2019
DOI: 10.1007/s10796-018-9892-1
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Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach

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Cited by 25 publications
(27 citation statements)
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“…It is proven that the UTAUT2 model is able to predict the adoption and the use of technology in the case of digital banking of Islamic banks (Venkatesh et al, 2012;Venkatesh et al, 2016;Venkatesh et al, 2003). An increase of Trust variable shows a positive result (Akhtar et al, 2019;Alharbi, 2017;Slade et al, 2015) and Satisfaction has a strong influence on Behavioral Intention (Albashrawi et al, 2019;Chao, 2019;Wijayanti & Riza, 2017).…”
Section: Resultsmentioning
confidence: 99%
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“…It is proven that the UTAUT2 model is able to predict the adoption and the use of technology in the case of digital banking of Islamic banks (Venkatesh et al, 2012;Venkatesh et al, 2016;Venkatesh et al, 2003). An increase of Trust variable shows a positive result (Akhtar et al, 2019;Alharbi, 2017;Slade et al, 2015) and Satisfaction has a strong influence on Behavioral Intention (Albashrawi et al, 2019;Chao, 2019;Wijayanti & Riza, 2017).…”
Section: Resultsmentioning
confidence: 99%
“…Unified Theory of Acceptance and Use of Technology (UTAUT) popularized by Venkatesh et al, (2003) In this first study, the researchers selected the constructs that best fit the context of digital banking service of Islamic banks and proposed two new constructs, namely customer trust (Akhtar et al, 2019;Alharbi, 2017;Giri & Wellang, 2016) and Customer Satisfaction (Albashrawi et al, 2019;Chao, 2019;Rahi et al, 2019) for digital banking service of Islamic banks.…”
Section: Theoretical Foundation and Formulation Of Hypothesismentioning
confidence: 99%
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“…This needs to be done so that high investment in information technology facilities becomes useful and provides value for consumers and companies (Bharata & Widyaningrum, 2020). Several previous research results found facts that lead to constituents that influence the behavioral intention of mobile banking, which is determined by the performance expectancy factor (Farah et al, 2018); effort expectancy (Zhou et al, 2010); social influence (Albashrawi et al, 2019); facilitating conditions (Chawla & Joshi, 2019); hedonic motivation (Alalwan et al, 2017); and habits (Gupta & Arora, 2019).…”
Section: Introductionmentioning
confidence: 99%
“…In addition, actual usage does not rely on participant's self-reported behaviour. This is advantageous because usage data retrieved from system logs is not necessarily correlated with subjective self-reported usage (Albashrawi et al 2019). In addition, measuring the actual behaviour of consumers can create business insights by offering valuable usage-based segmentation (Trivedi et al 2018).…”
Section: Introductionmentioning
confidence: 99%