Automation and augmentation in professional services: Exploring the impact of financial self-service technology

Vol.:(0123456789) Electronic Markets (2025) 35:94 https://doi.org/10.1007/s12525-025-00835-2 RESEARCH PAPER Automation andaugmentation inprofessional services: Exploring theimpact offinancial self‑service technology SophieAltrock1,2 · Anne‑LaureMention1,3,4,5· TorHelgeAas2,6 Received: 4 October 2024 / Accepted: 10 September 2025 © The Author(s) 2025 Abstract Despite the rise of increasingly smart technologies in professional services, financial services firms require clear strategies for integrating automation, augmentation, and hybrid service models alongside human workers. This paper employs a qualitative approach to explore the considerations necessary for adopting robo-advisors (RAs) in self-service investment management and the actions required to support their integration. The findings show how self-service technology (SST) such as RAs at the intersection of customer and worker can enable automation and augmentation simultaneously. Overall, six factors are identified across technical, organisational, and external dimensions that guide firms in determining when, where and how to effectively adopt RAs as an integral part of financial services. For each factor, we present specific firm-level actions to achieve successful service automation and augmentation. As such, this research contributes to a holistic approach to digital transformation at large and service automation in particular by proposing hybrid service models that leverage SST. Keywords Financial technology· Self-service technology· Service automation· Augmentation· Robo-advisor· Professional service JEL Classification M15 Introduction Since the first applications of tools to enhance human capability, the rise of digital technologies has increasingly challenged their co-occurrence in the workplace. While technology has brought about new products and services, it has also disrupted traditional processes within established firms, affecting everyone from customers to workers (Svahn etal., 2017; Z. Zhang etal., 2023). Scholars often refer to “automation” to describe the replacement of human workers by machines (Bessen etal., 2020; Raisch & Krakowski, 2021). With the advancement of technology, especially artificial intelligence (AI), conversations about intelligent automation and intelligent process automation have emerged (Christou etal., 2023; Coombs etal., 2020; Kholiya etal., 2021; Wirtz etal., 2021), suggesting a decreasing gap between human and artificial skills. As automation and AI have spread across various industries (Blöcher & Alt, 2021; Mingotto etal., 2021; Nam etal., 2021), the role of human workers has been increasingly challenged. However, with the advent of augmentation, humans are brought back into the equation, resulting in technologically-enhanced work processes through human–machine Responsible Editor: Daniel Beverungen * Sophie Altrock [email protected] Anne-Laure Mention [email protected] Tor Helge Aas tor[email protected] 1 School ofManagement, RMIT University, VIC, Building 80, 445 Swanston Street, Melbourne3000, Australia 2 School ofBusiness andLaw, University ofAgder, Universitetsveien 19, 4630Kristiansand, Norway 3 Tampere University, Kalevantie 4, 33100Tampere, Finland 4 Singapore University ofSocial Sciences, 463 Clementi Rd, 599494Singapore, Singapore 5 INESC TEC Portugal, R. Dr. Roberto Frias, 4200-465Porto, Portugal 6 Kristiania University ofApplied Sciences, Kirkegata 24-26, 0153Oslo, Norway Electronic Markets (2025) 35:94 94 Page 2 of 25 collaboration (Raisamo etal., 2019; Raisch & Krakowski, 2021). While it is often argued that automation increases efficiency and replaces human labour, automation and augmentation can in fact also adversely affect workers by increasing stress and impacting well-being (Nazareno & Schiff, 2021). Thus, substitution, complementation, augmentation and automation all rely on a comprehensive understanding of the resulting organisational changes (Nazareno & Schiff, 2021; Raisch & Krakowski, 2021) to fulfil the promise of increased business value generation (Coombs etal., 2020). This aligns with the much broader need for “socioeconomic change across individuals, organizations, ecosystems, and societies that are shaped by the adoption and utilization of digital technologies” (Dąbrowska etal., 2022, p. 931). Another aspect to consider is the interplay between humans and machines within socio-technical systems (Mateescu & Elish, 2019). This dynamic is especially relevant in understanding the interactions between workers and algorithms, AI, or robots (Blöcher & Alt, 2021; Lazo & Ebardo, 2023; Ritala etal., 2023; Son etal., 2024; Tarafdar etal., 2022). While human–machine interactions driven by automation are a concern across industries, the service sector faces unique challenges. One challenge is how the rise of AI has challenged the meaning of human interaction in service encounters. Today, various forms of AI can be applied through service delivery, creation, and interaction (Huang & Rust, 2021). As a result, service encounters that used to take place with workers and customers present (Meuter etal., 2000) are shifting to the digital space or evolving into hybrid service models that do “not reject or ignore the human factor” (Christou etal., 2023, p. 8). Alongside these service transformations, digital and increasingly smart self-service technology (SST) is reshaping traditional roles and service delivery through customer automation (Sampson, 2021). As such, SST offerings not only target new customer segments but also enhance customer satisfaction and reduce costs (Bitner etal., 2002). As service delivery is redefined, the relationships between workers and customers undergo change (Pentzold & Bischof, 2023), and customer expectations evolve (Narteh, 2015). Despite this evolution, customer–firm interactions remain crucial (Meuter etal., 2000); fully automated service interactions often face significant challenges, prompting a partial return to more traditional practices (Pentzold & Bischof, 2023). Interestingly, “although employees have experienced an intensification of their workload to compensate for human and machine shortcomings”, the value of workers is being neglected (Pentzold & Bischof, 2023, p. 6), reinforcing the need for organisations to pay attention to the changes workers and customers are experiencing in hybrid and automated service models (Mateescu & Elish, 2019; Pentzold & Bischof, 2023). A recent example of service automation and its challenges is the adoption of robo-advisors (RAs), a self-service application for automated investment management offered by financial services firms. Despite the financial sector’s long history with digital technologies, it still lacks a fully successful and futureoriented service model. Nonetheless, change is inevitable, necessitating strategies to integrate AI-driven technologies alongside the traditional role of financial advisors (Johnson, 2023). RAs have digitalised and automated tasks traditionally performed by human financial advisors, allowing customers to invest without human interaction. While many businesses offering RAs eliminate the advisor role, some retain a human touchpoint for customers or use RAs as a tool to assist advisors (Phoon & Koh, 2018). RAs provide numerous benefits, contributing to a seamless client journey, omnichannel service provision and increased advisor flexibility (Dietzmann etal., 2023). The integration of AI in robo-advice also offers potential advantages in mitigating advisor bias by combining human and artificial intelligence (Athota etal., 2023). However, despite these practical benefits, recent reports highlight a backlash against the expected success of RAs, emphasizing organisations’ struggles to create value (Rosenbaum, 2022; Vickovich & Patten, 2022). Although robo-advisory services are still in the relatively early stages of development, the topic is attracting increasing interest, with limited empirical research conducted so far (e.g., Athota etal., 2023; Bhatia etal., 2020, 2021; Coombs & Redman, 2018; Shanmuganathan, 2020; Waliszewski & Warchlewska, 2020). Most existing studies focus primarily on the customer perspective (e.g., Atwal & Bryson, 2021; Gan etal., 2021; Jung etal., 2018a, 2018b; Ko etal., 2021; Lewis, 2018; Manrai & Gupta, 2022; Oehler etal., 2021), with relatively little research exploring the impact of RAs on organisations, service delivery, or technology adoption from the workers’ perspective (e.g., Altrock etal., 2023; Bhatia etal., 2021; Dietzmann etal., 2023; Flavián etal., 2022; Lopez etal., 2015; Wexler & Oberlander, 2020). On a broader scale, financial technology research has yet to sufficiently “explain the fundamental issues of the technology implemented or the effects of these technologies on individuals, organisations, and society” (Jourdan etal., 2023, p. 11), highlighting the need for more comprehensive studies. This is further underscored in the realm of digital transformation research, suggesting a lacking awareness on how incumbent firms can adapt and renew themselves with digital technology (Dąbrowska etal., 2022). Additionally, the unique capabilities of SST at the intersection of customer and worker raise questions about the evolving role of human services (Roberts & Maier, 2024). Despite the increasing possibilities with technological automation, there remains a need for future-proof service delivery concepts that meet customer expectations, optimise services, and understand workers’ everchanging roles. An interdisciplinary perspective is particularly essential. Service professionals, such as financial advisors, are increasingly witnessing a shift in how services are delivered and the types of services required. The growing adoption of AI is expected to Electronic Markets (2025) 35:94 Page 3 of 25 94 reshape human interactions (De Witte, 2023) and highlights the importance of maintaining human involvement in service processes (Raisch & Krakowski, 2021). This shift emphasizes augmenting human capabilities rather than simply automating them, necessitating innovative approaches for traditional services. Therefore, the purpose of the present study is to provide new insights into how increasingly smart SSTs in financial services can be used to create value both within and beyond the boundaries of incumbent firms. By employing an empirical approach, we aim to enhance our understanding of SSTs in service professions through the perspectives of automation and augmentation. Focusing on the emerging practice of RAs, we conduct in-depth interviews with various financial stakeholders to explore the current state of RAs in financial service firms. To address these objectives, the study explores the following research questions (RQs): RQ 1. What considerations are necessary for financial services firms adopting robo-advisors for automation and augmentation? RQ 2. What actions are required of financial services firms to support the adoption of robo-advisors for automation and augmentation? Literature review Automation andaugmentation inprofessional service sectors Service work has been roughly defined as the process of using one’s resources (e.g., knowledge) for someone’s (self or other) benefit (Barrett etal., 2015). With the introduction of digital technology, service automation has grown to affect various types of work and workers, from ‘low-skilled’ to ‘highskilled’ (Bessen & Kossuth, 2019; Frey & Osborne, 2017). In professional services, workers follow "accepted professional practices and a code of professional ethics, often codified through professional certification" (Stumpf etal., 2002, p. 260). Firms in sectors such as healthcare, finance, insurance, and law often require service professionals who are not only knowledgeable but also empathetic. With the adoption of digital technologies, however, the skills required of service professionals