Over 100 years of research have shown that social loafing is a real and material psychological phenomenon that reduces performance among humans in groups. It is known that increasing evaluation potential, decreasing deindividuation, and cohesion all lead to reduced social loafing in physical environments. What has not yet been well researched is whether or not the findings associated with many of these variables also apply to virtualized working environments.
In the present study, 200 individuals were recruited through Amazon Mechanical Turk (an online work community) and were split into experimental and control groups. While both sets of participants engaged in identical alphabetization tasks and were informed that they were the final member of a five-person team, only the experimental subjects were asked to read the short biographies of their teammates and write a short biography of themselves for their teammates to read. By having experimental participants engage in self-disclosure it was expected they would experience reduced deindividuation (which is common with virtual teams), increased perceived evaluation potential, and increased feelings of cohesion.
Differences in quantity and quality of performance, and differences in morale (although not cohesion itself), were all in the expected direction, though none achieved levels of statistical significance driven largely by material performance variances within the datasets. Experimental subjects produced, on average, 1.9% additional correct alphabetization groupings (averaging 16.64 of 50 among experimental participants and 16.33 of 50 among control participants) compared to control subjects who were only aware they were a member of a team (F(1, 164) = .04, p >.05). Furthermore, in addition to attempting to alphabetize more groups, the experimental subjects’ answers were, on average, 0.38% more likely to be alphabetized correctly (F (1, 164) = .03, p > .05).
The Perceived Cohesion Scale (PCS) (Bollen & Hoyle, 1990) was included in 160 of the participants’ tasks and experimental subjects scored 2.3% higher (F(1, 122) = .209, p > .05) in feelings of “being enthusiastic about working online”, 3.0% higher (F(1, 122) = .49, p > .05) on being “happy to be working on Amazon Mechanical Turk”, 1.4% higher (F(1, 122) = .04, p > .05) on believing that Amazon Mechanical Turk was “one of the best working communities in the world”, and 2.3% higher (F(1, 122) = .22, p > .05) on the entire category of “Feelings of Morale”.
As an example of the level of variance within the data, the standard deviation (SD) for number of groupings that the experimental participants attempted to alphabetize (out of 50) was 11.57 on an average of 19.84 attempts. For control participants the standard deviation (SD) for number of attempted groupings (out of 50) was 11.31 out of 19.39 average completed attempts. The sample size required to achieve statistical significance at those levels of SD paired with the level of differences in sample mean performance would measure in the thousands.
Furthermore, while some heteroskedasticity was discovered among pilot PCS-statement category data while testing for homogeneity (and were subsequently analyzed using additional robust Welch and Forsythe equality of means testing), the performance data overall passed Levene’s test. Once the heteroskedastic pilot data was tested to accommodate for the heteroskedasticity within, one PCS statement category (HTB, where experimental participants expressed a happiness to be working on AMT) approached the .05 alpha of statistical significance (p = .08).
Despite the lack of statistical significance, this work should be considered valuable as a reference point. Finding any difference at all, in the expected direction, is within itself notable given how tiny the actual cohesion-inducing treatment was (i.e., a self-statement of only a few lines) and how short-term the perceived association with their teammates was (i.e., less than half an hour).
| Identifer | oai:union.ndltd.org:harvard.edu/oai:dash.harvard.edu:1/24078371 |
| Date | 11 January 2016 |
| Creators | Hagen, Matthew Howard |
| Publisher | Harvard University |
| Source Sets | Harvard University |
| Detected Language | English |
| Type | Thesis or Dissertation, text |
| Format | application/pdf |
| Rights | open |
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