Towards Designing Robo-Advisory to Promote Consensus Efficient Group Decision-Making in New Types of Economic Scenarios

Robo-advisors are a new type of FinTech increasingly used by millennials in place of traditional financial advice. Building on artificial intelligence, robo-advisors provide personalized asset and wealth management services. Their application and study have hitherto focused exclusively on individual advisory regarding asset management. We observe a pressing need to investigate robo- advisors’ application for complex artificial intelligence based recommendation tasks both, in context of group decision-making and in contexts beyond asset management, due to robo-advisors’ potential as a lever for integrating artificial intelligence in the entire decision-making process. Thus, we present a action design research in progress aimed at designing such a robo-advisor. More specifically, this study investigates whether and how robo-advisory promotes consensus-efficient group decision-making in new types of economic scenarios (after-sales). Based on a comprehensive problem formulation, we aim towards deriving a set of meta-requirements and design principles that are embodied in a preliminary prototypical instantiation of a robo-advisor

Nudged to Win: Designing Robo-Advisory to Overcome Decision Inertia

Decision inertia is a serious problem in financial decision-making and thus a challenge for decision support systems. We discuss recent findings and review antecedents and consequences of decision inertia from a psychological perspective. We use these insights to develop IT-based methods designed to overcome decision inertia using psychologically optimized financial decision support systems. Furthermore, we propose an experimental study to evaluate the design features of such a system. Our work is a first step in designing adaptive decision support systems that detect situations in which the user is prone to decision inertia and react by adapting interface elements appropriately that might otherwise exacerbate decision inertia – for a specific user in a specific decision situation

Robo-Advisory and Decision Inertia - Experimental Studies of Human Behaviour in Economic Decision-Making

Investing in the stock market is a complicated and risky undertaking for private households. In particular, private investors face numerous decisions: for instance, whether to invest in stocks or bonds, buy passively or actively managed investment products, or try something new like Bitcoin. They must decide where they can get independent financial advice, and whether this advice is trustworthy. As a consequence, information systems researchers design and build financial decision support systems. Robo-advisors are such decision support systems aiming to provide independent advice, and support private households in investment decisions and wealth management. This thesis evaluates robo-advisors, their design and use and thus their ability to support financial decision-making. Addressing this research need, my thesis is organized in three parts (part I-III ) consisting of four quantitative experimental studies, two qualitative friendly-user-studies, and one qualitative interview study. In Part I, Chapter 3 examines how robo-advisors can be designed for inexperienced investors. In particular, I derive design recommendations for the development of robo-advisor solutions and evaluate them in a three-cycle design sciences process. Requirements related to the clusters ease of interaction, work efficiency, information processing and cognitive load are identified as key elements for robo-advisory design. In Part II, Chapter 4 focuses on an important bias in economic decision-making - decision inertia, the tendency to repeat a decision regardless of the consequences. As a result, a decision-maker can make repeated suboptimal investments. To understand this bias more deeply, I investigate decision inertia in a general experimental setting and identify motivational and cognitive drivers of this phenomenon. Thus, I relied on behavioural, on self-reported, and on bio-physiological measures in three laboratory studies. In Part III, Chapter 5 specifies the findings from Part II to find and evaluate strategies to reduce decision inertia in financial decision support systems. For that purpose, I investigate two nudges (design features) to reduce inertia in investment decisions. My results suggest that defaults and warning messages can help participants to overcome decision inertia. Furthermore, the results illustrate that designers have to be careful not to push decision-makers into the decision inertia bias by accident. In summary, this thesis gives design recommendations for practitioners and scholars building robo-advisors. The insights can help to develop robo-advisors, and to increase advisor quality by considering decision inertia in the system design phase and consequently, it illustrates how to counteract this malicious decision bias for private investors

DIGITAL NUDGES FOR USER ONBOARDING: TURNING VISITORS INTO USERS

Two design recommendations (digital nudges) for decreasing user churn in mobile apps are presented. We examine commitment and personalization nudges, both of which are linked to the extant literature, in the context of a randomized online experiment with 150 participants. Our experimental study reveals that commitment and personalization cues distinctly affect consumers\u27 intention to use a mobile app. Moreover, our study demonstrates that personalization amplifies the effect of commitment cues on users\u27 intention to use a mobile app

A Framework for Formal Verification of DRAM Controllers

The large number of recent JEDEC DRAM standard releases and their increasing feature set makes it difficult for designers to rapidly upgrade the memory controller IPs to each new standard. Especially the hardware verification is challenging due to the higher protocol complexity of standards like DDR5, LPDDR5 or HBM3 in comparison with their predecessors. With traditional simulation-based verification it is laborious to guarantee the coverage of all possible states, especially for control flow rich memory controllers. This has a direct impact on the time-to-market. A promising alternative is formal verification because it allows to ensure protocol compliance based on mathematical proofs. However, with regard to memory controllers no fully-automated verification process has been presented in the state-of-the-art yet, which means there is still a potential risk of human error. In this paper we present a framework that automatically generates SystemVerilog Assertions for a DRAM protocol. In addition, we show how the framework can be used efficiently for different tasks of memory controller development.Comment: ACM/IEEE International Symposium on Memory Systems (MEMSYS 2022

Why IT-Projects Fail: A Meta-Analysis of the Construct ‘Escalating Commitment’ in Information Systems Research

Escalating commitment refers to situations where decision makers tend to persist with failing courses of action by committing themselves more and more to the course of action as they invest further resources even when they face substantial negative feedback (Brockner, 1992; Newman & Sabherwal, 1996). This article examines the phenomenon of escalating commitment in the domain of information systems by i) systematically conducting a literature review where 23 triggers for escalating commitment in IS were identified. The results of the literature review yield that the main research in this field clearly focuses on studying the phenomenon in the context of it-projects but also on the upcoming research field of online services such as online-auctions. On the other hand ii) we conducted a meta-analysis with the aim of quantifying the power of the phenomenon under discussion based on the literature identified in the first part of our study. The computed overall effect size turned out to be significantly different from zero, but had to be put under reservation after testing the population for homogeneity

Nudging Flexibility – Increasing Electric Vehicle User’s Charging Flexibility with Digital Nudges

Smart charging systems can prevent problems with the integration of battery electric vehicles (BEV) and allow the user to optimize the charging process according to his preferences. To do this, however, the user must enter his flexibility into the smart charging system. We propose that this flexibility can be increased by the means of choice architecture and digital nudging. Setting defaults and presenta-tion of normative defaults can successfully encourage end users to conserve electric energy. We pro-pose an online experiment to investigate the transferability of these nudges to the provision of charg-ing flexibility