and how they work and interact with customers have evolved. Such “mastery of a particular expertise or knowledge base” (von Nordenflycht, 2010, p. 156) therefore necessitates particular sensitivity to the needs and requirements of such services when attempting automation. Furthermore, in service contexts, technology adoption can affect various organisational layers and stakeholders, including not only workers but technology suppliers, customers, competitors, and managers (Ivanov & Webster, 2019). At the same time, as autonomous users are increasingly driven to SST (Middelburg, 2017), arguably without the need for human workers, traditional service firms struggle to find a balance, which calls for new strategies to adapt to and incorporate automation. One way to automate is through robotic process automation (RPA) which is “an umbrella term for tools that operate on the user interface of other computer systems in the way a human would do” (van der Aalst etal., 2018, p. 269). Implementing RPA allows organisations to enhance “process performance, efficiency, scalability, auditability, security, convenience and compliance” (Hofmann etal., 2020, p. 103). However, achieving these benefits requires careful consideration. At the microlevel, for instance, the introduction of RPA impacts human labour by altering workers’ engagement, skills, and roles (Hofmann etal., 2020). Furthermore, as automated systems become more prevalent in services, questions arise regarding the necessity for human-to-human interaction. Despite increasing automation in service encounters, scholars argue for maintaining interpersonal elements and the human touch (Wünderlich etal., 2013). This has resulted in increasing recognition for augmentation and, thus, extending human capability rather than replacing it (Raisamo etal., 2019; Raisch & Krakowski, 2021; Spring etal., 2022). However, there remains a lack of guidance on how and where to shift towards augmenting services instead of automating them. Technology adoption infinancial services firms The long history of technology adoption in organisations, along with the recognition of its vast potential and significant challenges, has led to the development of various theoretical constructs within and across management studies. Among the frameworks developed, the Technology Acceptance Model (TAM) examines factors influencing behavioural intention and attitudes towards the actual use of technology (Davis, 1989). Building ontheTAM and other models, the Unified Theory of Acceptance and Use of Technology(UTAUT) incorporates factors such as performance expectancy, effort expectancy, social influence, and facilitating conditions (Venkatesh etal., 2003). Yet another perspective on exploring technology adoption is through work systems, where humans and machines collaborate as components of a system; it is a “view of work as occurring through a purposeful system” (Alter, 2002, p. 91). From a broader perspective, emphasizing the process of adopting and implementing technology, the TechnologyOrganisation-Environment (TOE) framework has been used to explore the impact of varying contexts (Tornatzky & Fleischer, 1990). Although the framework may initially appear simplistic, it still remains applicable today (e.g., Abed, 2020; Ullah etal., 2021), providing a foundation for the components involved and considerations necessary when integrating a new technology solution into existing organisations. Previous scholars also tried adaptations of the framework; for instance, looking at robot adoption, Firescu Electronic Markets (2025) 35:94 94 Page 4 of 25 etal. (2022, p. 9) suggest including humans in the equation, stating that “management strategies have to be focused on employees’ identity, personality, needs, and expectations”. Similarly, Awa etal. (2017) expand the TOE model by introducing individuals but find that adoption is driven more by the original TOE factors than by individual factors. Sticking to the original components but instead emphasizing a holistic approach, Ullah etal. (2021) use the TOE framework in a multilayered approach to risk analysis, underpinning its various applications areas and comprehensiveness. Taken together, technology adoption models provide individual, organisational, or more holistic viewpoints for technology adoption. Although many of these theoretical models remain in use today, recent research highlights the need for a comprehensive understanding of digital transformation and challenges the status quo of organisations, individuals, ecosystems and geopolitics (Dąbrowska etal., 2022). Simultaneously, innovation with AI is posited to involve organisational, social, economic and technological factors (Mariani etal., 2023). Across sectors and the varying work environments, individual factors can influence adoption differently. For instance, in response to user demand and technological advancements, the traditional, highly regulated financial sector has perpetually introduced new products and services. Financial technology (FinTech) actors have risen to the bar and challenged traditional firms to keep up with the varied and dynamic needs of customers in the digital era (Mention, 2021). Additionally, the Covid-19 pandemic necessitated changes in how financial service firms deliver wealth management advice (Graseck etal., 2020). As Graseck etal. (2020, p. 4) note, “Covid-19 has altered clients’ expectations for financial advisor (FA)/relationship manager (RM) interaction, while also underscoring the value of human advice”. Today, investors are most likely to use digital offerings for moving money and servicing their accounts, although in-person advice might remain most relevant for transactions like opening an account (Baghai etal., 2022). Yet, client engagement has increased seven to ten times across all digital channels; 25% of this engagement is anticipated to be based on digital applications by the end of 2024, with only one in five interactions occurring face-toface (Graseck etal., 2020). As the new way forward, affecting both organisations and workers, omnichannel offerings and digital advice models are becoming the key to success (Baghai etal., 2022; Graseck etal., 2020) and driving the sector to change. Yet, clear pathways on how and when to move to machine automated and augmented services continue to be questioned (Pentzold & Bischof, 2023). Resulting from increasing technology adoption and a shift to increasingly digital service offerings, organisational changes significantly affect internal operations and workers. In the financial sector, service offerings are expected to grow beyond simple money management. Thus, for advisors to remain relevant, digital offerings have to be integrated with traditional advice delivery and human counterparts as more investors become comfortable with digital-only models (Baghai etal., 2022). Technological advances in the area of finance are becoming “inclusive, egalitarian and decentralized” (Paul & Sadath, 2021, p. 134), represented by new FinTech solutions on the market, including the development of the RA, that cater to the masses by providing investment advice through algorithms to build and manage client portfolios (Jung etal., 2018a, 2018b; Phoon & Koh, 2018). Ranging from simple automation of asset allocation to self-learning AI (Tokic, 2018), RAs have the ability to bring together different user groups (De Reuver etal., 2017) and finally cater to the do-it-yourself investor as a SST (Wexler & Oberlander, 2020; Zhang etal., 2021). The introduction of RAs into financial institutions entails a substantial shift in a service profession long reliant on traditional face-to-face interactions. Apart from high-net-worth clients who remain loyal to traditional solutions (Altrock etal., 2023), the new tech-savvy customer base (Merkle, 2020) seeks digital solutions and ways to invest at a small scale, which demands hybrid solutions and new ways for human financial advisors to deliver advice. Researchers thus continue to debate the implications of RAs on the delivery and provision of financial advice and services, with some viewing them as a complement to human advisors, while others consider them a transformative force that may redefine the industry landscape, calling for more in-depth studies with practical relevance (Altrock etal., 2023). Human enhancement inrobo‑advice Robots have long captured the human imagination and are now evolving into central components in business operations across many sectors. Although robots are often characterised as embodied (Young etal., 2011) and used in industrial settings, the RA has also been categorised as a virtual robot or disembodied professional service provider (Wexler & Oberlander, 2020). Meanwhile, the extension of human work and combining it with technological capabilities goes back decades and has been approached in various ways. Augmentation refers to the enhancement of human capabilities or productivity through technologies in a way that “somehow add[s] to the human body or mind” (Raisamo etal., 2019, p. 132). The interaction of machine and human becomes relevant, with the latter at the core (Raisamo etal., 2019). Automation, by contrast, is frequently used in research to describe the impact of digital technologies and the changing nature of work (e.g., Acemoglu & Restrepo, 2019; Hanelt etal., 2021; Rotatori etal., 2021). Automation describes “the machine execution of functions that at one time could only be performed by humans” and extends to functions “that humans do not wish to perform or cannot perform as accurately or reliably Electronic Markets (2025) 35:94 Page 5 of 25 94 as machines” (Parasuraman etal., 2000, p. 286). In services, automation is also used as a way to automate tasks not from workers but from customers directly by offering self-service solutions to enable quick and efficient access to services digitally (Sampson, 2021). Arguably, human beings remain relevant for various tasks, though scholars continue debating the types of tasks requiring human intervention. Some argue that what infuses human relevance to technologically enhanced processes can be closely tied to tacit knowledge, where “knowing how” to do something evolves into “tacit knowing”, forming an extension of existence (Oǧuz & Şengün, 2011). However, the impact of robotic and AI-driven technologies on jobs, knowledge and required skills can be diverse, which consequently provides a well-researched topic across service sectors (Mingotto etal., 2021; Paluch & Wirtz, 2020; Panch etal., 2019; Wirtz etal., 2018; Xu & Wang, 2021). While changes for workers are becoming more well-defined, researchers point to the need for practical organisational strategies addressing direct and indirect effects on organisations (Hofmann etal., 2020). In robo-advice we see the same need, as research on organisational adoption and strategies for adapting existing practices and advisor skills remains limited, despite scholars confirming the disruptive influence on traditional financial firms and their employees (Altrock etal., 2023). Despite RA solutions offered by FinTech companies directly to customers, traditional financial institutions maintain high levels of acceptance, expertise and experience (Gerlach & Lutz, 2021), encouraging full integration of RAs into existing business models and practices. A critical challenge thus remains in understanding the dynamic interplay between human and machine at incumbent firms. In particular, to the best of our knowledge, current research misses a holistic view on automation and augmentation in the context of SST which provides a particular opportunity for technology-driven service provision that maintains human connection as needed. Thus, further in-depth investigation is needed to uncover the necessary organisational considerations and strategic actions, balancing automation with augmentation. Based on the emergent and interesting case of robo-advice, this exploration, therefore, provides