e-learning motivation and educational portal acceptance in...

E-learning motivation andeducational portal acceptance in

developing countriesUrsula Paola Torres Maldonado

Ministry of Transportation and Communications, Lima, Peru

Gohar Feroz KhanSchool of Innovation, Global Information and Telecommunication TechnologyProgramme, Korea Advanced Institute of Science and Technology (KAIST),

Daejeon, South Korea

Junghoon MoonRegional Information Programme, Seoul National University, Seoul,

South Korea, and

Jae Jeung RhoDepartment of Management Science,

Korea Advanced Institute of Science and Technology, Daejeon, South Korea

Abstract

Purpose – The purpose of this paper is to: empirically validate a modified unified theory ofacceptance and use of technology (UTAUT) model by adding an “e-learning motivation” construct inthe South American context; try to determine the role of e-learning motivation in the use and adoptionof e-learning systems and conversely the effect of technology on students’ e-learning motivation; andto test region and gender as moderators in the model.

Design/methodology/approach – A survey method was used to collect data from 47 schoolslocated at different regions: the coast, Andes, and jungle of Peru. The partial least square techniquewas used for data analysis.

Findings – It was found that “e-learning motivation” and “social influence” had a positive influenceon behavioural intention, while “facilitating condition” had no effect on e-learning portal use.Furthermore, use behaviour had a positive influence on e-learning motivation. Also found was themoderating role of “region”.

Research limitations/implications – The analysis is carried out in a single country, thus, cautionshould be taken in generalisation of the results.

Practical implications – The findings will help policy makers and practitioners in developingcountries to better understand students’ e-learning motivation.

Originality/value – By adopting the UTAUT model, a new construct of “e-learning motivation” isadded, and applied to the South American context.

Keywords Acceptance sampling, E-learning, Developing countries, Information technology, Peru

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1468-4527.htm

The authors wish to acknowledge the support of the Peruvian Engineering CollegiateAssociation, Ms Susana Maldonado Villanueva and colleagues, who helped them to reach out tothe students. They also acknowledge the comments of anonymous reviewers who reviewed thepaper and provided useful feedback, and the respondents who participated in the study.

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Refereed article received10 December 2009Approved for publication18 April 2010

Online Information ReviewVol. 35 No. 1, 2011pp. 66-85q Emerald Group Publishing Limited1468-4527DOI 10.1108/14684521111113597

IntroductionOrganisations and educational institutions have been investing in informationtechnologies to improve education and training through electronic learning, known ase-learning (Bates, 2001). E-learning refers to the education and training deliveredthrough ICTs (information and communications technologies), specially designed tosupport individual learning or organisational performance goals (Clark and Mayer,2003; Sun et al., 2008). E-learning has been identified as an enabler for people andorganisations to keep up with changes in the global economy, particularly in theinternet era. Being economical, flexible, and easy to deliver without the constraints oftime and distance (Carey and Blatnik, 2005), e-learning is an attractive option fordeveloping countries.

Following the path of its counterparts (Bates, 2001), the Peruvian governmentformed The General Direction of Educational Technologies in 2007 with the aim toincorporate ICTs in education. It has the following main objectives (MinEdu, 2007a):

. develop and execute a trustworthy network capable of accessing all sources ofinformation, and to broadcast multimedia content to improve educational qualityin the rural and urban regions;

. guarantee connectivity in schools under the criterion of equity and providefacilities according to their needs;

. articulate and coordinate inter-sector actions with other organisations thatfacilitate expansion of educational services through ICT and media;

. establish guidelines for implementing technological platforms in educationalinstitutions such as innovative classrooms and other educational facilities usingICT; and

. promote e-learning initiatives by incorporating learning strategies andmultimedia technologies into the educational process.

The national educational portal called “Peru EDUCA” is one of several initiatives of thePeruvian government aimed at modernising education utilising ICT. The Peruviangovernment has invested and continues investing money and effort in the PeruEDUCA e-learning portal (MinEdu, 2007b; BFPE, 2008). However, there is no empiricalproof regarding students’ motivation to adopt and use the national educational portal,and the portal’s effect on the students’ motivation toward technology, particularly inthe South American context.

Therefore, the aim of this research is to:. empirically validate a modified unified theory of acceptance and use of

technology (UTAUT) model, by adding an “e-learning motivation” (ELM)construct in the South American context, especially in the case of Peru;

. try to determine the role of e-learning motivation in the adoption of the e-learningsystem and conversely technology’s effect on students’ e-learning motivation(Allen and Kishore, 2006); and

. consider “region” and “gender” as moderator variables in our model.

