Technology Acceptance, Path Dependence, and the Demand for Robo-Advisory Services

Name: Technology Acceptance, Path Dependence, and the Demand for Robo-Advisory Services
Brand: Springer Gabler
Price: 106.99 EUR
Availability: OnlineOnly

Maximilian Horn(Autor*in)

Springer Gabler (Verlag)

1. Auflage

Erschienen am 10. Juni 2024

XIV, 236 Seiten

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978-3-658-44679-6 (ISBN)

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Inhalt

Intro
Acknowledgements
Abstract
Contents
Abbreviation
1 Introduction
2 Uniting Individual Path Dependence and UTAUT: The PDA-UTAUT Model
2.1 Introduction to UTAUT
2.2 Overview of the Robo-Advisory Adoption Literature
2.3 Individual Path Dependence and Technology Adoption
2.3.1 The Logic Between Investment and Robo-Advisors
2.3.2 Individual Path Dependence
2.3.3 Individual Path Dependence and Robo-Advice
2.4 The PDA-UTAUT Model
2.4.1 The UTAUT Model with Exogenous Path Dependence
2.4.2 Endogenizing the Basic Product Adoption
3 Adapting the Model to Robo-Advice
3.1 The Constructs of the Traditional UTAUT Model
3.1.1 Performance Expectancy
3.1.2 Effort Expectancy
3.1.3 Interpersonal and External Influence
3.1.4 Facilitating Conditions
3.1.5 Control and Moderator Variables
3.2 Personal Factors Extending the UTAUT Model
3.2.1 Perceived Privacy Risk
3.2.2 Personal Innovativeness
3.2.3 Financial Literacy
3.3 The Inclusion of Path Dependence Into the Model
3.4 Endogenizing the Basic Technology
3.4.1 Financial Literacy
3.4.2 Ethical Stance Towards the Stock Market
3.4.3 Financial Risk Attitude
4 Empirical Evaluation of the PDA-UTAUT Model Adapted to Robo-Advisors
4.1 Empirical Methodology
4.1.1 Online Survey
4.1.2 Survey Data Quality Control
4.2 Results and Model Evaluation
4.2.1 Measurement Model Adaptation and Evaluation
4.2.2 Structural Model Adaptation
4.2.3 Structural Model Summary
4.2.4 Model Evaluation
4.3 Hypothesis Testing
4.3.1 The classical UTAUT Model with Additional Drivers
4.3.2 The Addition of path Dependence to the Model
4.3.3 Endogenizing path Dependence
4.4 Subgroup Analyses
4.4.1 Subgroup Analysis Between INRA and NI
4.4.2 Subgroup Analysis Between Germany and the USA
4.5 Discussion
4.5.1 Empirical Findings
4.5.2 Practical Implications Based on the Empirical Findings
4.5.3 Limitations
4.5.4 Future Research
5 Incorporating the Supply Side: Product Features of Robo-Advisors
5.1 Overview of the Literature on Robo-Advisors' Attributes
5.1.1 Human Versus Robot Financial Advisors and Trust Mechanisms
5.1.2 Robo-Advisor Characteristics and Customer Acceptance
5.1.3 Overview of Other Relevant Literature
5.1.4 Davis' Perspective on Objective Features
5.2 Empirical Methodology
5.2.1 Robo-Advisor Features in Germany and the USA
5.2.2 Derivation of Product Features
5.3 Results
5.3.1 Data Collection
5.3.2 Findings
5.3.3 Personal Take
5.4 Discussion
5.4.1 Theoretical Contributions
5.4.2 Management Implications
5.4.3 Limitations
5.4.4 Future Research
6 Analyzing Preferences of German and American Consumers using Choice-Based Conjoint Analysis (CBC)
6.1 Introduction to Conjoint Analyses (CA)
6.1.1 The Development of Conjoint Analyses
6.1.2 A primer on Choice-based Conjoint Analyses
6.1.3 Introduction to Part-worth Utility Estimation
6.2 Empirical Methodology
6.2.1 Decision on Attributes Included in the Study
6.2.2 Questionnaire Design
6.3 Results
6.3.1 Data Collection and Cleansing
6.3.2 Empirical Strategy
6.3.3 Findings
6.4 Discussion
6.4.1 Theoretical Contributions
6.4.2 Management Implications
6.4.3 Limitations
6.4.4 Future Research
7 Studying the Impact of Objective Product Features on the Independent UTAUT Constructs
7.1 Empirical Concept
7.1.1 Accounting for Features in Models of Technology Acceptance
7.1.2 Adapting the Model to Robo-advisor Technology
7.1.3 Study Design
7.2 Results
7.2.1 Online Survey
7.2.2 Survey Data Quality Control
7.2.3 Findings
7.3 Discussion
7.3.1 Empirical Findings
7.3.2 Management Implications
7.3.3 Limitations
7.3.4 Future Research
8 Summary
8.1 Introducing the PDA-UTAUT Model to Account for Path Dependence in Technology Acceptance Research
8.1.1 Insights on Individual Path Dependence and Technology Acceptance
8.1.2 Integrating Individual Path Dependence into UTAUT Models
8.2 Deriving Research Models and Hypotheses
8.2.1 Adapting the PDA-UTAUT Model to Robo-advisor Technology
8.2.2 An approach to Quantify the Influence of Features on the Demand for Robo-advisory Services
8.3 Empirical Findings
8.3.1 Empirical Evidence on the Influence of Path Dependence on the Demand for Robo-advisory Services
8.3.2 Empirical Evidence on the Influence of features on the Demand for Robo-advisory Services
8.4 Practical Implications Drawn from the Empirical Findings
8.4.1 Implications for Suppliers of Robo-advisory Services
8.4.2 Implications for Governmental Institutions
8.5 Limitations and Future Research
References

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