Compensating crowdworkers for their research participation often entails paying a flat rate to all participants, regardless of the amount of time they spend on the task or skill level. If the actual time required varies considerably between workers, flat rates may yield unfair compensation. To study this matter, we analyzed three survey studies with varying complexity. Based on the United Kingdom minimum wage and actual task completion times, we found that more than 3 in 4 (76.5%) of the crowdworkers studied were paid more than the intended hourly wage, and around one in four (23.5%) was paid less than the intended hourly wage when using a flat rate compensation model based on estimated completion time. The results indicate that the popular flat rate model falls short as a form of equitable remuneration, when perceiving fairness in the form of compensating one’s time. Flat rate compensation would not be problematic if the workers’ completion times were similar, but this is not the case in reality, as skills and motivation can vary. To overcome this problem, the study proposes three alternative compensation models: Compensation by Normal Distribution, Multi-Objective Fairness, and Post-Hoc Bonuses.

We conduct two studies to evaluate the suitability of artificially generated facial pictures for use in a customer-facing system using data-driven personas. STUDY 1 investigates the quality of a sample of 1,000 artificially generated facial pictures. Obtaining 6,812 crowd judgments, we find that 90% of the images are rated medium quality or better. STUDY 2 examines the application of artificially generated facial pictures in data-driven personas using an experimental setting where the high-quality pictures are implemented in persona profiles. Based on 496 participants using 4 persona treatments (2 × 2 research design), findings of Bayesian analysis show that using the artificial pictures in persona profiles did not decrease the scores for Authenticity, Clarity, Empathy, and Willingness to Use of the data-driven personas.

Though photographs of real people are typically used to portray personas, there is little research into the potential advantages or disadvantages of using such images, relative to other image styles. We conducted an experiment with 149 participants, testing the effects of six different image styles on user perceptions and personality traits that are attributed to personas by the participants. Results show that perceptions of clarity, completeness, consistency, credibility, and empathy for a persona increase with picture realism. Personas with more realistic pictures are also perceived as more agreeable, open, and emotionally stable, with higher confidence in these assessments. We also find evidence of the uncanny valley effect, with realistic cartoon personas experiencing a decrease in the user perception scores.

This research explores the value of personas for supporting professional advertisers to design adverts for social media. We test if a personified user group (PUG), when provided to online ad designers, results in better ad performance than when using a non-personified user group (NUG) that had no face picture or name. Our experiment has 30 participants that created Facebook ads using both PUG and NUG. We found that using PUG did increase advertising click performance of ads created by people who are more experienced with ads and personas. Moreover, an analysis of the ad texts showed that the use of PUG increased the empathy of the created ads, supporting the foundational empathy benefit cited in HCI literature. However, the use of PUG did not significantly increase purchase intent. The results imply that using PUG for online ad design evokes more empathy and improves click-through performance. More empathetic ads can have a positive impact on social media users, given that they appear to increase relevance.

Analyzing 5,665 crowd ratings on 1,133 social media comments, we find that individuals tend to agree on the extremes of a hate rating scale more than in the middle when evaluating the hatefulness of online comments. The agreement is higher for less hateful comments and lowest on moderately hateful comments. The results have implications for researchers developing machine learning models for online hate processing, as the extreme classes are likely to require fewer annotations for reaching statistical stability. Our findings suggest that the models developed in this domain should consider the distributions of hate ratings rather than average hate scores.