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Robo-advisory has become an increasingly popular asset management tool in recent decades. This paper studies the association between robo-advisor usage and perceived financial satisfaction.

Using data extracted from the National Financial Capability Study 2015 (NFCS2015), the present study carried out a logistic analysis that examines the association between robo-advisory and perceived financial satisfaction. This model also studies the interaction effect of age on this association.

The present study finds that robo-advisor usage is positively correlated with a person’s perceived financial satisfaction after controlling for covariates related to financial literacy and other demographic factors. Moreover, the present study reveals that age moderates the association between robo-advisory usage and financial satisfaction. The results are robust after regressing financial satisfaction on robo-advisory by different age groups.

This paper extends existing literature on robo-advisory by showing that robo-advisory usage relates to a higher level of financial satisfaction. This finding helps understand the rapidly increasing trend of robo-advisory in the financial industry. Moreover, the present study reveals a moderate effect of age on the association between robo-advisory usage and perceived financial satisfaction.

Artificial intelligence and robo-advisory have become prevalent in the finance industry, and many people rely on robots instead of humans for financial advice. This study aims to examine whether robo-advisory increases retirement worry based on agency theory and rational choice theory.

The present study investigate whether relying on robots for financial advice increases retirement-related worry in the present study. Using a sample of 1915 investors from the National Financial Capability Study (NCFS) survey, the author conducted instrumental variable regression analysis to examine the causal linkage.

Using fear of financial fraud as an instrument variable, the study provides a causal explanation of the linkage between robo-advisory usage and retirement worry. After controlling for sociodemographic and financial literacy-related variables, it is found that robo-advisory increases retirement worry.

Findings of the study emphasize on downsides of the artificial intelligence-enabled robo-advisory for financial planning. This article provides evidence that a lack of human involvement in financial planning may lead to increased worry among investors, which calls for attention from the regulators and policymakers.

The purpose of this study is to gauge the awareness and perception of Indian individual investors about a new fintech innovation known as robo-advisors in the wealth management scenario. Robo-advisors are comprehensive automated online advisory platforms that help investors in managing wealth by recommending portfolio allocations, which are based on certain algorithms.

This is a phenomenological qualitative study that used five focussed group discussions to gather the stipulated information. Purposive sampling was used and the sample comprised investors who actively invest in the Indian stock market. A semi-structured questionnaire and homogeneous discussions were used for this study. Discussion time for all the groups was 203 min. One of the authors moderated the discussions and translated the audio recordings verbatim. Subsequently, content analysis was carried out by using the NVIVO 12 software (QSR International) to derive different themes.

Factors such as cost-effectiveness, trust, data security, behavioural biases and sentiments of the investors were observed as crucial points which significantly impacted the perception of the investors. Furthermore, several suggestions on different ways to enhance the awareness levels of investors were brought up by the participants during the discussions. It was observed that some investors perceive robo-advisors as only an alternative for fund/wealth managers/brokers for quantitative analysis. Also, they strongly believe that human intervention is necessary to gauge the emotions of the investors. Hence, at present, robo-advisors for the Indian stock market, act only as a supplementary service rather than a substitute for financial advisors.

Due to the explorative nature of the study and limited participants, the findings of the study cannot be generalised to the overall population. Future research is imperative to study the dynamic nature of artificial intelligence (AI) theories and investigate whether they are able to capture the sentiments of individual investors and human sentiments impacting the market.

This study gives an insight into the awareness, perception and opinion of the investors about robo-advisory services. From a managerial perspective, the findings suggest that additional attention needs to be devoted to the adoption and inculcation of AI and machine learning theories while building algorithms or logic to come up with effective models. Many investors expressed discontent with the current design of risk profiles of the investors. This helps to provide feedback for developers and designers of robo-advisors to include advanced and detailed programming to be able to do risk profiling in a more comprehensive and precise manner.

In the future, robo-advisors will change the wealth management scenario. It is well-established that data is the new oil for all businesses in the present times. Technologies such as robo-advisor, need to evolve further in terms of predicting unstructured data, improvising qualitative analysis techniques to include the ability to gauge emotions of investors and markets in real-time. Additionally, the behavioural biases of both the programmers and the investors need to be taken care of simultaneously while designing these automated decision support systems.

This study fulfils an identified gap in the literature regarding the investors’ perception of new fintech innovation, that is, robo-advisors. It also clarifies the confusion about the awareness level of robo-advisors amongst Indian individual investors by examining their attitudes and by suggesting innovations for future research. To the best of the authors’ knowledge, this study is the first to investigate the awareness, perception and attitudes of individual investors towards robo-advisors.

