A large scale survey of motivation in software development

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 would like to predict motivation, in various settings. We identify 11 motivators from the literature (enjoying programming, ownership of code, learning, self-use, etc.), and evaluate their relative effect on motivation using supervised learning. 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 also conducted a follow-up survey, enabling investigation of motivation improvement given improvement in motivators. Results: 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 an increased probability of high motivation. In addition, improvement analysis shows that an increase in most motivators predicts an increase in general motivation. Conclusions: All 11 motivators indeed support motivation, but only moderately. No single motivator suffices to predict high motivation or motivation improvement, and each motivator sheds light on a different aspect of motivation. Models based on multiple motivators predict motivation improvement with up to 94% accuracy, better than any single motivator. Editor's note: Open Science material was validated by the Journal of Systems and Software Open Science Board.