The rest of the paper is organised as follows. In the next section we provide a brief butcomprehensive literature review related to e-learning, technology adoption, and

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motivation to learn constructs alongside our research model and hypothesis. Then wediscuss the research method employed in this study followed by analysis of the results.Finally, we draw conclusions about our findings.

Theoretical backgroundE-learning or learning through web based information and communicationtechnological tools has a profound effect on performance, academic achievements,and students’ satisfaction (Katz, 2002). Several models have been employed to addressthe issues of e-learning and to identify the cause and effect of different variables in thetechnology acceptance and use literature. For instance, perceived usefulness, perceivedease of use, and flexibility (Arbaugh, 2000, 2002), satisfaction and academicachievements (Katz, 2002), motivation, being comfortable with technology, andavailability (Piccoli et al., 2001), computer skills and initial knowledgeabout e-learning technology (Thurmond et al., 2002), interpersonal behaviours,motivating aims, and cognitive modes (Kanuka and Nocente, 2003), have all beenlinked with web based learning and ICT utilisation for educational purposes.

In e-learning a student’s motivation plays an important role (Conati, 2002).Motivation to learn is a “student’s tendency to find academic activities meaningful andworthwhile and try to derive the intended academic benefits from it” (Brophy, 2004, p.249). There are different research streams dealing with motivation in e-learning.Marzano (2003) for example outlined five lines of research on student motivation:

(1) drive theories (Hull, 1935) – students are either driven by striving for success orfear of failure;

(2) attribution theory (Weiner, 1992, 1980) – students attribute success to ability,luck, effort, and task difficulty;

(3) self-worth theory (Covington, 1984) – self-acceptance is one of our highestpriorities as humans;

(4) emotions (Conati, 2002; Kort and Reilly, 2001); and

(5) self-efficacy and self-regulation, freedom, meaningful individual goals, andself-awareness (McCombs and Whisler, 1989; Bandura, 1986; Pintrich andSchunk, 2002).

Other research streams related to students’ motivation include the motivationalplanner approach (Del Soldato and Du Boulay, 1995), which deals with practicalstrategies and tactics to be employed based on the e-learner’s motivational state.Another is the attention, relevance, confidence, and satisfaction approach (Keller, 1987)based on motivational design of instructions. This approach argues that these fourelements of motivation that should be considered in order to facilitate successfullearning. Similarly social cognitive learning theory and theories of intrinsic andextrinsic motivation are well known (Ryan and Deci, 2000a).

So far students’ motivation to learn has been widely studied in the traditionalclassroom environment, but not its influence on technology adoption, nor has the effectof technology on e-learning motivation been tested (Ryan and Deci, 2000a, b). We arguethat e-learning motivation can play an important role in technology acceptance andconversely technology use will affect a student’s e-learning motivation (Kim andMalhotra, 2005). In addition, we suggest that e-learning motivation is different from

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conventional learning motivation, which is based on an instruction design approach(Keller, 1987). For e-learning motivation, technology characteristics such as effortexpectancy must also be considered (Arbaugh, 2000) as shown in our research model(Figure 1). Our research model is based on UTAUT Model (Venkatesh et al., 2003), butwe modified the original model by adding the “e-learning motivation” construct.

E-learning motivation and technology useThe UTAUT model (Venkatesh et al., 2003) aims to explain user intentions to use aninformation system and the usage behaviour; it was developed through a review andmerger of different theories, which include the theory of reasoned action (Ajzen, 1985),technology acceptance model (Davis, 1989a), the theory of planned behaviour (Ajzen,1991), a combined theory of planned behaviour/technology acceptance model (Taylorand Todd, 1995), a model of PC utilisation behaviour (Thompson et al., 1991),innovation diffusion theory (Rodger et al., 1996), and social cognitive theory (Compeauet al., 1999). The model states that four key constructs – performance expectancy,effort expectancy, social influence, and facilitating conditions – predict technologyusage intention and behaviour (Venkatesh et al., 2003).

UTAUT defines performance expectancy as “the degree to which an individualbelieves that using the system will help him or her gain the desired performance” andeffort expectancy as “the degree of ease associated with the use of the system”(Venkatesh et al., 2003, pp. 447, 450). The performance expectancy construct iscomposed of perceived usefulness adopted from Davis (1989) and Taylor and Todd(1995), extrinsic motivation (Davis et al., 1992; Thompson et al., 1991), and relativeadvantage and outcome expectation from Compeau et al. (1999). Effort expectancycomprises three constructs:

(1) perceived ease of use (Davis, 1989);

(2) complexity associated with the task derived from the model of PC utilisation(Thompson et al., 1991); and

(3) ease of use (Davis, 1989).

