Improving Productivity through
Experience Sampling Method
FEATURED: TU Delft's pick-a-mood product manual
CLIENT: school of general nursing, tuen mun hospital in hong kong
OBJECTIVE: improving staff productivity
METHODOLOGY: ESM, Interviews, Observation, Questionnaires, affinity diagrams, grounded theory
ROLE: interaction design, lead researcher, project manager
TOOLS: IFTTT for automation & triggers, google apps, illustrator, arduino
WHY ESM? Experience Sampling Method (ESM) is a qualitative research method that studies users "in the wild". It not only asks what users do, but how they feel and why. Emotions are a crucial consideration in this human-centred research method, and juxtaposing traditional interview methods, we found that ESM is able to reveal robust, longitudinal data while building rapport from users. It is a highly customizable and scalable method that incorporates users' self-reports during or close to the moment of their experiences.
DESIGN RATIONALE: Through baseline interviews and observation with our volunteer administrative staff, we drew insights on how to design our ESM tool that would elicit self-reports. To motivate users and ensure we got responses at each checkpoint, we designed a desktop toy (compact, interactive) that would serve as a fun reminder to collect responses from users four times a day, for five working days. Mobile-friendly web survey links customized for the different checkpoints were written in English and spoken Cantonese. These were sent via an auto-reply email, which also included the photo they sent to us as an attachment to mimic the affirmation and response real conversations have.
The ESM tool involved:
1) lighting up automatically as a reminder for users to
2) share their mood by spinning a carousel using Pick-A-Mood avatars developed in IDStudioLab at TU Delft (N.R. Herrera, 2016)
3) and sliding a scale to share how they would rate their productivity
USER FEEDBACK: The users were so impressed with our ESM tool or "desktop toy" that they asked if they could keep it to play with.
INSIGHTS AND OPPORTUNITIES: By asking a simple "can you tell us a bit more about why...", we were pleasantly surprised at how much we learned about our users' daily routine, their difficulties, and what boosted their mood and productivity through daily diary responses. From our raw data that was automatically recorded using IFTTT triggers, we narrowed down three factors that served as design guidelines for the school using grounded theory including opportunities for machine-learning and predicting low mood before it effected productivity.