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The study follows platform theory (PT) and information processing theory (IPT) to understand the determinants of customer engagement (CE) on an over-the-top (OTT) platform. Platform-based factors include superior streaming infrastructure (SSI), multilayer analytics (MA), secure monetisation (SM) and convenient navigability (CN), while message-based factors include content diversification (CD), interactive elements (IE) and content localisation (CL). The study further investigates the impact of CE on brand advocacy (BA).

The study employed the quantitative method using the cross-sectional survey to collect the data using judgemental sampling. Data were collected from 650 users of OTT services. Partial least square-structural equation modelling (PLS-SEM) was used to test the hypothesised relationship.

The impact of platform and message-based factors on CE is significant, except for the IE. As per the results, SSI is the most significant platform-based factor of CE, followed by MA, CN and SM. The study also found that CL followed by CD has a substantial influence as a message-based factor for OTT providers. The significant impact of CE is also established on BA, as per the findings of the study.

The outcome of the study is relevant to managers and practitioners in the highly competitive OTT industry. The new research framework emphasises the increasing importance of platform- and message-based factors for CE and BA. The study will also assist OTT providers in guiding strategic and operational decisions in the context of the OTT industry to increase customer loyalty in emerging economies.

The study introduces a novel approach to assessing OTT subscriber engagement by integrating PT and IPT. The final outcome of the research model is BA, which is highly relevant for an OTT operator as it helps retain existing subscribers and attract new ones through BA.

This study aims to analyse and understand customer sentiments and perceptions from neobanking mobile applications by using advanced machine learning and text mining techniques.

This study explores a substantial large data set of 330,399 user reviews available in the form of unstructured textual data from neobanking mobile applications. This study is aimed to extract meaningful patterns, topics, sentiments and themes from the data.

The results show that the success of neobanking mobile applications depends on user experience, security features, personalised services and technological innovation.

This study is limited to textual resources available in the public domain, and hence may not present the entire range of user experiences. Further studies should incorporate a wider range of data sources and investigate the impact of regional disparities on user preferences.

This study provides actionable ideas for neobanking service providers, enabling them to improve service quality and mobile application user experience by integrating customer input and the latest trends. These results can offer important inputs to the process of user interaction design, implementation of new features and customer support services.

This study uses text mining approaches to analyse neobanking mobile applications, which further contribute to the growing literature on digital banking and FinTech. This study offers a unique view of consumer behaviour and preferences in the realm of digital banking, which will add to the literature on the quality of service concerning mobile applications.

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.

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.

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.

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.

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.

The purpose of this study is to integrate customer value theory (CVT) and protection motivation theory (PMT) to understand the factors that have an impact on customer experience, e-trust and intention to recommend, which influence the adoption behaviour of digital currency users.

A purposive sampling technique was used, and data were gathered through an online survey of 414 respondents. The measurement and structural models were tested using partial least squares structural equation modelling to establish linkages between the constructs.

Functional, emotional and social values positively impact customer experience. Furthermore, perceived severity, perceived vulnerability, response efficacy and self-efficacy had a positive impact on e-trust. E-trust positively affects customer experience and intention to use the digital currency directly. The study demonstrated that perceived value and protection motivation factors play a significant role in influencing the use of digital currency.

For managers and policymakers interested in the Indian digital currency market, it is suggested that functional utility and emotional and social benefits can enhance user satisfaction. In an e-trust model, user education to increase risk and protection awareness, effectiveness of responses and self-efficacy are critical to building e-trust.

Building on CVT and PMT’s usage in the broader financial services domain, this research empirically confirms the significance of perceived value and protection motivation factors while adopting digital currency. It provides an extensive and multifaceted approach to comprehending customer involvement and trust in digital financial services, thus enhancing the theoretical and empirical knowledge of both the fintech and blockchain industries.

This study builds on a conceptual model by integrating AI features – Perceived intelligence (PIN) and anthropomorphism (PAN) – while extending expectation confirmation theory (ECT) factors – interaction quality (IQU), confirmation (CON), and customer experience (CSE) – to evaluate the continued intention to use (CIU) of AI-enabled digital banking services.

Data were collected through an online questionnaire administered to 390 digital banking customers in India. The data were further analysed, and the presented hypotheses were evaluated using partial least squares structural equation modelling (PLS-SEM).

The research indicates that perceived intelligence and anthropomorphism predict interaction quality. Interaction quality significantly impacts expectation confirmation, consumer experience, and the continuous intention to use digital banking services powered by AI technology. AI design will become a fundamental factor; thus, all interactions should be user-friendly, efficient, and reliable, and the successful implementation of AI in digital banking will largely depend on AI features.

This study is the first to demonstrate the effectiveness of an AI-ECT model for AI-enabled Indian digital banks. The user continuance intention to use digital banking in the context of AI has not yet been studied. These findings further enrich the literature on AI, digital banking, and information systems by focusing on the AI's Intelligence and Anthropomorphism variables in digital banks.

