The development of TPB originated from the TRA (Ajzen and Fishbein 1980, Ajzen 1991, Ajzen 2011) and is designed to predict and explain human behaviour across various information technologies (Wu and Chen 2005, Wang and Ritchie 2012). According to TPB, a person’s actual behaviour in performing certain actions is directly influenced by his or her behavioural intention and, in turn, is jointly determined by his or her attitude, subjective norms (SN) and perceived behavioural controls (PBC) toward performing the behaviour. In essence, TPB differs from TRA in its addition of the component of PBC (Taylor and Todd 1995c, Bagozzi and Kimmel 2011). PBC refers to the individual’s perception of ease or difficulty in performing the behaviour of interest (Ajzen 1991). It is believed that behaviour is strongly influenced by an individual’s confidence in his/her ability to perform a behaviour (Ajzen 1991). The more an individual believes that the resources and opportunities exist to perform the behaviour, the greater their PBC over the behaviour should be. SN refers to “the
perceived social pressure to perform or not to perform the behaviour”. In other words, SN is related to the normative beliefs about expectation from other people (Wu and Chen 2005).
TPB has received good empirical support in a variety of application areas (Armitage and Conner 2001, Ajzen and Fishbein 2005, Sutton 2006). It has been applied to a variety of human behaviours, including adoption behaviour of internet banking (Lee 2009a, Yousafzai et al. 2010, Nasri and Charfeddine 2012), online tax (Wu and Chen 2005), e-service (Chen and Li 2010, Lee 2010), e-learning (Lee 2010), e-procurement (Aboelmaged 2010), users’ acceptance of instant messaging (Lu et al. 2009), health-related services (e.g., diet, drinking, drug usage, smoking, weight loss, etc.) (Godin and Kok 1996, Hoie et al. 2012), tourists’ behavioural intention and actual behaviour of visiting the destination (Quintal et al. 2010, Filho et al. 2012, Hsu and Huang 2012), environmental behaviour (Chao 2012), business start-up intentions and subsequent behavior (Kautonen et al. 2013), crisis planning intention (Wang and Ritchie 2012), intention to exercise (Spink et al. 2012), and so on.
Although current studies demonstrate that the TPB has great power in predicting and understanding consumers’ adoption behaviour across a variety of service contexts, it does not mean that TPB has no limitations. The main argument focuses on whether perceived behavioural control can be regarded as a good representative of actual behavioural control (Armitage and Conner 1999, Armitage and Conner 2001). In the literature, support for the PBC as an accurate proxy for actual control remains equivocal (Armitage and Conner 2001). In addition, the TPB is based on a specific behaviour, thus, each behaviour requires its own distinctive and specific belief set. Each behaviour in the TPB is explained by a salient belief set, so the application of TPB across a variety of situations may not be consistent (Hoie et al. 2012). Another limitation of TPB derives from the fact that this theory treats a set of beliefs
(those influencing attitude, SN, or PBC) as a one-dimensional construct (Hoie et al. 2012), which makes it difficult for understanding the specific beliefs that affect user behaviour in the different technology adoption contexts (Taylor and Todd 1995c, Riemenschneider et al. 2003, Lin 2008).
In an attempt to address the potential limitations of TPB, scholars argue that extending TPB by incorporating the additional key constructs which are deemed important to the specific usage context can increase the variance of explanation of usage behaviour (Hsu and Huang 2012). The major constructs, such as the achievement of personal goals (Perugini and Bagozzi 2001), self-identity processes (Shaw et al. 2000, Booth et al. 2014), descriptive norms (Hoie et al. 2012, Leyland et al. 2014), moral norms (Hoie et al. 2012, Newton et al. 2013), anticipated emotions (Ajzen and Sheikh 2013, Kim et al. 2013e), perceived risk and benefit (Lee 2009a), uncertainty (Quintal et al. 2010), past behaviours (Lam and Hsu 2006), user’ satisfaction (Baker and Crompton 2000, Liao et al. 2007), and technology readiness (Chen and Li 2010) were added to enhance the TPB’s predictive power. The extended TPB provided a more complete understanding of behaviour and behavioural intention.
In particular in the e-commerce setting, extant studies have applied TPB to online consumer behaviour (Bhattacherjee 2002, Choi and Geistfeld 2004, Hsu and Lu 2004, Ramus and Nielsen 2005, Wu 2006, Hansen 2008, Lee 2009a, Su and Huang 2010, Burns and Roberts 2013). Researchers usually draw upon TPB to build a new research model by integrating other theories or constructs into it. For example, Hsu et al. (2006) extended TPB by incorporating constructs drawn from the expectation disconfirmation theory (EDT) and examined the antecedents of users’ intention to continue using online shopping. The results indicated that disconfirmation from EDT and satisfaction with prior online shopping exerted
dominant influence on the continuance intention compared to the impacts of attitude, social norms, and PBC in the online shopping process. Limayem et al. (2000) introduced perceived innovativeness and perceived consequences, both as antecedents to attitude and intention into TPB. The results of their longitudinal study showed the positive effects of personal innovativeness and perceived consequences on attitude and intentions to online shopping. Behjati and Pandya (2012) extended TPB by including the effects of perceived reliability, trust and faithfulness on online purchasing intention. The findings showed that trust and faithfulness have significant relationships on online purchasing behaviour while perceived reliability has insignificantly relationship on online purchasing intention.
