Jacob Solomon

Don't Wait!: How Timing Affects Coordination of Crowdfunding Donations

Crowdfunding sites often impose deadlines for projects to receive their requested funds. This deadline structure creates a difficult decision for potential donors. Donors can donate early to a project to help it reach its goal and to signal to other donors that the project is worthwhile. But donors may also want to wait for a similar signal from others. We conduct an experimental simulation of a crowdfunding website to explore how potential donors to projects make this decision. We find evidence for both strategies in our experiment; some donate early while others wait till the last second. However, we also find that making an early donation is usually a better strategy for donors because the amount of donations made early in a projects campaign is often the only difference between that project being funded or not. This finding suggests that crowdfunding sites need to develop designs, policies and incentives that encourage people to make immediate donations so that the site can most efficiently fund projects.

Bootstrapping wikis: developing critical mass in a fledgling community by seeding content

Online communities depend on content contributed by their members. However, new communities have not yet achieved critical mass and are vulnerable to inadequate contribution. To encourage contribution, many fledgling communities seed the site with data from 3rd parties. We study the effectiveness of such seeding by looking at how people react to different types of seeded content. We found that people make larger contributions when there is no seeded content. But when there is seeded content, users learn from that content and contribute similar types of content. Therefore, if websites prefer specific types of contributions, seeding that type of contribution can be a valuable way to elicit appropriate contributions.

Graphics help patients distinguish between urgent and non-urgent deviations in laboratory test results.

ABSTRACT Objective: Most electronic health record systems provide laboratory test results to patients in table format. We tested whether presenting such results in visual displays (number lines) could improve understanding. Materials and Methods: We presented 1620 adults recruited from a demographically diverse Internet panel with hypothetical results from several common laboratory tests, first showing near-normal results and then more extreme values. Participants viewed results in either table format (with a “standard range” provided) or one of 3 number line formats: a simple 2-color format, a format with diagnostic categories such as “borderline high” indicated by colored blocks, and a gradient format that used color gradients to smoothly represent increasing risk as values deviated from standard ranges. We measured respondents’ subjective sense of urgency about each test result, their behavioral intentions, and their perceptions of the display format. Results: Visual displays reduced respondents’ perceived urgency and desire to contact health care providers immediately for near-normal test results compared to tables but did not affect their perceptions of extreme values. In regression analyses controlling for respondent health literacy, numeracy, and graphical literacy, gradient line displays resulted in the greatest sensitivity to changes in test results. Discussion: Unlike tables, which only tell patients whether test results are normal or not, visual displays can increase the meaningfulness of test results by clearly defining possible values and leveraging color cues and evaluative labels. Conclusion: Patient-facing displays of laboratory test results should use visual displays rather than tables to increase people’s sensitivity to variations in their results.

Is This Good or Bad?: Redesigning Visual Displays of Medical Test Results in Patient Portals to Provide Context and Meaning

We describe an ongoing effort to improve communication of medical test results directly to patients by providing important contextual information about the test results in a graphical format that is well suited to online patient portals. We undertook an iterative, user-centered design process to explore ways to design meaningful representations of test results. We present findings from this process that have important design implications for communicating test results via online patient portals.

Customization bias in decision support systems

Many Decision Support Systems (DSS) afford customization of inputs or algorithms before generating recommendations to a decision maker. This paper describes an experiment in which users make decisions assisted by recommendations of a DSS in a fantasy baseball game. This experiment shows that the act of customizing a DSS can lead to biased decision making. I show that users who believe they have customized a DSSs recommendation algorithm are more likely to follow the recommendations regardless of their accuracy. I also show that this customization bias is the result of using a DSS to seek confirmatory information in a recommendation.

HUMAN-WHAT INTERACTION? UNDERSTANDING USER SOURCE ORIENTATION

Interaction with internet-connected computing devices involves interaction with many distinct agents or sources simultaneously. Hardware, operating systems, web browsers, networking devices, and servers all influence the user experience, as do the engineers and programmers who designed them, the companies or organizations that have developed the systems, other users on the web, and various third-parties such as advertisers. We argue that users cannot be simultaneously engaged towards all of these sources of interaction, and instead must orient themselves to only a subset at any given time. We propose a model of source orientation based on literature from psychology, communications and human-computer interaction. This model describes how users select their source orientation when interacting with computers. We also present examples of how this model can be applied to promote usability in computing systems.

Improving the Understanding of Test Results by Substituting (Not Adding) Goal Ranges: Web-Based Between-Subjects Experiment

Background Most displays of laboratory test results include a standard reference range. For some patients (eg, those with chronic conditions), however, getting a result within the standard range may be unachievable, inappropriate, or even harmful. Objective The objective of our study was to test the impact of including clinically appropriate goal ranges outside the standard range in the visual displays of laboratory test results. Methods Participants (N=6776) from a demographically diverse Web-based panel viewed hypothetical glycated hemoglobin (HbA1c) test results (HbA1c either 6.2% or 8.2%) as part of a type 2 diabetes management scenario. Test result visual displays included either a standard range (4.5%-5.7%) only, a goal range (6.5%-7.5%) added to the standard range, or the goal range only. The results were displayed in 1 of the following 3 display formats: (1) a table; (2) a simple, two-colored number line (simple line); or (3) a number line with diagnostic categories indicated via colored blocks (block line). Primary outcome measures were comprehension of and negative reactions to test results. Results While goal range information did not influence the understanding of HbA1c=8.2% results, the goal range only display produced higher levels of comprehension and decreased negative reactions to HbA1c=6.2% test results compared with the no goal range and goal range added conditions. Goal range information was less helpful in the block line condition versus the other formats. Conclusions Replacing the standard range with a clinically appropriate goal range could help patients better understand how their test results relate to their personal targets.

Heterogeneity in Customization of Recommender Systems By Users with Homogenous Preferences

Recommender systems must find items that match the heterogeneous preferences of its users. Customizable recommenders allow users to directly manipulate the systems algorithm in order to help it match those preferences. However, customizing may demand a certain degree of skill and new users particularly may struggle to effectively customize the system. In user studies of two different systems, I show that there is considerable heterogeneity in the way that new users will try to customize a recommender, even within groups of users with similar underlying preferences. Furthermore, I show that this heterogeneity persists beyond the first few interactions with the recommender. System designs should consider this heterogeneity so that new users can both receive good recommendations in their early interactions as well as learn how to effectively customize the system for their preferences.