1 9 Behavioral Intention Use Behavior and the Acceptance of Electronic Learning Systems Spain

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Behavioral intention, use behavior and the acceptance of electroniclearning systems: Differences between higher educationand lifelong learning

Ángel F. Agudo-Peregrina, Ángel Hernández-García ⇑, Félix J. Pascual-MiguelUniversidad Politécnica de Madrid, Departamento de Ingeniería de Organización, Administración de Empresas y Estadística, Escuela Técnica Superior de Ingenieros deTelecomunicación, Despacho A-126. Av. Complutense, 30, 28040 Madrid, Spain

a r t i c l e i n f o

Article history:Available online 8 November 2013

Keywords:Educational technology acceptanceBehavioral intentionUse behaviorSelf-reported useTAM3

a b s t r a c t

Widespread implementation of e-learning systems – learning management systems, virtual learningenvironments – across higher education institutions has aroused great interest on the study of e-learningacceptance. Acceptance studies focus on the predictors of system adoption and use, with behavioralintention to use the system as a proxy for actual use. This study proposes a TAM3-based model – withthe inclusion of two additional variables: personal innovativeness in the domain of information technol-ogy and perceived interaction – to study the factors influencing the acceptance of e-learning systems.Attention is also brought towards the role of behavioral intention, especially in its relation with usebehavior. In order to do so, two different settings were considered: higher education and lifelong learn-ing; data was gathered from a survey administrated to Spanish graduate and lifelong learning students,and partial least squares analysis was used to test the research model. Results supported TAM relations,except for the intention-behavior linkage, and unveiled a dual nature of perceived usefulness – with onecomponent related to efficiency and performance, and another component related to flexibility. The ade-quacy of applying TAM3-based models in educational contexts and suitability of actual system usage

measures are also discussed. 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Acceptance models aim to identify the factors that allow pre-dicting user behavior and explaining the adoption process. Sincethe formulation of the Theory of Reasoned Action (TRA) (Fishbein& Ajzen, 1975) and the Technology Acceptance Model (TAM) (Da-vis, 1989), the constant search for a better explanation of technol-ogy acceptance and its antecedents has led to the development of models of increasing complexity.

Computer-mediated education, or electronic learning, has not

been exempt from this kind of analysis. But the relatively recentdevelopment of learning management systems and virtual learningenvironments has caused a relative gap between acceptance mod-els and empirical studies of educational technology acceptance. Asa result, the different models have gradually been tested in e-learn-ing contexts. Thus, most studies in the last decade were groundedonly on TAM or the Theory of Planned Behavior (TPB) (Ajzen,1991), with very few using more recently developed acceptancemodels, such as the Unified Theory of Acceptance and Use of the

Technology (UTAUT) (Venkatesh, Morris, Davis, & Davis, 2003) –e.g. Teo (2010), Dueñas-Rugnon, Iglesias-Pradas, and Agudo-Pere-grina (2012) – or TAM2 – e.g. Van Raaij and Schepers (2008) –,and close to none using the third version of the Technology Accep-tance Model (TAM3) (Venkatesh & Bala, 2008).

Two important issues in technology acceptance research are re-lated to the concept of actual use behavior. In the first place, accep-tance models are based on the assumption that behavioralintention is a valid predictor of actual use behavior; this leads tomany empirical studies just focusing on explaining behavioral

intention as they take the linkage between intention and usebehavior for granted; but recent literature (Bagozzi, 2007) has be-gun to question the validity of traditional acceptance models, andmainly the causality of this relation.

The second issue is related to the controversy about how toactually measure use behavior, as information technologies makeit possible to collect objective usage data but many measurementinstruments used in educational technology acceptance studiesstill rely on self-reported system usage. But when acceptance mod-els are used to predict future adoption of a system in pre-imple-mentation stages, objective usage data, and even self-reportedsystem usage, may not be available; in these cases, it is still possi-ble to explain behavioral intention, and it might be necessary to

0747-5632/$ - see front matter 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.chb.2013.10.035

⇑ Corresponding author.E-mail addresses: [email protected] (Á.F. Agudo-Peregrina), angel.hernandez@

upm.es (Á. Hernández-García), [email protected] (F.J. Pascual-Miguel).

Computers in Human Behavior 34 (2014) 301–314

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rely on other indirect measures of use behavior – such as pastbehavior.

In the light of the above mentioned, this study aims to answertwo research questions:

RQ1. Is TAM3 adequate to explain electronic systems accep-tance and use by students?

RQ2. Is the relation between behavioral intention and usebehavior valid in a TAM3-based framework?

In order to address these two questions, we have designed anacceptance model based on TAM3, adapted to the characteristicsof e-learning – understood as the use of learning management sys-tems and virtual learning environments – and applied it in two set-tings with different contexts and samples, from which we expect togain insight about the process of acceptance and use of electroniclearning systems.

The remainder of this document is structured as follows: sec-tion two will present a brief note about technology acceptancemodels and then focus on presenting the different variables usedin the research model, as well as the relations between them; sec-tion three will detail the study methodology, including a descrip-tion of the two settings, sample and the measurementinstrument used for validation of the research model; section fourwill show the results from the empirical analysis, which will bediscussed in section five; finally, section six will summarize themain conclusions from this research.

2. Theoretical background and research hypotheses

2.1. Technology acceptance models and determinants of use behavior

As mentioned in the introduction, the last three decades haveseen the emergence of some theoretical frameworks to study tech-nology acceptance and use, starting with TRA and the rest of mod-els stemming from it, such as TAM, TPB or UTAUT. They originatefrom the idea that salient beliefs of an individual determine hisattitude towards a stimulus object, which in turn determines hisintention to perform a certain behavior; and that behavioral inten-tion is the ultimate predictor of actual behavior.

In TAM, attitudes and intention to use a given technology arepredicted by perceived ease of use and perceived usefulness, offer-ing a simple but effective way to evaluate technology acceptance.The latest evolution of TAM, TAM3 (Venkatesh & Bala, 2008), fo-cuses on integrating the antecedents of perceived usefulness andperceived ease of use. But, although TAM3 addresses some of theissues pointed out by Bagozzi (2007) in technology acceptance re-search – e.g. the inclusion of elements related to emotions in themodel–, it has barely been applied to the specific characteristicsof technology-enhanced learning.

2.2. Technology Acceptance Model 3 (TAM3) and antecedents of usebehavior

Since we will build upon the acceptance framework proposedby TAM3, adapting it to the case of educational technology, and fol-lowing from the relation between behavioral intention and usebehavior in TAM3, we posit that:

H1a. Behavioral intention to use e-learning systems positivelypredicts use of e-learning systems by students.