Innovation is the way of life and we see various innovative techniques and methods being introduced in our daily life. This study aims to focus on digital innovation in the wealth management domain. This study examines the effect of usage of robo-advisory services in investment decision-making and behavioural biases, i.e. overconfidence and loss aversion. Such studies are more pronounced in developed countries and little has been studied about investor behaviour in association with advisory services in developing countries such as India.

Overconfidence and loss-aversion biases, investment decision-making and advisory services questions are measured using a five-point Likert scale. The number of respondents was 172 investors. A purposive sampling is used for gathering responses from investors. Structural equation modeling model was run using AMOS 22 version software package.

The authors found that behavioural biases positively and significantly influence the irrationalities of investment decision-making. The findings of this study also provide empirical evidence that the usage of robo-advisory services, by individual investors, is still incapable of mitigating behavioural biases, such as overconfidence bias and loss-aversion bias.

The sample size of this study could be a limiting factor. This study is limited only to two biases, while other behavioural biases affect the investment decision-making of the investors, which can be considered for future research along with the impact of robo-advisory services in different socio-cultural backgrounds.

This study will assist fintech start-ups, banks, architecture of robo advisors, product owners and wealth management service providers improvise their products, platforms and offerings of these automated advisory services. This could help individual investors to mitigate their behavioural biases in investment decision-making.

This study is useful to society as the awareness of robo-advisory services is very less, at present, and there is a need to increase the usage of these services to extend the benefit of this to the lower stratum of society. These services would be useful to all investors who find it difficult to afford financial advisors and help them mitigate their behavioural biases for investment decision-making.

This study is the first of its type that establishes the linkage between behavioural biases, digital innovation in fintech, i.e. robo-advisory services and individual investor’s investment decision-making in individual investor of the Indian stock market.

In recent years, investing with robo-advisors has gained momentum and is seen as a simplifying approach for individual investors to participate in financial markets. This chapter contributes to a better understanding of the concept of a robo-advisory and its implications for private investors by discussing its past, present, and future. It explores key issues, like cost-efficiency, historical performance, and automation levels, based on research and industry insights. Moreover, this chapter examines a robo-advisor's benefits, limitations, and challenges, like behavioral biases, regulation, and risk profiling. Finally, the importance of the ongoing megatrends of AI and green investing is examined concerning a robo-advisory.

This paper explores the current state of Robo-advisory services in India. This paper further highlights the problems experienced by the service providers in disseminating the innovative business model among the Indians.

The study adopts a qualitative approach to investigate the industry experts by conducting semi-structured interviews. The data collected were transcripted and further analyzed using the content analysis technique. Finally, the authors utilized categorization and coding techniques to frame broad study themes.

The study findings reveal that the three pillars of Robo-advisory are ease and convenience, the time factor and transparency in operations. Robo-advisory services are still at a nascent stage in India. Furthermore, keeping the sentiments of Indians in mind, FinTech companies could combine automated Robo-advisory with a human touch of a wealth manager for optimal advisory services.

Since the present study is qualitative, the authors cannot generalize the study results. Future research can focus on empirically proving the constructs of the study using quantitative methods.

Robo-advisors have a well-established market in developed nations but are still nascent in developing countries like India. The current focus of service providers and regulatory authorities must be to increase awareness among investors by educating the investors and building trust.

The present study is the first to qualitatively synthesize the challenges faced by the FinTech service providers in the Indian market.

Recognizing the importance of Robo-advisors in digital financial services, this paper aims to analyse the users’ perception and acceptability of artificial intelligence (AI) in digital investment solutions using an extended “Technology Acceptance Model” (TAM).

The model is tested using 454 online valid responses received from Indian Fintech users via direct path analysis, mediation and moderation.

The study’s findings show that trust, perceived usefulness and perceived risk all significantly impact users’ attitudes towards Robo-advisors. In contrast, ease of use and social influence did not impact users’ attitudes statistically. Furthermore, the results indicate that their attitudes and ease of use influence users’ intentions to adopt Robo-advisors. Moreover, the moderation effect of gender partly supports the overall model. Specifically, in the path between attitudes and their antecedents, gender plays a role in influencing the relationships among these variables. This aligns with preliminary research in the field, providing additional insight into how gender may moderate the factors influencing users’ attitudes and intentions regarding Robo-advisory services.

This research study also reveals that trust, perceived risk, ease of use and demographic factors influence the adoption of Robo-advisory services. It is functional, but its sample selection is not probabilistic and overly emphasizes gender. Future research should use probabilistic sampling, other demographic factors and experience and situational factors. Also, it is necessary to examine how convenient and satisfying it is to communicate with service providers. Filling these gaps will improve the knowledge of consumer behaviour in the context of Fintech adoption and develop the current research.

