Critical factors influencing the adoption of Robo-Advisors

An empirical study of the banking industry in Germany


Master's Thesis, 2020

97 Pages, Grade: 1,3


Excerpt

iv. TABLE OF CONTENTS

Chinese Abstract

English Abstract

Acknowledgement

Table of Contents

List of Figures

List of Tables

Chapter I: Introduction
1.1 Background
1.2 Case Study of a German bank adopting a Robo Advisory solution
1.3 Research Purpose and Questions
1.4 Importance of Research
1.5 Scope of Research
1.6 Organization of the Paper

Chapter II: Literature Review
2.1 Robo Advisory Landscape and Market Situation in Germany
2.2 Robo Advisory – Definition and Characteristics
2.3 Introduction of TEO (Technology Environment and Organization theory
2.3.1 The Technological Context
2.3.2 The Organizational Context
2.3.3 The Environmental Context
2.3.4 Conclusion
2.4 Previous Research and Findings about adoption of New Technology and its application in the implementation of Robo Advisory solutions (15)
2.4.1 Crucial Factors of Implementation
2.4.2 Organizational Dimension
2.4.3 Project-Planning Dimension
2.4.4 Environmental Dimension

Chapter III: Research Framework
3.1 Research Framework
3.2 Research Hypotheses and Variables
3.2.1 Organizational Dimension
3.2.2 Project-Planning Dimension
3.2.3 Environmental Dimension

Chapter IV: Survey and Data Collection
4.1 Research Survey Design
4.2 Survey Objectives
4.3 Selection of Survey´s Participants

Chapter V: Data Analysis and Results
5.1 Data Analysis
5.2 Analysis of the Status Quo
5.3 Exploratory Factor Analysis (EFA) with SPSS
5.3.1 Preliminary Analysis
5.3.2 Factor Extraction
5.4 Confirmatory Factor Analysis (CFA) with SmartPLS
5.4.1 Preliminary Analysis
5.4.2 Reliability and Validity Analysis
5.4.3 Hypothesis Testing

Chapter VI: Discussion of Findings and Conclusions
6.1 Discussion of Findings
6.1.1 Characteristics of Cooperating/Acquirable Robo Advisory Companies
6.1.2 Regulatory Issues regarding Robo Advisors
6.1.3 IT, Conversational and Coordination Skills of the Project Team and Top Management Support
6.2 Implications of the Study
6.3 Limitations and Directions for Future Study

Bibliographies

Appendix 1 (Survey Questions and Indexation)

Appendix 2 (Indexed Answers of Survey – Factors only)

Autobiography

i. CHINESE ABSTRACT

影響導入機器人理財顧問之關鍵因素

德國銀行產業實證性研究

國 立 交 通 大 學

管理學院

企業管理碩士學位學程

碩 士 論 文

關鍵字 : 機器人理財, 銀行業, 金融科技

機器人理財顧問是近期金融科技公司使用其商業模式進入金融行業且挑戰傳統銀行機構的例子之一。金融科技公司通常只在一個銀行領域開展業務,且試圖通過基於互聯網的產品來吸引客戶,這些產品側重於簡單的用戶界面,效率,透明度和自動化等方向。機器人理財顧問讓公司提供自動化的金融投資工具之服務給顧客,因此可以列於資產管理和投資領域。相關文獻指出傳統銀行可以藉由機器人理財顧問公司合作、收購或是自己創造自動化投資方案來為顧客提供新穎的服務。

這項研究主要在確認德國銀行目前採用的狀況,以及當傳統機構使用機器人理財顧問作為解決方案的工具時需要考慮的幾項重要因素。藉由問卷,在德國銀行的工作者提供了他們寶貴的意見作為數據研究的資源。經由分析,可以判定選擇標準對於銀行是否收購機器人諮詢公司或與此類金融科技公司合作非常重要。特別是需要考慮到機器人理財顧問公司性能、聲譽,共享足夠訊息的意願與充分的技術能力。研究也顯示傳統銀行的資訊科技基礎架構經常與機器人理財顧問公司所提供的技術並不相容。這有可能導致整合的過程中會有問題,也因此成為在項目啟動階段就必須要注意這方面,讓系統可以更順暢且提供精準的服務給顧客。

ii. ENGLISH ABSTRACT

Critical Factors influencing the Adoption of Robo Advisors:

