A laboratory experiment on using different financial-incentivization schemes in software-engineering experimentation

• Published in: PeerJ. Computer science Vol. 11; p. e2650
• Main Authors: Bershadskyy, Dmitri, Krüger, Jacob, Calıklı, Gül, Otto, Siegmar, Zabel, Sarah, Greif, Jannik, Heyer, Robert
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 12.03.2025; PeerJ Inc
• Subjects: Algorithms and Analysis of Algorithms; Economic incentives; Experimentation; Financial incentives; Human-Computer Interaction; Psychological research; Social Computing; Software Engineering; Surveys; New York; United States; Experimentation; Financial incentives; Software engineering
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2650

In software-engineering research, many empirical studies are conducted with open-source or industry developers. However, in contrast to other research communities like economics or psychology, only few experiments use financial incentives ( i.e ., paying money) as a strategy to motivate participants’ behavior and reward their performance. The most recent version of the SIGSOFT Empirical Standards mentions payouts only for increasing participation in surveys, but not for mimicking real-world motivations and behavior in experiments. Within this article, we report a controlled experiment in which we tackled this gap by studying how different financial incentivization schemes impact developers. For this purpose, we first conducted a survey on financial incentives used in the real-world, based on which we designed three incentivization schemes: (1) a performance-dependent scheme that employees prefer, (2) a scheme that is performance-independent, and (3) a scheme that mimics open-source development. Then, using a between-subject experimental design, we explored how these three schemes impact participants’ performance. Our findings indicate that the different schemes can impact participants’ performance in software-engineering experiments. Our results are not statistically significant, possibly due to small sample sizes and the consequent lack of statistical power, but with some notable trends that may inspire future hypothesis generation. Our contributions help understand the impact of financial incentives on participants in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.