The Theory of Reasoned Action (TRA) and Theory of Planned Behaviour (TPB) have influenced the Theory of Technology Acceptance Model (TAM) and its extended models, which mainly focus on the adoption and use of ICT. While TRA states that people are, more often than not, rational beings who make systematic use of available information, considering the repercussions of their actions before deciding whether or not to engage in a given behaviour, TAM assigns considerable weight to two key determinants - perceived usefulness and perceived ease of use (Davis 1989). The presence of behavioural intention (BI) in the TAM is one of the major differences with TRA. BI is considered to be an immediate antecedent of usage behaviour and gives an indication about an individual’ readiness to perform a specific behaviour. In TAM, both PU and PEOU influence an individual’s intention to use the technology, which in turn influences the usage behaviour. There were many confirmations in the literature for the relationship between BI and usage behaviour in general, and this has recently been extended to the e-learning context.
The Technology Acceptance Model (TAM) was developed by Davis (1989) to study diffusion and adoption of new technology at individual levels, and to clarify computer usage behaviour. Davis in Suryaningrum (2012) presented the TAM to explain the determinants of user acceptance of a wide range of end-user computing technologies. The basic factors in TAM are Perceived Usefulness (PU) and Perceived Ease of Use (PEOU). Davis defines PU as the degree to which individual beliefs regarding using the Information System will enhance the performance while PEOU relates to how ‘Individual believes the given Information System will reduce the intensity of their work’. Out of the two factors (PU & PEOU), Davis concluded that PU was the most important, the reason is that a after period of time in actually
using the innovation (post adoption) the beliefs of Perceived Ease of Use (PEOU) havedeclining effect on intention, while Perceived Usefulness has cohesiveness and a strong positive effect on intention.
In this model, Davis (1989) identified two theoretical constructs of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) that affect the intention to use a system. PE - is the extent an individual believes the system will help them do their jobs better. (PU), EE relates to how easy an individual believes the system is to use. (PEOU), IS - relates to whether or not important others ‟influence an individual’s” intention to use the system. FC denotes whether individuals have the personal knowledge and institutional resources available to use the system.
3.9.1 Studies on Technology Acceptance Model (TAM)
The design, adoption and development of a framework for the adoption and implementation of using ICT technologies to enhance and support distance learning must be looked at based on the existing models and theories as a way of addressing the existing gaps. Such design must also be informed by a thorough literature review alongside the environmental and infrastructural context as this forms the basis for financial, human resource, technological and physical resources-related challenges in most countries.
Roca et al. (2006) applied the technology acceptance model (TAM) and found that the users’ intention is determined by student satisfaction, which is determined by the perceived usefulness, information quality, confirmation, service quality, system quality, perceived ease of use, and ability to support cognitive development. Germann and Sasse (1997) found that lecturers who participated in a two-year technology integration program improved their technology self-efficacy and their interest in learning more about how technology could impact the curriculum improved. Ross, Hogaboam-Gray, and Hannay (1999: 87) reported that access to technologies increased Lecturers’ “opportunities for successful teaching experiences, thereby contributing to greater confidence in their instructional ability” (p. 87). Additionally, they also noted, “Lecturers who interpret their interactions with computers as indicative of high ability grow in self-confidence, regardless of their experiences” (p. 93). Research reveals also that before lecturers use technology for instruction they must be personally convinced of its benefits and must see the utility of using a particular technology (Lam 2000). Before technology is used in the classroom, lecturers focus attention on their
students. They want to know what impact it will have on students’ learning outcomes (Higgins & Moseley 2001).
Lecturers use technology because it motivates students and offers a different mode of presentation. Instead of using computers for drill and practice, more confident lecturers use technology as an instructional tool to enhance students’ learning (Lam 2000). Successful technology adoption in lecturers’ classrooms is dependent upon school administrators providing an individualized, differentiated process of training and implementation (Gray 2001). Glenn (1997: 126) commented, “often institutions rely upon a ‘one size fits all’ approach that meets the needs of only a few participants”. Lecturers must see how technology fits within their localized classroom setting (Stein, Smith & Silver 1999). Lecturers’ technology beliefs are influenced by their teaching philosophy. Resistance to adopting new technologies stem from lecturers’ existing teaching beliefs (Norton, McRobbie & Cooper 2000).
For technology adoption to be successful, lecturers must be willing to change their role in the classroom (Hardy 1998). When technology is used as a tool, the lecturer becomes a facilitator and students take on a proactive role in learning. Niederhauser and Stoddart (2001) noted a “consistent relationship between lecturers’ perspectives about the instructional uses of computers and the types of software they used with their students” (p. 27). Often, this change of teaching philosophy and methods focuses on learner-centred teaching and constructivist teaching practices (Rakes, Flowers, Casey & Santana 1999). Ertmer, Gopalakrishnan, and Ross (2001) found that exemplary technology-using lecturers exhibit more constructivist teaching practices. Successful integration of technology into teaching depends on transforming lecturers’ beliefs and philosophy concurrently (Windschitl & Sahl 2002).
