A Large Scale Survey of Motivation in Software Development and Analysis of its Validity
Idan Amit, Dror G. Feitelson
TL;DR
This study conducts a large-scale survey to quantify motivators of software developers and validate self-reported motivation. It identifies 11 motivators drawn from prior work and evaluates their independent and joint predictive power using a 66-question instrument answered by 521 complete responses and a year-later follow-up with 124 participants; validity analyses compare related questions, GitHub behavior, and cross-time stability. The results show that while each motivator contributes, no single motivator is sufficient, and a combined approach provides better prediction of high motivation and motivation improvement. The study highlights that motivation remains a complex, multi-faceted phenomenon with moderate validity in self-reports, and offers practical implications for sustaining OSS contributor engagement through recognition and community-building.
Abstract
Context: Motivation is known to improve performance. In software development in particular, there has been considerable interest in the motivation of contributors to open source. Objective: We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self use, etc.), and evaluate their relative effect on motivation. Since motivation is an internal subjective feeling, we also analyze the validity of the answers. Method: We conducted a survey with 66 questions on motivation which was completed by 521 developers. Most of the questions used an 11 point scale. We evaluated the validity of the answers validity by comparing related questions, comparing to actual behavior on GitHub, and comparison with the same developer in a follow up survey. Results: Validity problems include moderate correlations between answers to related questions, as well as self promotion and mistakes in the answers. Despite these problems, predictive analysis, investigating how diverse motivators influence the probability of high motivation, provided valuable insights. The correlations between the different motivators are low, implying their independence. High values in all 11 motivators predict increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation.