The main purpose of this experimental study is to investigate the influence of the derived nudges (implemented by two design features) on decision inertia, and (sub-) optimal invest- ment decisions in the context of a robo-advisor. The research model in Figure 36, illustrates the relationship of the different nudges implemented by different design features, and individ-
ual’s tendency to rely on decision inertia, which can be objectively measured by valuations that are contrary to the Rational Utility Theory (see experimental design below).
Figure 36: Research framework for this investigation (adopted from Section 1). The implemented nudges of the choice architecture toolbox and the resulting choice environ- ment are part of an overarching system design to guide decision makers through a decision process (Silver, 1991). This work aims to design the choice architecture of the financial deci- sion support system to nudge decision-makers to have less decision inertia. In the next part of this work, the hypotheses based on the tools of the choice architecture are illustrated. 5.3.1 Default Nudge (H1)
A choice architecture approach to design the choice environment sensitive to inertia in decision-making requires addressing the behavioural drivers of the tendency to repeat a deci- sion (or option) without shifting away. The rationale behind this approach is that people rely on heuristics and inertia because they do not want to expend cognitive resources.
Choice architecture research suggests that defaults are an appropriate tool to counter-act this suboptimal behaviour by nudging people towards other heuristic processing (Johnson et al., 2012). When deciding between different options, where one of them is pre-selected, decision- makers tend to rely on the default heuristic (Johnson & Goldstein, 2003), which means that they usually choose the default option a significant number of times. Following this rationale, this study assumes that if decision-makers show decision inertia, they repeat a previous investment without considering the alternatives. If the optimal decision is pre-selected and if that option is not the previous one, however, decision inertia and the default bias are in conflict. Based on the choice architecture literature, it is assumed that this situation results in the behaviour that the decision-maker repeats the default instead of the previous decision. Many different studies in the field of behavioural design support these conclusions. So far, defaults are also successfully applied in other scenarios to nudge people to certain behavioural changes. For instance, Stryja et al. (see Stryja, Satzger, and Dorner (2017); Stryja, Dorner, and Riefle (2017)),
proposed defaults and priming as possible nudges to overcome the resistance to change in innovation acceptance. In their study, default nudges significantly influenced resistance to acceptance of electronic cars (Stryja, Satzger, & Dorner, 2017). In another study, the tendency for air travellers to pay for carbon-offsets could be increased by default nudges (Brouwer, Brander, & Van Beukering, 2008).
Another point is that defaults could also be perceived as choice recommendations. As a result, decision-makers who are uncertain of their decisions, or who do not know how best to decide, perceive the default as a socially desired option and are therefore more inclined to follow it (Pichert & Katsikopoulos, 2008). It is assumed that investors use decision-support systems like robo-advisors because they want to relinquish a part of their responsibility (e.g. send orders to the market on their own or gather information about investments). On the other hand, they want to have the possibility to monitor their investment, which is a core feature of robo-advisors. Furthermore, they want to retain control or the feeling of control over their investment decisions. Nudging with a default option seems to be a fair compromise between these considerations and would nudge users of robo-advisory towards the optimal decision, without reducing the feeling of being in control.
These considerations suggests the following:
Hypothesis 1 (H1). Preselecting the optimal option based on Bayesian rationality in the user- interface, will increase the decision-maker’s tendency to choose that option, and hence reduce the decision-maker’s decision inertia.
5.3.2 Warning Message Nudge (H2)
Furthermore, this study proposes warning messages as a second nudge to reduce the tendency to rely on decision inertia. Warning messages are built on cognitive feedback theory (Balzer, Doherty, et al., 1989), which provides a framework to design feedback giving information about the cognitive system and the current decision-making related to the decision maker’s own strategy. Compared to other feedback approaches, cognitive feedback is intended to encour- age users of the information system to think more about their decisions and thereby prevent premature decisions (Sieck & Yates, 1997). If participants are subject to inertia, warning mes- sages building on cognitive feedback should encourage biased decision-makers to reconsider their decision and, if necessary, to consider alternative options. Warning messages relying on cognitive feedback have been successfully used not only in decision support systems in other economic decision environments (see e.g., Xiao and Benbasat (2015) or Winkler and Moser (2016), but also in financial decision support systems to reduce biased decision-making (see e.g. Bhandari et al. (2008)). For instance, Sengupta et al. measured the performance of participants hired as virtual project managers in an economic market simulation (Sengupta & Abdel-Hamid, 1993). The market simulation consisted of a spontaneously changing envi- ronment and the participants’ economic performance of the given task was measured. Con- sidering the different feedback and project groups, Sengupta and Abdel-Hamid (1993) found that subjects induced with cognitive feedback performed significantly better than the other subjects with feed-forward or outcome feedback.
However, the effectiveness of warning messages depends mainly on how the messaging pre- sented to the decision-maker (Xiao and Benbasat 2015). The complexity of the warning mes- sage, for example, tends to decrease fault correctability as well as benchmark efficiency among individuals (Kulhavy et al. 1985). For that purpose, this study relies on the evaluated design of warning messages as proposed by Bhandari et al. (2008).
Following this rationale, it is assumed that the warning messages building on cognitive feedback, may decrease the decision inertia of the users of the decision support system. Such a system simultaneously aims at choice accuracy and a more directive approach to influence an individual’s understanding. Hence, it is postulated that:
Hypothesis 2 (H2). Proving warning messages relying on cognitive feedback by the decision support system will decrease the decision-maker’s decision inertia.