Information Systems Research on Digital Platforms for Knowledge Work: A Scoping Review

Digital platforms for knowledge work, such as Upwork, Freelancer.com and Amazon Mechanical Turk connect clients with millions of workers for a range of tasks, including software development, virtual assistance, and micro-tasks. Information systems research on this emergent phenomenon has gained traction in recent years regarding publication volume and research diversity. To identify relevant papers, to distinguish them from related types of digital platforms, and to guide future research, we conducted a scoping review, focusing on the information systems literature. Results are structured according to a theoretical framework of the knowledge work process, covering three phases: Worker-client matching, committing for future action, and executing commitments. While the first phase has been analyzed extensively, we contend that the main phases of the knowledge work process have received scant attention. In this emergent stage of extant research, our review identifies promising research directions to guide prospective studies

The Perils and Promises of Big Data Research in Information Systems

With the proliferation of “big data” and powerful analytical techniques, information systems (IS) researchers are increasingly engaged in what we label as big data research (BDR)—research based on large digital trace datasets and computationally intensive methods. The number of such research papers has been growing rapidly in the top IS journals during the last decade, with roughly 16% of papers in 2018 employing this approach. In this editorial, we propose five conjectures that articulate the potential consequences of increasing BDR prevalence for the IS field’s research goals and outputs. We discuss ways in which IS researchers may be able to better leverage big data and new analysis techniques to conduct more impactful research. Our intent with these conjectures and analyses is to stimulate debate in the IS community. Indeed, we need a productive discussion about how emerging new research methods, digital trace data, and the development of indigenous theory relate to and can support one another

Who Gets the Job? Synthesis of Literature Findings on Provider Success in Crowdsourcing Marketplaces

Background: Over the past decade, crowdsourcing marketplaces — online exchange platforms which facilitate commercial outsourcing of services — have witnessed a dramatic growth in the number of participants (service providers and customers) and the value of outsourced services. Deciding about the most appropriate provider is a key challenge for customers in crowdsourcing marketplaces because available information about providers may be incomplete and sometimes irrelevant for customer decisions. Ineffective information impedes many service providers to develop long-term relationships with customers, obtain projects on a regular basis and survive on crowdsourcing marketplaces. Previous studies have investigated the impact of a range of factors on customers’ choice decisions and providers’ success, given the important role of customer–provider relationship development for long-term success on crowdsourcing marketplaces. Method: This paper reviews the literature of crowdsourcing marketplaces with the aim of developing a comprehensive list of factors that influence customers’ choice decisions and providers’ success. Results: We found 31 conceptually distinct profile information components/factors that determine customers’ choices and providers’ business outcomes on crowdsourcing marketplaces. Conclusion: We classified these 31 factors into five major categories: 1) prior relationship between a customer and a provider or a customer’s invitation, 2) providers’ bidding behavior, 3) crowdsourcing marketplace or auction characteristics, 4) providers’ profile information, and 5) customer characteristics. The main factors in each category, associated considerations, related literature gaps and avenues for future research are discussed in detail