essential empirical insights for financial service firms moving towards enhanced operational efficiency and effectiveness. Research design For this study, we adopted a phenomenon-driven approach that is well suited to organisational research (Schwarz & Stensaker, 2016) that explored the considerations that are necessary for financial services firms adopting RAs and the actions required for successful implementation. Given the fragmented literature across varying research streams, we emphasized a holistic approach where technology adoption in service interactions was viewed from several perspectives. In light of the persistent difficulties in adopting digital technologies and facilitating organisational transformation from the microto macro-level (Dąbrowska etal., 2022; Raisamo etal., 2019; Ramesh & Delen, 2021), we utilized a phenomenon-driven approach that allowed us to explore new theories based on the data collected to better understand technology adoption in service industries that were highly affected. We focused on the financial sector to examine how incumbent firms are gradually adapting traditional services. This sector has long been a pioneer in digital technology adoption, notably introducing the Automated Teller Machine (ATM), which remains one of the most commonly used SST even today (Curran & Meuter, 2005). Though the convergence of finance and technology and the emergence of fintech have been progressing for some time, the field remains largely unexplored (Jourdan etal., 2023), inviting further research. Data collection Due to the emergent and novel nature of the topic, we chose an exploratory qualitative approach, utilizing interviews to gather in-depth insights into the early adoption of RAs. We decided against a (comparative) case study approach for the same reason that adoption of RAs is still nascent, with financial services firms at varying stages of adoption (if at all) and employing RAs in many different ways. Interviews provided the necessary flexibility to explore the considerations and actions required for integrating RAs into financial services, thereby addressing our RQs directly. The collection of primary data included unstructured and semi-structured expert interviews (L. Bartlett & Vavrus, 2017) entailing transcripts (for semi-structured interviews) and/or written notes. The interview questions sought to understand the use and purpose of RAs, their potential for automation and augmentation of service interactions, their impact on financial advisors and the organisation at large, and what is needed to optimise RA adoption. Participants were selected from financial advisors, leaders, and managers to understand considerations regarding RA adoption and service automation and augmentation. In addition, we included stakeholders from organisations such as FinTech companies, consulting firms, and academia to provide a more comprehensive view of RA adoption and its impact on industry specifics and the changing landscape. Due to the emergent nature of the topic and the varied expertise of informants, we conducted a combination of unstructured and semi-structured interviews. In total, we conducted 20 interviews (n = 20), including unstructured scoping interviews for the study’s framing and the Electronic Markets (2025) 35:94 94 Page 6 of 25 exploration of financial technology adoption at large, and semi-structured interviews for targeted exploration of RA adoption and integration in financial services firms. Consultations and review of the literature revealed that banks were still in the early adoption phase of robo-advice, utilizing primarily customer-facing tools that had not yet impacted advisors as expected in fully developed hybrid models. As a consequence, we prioritized interviews with management-level informants involved in the decisionmaking processes of adopting RA technology, rather than financial advisors. Table1details each interviewee’s role and sector. Interviews were conducted virtually and lasted approximately 60min each. They were recorded and transcribed, with notes taken during interviews for data analysis purposes. Among the financial institutions represented by participants, five offered RAs, with most having launched these services in 2017 or 2018. Two financial institutions did not have any RA offerings at the time of data collection. The participant pool consisted primarily of mediumto largesized enterprises, both with and without local branches. Common among all participating banks was their location in high-income, highly developed countries with robust social welfare systems and advanced economies. Four interviews, conducted in German, were translated into English prior to analysis. Financial firms that offered RAs at the time of data collection are presented in Table2. We complemented our interview data through the collection of a variety of publicly available data. In general, any primary data made available can become secondary data (Hox & Boeije, 2005). Consequently, we included grey literature, defined as publicly available, open-source information from government, academia, business, and industry, in both print and electronic formats (Benzies etal., 2006). To increase the number of individual viewpoints from financial professionals and topical experts on RA adoption, we searched for relevant podcasts on the streaming platform Spotify on the topic of robo-advice, adding 18 podcasts in English and German. These included insights from certified financial planners, founders, and providers of RAs. Additional data was selectively collected through web searches following an initial review of primary data. Through that effort, we added insights into RA offerings from financial institutions’ websites, publications by recognised international financial institutions on robo-advice and AI more broadly, publicly available viewpoints, blogs and studies from industry sources, consulting firm publications Table 1 Overview of informants Code Role (specialisation) Industry ABN1 Financial advisor Finance MFN1 Chief executive officer Finance (FinTech) MCD1 Partner (risk) Consulting MCD2 Partner (technology consulting financial services) Consulting OEA1 Professor (financial technologies) Education MBA3 Chief executive officer Finance OEN1 Professor (artificial intelligence) Education OEU1 Professor (business ecosystems and platforms) Education MBN1 Chief innovation officer Finance MCN1 Partner (financial services risk) Consulting MFA1 Founder (investing) Finance (FinTech) OPN1 Subject director Finance MBN2 Chief data officer Finance MCG1 Senior manager Consulting MBN3 Head of investment advisory Finance MCD3 Consulting lead (financial services) Consulting MBG1 Chair of the board of directors Finance MBG2 Director (private and commercial banking) Finance MBG3 Expert (investment and strategy) Finance MBS1 Product stream lead (financial messaging and tracking) Finance Table 2 Overview of types of financial service firms included in the study Financial firm type Large financial services group Medium-sized online bank Medium-sized co-operative bank Large investment bank and financial services company Public savings bank Electronic Markets (2025) 35:94 Page 7 of 25 94 and news items. A few sources represent the US market, which had the highest Assets under Management (AuM) in RAs in 2023, providing insight into more established RA use (Statista, 2023). TableA1 in the Appendix provides an overview of all sources. We acknowledge that the selection of secondary data has limitations. Locating relevant data sources, retrieving them, and evaluating their quality can be challenging (Hox & Boeije, 2005). While the use of grey literature is debated, it can represent a useful tool in earlystage research “to uncover innovative information and to shorten the time between research and practice” (Pappas & Williams, 2011, p. 228). Thus, we utilized these data sources and triangulated them to increase our understanding of the emergence of RAs. Data analysis Following the collection of data, the analysis involved several steps: First, in an iterative process, the combined insights from primary and secondary sources were used to generate a comprehensive picture of the current RA market, adoption and challenges at incumbent firms. Rather than using sample-to-population statistical generalisations—which require large samples—we employed analytical generalisation. This method allowed us to compare our data to existing theory by making projections about the likely transferability of findings through theoretical analysis of the factors producing the outcomes and their context. The study focuses on “particularization, not generalization” (Stake, 1995), emphasizing theory building instead of theory testing. Reflecting on recent debates about qualitative data analysis, Kevin Corley notes that “there is no one best way to gather or analyse qualitative data for inductive/abductive purposes” and that using a systematic approach can sometimes be appropriate (Gioia etal., 2022, p. 247). Therefore, in a second step, to structure and organise our interview data, we chose the Gioia technique for data coding and analysis. Through this process, we aimed for qualitative rigour by presenting our research process and the connection from data to concepts and theory in a structured and transparent way. Initially, we compiled the data and familiarized ourselves with the NVivo software package for qualitative data analysis by searching for common topics mentioned by participants. We then chose first-order codes to categorise statements “as close as possible to the voice of the informants themselves” (Mees-Buss etal., 2022, p. 411) in Microsoft Excel. Following this initial conceptualisation, we explored whether “emerging themes suggest[ed] concepts that might help us describe and explain the phenomena we are [were] observing” (D. A. Gioia etal., 2013, p. 20). During this step, we also merged and separated first-order codes that were either very similar or overly complex. Emerging from these two stages of synthesis, we began to iterate between “emergent data, themes, concepts […] and the relevant literature” (D. A. Gioia etal., 2013, p. 21) to form aggregate dimensions. To further validate emerging themes and dimensions, we used secondary sources to review and confirm our interview data, continuing an iterative process between the two. Figure1 shows the resulting data structure, which we elaborate on further in the findings. In a third step, based on the data and insights from the literature, we derived six factors (aggregate dimensions) for successful RA adoption, representing firm-level considerations (RQ1) and actions (RQ2). In alignment with the TOE framework, factors reflect technological, organisational, and external aspects. In continuation, aiming to contribute to theory development (Stake, 1995), we derived propositions, following established guidance on theorizing (Cornelissen, 2017) and consistent with common practices in qualitative research (e.g., Cañibano etal., 2025). These propositions articulate (1) the considerations required for automation, augmentation, and their interplay, and (2) the corresponding actions that support each consideration. Accordingly, automation strategies emphasize a technology-centred perspective, augmentation strategies highlight a human-centred approach, and combined automation-augmentation strategies necessitate a holistic view that integrates both. The propositions were primarily derived from empirical insights, complemented by theoretical reflection (Ulaga etal., 2021). Building on these, we developed a conceptual model aligned with our RQs and propositions. The propositions were embedded in our conceptual model (similar to Alaassar etal., 2022). The model delineates six factors, each representing considerations and corresponding actions for RA adoption, and their links to automation and augmentation. Finally, we translated these propositions into practical guidelines for financial service firms aiming to adopt RAs—providing one overarching guideline as key consideration and six actionable guidelines based on six dimensions. Findings As Fig.1 shows, our study identified six key aggregated dimensions to consider for the adoption of RAs at incumbent firms: drivers for RA adoption, technology-driven service provision, redefinition of advisor-client relationships, organisational integration of RAs alongside human advisors, technical requirements and integration of RAs, and resource acquisition and optimisation. The sections below delve into each aggregated dimension by examining both primary (see interviewee codes in Table1) and secondary data.