In contrast the conventional motivation construct includes intrinsic motivation,extrinsic motivation, goal orientation, self-determination, self efficacy, and assessment

Figure 1.Proposed research model


69

anxiety (Glynn and Koballa, 2006). Intrinsic motivation comes from the rewardsinherent to a task or activity (Ryan and Deci, 2000a, b) while motivation that comesfrom outside the performer is mainly extrinsic (Pintrich and Schunk, 2002). Similaritiesamong performance expectancy, effort expectancy, and the motivation construct havebeen observed in prior research (Davis et al., 1989; Thompson et al., 1991; Moore andBenbasat, 1991). The motivation and performance expectancy constructs address theseconcepts: usefulness and extrinsic motivation, usefulness and outcome expectation,satisfaction and beliefs about one’s capabilities, and beliefs about attaining the gains.These concepts are common to both constructs (Davis et al., 1989, 1992; Thompsonet al., 1991; Moore and Benbasat, 1991). The following concepts are unique to eachconstruct: intrinsic motivation, effort expectancy, and perceived enjoyment. Therefore,we introduce a new construct of “e-learning motivation” (ELM) by the merger ofperformance expectancy, effort expectancy, and the motivation construct. However,according to UTAUT assessment anxiety and self-efficacy are not direct determinantsof intention, so they are not considered in our model either. Based on Agarwal andPrasad’s (1999) recommendations we removed the items that were very similar to otheritems. We define the e-learning motivation construct as a student’s tendency to find ane-learning system useful, easy to use, and to try to derive the intended academicbenefits from it. The e-learning motivation construct is composed of items adoptedfrom the motivation, performance, and effort expectancy constructs which have adirect influence on intention to use (Davis et al., 1992; Moon and Kim, 2001) as shown inour proposed research model (Figure 1).

Previous studies (Davis et al., 1992; Moon and Kim, 2001) have reported that bothextrinsic and intrinsic motivation have a significant effect on intentions to useinformation technology systems (Coovert and Goldstein, 1980). Similarly, Pintrich andSchrauben (1992) suggested that a student’s motivation is affected by the value theyassociate with the outcome and this motivation leads to cognitive engagement, whichin turn, leads to the use behaviour. According to Pintrich and Schrauben (1992)cognitive engagement represents the amount of effort spent in either studying orcompleting assignments. Incentive theories of motivation, for example Rotter et al.(1972) and Overmier and Lawry (1979), suggest that people will carry out an act onlywhen the desired outcome is to be attained, or they will perform an action that is ofvalue to them. Furthermore, perceived usefulness, perceived ease of use, intention touse, and usage behaviours have a circular rather than linear relationship (Allen andKishore, 2006; Kim and Malhotra, 2005). Based on the above reasoning we propose thefollowing two hypotheses:

H1. E-learning motivation will positively influence a student’s behaviouralintentions to use an educational portal.

H2. Educational portal use will positively influence a student’s e-learningmotivation (ELM).

Social influence, facilitating conditions, behaviour intention, and technology useSocial influence (SI) has been examined as an important factor in predicting technologyuse behaviour and intention to use (Venkatesh and Davis, 2000). According to thetheory of reasoned action, people’s behaviour is influenced by the way in which theybelieve others important to them think certain behaviour should or should not be

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followed. Where use is mandatory the role of social influence weakens over time andeventually becomes irrelevant with constant technology usage; however, it isimportant in the early stage of technology usage (Venkatesh and Davis, 2000). UTAUTalso postulates that facilitating conditions (FC) have a direct influence on usebehaviour (UB); it is composed of perceived behavioural control (Ajzen, 1991; Taylorand Todd, 1995), facilitating conditions (Thompson et al., 1991) and compatibility.Social influence is defined by UTAUT as: “the degree to which an individual perceivesthat important others believe he or she should use the new system”, while facilitatingconditions are defined as “the degree to which an individual believes that anorganisational and technical infrastructure exists to support use of the system”(Venkatesh et al., 2003, pp. 451, 453). Based on the above reasoning the followinghypotheses are constructed:

H3. Social influence will positively affect a student’s behavioural intention to usean educational portal.

H4. Facilitating conditions will positively influence educational portal usebehaviour.

H5. A student’s intention to use an educational portal will positively influencetheir use of an educational portal.