For this research, TAM3 has been adapted to address the specificcharacteristics of educationaltechnology acceptance.Thus,from the

original variables in TAM3, we havediscarded three determinantsof perceived usefulness – image or self-image, output quality and re-

sult demonstrability – and one antecedent of perceived ease of use–objectivesystemusability–,butwehaveincludedtwofactorsfrome-learningacceptanceliterature:perceived interaction andpersonalinnovativeness in the domain of information technology.

Of these, image was omitted because it was considered thatcourse delivery mode does not affect the status of an individual –and that, in general, learning status is more related to academic re-

cords. With regard to output quality and result demonstrability,there is not yet enough evidence of their influence on the domainof e-learning; furthermore, this study is more oriented toward indi-vidual acceptance from a pre-adoption perspective than towardcourse final outputs, and therefore it was considered convenient toexclude them from the study. Finally, system usability is more ori-ented toward comparison of systems, with an emphasis on efficacyandefficiency,thantoindividualperceptionsofthesystem;thisfact,togetherwithitsobjectivenature,incontrasttotherestofsubjectiveparameters of the study, advised against its inclusion in this study.

2.2.1. Perceived usefulness and perceived ease of usePerceived usefulness was defined by Davis (1989) as the extent

to which a person believes that a system may contribute to im-prove his work performance. In the educational context it maybe redefined as the extent to which a student believes that the e-learning system may help to improve his or her academic perfor-mance, by facilitating the whole learning process in general andthe completion of learning-related tasks in particular. Accordingto Umrani-Khan and Iyer (2009), in the case of educational learningsystems, perceived usefulness would additionally include the no-tion of flexibility, or the degree to which the tools and contentsof an e-learning system fit the student’s preferences; this includespreferred time, location/place and learning style, and favours thefeeling of independence and self-directed learning. Therefore, fromthe original formulation of TAM3:

H2a. Perceived usefulness positively predicts behavioral intentionto use e-learning systems by students.

Perceived ease of use was defined by Davis (1989) as the extentto which a person considers that the use of a system is free of ef-fort. From this broad definition, it follows that perceived ease of use includes aspects related to ease of access and navigation (Park,2009; Volery & Lord, 2000) and interface design (Selim, 2005,2007). In sum, an easy access to the system and browsing, and afriendly interface will have an influence on the students’ percep-tion of complexity of an e-learning system.

TAM posits that perceived ease of use is not only a determinantof behavioral intention but it also influences the perceived useful-ness (Venkatesh & Davis, 2000) – although there is some debateabout this relation, especially in contexts where users have a highlevel of expertise or experience in the use of the system (Venkatesh

& Bala, 2008). Thus:

H2b. Perceived ease of use positively predicts behavioral intentionto use e-learning systems by students.

H3a. Perceived ease of use positively predicts perceived usefulnessof e-learning systems by students.

2.2.2. Subjective normSubjective norm refers to the social pressure exerted toward a

person by the opinions of other people – referents and significantothers, such as family or friends – about whether or not performinga given behavior. Models derived from the original TAM proposed adouble influence of subjective norm in behavioral intention, both

directly and indirectly through perceived usefulness (Schepers &Wetzels, 2007).

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In educational settings, subjective norm focuses not on the so-cial influence toward decision making but on how the opinionsfrom peers, family, teachers and educational institution policiesmay influence the student’s predisposition to the use of e-learningsystems; therefore, we could redefine the concept as the extent towhich a student perceives a pressure from members in his or herenvironment to use e-learning systems. In this sense, Nanayakkara

and Whiddett (2005) confirmed that influence from peers wasespecially relevant in the decision to adopt e-learning by students.Nonetheless, some authors are skeptical about the influence of subjective norm, since they consider that e-learning may be per-ceived by students as an individual opportunity, rather than agroup obligation (Dueñas-Rugnon, Iglesias-Pradas, & Hernández-García, 2010; Dueñas-Rugnon et al., 2012). From the previous dis-cussion, we posit the following hypotheses:

H2c. Subjective norm positively predicts behavioral intention touse e-learning systems by students.

H3b. Subjective norm positively predicts perceived usefulness of e-learning systems by students.

2.2.3. Antecedents of perceived usefulnessVenkatesh and Davis (2000) introduced the concept of job rele-

vance in TAM3 to characterize the correspondence between a sys-tem and the job it is used for – i.e., the task-technology fit. In thecontext of this study, we have decided to rename it as relevancefor learning, and it may be defined as the extent to which a studentconsiders that the use of an e-learning system is suitable for learn-ing and performing learning-related tasks. From a conceptual view,this construct is related to perceived usefulness, since the capabil-ities of the system to carry out and successfully fulfill the students’learning needs are a prerequisite in order for that system to beevaluated as useful, and thus:

H3c. Relevance for learning positively predicts perceived useful-ness of e-learning systems by students.

Interactions have become an essential part of learning processesin e-learning (Donnelly, 2010) among all the agents involved –learners, instructors, administrative staff, contents, etc. Learningmanagement systems allow crossing time and spatial boundariesand enable synchronous and asynchronous interaction among par-ticipants, which is considered one of the greatest advantages of IT-supported distance learning. Therefore, the enhanced level of inter-action perceived by a student – i.e., the degree to which a studentperceives that the e-learning system enhances his or her commu-nication capabilities to interact with other students and teachers– may contribute to improve perceived usefulness and, as Liu,Chang Chen, Sun, Wible, and Kuo (2010) confirmed, even to an in-crease in the intention to use e-learning systems. Therefore, weformulate the following hypotheses:

H2d. Perceived interaction positively predicts behavioral intentionto use e-learning systems by students.

H3d. Perceived interaction positively predicts perceived useful-ness of e-learning systems by students.

