update abstract again

Author: xehu

index.Rmd

@@ -11,7 +11,7 @@ Author(s): Xinlan Emily Hu, Mark E. Whiting, Linnea Gandhi, Duncan J. Watts, and
 
 # Abstract
 
-Research on teams spans many diverse contexts, but integrating knowledge from heterogeneous sources is challenging because studies typically examine different tasks that cannot be directly compared. Most investigations involve teams working on just one or a handful of tasks, and researchers lack principled ways to quantify how similar or different these tasks are from one another. We address this challenge by introducing the “Task Space,” a multidimensional framework that represents tasks along 24 theoretically-motivated dimensions. We also build a crowd-annotated repository of 102 tasks from published literature, which serves as the basis of an integrative experiment that demonstrates the Task Space’s utility. Our experiment answers a fundamental question in team research: *when do interacting groups outperform individuals*? We use the annotated repository to systematically sample 20 diverse tasks, and we recruit 1,231 participants to work at three complexity levels, either individually or in groups of three or six (180 experimental conditions). Our experiment reveals striking heterogeneity in group advantage, with groups performing anywhere from three times worse to 60% better than the best individual working alone, depending on the task context. Critically, the Task Space makes this heterogeneity predictable, and it significantly outperforms traditional typologies in predicting group advantage on unseen tasks. Our models also reveal theoretically meaningful interactions between task features; for example, group advantage on creative tasks depends on whether the answers are objectively verifiable. The Task Space ultimately enables researchers to move beyond isolated findings to identify boundary conditions and build cumulative knowledge about team performance.
+Research on teams spans many contexts, but integrating knowledge from heterogeneous sources is challenging because studies typically examine different tasks that cannot be directly compared. Most investigations involve teams working on just one or a handful of tasks, and researchers lack principled ways to quantify how similar or different these tasks are from one another. We address this challenge by introducing the “Task Space,” a multidimensional space in which tasks—and the distances between them—can be represented formally, and use it to create a “Task Map” of 102 crowd-annotated tasks from the published experimental literature. We then demonstrate the Task Space’s utility by performing an integrative experiment that addresses a fundamental question in team research: *when do interacting groups outperform individuals?* Our experiment samples 20 diverse tasks from the Task Map at three complexity levels and recruits 1,231 participants to work either individually or in groups of three or six (180 experimental conditions). We find striking heterogeneity in group advantage, with groups performing anywhere from three times worse to 60% better than the best individual working alone, depending on the task context. Critically, the Task Space makes this heterogeneity predictable: it significantly outperforms traditional typologies in predicting group advantage on unseen tasks. Our models also reveal theoretically meaningful interactions between task features; for example, group advantage on creative tasks depends on whether the answers are objectively verifiable. We conclude by arguing that the Task Space enables researchers to integrate findings across different experiments, thereby building cumulative knowledge about team performance.
 
 # Understanding teams requires understanding their tasks.
 

index.html

@@ -359,33 +359,32 @@ <h1 class="title toc-ignore">The Task Space: An Integrative Framework
 Watts, and Abdullah Almaatouq</p>
 <div id="abstract" class="section level1">
 <h1>Abstract</h1>
-<p>Research on teams spans many diverse contexts, but integrating
-knowledge from heterogeneous sources is challenging because studies
-typically examine different tasks that cannot be directly compared. Most
-investigations involve teams working on just one or a handful of tasks,
-and researchers lack principled ways to quantify how similar or
-different these tasks are from one another. We address this challenge by
-introducing the “Task Space,” a multidimensional framework that
-represents tasks along 24 theoretically-motivated dimensions. We also
-build a crowd-annotated repository of 102 tasks from published
-literature, which serves as the basis of an integrative experiment that
-demonstrates the Task Space’s utility. Our experiment answers a
-fundamental question in team research: <em>when do interacting groups
-outperform individuals</em>? We use the annotated repository to
-systematically sample 20 diverse tasks, and we recruit 1,231
-participants to work at three complexity levels, either individually or
-in groups of three or six (180 experimental conditions). Our experiment
-reveals striking heterogeneity in group advantage, with groups
+<p>Research on teams spans many contexts, but integrating knowledge from
+heterogeneous sources is challenging because studies typically examine
+different tasks that cannot be directly compared. Most investigations
+involve teams working on just one or a handful of tasks, and researchers
+lack principled ways to quantify how similar or different these tasks
+are from one another. We address this challenge by introducing the “Task
+Space,” a multidimensional space in which tasks—and the distances
+between them—can be represented formally, and use it to create a “Task
+Map” of 102 crowd-annotated tasks from the published experimental
+literature. We then demonstrate the Task Space’s utility by performing
+an integrative experiment that addresses a fundamental question in team
+research: <em>when do interacting groups outperform individuals?</em>
+Our experiment samples 20 diverse tasks from the Task Map at three
+complexity levels and recruits 1,231 participants to work either
+individually or in groups of three or six (180 experimental conditions).
+We find striking heterogeneity in group advantage, with groups
 performing anywhere from three times worse to 60% better than the best
 individual working alone, depending on the task context. Critically, the
-Task Space makes this heterogeneity predictable, and it significantly
+Task Space makes this heterogeneity predictable: it significantly
 outperforms traditional typologies in predicting group advantage on
 unseen tasks. Our models also reveal theoretically meaningful
 interactions between task features; for example, group advantage on
 creative tasks depends on whether the answers are objectively
-verifiable. The Task Space ultimately enables researchers to move beyond
-isolated findings to identify boundary conditions and build cumulative
-knowledge about team performance.</p>
+verifiable. We conclude by arguing that the Task Space enables
+researchers to integrate findings across different experiments, thereby
+building cumulative knowledge about team performance.</p>
 </div>
 <div id="understanding-teams-requires-understanding-their-tasks." class="section level1">
 <h1>Understanding teams requires understanding their tasks.</h1>