Generally speaking, the extended TPB has improved the understanding of online consumer behaviour. Despite the growing body of knowledge of TPB in online environments, several issues exist in the literature in relation to the application of TPB in explaining online purchasing behaviour amd deserve attention from researchers. One obstacle in using TPB has been found in applying it to the research of continued online shopping behaviour. Recently, some researchers pointed out that a weakness of TPB is its lack of explanatory power of continued online shopping behaviour (Hsu et al. 2006). This is because TPB constructs do not fully reflect the context of user continuance decisions. Karahanna et al. (1999) also indicated that the beliefs users hold for continuance intention may not be the same set of beliefs which lead to initial adoption.
Additionally, among the existing studies, there is a relative paucity of knowledge about the roles that cost-related constructs reflected in time and effort expended have on the consumers’ decision-making (Mukherjee et al. 2012, Kim et al. 2013b, Wu et al. 2014). Darley et al. (2010) and Kim et al. (2014) further assert that the research on online consumer behaviour
needs a more comprehensive model, describing not only the effect of personal motivation beliefs, but also the impacts of TCs incurred during the online transaction process. Indeed, there is growing recognition that cost reduction appears central to the business models pursued by firms (Williamson and Ghani 2012). However, little is still known about the specific costs associated with online shopping and how they determine consumers’ online shopping behaviour (Wu et al. 2014). As such, understanding how the costs involved in online transaction-related activities together with the key constructs in TPB affect consumers’ online decision making is an important research topic that needs further theoretical and empirical attention.
2.2.4 Comparison between TAM and TPB
A group of studies have compared TAM and TPB (Taylor and Todd 1995c, Chau and Hu 2001a, Riemenschneider et al. 2003, Lin 2008, Huh et al. 2009). For example, Mathieson (1991) compared TAM and TPB in terms of how well they predict an individual’s intention to use an information system and stated the following: “Both TAM and TPB predicted intention to use an IS quite well, with TAM having a slight empirical advantage; TAM is easier to apply, but only supplies very general information on users’ opinions about a system; TPB provides more specific information that can better guide development” (Mathieson, 1991, pp. 186). In line with Mathieson (1991), Hansen et al. (2004) tested both TRA and TPB in the context of online shopping, and found that TPB provided the better explanation to online consumer behaviour than TRA did. Similarly, Lin (2008) found that TAM explains 41 per cent of the variance in behavioural intention, while TPB explains 46 per cent.
The prior literature has revealed three main differences between TAM and TPB (Mathieson 1991, Hansen et al. 2004). Firstly, TAM assumes that beliefs about PU and PEOU are always
the primary determinants of use decisions, that is, these two beliefs are general regardless of the usage context (Lin 2008), while TPB asserts that beliefs are specific to each usage situation (Ajzen and Sheikh 2013). In some situations there may be other variables besides PU and PEOU that would well predict intention. In the e-commerce context, consumer behaviour is not only influenced by PU and PEOU, but also shaped by other important constructs such as trust, privacy concerns and hedonic values (Wu et al. 2012, Meskaran et al. 2013). On this point, TPB can provide a more accurate explanation to online consumer behaviour than TAM does.
The second difference is that TAM does not include social norm. In TPB, the social norm is an important construct to capture unique variance in intention (Ajzen 2011, Ajzen and Sheikh 2013). It plays an inevitable role in determining online consumer behaviour since consumers usually follow the recommendations from their relatives, friends and people who share the same social value when they make purchase decision on certain brands (Manning 2009, Behjati and Pandya 2012).
Thirdly, TPB introduces PBC to take into account the effect of behavioural control. PBC is assumed to reflect past experience as well as anticipated impediments and obstacles (Ajzen 2011). Pavlou and Fygenson (2006) pointed out that “most e-commerce studies follow TAM, implicitly assuming that behaviour is volitional. However, online shopping is indeed low in volitional control and online consumers face several new constraints, such as the impersonal nature of the online environment, the extensive use of IT, and the uncertainty of the open Internet infrastructure (Al-Swidi et al. 2012). These issues call for the inclusion of PBC in e- commerce adoption models, implying the superiority of TPB over the TAM.