‘Strongly agree’’. The questionnaire included items worded with proper negation and a shuffle of the items to reduce monotony of questions measuring the same construct.

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Table 1 Question items used in the study Construct

Item

Measure

Perceived usefulness (PU)

PU1 PU2 PU3 PU4 PU5

Using the ILS would reduce my ability to perform well in the course? I think that an ILS such as this one should be part of each and every course in the university Using the ILS in the course would enhance my performance in the course Using the ILS in the course would make it easier for me to study for tests and exams Using the ILS in the course would make it easier for me to do my assignment(s)

Perceived ease of use (PEU)

PEU1 PEU2 PEU3 PEU4 PEU5

Learning to use the ILS is hard for me I find that the process of using the ILS was clear, understandable and straight forward Navigating through the ILS was easy for me It would be easy to become skillful at using the ILS I find the ILS easy to use?

Intention to use (IUC)

IUC1 IUC2

I intend to take more courses using online ILS in the future I intend to show others this ILS

Temporal dissociation (TD)

TD1 TD2 TD3 TD4

Sometimes I lose track of time when I am using the ILS Time flies when I am using ILS Most times when I get on to the ILS, I end up spending more time than I had planned I often spend more time on the ILS than I intended

Focused immersion (FI)

FI1 FI2 FI3

When I am using the ILS I am able to block out most other distraction While using the ILS, I am absorbed in what I am doing While using the ILS, I am immersed in the task I am performing

Heightened enjoyment (HE)

HE1 HE2 HE3

I have fun interacting with the ILS Using the ILS bores me I enjoy using the ILS

4.2. Setup and procedure A survey methodology approach was taken to test the relationships in the research model. Throughout two semesters, students in an introductory undergraduate management information systems course at Concordia University in Montreal, Canada, were asked to use an ILS to help them understand content material and rehearse for questions that may appear in the midterm and final exams. The ILS was developed to be used via the web and students were able to use the learning tool anywhere, anytime. The system monitored the students’ efforts in terms of three variables: time spent on the system, chapters that the student used, and detailed and average scores. Selection of the web to implement the learning system was appropriate for at least three reasons: (1) the technology exemplifies the characteristics of contemporary IT that underscore the importance of notions of cognitive absorption, (2) the technology is available from many locations around the campus, friends, Internet cafes and homes, thus access would not count as a barrier to

the usage of the technology, and (3) the number of instructors and schools publishing courses, course material, and learning objects on the web is continuously increasing. Using the ILS, students had to log in and identify whether they wished to practice their knowledge of the course content or would like to perform the required exam for a specific topic. Choosing the first option, students were not scored. Students had to take tests for each topic. The interface for both practice and test was the same. During the last week of the semester, the professor administered the survey questionnaire to the students in class. From pedagogical and cognitive perspectives there were three interesting elements. First, the questions for practice and assessment were obtained from the same pool-of-questions database. This required the students to use their cognitive skills (such as shortterm memory, working memory, recognition, and recollection). They had to be very attentive during the practice exercises. Questions included multiple choice as well as true or false and the student was

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Table 2 Basic descriptive statistics Min Experience Professional Internet Daily use of Internet Basic software knowledge

1.0 3.0 1.0 1.0

Max

Mean

5.0 5.0 5.0 4.0

3.9 4.2 2.8 2.2

S.D. 1.1 0.6 0.9 0.8

Note: The table below explain the values Scale Experience Professional Internet Daily use of Internet Basic software knowledge

1

2

3

4

5

<6 m <6 m <15 min Very high

6 month to 1 year 6 month to 1 year 15 min to 1 h High

þ1–2 years þ1–2 years >1–2 h Neutral

þ2–5 years þ2–5 years >2–5 h Low

>5 years >5 years >5 h Very low

given feedback instantaneously. The second element to the system was that the assessment was not a one time evaluation. The student had the choice of practicing again and then being re-assessed. The final assessment, however, was calculated as the average of all the assessments taken. Thirdly, the final element to using the learning tool was the presence of few wrong questions/answer sets per topic, and students were encouraged to find them and report them to the teacher. 4.3. Participants characteristics A total of 102 students participated in this study. The sample consisted of 52% female and 48% male participants with an average age of 23 years. The majority of students (85%) were majoring in Accountancy, Management Information Systems, Finance, and Marketing. Table 2 provides the basic demographics for the participants.

5. Analysis and findings 5.1. Assessment of the measurement model Prior to the assessment of the measurement model, guidelines for screening missing data, outliers, and assumptions of multivariate analysis were followed as suggested by [26]. The 102 usable questionnaires were

examined for missing data: they showed a few missing values and a mean substitution was used to generate replacement values for all the missing data. Both univariate and multivariate outliers were searched in the data set and since most of the variables under study were measured on a five-point scale, and none of the observations appeared to be extreme, all the data were kept for analysis. Data normality was checked using Skewness, Kurtosis, and Kolmogorov–Smirnov normality tests. The results of this examination led us to assume data normality. The partial least-squares (PLS) approach to multiple indicator structural equation analysis was used to assess both the measurement and structural models. This is a latent structural equation modeling technique that utilizes a component-based approach. This minimizes the sample size demands [12]. The largest construct in this study had five items, which made a required sample by PLS of 50 observations (5  10). The sample consisted of 102 observations. The assessment of the measurement model implied that individual item loadings and internal consistency reliabilities were examined as a test of reliability. As for discriminant validity, items should load higher on their own construct than on the others in the model, and the average variance shared between the constructs and their measures should be greater than the variances shared between the constructs themselves. The structural model and hypotheses were tested by examining the path coefficients and their significance.