2.2.4. Antecedents of perceived ease of useComputer self-efficacy refers to the extent to which an individ-

ual believes that he is able to perform a specific task with the use of a computer (Compeau & Higgins, 1995). In e-learning, computer

self-efficacy is related to students’ self-confidence in their abilitiesto search for information, communicate with others and their skill

with the use of computers, and has been considered a critical factorfor adoption of educational technology systems (Mungania & Reio,2005). Computer self-efficacy has been confirmed as an antecedentof perceived ease of use – e.g. Grandon, Alshare, and Kwun (2005),

Jong and Wang (2009)–, and therefore:

H4a. Self-efficacy positively predicts perceived ease of use of e-

learning systems by students.Venkatesh and Morris (2000) define computer anxiety as the

degree of apprehension, or even fear, that an individual experi-ences when using a computer. Students may express these nega-tive feelings caused by their lack of computer skills or becausethey feel more comfortable with other learning modalities, whichoffer a more traditional way to study and share course materials.Several studies have confirmed that computer anxiety plays animportant role as an antecedent of perceived ease of use in educa-tional technology acceptance (Hara & Kling, 2000; Jong & Wang,2009; Marchewka, Liu, & Kostiwa, 2007; Nistor, Göğüs, & Lerche,2013; Piccoli, Ahmad, & Ives, 2001; Smart & Cappel, 2006; VanRaaij & Schepers, 2008), and hence:

H4b. Computer anxiety negatively predicts perceived ease of useof e-learning systems by students.

Facilitating conditions, a factor accounting for perception of external control, is related to the concept of facilitating resources(Taylor & Todd, 1995), and to the extent to which an individualconsiders that he possesses the organizational resources and infra-structure support to use the system (Venkatesh & Bala, 2008). Infact, lack of a support infrastructure has been pointed out as a deci-sive barrier for e-learning systems implementation (Engelbrecht,2005; Selim, 2007).

In his e-Learning Acceptance Model (ELAM), Selim (2006) iden-tifies support as a key element, including aspects related to reli-ability and availability of technical support and online resources.

Other authors have also emphasized the capital role of support(Abdel-Wahab, 2008; Carlsson, Henningsson, Hrastinski, & Keller,2008; Jong & Wang, 2009; Nanayakkara & Whiddett, 2005; Umra-ni-Khan & Iyer, 2009); but besides this role of support, Selim alsoincluded other technology-related elements which facilitate thelearning process, such as availability of classrooms equipped withadequate computers, network reliability or existence of informa-tion repositories and digital libraries.

Although TAM3 only considered the role of facilitating condi-tions as antecedent of perceived ease of use, UTAUT (Venkateshet al., 2003) and UTAUT2 (Venkatesh, Thong, & Xin, 2012) con-firmed a direct influence of facilitating conditions in actual use of the system. Therefore, we posit that:

H1b. Facilitating conditions positively predict use of e-learningsystems by students.

H4c. Facilitating conditions positively predict perceived ease of use of e-learning systems by students.

Traditionally, technology acceptance research has been fo-cused on utilitarian systems. Therefore, affective and hedonic fac-tors were often neglected in most empirical studies. But, asinformation technology permeated through different activities,it was deemed necessary to consider the influence of these affec-tive elements in the adoption of systems which were not intrin-sically utilitarian. This is the case of e-learning systems, where itis accepted that the inclusion of elements of fun favour the learn-

ing process and contribute to an increase in the comfort the stu-dents experience with the use of the system (Chen, Chen, Lin, &

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Yeh, 2007; Ramírez-Correa, Rondán-Cataluña, & Arenas-Gaitán,2010; Roca & Gagné, 2008; Zhang, Zhao, & Tan, 2008; Šumak,Herič ko, Pušnik, & Polanč ič , 2011). However, it must not be for-gotten that the main objective of an e-learning system is not toprovide pleasure or enjoyment, and therefore we will conceptual-ize the hedonic elements with the term perceived playfulness; inline with Venkatesh et al. (2003), we will define perceived

playfulness as the degree in which the use of the e-learningsystem is perceived as fun or enjoyable by the student, regardlessof the final results achieved, and then formulate the followinghypothesis:

H4d. Perceived playfulness positively predict perceived ease of useof e-learning systems by students.

In their study of virtual learning environments acceptance inChina, Van Raaij and Schepers (2008) included a factor called per-sonal innovativeness towards IT. Personal innovativeness in thedomain of IT (PIIT) was first conceptualized by Agarwal and Prasad(1998), and is related to the predisposition of an individual to useand experiment with new information technologies, regardless of

external opinions (Schillewaert, Ahearneb, Frambachc, & Moena-ertd, 2005). Therefore, it reflects a tendency towards trying newinnovations, assuming the risks inherent to untested technologies(Bommer & Jalajas, 1999). Van Raaij and Schepers (2008) con-firmed the influence of this construct on perceived ease of usebut not on perceived usefulness in the context of e-learning, andtherefore we posit that:

H4e. PIIT positively predict perceived ease of use of e-learningsystems by students.

2.2.5. Use behavior and habit So far, we have proposed a series of variables and relations

which allow us to study electronic learning systems acceptance

and use behaviors, but we have intentionally avoided giving acharacterization of actual use behavior. As it was mentioned inthe introductory section, actual system usage is a controversialconstruct in acceptance models because there is no consensus onhow it should be measured – see, for example, Burton-Jones andStraub (2006) – or about the causality in its relation with behav-ioral intention.

In general, the two most common ways to measure actual useare objective and subjective measures (Straub, Limayem & Kara-hanna-Evaristo, 1995). Objective measures are generally usagedata extracted from system logs, including time spent in the sys-tem, number of logins or total number of interactions with the sys-tem; while they may provide accurate usage information, objectivemeasures require data processing and are not available in pre-

adoption stages. Subjective measures, on the other hand, are moreoften gathered via self-reported values about frequency or inten-sity of use of a system (Turner, Kitchenham, Brereton, Charters, &Budgen, 2010); of course, this measure of actual system usage issubject to response bias and is not generally available in pre-adop-tion stages either. Although both measures tend to be correlated,the relation between self-reported use and objective measures of use is not clear (Straub et al., 1995).

As stated before, none of these measures are helpful when thesystem is not implemented or the individuals have not had ahands-on experience with the system yet. Furthermore, it is possi-ble that access to usage log data is not available and the interval of time between uses of the system is long enough to affect self-re-ported measures; this is especially true in the case of electronic

learning in higher education, where e-learning courses availablemay often be separated by more than six months.

Therefore, there should be some way to obtain a more preciseself-reported measure of actual use behavior in these cases. Trian-dis’s (1977) Theory of Interpersonal Behavior includes a factorcalled ‘‘habits’’, predicted by past behavior and which, togetherwith behavioral intention and moderated by facilitating condi-tions, ultimately determines actual behavior. This variable is re-lated to one subjective measure of system usage identified by

Straub et al. (1995) regarding the self-perception of own usageby the individual, and it also provides a helpful instrument to com-pensate for delay effects in the capture of self-reported data.