Moderating effectsIn addition UTAUT hypothesises the role of four key moderator variables: gender, age,experience, and voluntariness of use. However, the knowledge gap literature argues thatpeople with higher socio-economic status may acquire political and scientific knowledgeat a faster rate than people with lower socio-economic status (Trichenor et al., 1970). Theimpact of culture, family income, practice of religion, political activities, personalrelationships, individual habits, and race on technology adoption, have been widelystudied (Hoffman and Novak, 1998; Eamon, 2004; Mehra et al., 2004). Peru isgeographically divided into three regions – coast, Andes and jungle – with each regionhaving diverse cultures, income levels, infrastructure, and race (see Ministry ofCommerce and Tourism web portal, www.mincetur.gob.pe/newweb/Default.aspx?tabid ¼ 1581). Therefore, in the context of Peru while applying the UTAUTmodel we introduce region and gender as moderating variables keeping in considerationthe political differences, race, family income, and geographical location of the students,and the effects of gender and cultural differences on technology adoption (Minton andSchneider, 1980; Weiner et al., 2003). We eliminated voluntariness of use and experiencedue to their irrelevance in the school context; most of the students in our sample are ofthe same age group and have the same level of use experience. Thus, the followinghypotheses are designed to test the moderating effects:

H6. Region will negatively moderate the effect of social influence on intention touse an educational portal.

H7. Region will negatively moderate the effect of facilitating conditions oneducational portal usage behaviour.

H8. Region will negatively moderate the effect of e-learning motivation onintention to use an educational portal.


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H9. Gender will positively moderate the effect of social influence on intention touse an educational portal.

H10. Gender will positively moderate the effect of e-learning motivation onintention to use an educational portal.

Research methodThe portalThe national educational portal, Peru EDUCA (2009), is one of several initiatives takenin order to achieve the goals of modernising education through promoting e-learningand to increase the supply of education in Peru, especially in the rural areas. The portalis used by more than 3,000 students from schools throughout Peru. The portal providescourse materials of instant audio and video content related to a wide range of subjectsincluding physics, religious education, history, economics, geography, English, andmathematics. Apart from the academic content Peru EDUCA provides access to otheronline services such as chat, forums, and customised desktop services.

Instrument developmentThe research instrument was developed based on the items adopted from the Venkateshet al. (2003) model and motivational theories (Brophy, 1988, 2004; Tuckman and Sexton,1992; Glynn and Koballa, 2006). To fully represent the “e-learning motivation” constructand to keep the length of the instrument reasonable, five items were adopted from themotivation construct (MC), four items from performance expectancy (PE), and three fromeffort expectancy (EE). However, based on Agarwal and Prasad’s (1999)recommendations we removed the items that were not relevant to the study, and wedeleted items that were very similar to other items leaving eight items in totalrepresenting the e-learning motivation construct. By using these criteria the itemsselected ensure complete coverage of the e-learning motivation construct. Furthermore,the items for measuring social influence, facilitating conditions, behavioural intention,and use behaviour were also adopted from Venkatesh et al. (2003).

The back translation method (Brislin, 1970, 1986) was used for translating theEnglish version of the original survey items into Spanish. Every effort was made topresent the items in a way that could be understood by the respondents whilepreserving the original meaning of the items until both the versions converged (Wernerand Campbell, 1970). A total of 21 question items (see Appendix) were used andrespondents had to rate each item on a Likert scale ranging from “totally disagree” (1)to “totally agree” (5) in order to measure the latent constructs.

Data collectionWe randomly identified 47 secondary schools located in different regions – the coast,Andes and jungle of Peru – which were connected to the Peru EDUCA nationaleducational portal. The survey instrument was administered to all the students fordata collection with the help of the Peruvian Engineering Collegiate Association(www.cip.org.pe) and some assistance from the Ministry of Education’s website.Initially, 240 students responded to the survey. After excluding cases with missing orincomplete responses 150 complete and usable surveys were retained for quantitativeanalysis. Among the respondents 70 per cent were male and 30 per cent were femalestudents. Most (62 per cent) of the responses were from the coast region while 38 per

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cent were from the Andes region. We were unable to receive enough responses from thejungle region; therefore we eliminated it from our analysis. The demographic profile ofthe respondents is shown in Table I.

