Markus Weinmann

How Is Your User Feeling? Inferring Emotion Through Human-Computer Interaction Devices

City University of Hong Kong [7002626, 7004123]; Research Grants Council of the Hong Kong Special Administrative Region [CityU149512]

Which Reward Should I Choose? Preliminary Evidence for the Middle-Option Bias in Reward-Based Crowdfunding

Crowdfunding has become an important research area, but we know little about how rewards influence fundraising success. This research-in-progress studies reward-based crowdfunding through a behavioraleconomics lens. We draw on dual-process theory and provide preliminary evidence for the middle-option bias in crowdfunding. Two empirical studies in a simple, controlled environment confirm the significance of the middle-option bias, both for varying numbers of donation options and for varying price ranges. Since our findings suggest that the positioning of rewards in a menu of rewards can influence support behavior and how much money project creators collect, they can inform the design of crowdfunding projects. Our future research will develop a mock crowdfunding website to study the middle-option bias in a more realistic environment.

Choice Architecture: Using Fixation Patterns to Analyze the Effects of Form Design on Cognitive Biases

User-generated online reviews are an important input into purchase decisions, but are susceptible to cognitive biases, which ultimately undermine the reviews’ value. As even minor changes to the design of online environments (such as Web pages) can influence people’s behavior, design modifications to online review forms could help reduce biases. We hypothesize that design modifications to online forms can help reduce three common sources of biases (availability, anchoring, and response style), and propose an experiment that employs eye tracking and recording of mousing behavior to test the hypotheses.

Digital nudging: guiding online user choices through interface design

Designers can create designs that nudge users toward the most desirable option.

Combating the Influence of the Heuristic Thinking in Online Star Ratings: Preliminary Evidence

Online reviews, often considered more credible and less biased than marketing information, have become an important aspect of making purchase decisions. Yet, online star ratings can be affected by reviewers’ heuristic evaluations, potentially leading to suboptimal purchase decisions. For example, star ratings may be biased due to the availability heuristic, i.e., users giving disproportionate weight to one—often negative—attribute. As research has demonstrated that even minor modifications of the presentation of options can have a large influence on people’s behavior, we test the effects of prior attribute rating on overall star rating. An experiment (n = 56) conducted in the context of restaurant ratings showed that merely asking people first to rate individual attributes can significantly influence overall ratings. These findings can have important implications, as uncovering the effects specific design parameters of review forms have on people’s evaluation results will allow for reducing unintended biases in review form design.

Beyond crowd judgments: Data-driven estimation of market value in association football

Association football is a popular sport, but it is also a big business. From a managerial perspective, the most important decisions that team managers make concern player transfers, so issues related to player valuation, especially the determination of transfer fees and market values, are of major concern. Market values can be understood as estimates of transfer fees—that is, prices that could be paid for a player on the football market—so they play an important role in transfer negotiations. These values have traditionally been estimated by football experts, but crowdsourcing has emerged as an increasingly popular approach to estimating market value. While researchers have found high correlations between crowdsourced market values and actual transfer fees, the process behind crowd judgments is not transparent, crowd estimates are not replicable, and they are updated infrequently because they require the participation of many users. Data analytics may thus provide a sound alternative or a complementary approach to crowd-based estimations of market value. Based on a unique data set that is comprised of 4217 players from the top five European leagues and a period of six playing seasons, we estimate players’ market values using multilevel regression analysis. The regression results suggest that data-driven estimates of market value can overcome several of the crowds practical limitations while producing comparably accurate numbers. Our results have important implications for football managers and scouts, as data analytics facilitates precise, objective, and reliable estimates of market value that can be updated at any time.