An empirical study of the banking industry in Germany

Student: Michael Rögele

College of Management

Global Master of Business Administration

National Chiao Tung University

Keywords: Robo Advisory, Banking Industry, Adoption of Technology, Fintech

Robo Advisors are one example of Fintech companies that recently have moved into the financial industry and challenge traditional banking institutes with their business models. Mostly, Fintech companies are only operating in one field of banking and try to attract customers via internet-based offers that focus on simple user interface, efficiency, transparency and automation. Robo Advisors are companies offering automated financial investment tools and can therefore be located in the segment of asset management and investment. Relevant literature has stated the need for traditional banks to react in form of cooperation with such companies, acquisition of Robo Advisors or creation of own automated investment solutions. This study aims to identify the current state of adoption in German banks and critical factors for traditional institutes that need to be considered when it comes to the implementation of Robo Advisory solutions. Data were collected via a questionnaire that was filled by banking employees in Germany. Factor Analysis revealed the result that selection criteria are a very important factor for banks when they decide to acquire a Robo Advisory company or cooperate with such a Fintech firm. Especially characteristics like the performance, reputation, willingness to share sufficient information and technological capability of a Robo Advisory company need to be considered. The study also revealed that the IT-infrastructure of traditional banks is often not compatible with the technology offered by Robo Advisors. This can lead to problems in the integration process and need to be an aspect in the initiation phase of such a project.

iii. ACKNOWLEDGEMENT

I would like to thank all my classmates, groupmates, friends, professors and the GMBA office who made these two years so special. There were challenging group projects, tight deadlines, enriching courses, an internship and finally yet importantly the Master Thesis that made my journey amazing.

Especially, I would like to acknowledge my advisor, professor Hwang, from whom I learnt many things. During the last 1.5 years, his guidance was extraordinarily helpful. Without that, I would not have been able to achieve the results presented in this paper. Additionally, I would like to thank my advisor´s PhD-student, Elisa Lin, whose support in data analysis was a great help.

v. LIST OF FIGURES

Figure 1: FinTech Landscape (Taxonomy)

Figure 2: AUM Robo Advisory Providers Top 5 Countries (September 2019)

Figure 3: Robo Advisory AUM 2017 – 2023 (2020 – 2023 prediction)

Figure 4: Customer Assessment

Figure 5: Customer Portfolio Management

Figure 6: The TEO Framework

Figure 7: Research Model

Figure 8: Number of Banks in Germany from 2004 until 2018

Figure 9: Sectoral Sample Distribution

Figure 10: Distribution of Current Stage of Implementation

Figure 11: Distribution of Form of Implementation

Figure 12: KMO/MSA-value distribution

Figure 13: KMO and Bartlett´s Test

Figure 14: Extraction Method: Principal Component Analysis

Figure 15: Extraction Method: Principal Component Analysis (4 components extracted)

Figure 16: Extraction Method: Principal Component Analysis, Rotation Method: Varimax with Kaiser Normalization (Rotation conv. in 5 iterations)

Figure 17: Maximum # of arrows in model indicating minimum sample size

Figure 18: SmartPLS CFA Output Model (1)

Figure 19: SmartPLS CFA Output Model (2)

Figure 20: Fornell-Larcker Table (Model 1)

Figure 21: Fornell-Larcker Table (Model 2)

Figure 22: T-Statistics and P-Values after Bootstrapping Algorithm (Model 1)

Figure 23: T-Statistics and P-Values after Bootstrapping Algorithm (Model 2)

Figure 24: Job Positions of Survey´s Participants

vi. LIST OF TABLES

Table 1: Organizational Factors

Table 2: Project-Planning-related Factors

Table 3: Environmental Factors

Table 4: Development of Organizational Questions

Table 5: Development of Project-Planning-related Questions

Table 6: Development of Environmental Questions

Table 7: Summary Results for Reliability and Validity of Refl. Outer Model (1)

Table 8: Summary Results for Reliability and Validity of Refl. Outer Model (2)

Chapter I: Introduction

1.1 Background

„Banking is necessary, banks are not! “1 This famous citation from Bill Gates goes back to 1994, but it describes the overall issue that is going to be covered in this thesis very well. The internet and the digitalization have already changed many industries in the past few years and of course, the same is happening in the banking industry as well.