This study aims to integrate Delone and McLean’s information system success (DMISS) model with the innovation resistance model to evaluate the relationship between behavioural intention to use (BIU) and innovation resistance in the context of neo-banking. The primary objective of this study is to identify the drivers of neo-banking adoption and the barriers to its adoption and incorporate constructs such as e-trust (ETR) and personal innovativeness (PIV) to provide a more comprehensive understanding of the factors influencing neo-banking adoption.

A structured survey-based questionnaire was used to gather data from a diverse sample population in India. The Partial Least Squares Structural Equation Modeling (PLS-SEM) model was employed to further examine the adoption of neobanking services and users' intention to use neobanking services.

This study reveals a significant correlation between BIU and the uptake of neobanking services, demonstrating the value of consumers' readiness to embrace these offerings. However, resistance to usage has emerged as a major obstacle for consumers concerned about data security, technology reluctance and perceived risks associated with digital-only neobanks.

Analysing the driving and restraining factors will provide substantial information on the formation of consumers' decision-making processes in the Indian banking industry, which is undergoing rapid digital transformation. This information is of great importance to scholars, practitioners and policymakers, as it highlights the factors that may facilitate or impede the adoption of neobanking in India. The outcomes of this analysis will be of particular interest to researchers, experts and stakeholders in the field as they will provide valuable insights into the dynamics of consumer behaviour in the Indian banking sector.

This study represents an initial effort to examine BIUs and usage resistance within the rapidly developing neobanking sector in India. The findings of this study build on the existing research in this area and contribute to the ongoing discussion on the adoption of neo-banking.

This study aimed to explore the impact of Artificial Intelligence (AI) characteristics, namely Perceived Animacy (PAN), perceived intelligence (PIN), and perceived anthropomorphism (PAI), on user satisfaction (ESA) and continuous intentions (CIN) by integrating Expectation Confirmation Theory (ECT), with a particular focus on Generation Y and Z.

Using a quantitative method, the study collected 495 data from Gen Y (204) and Z (291) respondents who were users of digital banking apps through structured questionnaires that were analysed using PLS-SEM. The latter helped investigate the driving forces of AI characteristics and user behavioural intentions as well as reveal generation-specific features of digital banking engagement.

The study revealed that PAN and PIN have significant positive effects on the anthropomorphic perceptions of digital banking apps, which in turn increases perceived usefulness, satisfaction, and continuous intentions. In particular, the influence of these AI attributes varies across generations; Gen Y’s loyalty is mostly based on the benefits derived from AI features, whereas Gen Z places a greater value on the anthropomorphic factor of AI. This marked a generational shift in the demand for digital banking services.

The specificity of Indian Gen Y and Z users defines the scope of this study, suggesting that demographic and geographical boundaries can be broadened in future AI-related banking research.

The results have important implications for bank executive officers and policymakers in developing AI-supported digital banking interfaces that appeal to the unique tastes of millennial customers, thus emphasising the importance of personalising AI functionalities to enhance user participation and loyalty.

This study enriches the digital banking literature by combining AI attributes with ECT, offering a granular understanding of AI’s role in modulating young consumers' satisfaction and continuance intentions. It underscores the strategic imperative of AI in cultivating compelling and loyalty-inducing digital banking environments tailored to the evolving expectations of Generations Y and Z.

This study aims to conceptualise a customer-centric model based on an online customer experience (OCE) construct, mediated by e-loyalty (EL) and e-trust (ET), to improve the continuous usage intention (CUI) of Indian digital banks from Generation Y and Z perspectives.

This study used an online survey method to gather data from a sample of 466 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using PLS-SEM to further study the research hypotheses.

The main factors that determine digital banks’ OCE are perceived enjoyment, e-service quality, information quality and e-convenience. Additionally, relevant constructs were evaluated using an importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking; therefore, the outcome may be generalised to a limited extent. It would be valuable to imitate studies in other countries to strengthen digital banking further.

There is a lack of research on digital banking and OCE in India; thus, this study helps rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between OCE, EL, ET and the bottom line of financial institutions, using these factors as dependent variables instead of traditional measures.

This study aims to develop a customer-centric model based on an online customer experience (OCE) construct relating to e-loyalty, e-trust and e-satisfaction, resulting in improved Net Promoter Score for Indian digital banks.

This study used an online survey method to gather data from a sample of 485 digital banking users, from which usable questionnaires were obtained. The obtained data were subjected to thorough analysis using partial least squares structural equation modelling to further investigate the research hypotheses.

The main factors determining digital banks’ OCE were perceived customer centrality, perceived value and perceived usability. Additionally, relevant constructs were evaluated using importance-performance map analysis.

This study used convenience sampling for the urban population using digital banking services; therefore, the outcome may be generalized to a limited extent. To further strengthen digital banking, it would be valuable to imitate studies in other countries.

There is a lack of research on digital banking and OCE in India; thus, this study will help rectify this issue while providing valuable insights. This study differs from others in that it examines the connections between online customer satisfaction, loyalty, trust and the bottom line of financial institutions using these factors as dependent variables instead of traditional measures.