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is related to automaticity behaviour, which is formed by an accumulation of knowledge and skills over time (Limayem et al. 2007; Venkatesh et al., 2012).
In their efforts, Venkatesh et al.’s (2012) methodology included a longitudinal study among the users of mobile Internet technology by doing a two-stage online survey; it was conducted to obtain data from 1,512 customers. Empirical conclusions found that about 56 percent of variance in behavioural intention as well as 40 percent of variance in usage behaviour could be predicted by the original model of the UTAUT (Venkatesh et al., 2012). More particularly, three main factors of the UTAUT (performance expectancy, effort expectancy, and socials influences) were able to maintain their considerable effect on behavioural intention. Usage behaviour is also significantly influenced by facilitating conditions and behavioural intention. In view of that, Venkatesh et al. (2012) approved that the UTAUT, across the clients’ context, still enjoys good predictive power as well as a good level of generalisability.
Statistical findings from Venkatesh et al. (2012) also supported very strongly the predictive power of UTAUT2. It is somewhat higher than the 2003 findings of Venkatesh et al. with 74 percent of variance in behavioural intention and 52 percent of variance in the actual usage being accounted for by UTAUT2. Mainly, hedonic motivation, price value, and habit are all statistically proven to be significant predictors of a customers’ intention to use technology. In addition, the statistical results confirmed a positive significant relationship between facilitating conditions and behavioural intention (Venkatesh et al., 2012). With respect to the impact of moderating factors, empirical results showed that the relationship between hedonic motivation and behavioural intention is moderated by age, gender, and experience. This relationship reached a high level among younger men who have less experience. As for price value, Venkatesh et al. (2003) revealed that older women pay more attention to price value in formulating their decision to use technology. Consistent with their hypotheses, Venkatesh et al. (2012) concluded that older men who have more experience indicate that their intention to use technology is statistically predicted by habit.
It is worth asserting that, along with the advantages that pertain to the original model of UTAUT as discussed earlier, UTAUT2 represents a significant contribution in the technology acceptance field (Venkatesh et al., 2012). Indeed, by modelling the hedonic motivation along with performance expectancy, both types of motivation – intrinsic and extrinsic – are represented within the UTAUT2 which is consistent with Davis et al. (1992). Furthermore, formulating price value has added a new dimension for the cost concept; UTAUT2 has identified monetary cost as a crucial predictor in the adoption of technology by customers (Venkatesh et al., 2012).Another contribution of UTAUT2 is that by including habit, new mechanisms such as stored intention and automaticity, which may predict individual behaviour, have been introduced (Venkatesh et al., 2012). In terms of predictive power, while UTAUT has accounted for the highest degree of variance among prior models, UTAUT2 was able to reach the highest degree of variance in behavioural intention (74 percent) which has never been achieved by previous models within the technology acceptance stream (Venkatesh et al., 2012).
3.3 Selecting an Appropriate Theory for Proposing the Conceptual Model
Fundamentally, UTAUT2 is specifically proposed to clarify the technology acceptance from the customer perspective which is consistent with the current study’s aim and objectives to provide further understanding about behavioural intention and usage of three kinds of SSTs (Internet banking, Mobile banking, Telebanking) by focusing on Jordanian customers (Venkatesh et al., 2012). Further, according to the current study’s aim and objectives (Chapter 1, Section 1.4), there is a need to choose an appropriate model covering almost all constructs that will determine consumer behaviour in this respect. From an in-depth analysis of the SST literature, it can be seen that the main constructs in UTAUT2 such as performance expectancy, effort expectancy (AbuShanab et al., 2010; Wang and Shih, 2009) or captured constructs such as usefulness, relative advantages, perceived enjoyment, (Berger, 2009, Eriksson et al., 2005; Zhou et al., 2010) are identified as key drivers of customer intention and the usage of SST (Eriksson et al., 2005). Similarly, there are many factors recognised as key determinants of the customers’ reaction towards SST that are difficult to summarise and be included in a single model as can be done by the UTAUT2.
UTAUT2 is based on UTAUT which is considered to be the most predictive, inclusive, and parsimonious theory over the information system domain (Bagozzi, 2007). By the same token, as discussed earlier, predictability and validity of UTAUT have been supported to explain the customers’ intention and behaviour towards different types of SST (e.g. Mobile banking and Internet banking) in both developed countries (e.g. USA) and developing countries (e.g. Oman) (AbuShanab et al., 2010; Chiu et al., 2010; Martins et al., 2014; Riffai et al., 2012; Zhou et al., 2010).
Of note is that UTAUT2 was validated to explain the usage of mobile Internet services in Hong Kong, a highly developed country (Venkatesh et al., 2012). Therefore, expanding the validity and applicability of UTAUT2 to other countries (Jordan, for example, as a developing country) in order to encourage adoption and use of new technologies (SSTs: Internet banking, Mobile banking, Telebanking) is considered a fruitful direction to be examined as highly recommended by Venkatesh et al. (2012).
