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Strategic HR Review, vol. 16 no. 3
Type: Research Article
ISSN: 1475-4398

Open Access
Article
Publication date: 2 May 2023

Puneett Bhatnagr and Anupama Rajesh

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

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Abstract

Purpose

The authors aim to study a conceptual model based on behavioural theories (UTAUT-3 model) to evaluate the adoption, usage and recommendation for neobanking services in India.

Design/methodology/approach

The authors propose this model based on the UTAUT-3 integrated with perceived risk constructs. Hypotheses were developed to determine the relationships and empirically validated using the PLSs-SEM method. Using the survey method, 680 Delhi NCR respondents participated in the survey.

Findings

Empirical results suggested that behavioural intention (BI) to usage, adoption and recommendation affects neobanking adoption positively. The research observed that performance expectancy (PE), effort expectancy (EE), perceived privacy risk (PYR) and perceived performance risk (PPR) are the essential constructs influencing the adoption of neobanking services.

Research limitations/implications

Limited by geographic and Covid-19 constraints, a cross-sectional study was conducted. It highlights the BI of neobanking users tested using the UTAUT-3 model during the Covid-19 period.

Originality/value

The study's outcome offers valuable insights into Indian Neobanking services that researchers have not studied earlier. These insights will help bank managers, risk professionals, IT Developers, regulators, financial intermediaries and Fintech companies planning to invest or develop similar neobanking services. Additionally, this research provides significant insight into how perceived risk determinants may impact adoption independently for the neobanking service.

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South Asian Journal of Marketing, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2719-2377

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Book part
Publication date: 4 December 2020

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Application of Big Data and Business Analytics
Type: Book
ISBN: 978-1-80043-884-2

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