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As summarized in Table 3, the reliability of most of the constructs, except for CA, were above or close to 0.70 while values of Rho were significantly greater than 0.5; this represented a commonly acceptable level for exploratory research.

The PLS approach can be used for theory confirmation and to suggest where relationships might exist. For prediction, the PLS approach was more suitable than others, such as LISREL and EQS, because it assumed that all the measured variance in the study were to be explained. PLS loadings of measures of each construct were similar to a principle components factor analysis and path coefficients were similar to standardized beta weights in a regression analysis. R2 values for dependent constructs were also produced. The analysis of the data was made in two steps: Measurement and Structural model. The measurement model using PLS was assessed in terms of item loadings, internal consistency, and discriminant validity. The structural model and hypotheses were investigated by examining the path coefficients represented as standardized betas. The explained variance in the dependent constructs was assessed as an indication of the overall predictive strength of the model.

5.1.2. Convergent and discriminant validity assessment To evaluate discriminant validity, [21] suggested a comparison between the average extracted variance of each factor and the variance shared between the constructs (the squared correlations between the constructs). It is expected that the loadings of all items within a construct should be high on that construct, indicating high convergent validity, and low on the others. Cross-loadings of items are given in Table 4. Item PU5 was dropped from the analysis because it showed low loading on its specified construct. Items of each of the three dimensions of CA construct were averaged since PLS does not allow the modeling of second-order factors. These loadings showed a clear discriminant and convergent validity for all constructs. Table 5 reflects AVE (average variance extracted) values. Squared correlations are reported on the off-diagonals and AVE squared roots are reported on the diagonal. The numbers on the diagonal should be greater than twice those off diagonal. The largest correlation (off-diagonal) is 0.24. The lowest AVE squared root (on-diagonal) is 0.72. Hence, the smallest on-diagonal value is larger than twice the largest off-diagonal values displaying the expected pattern. Hence, the results indicate that discriminant and convergent validity of the measures was reasonable.

5.1.1. Reliability assessment PLS estimates the loading parameters (i.e. links between measures and constructs) and path coefficients and links between different constructs at the same time. Reliabilities of individual items were assessed by examining the loadings of the items on their respective constructs. These loadings should be higher than 0.5, following the criterion indicated by [45] to indicate that significant variance was shared between each item and the construct. The second indicator assessed was the Rho coefficient for internal consistency. This was not influenced by the number of items in the scale but by the relative loadings of the items. Rho is based on the ratio of construct variance to the sum of construct and error variance. As shown in Table 3, the value of Rho greater than 0.50 indicated that the construct variance accounts for at least 50% of the measurement variance. The average variance extracted should be higher than 50% [46].

5.2. Assessment of the structural model The second step of PLS is the assessment of the structural model of Fig. 2. Each hypothesis was tested

Table 3 Reliability assessment Variables

No. of items

Rho

Reliability, a

Mean

Min

Max

S.D.

Cognitive absorption Perceived usefulness Perceived ease of use Intention to use

3 4 5 4

0.76 0.92 0.91 0.87

0.52 0.74 0.67 0.62

3.4 3.0 3.4 2.9

1.4 1.4 1.0 1.0

4.7 4.4 4.2 5.0

0.7 0.7 0.6 0.9

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Table 4 PLS loadings: convergent and discriminant validity

Table 5 Variance shared between constructs

Loadings

PU

PEU

IUC

CA

PU1 PU2 PU3 PU4 PEU1 PEU2 PEU3 PEU4 PEU5 IUC1 IUC2 TD FI HE

0.735 0.905 0.921 0.880 0.254 0.410 0.233 0.320 0.306 0.437 0.402 0.169 0.315 0.406

0.302 0.295 0.336 0.354 0.741 0.824 0.835 0.820 0.899 0.268 0.290 0.011 0.232 0.215

0.418 0.403 0.478 0.407 0.310 0.171 0.252 0.321 0.299 0.858 0.841 0.009 0.238 0.358

0.235 0.458 0.417 0.386 0.174 0.218 0.278 0.212 0.181 0.256 0.330 0.513 0.828 0.835

Values in bold show high loading of items on respective construct indicating high convergent validity.

using PLS Graph [10] and by looking at the path coefficients. The estimated path effects are given along with their degree of significance [41]. A bootstrapping procedure was used to assess the level of significance of the paths computed by PLS. T-values were computed from a series of PLS evaluations made against several partitions of the data set. As can be seen, the positive correlations between the constructs suggest that there were grounds for expecting significant effects between them. Hypothesis 4b tested the relationship between CA and PU. A strong