Following this discussion, if we define habit as the self-percep-tion of own usage and frequency/intensity of past electronic learn-ing use behavior of an individual, we may hypothesize that:

H1c. Habit positively predicts use of e-learning systems bystudents.

2.3. Research model

From the above research hypotheses, the research model forthis study may be graphically depicted as shown in Fig. 1.

3. Method

3.1. Research design and setting

In order to test the proposed research model, two settings werestudied: the first one was based on a sample of higher educationstudents of different public universities in the Madrid area fromhigher courses – i.e. within their last two years before gradua-tion–, while the second was based on a sample of people qualifyingfor courses from the lifelong learning program of UniversidadPolitécnica de Madrid (UPM).

This research design aimed to uncover differences in the accep-tance and use behaviors of e-learning systems between two differ-

ent groups of students – from a demographic and even amotivational perspective – and therefore sought for further gener-alization of results. Data was gathered by means of online ques-tionnaires distributed to respondents via e-mail during twodifferent periods of time: summer holiday of 2012 for graduatestudents and Easter 2013 for lifelong learning students. Question-naires were the same for both sample groups. The online surveyconsisted of two parts: the first one asked four questions relatedto sample characteristics – gender and experience with computers,Internet and electronic learning systems–, while the second partincluded the different items used to measure the research modelvariables, without any reference to specific educational technolo-gies or systems. Since no actual data usage was available for thegroup of graduate students, self-reported frequency of use was

chosen to measure use behavior, using habit to compensate forpossible delay effects. In addition to the former, lifelong learningstudents’ identifiers were retrieved in order to compare theirself-reported measures about time spent and actual computer-re-corded data of use – a token paired each questionnaire to the userIDs in the learning management system (Moodle)–; individualnames were not retrieved to preserve privacy. Questions in the sec-ond part of the questionnaire were distributed randomly by thesurvey program into four different blocks in order to reduce meth-od bias.

3.2. Population and sample

The questionnaire was initially distributed to 95 third and

fourth year graduate students from different public universitiesin the Madrid area; a total of 77 surveys were answered, with 66



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complete and valid responses. Then, it was distributed to 125 stu-dents eligible to take part in online courses from UPM’s lifelonglearning global offer; 85 surveys were answered, with a total of 81 valid responses. According to Green (1991), sample size wasconsidered adequate for analysis, with large effect size.

Sample characteristics of the sample is shown in Table 1 (gen-der) and Table 2 (experience with the use of computers, Internetand e-learning systems).

3.3. Variables and instruments

The measurement instrument was developed following the ori-

ginal instrument used by Venkatesh and Bala (2008) for TAM3 con-structs, and adapting the items to the context of e-learningsystems. The instrument for self-efficacy was adapted from Park(2009), facilitating conditions was expanded by including itemsfrom Selim (2007), and indicators related to flexibility adaptedfrom Paechter, Maier, and Macher (2010) were added to the mea-surement instrument for perceived usefulness. Additionally, the

measurement instrument for perceived interaction was alsoadapted from Paechter et al. (2010) and personal innovativenesswas adapted from Van Raaij and Schepers (2008).

As indicated before, use behavior was measured through self-reported time spent using the system, although log usage datawas also collected during the fifth and sixth week of lifelong learn-ing courses – halfway in the course. Habit was measured as self-perceived own usage and self-reported past usage of electroniclearning systems.

All items, except for self-reported intensity of use and past use,were measured in Likert-7 scales, ranging from 1 – ‘‘totally dis-agree’’ – to 7 – ‘‘totally agree’’–. Past use of the system was mea-sured by asking the number of online courses taken in the lastyear and self-reported time spent was questioned asking partici-pants how many hours a week, on average, they spent on an onlinecourse. Possible answers were divided into different intervals andthe responses relative to habit and use behavior were later normal-ized for analysis.

3.4. Procedure

Once data was gathered, the research model was validatedusing a Partial Least Squares (PLS) approach method to test thestructural model, with the help of the software SmartPLS 2.0(Ringle, Wende, & Will, 2005). PLS allows independence of datadistribution, small sample sizes and no assumptions concerningmeasurement scales (Haenlein & Kaplan, 2004), and focuses onprediction (Hair, Ringle, & Sarstedt, 2011). Therefore, it was

Fig. 1. Research model.

Table 1

Sample characteristics (Gender).

Gender

Higher education Lifelong learning

Male 38 (57.6%) 27 (33.3%)Female 28 (42.4%) 54 (66.7%)

Table 2

Sample characteristics.

Experience with computers Experience using the Internet Exper ience with e-learning systems

Higher education Lifelong learning Higher education Lifelong learning Higher education Lifelong learning

None – – – – 5 (7.6%) 5 (6.2%)Low (5 years) 60 (90.9%) 78 (96.3%) 61 (92.4%) 75 (92.6%) 16 (24.2%) 32 (39.5%)


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considered adequate for the analysis. Finally, a bootstrapresampling procedure was made in order to assess the stabilityof estimates (Chin, Marcolin, & Newsted, 2003).

4. Results

4.1. Measurement model: item reliability, convergent and discriminant validity

In the first place, descriptive statistics were calculated for eachconstruct (see Table 3; as normalized z-values were used forassessment of habit and system usage, they have not been includedin that table, since they had mean zero and variance equal to one).

From Table 3, mean values were generally higher in the case of lifelong learning studies, except for subjective norm and computeranxiety – in which the differences were not significant. This resultgoes in line with those found in Table 1, where lifelong learningstudies are in general more experienced in the use of e-learningsystems than higher education students.

Standardized loadings of the latent variable indicators were ob-served for assessment of item reliability – with all the indicatorsdefined as reflective. Indicators with loadings not exceeding theideal cut-off level of 0.7 (Nunnally, 1978) were dropped for subse-quent analysis. In particular, one item was dropped from the scaleof perceived ease of use (‘‘Using e-learning systems does not re-quire a lot of my mental effort’’), one from PIIT (‘‘In general, I amhesitant to try out new information technologies’’, reverse-scored),one from behavioral intention (‘‘I intend to use e-learning systemsin the next six months’’) and three from facilitating conditions (allof them except for ‘‘I have the resources necessary to use e-learn-ing systems’’). Interestingly, in both settings we found very lowloadings for the items related to flexibility in the measurementinstrument for perceived usefulness; also, they showed very low

communality with the rest of indicators of the construct but highcommunality between them. Therefore, it was decided to separatethe construct into two different constructs, one of them related tothe ‘‘traditional’’ definition of perceived usefulness (PU1) and theother accounting for the aspects related to flexibility (PU2).