Data analysis and resultsThe partial least square (PLS) technique was used for the data analysis. PLS is astructured equation modelling technique that can analyse structural equation modelsinvolving multiple-item constructs, with direct and indirect paths. PLS has severaladvantages over other analysis tools; for instance it uses component-based estimation,maximises the variance explained in the dependent variable, does not requiremultivariate normality of the data, and is less demanding as regards sample size (Chin,1998). PLS analysis is a two-stage process. First, the test of the measurement modelincludes the estimation of internal consistency (composite reliability) and theconvergent and discriminant validity of the instrument items. The second stagecomprises the assessment of the structural model.

Reliability and validity testsThe reliability results are depicted in Table II. The data indicates that all reliabilitymeasures are robust and well above the recommended level of 0.70 (Nunnally, 1978),thus indicating adequate composite reliability (internal consistency). The averagevariance extracted (AVE) was also calculated. AVE measures the amount ofvariance that a construct captures from its indicators relative to the variance

Criteria Frequency %

GenderMale 105 70Female 45 30

Age15-18 150 100

RegionCoast 93 62Andes 57 38

OccupationStudent 150 100

EducationHigh School 150 100

Table I.Demographic profile of

the respondents

Variable constructs Composite reliability Average variance extracted/explained

E-learning motivation (ELM) 0.937 0.654Social influence (SI) 0.955 0.877Facilitating conditions (FC) 0.938 0.836Behavioural intention (BI) 0.934 0.825Use behaviour (UB) 0.955 0.875

Table II.Reliability


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contained in measurement error; it is interpreted as a measure of reliability for theconstruct, and as a means of evaluating discriminant validity. The average varianceextracted (AVE) for each measure exceeded the recommended level of 0.50 (Fornelland Larcker, 1981).

For satisfactory discriminant validity the AVE from the construct should be greaterthan the variance shared between the construct and other constructs in the model.Table III shows the results of testing the discriminant validity of the measured scales.The elements in the matrix diagonals, representing the square roots of the AVEs, aregreater in all cases than the off-diagonal elements in their corresponding row andcolumn, supporting the discriminant validity of our scales. Convergent validity isdemonstrated when items load highly (. 0.50) on their associated factors. Table IVshows that all the measures have significant loadings that load much higher than thesuggested threshold. Furthermore, each item’s factor loading on its respectiveconstruct was highly significant ( p, 0.0001) as the values of t-statistics were between63 and 730.

Latent variable ELM BI SI UB FC

E-learning motivation 0.817Behavioural intention 0.734 0.945Social influence 0.737 0.744 0.914Use behaviour 0.585 0.735 0.536 0.917Facilitating conditions 0.584 0.648 0.653 0.537 0.944

Table III.Discriminant validity(inter-correlations) ofvariables


Socialinfluence

Facilitatingconditions

Behaviourintention

Usebehaviour

ELM1 0.890 20.066 20.055 20.108 0.757ELM 2 0.805 0.207 20.140 20.130 0.372ELM 3 0.661 20.047 0.143 0.396 0.102ELM 4 0.801 0.354 0.038 20.125 20.041ELM 5 0.837 20.036 20.083 0.471 20.086ELM 6 0.836 0.325 20.048 0.096 20.324ELM 7 0.790 0.408 20.030 20.280 20.013ELM 8 0.889 20.054 20.136 0.293 20.107SI1 0.146 0.859 20.105 0.236 0.015SI2 0.228 0.834 20.139 20.010 20.180SI3 0.115 0.892 20.049 0.200 0.059FC1 0.224 0.108 0.791 20.072 20.149FC2 0.325 0.206 0.797 20.349 20.148FC3 0.178 20.100 0.786 20.053 0.084BI1 0.119 20.160 0.019 0.842 0.230BI2 20.077 0.005 0.254 0.870 20.113BI3 0.264 20.039 20.090 0.809 0.258UB1 20.099 20.028 0.412 20.140 0.808UB2 20.190 0.016 0.458 0.006 0.865UB3 20.206 20.064 0.480 0.119 0.874

Table IV.Loadings and crossloadings

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Assessment of the structural modelThe test of the structural model was performed using a PLS graph. The test includes:

. estimating the goodness of fit indices, which indicates how well the model isperforming;

. estimating the path coefficients, which indicate the strengths of the relationshipsbetween the dependent variables and independent variables; and

. the R 2 value, which represents the amount of variance explained by theindependent variables.

The path coefficients in the PLS model represent standardised regression coefficients.The suggested lower limit of substantive significance for regression coefficients is 0.05.

