Events

Event Information:

  • Tue
    18
    Apr
    2017

    [Talk] Carol Moser, Chanda Phelan, and Yan Chen

    12:00 pm - 01:00 pmNQ 3100 (The Ehrlicher Room), North Quad, 105 S. State St. Ann Arbor MI 48109

    Facebook_4-18

    Carol and Chanda will be presenting their work on choice overload effect in an e-commerce context and discuss how this effect is influenced by an individual’s tendency to maximize or satisfice decisions. Yan will be sharing his work on Codeon, a system that he and his coauthors developed to enable more effective task hand-off between end-user developers and remote helpers by allowing asynchronous responses to on-demand requests.

    Everyone is welcome -- light lunch will be served on a first-come-first-served basis; make sure to RSVP, so that we will be prepared (add to calendar).

    Carol Moser and Chanda Phelan: "No Such Thing as Too Much Chocolate: Evidence Against Choice Overload in E-Commerce"

    Abstract:

    E-commerce designers must decide how many products to display at one time. Choice overload research has demonstrated the surprising finding that more choice is not necessarily better—selecting from larger choice sets can be more cognitively demanding and can result in lower levels of choice satisfaction. This research tests the choice overload effect in an e-commerce context and explores how the choice overload effect is influenced by an individual’s tendency to maximize or satisfice decisions. We conducted an online experiment with 611 participants randomly assigned to select a gourmet chocolate bar from either 12, 24, 40, 50, 60, or 72 different options. Consistent with prior work, we find that maximizers are less satisfied with their product choice than satisficers. However, using Bayesian analysis, we find that it’s unlikely that choice set size affects choice satisfaction by much, if at all. We discuss why the decision-making process may be different in e-commerce contexts than the physical settings used in previous choice overload experiments.

    Bios:

    Carol Moser is a PhD Candidate in Human-Computer Interaction and Social Computing at the University of Michigan School of Information. Carol studies how web design and other sociotechnical factors influence consumer decision-making and behavior online. She is advised by Paul Resnick and Sarita Schoenebeck. Carol is a Rackham Merit Fellow and holds a BA in Communication Studies from the University of Michigan.

    Chanda Phelan is a PhD candidate in human-computer interaction working with Dr. Paul Resnick at UMSI. Her research focus is designing for rural and low-income users, particularly as related to health behavior change. Her current research projects include designing data-driven feedback to increase user motivation and confidence in a strength-training exercise program. She is also a Rackham Merit Fellow. She holds a MS in information economics from UMSI and a BA in English from Pomona College.

     

    Yan Chen: Codeon: On-Demand Software Development Assistance

    Abstract:

    Software developers rely on support from a variety of resources—including other developers—but the coordination cost of finding another developer with relevant experience, explaining the context of the problem, composing a specific help request, and providing access to relevant code is prohibitively high for all but the largest of tasks. In this talk, I'm going to introduce Codeon, a system that we developed to enable more effective task hand-off between end-user developers and remote helpers by allowing asynchronous responses to on-demand requests.

    I will discuss the design process of how we developed the main components in our system, such as what we tried for each feature, what fails, what we decided, etc. Then I will talk about our final system evaluation study and show the results that developers using Codeon completed nearly twice as many tasks as those who used state-of-the-art synchronous video and code sharing tools, by reducing the coordination costs of seeking assistance from other developers.

    Bio:

    Yan Chen is a doctoral student at School of Information, University of Michigan, advised by Dr. Walter S. Lasecki and Dr. Steve Oney. His research spans human-computer interaction, programming collaboration and computing education. In particular, he is interested in creating interactive programming tools to support code specific questions in educational settings by leveraging relevant information (e.g. students' expertise, or code context). He holds a bachelor's degree in Applied Mathematics and Electrical and Computer Engineering, and a master's in Applied Mathematics all from University of Colorado at Boulder.