Results from the item reliability assessment are shown inTables 4 and 5. A bootstrap resampling procedure was used to testthe stability of the estimates (Chin et al., 2003), with valuescorresponding to a significance level of p < 0.001 in all cases.

To ensure validity at the construct level – i.e., convergentvalidity–, composite reliability and average variance extracted(AVE) were calculated (see Table 6). Although values of Cronbach’salpha are also shown for reference, composite reliability coefficientis preferred instead because it does not assume that all indicators

are equally weighted (Barclay, Higgins, & Thompson, 1995), andtherefore offers a more general measure (Fornell & Larcker,

1981). Values were higher than 0.81 and 0.59, respectively, wellover the acceptable threshold values of 0.7 (Hair, Anderson,Tatham, & Black, 1998) and 0.5 (Fornell & Larcker, 1981; Hulland,1999).

For assessment of discriminant validity a comparison was re-

quired between average variance extracted (AVE) and inter-con-struct correlations. Upon Fornell and Larcker’s (1981)recommendation, the square root of AVE should be greater thanbivariate correlations between each construct and the rest of con-structs. As shown in Tables 7 and 8, this method allows us to con-firm that the different items are measuring their correspondentconstruct and not others (Gefen & Straub, 2005), therefore confirm-ing discriminant validity for this analysis. It must be noted, how-ever, that a strong correlation was found between relevance forlearning and behavioral intention in setting 1 – higher educationstudents.

As mentioned earlier in this text, we also calculated the correla-tion between self-reported time spent using the system – calcu-lated in hours per week – and activity data from the system log

– also in hours per week–, and we obtained a significant but lowcorrelation between them (Pearson’s correlation 0.359, p < 0.001).

4.2. Structural model analysis

Standardized path coefficients and significance levels were usedto test the research hypothesis (Chin, 1998). The hypotheses testwas based on the research model in Fig. 1, and the results from set-tings 1 and 2 are shown in Table 9, and Figs. 2 and 3, respectively.

From Table 9, the research model seems to be able to predictbehavioral intention based on perceived usefulness and subjectivenorm, but not on perceived ease of use. It also seems to confirmmost of the antecedents of perceived usefulness – except for sub-

jective norm, which seems to only have a direct influence on

behavioral intention to use e-learning systems–, with a stronginfluence of relevance for learning. Besides, the results from bothsettings are contradictory in regard to the antecedents of perceivedease of use, which were very different in both settings.

With regard to use, the results show a very weak or inexistentlink between behavioral intention and self-reported frequency of use, and a surprising negative relation between facilitating condi-tions and use in setting 1. The influence of habit was very strongin setting 1 – standardized path coefficient value of 0.85, p < 0.001 – but non-significant in setting 2.

Based on Chin (1998) we also performed a multigroup analysisto discover differences between the samples from the two settings– see Table 10–. From Table 10, we found significant differences infive relations between both settings: three of them were related to

the antecedents of perceived ease of use – namely, the relations be-tween perceived ease of use and computer anxiety, perceived play-

Table 3

Descriptive statistics.

Higher education Lifelong learning t -Test (two-tailed)

Mean Std. Err. Mean Std. Err. p

Perceived usefulness 1 (PU1)a 4.81 1.41 5.25 1.34 0.00Perceived usefulness 2 (PU2)a 5.86 1.22 6.43 0.82 0.00Perceived ease of use (PEOU) 5.05 1.24 5.64 1.04 0.00Subjective norm (SN) 4.35 1.39 4.28 1.23 0.56 (n.s.)Relevance for learning (LREL) 4.98 1.41 5.51 1.04 0.00Personal innovativeness in the domain of IT (PIIT) 5.09 1.51 5.62 1.28 0.00Self-efficacy (SEFF) 5.96 1.10 6.15 0.94 0.12 (n.s)Perceived interaction (PI) 5.12 1.63 5.31 1.53 0.10Facilitating conditions (FC) 5.06 1.35 5.58 1.16 0.01Computer anxiety (ANX) 2.53 1.58 2.42 1.69 0.39 (n.s.)Perceived playfulness (PP) 4.76 1.38 4.68 1.56 0.65 (n.s.)Behavioral intention (BI) 4.89 1.58 5.59 1.35 0.00

a Perceived usefulness was separated into two variables after item reliability analysis.



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Table 4

Item factorial loadings after depuration of indicators (Setting 1, Higher education).

Self-efficacy(SEFF)

Computeranxiety(ANX)

Facilitatingconditions(FC)

Perceivedplayfulness(PP)

Perceivedease of use(PEOU)

Perceivedinteraction(PI)

Subjectivenorm (SN)

Personalinnovation inIT (PIIT)

Relevance forlearning(LREL)

Perceivedusefulness –performance (PU1)

Perceivusefulnflexibil

SEFF1 0.95SEFF2 0.95ANX1 0.78ANX2 0.73ANX3 0.92ANX4 0.85FC3 1.00PP1 0.89PP2 0.86PEOU2 0.86PEOU3 0.83PEOU4 0.81PI1 0.91PI2 0.90SN1 0.79SN2 0.81SN3 0.70PIIT1 0.81PIIT2 0.86LREL1 0.91LREL2 0.89PU1 0.87PU2 0.89PU3 0.86PU4 0.90PU5 0.93BI1 BI2 ZHAB1 ZHAB2 ZUSE1

ZHAB: Habit indicators (normalized); ZUSE: Use (normalized).


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Table 5

Item factorial loadings after depuration of indicators (Setting 2, Lifelong learning).

Self-efficacy

(SEFF)

ComputerAnxiety

(ANX)

FacilitatingConditions

(FC)

PerceivedPlayfulness

(PP)

PerceivedEase of Use

(PEOU)

PerceivedInteraction

(PI)

SubjectiveNorm (SN)

PersonalInnovation in

IT (PIIT)

Relevance forlearning

(LREL)

PerceivedUsefulness -

Performance (PU1)

PerceivUsefuln

FlexibilSEFF1 0.90SEFF2 0.94ANX1 0.89ANX2 0.87ANX3 0.74ANX4 0.79FC3 1.00PP1 0.93PP2 0.94PEOU2 0.78PEOU3 0.87PEOU4 0.86PI1 0.98PI2 0.97SN1 0.85SN2 0.92

SN3 0.86PIIT1 0.98PIIT2 0.72LREL1 0.90LREL2 0.82PU1 0.89PU2 0.87PU3 0.88PU4 0.94PU5 0.94BI1 BI2 ZHAB1 ZHAB2 ZUSE1

ZHAB: Habit indicators (normalized); ZUSE: Use (normalized).


