System and User Characteristics in the Adoption and Use of e-learning Management Systems: A Cross-Age Study

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1 System and User Characteristics in the Adoption and Use of e-learning Management Systems: A Cross-Age Study Oscar Lorenzo Dueñas-Rugnon, Santiago Iglesias-Pradas, and Ángel Hernández-García Grupo de Tecnologías de la Información para la Gestión Empresarial, Escuela Técnica Superior de Ingenieros de Telecomunicación, Av. Complutense 30, Madrid oscarlorenzo.duenas@gmail.com, {s.iglesias,angel.hernandez}@upm.es Abstract. This study analyses the most relevant factors which affect users' intention to use e-learning management systems, with special focus in the difference of age. The fundamental hypothesis is that there can be established a influence of behavioural intention, attitude toward behaviour, perceived usefulness, perceived ease of use, subjective norms and social influence in the intention to use of e-learning management systems, with user and system factors as antecedents. In order to design the theoretical model, the most relevant technology acceptance models have been reviewed from literature, with special focus in learning management systems (LMS) adoption. The result of this review is a unified model which has been validated with data collected from 94 people through a web questionnaire. The results from this data have been analysed using the partial least squares (PLS) method. The analysis has demonstrated the predictive relevance and the validation of the model for users between 26 to 35 years old and 36 to 45 years old. Keywords: LMS, technology acceptance models, e-learning, TAM, partial least squares. 1 Introduction In the recent years the use and implementation of LMS is rapidly growing both in the industrial and academic environment. The use of TEL (Technology Enhanced Learning) is a fundamental concept for the current learning processes because technology allows expanding the learning scope. In the last 40 years, the technologies supporting these learning processes have received different names, although the more used term is e-learning, which can be defined as the use of the new multimedia technology and Internet, to improve the learning quality, supporting the access to resources and services, as well as the interchange and the remote collaboration [1]. This study covers the factors which have a greater influence in the users intention to use LMS systems, from a review of technology adoption models, with focus on LMS systems, and an analysis of LMS implementation case studies, and proposing a study framework based on a unified model. M.D. Lytras et al. (Eds.): TECH-EDUCATION 2010, CCIS 73, pp , Springer-Verlag Berlin Heidelberg 2010

2 302 O.L. Dueñas-Rugnon, S. Iglesias-Pradas, and Á. Hernández-García 2 Theoretical Background In order to examine the intention to use LMS, there is a wide range of theoretical models for characterizing technological acceptance, being the most relevant TAM (Technology Acceptance Model) [2], UTAUT (Unified Theory of Acceptance and Use of Technology) [3], TRA (Theory of Reasoned Action) [4], TAM2 [5], TPB (Theory of Planned Behaviour) [6], MM (Motivational Model) [7], PC MPCU (Model of PC Utilization) [8], IDT (Innovation Diffusion Theory) [9], and SCT (Social Cognitive Theory) [10]. Even though their approaches vary from one theory to another, they share some elements in common. Based upon the common constructs presented in these theories, and extracting other factors identified on LMS adoption focused studies [11], [12], [8], four types of constructs have been identified: 1. Actual use of technology 2. Intention to use/attitude towards use, as a predisposition against or towards a stimulus object [4]; in this particular case, the use of a LMS 3. Attitude antecedents, being the most relevant those related to TAM (perceived ease of use, perceived usefulness) and TPB (normative and social factors, such as normative beliefs and subjective norm) 4. A group of different factors which affect the attitude antecedents. These factors include self-efficacy, anxiety, system functionality, performance expectations, and quality of contents, and can be classified into two separate groups: those user related (i.e., self-efficacy, anxiety, performance expectations) and those system related (i.e., functionality, contents) 2.1 Research Model and Hypotheses In order to define the research model, the aformentioned technological acceptance models, as well as use cases related to e-learning system acceptance, have been reviewed and compared in order to extract the latent constructs. According to these theoretical and practical approaches, the research model was formulated. Figure 1 illustrates the research model, hypotheses and relations between constructs. The following hypotheses were formulated: H1, H2: Behavioural intention is positively influenced by attitude towards behaviour and the perceived usefulness. H3, H4, H5, H6: Attitude towards behaviour is positively affected by perceived usefulness, perceived ease of use, subjective norms and social influence. H7: Perceived usefulness is positively influenced by perceived ease of use. H8, H9, H10, H11: User related factors have a positive influence on perceived usefulness, perceived ease of use, subjective norms and social influence. H12, H13 H14, H15: System related factors have a positive influence on perceived usefulness, perceived ease of use, subjective norms and social influence.

