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1.
Heliyon ; 8(11): e11654, 2022 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-36411928

RESUMO

Purpose: The purpose of the present study is to identify the determinants of continuance intentions to use FinTech peer-to-peer (P2P) payment apps in India. Design: A cross-sectional survey of 463 FinTech users was carried out during the pandemic with the help of a digital questionnaire. The study has empirically tested an extended expectancy confirmation model and theory of acceptance to examine the determinants of continuance intentions to use FinTech P2P payment apps. Findings: We demonstrate that confirmation of prior expectations and post-adoption perceived usefulness determine continuance intention to use FinTech payment services. Cognitive acceptance (trust) and normative acceptance (social influence) not only determine behavioural acceptance (willingness to co-create) but also influences continuance intentions. Confirmation of initial expectations during service use determines satisfaction and post-purchase perceived usefulness. Practical implications: The continuous use of FinTech P2P services can be ensured by fulfilling users' post-purchase expected benefits, fostering trust, and social influence. These can be achieved by gearing up internal resources to provide customized experiences that meet customers' expectations and facilitate fruitful interactions. Only if the mobile experience is accessible and meets the expectations of customers, they would appreciate its performance and value in the offerings leading to extensive use of FinTech P2P services. Originality/value: The present paper is based on extended ECM and the theory of acceptance that aim to explain continuance intention to use FinTech P2P payments. The study findings add to the limited body of research in the context of FinTech P2P payments.

2.
Heliyon ; 8(3): e09074, 2022 Mar.
Artigo em Inglês | MEDLINE | ID: mdl-35295660

RESUMO

The emerging-market banking sector plays a significant role in modern-day banking sector stability. In this study, we have used the dynamic conditional correlation (DCC) version of the Generalised autoregressive conditional heteroscedasticity (GARCH) model to estimate the correlation among Emerging Markets (BANKSEK), Latin America (BANKSLA), Brazil, Russia, India, and China (BRIC) (BANKSBC), Portugal, Ireland, Italy, Greece, and Spain (PIIGS) (BANKSPI) and Far East (BANKSFE). The study covers more than 100, 200 and 300 trading days of the GFC (starting July 8, 2008) and the COVID-19 pandemic (starting January 1, 2020). We have found that generally, in the short-term excluding PIIGS, all banks show similar pairwise correlation, and the pattern holds in the medium and long term. The far east banking sector displays a reduced correlation than their counterparts, even following the same pattern.

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