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fulness and personal innovativeness – and the other two were re-lated to system use behavior – relations with facilitating conditionsand habit.

The structural model was also evaluated by observation of thevariance explained (R2) of the endogenous latent variables (Chin,1998); predictive ability was calculated using the Stone-Geisser’s(Q 2) test (see Table 11).

Concerning R2 values, our research model offers a good explana-tion of e-learning systems acceptance by students in higher educa-

tion – more than 50 percent of the variance of behavioral intention

and almost 70 percent of the variance explained for usebehavior. However, although it also offers a good explanation of intention to use e-learning systems by lifelong learning students,it fails to explain use in this case – a low 4 percent of varianceexplained.

After a blindfolding procedure (Tenenhaus, Esposito Vinzi,Chatelin, & Lauro, 2005) with an omission distance of 7 (Wold,1982) we calculated the Stone-Geisser (Q 2) values, with all valuesgreater than zero, which indicates that the relations between the

exogenous and endogenous constructs have predictive relevance.

Table 6

Convergent validity.

Composite Reliability Cronbach’s Alpha AVE

Highereducation

Lifelonglearning

Highereducation

Lifelonglearning

Highereducation

Lifelonglearning

Computer anxiety (ANX) 0.89 0.89 0.84 0.85 0.67 0.68Facilitating conditions (FC) 1.00 1.00 1.00 1.00 1.00 1.00Relevance for learning (LREL) 0.89 0.85 0.76 0.67 0.81 0.75Perceived playfulness (PP) 0.87 0.93 0.69 0.85 0.76 0.87Personal innovativeness in the domain of IT (PIIT) 0.82 0.85 0.57 0.72 0.70 0.74Perceived interaction (PI) 0.90 0.97 0.78 0.95 0.82 0.95Self-efficacy (SEFF) 0.95 0.92 0.88 0.82 0.90 0.85Subjective norm (SN) 0.81 0.91 0.66 0.85 0.59 0.77Perceived ease of use (PEOU) 0.87 0.88 0.79 0.79 0.70 0.70Perceived usefulness 1 (PU1) 0.91 0.91 0.85 0.85 0.77 0.77Perceived usefulness 1 (PU2) 0.91 0.94 0.80 0.87 0.84 0.88Behavioral intention (BI) 0.93 0.95 0.86 0.89 0.88 0.90Habit (HAB) 0.88 0.87 0.73 0.73 0.79 0.77Use (USE) 1.00 1.00 1.00 1.00 1.00 1.00

Table 7

Discriminant validity (Setting 1, Higher education): bivariate correlations. In the main diagonal, the squared-root AVE of each construct.

ANX BI FC LREL USE PP PEOU PIIT PI PU1 PU2 SEFF SN HAB

ANX 0.82BI 0.15 0.94FC 0.26 0.17 1.00LREL 0.11 0.77 0.29 0.90USE 0.08 0.15 0.07 0.07 1.00PP 0.02 0.47 0.18 0.47 0.10 0.87PEOU 0.33 0.19 0.41 0.18 0.13 0.24 0.84PIIT 0.29 0.38 0.16 0.42 0.37 0.35 0.55 0.83PI 0.02 0.55 0.20 0.41 0.24 0.34 0.19 0.37 0.90PU 0.02 0.59 0.41 0.66 0.22 0.48 0.43 0.40 0.47 0.88PU2 0.14 0.46 0.27 0.51 0.03 0.28 0.35 0.48 0.36 0.42 0.91SEFF 0.57 0.09 0.47 0.07 0.17 0.23 0.50 0.47 0.15 0.16 0.26 0.95SN 0.13 0.51 0.31 0.58 0.06 0.41 0.33 0.30 0.30 0.51 0.30 0.23 0.77HAB 0.19 0.17 0.28 0.09 0.81 0.22 0.35 0.32 0.28 0.33 0.06 0.32 0.18 0.89

Table 8

Discriminant validity (Setting 2, Lifelong learning): bivariate correlations. In the main diagonal, the squared-root AVE of each construct.

ANX BI FC LREL USE PP PEOU PIIT PI PU1 PU2 SEFF SN HAB

ANX 0.82BI 0.14 0.95FC 0.01 0.35 1.00LREL 0.16 0.60 0.58 0.86USE 0.16 0.14 0.11 0.01 1.00PP 0.08 0.60 0.56 0.60 0.02 0.93PEOU 0.35 0.36 0.50 0.55 0.05 0.59 0.84PIIT 0.15 0.40 0.21 0.41 0.18 0.46 0.25 0.86PI 0.05 0.42 0.49 0.38 0.08 0.56 0.46 0.33 0.97PU 0.31 0.53 0.36 0.62 0.06 0.64 0.55 0.32 0.46 0.88PU2 0.32 0.41 0.35 0.45 0.07 0.36 0.46 0.44 0.31 0.42 0.94

SEFF 0.44 0.33 0.17 0.33 0.08 0.32 0.39 0.52 0.15 0.24 0.48 0.92SN 0.14 0.49 0.19 0.37 0.01 0.53 0.35 0.28 0.31 0.42 0.15 0.13 0.88HAB 0.22 0.29 0.09 0.22 0.08 0.34 0.20 0.35 0.08 0.25 0.32 0.45 0.08 0.88


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5. Discussion

5.1. Technology Acceptance Model 3 (TAM3) and use behavior

The results from the two settings in this study offer relevantfindings which might be of interest both for scholars and practitio-ners. The most straightforward finding, which addresses the firstresearch question, is that TAM3 does not offer a much better expla-nation than previous and more parsimonious models, such as TAM,

TAM2 or hybrid models such as the Combined TAM-TPB (C-TAM-TPB) (Taylor & Todd, 1995). In fact, we observed that almost onlythe core components of these models and their relations are sus-tained in this study for both settings – i.e., direct influence of sub-

jective norm and perceived usefulness in behavioral intention, andindirect influence of perceived ease of use in behavioral intentionthrough perceived usefulness. Moreover, the differences found inthe relations of antecedents of perceived ease of use and the lowvariance explained of system use in setting 2 suggest that the com-

Table 9

Path coefficients and contrast of research hypotheses.

Hypotheses Path coefficient Supported?

