Constructs selection and mapping

As mentioned before, this study identifies the factors that influence bank customers' usage of mobile banking apps, from the perspective of the customers themselves. This identification of the most important constructs leads to the development of an adapted form of the UTAUT model in terms of predicting the usage of mobile banking in Jordan. Therefore, an analysis has been conducted to identify the main constructs and their relations among different contexts, as abovementioned in table 3.2. It is obvious the constructs of the UTAUT model such as performance expectancy, effort expectancy, social influence and facilitating conditions, have a key role in identifying the customers’ actual behaviour towards technology usage; besides they captured several of the previous constructs in this field of study (Venkatesh et al., 2012; Zhou et al., 2010; AbuShanab et al., 2010; Venkatesh et al., 2003).

Therefore, the UTAUT’s constructs (performance expectancy, effort expectancy, social influence) have been selected as main constructs in the proposed research model. Despite the importance of facilitating conditions and behavioural intention, both constructs were

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excluded. Facilitating conditions were defined and explained previously in subsection 3.2.9 as “the degree to which an individual believes that an organizational and technical infrastructure exists to support use of the system” (Venkatesh et al., 2003, p. 453): it is clear that this construct reflects the concept of system infrastructure. Thus, the construct has been broken into two similar concepts in the context of mobile banking services (App quality and Mobile performance), which represent the infrastructure of mobile banking from the perspectives of both software and hardware, further details about both constructs will be provided later in this chapter.

Regarding the construct of behavioural intention, it has been excluded due to the practical nature of this research. While this research targets "actual users" of mobile banking apps in Jordan, there is no necessity to study their behavioural intention because the actual behaviour (usage) already has been happening, which mitigates and may conceal totally the effect of intention. This agrees with the practical research objectives of developing an adapted form of UTAUT to understand the behaviour of mobile banking users. Another reason is that, from reviewing the literature on technology acceptance, it is obvious that most researchers reported their prediction power is disproportionately based on users' behavioural intention, while there is a huge gap between intention and actual behaviour.

The model of PC Utilisation (MPCU) by Thompson et al. (1991) is considered an example or evidence of excluding behavioural intention. Thompson et al. (1991) theorised a direct relation between individual effect and actual behaviour (usage) of PC, he presumed that the individual’s effect, feeling, or emotion has a direct effect on the usage of the PC without the mediating role of behavioural intention, thus it has been excluded from the model. Moreover, Chang and Cheung (2001) mentioned that in spite of the critical role of behavioural intention as a key determinant to predict actual behaviour, MPCU only considers the actual behaviour while ignoring behavioural intention.

The above-mentioned perspective, stated by Thompson et al. (1991), agrees with this research’s objectives. Behavioural intentions to use such technology is only important in the early stages of system implementation. In contrast, in the evaluation and validation stages of such technology, the actual behaviour (usage) provides robust insights for the

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service provider by revealing information about the systems’ strengths and weaknesses. This kind of information is useful to analyse the current usage rate of mobile banking apps and therefore assists in planning future strategies. To sum up, first this study is targeting actual users of mobile banking apps, second the mobile banking services in Jordan have already been implemented and are in the stage of evaluation, which justifies the focus on actual behaviour (usage) rather than behavioural intention, with the purpose of presenting the current users’ perceptions and usage decisions about mobile banking apps.

As claimed by Venkatesh et al. (2012), UTAUT needs an ongoing update by including and testing other related factors to broaden the applicability of the model across different contexts. Therefore, this research proceeded to further survey and analysis of other factors, as seen in table 3.2, to capture the appropriate constructs that will be added to UTAUT’s selected constructs. In addition to conducting construct/relation analysis, this study utilised exploratory interviews with online banking experts to provide more explanation about other factors. Furthermore, the data that has been gathered from the pilot-study have been used in the same manner. Consequently, there are four constructs have been selected/extracted from the literature (app quality, mobile performance risk, app security risk, app transactional risk). These constructs were captured mainly from both perceived risk and system quality constructs, which in turn, are found frequently cited as important factors to affect both behavioural intention and actual usage.

It was stated by the participants of pilot study, that they had serious concerns about the use of mobile banking apps due to the financial nature and the sensitivity of its data. This contributed to further study of the perception of potential hazards that may face mobile banking users. The concept of perceived risk encompasses numerous kinds of risk, which depend on the nature of the process or technology. Perceived risk influences the users’ actual behaviour to use or not use an online service. This perception could change from client to customer (Hong & Yi, 2012). Perceived risk, as mentioned before, is one of the most important factors to influence the usage of new technology. It is worthwhile to mention that perceived risk can be observed in several areas, such as security, financial, transactional, social, privacy, psychological, and performance risks (Yoon & Occeña, 2014; Benjamin & Samson, 2011; Aransiola & Asindemade, 2011; Masocha et al., 2010).

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For example, Charfeddine (2012) recommended considering some risk factors such as system reliability, system security and system responsiveness, to reduce the customers’ perceived risk that negatively affects the use of e-banking services. Because there are several dimensions of perceived risk, this study focuses mainly on three dimensions (mobile performance risk, app security risk, app transactional risk), which are linked to the nature of mobile banking apps. Also these kinds of risk were mentioned by both of mobile banking customers and experts during the pilot study and exploratory interview respectively.