3 System and User Characteristics in the Adoption and Use of e-lms 303 Fig. 1. Research Model 3 Research Design and Methodology A survey from the research model was taken to 200 students, grouped by age (from 18 to 25 years, from 26 to 35 years, and from 36 to 45 years), who had previous experience with e-learning systems, from both the academic and industrial environment in Spain. The survey included 41 items measured using a seven-point Likert-type scale and ranging from Strongly disagree to Strongly agree. The survey data was gathered with the help of the web tool LimeWire Survey. This survey was anonymous and was sent to respondents by with the link reference. In order to analyse the results, SEM (Structured Equations Models) was used with a Partial Least Squares (PLS) method approach, used for prediction and confirmatory analysis. According to Fornell and Bookstein [14], PLS presents relevant advantages, such as: minimal sample size requirements, with different measurement scales and kind of variables. PLS-GRAPH software was used for extraction and analysis of results. 4 Data Analysis A total of 94 complete surveys were received (47% answering rate). Data from this survey were exported from the web tool to Microsoft Excel, and then converted to PLS-Graph readable files. The sample size was adequate for analysis according to the criteria from [14] except for the group of students ranging from 18 to 25 years, with a sample size of 20. Next, measurement model and structural model validity were performed. For measurement model validity, factorial analysis, convergent validity and discriminant validity were tested. Table 1 displays the results from the factorial analysis and convergent validity.

4 304 O.L. Dueñas-Rugnon, S. Iglesias-Pradas, and Á. Hernández-García The factorial loadings for each construct and item are significantly greater than the acceptable limit of 0.5, and most of them are greater than the optimal limit of Composite reliability values are significantly greater than the optimal limit of 0.7; with most of them higher than 0.9. On the other hand, the AVE (Average Variance Extracted) values are equal or greater than the minimal limit of 0.5. AVE values for the construct User Factors fell slightly below the limit of 0.5. Furthermore, another discriminant validity analysis was done, taking into account the correlations between item weights and constructs. Finally, structural model validity was studied. This analysis includes the construct variance explained (R 2 ), the predictive relevance (Q 2 ), and the values of the significant structural paths. Table 2 illustrates R 2 and Q 2 analysis. As shown in Table 2, the construct variance explained (R 2 ), for the ranges between 26 to 35 years old and 36 to 45 years old indicate a good explanation of the model (more than 50% of the variance explained in the former case and more than 75% in the latter). For the range between 18 to 25 years old, values were lower than 50% mainly for behavioural intention. Table 1. Measurement Model Validity (Factorial Analysis and Convergent Validity) Table 2. Construct Variance Explained (R 2 ) and Predictive Relevance (Q 2 )

5 System and User Characteristics in the Adoption and Use of e-lms 305 Observing model's predictive relevance analysis, the construct of the ranges between 26 to 35 years old and 36 to 45 years old indicate predictive relevance (values greater than 0). On the other hand, values for the group of students between 18 and 25 years showed no predictive relevance with a very low value of variance explained for the total model of 12,4%. Then, structural paths were analyzed (results are shown in figure 2). Although predictive relevance may be question for the group of students between 18 to 25 years old, behavioural intention would be influenced by attitude towards behaviour, which is influenced by perceived usefulness (strongly) and social influence. User factors would have a more notable influence on perceived usefulness than perceived ease of use., the social influence is influenced by the user (0.406) and system factors (0.285). The range between 26 to 35 years old indicates that the behavioural intention is influenced by the attitude toward behaviour (0.824). Consequently, the attitude toward behaviour is influenced by the perceived usefulness (0.468) and the perceived ease of use (0.335). Finally, the perceived usefulness and the perceived ease of use are influenced by the user (0.350 and 0.402) and system factors (0.532 and 0.499). The range between 36 to 45 years old indicates that the behavioural intention is influenced by the attitude toward behaviour (0.551) and the perceived usefulness (0.375). Consequently, the attitude toward behaviour is influenced by the perceived usefulness (0.638) and the perceived ease of use (0.340). Finally, the perceived usefulness is influenced by the user factors (0.824), and the perceived ease of use is influenced by the user and system factors (0.455 and 0.510). Fig. 2. Structural Paths (significant paths are underlined)