Higher education Lifelong learning Higher education Lifelong learning

H1a Behavioral intention? use 0.03ns 0.15 No –H1b Facilitating conditions? use 0.18* 0.07ns No NoH1c Habit? use 0.85*** 0.13ns Yes NoH2a1 Perceived usefulness 1? behavioral intention 0.31* 0.26* Yes YesH2a2 Perceived usefulness 2? behavioral intention 0.21* 0.24* Yes YesH2b Perceived ease of use? behavioral intention 0.15ns 0.08ns No NoH2c Subjective norm? behavioral intention 0.26** 0.32** Yes YesH2d Perceived interaction? behavioral intention 0.27* 0.16ns Yes NoH3a1 Perceived ease of use? perceived usefulness 1 0.28** 0.20 Yes –H3b1 Subjective Norm? perceived usefulness 1 0.08ns 0.15 No –H3c1 Relevance for learning? perceived usefulness 1 0.48*** 0.39** Yes YesH3d1 Perceived interaction? perceived usefulness 1 0.20 0.18 – –H3a2 Perceived ease of use? perceived usefulness 2 0.27** 0.29* Yes YesH3b2 Subjective norm? perceived usefulness 2 0.10ns 0.09ns No NoH3c2 Relevance for learning? perceived usefulness 2 0.46*** 0.29*** Yes YesH3d2 Perceived interaction? perceived usefulness 2 0.15 0.09ns – NoH4a Self-efficacy? perceived ease of use 0.15ns 0.15ns No NoH4b Computer anxiety? perceived ease of use 0.06ns 0.27*** No YesH4c Facilitating conditions? perceived ease of use 0.25 0.26* – YesH4d Perceived playfulness? perceived ease of use 0.01ns 0.43* No YesH4e PIIT? perceived ease of use 0.42*** 0.12ns Yes No

Note: Hypotheses marked with a dash represent no conclusive evidence for support, but marginally significant relations found.ns non-significant.

p < 0.1 (two-tailed).* p < 0.05 (two-tailed).** p < 0.01 (two-tailed).*** p < 0.001 (two-tailed).

Fig. 2. Structural model analysis (Setting 1, higher education).



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plexity introduced by TAM3 does not help to understand better theacceptance and use process. These results, however, may be relatedto the characteristics of the sample and should be confirmed by fu-ture research. It is noteworthy that the factor with strongest influ-ence in behavioral intention in setting 2 was subjective norm,contrary to most acceptance studies where perceived usefulnessis usually the most important predictor of behavioral intention; areason behind this result might be that in the present economic sit-uation there is a high pressure over workers and unemployed peo-ple to take lifelong learning online courses in order to access new

opportunities in the labour market.

Fig. 3. Structural model analysis (Setting 2, Lifelong learning).

Table 10

Multigroup analysis. In bold, significant differences between both settings.

Regression weight Std. Err t -Statistic p-Value

Higher education Lifelong learning Higher education Lifelong learning

Computer anxiety? perceived ease of use 0.06 0.27 0.09 0.08 1.808 0.036Facilitating conditions? perceived ease of use 0.25 0.26 0.13 0.11 0.072 0.471

Perceived playfulness? perceived ease of use 0.01 0.43 0.07 0.09 3.517 0.000Self-efficacy? perceived ease of use 0.15 0.15 0.12 0.09 0.008 0.497PIIT? perceived ease of use 0.42 0.12 0.12 0.08 3.837 0.000Perceived ease of use? perceived usefulness 1 0.28 0.20 0.11 0.10 0.552 0.291Perceived ease of use? perceived usefulness 2 0.27 0.29 0.11 0.15 0.103 0.459Relevance for learning? perceived usefulness 1 0.48 0.39 0.12 0.13 0.522 0.301Relevance for learning? perceived usefulness 2 0.46 0.29 0.13 0.09 1.117 0.133Perceived interaction? perceived usefulness 1 0.20 0.18 0.11 0.10 0.101 0.460Perceived interaction? perceived usefulness 2 0.15 0.09 0.09 0.10 0.459 0.324Subjective norm? perceived usefulness 1 0.08 0.15 0.07 0.08 0.645 0.260Subjective norm? perceived usefulness 2 0.10 0.09 0.10 0.07 0.079 0.469Perceived usefulness 1? behavioral intention 0.31 0.27 0.15 0.13 0.208 0.418Perceived usefulness 2?behavioral intention 0.21 0.24 0.09 0.12 0.176 0.430Perceived interaction? behavioral intention 0.27 0.16 0.11 0.10 0.768 0.222Subjective norm? behavioral intention 0.26 0.32 0.09 0.11 0.450 0.327Perceived ease of use? behavioral intention 0.15 0.08 0.11 0.09 0.518 0.303Facilitating conditions? use 0.18 0.07 0.07 0.08 2.358 0.010Behavioral intention? use 0.03 0.15 0.04 0.09 1.202 0.116Habit? use 0.85 0.13 0.05 0.09 8.976 0.000

Table 11

Variance explained (R2) and Q 2.

R2 Q 2

Highereducation

Lifelonglearning

Highereducation

Lifelonglearning

PEOU 0.43 0.51 0.27 0.34PU 0.57 0.50 0.45 0.37PU2 0.36 0.28 0.29 0.23BI 0.53 0.44 0.48 0.36USE 0.68 0.04 0.69 0.07


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The second research question bears a more difficult answer.Turner et al.’s (2010) review of TAM-based studies concluded thatbehavioral intention is likely to be correlated to actual usage, espe-cially when actual use measures are self-reported. However, wefound that there was no significant relation in one of the settingsand only a weak link with a very low variance explained for systemuse in the other. Moreover, we found only a low correlation be-

tween self-reported intensity of use and actual time spent usingthe system – a similar result to Straub et al. (1995). Nonetheless,this result might be biased by the difficulty to measure onlinestudy work performed by the students outside the system, andtherefore we believe it would be necessary to extend the studyto include all the activity carried out in the student’s personallearning environment (PLE) before being able to generalize theseresults.

The non-significant relation between behavioral intention andself-reported usage, and a close look at the empirical results de-mand a further insight in how – i.e., scales for the measurementinstrument (Burton-Jones & Straub, 2006), self-reported vs. actualuse (Pynoo et al., 2011) – and when both variables are measuredin educational contexts. Moreover, the results suggest a trade-off effect between habit and behavioral intention as predictors of use, which is in line with previous research on information systemsacceptance – Limayem, Hirt, and Cheung (2007) present it as amoderation effect. Given that the offer of graduate courses is gen-erally renewed half-yearly while the offer of lifelong learningcourses is not regular, this finding might also emphasize that therelative relevance of habit and intention in the acceptance anduse of e-learning systems may be influenced by the stability of the context (Ouellette & Wood, 1998).

