Understanding intention to use mobile learning: a perspective of the extended unified theory of acceptance and use of technology

Reham Adel Ali,Muhammad Rafie Mohd Arshad / International Journal of Advanced and Applied Sciences, 3(7) 2016, Pages: 81‐88 82 education, only a few studies have examined whether m-learning has the capacity to attract increased number of K-12 learners or not, as most of the studies have been on university students. Accordingly, it is of paramount importance that a more thorough investigation is made of the factors influencing the intention of learners to utilize mlearning in the interactive learning environment of mobile-based devices (Cheng, 2015). Therefore, on the basis of the unified theory of acceptance and use of technology (UTAUT) as proposed by Venkatesh et al. (2003), this study’s objective is to determine and examine the determinants, in addition to the gender and experience differences in relation to the acceptance of m-learning. In the next section of this paper, a review of the UTAUT is discussed along with the rationale for the adoption of UTAUT as this study’s theoretical framework. This is followed by descriptions of the research model and the concluding argument. 2. Literature review and the conceptual foundation 2.1. Technology acceptance model (TAM) Technology acceptance model (TAM) is one of the powerful models used in explaining the acceptance of information technology (Lee et al., 2003). Davis et al. (1989) developed TAM by proposing two factors, the perceived usefulness (PU) and perceived ease of use (PEU), are critical factors for explaining the adoption of new technology. External variables can be effect on PU and PEOU. TAM states that actual use of a system is determined by behavioral intention to use a system, where the intention to use the system is determined by the attitude of a person toward using the system. Person’s attitude towards using the system is affected by perceived usefulness (PU) and perceived ease of use (PEU). 2.2. Unified theory of acceptance and use of technology (UTAUT) The model of the unified theory of acceptance and use of technology (UTAUT) was proposed and developed by researchers through the combination of eight major theories in behavioral prediction. In relation to UTAUT it is comprised of four independent variables: social influences; effort expectancy; performance expectancy; and facilitating conditions. It is these variables which are the determinants of behavior; in other words a behavioral intention. Effort expectancy relates to the level to which an individual perceives the system can be easy to adopt and use and this is similar to the ease of use construct as denoted in the TAM. Performance expectancy is the measurement of the degree to which an individual perceives that using the system could assist to increase the level of their performance, and this concept is also similar to the construct in the TAM. Social influence is a measure of the degree to which an individual believes that others whom they care about are of the view that a particular system should be used. In measuring facilitating conditions, it is the degree to which an individual perceives that there is a presence of organizational assistance to facilitate the use of the system. The two variables of effort expectancy and performance expectancy in UTAUT are similar concepts to perceived ease of use and the perceived usefulness in TAM. Furthermore, social influences can be likened to the factor of a ‘subjective norm’ in TAM2, which is an extension of TAM. Also, facilitating conditions is having the same meaning of compatibility construct from diffusion of innovation theory (DOI) in accordance with Venkatesh et al. (2003). The UTAUT also takes into consideration the moderating variables. These variables are: age; gender; voluntariness of use; and experience. In selecting UTAUT as the underlying theory, this was made on the basis of its comprehensiveness and its global approach. As previously mentioned, the UTAUT constructs have been construed from eight acceptance models of behavioral prediction (Venkatesh et al., 2003). The UTAUT covers off on the significant influencing factors of the acceptance of technology by users such as technology factor that describes the characteristics of a technology and implementation environment factor that includes organization characteristics (Marchewka and Kostiwa, 2014; Venkatesh et al., 2003). Furthermore, the UTAUT moderating variables of age, gender, voluntariness of use and experience can be defined as individual differences influencing an individual’s attitudes concerning a given technology. Indeed UTAUT can effectively predict about 70 percent of the cases for an uptake of information technology, whereas other models to measure user adoption could only do so in approximately 40 percent of the cases (Davis et al., 1989; Venkatesh et al., 2003). Significantly, as UTAUT is a new theoretical framework there ought to be consideration given to more investigation to corroborate its