In view of the justifications discussed above, UTAUT2 has been selected as an appropriate theoretical foundation for proposing the conceptual model utilised in this study to explain the Jordanian customers’ intention and usage of three kinds of SSTs: Internet banking, Mobile banking, and Telebanking.
Nevertheless, a more thorough analysis of the prior studies of SST leads to the observation that other constructs, recognised as critical factors in the context of SSTs, are excluded by UTAUT2. More importantly, Venkatesh et al. (2012, p.173) has highly recommended that:
“Future research can identify other relevant factors that may help increase the applicability of UTAUT2 to a wide range of consumer technology use contexts.”
Accordingly, this study has conducted further analyses and comparisons of the relevant factors to identify the constructs that will form extensions to UTAUT2. The next section (3.4) presents the constructs/relationship analyses and mapping.
3.4 Constructs/ Relationship Analyses
In reviewing related studies addressing customer behaviour toward SSTs, it has been noticed that a large number of factors examined in this area are likely to vary by author and context (see Table 3.2).Among these factors are perceived ease of use and perceived usefulness, which are both recognised as the most cited and considerable factors influencing behavioural intention and acceptance in this stream (e.g. Curran and Meuter, 2005; Eriksson and Nilsson, 2007; Jaruwachirathanakul and Fink, 2005; Lin, 2011). In addition, attitudes have received a great deal of attention from researchers in the context of SST (e.g. Berger, 2009; Shih and Fang, 2004). Also, both self-efficacy and technology anxiety have received attention by a number of SST studies (e.g. AbuShanab et al., 2010; Kim and Forsythe, 2009; Lee et al., 2010; Zhao et al., 2008). Perceived behavioural control and accessibility have also been recognised as major constructs influencing individual reactions to SST (e.g. Chang and Yang, 2008; Gan et al., 2006; Lee and Allaway, 2002). Similarly, innovation characteristics such as complexity, result demonstrability, observability, trialability, design, relative advantage, and compatibility are all identified as important predictors of behavioural intention and adoption of SSTs (e.g. Gerrard and Cunningham, 2003; Howcroft et al. 2002; Lin and Hsieh, 2011; Meuter et al., 2005).
Several studies have paid particular attention to issues concerned with trust, perceived risk, privacy and security, assurances, integrity, competence, credibility, confidently, and reliability (Curran and Meuter, 2005; Flavián et al., 2006; Gelderman et al., 2011; Kim et al., 2009; Laukkanen and Cruz, 2009; Lin, 2011). These constructs have been widely regarded as crucial determinants of the adoption of SST, particularly in the banking sector (Flavián et al., 2006). Laukkanen et al. (2008) and Laukkanen and Cruz (2009) discussed the main barriers mitigating the customers’ intention and use of SST such as usage and tradition barriers.The main role of social factors such as subjective norms, social influence, reference groups, and image have been noted by many authors; for example, Jaruwachirathanakul and Fink (2005) and Laukkanen and Cruz (2009).
A number of studies have mentioned that service quality and value are correlated with the individuals’ propensity and evaluation for the SST (Al-Hawari and Ward, 2006; Al-Hawari et al., 2009; Dabholkar, 1996; Ho and Ko, 2008; Hwang and Kim, 2007; Lee et al., 2011; Lin and Hsieh, 2011; Lu et al., 2009). The benefits of using SST such as convenience, customisation, economic benefits, lower fees, anticipated outcome, time saving, mobility, avoid crowdedness have been strongly supported as positive drivers of customer acceptance of SST (Bateson, 1985; Ding et al., 2007; Globerson and Maggard, 1991; Meuter et al., 2003). Numerous studies have focused on intrinsic utilities attained by using SST such as perceived enjoyment, playfulness, and fun as decisive drivers of the intention and usage of SST (Dabholkar and Bagozzi, 2002; Dabholkar et al., 2003; Esman et al., 2010; Gan et al., 2006; Lee et al., 2011; Lin and Hsieh, 2011; Riffai et al., 2012). Remarkably, customer and technology readiness have been included by some studies to explain the adoption of SST (Chiu et al., 2010; Ho and Ko, 2008; Liljander et al., 2006; Lin et al., 2007; Meuter et al., 2005; Parasuraman, 2000).
Several studies have discussed the need for interaction and the role of human contact on customer inclination and acceptance of SST (e.g. Dabholkar et al., 2003; Gelderman et al., 2011; Lee et al., 2010; Lu et al., 2009; Reinders et al., 2008). In addition, customer satisfaction with SST or its impact on the future intention and use have been mentioned by a number of studies (e.g. Eriksson and Nilsson, 2007; Lin and Hsieh, 2007). In their study, Simon and Usunier (2007) formulated the cognitive factors, particularly rational engagement and experiential style as a key predictor of customer willingness to adopt SST. Furthermore, the contextual factors are examined by Mallat et al. (2008) as a significant influencer on customers’ intention to adopt SST. Finally, experience, skills, and PC proficiency which individuals have about SST have been considered as critical factors influencing the intention and acceptance of SST (Jayawardhena et al., 2007; Mallat et al., 2008; Reinders et al., 2008).

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