(Appendix A1) Drivers forRA adoption To understand the effect RAs have on incumbent financial institutions, it is necessary to understand the origin and purpose of robo-advice. In the search for the early beginnings of Electronic Markets (2025) 35:94 94 Page 8 of 25 robo-advice just a few years ago, storylines about its origin denote the promise of an innovative, low-cost financial solution for the masses (Banarjee, 2017): “While bankers were walking around Wall Street with their impeccably tailored suits and their designer leather briefcases, 29-year-old Jon Stein came up with a new idea and decided to compete with Wall Street’s “wolves” with an innovative new financial product. […] In August 2008, Jon Stein, with his roommate and friends, signed a simple Betterment founder agreement. With the mission to help people live a better lives using a more convenient investment solution, they built Betterment from the perspective of enhanced customer experience, improving the customer’s operational convenience and overall user experience. At this Fig. 1 Gioia data structure Electronic Markets (2025) 35:94 Page 9 of 25 94 Fig. 1 (continued) Electronic Markets (2025) 35:94 94 Page 16 of 25 may facilitate work augmentation as workers share tasks with the RA/AI technology. Although scholars continue to debate automation and augmentation, as of recently, there is no “silver bullet” for its adoption, and it often will depend on the particular situation or task at hand (Nguyen & Elbanna, 2025). To provide a basis for further research, we therefore selected the specific scenario of robo-advice to show the possible interplay of automation and augmentation in service transactions. This further underpins the breadth of such endeavours and the need for organisations to adopt holistic approaches considerate of people, culture, technology, and processes—reflecting approaches known from digital transformation (Evans etal., 2022; Satwekar etal., 2024). Consistent with Raisch and Krakowski (2021), our findings show that managing the balance between automation and augmentation involves a certain dynamic throughout the adoption process. Even more so, we find that organisations in the early adoption phase may be driven to automate with the aim of catering to customers in order to stay competitive and relevant. Augmentation, as such, not only becomes part of integrating automation (e.g., by requiring human oversight or input for automating certain tasks and processes) but evolves as a requirement for fully scaled integration. Thus, what researchers refer to as intertwining and entangling (e.g., Isaza & Cepa, 2024; Pentzold & Bischof, 2023), we find reflected in combined automation and augmentation strategies, where both coexist in the adoption process. Throughout the process, our findings therefore suggest an iterative process thereby shifting back and forth from exploring technological aspects and human aspects. Considering both automation and augmentation then entails mapping when and where in each step (e.g., of the investment management process), human worker or machine (here the RA) are the main actor. Thus, instead of shifting the process to be machine operated (only), a holistic and augmentation-oriented approach considers the effect on workers, their abilities (compared to machine), shift in required skills, shift in client relationship and contact (compared to prior process). From that point onward, adoption necessitates technological, organisational, and external factors to sustain the transformation process—reaffirming the TOE framework as a relevant tool for exploring automation and augmentation. Second, our findings have noteworthy implications for the evolution of the advisory role and customers in service interactions. Beyond the concepts of automation and augmentation applicable across contexts, our findings further Fig. 2 Firm-level considerations and actions for automating and augmenting with SST Electronic Markets (2025) 35:94 Page 17 of 25 94 our understanding in services. As such, we integrate and emphasize the notion of hybrid service models and the emergent role of digital (increasingly smart) SST—fundamental in balancing worker and skills shortages as well as lacking financial inclusion. Building on Beverungen etal. (2019) and their conceptualisation of smart products as boundary objects, we position service consumer and service provider connected through SST in a comprehensive service system, with varying tasks—dependent on abilities and service requirements. In particular, we find that—in this way—SST contributes not only to automation (through customers) but to augmentation by integrating self-service solutions with workers, providing as-needed touchpoints for interaction, communication, and data-driven insights. In the specific context of robo-advice, Dietzmann etal. (2023) outline three dimensions of human-AI interaction, from automation to an omnichannel approach, where advisors serve as network coordinators delivering AI-assisted services. We extend this concept by showing that these hybrid services, tailored for banking customers and advisors, effectively achieve both automation and augmentation. Doing so gives rise to new services on the frontend, requiring active participation of customers, while adapting established practices at the backend, integrating human workers. Consequently, both RAs and human advisors need to maintain a balanced operation where advisor involvement varies with RA maturity (increasing AI adoption) and customer preferences (Altrock etal., 2023; Dietzmann etal., 2023). Adding to Fabri etal. (2023), this confirms the needed mutual recognition whereby then, SST and worker augment each other in their functions. Without a comprehensive hybrid model, a resulting imbalance might therefore have negative consequences such as reducing individuals’ performance (M. L. Bartlett & McCarley, 2019). Thus, as emphasized by our findings, holistic hybrid strategies that link worker and customer with SST help establish human-RA collaboration that ensures coexistence while maintaining accountability and human touchpoint. In particular, as AI emerges across sectors, we have yet to explore its practical implications for service workers, behaviours, skills, strategies, organisational performance and other stakeholders, along with the role of contextual factors (Coombs etal., 2020). According to Spring etal. (2022), AI systems not only automate but also augment by freeing up professionals’ time for “value-adding advisory work” and providing new technological insights and actions. While our research confirms that advisors may shift their tasks, we find differing opinions regarding their exact nature. For instance, as financial service firms may introduce (third-party) RAs, new tasks emerge such as managing and collaborating with (external) technology providers or development teams, fostering collaborations with FinTech entities, ensuring maintenance and technical integration within existing structures and workflows, or guaranteeing regulatory compliance. Introducing RAs thus is not only a matter of automation and new service offerings but of process adaptations, organisational transformation, and human resource development as the role of financial advisors evolves (Dietzmann etal., 2023), as underlined by our findings. As such, RAs may require a shift in thinking from simple automated investment management tools to integrated self-service solutions as part of a bigger systemic change towards comprehensive and hybrid service platforms. As a third contribution, our study provides interdisciplinary insight, connected research streams, and advancing broader debates on digital transformation, service automation, and AI adoption. Scholars have continuously highlighted that introducing digital technologies and innovation pose challenges to a wide array of social, economic and organisational norms and their respective stakeholders (Dąbrowska etal., 2022; Mariani etal., 2023; Tornatzky & Fleischer, 1990). While RAs provide new opportunities for human financial advisors, our findings show that such collaborative work scenarios lack the technological and organisational foundation to not only adopt but to fully integrate RAs in a way that fulfils their potential. As such, in order to accelerate robo-advice, we argue for adopting a holistic view of SST-driven automation and augmentation that understands all its sociotechnical implications “to enhance the performance of work systems” (Pasmore etal., 2019, p. 67). Designing next-generation sociotechnical systems then requires balanced optimisation, evolution, and alignment across ecosystems, organisations, technical, and social systems (Pasmore etal., 2019). This approach necessitates interdisciplinary collaboration to create comprehensive hybrid service models for both robo-advice and automated services across sectors, such as healthcare, consulting, social, or legal services. Moving towards hybrid services, as we show, combines the technical aspects of offering fully digitalised and automated access (automation) with the human aspects of retaining the interpersonal touchpoint for advice (augmentation), resulting in comprehensive transformations. In a reciprocal way, then, machine capabilities become extended by human capability and vice versa, providing holistic, integrated, and future-proof services combining the best of each. Limitations andfurther research While we believe that the firm-level considerations and actions identified in our study may have broad applicability across various sectors, we acknowledge that organisational and sector-specific differences may necessitate alternative or modified approaches. Changes in roles and relationships between service providers and users, as well as the specific activities and SST involved, will be unique to each sector. Electronic Markets (2025) 35:94 94 Page 18 of 25 For instance, a digital therapy solution for patients seeking mental health treatment online could similarly enable hybrid services, allowing healthcare professionals to provide enhanced and more accessible support (Bond etal., 2023). However, compared to investment management, the needed guidance and involvement will be playing a more important role requiring more specific mechanisms to ensure that human interaction remains whereas in robo-advice that option may be more voluntary. Similarly to financial advice, healthcare professionals may need to increase digital literacy in order to utilize and adopt such tools; however, while financial advisors may be able to redirect to other activities (Altrock etal., 2023), it may be debatable to what extent healthcare professionals will be able to do so. More research will thus be required to explore these aspects across sectors and contexts. Though we have explored RAs through the lens of service automation and augmentation, other perspectives can be applied. As a SST, the RA takes on the role of a customer tool, although the impact on employees is increasingly being noticed. From another angle, the RA can be viewed as a human advisor’s co-worker (Tarafdar etal., 2022), where the employee’s contribution will extend beyond “education, support and recovery” (Hilton etal., 2013), such as supporting the