The structural model results without moderating effects are shown in Figure 2. Allbeta path coefficients are positive and statistically significant (at *p , 0.0005) exceptin the case of the relationship between facilitating conditions and use behaviourðbeta ¼ 0:09; nsÞ: E-learning motivation and social influence both had a positiveinfluence ðbeta ¼ 0:43; p , 0.0005) on behavioural intention and accounted for 64 percent of the variance in intention. Furthermore behavioural intention had a positiveinfluence ðbeta ¼ 0:69; p , 0.0005) on use behaviour. As predicted, use behaviour hada positive influence ðbeta ¼ 0:59; *p , 0.0005) on e-learning motivation in a cyclicmanner and accounted for 40 per cent of the variance in e-learning motivation.

ThestructuralmodelwithsignificantmoderatoreffectsisshowninFigure3.Initially,weconsidered gender and region as moderator variables in our model (see Figure 1). However,we found thatonly region had a negative (beta ¼ 20.23, * *p , 0.1) significant interactingeffect with the social influence upon behavioural intention. Furthermore, both with andwithout moderating effects the model explains 64 per cent of the variance in behaviouralintention and 60 per cent of the variance in use behaviour. In the cyclic effect the varianceobserved was as much as 40 per cent ine-learning motivation withand without moderating

Figure 2.Structural model results

(without moderatingeffect)


75

effects in our model. A summary of the findings after the introduction of moderatingvariables is shown in Table V.

Discussion and implicationsThe goal of this study was to empirically extend the understanding of students’e-learning motivation and e-learning portal usage. The findings of this study provide apreliminary test of the viability of the research model and are consistent with theproposed theoretical foundation within the context of developing countries, especially inthe case of Peru. We found that e-learning motivation had a positive influence onbehavioural intention toward the e-learning portal. As predicted, use behaviour had apositive influence on e-learning motivation in a cyclic way and accounted for as much as40 per cent of the variance in e-learning motivation. Also, we found that social influencehad a significant effect on behavioural intention and accounted for 64 per cent of thevariance in behavioural intention. Similarly, we found that facilitating conditions had noeffect on use behaviour. Regarding moderation effects, we found that region moderatedthe relationship between social influence (SI) and behavioural intention.

Several implications can be drawn from our findings. We can conclude from thepositive cyclic relation between e-learning portal use and e-learning motivation thatmotivation plays a significant role in the adoption and use of e-educational portals in aschool context. Conversely, the more students use educational portals the higher will betheir motivation toward e-learning. The role of educational portals in enhancingstudents’ motivation toward e-learning gives us a clear indication that we mustencourage students to use educational portals. Similarly, for the students to bemotivated to use the e-educational system, technology characteristics such as ease ofuse and effort expectancy must also be considered while designing educational portals,apart from conventional motivational factors such as intrinsic and extrinsicmotivation, goal orientation, self-determination, and self efficacy. The positive

Figure 3.Structural model results(with moderating effect)

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Table V.Moderating variables


77

relationship between social influence and behavioural intention is consistent with priorliterature that stresses the role of social influence in adoption and usage of technology(McInerney, 2005). In the school context these findings explain the role of teachers,parents, and other peers in the adoption and diffusion of an e-educational portal. Alsoduring the data collection phase we came across several schools and students whowere not aware of the Peru EDUCA portal or who did not want to use it; this suggeststhat either they are not aware of the cyclic benefit of using e-portals or maybe they arenot influenced by their teachers, family members, or other peers to use the portal. Wecan conclude that in developing countries such as Peru where technology diffusion andawareness is low, the students must be influenced through teachers, parents, and otherpeers to adopt and use educational portals.

In addition, we can conclude from the negative moderating effect of region on therelationship of social influence and behavioural intention that the level of social influencefor students in the Andes region is stronger than that of students in the coastal region ofPeru. Simply put, technology can be more easily adopted by the students in the Andesregion, due to the stronger influence of teachers, friends, and family. This sociallyinfluenced behaviour must be reflected in government policies for motivating students touse e-learning portals. This also implies that different strategies must be used byauthorities to motivate students toward e-learning system use depending on the region.The limited response rate from the jungle region during our data collection phase reflectsthe lack of awareness of the students about the e-learning portal. So there is a need forcreating awareness regarding use of the e-learning portal in the jungle region of Peru. Inaddition, we did not find gender as a moderator in our model. These findings suggestthat, in Peru, male and female students can be equally motivated toward use ofe-learning portals and similar polices can be used to motivate both genders towarde-learning. Furthermore, this implies that in the Peruvian context while using e-learningsystems male and female students who live in the same regions have the same level ofsocial influence. The lack of a correlation between facilitating conditions and e-learningportal use suggests that making all resources available does not necessarily guaranteethe adoption of e-learning portals. These findings further highlight the importance ofe-learning motivation in educational portal use among students.