robustness as the underlying theoretical basis for research endeavors (Straub, 2009). In this regard, recent studies in wide-ranging research domains have adopted the UTAUT as the underlying theoretical basis. These studies have included an examination of organizational learning systems (Wong and Huang, 2011), mobile banking implementations (AbuShanab and Pearson, 2007; Zhou, 2012), 3G mobile communication (Mardikyan et al., 2012; Wu et al., 2012) and wireless communications (Anderson and Schwager, 2004). Furthermore, the UTAUT has been adopted as the theory to examine the acceptance by students of blackboard technology as indicated by Marchewka and Kostiwa (2014); Pynoo et al. (2011) applied UTAUT to measure the level of acceptance and utilization of a digital learning environment by secondary school teachers, and UTAUT has been adopted as the theoretical framework to examine Reham Adel Ali,Muhammad Rafie Mohd Arshad / International Journal of Advanced and Applied Sciences, 3(7) 2016, Pages: 81‐88 83 training in health care systems (Marshall et al., 2011). The review of the literature indicates that there are three main factors influencing the acceptance of technology: (1) the technology factor; (2) the individual factor; and (3) the factor of the implementation environment (Chau and Hu, 2002; Hu et al., 1999; Nanayakkara, 2007; Sekaran and Bougie, 2010). In relation to the factor of the implementation environment, it involves the organizational characteristics (Dadayan and Ferro, 2005; Nanayakkara, 2007), whilst it is the technology characteristics that are associated with the factor of the technology. Measurement of these characteristics was made by examining the ease of use and the usefulness of the technology (Dadayan and Ferro, 2005; Nanayakkara, 2007; Venkatesh et al., 2003). For this study, the factor of the implementation environment was measured by way of two variables: social influence; and the facilitating conditions. The underlying framework of the UTAUT was originally formulated to describe and better understand organizational adoption of information technologies. Of note is that the uptake of mobile technology is more personalized in relation to the individual (Carlsson et al., 2006). However, it has been claimed the UTAUT does not take into consideration the factor of an individual’s characteristics as claimed by Dadayan and Ferro (2005) and Nanayakkara (2007). Accordingly, this paper has included the individual factor in order to ascertain an individual’s characteristics such as their knowledge and level of skills and their perceptions such as their capacity to utilize m-learning in a meaningful way. In examining the literature concerning information technology and the models of user acceptance, a number of constructs have been utilized to measure the individual factor. For this paper, the individual factor was measured by way of two determinants: self-efficacy; and selfregulation (Chung et al., 2015; Liaw et al., 2014; Park et al., 2012). Fig. 1 below presents the mlearning factors as well as the important sub-factors. Indeed it is these factors which will be utilized for this study as the research model’s underlying foundation. Fig. 1: Factors and sub-factors of m-learning acceptance 3. Research model and hypotheses As mentioned above, the UTAUT has been adopted as the underlying theoretical basis for this study by applying some modifications to the traditional UTAUT model to take into consideration the individual factor. Indeed, the actual usage concept was removed from the revised model of the UTAUT, as the technology of m-learning is still in the stage of infancy in relation to development. In fact the purpose of this `paper is to examine the future acceptance of the m-learning technology emergence, rather than the current usage of the technology as previous studies have indicated that the actual usage of m-learning is not a cogent method of measurement of the m-learning value (Lu et al., 2003; Yang, 2005). Accordingly, behavioral intention was identified as a dependent variable of the acceptance of m-learning and the actual use or the construct of use behavior was eliminated. Furthermore, this study has examined the acceptance by students of m-learning in the context of totally voluntary usage and by using a population of students of the same age and, therefore, voluntariness of usage and age have been eliminated as moderators. The proposed research model utilized in this research endeavor is shown in Fig. 2. Furthermore, the measurement items for the constructs have been identified from prior studies, as shown in Table 1. The constructs of the UTAUT are described in detail as well as its relevance to this research endeavor in the following sections. Fig. 2: Proposed research model 3.1. Performance expectancy (perceived usefulness) In relation to the new technology, performance expectancy (PE) can be defined as the degree to which a person perceives that utilizing the novel technology will assist them to achieve a benefit in relation to performance of a