FinTechs, companies that combine innovative technologies with financial products and services2, entered the market and try to revolutionize the financial industry in each of its sectors.3 This is exactly what Bill Gates is referring to in the introducing quote. According to that, FinTechs are putting a lot of pressure on traditional financial institutes and try to establish themselves in an already “overbanked”4 market. Mostly, they are only operating in one field of the banking business which gives them the opportunity to increase profit and customer satisfaction via internet based offers that focus on simple user interface, efficiency, transparency and automation. Many traditional banks are not using their full potential among these disciplines.5

In general, FinTech companies can be divided like the classic core businesses of universal banks. There are the following segments: Financing/Loan Business, Asset Management, Payment and Other FinTechs (see Figure 1). In frame of this paper the area of Asset Management, more specifically Robo Advisory, is going to be analyzed further.

Figure 1 : FinTech Landscape (Taxonomoy)6

Robo Advisors are decision support systems optimized for financial investments. The algorithm-based software can provide simple portfolio suggestions (usually consisting of Exchange Traded Funds (ETF)) according to the desired investment goal, risk tolerance and time horizon of the customer.7

These new specialized service providers in the field of financial investment and asset management are a certain threat for traditional universal banking institutes. Due to the new competitive pressure, they have three choices: Acquisition of more or less established Robo Advisory companies, cooperation with successful Robo Advisory service providers or the creation of own Robo Advisory tools.8

The banking industry in Germany is in an advanced stage of development and results of this empirical investigation are supposed to benefit the target country, but can also be seen as a case study for countries in less advanced stages.

In detail, it will be essential to have a comprehensive understanding of critical success factors for creating and implementing Robo Advisory solutions in traditional banks. Surveys have shown that there is demand, but very little research about operational, managerial and strategic aspects of creating own Robo Advisory tools.9

It is obvious that the findings are a good reference especially for global large banks. Smaller banks usually can´t afford the creation of own solutions, because the whole process is labor and cost intensive and has a high risk of failure. They may only consider a cooperation with an existing Robo Advisory company. The results presented in this paper can definitely help those banks, which desire to implement Robo Advisory solutions by overcoming potential obstacles and furthermore reducing the risk of failure during implementation. For other industries, e.g. insurance companies the findings can be used as a case study for the implementation of Robo Advisory tools, as more and more insurance companies also claim investment and asset management as a core business field. Besides, academia can use the findings of this study as a basis to initiate other related studies in the area of Robo Advisory.

1.2 Case Study of a German bank adopting a Robo Advisory solution

Among others, there is a very successful cooperation between the German “Volkswagen Bank” and the Robo Advisory firm “Whitebox” since 2017. With the integration of the FinTech the bank accessed a completely new distribution channel in the area of online portfolio management.

A representative of Volkswagen Bank stressed that careful selection of the Robo Advisory firm was a very important issue. Besides obvious factors like reputation and business performance there is also the need that the FinTech company is willing to share sufficient information.

In this case, the cooperation worked out very well because both sides realized the opportunities of such a cooperation. Not only the bank profits, also the Robo Advisory firm increases its potential customers and may improve its overall reputation and customer awareness.10

1.3 Research Purposes and Questions

The purpose of this research is to develop a set of criteria that are crucial for banks when it comes to the adoption of Robo Advisory systems. As mentioned before the target country of the study is Germany. A questionnaire will be used to investigate the current state and need of German banks respectively. When the question of implementing a Robo Advisory solution arises, some interesting questions can be proposed:

(a) What is the current stage of adoption of Robo Advisors in our target market?
(b) What is the preferred method of implementation in our target market (acquisition, cooperation or creation of own Robo Advisory tools)?
(c) What are the crucial factors (needs) when it comes to the adoption of Robo Advisory systems?

Therefore the main research questions to be investigated in this study are:

Q1: What is the overall stage of adoption of Robo Advisory in our target country and how does the distribution look like?

Q2: What is the preferred method of adoption and how does the distribution look like? (Acquisition, cooperation or creation of own Robo Advisory systems)

Q3: What are the critical factors for banks to proceed in the development of adoption?

1.4 Importance of Research

Most research regarding Robo Advisors has focused on its conceptual core elements such as performance improvement, regulatory problems or trust-related approaches. The outcomes of trust-related studies have shown that there is no common standard in this industry.11 In other words, the market players who claim to be Robo Advisors could not be more various. While some players only give limited financial advice or orientation, the offered service of others can even cover comprehensive portfolio management. Because of that, the adoption rate is quite slow.12 13 Therefore, it is crucial to investigate critical factors for the adoption of Robo Advisors in combination with actionable suggestions for banks in order to contribute positively to the current literature.