In order to further elaborate our findings we collected qualitative data fromsecondary sources about e-learning initiatives in Peru and compared it with theempirical findings of this study. As expected the qualitative data fully support thefindings. For example, basic infrastructure such as computers and the internet isavailable in more than 41 per cent of the schools in the coastal and Andes regions ofPeru (Escale, 2008). However, our findings indicate that even though basicinfrastructure is available in Peru it does not guarantee educational portal use,although the authorities believe that making infrastructure available may promoteinternet use. Furthermore, our findings indicate that contextual policies must beenacted in different regions for motivating students toward e-learning portal use, butthe government of Peru has not made such policy differentiation and initiated similarpolicies and projects irrespective of the regions (MinEdu, 2007b). This furtherelaborates our findings regarding the lack of awareness and use of the e-learning portalin the jungle region of Peru, as most of the infrastructure and policies are directedtoward the coastal and Andes regions rather than the jungle region (MinEdu, 2007b;Escale, 2008). Our empirical findings regarding the lack of awareness and motivation

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among students in the jungle region can also be reflected in the number of schoolshaving internet connectivity by region, for example, 46.3 per cent schools from urbanareas and only 15 per cent from rural areas had internet connectivity (Escale, 2008).Our research demonstrates that student e-learning motivation plays a significant rolein educational portal use and adoption, but the data from the Peru government indicatethat there are no special policies and projects designed to encourage students’motivation toward educational portal use (MinEdu, 2009). We found that socialinfluence plays an important role in fostering educational portal use and people in theAndes region are more likely to adopt technology due to comparatively strong socialinfluence; which is consistent with the fact that people in the Andes region have closeties and the level of embeddedness is high compared to the coastal region, mainly dueto the cultural and ethnic differences among these regions (Degregori, 1977).

This study has some limitations that must be mentioned. We carried out ouranalysis in only one country, but which can be representative of the South Americancontext; thus caution should be taken in generalisation of the study. Also the samplesize ðN ¼ 150Þ was not large enough due to no responses from the jungle region ofPeru. Furthermore, we could not include the jungle area in our analysis due to limitedresponses from this area. Future research may extend the study to other SouthAmerican countries for testing the effect of e-learning motivation on use of educationalportals, and vice versa while utilising a larger sample size. Moreover, future researchcan use the e-learning motivation construct for exploring other e-learning technologies’acceptance in schools, for instance e-learning delivered through mobile, digital TV, andIP TV in developing countries.

ConclusionAdopting and modifying the UTAUT model, we added a new construct of e-learningmotivation and applied it to the Peruvian context for predicting the role of e-learningmotivation in educational portal adoption and use. We found that e-learning motivationplays a crucial role in the adoption and use of e-educational portals; we demonstratedthat it is different from conventional learning motivation by adding technologycharacteristics (effort expectancy) to the traditional motivational construct. In additionwe examined the cyclic effect of the technology use on e-learning motivation; we foundthat e-educational portal use simulates students’ e-learning motivation. We alsodemonstrated the importance of the influence of teachers, parents, and other peers in theadoption of educational portals in schools. In addition, we used region and gender asmoderating variables in our study. Based on the findings we provided some usefulsuggestions to policy makers and practitioners in developing countries.

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Appendix. Peruvian predictor latent construct itemsE-learning motivation (Five-point Likert scale from “totally disagree” to “totally agree”):

ELM 1: I enjoy learning by using the Peru EDUCA portal. (MC).

ELM 2: I find the Peru EDUCA portal useful in my studies. (MC, PE).

ELM 3: Using the Peru EDUCA portal in my studies enables me to accomplish tasks morequickly. (MC, PE).

ELM 5: Using the Peru EDUCA portal helps me do better than others in my studies. (MC, PE).

ELM 6: My interactions with the Peru EDUCA portal are clear and understandable. (EE).

ELM 7: Learning by using the Peru EDUCA portal is easy for me. (EE).

ELM 8: It is easy for me to learn more using the Peru EDUCA portal. (EE).

Social influence (Five-point Likert scale from “totally disagree” to “totally agree”):

SN 1: Most people who are important to me think I should use the Peru EDUCA portal.

SN 2: Most people who are important to me would want me to use the Peru EDUCA portal.

SN 3: People whose opinions I value would prefer me to use the Peru EDUCA portal.

