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These data files correspond to the results produced in the following paper currently accepted for publication. Pradeep K. Murukannaiah, Nirav Ajmeri, and Munindar P. Singh. 2022. Enhancing Creativity as Innovation via Asynchronous Crowdwork. In Proceedings of the 14th ACM Web Science Conference. Pages 1--9. To Appear. ---------- Data files ---------- * all_scenarios.csv: 1,823 scenarios produced by MTurk workers * rated_scenarios.csv: 639 scenarios rated for creativity by three authors (700 scenarios were randomly selected for rating. 61 scenarios of these 700 scenarios were unclear or irrelevant and thus were discarded) * creativity.csv: data for RQ1 (Creativity) * personality-creativty.csv and team-composition-creativity.csv: data for RQ2 (Personality) * efficiency.csv: data for RQ3 (Efficiency) * emotions.csv: data for RQ4 (Emotions)
Creativity, Crowdsourcing, Smarthome requirements
Creativity, Crowdsourcing, Smarthome requirements
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
| views | 62 | |
| downloads | 46 |

Views provided by UsageCounts
Downloads provided by UsageCounts