1.5 Scope of Research

This research covers an empirical approach towards adoption of Robo Advisory in banks and financial institutions. Traditional universal banking institutes in in Germany are chosen as research subjects, while specialized institutes (banks that only focus on one core business or one specific target customer) were excluded. Typically, asset managers, controllers, IT specialists or Investment Managers are respondents of the survey as they are most familiar with the process of implementation. With the valuable answers and insights of the respondents, the author is aiming to receive a clear picture about the factors that are crucial for successful adoption of Robo Advisory systems.

1.6 Organization of the Paper

The paper is divided into six main chapters. The first chapter will provide an introduction and relevant background information to the topic. From there, the research purposes, questions and problems are stated. After that, the importance of the research as well as the research scope are set.

Chapter 2 will review the FinTech landscape in Germany. First, a comprehensive definition of Robo Advisory will be given based on a detailed literature review. Having the conceptual terminology in mind, an in-depth market analysis in the target country can be conducted. Afterwards, the chapter is going to be closed by a summative literature review, mentioning the most significant previous findings of technology implementation and its application to the adaption of Robo Advisory systems in banks.

The next chapter is one of the most important parts of the study as it introduces the framework of the research. After introducing the Technology-Organization-Environment Framework (TOE) theory, the core of the paper is going to be stated. Hence, the inference process of the research model development will also be discussed in this chapter. All variables and their underlying hypotheses are going to be explained in detail.

In chapter 4, we will describe the methodology employed in the study. It consists of the survey subjects and the research design. The expected data analysis approaches are elaborated in this chapter as well.

Based on that, Chapter 5 will provide further insights regarding data analysis and its results. Every single variable will have to be validated by the proposed statistical methods. Finally, hypotheses testing can be conducted.

In the last chapter, a controversial discussion based on the findings from Chapter three to five will help to suggest implications of the study. The conclusion will address the value of our findings as well as their limitations. The paper will also conclude by providing actionable suggestions for banks and research directions for future studies.

Chapter II: Literature Review

2.1 Robo Advisory Landscape and Market Situation in Germany

In a global comparison, the Robo Advisory market in Germany is quite advanced and in terms of Assets under Management (AUM) with 8,460 million USD currently ranking on place four behind USA, China and UK.14

Abbildung in dieser Leseprobe nicht enthalten

Figure 2: AUM Robo Advisory providers top five countries (September 2019)

Those markets have been first movers and to date claim to have the most developed Robo Advisors. As in all FinTech sectors, adoption is very fast and some lessons learnt from providers in UK and US, but also China have already been adopted by German players. One of the examples is the expansion of services into the B2B segment.15 Given the fact, that Germany enjoys the advantages of a second mover, it can easily learn from service providers abroad and avoid common mistakes made by them. Nevertheless, the target country already has a reasonable level of AUM while further growth is predicted. Therefore, we think that Germany is a good target country for the purpose of this research and can serve as a case study for other countries, where the stage of Robo Advisory adoption is in earlier stages.

Abbildung in dieser Leseprobe nicht enthalten

Figure 3: Robo Advisory AUM 2017 – 2023 (2020 – 2023 prediction)16

Coming back to the above-mentioned growth of Assets under Management in Germany: In 2016, there were less than a million €, in 2017 already 2,020 million €, in 2018 and 2019 the number increased by 120% and 90% respectively. The monthly growth is predicted to be 120% for the next year.17 Therefore, a great potential can be assumed.18

More and more providers have entered the competition. Following the first domestic launch in 2013, the number of institutions offering Robo Advisors jumped to 40 in just a few years of time. With takeovers and consolidation though, the number of providers came down to 25 recently.19 These players currently hold AUM of ~ 8,460 million € (September 2019).

2.2 Robo Advisory – Definition and Characteristics

When wrapping up the term Robo Advice you need to state that the actual tool is neither a robot nor a real advisor, as the initial perception would assume. According to the literature, a robot is a machine build to imitate human beings. Obviously, this concept has nothing to do with the characteristics of nowadays´ Robo Advisors. Furthermore, talking about “Advice” and its actual meaning is not completely correct as well. The comprehensive financial consulting or advisory of retail customers is more than just a portfolio suggestion based on a questionnaire filled in by the client in advance. A conceptual definition states that advisory is not only mastering communication, but also a contextual mix of behavioral and reflective competencies.20

These above-mentioned reflective skills are not implemented in Robo Advisory tools (yet); it is more a form of automated portfolio suggestion. With further improvement of Artificial Intelligence, scenarios where non-human advisory tools imitate human behaviors are definitely in the pipeline.

