The Impact of Subjective and Objective Experience on Mobile Banking Usage: An Analytical Approach

Albashrawi, Mousa; Kartal, Hasan Basri; Oztekin, Asil; Motiwalla, Luvai

doi:10.24251/hicss.2017.137

Cited by 10 publications

(7 citation statements)

References 27 publications

(63 reference statements)

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“…In their research, Bankole et al (2011), exploring the antecedents of mobile banking in Nigeria, have proven that the effort expectancy has a positive impact on the behavioral intention to use mobile banking services. Bhatiasevi (2016) came to the same conclusion in his research conducted to identify the factors leading to the adoption of mobile banking in Thailand, as did Albashrawi et al (2017) by observing a sample of U.S. bank clients. Starting from the above, one can assume the following:…”

Section: Literature Review and Hypotheses Developmentmentioning

confidence: 68%

Antecedents of mobile banking: UTAUT model

Lazarević¹,

Pešterac²

2019

EurJ Appl Econ

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The development of modern information and communication technologies enabled banks to rely on mobile banking as an important distribution channel in their businesses. Given that investments in the development of mobile banking systems are extremely high, knowledge of which factors affect the intentions of individuals to use mobile banking services can be of great importance. For this purpose, empirical research was conducted and 313 respondents were surveyed in the territory of Sumadija, Central Serbia. The collected primary data were analyzed using the statistical software SPSS v. 20. To examine the factors in the work, the UTAUT model (Unified Theory of Acceptance and Use of Technology) was used. The results of empirical research indicate that all components of the UTAUT model have statistically significant influence on intention to use mobile banking, with performance expectancy singled out as the most important antecedent, while effort expectancy has the weakest impact. The paper confirms the success of the UTAUT model for testing mobile banking antecedents, and gains new insights regarding the intention of using mobile banking in Serbia that can serve for managerial purposes.

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Section: Literature Review and Hypotheses Developmentmentioning

confidence: 68%

Antecedents of mobile banking: UTAUT model

Lazarević¹,

Pešterac²

2019

EurJ Appl Econ

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“…Specifically, USM derives starting values for the model's latent variables through principal component analysis, and then applies the Bayesian neural network approach to discover the optimal system of linear, nonlinear, and interactive pathways among the latent variables, with the aim of maximizing the variance explained (e.g., Henseler, Hubona, & Ray, 2016; McIntosh, Edwards, & Antonakis, 2014; Rigdon, Ringle, & Sarstedt, 2010). As a result, the USM approach typically yields higher explanatory power than PLS‐PM‐based linear modeling (Albashrawi, Kartal, Oztekin, & Motiwalla, 2017). Because USM has recently received increasing attention in business research (e.g., Al‐Ebbini, Oztekin, & Chen, 2016; Garbe & Richter, 2009; Oztekin, Kong, & Delen, 2011; Turkyilmaz, Oztekin, Zaim, & Demirel, 2013; Turkyilmaz, Temizer, & Oztekin, 2018), investigating the method's predictive accuracy would be particularly promising.…”

Section: Discussionmentioning

confidence: 99%

Prediction‐Oriented Model Selection in Partial Least Squares Path Modeling

et al. 2018

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Partial least squares path modeling (PLS-PM) has become popular in various disciplines to model structural relationships among latent variables measured by manifest variables. To fully benefit from the predictive capabilities of PLS-PM, researchers must understand the efficacy of predictive metrics used. In this research, we compare the performance of standard PLS-PM criteria and model selection criteria derived from Information Theory, in terms of selecting the best predictive model among a cohort of competing models. We use Monte Carlo simulation to study this question under various sample sizes, effect sizes, item loadings, and model setups. Specifically, we explore whether, and when, the in-sample measures such as the model selection criteria can substitute for out-of-sample criteria that require a holdout sample. Such a substitution is advantageous when creating a holdout causes considerable loss of statistical and predictive power due to an overall small sample. We find that when the researcher does not have the luxury of a holdout sample, and the goal is selecting correctly specified models with low prediction error, the in-sample model selection criteria, in particular the Bayesian Information Criterion (BIC) and Geweke-Meese Criterion (GM), are useful substitutes for out-of-sample criteria. When a holdout sample is available, the best performing out-of-sample criteria include the root mean squared error (RMSE) and mean absolute deviation (MAD). Finally, we recommend against using standard the PLS-PM criteria (R 2 , Adjusted R 2 , and Q 2), and specifically the out-of-sample mean absolute percentage error (MAPE) for prediction-oriented model selection purposes. Finally, we illustrate the model selection criteria's practical utility using a well-known corporate reputation model.

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“…I think that using mobile banking secure in conducting my banking transactions. 4. I think that using mobile banking can offer me a wider range of banking products, services and investment opportunities.…”

Section: Think That Learning To Use Mobile Banking Would Be Easymentioning

confidence: 99%

Examining Impact of Perceived Utility, Ease of Use, Credibility, Self efficacy and Financial Cost on Behavioral Intention to Use Mobile Banking

2024

IRJMETS

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For Indian banks, mobile banking is one of the brilliant opportunity to attract new customers while maintaining operational cost. Whereas, for consumers, it is a terrific platform to efficiently manage their financial transactions on the go. On the other side, Indian banks are in the stage of transforming digital to truly digital to enhance their competitive edge. The paper aimed to examine the behavioural intension of mobile banking users in Gwalior City, (Madhya Pradesh, India). Multiple regression was used to test the casual relationship between dependent and independent variables. Perceived utility, perceived ease of use, perceived credibility, and selfefficacy were found to be the factors influencing mobile users' behavioral intention to embrace mobile banking services in Gwalior City. As anticipated, the only component in this study that was found to have a negative, negligible link with the adoption of mobile banking was the financial cost.

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The Impact of Subjective and Objective Experience on Mobile Banking Usage: An Analytical Approach

Abstract: This paper aims to investigate mobile banking (MB)

Cited by 10 publications

References 27 publications

Antecedents of mobile banking: UTAUT model

Antecedents of mobile banking: UTAUT model

Prediction‐Oriented Model Selection in Partial Least Squares Path Modeling

Examining Impact of Perceived Utility, Ease of Use, Credibility, Self efficacy and Financial Cost on Behavioral Intention to Use Mobile Banking

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