client–advisor relationship (Dietzmann etal., 2023) when customers seek more than self-service. Further research should explore RA adoption in organisations adopting RAs in hybrid service models (to date specifically in the USA) and explore RAs as co-workers. Complementing Altrock etal. (2023), who suggest a range of robo-solutions with varying degrees of automation, we argue that roboadvice as such is not an either/or proposition; rather, it provides various stages of interaction from more automated to more augmented. Future research should explore the definitions and application areas of RAs, inside and outside of the financial context, and how they affect RA adoption and integration into organisational contexts. Furthermore, the relevance of the six considerations and actions, as well as our propositions, should be further tested empirically for the integration of RAs and other SST in the financial and in related service organisations. Potential for automation and augmentation might differ across contexts depending on the services provided by professionals and the needs of respective users. Furthermore, we note the need to explore the interactions of automation, augmentation and SSTs in the context of service delivery to push for organisational responsibility in enhancing worker wellbeing and customer emancipation (Lobschat etal., 2021; Wirtz etal., 2023). Research should explore the role of corporate digital responsibility and how hybrid service models can contribute to worker wellbeing. Finally, adding to our study, we encourage continued efforts in interdisciplinary research. Current contributions on robo-advice are represented in fields like information systems (Ganbold etal., 2022; Ge etal., 2021; Jung etal., 2018a, 2018b; Maedche etal., 2019), service science (Flavián etal., 2022; Wexler & Oberlander, 2020) and service marketing (Hildebrand & Bergner, 2021; Lewis, 2018; Manrai & Gupta, 2022; L. Zhang etal., 2021). Although research in these areas provides relevant insights, we call for more interdisciplinary research that uses a wider lens on the full range of automation’s impact. Value creation thus should not be viewed only from a customer perspective but also from the intra-organisational perspective, as “SST will require effective management of customers and employees” (Hilton etal., 2013, p. 8). As such, integrating hybrid service models may entail, for instance, aspects of human-in-the-loop systems, service process design, work redesign, change management, interface design, or service marketing incorporating various micro-, meso-, and macrolevel aspects. Extending on pure service and management theory, transitioning hybrid service models into today’s days and age of AI and industry 5.0 therefore requires a broader lens incorporating the shift in services and the role of individuals in it. This further will allow generating a merging of automation and augmentation whereby automation focuses on the technological, and augmentation on the individual, organisational, and interpersonal. Despite the relevance of our findings, this study is further limited in scope and generalisability of results due to the early stages of RA adoption and the limited resources and cases to study. However, in order to develop theories that help explain present phenomena (Schwarz & Stensaker, 2016) and alter how we react to them in practice, we ought to “narrow the gap between understanding and living” (Weick, 1999, p. 135). We thus aim to fuel the discourse and provide a basis for further research on service automation and its impact on workers and service delivery. Practical implications For practitioners, our findings highlight a few things. First, they emphasize that the adoption of RAs necessitates a comprehensive adoption strategy that will allow for customer automation and worker augmentation at the same time. Second, we present six considerations for practitioners seeking to adopt RAs or those who are challenged by increasing the value added by RAs in existing operations: drivers of RA adoption, the increasing significance of technology in service provision, a redefinition of customer relationships and worker roles, integration on an organisational level, technical requirements, and the resources required. Thus, we find various reasons for financial service firms to adopt RAs. The adoption of RAs then becomes part of technology-driven service provision, requiring firms to explore how hybrid service provision models can be facilitated and how Electronic Markets (2025) 35:94 Page 19 of 25 94 they affect other organisational aspects. This, particularly, entails the impact of financial advisors, their roles, but also the changing involvement of customers. Next to the needed technological foundation to adopt RA technologies as a platform, organisational integration will be required. While financial firms will have different capabilities inside the firm, collaboration with external companies and the add-on of needed resources and capabilities should be considered, as well as regulatory guidance or other support coming from additional stakeholders. In summary of the firm-level actions outlined in Fig.2, we derive seven general guidelines from the propositions for RA adoption in financial service firms, as detailed in Table3. Further, we argue that a view of RAs independent from the organisation, its workers and the services provided fails to provide the anticipated benefits and thus calls for approaching RAs through holistic integration where, first, automation too involves augmentation (Raisch & Krakowski, 2021), and second, adaptation not only concerns the organisation internally in a human-centred approach (Patricio etal., 2022), but also must be integrated into the surrounding ecosystem as a whole (Hanelt etal., 2021). As a result, RAs do never fully replace advisors; they change how financial services operate, paving the way towards a more integrative view of the provision of democratised and holistic financial services (Finke & Blanchett, 2021; Silver, 2021). Holistic strategies for hybrid services, we argue, require approaching RA adoption from two angles. First, organisations may be driven by providing new solutions to customers emerging from the automation lens. RAs provide accessible solutions for an old and a new customer base. In order to provide RAs to customers, thus, organisations should start from the lens of offering a new digital service and ensuring the technological capabilities. Depending on the most feasible RA solution—which may depend on regulatory, monetary, or other factors—the adoption and integration strategy requires the second, and thus, the augmentation angle. Here, the objective extends beyond the customer solution towards exploring the RA impact holistically and, specifically, how the solution will become integral to the new service delivery. This entails strategizing for, e.g., changes in worker roles and skills, adaptation of work processes, establishing the boundaries and mutual benefit of RA and human advisor, the possibility to include external support, and ensuring compliance and service quality. As such, interdisciplinary teams may be indispensable to take digital services to the next level and establish hybrid services instead. To ensure RA success, we therefore propose taking RA adoption beyond the lens of customer automation and encourage financial institutions to investigate micro-, meso-, and macro-level changes. Conclusion Automation is spreading rapidly across industries, even as the growth in various AI technologies and robots continues to drive the debate on the organisational changes it entails. This paper has explored the adoption of RAs to understand changes to incumbent financial firms and their workers as they adopt SST. Building on a persistent discourse on augmentation, and thus, balancing human intervention and machine component calls for a deeper understanding of what is required of organisations to succeed. The findings of this qualitative empirical study show that the application of RAs at incumbent firms varies, although both drivers and challenges that affect firms can be found. Our conceptual framework presents key considerations and actions required to integrate SST strategically and holistically into existing businesses and thus provides a foundation for continued exploration into changing service delivery models affected by SST and similar platforms with varying degrees of AI. As a result, this paper sheds light on the topics of digital transformation, service automation, and worker augmentation in professional services and provides direction and guidance for scholars and organisational leaders alike. Table 3 Practitioner guidelines for RA Adoption Proposition (P) Guideline (P1), (P2), (P3) 1: Approach RA adoption not just as an automation project, but as an augmentation project: Consider changes in human advisor role, skills, and client relationships (P4) 2: Explore RA adoption and the role of RAs for new and existing clients: Match varying client groups and needs with varying types and abilities of the RA (P5) 3: Aim to adopt RAs in a hybrid multichannel service model with extension potential (P6) 4: Integrate human advisors throughout the RA adoption process (from development to service provision) (P7) 5: Enable and support full organisational integration of RAs: e.g., change mgmt., adaptation of service delivery and work processes (balancing of RA and human advisor) (P8) 6: Provide the necessary technical foundation for RA adoption: e.g., infrastructure, data, interface design (P9) 7: Consider collaboration potential with external stakeholders: e.g., for RA solution, technical skills Electronic Markets (2025) 35:94 94 Page 20 of 25 Supplementary Information The online version contains supplementary material available at https:// doi. or g/ 10. 1007/ s1252502500835-2. Funding Open Access funding enabled and organized by CAUL and its Member Institutions. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 956745. Results reflect the author’s view only. The European Commission is not responsible for any use that may be made of the information it contains. Data Availability Selected quotes from study informants are included in the article and an overview of collected data can be found in the presented data structure. Restrictions apply to the full availability of the dataof this study, which was collected with anonymity and limited data access ensured to informants taking part in the study. Summaries of the interview contents that support these findings areavailable from the corresponding author upon reasonable request. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. References Abed, S. S. (2020). Social commerce adoption using TOE framework: An empirical investigation of Saudi Arabian SMEs. International Journal of Information Management. https:// doi. org/ 10. 1016/j. ijinf omgt. 2020. 102118 Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3–30. https:// doi. org/ 10. 1257/ jep. 33.2.3 Aigner,T.(2019).Robo-Advisor-Studie mit Prof. Francesco D’Acunto [Podcast]. In Money, Markets & Machines. By Scalable Capital. Spotify. Available at: https:// open. spoti fy. com Alaassar, A., Mention, A. L., & Aas, T. H. (2022). Ecosystem dynamics: Exploring the interplay within fintech entrepreneurial ecosystems. Small Business Economics, 58(4), 2157–2182. https:// doi. org/ 10. 1007/ s1118702100505-5 Alter, S. (2002). The work system method for understanding information systems and information systems research. Communications of the Association for Information Systems, 9. https:// doi. org/ 10. 17705/ 1cais. 00906 Altrock, S., Mention, A.-L., & Aas, T. H. (2023). Being human in the digitally enabled workplace: Insights from the robo-advice literature. IEEE Transactions on Engineering Management, 1–16. https:// doi. org/ 10. 1109/ TEM. 2023. 