To sum it up, Robo Advisors can be defined as digital platforms comprising interactive and intelligent user assistance components while leveraging information technology to guide customers through an automated financial advisory process.21 22 23

Previous literature suggests that Robo Advisors differ from traditional advisory in two elementary levels (Customer Assessment and Customers´ Portfolio Management).

Abbildung in dieser Leseprobe nicht enthalten

Figure 4: Customer Assessment

Abbildung in dieser Leseprobe nicht enthalten

Figure 5: Customer Portfolio Management

The disruptive potential of Robo Advisors induced by these key aspects from the two tables above24 has several implications. The main difference compared to traditional financial advisory is that the whole process takes place without any human consultant. The client usually needs to fill a form developed by the Robo Advisory Company, which has the purpose to figure out the risk profile as well as time horizon of the investment.25 In fact, these questions are similar to these asked by a human advisor during the first meeting with a new customer. Based on the initial survey the algorithm developed by the Robo Advisory Company calculates one or more portfolio suggestions. Most of the market players have low minimum investment amount (0 EUR to 10,000 EUR in Germany26 ) and charge a relatively low fee, which can be explained by mostly choosing passively managed index funds (e.g. ETF).27 Consequently, the raise of Robo Advisors resulted in a new low budget investor class that has not been served by traditional financial advisors before. Therefore, millennials (people born mid-1990s to early 2000s) are a primary target group of Robo advisors, as they constitute an investor group rather attracted by using technology than discouraged by it like older investors.28

Some service providers are rebalancing the customer´s portfolio automatically when the client disposes or pays money in or the market situation is changing significantly (ongoing rebalancing). The advantage of this method is that small investors have the chance to participate the trend of the financial markets without a lot of background knowledge. After the customer conducts the investment as the algorithm suggested, he changes the fundamental direction of his portfolio allocation (e.g. defensive, balanced, offensive), but not based on investment in single company shares.29 30

2.3 Introduction of TOE (Technology-Organization-Environment)

The technology-organization-environment (TOE) framework is an organizational-level-theory and first introduced by De Pietro, Wiarda and Fleischer in “The Process of Technological Innovation” in 1990.31 According to them, the entire process of innovation starts with the development of ideas by engineers and entrepreneurs and ends by the adoption and implementation of those innovations within a company.

The TOE framework covers one part of the whole process, in particular how the firm´s environment influences the adoption of innovations. As the name of the framework already indicates, there are three different dimensions from a firm´s context, which can influence the adoption of decisions. In detail, the three aspects are technological environment, organizational dimension and environmental context.

Figure 6: The TEO Framework 32

The framework can be summarized with the model shown above. In the following section, the three dimensions and its interrelations will be explained further. Besides, the connection to the topic of this paper will be drawn. Briefly said, the aspects of the TOE framework will be applied on the adoption of Robo Advisory solutions in German banks. Therefore, it´s crucial to understand the three dimensions as well as their impacts and interrelations in detail first.

2.3.1 The Technological Context

Talking about the availability of technology, we need to think about the technologies that are already in use of the firm and about those that are available on the market, but not in use yet. The technologies that are currently used by a company are of essential importance in the adoption process of Robo Advisory solutions, because they limit the scope and pace of technological change, which is feasible for the bank.33 On the other hand, there are innovations on the market that are not in use yet by a given company, but they can also influence innovation. In particular, it can show what the status of technological innovation is and more practical, how they can leverage these technologies in order to improve their existing processes.

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Excerpt out of 97 pages

Details

Title
Critical factors influencing the adoption of Robo-Advisors
Subtitle
An empirical study of the banking industry in Germany
Grade
1,3
Author
Year
2020
Pages
97
Catalog Number
V916119
ISBN (eBook)
9783346227218
ISBN (Book)
9783346227225
Language
English
Tags
Robo Advisory, Banking Industry, Adoption of Technology, Fintech
Quote paper
Michael Rögele (Author), 2020, Critical factors influencing the adoption of Robo-Advisors, Munich, GRIN Verlag, https://www.grin.com/document/916119

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