This study posits that perceived usefulness, trust, perceived risk and ease of use remain core determinants of adopting Robo-advisory services. So, to improve the level of trust of users, it is necessary to develop security measures, data clarity and quality and customer support. Enhancing ease of use by incorporating better interface gestures is always beneficial for increasing the number of users and their level of satisfaction. As identified in previous studies, practical solutions will be achieved by pursuing the increased use of technology while leveraging AI for personal services and minimizing perceived risks, which will strengthen more advanced security measures as well as sufficiently clear communication.

The paper aims to extend the TAM by incorporating measures of trust and social influence to identify the factors that drive the adoption of Robo-advisors. In doing so, the paper may contribute to developing a more comprehensive understanding of the factors that shape consumers’ attitudes and intentions towards these technologies. Moreover, the paper appears to examine the moderating effect of gender on attitude and its predictors, which could provide insights into how gender characteristics may impact the adoption of Robo-advisors.

Financial services industry is increasingly showing interest in automated financial advisors, or robo-advisors, with the aim of democratizing access to financial advice and stimulating investment behavior among populations that were previously less active and less served. However, the extent to which consumers trust this technology influences the adoption of rob-advisors. The resemblance to a human, or anthropomorphism, can provide a sense of social presence and increase trust.

In this paper, we conduct an experiment (N = 223) to test the effect of anthropomorphism (low vs medium vs high) and gender (male vs female) of the robo-advisor on social presence. This perception, in turn, enables consumers to evaluate personality characteristics of the robo-advisor, such as competence, warmth, and persuasiveness, all of which are related to trust in the robo-advisor. We separately conduct an experimental study (N = 206) testing the effect of gender neutrality on consumer responses to robo-advisory anthropomorphism.

Our results show that consumers prefer human-alike robo-advisors over machinelike or humanoid robo-advisors. This preference is only observed for male robo-advisors and is explained by perceived competence and perceived persuasiveness. Furthermore, highlighting gender neutrality undermines the positive effect of robo-advisor anthropomorphism on trust.

We contribute to the body of knowledge on robo-advisor design by showing the effect of robot’s anthropomorphism and gender on consumer perceptions and trust. Consequently, we offer insightful recommendations to promote the adoption of robo-advisory services in the financial sector.

This research aims to comprehend the factors that impact the emerging inclination of consumers toward mobile finance technology (fintech) services over banking institutions. The study focuses on users' demographics and psychographics to delineate their unique segments and profiles.

The study proposes a segmentation and profiling framework that includes variance analysis, two-step cluster analysis and pairwise statistical tests. This framework is applied to a dataset of customers using a range of mobile fintech services, specifically robo-investment, peer-to-peer (P2P) payments, robo-advisory and digital savings. The analysis creates distinct customer profile clusters, which are later validated using pairwise statistical tests based on segmentation output.

Empirical results reveal that P2P payment service users exhibit a higher frequency of usage, proficiency and intention to continue using the service compared to users of robo-investment or digital savings platforms. In contrast, individuals utilizing robo-advisory services are identified to have a significantly greater familiarity and intention to sustain engagement with the service compared to digital savings users.

The findings provide financial institutions, especially traditional banks with actionable insights into their customer base. This information enables them to identify specific customer needs and preferences, thereby allowing them to tailor products and services accordingly. Ultimately, this understanding may strategically position traditional banks to maintain competitiveness amidst the increasing prominence of fintech enterprises.

This research provides an in-depth examination of customer segments and profiles within the mobile fintech services sphere, thus giving a nuanced understanding of customer behavior and preferences and generating practical recommendations for banks and other financial institutions. This study thereby sets the stage for further research and paves the way for developing personalized products and services in the evolving fintech landscape.

AI-based robo-advisory (RA) represents a FinTech application that is already replacing retail investment advisors. In private banking (PB), clients also increasingly expect service provision across different digital channels, but with a higher degree of personalization. Hence, the present study investigates the impact of intelligent RA on the PB investment advisory process to derive both process (re)design knowledge and strategic guidance for artificial intelligence (AI) usage for PB investment advisory.

The present study applies an AI process impact analysis approach by decomposing AI-based RA into three AI application types: conversational agent, customer segmentation and predictive analytics. The analysis results along a reference PB investment advisory process reveal sub-process transformations which are applied for process redesign integrating AI.

The study results imply that AI systems (1) enable seamless client journeys, (2) increase advisor flexibility, (3) support the client–advisor relationship by applying an omnichannel approach and (4) demand advisor skills to be augmented with technical and statistical knowledge.

The research study contributes (1) an AI process impact analysis approach, (2) derives process (re)design knowledge for AI deployment and (3) develops strategic guidance for AI usage in PB investment advisory.