Regarding to the quality construct in the context of electronic banking services, there were found to be several dimensions of quality in this stream, such as portal, product and service quality. \in this context, Treiblmaier (2006) measured the quality of a website by testing the dimensions of design, content and structure; in addition, he measured the effect of the website’s quality upon satisfaction and trust in the online context. He affirmed that satisfaction and trust was determined by website quality. This study focuses on mobile banking app quality, in terms of structure, design and content; this construct, in addition to the previous constructs, have been matched with the related constructs derived from the literature review. This construct mapping is summarised below in table 4.2

Table 4.2 Construct mapping

Research selected constructs Literature related constructs References

Performance expectancy Perceived usefulness Hanafizadeh et al. (2014); Kallweit

et al. (2014); Zhou (2012);

Kesharwani and Bisht (2012); Zhou (2011); Lee et al. (2011); Koenig- Lewis et al. (2010); Lin (2010); Wessels and Drennan (2010); Riquelme and Rios (2010); Gu et al. (2009); Al-Somali et al. (2009); Marler et al. (2009); Berger (2009); Lee (2009); Kim and Forsythe (2009); Jahangir and Begum (2008); Mallat et al. (2008); Celik (2008); Eriksson et al. (2007); Lin et al. (2007); Berger (2007); Eriksson and Nilsson (2007); Cheng et al. (2006);

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Curran and Meuter (2005); Eriksson et al. (2005); Jaruwachirathanakul and Fink (2005); Luarn and Lin (2005)

Relative advantage Kapoor et al. (2014); Khraim et al.

(2011); Lin (2011); Püschel et al. (2010); Kim et al. (2009); Meuter et al. (2005); Kolodinsky et al. (2004); Shih and Fang (2004) Brown et al. (2003); Fitzgerald and Kiel (2001) Lee et al. (2003);

Convenience Demirci Orel and Kara (2014); Ding

et al. (2011); Lin and Hsieh (2011); Pujari (2004); Gerrard and

Cunningham (2003); Dabholkar et al. (2003); Meuter et al. (2003); Yen and Gwinner (2003)

Effort expectancy Perceived ease of use Kallweit et al. (2014); Hanafizadeh

et al. (2014); Akturan and Tezcan (2012); Lin (2011); Lee et al. (2011); Lin (2010); Kim and Forsythe (2009); Al-Somali et al. (2009); Lu et al. (2009); Berger (2009); Gu et al. (2009); Cheng Amin et al. (2008); Zhao et al. (2008); Berger (2007); Lin et al. (2007); et al. (2006); Curran and Meuter (2005); Meuter et al. (2003); Dabholkar et al. (2003); Dabholkar and Bagozzi (2002)

Complexity Kapoor et al. (2014); Al-Jabri and

Sohail (2012); Yoon (2010); Kim and Forsythe (2010); Meuter et al. (2005); Jaruwachirathanakul and Fink (2005); Shih and Fang (2004); Gerrard and Cunningham (2003); Lee et al. (2003)

Social influence Subjective norms Al-Majali (2011); Riquelme and

Rios (2010); Lee (2009); Marler et al. (2009); Jaruwachirathanakul and Fink (2005); Shih and Fang (2004)

Image Laukkanen and Cruz (2009),

Apps quality Technology readiness Gelderman et al. (2011); Lee et al.

(2010); Ho and Ko (2008); Berger (2007); Lin and Hsieh (2007); Meuter et al. (2005)

Service quality Gan et al. (2006); Floh and

Treiblmaier (2006)

Website quality Qutaishatal (2012); Shareef et al.,

(2011); Connolly and Bannister (2007); Lin (2007); Gan et al. (2006); Floh and Treiblmaier (2006); Bauer et al. (2005); Kim et al. (2005)

Perceived speed Shamdasani et al. (2008);

Dabholkar et al. (2003)

Mobile performance risk Usage barriers Laukkanen and Cruz (2009);

Laukkanen et al. (2008)

Performance Kolodinsky et al. (2004); Polatoglu

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App security risk Security and privacy Al-Tarawneh et al. (2017); Li

(2012); Demirdogen et al. (2010); Gelderman et al. (2011); Masocha et al. (2011); Lin and Hsieh (2011); Chen et al. (2008);

Jaruwachirathanakul and Fink (2005)

App transactional risk Credibility Hanafizadeh et al. (2014); Yu

(2012); Koenig-Lewis et al. (2010); Grabner-Kraeuter and Faullant (2008); Reichheld and Schefter (2000);

Reliability Sayar and Wolfe (2007); Casalo et

al. (2007); Lichtenstein and Williamson (2006); Chang and Yang (2008); Gan et al. (2006)

Performance risk Security risk Transactional risk

Perceived risk Alalwan et al. (2016); Purwanegara

et al. (2014); Farzianpour et al. (2014); Martins et al. (2014); Jeong and Yoon (2013); Akhlaq and Ahmad (2013); Jeong et al. (2013); Chiou and Shen (2012); Kesharwani and Bisht (2012); Akturan and Tezcan (2012); Akhlaq and Shah (2011); Al-Majali (2011); Taylor and Strutton, (2010); Cruz et al. (2010); Koenig-Lewis et al. (2010); Riquelme and Rios (2010);

Laukkanen and Cruz (2009); Ruiz- Mafe et al. (2009); Mallat et al. (2008); Curran and Meuter (2007)