ISSN 2249-0582 World J of Engineering and Pure and Applied Sci. 2011;1(2): (2): (2): (2):19 19 19 19 Fadare et al Fadare et al Fadare et al Fadare et al. Behavioral intention for m- learning on 3G Mobile Internet Technology © Research | Reviews | Publications, 2011 http://www.rrpjournals.com/ OPEN ACCESS OPEN ACCESS Original Original Original Original Article Education and Technology Research Behavioral Intention for Mobile Learning on 3G Mobile Internet Technology in South-West Part of Nigeria Oluwaseun Gbenga FADARE 1, *, Oluleye Hezekiah BABATUNDE 2 , Dipo Theophilus AKOMOLAFE 1 ,Olayinka Olusegun LAWAL 1 ABSTRACT [ENGLISH/ANGLAIS] ABSTRACT [ENGLISH/ANGLAIS] ABSTRACT [ENGLISH/ANGLAIS] ABSTRACT [ENGLISH/ANGLAIS] Affiliations: 1 Department of Computer Science, Joseph Ayo Babalola University, Ikeji- Arakeji, NIGERIA 2 Department of Computer Science, Osun-State University, NIGERIA Address for Correspondence/ Adresse pour la Correspondance: fadareoluwaseun@ya hoo.com Accepted/Accepté: September, 2011 Citation: Fadare OG, Babatunde OG, Akomolafe DT, Lawal OO. Behavioral Intention for Mobile Learning on 3G Mobile Internet Technology in South- West Part of Nigeria World Journal of Engineering and Pure and Applied Sciences 2011;1(2):19-28. With the evolution and adaptability of 3G telecommunication features on handheld devices, education tends to go out of conventional campus into a feat where teaching and learning could be ubiquity, convenient, location independent and personalized. The rapid growth of mobile users will push educational institutions to adopt mobile learning solution. We propose and verify a theoretical framework of university students’ m-learning acceptance and intention to use, based mainly on the Technology Acceptance Model (TAM). A sample of 458 university students took part in this research. The structural equation modeling techniques are employed to explain the adoption processes of hypothesized research model. A theoretical general framework TAMM is developed based on TAM. Our result proved that psychometric constructs of TAM can be extended and that TAMM is well suited, and of good theoretical tool in understanding users’ acceptance of mobile learning in south west part of Nigeria. Mobile learning self-efficacy is the most importance construct influencing behavioral intention to use m-learning with path co-efficient of 0.77 and t-value of 1.76* at p < 0.05 which is significant. Self efficacy is able to explain the highest percent of the variance (70) observed in behavioral intention to use m-learning in south-west part of Nigeria. Keywords: Mobile learning ,TAM, TAMM, mobile learning self-efficacy RÉS RÉS RÉS RÉSUMÉ [FRANÇAIS/FRENCH] UMÉ [FRANÇAIS/FRENCH] UMÉ [FRANÇAIS/FRENCH] UMÉ [FRANÇAIS/FRENCH] Avec l'évolution et l'adaptabilité des fonctions de télécommunication 3G sur des appareils portables, l'éducation tend à sortir du campus conventionnel en un exploit où l'enseignement et l'apprentissage pourrait être ubiquité, emplacement pratique et indépendant et personnalisé. La croissance rapide des utilisateurs mobiles va pousser les établissements d'enseignement à adopter solution mobile d'apprentissage. Nous proposons et vérifier un cadre théorique des étudiants d'université «m-learning acceptation et l'intention d'utiliser, basée principalement sur le modèle d'acceptation de la technologie (TAM). Un échantillon de 458 étudiants universitaires ont pris part à cette recherche. Les techniques de modélisation par équation structurale sont utilisées pour expliquer le processus d'adoption du modèle de recherche hypothétique. Un cadre général théorique TAMM est développé sur la base de TAM. Notre résultat a prouvé que les constructions psychométriques de TAM peut être étendu et que TAMM est bien adapté, et d'outil théorique pour comprendre l'acceptation de bons utilisateurs de l'apprentissage mobile dans le sud-ouest partie du Nigeria. L'apprentissage mobile auto-efficacité est le plus important de construire influençant l'intention comportementale d'utiliser m-learning avec le chemin de la co-efficacité de 0,77 et t-value de 1,76 * p <0,05 ce qui est important. L'auto- efficacité est capable d'expliquer le plus élevé pour cent de la variance (70) observée dans l'intention comportementale d'utiliser m-learning dans le sud-ouest partie du Nigeria. Mots-clés: Mobile learning ,TAM, TAMM, mobile learning self-efficacy INTRODUCTION INTRODUCTION INTRODUCTION INTRODUCTION The proliferation of mobile computer technology and handheld IT devices play a vital role in mobile learning, which allow users to access learning materials anytime and anywhere [1,_2,_3,_4,_5,_6]. Chen [7] summarized the characteristics of mobile learning which distinguish it from conventional learning where all the educational activities are carried out at a designated time and place, and from desktop computerized education where leanings are confined to places where wired connection are available. The IT and telecommunication industries need to understand what factors will influence the user’ intention to adopt m-learning technology to support their implementation. The 3G mobile internet technology can be used as an efficient and instructional learning tool. Our aim is to examine factors affecting the user’s adopting mobile learning on 3G mobile telecommunication. The objectives of the study are to