Robo-advisors, also known as robo-advisory services, significantly reshape customer service in financial advisory industries, transforming retail investor markets by substituting human financial advisory experts with artificial intelligence empowered services. However, existing literature remains scattered across disciplines, with theories on financial customer service predominantly focused on Internet banking, neglecting artificial intelligence empowered interactions. Thus, service providers need a framework for implementing robo-advisors in frontline service and researchers require an advanced agenda to stimulate future research. Through a systematic, interdisciplinary literature review based on Belanche et al.’s service robot framework, this article contextualizes service robot theories into financial advisory services, synthesizing knowledge on artificial intelligence empowered customer service. We contribute to literature on service robots by contextualizing, refining, and extending the original framework by Belanche et al. and by developing a research agenda with critical perspectives. Moreover, the study yields practical and theoretical insights into artificial intelligence empowered financial advisory services.

Playing outdoors in nature with peers has been attributed most importance for children's healthy development but is increasingly marginalized because of the attractiveness of screen-based play. Careful merging of digital technology into outdoor play environments rich on nature elements could potentially help bridge digital play with more traditional play activities outdoors. A systematic comparison was made of outdoor play in more or less green settings, with and without digital installations or traditional play equipment. The separate and combined role of digital artefacts, play equipment and natural elements, were investigated, with particular focus on the effects of merging digital materials into nature. A group of children aged 6-8 were involved in a field study in a three-week period playing in a traditional playground, a forest and in a forest with digitally enhanced play artefacts. Children ' s play behavior was evaluated using a behavioral tracking method, a questionnaire and a contextual interview with the children, and a physical activity measure, in combination with inventories including maps to document the design, and the ecological and physical status of the settings. The study documents differences in children's play behavior across the three settings. It differs most between the digital forest setting and the forest setting regarding the play categories imaginative play, physical play and rule play and the digital forest setting stands out when it comes to expressive play. It is discussed how particular attributes in the physical environment influence the overall play flow and the interactive effects of natural material and digital material. Ecologically, the forest and the forest with digitally enhanced artefacts were more diverse than the traditional playground, but the natural material present was important for play in all settings.

AI-empowered financial advisory services (or robo-advisory services) emerge as one of the channels to attract customers to invest in financial products that integrate the environment, social, and governance (ESG) criteria to their investment objectives. This reasoning is often based on the assumption that AI is more transparent in fees and unbiased than conventional human advisors, while at the same time, lowering the entry bar for young and low-budget customers. In automated services with little or no human intervention, users’ experience and ability to use this type of service play a critical role in supporting consumer investment decisions. However, the impact of robo-advisor user experience towards customers’ preference for sustainable investment choices has not been addressed by previous studies. In the long run, can robo-advisory services significantly support or promote a more sustainable investment portfolio to customers? We provide initial insights on these questions based on a mixed-method user test, including a pre-test survey, observations of robo-advisor usage, and a post-test retrospective interview. The preliminary results show that this AI-empowered system and its service have not fulfilled the expectations to support customers’ sustainable investment decision-making due to the lack of comprehensible information and transparency. Firstly, we explain the relationships between customers’ intention to select a sustainable portfolio with their features and attitudes towards AI. The observation and interview data reveal that a standardized definition and criteria of sustainable investment and portfolios are needed for customers to establish a fundamental understanding of this service. Also, customers demand more explanations in the final recommendation automatically generated by AI.

AI-empowered and algorithm-driven automated financial advisory systems, also known as Robo-advisors, have been rapidly implemented by service providers and customers in financial service markets. Yet, few empirical studies investigate customers’ experience interacting with fully functional Robo-advisors in real-life scenarios. Also, it is still unknown how the design of the automated system can affect customers’ perception and adoption of this new technology. To mitigate these gaps, 24 participants with different levels of experience and understanding of financial investment were asked to use a Robo-advisor from a retail bank and perform the tasks. By conducting observations and retrospective post-test interviews, we find that participants do not fully perceive the social aspects supposed to be provided by Robo-advisors. The overarching problems are, among others, a lack of transparency and incomprehensible information. This results in distrust of the results generated by this system, which negatively affects customers’ adoption of the investment advice provided by the Robo-advisor. The potential of interactive data visualization is also detected. This work contributes to the understanding of customers regarding their perception and adoption based on their use of a functional Robo-advisor and proposes design takeaways for transparent and comprehensible automated advisory systems in financial service contexts.

AI-empowered financial advisory services, also known as robo-advisors, present both innovations and challenges as they replace human financial advisors, reshape customer service, and attract customers with different characteristics than their predecessors. Therefore, it is more important than ever for financial service providers to understand customers’ perception and experience of using robo-advisors on the service front line. However, our systematic literature review indicates that research on robo-advisors is scattered across different disciplines with a narrow focus on sharing knowledge across fields. Moreover, empirical studies on robo-advisors have primarily focused on customer acceptance and intentional behaviors, often based on data collected through surveys. Also, these studies have paid less attention to the role of robo-advisor design and the context in which customers interact with fully functional robo-advisors in real-life situations. To address these gaps, this thesis aims to synthesize interdisciplinary knowledge and identify gaps in the research on robo-advisors. It also aims to explore customers’ experience of using and interacting with robo-advisors and how the experience affects their perception and adoption of the service. By conducting a systematic literature review and a qualitative user study, this thesis finds that customers’ perceptions of robo-advisors often do not meet their expectations. Nontransparency and incomprehensible information about the system’s decision-making are significant barriers to customers’ adoption of robo-advisors. This thesis contributes to a deeper understanding of customers in AI-empowered financial advisory services by using theories and approaches across different disciplines. It also provides practical implications for practitioners in the robo-advisory service industry.

AI-förstärkta finansiella robotrådgivare innebär både innovationer och utmaningar eftersom de ersätter mänskliga finansiella rådgivare, omformar kundservice och lockar till sig andra typer av kunder än deras föregångare. Därför är det viktigare än någonsin för tjänsteleverantörer i det finansiella systemet att förstå kunders erfarenheter och upplevelser av robotrådgivning i servicefronten. Dock indikerar avhandlingens systematiska litteraturgenomgång att tidigare forskning om robotrådgivare är spridd över olika discipliner med begränsat fokus på att dela kunskap mellan disciplinerna. Dessutom har de empiriska studierna rörande robotrådgivare primärt fokuserat på kundacceptans och intentionsbeteenden – ofta baserat på data insamlad via enkäter. Tidigare studier har också ägnat mindre uppmärksamhet åt robotrådgivares design och den kontext där kunder interagerar med robotrådgivare i verkliga situationer. Baserat på dessa brister i tidigare studier syntetiserar avhandlingen tvärvetenskaplig kunskap och identifierar luckor i forskning om robotrådgivare. Avhandlingen utforskar också kunders erfarenhet av att använda och interagera med robotrådgivare och hur dessa erfarenheter påverkar deras upplevelser av och användande av tjänsten. Genom en systematisk litteraturgenomgång och en kvalitativ intervjubaserad användarstudie visar avhandlingens studier att kunders upplevelse av robotrådgivare inte motsvarar deras förväntningar. Bristande transparens och svårbegriplig information om systemets beslutsfattande är betydande hinder för kundernas antagande av sådana tjänster när de interagerar med robotrådgivare. Avhandlingen bidrar till en djupare förståelse för kundupplevelser av AI-förstärkta finansiella robotrådgive genom att integrera teori och metod från olika discipliner. Det ger också praktiska implikationer för finanssektorns företag med verksamhet inom robotrådgivning.