32918 20 Athota, V. S., Pereira, V., Hasan, Z., Vaz, D., Laker, B., & Reppas, D. (2023). Overcoming financial planners’ cognitive biases through digitalization: A qualitative study. Journal of Business Research. https:// doi. org/ 10. 1016/j. jbusr es. 2022. 08. 055 Atwal, G., & Bryson, D. (2021). Antecedents of intention to adopt artificial intelligence services by consumers in personal financial investing. Strategic Change, 30(3), 293–298. https:// doi. org/ 10. 1002/ jsc. 2412 Awa, H. O., Ukoha, O., & Igwe, S. R. (2017). Revisiting technologyorganization-environment (T-O-E) theory for enriched applicability. Bottom Line, 30(1), 2–22. https:// doi. org/ 10. 1108/ BL1220160044 Baer, I., Waardenburg, L., & Huysman, M. (2025). What is augmented? A metanarrative review of AI-based augmentation. Journal of the Association for Information Systems, 26(3), 760–798. https:// doi. org/ 10. 17705/ 1jais. 00921 Baghai, P., D’Amico, A., Golyk, V., Salvo, A., & Zucker, J. (2022). US wealth management: A growth agenda for the coming decade. McKinsey & Company. Available at: https:// www. mckin sey. com/ ~/ media/ mckin sey/ indus tries/ finan cial% 20ser vices/ our% 20ins ights/ us% 20wea lth% 20man ag eme nt% 20a% 20gro wth% 20age nda% 20for% 20the% 20com ing% 20dec ade/ usw ea lthmanag ement-agrowthag endaforthecomingdecade. pdf. Accessed 26 Aug 2022. Banarjee, P. (2017). 57: Dan Hallett on robo-advisors, financial advice, and CRM2. Mostly Money[Podcast].In Mostly Money. Spotify.Available at: https:// open. spoti fy. com Barrett, M., Davidson, E., Prabhu, J., & Vargo, S. L. (2015). Service innovation in the digital age: Key contributions and future directions. MIS Quarterly, 39(1), 135–154. https:// doi. org/ 10. 25300/ MISQ/ 2015/ 39:1. 03 Bartlett, L., & Vavrus, F. (2017). Rethinking Case Study Research. Taylor & Francis. Bartlett, M. L., & McCarley, J. S. (2019). Human interaction with automated aids: Implications for robo-advisors. Financial Planning Review. https:// doi. org/ 10. 1002/ cfp2. 1059 Benzies, K. M., Premji, S., Hayden, K. A., & Serrett, K. (2006). Stateof-the-evidence reviews: Advantages and challenges of including grey literature. Worldviews on Evidence-Based Nursing, 3(2), 55–61. https:// doi. org/ 10. 1111/j. 17416787. 2006. 00051.x Bessen, J., & Kossuth, J. (2019). Research: Automation affects highskill workers more often, but low-skill workers more deeply. Harvard Business Review. https:// hbr. org/ 2019/ 02/ resea rchautom ationaffec tshighskillworke rsmoreoftenbutlow - skillworke rsmoredeeply.Accessed 4 Mar 2025. Bessen, J., Goos, M., & Salomons, A. (2020). Automation: A Guide for Policymakers. Economic Studies at Brookings. https:// www. yang2 020. com/ whatisfreed omdivid endfaq/.Accessed 20 Nov 2023. Beverungen, D., Müller, O., Matzner, M., Mendling, J., & vom Brocke, J. (2019). Conceptualizing smart service systems. Electronic Markets, 29(1), 7–18. https:// doi. org/ 10. 1007/ s125250170270-5 Bhatia, A., Chandani, A., & Chhateja, J. (2020). Robo advisory and its potential in addressing the behavioral biases of investors — A qualitative study in Indian context. Journal of Behavioral and Experimental Finance. https:// doi. org/ 10. 1016/j. jbef. 2020. 100281 Bhatia, A., Chandani, A., Atiq, R., Mehta, M., & Divekar, R. (2021). Artificial intelligence in financial services: A qualitative research to discover robo-advisory services. Qualitative Research in Financial Markets, 13(5), 632–654. https:// doi. org/ 10. 1108/ QRFM1020200199 Bitner, M. J., Ostrom, A. L., & Meuter, M. L. (2002). Implementing successful self-service technologies. Academy of Management Executive, 16(4), 96–109. Blöcher, K., & Alt, R. (2021). AI and robotics in the European restaurant sector: Assessing potentials for process innovation in a highcontact service industry. Electronic Markets, 31(3), 529–551. https:// doi. org/ 10. 1007/ s1252502000443-2 Block, S., & von Schirach, T. (2023). Shortage of skilled workers – an existential threat to banks and insurance companies. zeb Electronic Markets (2025) 35:94 Page 21 of 25 94 Consulting. Available at: https:// www. banki nghub. eu/ human resou rces/ short ageskill edworke rsbanks Bond, R. R., Mulvenna, M. D., Potts, C., O’Neill, S., Ennis, E., & Torous, J. (2023). Digital transformation of mental health services. Npj Mental Health Research, 2(1), Article 13. https:// doi. org/ 10. 1038/ s4418402300033-y Cañibano, A., Dudau, A., & Muratbekova-Touron, M. (2025). Publishing qualitative research in EMJ: Editorial reflections. European Management Journal, 43(3), 344–350. https:/ / doi. o rg/ 10. 1016/j. emj. 2025. 03. 006 CFA Institute. (2022). CFA Annual Report 2021. CFA Institute. Available at: https:// www. cfain stitu te. org/ sites/ defau lt/ files/-/ media/ docum ents/ corpo raterecord/ annualreport2021. pdf Christou, P., Hadjielias, E., Simillidou, A., & Kvasova, O. (2023). The use of intelligent automation as a form of digital transformation in tourism: Towards a hybrid experiential offering. Journal of Business Research. https:// doi. org/ 10. 1016/j. jbusr es. 2022. 113415 Coombs, C., & Redman, A. (2018). The impact of robo-advice on financial advisers: A qualitative case study. UK Academy for Information Systems Conference Proceedings, 2018(17). Available at: https:// aisel. aisnet. org/ ukais 2018/ 17 Coombs, C., Hislop, D., Taneva, S. K., & Barnard, S. (2020). The strategic impacts of intelligent automation for knowledge and service work: An interdisciplinary review. The Journal of Strategic Information Systems. https:// doi. org/ 10. 1016/j. jsis. 2020. 101600 Connelly, A. (2020). #040 So helfen Robo Advisor, die passende Geldanlage zu finden [Podcast]. In Der herMoney Talk: Geldund Karrierepodcast für Frauen. Spotify. Available at: https:// open.spotify.com Cornelissen, J. (2017). Editor’s comments: Developing propositions, a process model, or a typology? Addressing the challenges of writing theory without a boilerplate. In Academy of Management Review (Vol. 42, Issue 1, pp. 1–9). Academy of Management. https:// doi. org/ 10. 5465/ amr. 2016. 0196 Curran, J. M., & Meuter, M. L. (2005). Self-service technology adoption: Comparing three technologies. Journal of Services Marketing, 19(2), 103–113. https:// doi. org/ 10. 1108/ 08876 04051 05914 11 Dąbrowska, J., Almpanopoulou, A., Brem, A., Chesbrough, H., Cucino, V., Di Minin, A., Giones, F., Hakala, H., Marullo, C., Mention, A., Mortara, L., Nørskov, S., Nylund, P. A., Oddo, C. M., Radziwon, A., & Ritala, P. (2022). Digital transformation, for better or worse: A critical multi-level research agenda. R & D Management, 52(5), 930–954. https:// doi. org/ 10. 1111/ radm. 12531 Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https:// doi. org/ 10. 2307/ 249008 De Reuver, M., Sørensen, C., & Basole, R. C. (2017). The Digital Platform: A Research Agenda. https:// doi. org/ 10. 1057/ s41265 De Witte, M. (2023). How will ChatGPT change the way we think and work? | Stanford News. Standford News. https:// news. st anf ord. edu/ 2023/ 02/ 13/ willchatg ptchangewaythinkwork/.Accessed 27 Feb 2023. Di Battista, A., Grayling, S., Hasselaar, E., Leopold, T., Li, R., Rayner, M., & Zahidi, S. (2023). Future of jobs report 2023.World Economic Forum. Available at: https:// www. wefor um. org. Accessed 24 Aug 2023. Dietzmann, C., Jaeggi, T., & Alt, R. (2023). Implications of AI-based robo-advisory for private banking investment advisory. Journal of Electronic Business & Digital Economics. https:// doi. org/ 10. 1108/ jebde0920220037 Euro Banking Association. (2019). Artificial intelligence in the era of open banking. Euro Banking Association. Available at: https:// www. abeeba. eu/ wpcon tent/ uploa ds/ 2025/ 04/ eba_ 201906_ ob wg_ ai_ in_ the_ era_ of_ open_ banki ng_ single_ pag e_ view. pdf Evans, N., Miklosik, A., Bosua, R., & Mahmood Ahmed Qureshi, A. (2022). Digital business transformation: An experience-based holistic framework. IEEE Access, 10, 121930–121939. https:// doi. org/ 10. 1109/ ACCESS. 2022. 32219 84 Fabri, L., Häckel, B., Oberländer, A. M., Rieg, M., & Stohr, A. (2023). Disentangling human-AI hybrids: Conceptualizing the interworking of humans and AI-enabled systems. Business and Information Systems Engineering, 65(6), 623–641. https:// doi. org/ 10. 1007/ s1259902300810-1 Fan, L., & Chatterjee, S. (2020). The utilization of robo-advisors by individual investors: An analysis using diffusion of innovation and information search frameworks. Journal of Financial Counseling and Planning, 31(1), 130–145. https:// doi. org/ 10. 1891/ JFCP1800078 Felix, B., & Passmore, C. (2019). The future of Canadian fintech: Leading the charge with robo-advisors (Ep. 41) [Podcast]. In Rational Reminder Podcast. Spotify. Available at: https:// open. spoti fy. com Finke, M., & Blanchett, D. (2021). Ep. 19: Robo-advisors [Podcast]. In Wealth, Managed. Spotify. Available at: https:// open. spoti fy. com Firescu, V., Gaşpar, M. L., Crucianu, I., & Rotariu, E. (2022). Collaboration between humans and robots in organizations: A macroergonomic, emotional, and spiritual approach. Frontiers in Psychology. https:// doi. org/ 10. 3389/ fpsyg. 2022. 855768 Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services – The effect of technology readiness and awareness. Journal of Service Management, 33(2), 293–320. https:// doi. org/ 10. 1108/ JOSM1020200378 French, O. (2019). How to build a robo-advisor [Podcast]. The Bid. By BlackRock. Spotify. Available at: https:// open. spoti fy. com Frey, C. B., & Osborne, M. A. (2017). The future of employment: How susceptible are jobs to computerisation? Technological Forecasting And Social Change, 114, 254–280. https:// doi. org/ 10. 1016/j. techf ore. 2016. 08. 019 Fürstenau, D., Klein, S., Vogel, A., & Auschra, C. (2021). Multisided platform and data-driven care research a longitudinal case study on business model innovation for improving care in complex neurological diseases. Electronic Markets, 31(4), 811–828. https:// doi. org/ 10. 1007/ s1252502100461-8 Gan, L. Y., Khan, M. T. I., & Liew, T. W. (2021). Understanding consumer’s adoption of financial robo-advisors at the outbreak of the COVID-19 crisis in Malaysia. Financial Planning Review. https:// doi. org/ 10. 1002/ cfp2. 1127 Ganbold, O., Rose, A. M., Rose, J. M., & Rotaru, K. (2022). Increasing reliance on financial advice with avatars: The effects of competence and complexity on algorithm aversion. Journal of Information Systems, 36(1), 7–17. https:// doi. org/ 10. 2308/ ISYS2021002 Ge, R., Zheng, Z., Tian, X., & Liao, L. (2021). Human-robot interaction: When investors adjust the usage of robo-advisors in peerto-peer lending. Information Systems Research, 32(3), 774–785. https:// doi. org/ 10. 1287/ ISRE. 2021. 1009 Gerlach, J. M., & Lutz, J. K. T. (2021). Digital financial advice solutions – Evidence on factors affecting the future usage intention and the moderating effect of experience. Journal of Economics and Business. https:// doi. org/ 10. 1016/j. jecon bus. 2021. 106009 Gioia, D., Corley, K., Eisenhardt, K., Feldman, M., Langley, A., Lê, J., Golden-Biddle, K., Locke, K., Mees-Buss, J., Piekkari, R., Ravasi, D., Rerup, C., Schmid, T., Silverman, D., & Welch, C. (2022). A curated debate: On using “templates” in qualitative research. Journal of Management Inquiry, 31(3), 231–252. https:// doi. org/ 10. 1177/ 10564 92622 10989 55 Electronic Markets (2025) 35:94 94 Page 22 of 25 Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational Research Methods, 16(1), 15–31. https:// doi. org/ 10. 1177/ 10944 28112 452151 Golba, B. (2017). In search of a sweet spot for wealth management digitisation: Human versus technology, provider versus customer perspective. Journal of Digital Banking, 2(3), 214–222. https:// doi. org/ 10. 69554/ VGBR8 639 Graseck, B. L., Stoklosa, M. L., Lord, N., Cyprys, M. J., Gosalia, M., Dobreva, I., Kenny, R., Upadek, K., Edelmann, C., Kellum, B., Zerafa, L., Gorski, J., Rodrigues, J. M., & Schroeder, P. (2020). After the storm. In Global wealth management report (pp. 1–39). Morgan Stanley & Oliver Wyman. Available at: https:// www. olive r wyman. com/ conte nt/ dam/ olive rwyman/ v2/ publi catio ns/ 2020/ jul/ 2020GlobalWealt hManag ementReportAftertheStorm. pdf. Accessed 26 Aug 2022. Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of Management Studies, 58(5), 1159–1197. https:// doi. org/ 10. 1111/ joms. 12639 Hildebrand, C., & Bergner, A. (2021). Conversational robo advisors as surrogates of trust: Onboarding experience, firm perception, and consumer financial decision making. Journal of the Academy of Marketing Science , 49. https:// doi. org/ 10. 1007/ s11747020 Hilton, T., Hughes, T., Little, ed, & Marandi, E. (2013). Adopting self-service technology to do more with less. Journal of Services Marketing, 27(1), 3–12. https:// doi. org/ 10. 1108/ 08876 04131 12963 38 Hofmann, P., Samp, C., & Urbach, N. (2020). Robotic process automation. Electronic Markets, 30(1), 99–106. https:// doi. org/ 10. 1007/ s1252501900365-8 Hox, J. J., & Boeije, H. R. (2005). Data collection, primary vs. secondary.In K. Kempf-Leonard (Ed.), Encyclopedia of social measurement (Vol. 1, pp. 593–599). Elsevier. Hsu, P. F., Nguyen, T. K., & Huang, J. Y. (2021). Value co-creation and co-destruction in self-service technology: A customer’s perspective. Electronic Commerce Research and Applications. https:// doi. org/ 10. 1016/j. elerap. 2021. 101029 Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30–41. https:// doi. org/ 10. 1177/ 10946 70520 902266 Isaza, L., & Cepa, K. (2024). Automation and augmentation: A process study of how robotization shapes tasks of operational employees. European Management Journal. https:// doi. org/ 10. 1016/j. emj. 2024. 11. 010 Iskowitz, M. (2020). Waiting for the other shoe to drop: After Motif closes, which robo-advisors will survive? WealthTech Today. Available at: https:// wealt htech today. com/ 2020/ 05/ 01/ waiti ngfortheot hershoetodropaftermotifcloseswhichroboa dviso rswillsurvi ve/ Ivanov, S., & Webster, C. (2019). Conceptual framework of the use of robots, artificial intelligence and service automation in travel, tourism, and hospitality companies. In Robots, Artificial Intelligence and Service Automation in Travel, Tourism and Hospitality (pp. 7–37). Emerald Group Publishing Ltd. https:// doi. org/ 10. 1108/ 978-17875668732019 1001 Johnson, M. (2023). A brave new world: ChatGPT’s potential to reshape the financial services landscape. Forbes. https:// www. forbes. com/ sites/ meagh anjoh nson/ 2023/ 03/ 20/abravenewworldchatg ptspoten tialtoresha pethefinan ciallands cape/? sh= 10070 3b764 04.Accessed 24 Mar 2023. Jourdan, Z., Corley, J. K., Valentine, R., & Tran, A. M. (2023). Fintech: A content analysis of the finance and information systems literature. Electronic Markets, 33, 2. https:// doi. org/ 10. 1007/ s1252502300624-9 Jung, D., & Weinhardt, C. (2018). Robo-advisors and financial decision inertia: How choice architecture helps to reduce inertia in financial planning tools. International conference on information systems (ICIS) 2018 proceedings, paper 4. Available at: https:// aisel. aisnet. org/ icis2 018/ gener al/4 Jung, D., Dorner, V., Glaser, F., & Morana, S. (2018a). Robo-advisory: Digitalization and automation of financial advisory. Business and Information Systems Engineering, 60(1), 81–86. https:// doi. org/ 10. 1007/ s125990180521-9 Jung, D., Dorner, V., Weinhardt, C., & Pusmaz, H. (2018b). Designing a robo-advisor for risk-averse, low-budget consumers. Electronic Markets, 28(3), 367–380. https:// doi. org/ 10. 1007/ s125250170279-9 Kholiya, P. S., Kapoor, A., Rana, M., & Bhushan, M. (2021). Intelligent process automation: The future of digital transformation. Proceedings of the 2021 10th International Conference on System Modeling and Advancement in Research Trends, SMART 2021, 185–190. https:// doi. org/ 10. 1109/ SMART 52563. 2021. 96762 22 Kitces, M. (2021). Ep. 051: What robo-advisors can teach human advisors about evidence-based behavioural finance with Dan Egan [Podcast]. In Financial Advisor Success. Available at: https:// open. spoti fy. com Ko, K.-C., Uchikata, S., & Liu, S. (2021). Consequences of humanizing robo-advisor by adding phatic communication ability: Perceived empathy and investor’s trust. Advances in Consumer Research, 49. Kramer, A. & Röhl, C. W. (2022). egtv #218 – 3-in-1: Lloyd Fonds AG – Vermögensverwalter, Fondsmanager & Robo-Advisor | Achim Plate im CEO-Update [Podcast]. In echtgeld.tv. Spotify. Available at: https:// open. spoti fy. com Lazo, M. P., & Ebardo, R. A. (2023). Artificial intelligence adoption in the banking industry: Current state and future prospects. Journal of Innovation Management, 11(3), 54–74. https:// doi. org/ 10. 24840/ 218 Levine, R., & Mackey, C. (2017). Humans versus robots: Who to turn to for investment advice? CPA Journal. Available at: https:// www. cpajo urnal. com/ 2017/ 05/ 16/ humansversusrobots/ Lewis, D. R. (2018). The perils of overconfidence: Why many consumers fail to seek advice when they really should. Journal of Financial Services Marketing, 23(2), 104–111. https:// doi. org/ 10. 1057/ s412640180048-7 Lobschat, L., Mueller, B., Eggers, F., Brandimarte, L., Diefenbach, S., Kroschke, M., & Wirtz, J. (2021). Corporate digital responsibility. Journal of Business Research, 122, 875–888. https:// doi. org/ 10. 1016/j. jbusr es. 2019. 10. 006 Lopez, J. C., Babcic, S., & De La Ossa, A. (2015). Advice goes virtual: how new digital investment services are changing the wealth management landscape. The Journal of Financial Perspectives, 3(3). https:// ssrn. com/ abstr act= 30840 57Ele ctron iccop y avai lable at.Accessed 30 May 2023. Lorenzen, F. (2021). #106 Erik Podzuweit: Neo-Broker, Aktienmarkt, Überbewertungen, Robo-Advisor, Aktionärskultur & mehr [Podcast]. In Aktienrebell – Rational anlegen & Vermögen aufbauen.Spotify. Available at: https:// open. spoti fy. com Maedche, A., Legner, C., Benlian, A., Berger, B., Gimpel, H., Hess, T., Hinz, O., Morana, S., & Söllner, M. (2019). AI-based digital assistants: Opportunities, threats, and research perspectives. Business and Information Systems Engineering, 61(4), 535–544. https:// doi. org/ 10. 1007/ s1259901900600-8 Manrai, R., & Gupta, K. P. (2022). Investor’s perceptions on artificial intelligence (AI) technology adoption in investment services in India. Journal of Financial Services Marketing. https:// doi. org/ 10. 1057/ s4126402100134-9 Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic Electronic Markets (2025) 35:94 Page 23 of 25 94 review, conceptual framework, and future research directions. Technovation. https:// doi. org/ 10. 1016/j. techn ovati on. 2022. 102623 Mateescu, A., & Elish, M. C. (2019). AI in context: The labor of integrating new technologies. https:// datas ociety. net/ libra ry/ aiinconte xt/.Accessed 20 Nov 2023. Maume, P. (2021). Robo-advisors: How do they fit in the existing EU regulatory framework, in particular with regard to investor protection? Policy department for economic, scientific and quality of life policies. Available at: https:// www. europ arl. eur opa. eu/ R egDa ta/ etudes/ S TUD/ 2021/ 662928/ IPOL_ STU(2021) 662928_ EN . pdf Mayer, E. J. (2023). Interview with Matt Reiner, CEO & Founder of Benjamin and Managing Partner with Capital Investment Advisors – How robo-advisors can’t take the human element out of investing [Podcast]. In For Advisors by Advisors. Spotify. Available at: https:// open. spoti fy. com Mees-Buss, J., Welch, C., & Piekkari, R. (2022). From templates to heuristics: How and why to move beyond the Gioia methodology. Organizational Research Methods, 25(2), 405–429. https:// doi. org/ 10. 1177/ 10944 28120 967716 Megaw, N., & Moise, I. (2022). Half a trillion dollars wiped from once high-flying fintechs. Financial Times. Available at: https:// www. ft. com/ conte nt/ 338ed a4604e546b3a71da429d 21c49 0c Mention, A.-L. (2021). The age of FinTech: Implications for research, policy and practice. The Journal of FinTech, 01(01), Article Article 2050002. https:// doi. org/ 10. 1142/ s2705 10992 05000 29 Merkle, C. (2020). Robo-advice and the future of delegated investment. The Capco Institute Journal of Financial Transformation, 51. https:// ssrn. com/ abstr act= 36129 86.Accessed 16 Feb 2022. Meuter, M. L., Ostrom, A. L., Roundtree, R. I., & Bitner, M. J. (2000). Self-service technologies: Understanding customer satisfaction with technology-based service encounters. Journal of Marketing, 64, 50–64. https:// doi. org/ 10. 1509/ jmkg. 64.3. 50. 18024 Middelburg, J. W. (2017). Service automation framework: For the design and delivery of automated services. Van Haren Publishing. Mingotto, E., Montaguti, F., & Tamma, M. (2021). Challenges in redesigning operations and jobs to embody AI and robotics in services. Findings from a case in the hospitality industry. Electronic Markets, 31(3). https:// doi. org/ 10. 1007/ s1252502000439-y Moulliet, D. etal. (2016). The expansion of robo-advisory in wealth management. Available at: https:// s3euwest-1. amazo n aws. com/ editor. produ ction. press matrix. com/ emags/ 72699/ pdfs/ origi nal/ e62cd a8e27f84ccaa1caf3bf6 d8fb5 50. pdf Nam, K., Dutt, C. S., Chathoth, P., Daghfous, A., & Sajid Khan, M. (2021). The adoption of artificial intelligence and robotics in the hotel industry: Prospects and challenges. Electronic Markets, 31(3). https:// doi. org/ 10. 1007/ s1252502000442-3 Narteh, B. (2015). Perceived service quality and satisfaction of selfservice technology: The case of automated teller machines. International Journal of Quality and Reliability Management, 32(4), 361–380. https:// doi. org/ 10. 1108/ IJQRM0820120113 Nasr, N. (2022). 10 experts share their thoughts on robo-advisors. Robo-AdvisorFinder. Available at: https:// www. roboadvis orfin der. com/ blog/ thoug htsroboadvis ors Nazareno, L., & Schiff, D. S. (2021). The impact of automation and artificial intelligence on worker well-being. Technology in Society. https:// doi. org/ 10. 1016/j. techs oc. 2021. 101679 Nguyen, T., & Elbanna, A. (2025). Understanding human-AI augmentation in the workplace: A review and a future research agenda. Information Systems Frontiers. https:// doi. org/ 10. 1007/ s1079602510591-5 Oehler, A., Horn, M., & Wendt, S. (2021). Investor characteristics and their impact on the decision to use a robo-advisor. Journal of Financial Services Research. https:// doi. org/ 10. 1007/ s1069302100367-8 Oǧuz, F., & Şengün, A. E. (2011). Mystery of the unknown: Revisiting tacit knowledge in the organizational literature. Journal of Knowledge Management, 15(3), 445–461. https:// doi. org/ 10. 1108/ 13673 27111 11374 20 Paluch, S., & Wirtz, J. (2020). Artificial intelligence and robots in the service encounter. Journal of Service Management Research, 4(1), 3–8. https:// doi. org/ 10. 15358/ 251186762020-1-3 Panch, T., Mattie, H., & Celi, L. A. (2019). The “inconvenient truth” about AI in healthcare. Npj Digital Medicine. https:// doi. org/ 10. 1038/ s417460190155-4 Panchanathan, S. (2019). Innovating for the future — Mindsets and skillsets. Technology & Innovation, 21(1), 1–2. https:// doi. org/ 10. 21300/ 21.1. 2019.1 Pappas, C., & Williams, I. (2011). Grey literature: Its emerging importance. Journal of Hospital Librarianship, 11(3), 228–234. https:// doi. org/ 10. 1080/ 15323 269. 2011. 587100 Parasuraman, R., Sheridan, T. B., & Wickens, C. D. (2000). A model for types and levels of human interaction with automation. IEEE Transactions on Systems, Man, and Cybernetics, 30(3), 286–297. https:// doi. org/ 10. 1109/ 3468. 844354 Pasmore, W., Winby, S., Mohrman, S. A., & Vanasse, R. (2019). Reflections: Sociotechnical systems design and organization change. Journal of Change Management, 19(2), 67–85. https:// doi. org/ 10. 1080/ 14697 017. 2018. 15537 61 Paterson, J. M. (2023). Making robo-advisers careful? Duties of care in providing automated financial advice to consumers. Law and Financial Markets Review. https:// doi. org/ 10. 1080/ 17521 440. 2023. 