Experimental economic laboratories run many studies to test theoretical predictions with actual human behaviour, including public goods games. With this experiment, participants in a group have the option to invest money in a public account or to keep it. All the invested money is multiplied and then evenly distributed. This structure incentivizes free riding, resulting in contributions to the public goods declining over time. Face-to-face Communication (FFC) diminishes free riding and thus positively affects contribution behaviour, but the question of how has remained mostly unknown. In this paper, we investigate two communication channels, aiming to explain what promotes cooperation and discourages free riding. Firstly, the facial expressions of the group in the 3-minute FFC videos are automatically analysed to predict the group behaviour towards the end of the game. The proposed automatic facial expressions analysis approach uses a new group activity descriptor and utilises random forest classification. Secondly, the contents of FFC are investigated by categorising strategy-relevant topics and using meta-data. The results show that it is possible to predict whether the group will fully contribute to the end of the games based on facial expression data from three minutes of FFC, but deeper understanding requires a larger dataset. Facial expression analysis and content analysis found that FFC and talking until the very end had a significant, positive effect on the contributions.

Using a public goods laboratory experiment, this paper analyzes the extent to which face-to-face communication keeps its efficiency gains even after its removal. This is important as communication in real world is costly (e.g. time). If the effect of communication is long-lasting, the number of communication periods could be minimized. This paper provides evidence that there is a lasting positive effect on contributions even after communication was removed. Yet, after the removal, the contributions are lower and abate over time to the previous magnitude. This is referred to as the reverberation effect of communication. As we do not observe an effect of endogenizing communication, the strongest driver of the size of the contributions is the existence of communication or its reverberation. Eventually, the experiment provides evidence for a strong end-game effect after communication was removed, insinuating communication does not protect from the end-game behavior. In total, the results of the paper imply, that the effects of communication are not permanent but communication should be repeated. Simultaneously, results indicate no need for permanent communication. Since communication is conducted using video-conference tools, we present results from a machine learning based analysis of facial expressions to predict contribution behavior on group level.

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Empirical studies in software engineering are often conducted with open-source developers or in industrial collaborations. Seemingly, this resulted in few experiments using financial incentives (e.g., money, vouchers) as a strategy to motivate the participants' behavior; which is typically done in other research communities, such as economics or psychology. Even the current version of the SIGSOFT Empirical Standards does mention payouts for completing surveys only, but not for mimicking the real-world or motivating realistic behavior during experiments. So, there is a lack of understanding regarding whether financial incentives can or cannot be useful for software-engineering experimentation. To tackle this problem, we plan a survey based on which we will conduct a controlled laboratory experiment. Precisely, we will use the survey to elicit incentivization schemes we will employ as (up to) four payoff functions (i.e., mappings of choices or performance in an experiment to a monetary payment) during a code-review task in the experiment: (1) a scheme that employees prefer, (2) a scheme that is actually employed, (3) a scheme that is performance-independent, and (4) a scheme that mimics an open-source scenario. Using a between-subject design, we aim to explore how the different schemes impact the participants' performance. Our contributions help understand the impact of financial incentives on developers in experiments as well as real-world scenarios, guiding researchers in designing experiments and organizations in compensating developers.

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