Variables

CA PU PEU IUC

Variance CA

PU

PEU

IUC

0.72 0.18 0.06 0.11

0.86 0.14 0.24

0.82 0.11

0.84

Values in bold show high loading of items on respective construct indicating high convergent validity.

positive relationship was observed (path ¼ 0.36, P < 0:001). Hypothesis 4a showed a positive relationship between CA and PEU (path ¼ 0.24, P < 0:05). Hypothesis 3 tested the relationship between PEU and PU: a positive and significant relationship was found (path ¼ 0.28, P < 0:01). The percentage of variance explained (R2) of PU was 26% while that of Perceived Ease of Use was 6% which is very weak. Hypothesis 1 tested the relationship between PU and IUC. This hypothesis was supported and a positive and significant effect was observed (path ¼ 0.43, P < 0:001). Hypothesis 2 indicates a positive effect between PEU and IUC (path ¼ 0.16, P < 0:05). The percentage of the variance explained (R2) of IUC was 26%. In addition, an analysis of competing models was conducted: the direct effect from CA to IUC. The results show a positive effect (path ¼ 0.17, P < 0:05) with an R2 of 28.9% of IUC explained variance.

Fig. 2. Model parameters for the research model.

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Evidently, the model explains intentions slightly better by accounting for the direct influence of CA on IUC. The results may suggest how people may think of absorption, flow, and engagement—they directly influenced what people do. Finally, we tested the effects of PEU and PU on CA. The results show a significant effect of PU on CA (path ¼ 0.38, P < 0:001) and a weak relationship between PEU and CA (path ¼ 0.094 P ¼ not significant). The percentage of the variance explained (R2) of CA was 26.2%.

6. Discussion and conclusions The results of the empirical analysis provide a number of interesting insights and suggestions: (1) cognitive absorption was shown to be an important antecedent to PU but less important to PEU; (2) TD did not load high on CA, (3) weak results were observed for PEU, however findings provided strong support for the PU-IUC component of the TAM model, and (4) CA seemed to play an important role in explaining intentions both directly and indirectly. Cognitive absorption was found to play an important role as an antecedent to PU which increases when an individual experiences a total engagement with the ILS (i.e. focused immersion) and enjoys the pleasure aspects of the interaction with the ILS (i.e. heightened enjoyment). The second interesting result was that cognitive absorption has a significant effect on PU with a higher variance explained of the latter construct but smaller variance of PEU. As advocated by prior research, the analysis also shed light on the positive effect of PEU on PU. As expected, there was a significant positive effect. Overall, the model showed weak results for PEU. The ILS entailed only three different web pages and the interface was simple, clean and without distractions to the task at hand. Also, students did perceive the ILS to be easy to use, yet PEU did not play an important role in our model. It seems logical for the group of students taking an MIS course that student’s knowledge of IT varies from those who have barely used a computer to those who are actually working in the IT field. Therefore the variance of PEU in our proposed model is poorly explained. Finally, there was a significant positive impact of both PU and PEU on the intention to use the ILS in

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another course, though the influence of PU was much more significant than PEU and approximately three times stronger.

7. Limitations The findings of this research must be considered in light of its limitations. First, the questionnaire approach is not free of subjectivity in the respondent. The questionnaire was a ‘‘snap-shot’’ instead of a longitudinal study. Second, while the respondents are undergraduate students and the subject matter is appropriate to them caution must to be taken in generalizing the results. Participants in this study came from four major cultural backgrounds: English, French, Asian, and Middle Eastern. The influence of cultural beliefs may vary drastically. Conclusions drawn are based on the concept of ILS usage. Other learning objects can be designed for different tasks and for different platforms (in this case it was web-based). In other words we admit that this study was based on a single distinct technology. This however, may not generalize across a wide set of technologies. Finally, the research used in this study predicts causal relationships between the constructs studied. Measures of the constructs were gathered at one point in time. Therefore, causality cannot be inferred. Although, PLS analysis provides strong support for the interpretation due to the fact that all of the relationships are tested simultaneously, conclusive statements about causality cannot be made since alternative explanations cannot be ignored.

8. Implications This research was motivated by a need to understand behavior towards ILSs and by the hope of gaining wisdom for the determinants of their intentional use. All this is guided towards providing a better and more effective learning experience to students. There are several potentially important implications. The findings demonstrate the value of the contributions of cognitive absorption as one antecedent to perceived usefulness of ILS in higher education. This study represents a theory-based empirical test

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with incorporation of CA in online learning. What was learned from this study include the value of TD for the assessment of the ILS. Creating ILSs that are multimedia based could increase the element of playfulness and hence increase TD. From a practical perspective, companies are highly interested in training issues. The setup, procedure, theoretical foundation and methodology presented here may apply for online training tools. The interplay of the relationships of different constructs is of interest to practitioners who assess different ILS. Certainly, firms could use the results to enhance their understanding of what makes individuals select ILSs for better performance.

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