Facilitating conditions (Five-point Likert scale from “totally disagree” to “totally agree”):

FC 1: I have the resources and the knowledge and the ability to make use of the PeruEDUCA portal.

FC 2: Central support was available to help with the Peru EDUCA portal’s problems.

FC 3: Management provided most of the necessary help and resources for use of the PeruEDUCA portal.

Behavioural intention (Five-point Likert scale from “strongly disagree” to “strongly agree”):

BI 1: I predict I will continue to use the Peru EDUCA portal on a regular basis.

BI 2: What are the chances in 100 that you will continue as a Peru EDUCA portal user? 1) 0;2) 1-10%; 3) 11-30%; 4) 31-50%; 5) 51-70%; 6) 71-100%.


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BI 3: To do my work, I would use the Peru EDUCA portal rather than any other means

available.

Use behaviour

USE 1: On an average working day, how much time do you spend using the Peru EDUCAportal? 1) Almost none; 2) less than 30 minutes; 3) from 30 minutes to 1 hour; 4)from one to two hours; 5) from two to three hours; and 6) more than three hours.

USE 2: On average, how frequently do you use the Peru EDUCA portal? 1) Less than once a

month; 2) once a month; 3) a few times a month; 4) a few times a week; 5) about once

a day; and 6) several times a day.

USE 3: How many different applications of the Peru EDUCA portal have you worked with

or used in your studies? 1) None; 2) one; 3) two; 4) three to five applications; 5) 6 to

10 applications; and 6) more than 10 applications.

About the authorsUrsula Paola Torres Maldonado works as a Researcher at Peru’s Ministry of Transportationand Communications. She obtained her Master’s degree from the Department ofManagement Science, Korea Advanced Institute of Science and Technology in SouthKorea. She received an Electronic Engineering degree from Callao National University inPeru. She has been working for the Ministry of Transportation and Communication for fouryears in areas related to telecommunication services. Her research interests include ICT ineducation, e-government, wireless broadband communication, and user acceptance ofinformation technologies.

Gohar Feroz Khan is a PhD researcher in the School of Innovation, Global Information andTelecommunication Technology Programme at Korea Advanced Institute of Science andTechnology in South Korea. He has a Master’s degree in Computer Science and has several yearsof work experience in the public sector. His research interests include e-government,m-government, user acceptance of information technologies, ICT policy, network economics, ande-learning. His work has been published in Asia Pacific Journal of Information Systems. He haspresented his research at information systems conferences such as the International Conferenceon Computer Sciences and Convergence Information Technology, Seoul (2009), InternationalConference on e-Democracy, e-Government and e-Society in Paris, and the Conference ofInnovation and Development, Seoul (2010). Gohar Feroz Khan is the corresponding author andcan be contacted at: [email protected]

Junghoon Moon is an Assistant Professor in the Regional Information Programme at SeoulNational University in Korea. He received his PhD from the State University of New York atBuffalo in 2006 and received his Master’s and Bachelor degrees from Seoul National University.He worked for several years as a Systems Analyst and Consultant. In addition, he is a member ofAuto-ID labs sponsored by EPCglobal. Junghoon Moon’s research interests include humanfactors in MIS/e-business, technology management, e-government, information policies for thefood industry, and business applications using a ubiquitous sensor network. He has presentedhis studies at many conferences including the Korean Conference on Management InformationSystems and the International Conference of the Information Resources ManagementAssociation. At the Americas Conference on Information Systems in 2006, one of his paperswas judged the Best Paper of the Year. At the Hawaiian International Conference on SystemSciences in 2007, one of his papers was nominated as the Best Paper of the Year. He haspublished articles in many journals, including Online Information Review, Information SystemsFrontiers, e-Business Studies, and the Journal of Information Technology Management.

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Jae Jeung Rho is an Associate Professor in the Department of Management Science and aDirector of the Global IT Technology Programme at Korea Advanced Institute of Science andTechnology. He received his PhD in Industrial Engineering from the University of Houston in1994, his Master’s from Texas A&M University, and Bachelor degrees from Seoul NationalUniversity. He worked for several years as a Systems Analyst and Consultant. In addition, he isan Associate Director. Jae Jeung Rho’s research interests include RFID and wireless sensornetworks in supply chain management, e-government and m-government frameworkdevelopment for developing countries. Currently, he is a member of the Board of Directors ofthe Korean Society for Supply Chain Management, a Business Director of Auto-ID Labs in Korea,sponsored by EPCglobal, and a member of the Advisory Committee of the NationalInformatisation Agency (NIA).


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