Ethics and the Future of Meaningful Work: Introduction to the Special Issue.

The world of work over the past 3 years has been characterized by a great reset due to the COVID-19 pandemic, giving an even more central role to scholarly discussions of ethics and the future of work. Such discussions have the potential to inform whether, when, and which work is viewed and experienced as meaningful. Yet, thus far, debates concerning ethics, meaningful work, and the future of work have largely pursued separate trajectories. Not only is bridging these research spheres important for the advancement of meaningful work as a field of study but doing so can potentially inform the organizations and societies of the future. In proposing this Special Issue, we were inspired to address these intersections, and we are grateful to have this platform for advancing an integrative conversation, together with the authors of the seven selected scholarly contributions. Each article in this issue takes a unique approach to addressing these topics, with some emphasizing ethics while others focus on the future aspects of meaningful work. Taken together, the papers indicate future research directions with regard to: (a) the meaning of meaningful work, (b) the future of meaningful work, and (c) how we can study the ethics of meaningful work in the future. We hope these insights will spark further relevant scholarly and practitioner conversations.

Coding Psychological Constructs in Text Using Mechanical Turk: A Reliable, Accurate, and Efficient Alternative.

In this paper we evaluate how to effectively use the crowdsourcing service, Amazon's Mechanical Turk (MTurk), to content analyze textual data for use in psychological research. MTurk is a marketplace for discrete tasks completed by workers, typically for small amounts of money. MTurk has been used to aid psychological research in general, and content analysis in particular. In the current study, MTurk workers content analyzed personally-written textual data using coding categories previously developed and validated in psychological research. These codes were evaluated for reliability, accuracy, completion time, and cost. Results indicate that MTurk workers categorized textual data with comparable reliability and accuracy to both previously published studies and expert raters. Further, the coding tasks were performed quickly and cheaply. These data suggest that crowdsourced content analysis can help advance psychological research.