Robo-Advisors versus Traditional Investment Advisors: <i>An Unequal Game</i>

Uhl, Matthias; Rohner, Philippe

doi:10.3905/jwm.2018.21.1.044

Cited by 53 publications

(29 citation statements)

References 11 publications

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“…The outline of the robo-advisor The robo-advisor refers to an online investment management platform comprising interactive and intelligent user assistance components that can provide users with ubiquitous access to sophisticated planning and portfolio management tools, use statistical techniques and machine learning algorithms to automatically analyze financial product risk levels and offer users personalized portfolio recommendations (Beltramini, 2018;Jung et al, 2018a;Jung et al, 2018b;Xue et al, 2018;Belanche et al, 2019;Bhatia et al, 2020;Lourenço et al, 2020). As compared with traditional human financial advisors, launching the robo-advisor may become a more beneficial and flexible way for financial institutions to provide their customers with ubiquitous access, automated financial services, sophisticated planning and portfolio management tools, wider investment options based on systematic and quantitative analyzes and lower management fees (Uhl and Rohner, 2018;Belanche et al, 2019;Brenner and Meyll, 2020). This is the reason why many financial institutions have launched robo-advisor services to enable their customers to conveniently access from anywhere, at any time, on any device, by logging in on a dedicated page (Beltramini, 2018;Belanche et al, 2019).…”

Section: Theoretical Background Hypotheses and Research Modelmentioning

confidence: 99%

Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention

Cheng

2020

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Purpose The purpose of this study is to propose a synthetic post-adoption model based on the expectation-confirmation model (ECM) and flow theory to examine whether the fit factor, network factors and psychological factors as antecedents to end-users’ beliefs can affect their continuance intention of the robo-advisor. Design/methodology/approach This study used the research model based on ECM and flow theory to examine the effects of the fit factor, network factors and psychological factors on end-users’ beliefs and continuance intention of the robo-advisor. Sample data were collected from end-users at three financial services companies in Taiwan. A total of 450 questionnaires were distributed and 360 (80.0%) usable questionnaires were analyzed using structural equation modeling. Findings This study proposes a solid research model that based on ECM and flow theory, three types of factors, namely, fit factor, network factors and psychological factors, as antecedents to end-users’ continuance intention of the robo-advisor have been examined and this study’s results strongly support the research model with all hypothesized links being significant. Originality/value It is particularly worth mentioning that a synthetic post-adoption model can be proposed in this study by introducing the fit factor extracted from task-technology fit model, network factors originated from the theory of network externalities and psychological factors derived from uses and gratifications theory as antecedents to perceived usefulness, confirmation, satisfaction and continuance intention referred in ECM and flow experience derived from flow theory. Thus, this study’s research model and findings can reveal deep insights into the evaluation of determinants in the field of end-users’ continuance intention of the robo-advisor.

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Section: Theoretical Background Hypotheses and Research Modelmentioning

confidence: 99%

Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention

Cheng

2020

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“…Generally, robo‐advisors have similarities, which integrate a questionnaire process coupled with various investment solutions (Bhatnagar, 2016; Hougan, 2015; Phoon & Koh, 2018). Normally, there is an allocation model filled with exchange‐traded funds (ETFs) or mutual funds (Bhatnagar, 2016; Uhl & Rohner, 2018). The robo‐advisor can also use standard investing techniques, such as mean–variance optimization (Phoon & Koh, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, he argued that robo‐advisors incorporating trading algorithms could replicate many active strategies, such as smart beta protocols. Furthermore, Uhl and Rohner (2018) argued that robo‐advisors can help investors because robo‐advisors use passive strategies, minimize cost, and reduce behavioral biases.…”

Section: Introductionmentioning

confidence: 99%

Financial attributes, financial behaviors,financial‐advisor‐usebeliefs, and investing characteristics associated with having used arobo‐advisor

Todd

Seay

2020

Fin Plan Rev

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This study investigates the financial attributes, financial behaviors, financialadvisor-use beliefs, and investing characteristics associated with having used an automated investment solution (i.e., a robo-advisor). Data are used from the 2015 National Financial Capability Study (both the State-by-State Survey and the Investor Survey) to investigate the use of robo-advisors among 1,393 individuals with non-retirement investment accounts. Multivariable results indicated that being younger, having low or midrange income, and having lower objective investment-knowledge scores was positively associated with robo-advisor use. This study provides important insight for policymakers, financial practitioners, and proprietors of robo-advisors and other fintech products.

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“…Study of them is relevant. Investors increasingly use them, because they have lower investment minimums and charge lower fees than human advisers and returns appear comparable (Uhl and Rohner, 2018).…”

Section: Who Are the Relevant Users?mentioning

confidence: 99%

The role of users’ engagement in shaping financial reporting: should activists target accounting more?

Osma

Grande-Herrera

2021

Accounting and Business Research

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We define accounting engagement as stakeholders' actions taken with the intention of influencing corporate reporting. Using this definition, we review the literature on such activism and discuss avenues for research. The evidence reviewed suggests accounting engagement is rare. We reflect on the reasons of this, given evidence on increasing overt engagement on other corporate issues, such as managerial compensation and governance, social, and environmental responsibility. Both information production and information acquisition costs have decreased over time, raising further questions about why engagement has not increased. We consider potential reasons linked to concerns over whether financial reporting meets users' information needs, particularly, given the emergence of new users and the role of new technologies in the diffusion and processing of information. These concerns have accompanied claims of increasing complexity of financial accounting and the threat of information overload.

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Robo-Advisors versus Traditional Investment Advisors: An Unequal Game

Cited by 53 publications

References 11 publications

Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention

Will robo-advisors continue? Roles of task-technology fit, network externalities, gratifications and flow experience in facilitating continuance intention

Financial attributes, financial behaviors,financial‐advisor‐usebeliefs, and investing characteristics associated with having used arobo‐advisor

The role of users’ engagement in shaping financial reporting: should activists target accounting more?

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