21960 27 Patricio, R., Gomide, P., & Rocha, L. (2022). Taking the digital innovation journey beyond technology: A human-centered design approach. Journal of Innovation Management, 10(4), 26–46. https:// doi. org/ 10. 24840/ 21830606_ 010. 004_ 0002 Paul, L. R., & Sadath, L. (2021). A systematic analysis on fintech and its applications. Proceedings of International Conference on Innovative Practices in Technology and Management, ICIPTM, 2021, 131–136. https:// doi. org/ 10. 1109/ ICIPT M52218. 2021. 93883 71 Pentzold, C., & Bischof, A. (2023). Achieving agency within imperfect automation: Working customers and self-service technologies. Convergence: THe International Journal of Research into New Media Technologies. https:// doi. org/ 10. 1177/ 13548 56523 11745 88 Phoon, K. F., & Koh, C. C. F. (2018). Robo-advisors and wealth management. Journal of Alternative Investments, 20(3), 79–94. https:// doi. org/ 10. 3905/ jati. 201 Piehlmaier, D. M. (2022). Overconfidence and the adoption of roboadvice: Why overconfident investors drive the expansion of automated financial advice. Financial Innovation, 8(1), Article 14. https:// doi. org/ 10. 1186/ s4085402100324-3 Preston, B. (2015). Are robo-advisors the enemy? With Jon Stein from Betterment [Podcast]. In Money Guy Show. Spotify. Available at: https:// open. spoti fy. com Quinn, P., & Hirsch, K. (2020). Modernizing legacy banking systems: Practical advice to help banks succeed at core and application modernization. Deloitte. Available at: https:// www. deloi tte. com/ us/ en/ Indus tries/ finan cialservi ces/ artic les/ moder nizinglegacysyste msinbanki ng. html Raisamo, R., Rakkolainen, I., Majaranta, P., Salminen, K., Rantala, J., & Farooq, A. (2019). Human augmentation: Past, present and future. International Journal of Human Computer Studies, 131, 131–143. https:// doi. org/ 10. 1016/j. ijhcs. 2019. 05. 008 Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210. https:// doi. org/ 10. 5465/ AMR. 2018. 0072 Electronic Markets (2025) 35:94 94 Page 24 of 25 Ramesh, N., & Delen, D. (2021). Digital transformation: How to beat the 90% failure rate? IEEE Engineering Management Review, 49(3), 22–25. https:// doi. org/ 10. 1109/ EMR. 2021. 30701 39 Ritala, P., Ruokonen, M., & Ramaul, L. (2023). Transforming boundaries: How does ChatGPT change knowledge work? Journal Of Business Strategy. https:// doi. org/ 10. 1108/ JBS0520230094 Ritzholtz, B. (2019). Jon Stein discusses fintech and robo-advisers [Podcast]. In Masters in Business. Spotify. Available at: https:// open. spoti fy. com Roberts, C., & Maier, T. (2024). The evolution of service toward automated customer assistance: There is a difference. International Journal of Contemporary Hospitality Management, 36(6), 1914– 1925. https:// doi. org/ 10. 1108/ IJCHM0820221037 Rosenbaum, E. (2022). Robo-advisor dream of disrupting Wall Street wealth is not working out exactly as planned. CNBC.Available at:https:// www. cnbc. com/ 2022/ 01/ 27/ roboa dvisordisru ptionofwallstreetwealthisnotworki ngout. html.Accessed 24 Aug 2023. Rotatori, D., Lee, E. J., & Sleeva, S. (2021). The evolution of the workforce during the fourth industrial revolution. Human Resource Development International, 24(1), 92–103. https:// doi. org/ 10. 1080/ 13678 868. 2020. 17674 53 Sampson, S. E. (2021). A strategic framework for task automation in professional services. Journal of Service Research, 24(1), 122–140. https:// doi. org/ 10. 1177/ 10946 70520 940407 Satwekar, A., Miozza, M., Abbattista, C., Palumbo, S., & Rossi, M. (2024). Triad of digital transformation: Holistic orchestration for people, process, and technology. IEEE Transactions on Engineering Management, 71, 7815–7831. https:// doi. org/ 10. 1109/ TEM. 2024. 33849 95 Schlegel, D., & Kraus, P. (2021). Skills and competencies for digital transformation – A critical analysis in the context of robotic process automation. International Journal of Organizational Analysis. https:// doi. org/ 10. 1108/ IJOA0420212707 Schmidt, K. (2021). Folge 41: Robo-Advisor: Geldanlage der Zukunft – oder nur Science Fiction? [Podcast]. In Klug anlegen – Der Podcast zur Geldanlage. Spotify. Available at: https:// open. spo t i fy. com Stratmann, M. (2020). #83 Geldanlage mit Robo Advisor mit Sabine Schoon [Podcast]. In Schwungmasse – Der finanz-heldinnen Podcast. Spotify. Available at: https:// open. spoti fy. com Schwarz, G. M., & Stensaker, I. G. (2016). Showcasing phenomenondriven research on organizational change. Journal of Change Management, 16(4), 245–264. https:// doi. org/ 10. 1080/ 14697 017. 2016. 12309 31 Shanmuganathan, M. (2020). Behavioural finance in an era of artificial intelligence: Longitudinal case study of robo-advisors in investment decisions. Journal of Behavioral and Experimental Finance. https:// doi. org/ 10. 1016/j. jbef. 2020. 100297 Shih, K. (2019). The robo-advisor origin story you must read (part 1). LinkedIn. Available at: https:// www. linke din. com/ pulse/ roboadvis ororiginstoryyoumustreadpart-1kenshih/ Silver, C. (2021). Betterment comes of age [Podcast]. In The investopedia express with Caleb Silver. Spotify. Available at: https:// open.spotify.com Son, A., Corina, A., Petcu, A., & Schuurman, D. (2024). Corporate AI living labs: A structured approach to accelerating AI adoption and transforming towards AI-empowered employees for operational excel-lence-letter. Journal of Innovation Management, 12(3), XV–XX. https:// doi. org/ 10. 24840/ 21830606_ 012 Spring, M., Faulconbridge, J., & Sarwar, A. (2022). How information technology automates and augments processes: Insights from artificial-intelligence-based systems in professional service operations. Journal of Operations Management, 68(6–7), 592–618. https:// doi. org/ 10. 1002/ joom. 1215 Stake, R. E. (1995). The art of case study research. Sage Publications. Statista (2023). Robo-advisors – worldwide 2023. Available at: https:// www. stati sta. com/ outlo ok/ dmo/ finte ch/ digit alinves tment/ roboadvis ors/ world wide. Accessed 24 Aug 2023. Stopka, A. (2021). #129 Was ist von einem Robo-Advisor zu halten? [Podcast]. In Finanzpodcast – Finanzieller Erfolg auf ganzer Linie. Spotify. Available at: https:// open. spoti fy. com Stumpf, S. A., Doh, J. P., & Clark, K. D. (2002). Professional services firms in transition: Challenges and opportunities for improving performance. Organizational Dynamics, 31(3), 259–279. https:// doi. org/ 10. 1016/ S00902616(02) 00113-4 Svahn, F., Mathiassen, L., & Lindgren, R. (2017). Embracing Digital Innovation in Incumbent Firms. Source: MIS Quarterly, 41(1), 239–254. https:// doi. org/ 10. 2307/ 26629 645 Tarafdar, M., Page, X., & Marabelli, M. (2022). Algorithms as coworkers: Human algorithm role interactions in algorithmic work. Information Systems Journal. https:// doi. org/ 10. 1111/ isj. 12389 Thrasher, B. (2020). Motif, a portfolio management fintech firm, suddenly closes. RIA Intel. Available at: https:// www. insti tutio nalin v es tor. com/ artic le/ 2aucv shv49 2qs0w u1h2io/ riaintel/ motifaport foliomanag ementfinte chfirmsudde nlycloses Tokic, D. (2018). BlackRock robo-advisor 4.0: When artificial intelligence replaces human discretion. Strategic Change, 27(4), 285–290. https:// doi. org/ 10. 1002/ jsc. 2201 Tolentino, J. A. (2021). 050: Rise of the robo advisors ft. Michele Ferrario [Podcast]. In Pera & Purpose Podcast. Spotify. Available at: https:// open. spoti fy. com Tornatzky, L. G., & Fleischer, M. (1990). The processes of technological innovation. Lexington Books. UBS. (2022). UBS and Wealthfront mutually agree to terminate merger agreement. Available at: https:// www. ubs. com/ global/ en/ media/ displ aypagendp/ en20220 902wealt hfront. html Ulaga, W., Kleinaltenkamp, M., Kashyap, V., & Eggert, A. (2021). Advancing marketing theory and practice: Guidelines for crafting research propositions. Ams Review, 11(3–4), 395–406. https:// doi. org/ 10. 1007/ s1316202100215-x Ullah, F., Qayyum, S., Thaheem, M. J., Al-Turjman, F., & Sepasgozar, S. M. E. (2021). Risk management in sustainable smart cities governance: A TOE framework. Technological Forecasting And Social Change. https:// doi. org/ 10. 1016/j. techf ore. 2021. 120743 Vahromovs, V. (2021). Legacy systems in banking: the major barrier for digital transformation. Fintech Futures. https:// www. finte chfut ures. com/ 2021/ 11/ legacysyste msinbanki ngthemajorbarri erfordigit altrans forma tion/.Accessed 24 Aug 2023. van der Aalst, W. M. P., Bichler, M., & Heinzl, A. (2018). Robotic process automation. Business and Information Systems Engineering, 60(4), 269–272. https:// doi. org/ 10. 1007/ s125990180542-4 Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view.MIS Quarterly, 27(3), 425–478. https:// doi. org/ 10. 2307/ 30036 540 Vickovich, A., & Patten, S. (2022). Robo advice declines as investors warm to ‘hybrid’ model. Australian Financial Review. https:// www. afr. com/ compa nies/ f inan cialservi ces/ roboadvicedecli nesasinves torswarmtohybridmodel20221 108p5bwi3.Accessed 3 Aug 2023. von Hirschhausen, C. (2017). #08 Dr. Thomas Bloch – Fintechs, RoboAdvisors and the new banking [Podcast]. In The Wall Street Lab. Spotify. Available at: https:// open. spoti fy. com von Nordenflycht, A. (2010). What is a professional service firm? Toward a theory and taxonomy of knowledge-intensive firms. Academy of Management Review, 35(1), 155–174. https:// doi. org/ 10. 5465/ amr. 35.1. zok155 Waliszewski, K., & Warchlewska, A. (2020). Attitudes towards artificial intelligence in the area of personal financial planning: A case Electronic Markets (2025) 35:94 Page 25 of 25 94 study of selected countries. Entrepreneurship and Sustainability Issues, 8(2), 399–420. https:// doi. org/ 10. 9770/ jesi. 2020.8. 2(24) Weick, K. E. (1999). That’s moving: Theories that matter. Journal of Management Inquiry, 8(2), 134–142. https:// doi. org/ 10. 1177/ 10564 92699 82005 Wexler, M. N., & Oberlander, J. (2020). Robo-advisors (RAs): The programmed self-service market for professional advice. Journal of Service Theory and Practice, 31(3), 351–365. https:// doi. org/ 10. 1108/ JSTP0720200153 Wirtz, J., Kunz, W., & Paluch, S. (2021). The Service Revolution, Intelligent Automation and Service Robots. www. europ eanbu sines srevi ew. com.Accessed 3 Mar 2023. Wirtz, J., Kunz, W. H., Hartley, N., & Tarbit, J. (2023). Corporate digital responsibility in service firms and their ecosystems. Journal of Service Research, 26(2), 173–190. https:// doi. org/ 10. 1177/ 10946 70522 11304 67 Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: Service robots in the frontline. Journal of Service Management, 29(5), 907–931. https:// doi. org/ 10. 1108/ JOSM0420180119 Wünderlich, N. V., Wangenheim, F. V., & Bitner, M. J. (2013). High tech and high touch: A framework for understanding user attitudes and behaviors related to smart interactive services. Journal of Service Research, 16(1), 3–20. https:// doi. org/ 10. 1177/ 10946 70512 448413 Xu, N., & Wang, K. J. (2021). Adopting robot lawyer? The extending artificial intelligence robot lawyer technology acceptance model for legal industry by an exploratory study. Journal of Management & Organization, 27(5), 867–885. https:// doi. org/ 10. 1017/ jmo. 2018. 81 Young, J. E., Sung, J., Voida, A., Sharlin, E., Igarashi, T., Christensen, H. I., & Grinter, R. E. (2011). Evaluating human-robot interaction: Focusing on the holistic interaction experience. International Journal of Social Robotics, 3(1), 53–67. https:// doi. org/ 10. 1007/ s123690100081-8 Zhang, L., Pentina, I., & Fan, Y. (2021). Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services. Journal of Services Marketing, 35(5), 634–646. https:// doi. org/ 10. 1108/ JSM0520200162 Zhang, Z., Jin, J., Li, S., & Zhang, Y. (2023). Digital transformation of incumbent firms from the perspective of portfolios of innovation. Technology in Society. https:// doi. org/ 10. 1016/j. techs oc. 2022. 102149 Publisher's Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.