When user needs do not align with system designers' visions, new technology implementation becomes a complex process as users appropriate the new technology to meet their needs. Prior studies recognize this complexity, but focus on the complex implementation of simple systems in which user groups are well defined and the IT artifact is the primary change. We extend the research lens by examining the implementation of the Brazilian correspondent banking system, a complex system involving multiple actors, system elements, and settings intended to address the social problem of financial exclusion. Our case study comparison of two settings-retail stores and post offices-reveals that actors' appropriations extended beyond the IT artifact to include technical, role, usage, social, and policy appropriations. The intended users (poor clients in remote and underserved areas) barely interacted with the IT artifact or other system elements; instead, they relied upon remote bankers (correspondents) to appropriate the system on their behalf. Because rewards, incentives, and constraints differed by setting, correspondents' appropriations differed by setting. We call the resulting mix of appropriations across multiple elements by multiple actors in multiple settings multiplex appropriation. Complex societal challenges often involve multiple users in multiple settings with varied needs and few technology skills; thus, designing systems to meet user requirements may prove impossible. Instead, allowing multiplex appropriation might foster system success because, rather than forcing a global alignment among system elements or trying to ascertain multiple user needs, it allows for multiple local alignments of system elements that fit local settings.

To improve the quality and efficiency of research, groups within the scientific community seek to exploit the value of data sharing. Funders, institutions, and specialist organizations are developing and implementing strategies to encourage or mandate data sharing within and across disciplines, with varying degrees of success. Academic journals in ecology and evolution have adopted several types of public data archiving policies requiring authors to make data underlying scholarly manuscripts freely available. Yet anecdotes from the community and studies evaluating data availability suggest that these policies have not obtained the desired effects, both in terms of quantity and quality of available datasets. We conducted a qualitative, interview-based study with journal editorial staff and other stakeholders in the academic publishing process to examine how journals enforce data archiving policies. We specifically sought to establish who editors and other stakeholders perceive as responsible for ensuring data completeness and quality in the peer review process. Our analysis revealed little consensus with regard to how data archiving policies should be enforced and who should hold authors accountable for dataset submissions. Themes in interviewee responses included hopefulness that reviewers would take the initiative to review datasets and trust in authors to ensure the completeness and quality of their datasets. We highlight problematic aspects of these thematic responses and offer potential starting points for improvement of the public data archiving process.

Modern organizations often employ data scientists to improve business processes using diverse sets of data. Researchers and practitioners have both touted the benefits and warned of the drawbacks associated with data science and big data approaches, but few studies investigate how data science is carried out "on the ground." In this paper, we first review the hype and criticisms surrounding data science and big data approaches. We then present the findings of semistructured interviews with 18 data analysts from various industries and organizational roles. Using qualitative coding techniques, we evaluated these interviews in light of the hype and criticisms surrounding data science in the popular discourse. We found that although the data analysts we interviewed were sensitive to both the allure and the potential pitfalls of data science, their motivations and evaluations of their work were more nuanced. We conclude by reflecting on the relationship between data analysts' work and the discourses around data science and big data, suggesting how future research can better account for the everyday practices of this profession.

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Recent events reveal the potential for information technologies to threaten democratic participation and destabilize knowledge institutions. These are core concerns for researchers working within the area of critical information studies—yet these companies have also demonstrated novel tactics for obscuring their operations, reducing the ability of scholars to speak about how harms are perpetuated or to link them to larger systems. While scholars' methods and ethical conventions have historically privileged the agency of research participants, the current landscape suggests the value of exploring methods that would reveal actions that are purposefully hidden. We propose investigation as a model for critical information studies and review the methods and epistemological conventions of investigative journalists as a provocative example, noting that their orientation toward those in power enables them to discuss societal harms in ways that academic researchers often cannot. We conclude by discussing key topics, such as process accountability and institutional norms, that should feature in discussions of how academic researchers might position investigation in relation to their own work.