Some scholars such as Dillon and Morris (2006: 15) assert that although technology has been used over the years, the adoption of technology for learning and teaching has always experienced challenges. This calls for designing of simple yet comprehensive technology adoption models in tandem with the local settings. A study by Venktesh (2008), stated that IT adoption is becoming increasingly complex and implementation costs are very high. Scholars like Zemel and Groves (2000) assert that the challenges faced by higher education students for this technological impact is daunting and that higher education faculty need to prepare competent professionals in the design and use of current and emerging technologies. A study by Al-alak and Alnawas (2009: 201) using Tam also showed that perceived usefulness and perceived ease of use highly affected the adoption in their study on measuring the acceptance
and adoption of E-Learning by academic staff and students. In another study by Holzweiss, Joyner, Fuller, Henderson and Young (2014: 311-323) it was found that the use of TAM however good has limitations of leaving some dimensions such as used behaviour, behavioural intentions and attitudes. In yet other studies by Kripanont (2006: 13-28 and 2007) using TAM, he found that although TAM may be missing out on some factors affecting technology adoption, it was still key in understanding not only technology adoption but also the other models of technology adoption.
Many researchers have used the TAM to measure students’ acceptance of Web-based learning tools. For example, Amoako-Gyampah (2004) found that the perceived ease of use (PEOU) has a direct and positive influence and effect on the intention to use the system, and his results were also supported by other researchers. In contrast, Chesney concluded that PEOU did not have a direct and significant influence on the intention to use the system.
Social norm was adopted and included in the TAM model, in order to overcome the limitation of TAM in measuring the influence of social environments. SN is defined as the person’s perception that most people who are important to him or her think he or she should or should not perform the behaviour in question. SN was studied in some research as an antecedent of BI and in other studies as an antecedent PU. However, as mentioned by Venkatesh et al (2008),the influence of SN is very complex.
It has been noted that there are a number of studies that have involved TAM as their theoretical background for explaining ICT adoption and use (Suryaningrum 2012) and in which scholars have already confirmed that Perceived Usefulness has a positive relationship with both adoption intention and continuance intention (Venkatesh 2000). In some post adoption studies, PU has been found to influence satisfaction (Anol Bhattacherjee 2001; Moez Limayem, Hirt & Cheung 2007) and attitude toward the technology (Anol Bhattacherjee & Hikmet 2008). PEOU has been found to influence both PU and adoption intention (Davis 1989). Even though TAM was found to be a valid theoretical framework in studying ICT adoption and use, it has been criticized for its several limitations including the original model’s intended generality and parsimony (Dishaw & Strong 1999), not considering non- organizational setting (Venkatesh & Davis 2000), and overlooking the moderating effects of ICT adoption and use in different situations (Sun & Zhang 2006; Suryaningrum 2012).
Researchers, on the other hand, can use external variables in the extended TAM to measure the acceptance of new innovation technology in their study (Venkatesh & Davis 1996, 2000). The external variables in TAM include: System design characteristics, User characteristics (Cognitive style and other personality variables), Task characteristics (Nature of the development or implementation, Political influences and organization structure) (Venkatesh & Davis 1996 2000). Other researchers have used the Technology Acceptance Model in their studies including the study of Adams et al. (1992), Suryaningrum (2012), Hendrickson et al. (1993), Segars and Grover (1993), Subramanian (1994), and Szajna (1994) to provide empirical evidence on the relationships that exist between usefulness, ease of use and system use.
According to Suryaningrum (2012), TAM assigns considerable weight to two key determinants perceived usefulness and perceived ease of use. In addition, the Technology Acceptance Model (TAM) will be used to determine the extent at which people adopt or use technologies. This is confirmed by Eben & Achampong (2010). Suryaningrum (2012) notes that according to Davis et al. (1989), perceived usefulness will directly influence the behavioural intention. Whenever the technology is free of effort, people will realize its usefulness.
Adams et al. (1992) replicated the work of Davis (1989) to demonstrate the validity and reliability of his instrument and his measurement scales. This model has also been used to examine the acceptance of email systems (Straub et al. 1997), personal digital assistants (Yi et
al. 2006), World Wide Web (Moon & Kim 2001), Enterprise Resource Planning Systems
Figure 3.5: Technology Acceptance Model (Davis 1989).
This theory due to its provision of a room for external variables seems to have key factors that could be inclusive of other factors that can to a greater extent explain the reasons as to why both individuals and organizations act differently in their adoption of new technologies. This seems to be why it has been used by many studies to shed light on adoption of technology.