The Experience Sampling Method (ESM) is used by scientists from various disciplines to gather insights into the intra-psychic elements of human life. Researchers have used the ESM in a wide variety of studies, with the method seeing increased popularity. Mobile technologies have enabled new possibilities for the use of the ESM, while simultaneously leading to new conceptual, methodological, and technological challenges. In this survey, we provide an overview of the history of the ESM, usage of this methodology in the computer science discipline, as well as its evolution over time. Next, we identify and discuss important considerations for ESM studies on mobile devices, and analyse the particular methodological parameters scientists should consider in their study design. We reflect on the existing tools that support the ESM methodology and discuss the future development of such tools. Finally, we discuss the effect of future technological developments on the use of the ESM and identify areas requiring further investigation.

The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the virus’ reproduction rate, these restrictions come with far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software engineers who were working from home. Although software professionals are accustomed to working with digital tools, but not all of them remotely, in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In a two-wave longitudinal study ( N = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity. Examples include anxiety, distractions, coping strategies, psychological and physical needs, office set-up, stress, and work motivation. This design allowed us to identify the variables that explained unique variance in well-being and productivity. Results include (1) the quality of social contacts predicted positively, and stress predicted an individual’s well-being negatively when controlling for other variables consistently across both waves; (2) boredom and distractions predicted productivity negatively; (3) productivity was less strongly associated with all predictor variables at time two compared to time one, suggesting that software engineers adapted to the lockdown situation over time; and (4) longitudinal analyses did not provide evidence that any predictor variable causal explained variance in well-being and productivity. Overall, we conclude that working from home was per se not a significant challenge for software engineers. Finally, our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provides tailored insights for software professionals.

Social conformity occurs when an individual changes their behaviour in line with the majority's expectations. Although social conformity has been investigated in small group settings, the effect of gender -of both the individual and the majority/minority -is not well understood in online settings. Here we systematically investigate the impact of groups' gender composition on social conformity in online settings. We use an online quiz in which participants submit their answers and confidence scores, both prior to and following the presentation of peer answers that are dynamically fabricated. Our results show an overall conformity rate of 39%, and a significant effect of gender that manifests in a number of ways: gender composition of the majority, the perceived nature of the question, participant gender, visual cues of the system, and final answer correctness. We conclude with a discussion on the implications of our findings in designing online group settings, accounting for the effects of gender on conformity.CCS Concepts: • Human-centered computing → Empirical studies in collaborative and social computing; Empirical studies in HCI .

Researchers who analyse smartphone usage logs often make the assumption that users who lock and unlock their phone for brief periods of time (e.g., less than a minute) are continuing the same "session" of interaction. However, this assumption is not empirically validated, and in fact different studies apply different arbitrary thresholds in their analysis. To validate this assumption, we conducted a field study where we collected user-labelled activity data through ESM and sensor logging. Our results indicate that for the majority of instances where users return to their smartphone, i.e., unlock their device, they in fact begin a new session as opposed to continuing a previous one. Our findings suggest that the commonly used approach of ignoring brief standby periods is not reliable, but optimisation is possible. We therefore propose various metrics related to usage sessions and evaluate various machine learning approaches to classify gaps in usage.

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