Modified Technology Acceptance Model (TAM2)

Before Venkatesh and Davis (2000) made a few adjustments from TAM to TAM2, the correlation between different aspects of technology acceptance had been explored for over ten years. As Venkatesh and Davis (2000) stipulate, during this time PU became consistently acknowledged as one of the strong factors that influence usage intentions, whereby the standardised regression coefficients are usually approximately 0.6. As a corollary, Venkatesh and Davis (2000) broadened their model to take account of subjective norms, image, relevance of the job, quality of output, demonstrability of results, as well as PEOU as an antecedent of PU – as illustrated in Figure 3.6. The antecedents of PU formed the core difference between TAM and TAM2. In TAM2, the major forces that influenced judgments of PU were meticulously expounded, whereby 60% of the differences in behavioural intention (BI) were explicated. In addition, user’s attitude was dropped in TAM2, and BI and usage behaviour were regarded as the ultimate dependent variables.

The determinants of PU in TAM2 take account of both social and cognitive factors. Subjective norms, voluntariness and image are the social factors that affect PU in TAM2. Voluntariness, according to Venkatesh and Davis (2000), can be defined as whether the user is directed to use the system or whether he/she uses it out of freewill. Voluntariness standardises the impact of subjective norms on a person’s intention to use IS technologies. Consequently, when usage of an IS technology is mandatory, the subjective norms will bring about a positive impact on intention to use the system. When usage is out of freewill, subjective norms will have no impact. Image reflects whether usage is perceived to enhance the social perception of the user (Venkatesh and Davis, 2000). The PU of an IS technology is said to be positively impacted by image and subjective norms. As a result, TAM2 looks into these relationships based on the user’s experience. According to the model, the subjective norms of a user will have less effect on the PU and usage intention if the user becomes progressively exposed to the IS technology.

61 Relevance of the system in the job, quality of output and the demonstrability result are the cognitive determinants of PU. Job relevance, according to Venkatesh and Davis (2000), can be defined as a user’s perception regarding the applicability of the IS technology in the work. On the other hand, output quality is defined by Venkatesh and Davis (2000) as the users’ perception regarding the system’s effectiveness when performing specific tasks. Finally, Venkatesh and Davis (2000) define result demonstrability as the tangibility of an IS technology on performance or contribution. Every cognitive determinant is said to have a positive effect on the PU of an IS technology.

Chau and Lai (2003) applied TAM2 in the evaluation of users’ acceptance of e-banking. They established that PU was a significant determinant of BI, but found no significant correlation between PEOU and acceptance. Hart et al. (2007) applied TAM2 to investigate a practical online analytic processing (OLAP) project for students. Based on the findings of the study, it was apparent that there is indeed a positive correlation between cognitive factors such as demonstrability of outcomes, quality of output and job relevance, as well as PEOU and acceptance of IT systems.

62

Figure 3.6: Modified Technology Acceptance Model (TAM2). Source: Venkatesh and

Davis (2000).

TAM has received sufficient attention in the literature, but not without criticism. For instance, the original version of TAM lacks rigorous and sufficient research (Chuttur, 2009). Another criticism noted by Segars and Grover (1993) was regarding TAM’s utilisation of CFA to re-construct another model using different variables (e.g., ease of use and usefulness). For over two decades, different research studies have attempted to extend TAM’s theoretical basis to overcome some of the mentioned drawbacks. For instance, results of extending the original TAM to TAM2 (proposed by Venkatesh and Davis, 2000) significantly supported the model formulation. Furthermore, other researchers (such as Jiang et al., 2000; Chau and Hu, 2001; Horton et al., 2001) have

63 modified TAM to suit new technologies, including internet, intranet and World Wide Web (WWW). In addition, several studies extended TAM by focusing specifically on antecedents of PEOU and PU (Venkatesh, 2000; Pavlou, 2003), or added additional components to the model – such as perceived self-efficacy (Al-Gahtani and King, 1999; Venkatesh and Morris, 2000; Kleijnen et al., 2004) – in order to account for their studies’ context.

Mathieson (1991) compared TAM and TRA in a study of spreadsheet acceptance. The results indicated that PEOU and PU were significant factors that affected usage. Moreover, Igbaria et al. (1997) described the TAM model as easier, much simpler and more accurate than the TRA model when examining technology acceptance. Taylor and Todd (1995) also made a comparison of TAM, TPB and DTPB and established that TAM had been successful in predicting the use of a computer resource centre. This was vital in adding to the growing support for the model.

In a meta-analysis of 26 studies, carried out by Ma and Liu (2004), a conclusion was made that there was a strong and significant correlation between PU and acceptance, as well as between PU and PEOU. Furthermore, Sun et al. (2009:351) stated that:

[M]uch prior IT usage research was based on Ajzen’s theory of planned behavior (TPB), shaped by three perceptions: attitude, subjective norm (SN), and perceived behavioral control (PBC). Though it did not identify specific beliefs or other perceptions salient to IT usage, TAM added perceived usefulness (PU) and perceived ease of use (PEOU) as attitudinal beliefs salient to IT usage.