6 306 O.L. Dueñas-Rugnon, S. Iglesias-Pradas, and Á. Hernández-García 5 Conclusions The analysis has demonstrated the predictive relevance and the validation of the model for users between 26 to 35 years old and 36 to 45 years old, whereas no significant relations were extracted from the analysis of users between 18 and 25 years old, most probably due to the small data sample. However, social influence outstands as a significant factor on attitude towards behaviour, whilst this relation is not confirmed on the other two groups. The main reasons behind this finding may be both the obligation to take courses under LMS environments, and/or the peer pressure when there has been a successful experience in the use of LMS. The importance of perceived usefulness stands out as the most relevant factor impacting attitude towards behaviour; this result recommends the use of LMS only where the benefits related to LMS use exceed those of traditional learning. Also, the authors think that a more detailed study should be made about the construct user factors, as it could be interesting to decompose it into three subconstructs: anxiety, self-efficacy and performance expectations, so that influence of each one could be observed. Surprisingly, both subjective norms and social influence seemed to have weak or no influence on attitude towards behaviour. The explanation to this result may lay on the non coercive nature of courses taken by the students, perceived more as an individual opportunity than a group obligation. Another point of interest rises from the results on the relevance of system factors, which only seem to be relevant for the group between 26 to 35 years. In the authors opinion, this result should be contrasted with another experiment; if this difference is maintained, reasons behind this finding should be imperative to study in order to be able to make a better design of LMS systems taking into account the age profile of students. References 1. Communication from the Commission to the Council and the European Parliament. The e- Learning Action Plan, European Commission (March 2001), /com2001_0172en01.pdf 2. Davis, F.D.: Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly 13(3), (1989) 3. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3), (2003) 4. Fishbein, M., Ajzen, I.: Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading (1975) 5. Venkatesh, V., Davis, F.D.: A theoretical extension of the technology acceptance model: four longitudinal field studies. Management Science 46(2), (2000) 6. Ajzen, I.: The theory of planned behavior. Organizational Behavior and Human Decision Processes 50, (1991) 7. Vallerand, R.J., Ratelle, C.F.: Intrinsic and extrinsic motivation: A hierarchical model. University of Rochester Press (2002)

7 System and User Characteristics in the Adoption and Use of e-lms Thompson, R.L., Higgins, C.A., Howell, J.M.: Personal Computing: Toward a Conceptual Model of Utilization. MIS Quarterly 15(1), (1991) 9. Moore, G.C., Benbasat, I.: Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation. Information Systems Research 2(3), (1991) 10. Bandura, A.: Social Foundations of Thought and Action: A Social Cognitive Theory. Prentice Hall, Englewood Cliffs (1986) 11. Moon, J., Kim, Y.: Extending the TAM for World-Wide-Web context. Information and Management 38, (2001) 12. Saadé, R., Bahli, B.: The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information and Management 42, (2005) 13. Fornell, C., Bookstein, F.: Two Structural Equation Models: LISREL and PLS Applied to Consumer Exit-Voice Theory. Journal of Marketing Research 19, (1982) 14. Barclay, D., Higgins, C., Thompson, R.: The Partial Least Squares (PLS) Approach to Causal Modeling: Personal Computing Adoption and Use as an Illustration. Technologies Studies (Special Issue on Research Methodology) 2(2), (1995)

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