Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial ‘domain knowledge’ of experts in relevant fields with ‘machine knowledge’, i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms – dubbed consolidation, algorithmic mediation, and naturalization – in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.

Continuous use of cloud computing and cloud sourcing has received limited research attention compared to cloud adoption. There are indications that cloud sourcing benefits are not easy to reap in continuous use for companies, calling for more research on the continuance use of cloud computing. The current study is one of the first studies of the continuance use of cloud computing processes at the organizational level, contributing to the management and business research literature on cloud computing. In particular, the present study has contributed with two case studies verifying the existence of barriers and more importantly identifying an additional type of barrier: management process barriers (MP), i.e., lack of objectives and strategies for cloud sourcing and lack of organizing cloud vendor communication. Overcoming or reducing management process barriers guides the continuance use of cloud computing process in a strategic direction for the company and enables cloud-related innovation. As a contribution, our research builds on and extends extant research by providing a TOMPE framework of barriers to the continuance use of cloud computing (based and modified from the technology–organization–environment (TOE) framework, by complementing it with the identified management process barriers).

Information systems (IS) are strategic in so far as they are used to realize strategic intent. Yet, while much has been said about aligning IS functionality with the strategic intent and how to organizationally implement strategically aligned systems, less is known of how to successfully implement strategic change associated with system use – a truly critical challenge within strategic IS implementation. Drawing on a strategy-as-practice perspective we address this gap by developing a multi-dimensional view of IS strategy, conceptualizing three key challenges in the IS strategy process, to explain how and why a paper mill, despite successfully implementing a strategic production management system, failed to produce intended strategic change. We call this outcome strategy blindness: organizational incapability to realize the strategic intent of implemented, available system capabilities. Using a longitudinal case study we investigate how cognitive rigidity of key actors and fixed, interrelated practices shaped the implementation of the new production system. We also identify core components and dynamics that constitute a richer multi-dimensional view of the IS strategy implementation (alignment) process. In particular, we identify three salient factors that contribute to strategy blindness – mistranslation of intent, flexibility of the IT artifact and cognitive entrenchment – and discuss how they affect strategic implementation processes. We conclude by discussing implications of our findings for IS strategy theory and practice, especially the contribution of strategy-as-practice to this stream of research.

In this paper we focus empirical and conceptual attention on the social construction of information systems (IS) requirements, and illustrate that IS developers too often choose to ignore, and thus effectively black-box, the complexities of gathering requirements in order to simplify both the difficulties of their work and their relations with customers. The empirical contribution of this paper is evidence drawn from a study of how IS developers pursue requirements engineering and how they conceive its value. The factors we found to be important in this process include: the changing needs of the organization, the ways in which structured IS methods are enacted via experience and social competency, the formation of project groups, and finally engagement in interpersonal conflict and negotiations. Our conceptual contribution is theorization on the nature of developing requirements as a process of social learning.