Today, crowdsourcing is used to "taskify" any job ranging from simple receipt transcription to collaborative editing, fan-subbing, citizen science, and citizen journalism. The crowd is typically volatile, its arrival and departure asynchronous, and its levels of attention and accuracy is diverse. Tasks vary in complexity and may necessitate the participation of workers with varying degrees of expertise. Sometimes, workers need to collaborate explicitly and build on each others' contributions to complete a single task. This uber-ization of human labor requires the understanding of workers motivation in completing a task, their ability to work together in collaborative tasks, as well as, helping workers find relevant tasks. For over 40 years, organization studies have thoroughly examined human factors that affect workers in physical workplaces. More recently, computer scientists have developed algorithms that verify and leverage those findings in a virtual marketplace, in this case, a crowdsourcing platform. This goal of this talk is to review those two areas and discuss how their combination may improve workers' experience, task throughput and outcome quality for both microtasks and collaborative tasks. We will start with a coverage of motivation theory, team formation, and learning worker profiles. We will then address open research questions that result from this review.