Megan Erway

About me

Four products in the past year, each starting with a problem I experienced or saw firsthand

Lubbock Coolers is the one with revenue attached. I own product strategy, customer experience, and operations end-to-end: the storefront, a 3D configurator that lets customers preview their artwork before paying, per-panel pricing, and a dashboard tracking conversion and drop-off at each step. I've sold 20 units, and every change since launch has come from a customer conversation or funnel data.

I’m a senior at Texas Tech University studying psychological sciences and an undergraduate researcher in the Greenlee Lab, where I study human factors in human-AI teams. That training shapes how I build products, which starts with understanding the user, then using data to figure out next steps.

I scoped the other three projects the same way. Weekender crowdsources what college group trips actually cost per person, using a two-field submission form and a five-stage funnel. LabMatch helps students find relevant research labs based on plain-language interests, shows why each lab was matched, and includes moderated anonymous reviews based on my previous experience. The Psi Chi app replaced a manual process I was responsible for running by routing each point submission to the appropriate officer chair and requiring photo proof

What I do

Product strategy Process improvement Root cause analysis User & customer interviews Survey design Usability testing UX research Human factors research KPI analysis Data visualization ANOVA & regression End-to-end Operations Capacity planning SQL Jira Excel Qualtrics Google Analytics 4 Stripe MATLAB

Research behind the products

Team-bound or Role-bound? Structure of Shared Cognition in Collaborative Tasks

Poster presentation, TTU Undergraduate Research Conference, April 2026. Analyzed a 170-participant, 85-team study and found team-based similarity significantly exceeded role-based similarity (p < .001), informing whether collaborative tools should be designed at the team or role level.

Mapping Trust Trajectories in Human-AI Teams: A Role-Based Analysis

Co-author, TTU Undergraduate Research Conference, April 2026. Analyzed trust survey data (TAS, HAIQ) across a three-agent human-AI team and found trust remained stable across roles and consistent over time.

Let's connect

Email me See the work