Working and organizing in the age of the learning algorithm

Learning algorithms, technologies that generate responses, classifications, or dynamic predictions that resemble those of a knowledge worker, raise important research questions for organizational scholars related to work and organizing. We suggest that such algorithms are distinguished by four consequential aspects: black-boxed performance, comprehensive digitization, anticipatory quantification, and hidden politics. These aspects are likely to alter work and organizing in qualitatively different ways beyond simply signaling an acceleration of long-term technology trends. Our analysis indicates that learning algorithms will transform expertise in organizations, reshape work and occupational boundaries, and offer novel forms of coordination and control. Thus, learning algorithms can be considered performative due to the extent to which their use can shape and alter work and organizational realities. Their rapid deployment requires scholarly attention to societal issues such as the extent to which the algorithm is authorized to make decisions, the need to incorporate morality in the technology, and their digital iron-cage potential

Make way for the algorithms: symbolic actions and change in a regime of knowing

When actors deem technological change undesirable, they may act symbolically by pretending to comply while avoiding real change. In our study of the introduction of an algorithmic technology in a sales organization, we found that such symbolic conformity led unintendedly to the full implementation of the suggested technological change. To explain this surprising outcome we advance a regime-of-knowing lens that helps to analyze deep challenges happening ‘under the surface’ during the process of technology introduction. A regime of knowing guides what is worth knowing, what actions matter to acquire this knowledge, and who has the authority to make decisions around those issues. We found that both the technologists who introduced the algorithmic technology, as well as the incumbent workers whose work was affected by the change, used symbolic actions to either defend the established regime of knowing or to advocate a radical change. While the incumbent workers enacted symbolic conformity by pretending to comply with suggested changes, the technologists performed symbolic advocacy by presenting a positive side of the technological change. Ironically, because the symbolic conformity enabled and was reinforced by symbolic advocacy, reinforcing cycles of symbolic actions yielded a radical change in the sales' regime of knowing: from one focused on a deep understanding of customers via personal contact and strong relationships, to one based upon model predictions from the processing of large datasets. We discuss the theoretical implications of these findings for the introduction of technology at work and for knowing in the workplace.Cambridge Judge Business School internal gran

Crunching the numbers Studying the enactment of analytics in an organization

Huysman, M.H. [Promotor]Berends, J.J. [Copromotor]Weerd, G.C. van de [Copromotor

Special issue editorial: artificial intelligence in organizations: implications for information systems research

Artificial intelligence (AI) technologies offer novel, distinctive opportunities and pose new significant challenges to organizations that set them apart from other forms of digital technologies. This article discusses the distinct effects of AI technologies in organizations, the tensions they raise and the opportunities they present for information systems (IS) research. We explore these opportunities in term of four business capabilities: automation, engagement, insight/decision making and innovation. We discuss the differentiated effects that AI brings about and the implications for future IS research

On the right track? Studying the use of biometric data to manage people in organizations

Organizations increasingly use people analytics tools to monitor and control their workforce. Yet, we know little about how such technologies support management in making informed decisions about their workers. We know even less about how biometric data gets collected and used in organizations, and with what consequences. Unlike other types of data processed by people analytics tools representing work performance, biometric data represents bodily performance. Drawing on a 16-month ethnographic study, we address this gap by exploring how biometric data is used to manage and control people in an elite sport setting. In particular, we follow how coaches and athletes negotiate over complementary rhythms, what to make visible and the degree of context required to make sense of biometric data. Our study reveals that biometric data, being temporal, intimate, and decontextualized, necessitates a close relationship between managers and workers. More specifically, we illuminate a new form of control enacted through the use of biometric data: a shared control of the body, where managers and workers together control the body as a primary object of work performance. Encompassing our findings as part of a theoretical framework, we highlight the implications of our discoveries for theory on organizational control and people analytics

Understanding users’ behavior with software operation data mining

Software usage concerns knowledge about how end-users use the software in the field, and how the software itself responds to their actions. In this paper, we present the Usage Mining Method to guide the analysis of data collected during software operation, in order to extract knowledge about how a software product is used by the end-users. Our method suggests three analysis tasks which employ data mining techniques for extracting usage knowledge from software operation data: users profiling, clickstream analysis and classification analysis. The Usage Mining Method was evaluated through a prototype that was executed in the case of Exact Online, the main online financial management application in the Netherlands. The evaluation confirmed the supportive role of the Usage Mining Method in software product management and development processes, as well as the applicability of the suggested data mining algorithms to carry out the usage analysis tasks. © 2013 Elsevier Ltd. All rights reserved

Towards crowd-based requirements engineering. A research preview

<b>[Context and motivation] </b>Stakeholders who are highly distributed form a large, heterogeneous online group, the so-called "crowd". The rise of mobile, social and cloud apps has led to a stark increase in crowd-based settings. <b>[Question/problem] </b>Traditional requirements engineering (RE) techniques face scalability issues and require the co-presence of stakeholders and engineers, which cannot be realized in a crowd setting. While different approaches have recently been introduced to partially automate RE in this context, a multi-method approach to (semi-)automate all RE activities is still needed. <b>[Principal ideas/results]</b> We propose "Crowd-based Requirements Engineering" as an approach that integrates existing elicitation and analysis techniques and fills existing gaps by introducing new concepts. It collects feedback through direct interactions and social collaboration, and by deploying mining techniques. <b>[Contribution] </b>This paper describes the initial state of the art of our approach, and previews our plans for further research