Keith Melissa G, Tay Louis, Harms Peter D
Department of Psychological Sciences, Purdue UniversityWest Lafayette, IN, United States.
Department of Management, The University of AlabamaTuscaloosa, AL, United States.
Front Psychol. 2017 Aug 8;8:1359. doi: 10.3389/fpsyg.2017.01359. eCollection 2017.
Amazon Mechanical Turk (MTurk) is becoming a prevalent source of quick and cost effective data for organizational research, but there are questions about the appropriateness of the platform for organizational research. To answer these questions, we conducted an integrative review based on 75 papers evaluating the MTurk platform and 250 MTurk samples used in organizational research. This integrative review provides four contributions: (1) we analyze the trends associated with the use of MTurk samples in organizational research; (2) we develop a systems perspective (recruitment system, selection system, and work management system) to synthesize and organize the key factors influencing data collected on MTurk that may affect generalizability and data quality; (3) within each factor, we also use available MTurk samples from the organizational literature to analyze key issues (e.g., sample characteristics, use of attention checks, payment); and (4) based on our review, we provide specific recommendations and a checklist for data reporting in order to improve data transparency and enable further research on this issue.
亚马逊土耳其机器人(MTurk)正成为组织研究中快速且经济高效的数据的一个普遍来源,但对于该平台在组织研究中的适用性存在一些问题。为回答这些问题,我们基于75篇评估MTurk平台的论文以及组织研究中使用的250个MTurk样本进行了一项综合综述。这项综合综述有四点贡献:(1)我们分析了组织研究中使用MTurk样本的相关趋势;(2)我们构建了一个系统视角(招聘系统、选拔系统和工作管理系统),以综合并组织影响在MTurk上收集的数据的关键因素,这些因素可能会影响普遍性和数据质量;(3)在每个因素内,我们还利用组织文献中可用的MTurk样本分析关键问题(如样本特征、注意力检查的使用、报酬);(4)基于我们的综述,我们提供了具体建议和数据报告清单,以提高数据透明度并推动对此问题的进一步研究。