Nudging Attention to Workplace Meeting Goals: A Large-Scale, Preregistered Field Experiment

TL;DR

This study tests a minimal, in-context intervention to encourage pre-meeting goal reflection by embedding brief surveys into a corporate collaboration platform. In a two-week, preregistered field experiment with 361 employees across a global technology firm, participants were randomized to receive pre-meeting goal-reflection prompts (treatment) or only post-meeting surveys (control). The primary finding is that pre-meeting prompts did not significantly improve per-meeting effectiveness, though both groups showed upstream gains in goal clarity and communication, and post-meeting prompts appeared to induce reflection across both groups. Mixed-methods analysis reveals mechanisms and barriers, including target selection, timing, interface design, and work culture, and suggests that regular post-meeting reflections can function as a form of micro-training that builds reflective habits. The study offers design implications for embedding goal reflection into workflows, emphasizing habitual, actionable, and reciprocal reflection to foster sustained meeting intentionality.

Abstract

Ineffective meetings are pervasive. Thinking ahead explicitly about meeting goals may improve effectiveness, but current collaboration platforms lack integrated support. We tested a lightweight goal-reflection intervention in a preregistered field experiment in a global technology company (361 employees, 7196 meetings). Over two weeks, workers in the treatment group completed brief pre-meeting surveys in their collaboration platform, nudging attention to goals for upcoming meetings. To measure impact, both treatment and control groups completed post-meeting surveys about meeting effectiveness. While the intervention impact on meeting effectiveness was not statistically significant, mixed-methods findings revealed improvements in self-reported awareness and behaviour across both groups, with post-meeting surveys unintentionally functioning as an intervention. We highlight the promise of supporting goal reflection, while noting challenges of evaluating and supporting workplace reflection for meetings, including workflow and collaboration norms, and attitudes and behaviours around meeting preparation. We conclude with implications for designing technological support for meeting intentionality.

Figures (12)

• Figure 1: Pre- and post- meeting surveys as they appeared in chat threads via our Meeting Survey application. In the pre-meeting survey, treatment group participants could select multiple goals from 11 options, as well as write in their own goals.
• Figure 2: Participants' flow in the study.
• Figure 3: Type and number of goals selected across meetings in the treatment group. For the type of goals, percentages do not add up to 100% because participants could select multiple goals for a given meeting.
• Figure 4: Distributions of intervention engagement and study compliance. Top-left: number of meetings targeted per person by our system, by group. Top-right: Number of completed post-meeting surveys per person, by group. Bottom-left: Percentage of completed post-meeting surveys per person, by group. Denominators vary widely, according to the number of surveys targeted for each person. Bottom-right: Number of completed pre- and post- meeting survey pairs per person in the treatment group.
• Figure 5: Analysis results for the meeting effectiveness ratings. Left panel displays regression coefficients (black markers) for the condition assignment ('TREATMENT') and the covariates, along with error bars indicating 95% confidence intervals (CIs). Positive coefficients indicate a positive association with meeting effectiveness ratings, and vice versa. The numerical values to the right show the coefficients, along with 95% CIs and p-values. Statistical significance indicators: ** p<0.001, ** p<0.01, * p<0.05, + p<0.1. The right panel visualises participants' average meeting ratings (coloured markers) for each condition, as estimated marginal means derived from a linear mixed model on the complete-case data (for visualisation only). To the right are density distributions of the participant data. Black markers indicate group averages and 95% CIs.