scholarly journals No evidence that a range of artificial monitoring cues influence online donations to charity in an MTurk sample

2016 ◽  
Vol 3 (10) ◽  
pp. 150710 ◽  
Author(s):  
Timothy J. Saunders ◽  
Alex H. Taylor ◽  
Quentin D. Atkinson
Keyword(s):  
Individual Differences ◽  
Cultural Differences ◽  
Charitable Giving ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Prosocial Behaviour ◽  
Culture Age ◽  
Work Supports ◽  
Internet Marketplace ◽  
Online Donations

Monitoring cues, such as an image of a face or pair of eyes, have been found to increase prosocial behaviour in several studies. However, other studies have found little or no support for this effect. Here, we examined whether monitoring cues affect online donations to charity while manipulating the emotion displayed, the number of watchers and the cue type. We also include as statistical controls a range of likely covariates of prosocial behaviour. Using the crowdsourcing Internet marketplace, Amazon Mechanical Turk (MTurk), 1535 participants completed our survey and were given the opportunity to donate to charity while being shown an image prime. None of the monitoring primes we tested had a significant effect on charitable giving. By contrast, the control variables of culture, age, sex and previous charity giving frequency did predict donations. This work supports the importance of cultural differences and enduring individual differences in prosocial behaviour and shows that a range of artificial monitoring cues do not reliably boost online charity donation on MTurk.

What Makes an Environmental Steward? An Individual Differences Approach

2021 ◽  
Author(s):  
Ryan Plummer ◽  
Julia Baird ◽  
Gillian Dale
Keyword(s):  
Individual Differences ◽  
Environmental Stewardship ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Future Research ◽  
Individual Factors ◽  
Environmental Perceptions ◽  
Leverage Point

Engaging in environmental stewardship is critical for sustainability. Understanding individual differences and engagement is an important gap in present scholarship and addressing it is necessary to understand individual factors that relate to the types of activities engaged in, motivations and barriers to environmental stewardship. We surveyed 637 Canadian and American adults via Amazon Mechanical Turk, querying a range of demographic, psychological and environmental perceptions factors as well as motivations and barriers to stewardship activities. Respondents were ultimately grouped into Non-Stewards, Home-Oriented Stewards and Community-Oriented Stewards. Few differences were found among these groups. However, Home-Oriented Stewards and Community-Oriented Stewards exhibited very different initial and ongoing motivations to engage in environmental stewardship. Accordingly, we identify stewardship motivations as a potential leverage point and as one of several promising avenues for future research related to enhancing engagement in environmental stewardship for the sustainability of the planet.

scholarly journals Signaling when no one is watching: A reputation heuristics account of outrage and punishment in one-shot anonymous interactions

2019 ◽  
Author(s):  
Jillian Jordan ◽  
David Gertler Rand
Keyword(s):  
Individual Differences ◽  
Third Party ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Moral Outrage ◽  
Total N ◽  
Mediation Analyses ◽  
Costly Punishment ◽  
Using Data

Moralistic punishment can confer reputation benefits by signaling trustworthiness to observers. But why do people punish even when nobody is watching? We argue that people often rely on the heuristic that reputation is typically at stake, such that reputation concerns can shape moral outrage and punishment even in one-shot anonymous interactions. We then support this account using data from Amazon Mechanical Turk. In anonymous experiments, subjects (total n = 8440) report more outrage in response to others’ selfishness when they cannot signal their trustworthiness through direct prosociality (sharing with a third party)—such that if the interaction were not anonymous, punishment would have greater signaling value. Furthermore, mediation analyses suggest that sharing opportunities reduce outrage by decreasing reputation concerns. Additionally, anonymous experiments measuring costly punishment (total n = 6076) show the same pattern: subjects punish more when sharing is not possible. And importantly, moderation analyses provide some evidence that sharing opportunities do not merely reduce outrage and punishment by inducing empathy towards selfishness or hypocrisy aversion among non-sharers. Finally, we support the specific role of heuristics by investigating individual differences in deliberateness. Less deliberative individuals (who typically rely more on heuristics) are more sensitive to sharing opportunities in our anonymous punishment experiments, but, critically, not in punishment experiments where reputation is at stake (total n = 3422); and not in our anonymous outrage experiments (where condemning is costless). Together, our results suggest that when nobody is watching, reputation cues nonetheless can shape outrage and—among individuals who rely on heuristics—costly punishment.

scholarly journals Evaluating extinction, renewal, and resurgence of operant behavior in humans with Amazon Mechanical Turk

2021 ◽  
Vol 74 ◽  
pp. 101728
Author(s):  
Carolyn M. Ritchey ◽  
Toshikazu Kuroda ◽  
Jillian M. Rung ◽  
Christopher A. Podlesnik
Keyword(s):  
Operant Behavior ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk

scholarly journals Amazon Mechanical Turk: Gold Mine or Coal Mine?

2011 ◽  
Vol 37 (2) ◽  
pp. 413-420 ◽  
Author(s):  
Karën Fort ◽  
Gilles Adda ◽  
K. Bretonnel Cohen
Keyword(s):  
Coal Mine ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Gold Mine

scholarly journals Developing and validating an isotrigon texture discrimination task using Amazon Mechanical Turk

2015 ◽  
Vol 16 (S1) ◽  
Author(s):  
John WG Seamons ◽  
Marconi S Barbosa ◽  
Jonathan D Victor ◽  
Dominique Coy ◽  
Ted Maddess
Keyword(s):  
Discrimination Task ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Texture Discrimination

Real user evaluation of spoken dialogue systems using Amazon Mechanical Turk

2011 ◽  
Author(s):  
F. Jurčíček ◽  
S. Keizer ◽  
Milica Gašić ◽  
François Mairesse ◽  
B. Thomson ◽  
...  
Keyword(s):  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
User Evaluation ◽  
Dialogue Systems ◽  
Spoken Dialogue Systems ◽  
Spoken Dialogue

Exploring Biases Between Human and Machine Generated Designs

2018 ◽  
Vol 141 (2) ◽  
Author(s):  
Christian E. Lopez ◽  
Scarlett R. Miller ◽  
Conrad S. Tucker
Keyword(s):  
Computer Simulation ◽  
Deep Learning ◽  
Domain Knowledge ◽  
Mechanical Turk ◽  
Amazon Mechanical Turk ◽  
Design Tools ◽  
Generative Design ◽  
Generate Functional ◽  
Intended Function ◽  
2D Sketches

The objective of this work is to explore the possible biases that individuals may have toward the perceived functionality of machine generated designs, compared to human created designs. Toward this end, 1187 participants were recruited via Amazon mechanical Turk (AMT) to analyze the perceived functional characteristics of both human created two-dimensional (2D) sketches and sketches generated by a deep learning generative model. In addition, a computer simulation was used to test the capability of the sketched ideas to perform their intended function and explore the validity of participants' responses. The results reveal that both participants and computer simulation evaluations were in agreement, indicating that sketches generated via the deep generative design model were more likely to perform their intended function, compared to human created sketches used to train the model. The results also reveal that participants were subject to biases while evaluating the sketches, and their age and domain knowledge were positively correlated with their perceived functionality of sketches. The results provide evidence that supports the capabilities of deep learning generative design tools to generate functional ideas and their potential to assist designers in creative tasks such as ideation.

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