Furthermore, since students may perceive that they have nocontrol over which courses are offered and especially when thecourses are delivered, intention to use an electronic learning sys-tem may be related to a declared learning modality preferencerather than to classical definitions of behavioral intention in TRAor TAM, which might be too general for its use in educational

settings.Interestingly enough, it was found that facilitating conditions

predicted negatively actual measured use of the system. The mea-surement instrument for facilitating conditions was reduced, afteritem depuration, to one question (‘‘I have the resources necessaryto use e-learning systems’’), and we have not found similar resultsin previous literature. While we are unable to explain this result,we believe that it is necessary to give a unified conceptualizationof this variable, since it may be seen from very different perspec-tives depending on the study and the context, ranging from theavailability of basic resources to perform the behavior to aspectsrelated to the existence of external support.

5.2. Perceived usefulness and perceived ease of use

From the results, perceived usefulness may be separated intotwo different constructs, one related to performance and the otherto an increase in flexibility enabled by electronic learning systems;that is, the decomposition of perceived usefulness means thatstudents perceive a difference between efficiency and performancerelated advantages of e-learning, on one side, and those related toflexibility and self-paced learning, on the other. Considering thatflexibility in the choice of learning strategies is highly related tolearning achievements (Paechter et al., 2010), and following theprevious discussion on behavioral intention in e-learning contexts,this finding leaves a door open to the formulation of differentintervention and course planning strategies depending on thecharacteristics of the study group and considering the aspects of

self-regulation and self-directed learning, in order to fostere-learning use preference.

With regard to perceived ease of use, prior research has made itevident that very high levels of complexity may cause a decrease inthe intention to use the system, especially in the case of users withlow experience in the use of the system. Therefore, it is possiblethat the lack of mention of specific systems may have had influ-ence on the non-significant relation between perceived ease of use and behavioral intention; thus, we would recommend main-

taining this relation in future studies focused on the study of a cer-tain educational technology.

5.3. Antecedents of perceived usefulness

The role of perceived interaction is also worth noting, since wefound no conclusive evidence of its relation to perceived useful-ness, but it was significantly related to behavioral intention in set-ting 1, which suggests that higher education students give arelative important to the creation of social ties in online learning,resembling a traditional classroom, while lifelong learning stu-dents focus more on learning outcomes and consider courses moreas an individual, self-directed way to learn.

Finally, the role of relevance for learning stands out as the mostimportant predictor of perceived usefulness; this suggests that,prior to implementation of any electronic learning system, it isimperative to confirm that prospective students perceive that e-learning systems are the appropriate way to deliver the course,which in turn will facilitate to communicate the advantages asso-ciated to online delivery of courses. Nevertheless, while it may beclear for some that job relevance or relevance for learning is a pre-requisite for successful implementation of e-learning systems, weshould question the role of the construct given its high correlationwith other constructs such as behavioral intention, and which sug-gest that it may be more a final determinant of technology accep-tance, as in the Technology-Task Fit model (Goodhue & Thompson,1995) than an antecedent of perceived usefulness.

5.4. Antecedents of perceived ease of use

The significant differences found in the analysis of the anteced-ents of perceived ease of use between settings are also remarkable,as they give insightful information about the different behaviorsand perceptions of students towards e-learning. Thus, it was inter-esting to observe that individuals’ perceived ease of use in setting 1is strongly predicted by perceived innovativeness, whereas this didnot happen in setting 2, where perceived ease of use was stronglypredicted by perceived playfulness – positively – and computeranxiety – negatively–. We believe that this result may be fully ex-plained by the nature of lifelong learning courses; in general, life-long learning courses are considered as a complement of organizational training and, since they try to build on specific com-petences, they are generally independent from one another. Since

they also tend to occur during a relatively short span of time, an er-ror would likely have more impact on passing the course, whichcontributes to increase anxiety. In addition to this, students whotake lifelong learning courses generally use their own spare timeto complete the courses, and therefore need more intrinsic motiva-tional elements for heightened enjoyment which help to create amore user-friendly environment. It was noteworthy that highereducation students do not have this kind of pressure and anxiety,as online courses are generally an optional part of a bigger whole– the academic program.

5.5. Habit

Habit is a strong determinant of actual use of e-learning sys-

tems for higher education students, but not for lifelong learningstudents. There may be a simple explanation for this: public uni-



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versities in Madrid usually offer a set of online elective courseswhich give credit for academic major; since the online course datesare planned to avoid overlapping with the schedule of majorcourses, students opting in for online elective credits in the firstyears seem to tend to repeat this behavior and become ‘‘habitualusers’’ of e-learning systems; adding to this, the high influence of personal innovativeness in perceived ease of use for higher educa-

tion students suggest that, in order to foster use of e-learning sys-tems, there is an opportunity to engage first-year students willingto try online courses into e-learning as a complementary way tocomplete their studies.

6. Conclusion

This is, to the best of our knowledge, one of the first studies totest a TAM3-based model in educational contexts. The results of the two empirical settings suggest that the increased complexityof TAM3 does not result in a significant improvement in the expla-nation of the acceptance and use process when compared to priorand more simple TAM-based models.

This research also tested the intention-behavior link, which is

often taken for granted in acceptance studies but has not beenput into question until recently (Bagozzi, 2007). From the resultsof the two empirical settings, it may follow that there is no signif-icant relation between intention to use a system and actual behav-ior. This seems particularly true in the presence of habitualbehaviors. This result, however, was mainly based on self-reportedsystem usage, and it is our belief that efforts should be made in or-der to clearly discern the nature of this relation and to develop con-sistent measures for subjective and objective report of systemusage in scholar research. This task may be especially difficult inlearning environments, given the diversity of e-learning systems,learning styles, course types, mandatory/optional contexts, etc.

Perceived usefulness and subjective norm were identified as themost relevant predictors of behavioral intention to use e-learningsystems, but the present study also found that there are two differ-ent dimensions of perceived usefulness when considering the useof e-learning systems, one of which is associated to performanceand the other to flexibility. Finally, findings from this study alsosuggest that antecedents of perceived ease of use are highly depen-dent on the type of course and the characteristics and learningobjectives of students.

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1 9 Behavioral Intention Use Behavior and the Acceptance of Electronic Learning Systems Spain