The Technology Acceptance Model (TAM)

TAM, introduced by Davis (1989) is one of the most widely used and accepted models researchers use to explain information technology and information systems acceptance and usage. TAM, rooted in TRA (Fishbein and Ajzen 1975) suggests the belief–attitude– intention–behaviour causal relationship for explaining and predicting technology acceptance among potential users. Building on TRA, TAM proposes that two beliefs about a new technology, perceived usefulness (PU) and perceived ease of use (PEOU), determine a person's attitude toward using thattechnology, which in turn determine their intention to use it. PU is the degree to which one believes that using the technology will enhance his/her performance (Davis et al. 1989). PEOU is the degree to which one believes that using the technology will be free of effort. TAM further suggests that PEOU is instrumental in explaining the variance inPU (Davis et al. 1989).

Prior studies have validated TAM as a robust and parsimonious framework for understanding the user's adoption of technology in a variety of contexts including banking technology (Gounaris and Koritos 2008, Al-Ajam and Nor 2013), adoption of online shopping (Svendsen et al. 2013), online trading (Lee 2009b), online auctions (Stern et al. 2008), online games (Zhu et al. 2012a), m-commerce (Bruner and Kumar 2005), mobile Internet services (Jiang 2009), mobile financial services (Lee et al. 2012b), mobile advertising (Zhang and Mao 2008), 3G mobile value-added services (Kuo and Yen 2009), online community participation (Wang et al. 2012a), adoption of e-health (Dünnebeil et al. 2012), e-learning (Lee et al. 2013), instant messaging services (Wang et al. 2004), Wi-Fi technology (Mehta 2013), and so on.

In the study of online shopping adoption, TAM often plays a backbone in the research models (O'Cass and Fenech 2003, Ahn et al. 2004) and other theories are usually integrated with it.

For example, Chen et al. (2004) investigated online consumer behaviour by integrating the innovation diffusion theory (IDT) with TAM. Their findings confirmed that TAM was a reliable and valid research model in investigating online consumer behaviour. The findings also suggested that a more positive consumer attitude could be achieved by enhancing consumer’s value, needs and lifestyle, PU, and PEOU. Koufaris (2002) integrated TAM and flow theory (Csikszentmihalyi 1990) into one theoretical framework of online consumer behaviour, aiming to examine how these factors from two theories together influence online consumer behaviour. The findings indicated that both enjoyment of the shopping experience and PU of the website strongly predicted consumers’ intention to return to the e-commerce website, implying that online consumers were not only purely utilitarian (focusing on efficiency in shopping), but were also enjoying online shopping. The study also further confirmed that TAM could be successfully applied in online shopping behaviour research, even when the behaviour was not restricted to pure system usage, but instead included purchase decision behaviour, etc. Many other studies have reached the similar conclusion that TAM was a good model in the study of online consumer behaviour (Chiu et al. 2009b, Ha and Stoel 2009, Svendsen et al. 2013).

Although TAM has been tested over a wide range of system settings (Tong 2010), including online shopping adoption, empirical tests of the model have rendered mixed and inconclusive results, leading to questions about its validity (Meuter et al. 2005). For example, several studies (Taylor and Todd 1995a, Jackson et al. 1997) reported that a positive attitude toward a new technology was not an invariably significant predictor of consumers’ intentions to use that technology. In addition, research has shown inconsistent findings regarding the effect of PEOU on attitude. Whereas some studies found positive and significant effects of PEOU on attitude (O'Cass and Fenech 2003, Chen and Tan 2004), others revealed insignificant

relationships (Chau and Hu 2001b, Townsend et al. 2001). Researchers suggested belief factors such as PU, enjoyment, trust, and performance may influence one's attitude toward using a technology more strongly than by PEOU (Van der Heijden and Verhagen 2004).

While some researchers favour TAM because it is a parsimonious model (Tong 2010) (Porter and Donthu 2006), others argue that this parsimony represents a major drawback (Venkatesh et al. 2012). According to social psychology theories, an individual’s behaviour is not just driven by evaluative beliefs and attitudes, but also by subjective norms, perceived behavioural control, and habits (Burton-Jones and Hubona 2006). Thus, a significant body of studies suggest improving or extending TAM constructs (Wixom and Todd 2005, Porter and Donthu 2006, Srite and Karahanna 2006, Cyr et al. 2007, Aggelidis and Chatzoglou 2009, Chiu et al. 2009b, Kim and Garrison 2009, Li and Huang 2009, Chung et al. 2010, Pan and Jordan-Marsh 2010, Belanche et al. 2012a, Chyou et al. 2012, Lee et al. 2012a, Teh and Ahmed 2012, Cheema et al. 2013, Hiramatsu and Nose 2013, Park et al. 2014).

One of the extensions of TAM, TAM2, was proposed by Venkatesh and Davis (2000). In TAM2, social influence (subjective norm, voluntariness, and image), cognitive instrumental processes (job relevance, output quality, and result demonstrability) and experience were included and found to have a significant influence on PU. The new model was tested in both voluntary and mandatory settings. The results strongly supported TAM2 and explained 60 percent of user adoption using the update version of TAM (Venkatesh and Davis 2000). Hsu and Lu (2004) and Park (2009) provided similar empirical evidence that supported the effect of social influence on user’s belief of a new technology.

In a web-based environment, many researchers found that there were a broader range of additional factors that were needed to investigate users’ adoption behaviour. For example, Venkatesh (2000) suggested that TAM could be further enhanced by adding control, intrinsic motivation, and emotion as variables within the PEOU dimension. Cai and Xu (2007) extended the original TAM to encompass perceived enjoyment as an additional motivational determinant of acceptance. Gefen and Straub (2003), Kim (2012), Pavlou (2003) and Shih (2004) integrated previous work by incorporating trust into TAM and found positive effect of trust on behavioural intention. Ha and Stoel (2009) integrated enjoyment and trust into TAM to understand consumer acceptance of e-shopping.

In addition, social influence was incorporated into TAM and further showed a significant effect on consumer’s intention toward technologies according to Hsu and Lu (2004). Chen et at. (2002) added compatibility to PU and PEOU in predicting an online consumer’s attitude. The model showed that compatibility was positively related to a consumer’s attitude about using technology along with PU and PEOU. Li and Huang (2009), Van der Heijden et al. (2003) and Pavlou (2003) augmented the TAM with the perceived risk in e-stores. Chen and Tan (2004) further expanded TAM by adding a link from perceived service quality to attitude toward using. Porter and Donthu (2006), in their work to study the attitude towards Internet usage used access barrier along with PU and PEOU as the additional construct based on the TAM.

In summary, a large number of empirical studies have applied TAM to examine online consumer behaviour. PU and PEOU together can routinely explain up to 40 per cent of usage intentions and 30 per cent of systems usage (Meister and Compeau 2002). However, the literature notes TAM's parsimony as a key limitation (Venkatesh 2000, Vijayasarathy 2004).

TAM has been criticized for only explaining consumer behaviour on the Internet based on a technological point of view. Since the online environment is quite complex and full of uncertainties, there are many potential factors, such as concerns about security and privacy, product quality, and e-service quality, site design, product return, consumers’ Internet skills, etc. that can affect online consumer purchase and post-purchase decision. Only focusing on two dimensions (PU and PEOU) in the TAM seems too simple in such a complex online environment. Moreover, the variables in TAM are better suited to decisions involving few technology usage choices than to situations involving users' voluntary choices (e.g., online shopping) (Vijayasarathy 2004). Therefore, the original TAM variables may not adequately capture key beliefs influencing consumers' attitudes toward online shopping.