Despite the limitations of the different frameworks that have been discussed in the previous subsections, some may wonder why not utilise another model – such as technology–organisation–environment (TOE) or DeLone and McLean’s (1992) IS success model – instead of using TAM? Despite these models having been used to develop frameworks and conceptual models in order to understand the relationship of various

64 factors that may affect ERP adoption, it is worth noting that the previous research on some of these models – such as DeLone and McLean’s (1992) IS success model – have not been empirically proven (Seddon, 1997).

The reasons for adopting ERP systems are different from other traditional information systems. Indeed, Ifinedo et al. (2010:1138) stated that “ERP is a different class of IS”. According to Ifinedo et al. (2010), the first reason is related to the implementation of ERP systems that requires business process engineering because such processes are intended to completely change the adopting company. For instance, the system users in the company need to be trained to use the new system, as well as to follow new processes and procedures (Holsapple et al., 2005). ERP was described by Ifinedo et al. (2010) as “deterministic technology” that requires the company’s work processes to be integrated with other software application modules (Klaus et al., 2000).

The second reason for adopting an ERP system is related to the complexity of implementing ERP systems in comparison with traditional information systems. In fact, companies that adopt ERP systems find it difficult to establish such endeavours without identifying the benefits that they may gain and having external resources and expertise when implementing these complex technologies (Wang et al., 2008). As a result, “success measurement models used for other typical IS success evaluations may not be adequate for ERP systems” (Ifinedo et al., 2010:1138).

Additionally, the majority of the research studies using the DeLone and McLean (1992) IS success model focus on people rather than systems (Fan and Fang, 2006). However, low usage of information systems could cause low return of IS investment (Sichel, 1997). Thus, the usage intention of the system users can be considered an important determinant to information system success (Fan and Fang, 2006). Further, this model suggests that information and system qualities are important factors for the success of information systems, since the ERP system is within the framework of information systems. Thus, PEOU and PU are functions of the information system quality.

65 Different research studies (such as Pan and Jang, 2008; Kouki et al., 2010; Ramdani et al., 2009) examined the adoption of ERP systems by the use of a TOE framework. However, some studies based on this framework have several limitations (Gangwar et al., 2014). For example, Low et al. (2011) indicated that TOE framework’s lack major constructs and that variables of TOE frameworks may differ from one context to another (Wang et al., 2010). Thus, TOE frameworks should include other variables – such as sociological and cognitive variables – to enrich them (Jang, 2010; Wen and Chen, 2010).

Musawa and Wahab (2012) investigated the adoption of Electronic Data Interchange (EDI) in Nigerian SMEs and extended to the TOE framework. The authors concluded that the TOE framework lacks the explanatory power of IS adoption, where nearly half of the EDI variance remains unclear. Dedrick and West (2003) argued that the TOE framework is only concerned with variables classifications and the framework cannot be considered as a well-developed theory because it does not act as an integrated conceptual framework.

The TOE framework has been integrated with other technology acceptance frameworks that have clear constructs and, more particularly, with TAM. However, integrating TOE and TAM raises concerns relating to the variables of the two models. First, TAM has many external variables that have been identified and examined by different research studies, whereas TOE’s variables differ from one research study to another and are not widely accepted (Gangwar and Raoot, 2014). Second, the significance of the variables for both frameworks differs from one county to another and from one technology to another. Some variables could be found to be consistently insignificant in a group of contexts or studies (Gangwar and Raoot, 2014).

Many researchers in the field of IT have tried to explain the utilisation and the adoption of technology. However, most of the existing models, theories and frameworks have failed to completely explain the reasons why a certain technology is unacceptable or acceptable by its users (Al-Jabri and Roztocki, 2015). Moreover, according to Brown et al. (2002), voluntary adoption of technology was presumed by many researchers where the rejection of new technology was optional. However, in the real sense, there are instances when a

66 specific IT is mandated, making it difficult for users to reject. ERP system implementation is an example of mandated IT (Al-Jabri and Al-Hadab, 2008).

A significant variation exists with regard to IT acceptance; in some instances, major users are not consulted when investing in IT (for instance, ERPs). According to Nah et al. (2004), the variation that characterises technology acceptance within mandatory contexts has not been explained by Davis’ (1989) TAM model or Venkatesh and Davis’ (2000) expanded TAM model. A vast amount of technological investments are conducted in involuntary environments (Al-Jabri and Roztocki, 2015). Morris and Venkatesh (2010) suggest that approximately 80% of Fortune 500 firms have adopted ERP. According to Momoh et al. (2010), ERP systems are very complex systems and such complexity may negatively affect an individual’s PEOU, as well as PU. Thus, developing a conceptual model to guide HEIs in ERP adoption is very important for researchers as well as managers, in order to help them overcome the complex nature of ERP systems.

The next section (Section 3.3) discusses the empirical studies that have generally drawn on TAM, specifically in the ERP context.