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Although many knowledge management (KM) initiatives are known to miscarry, there has been relative little critical, in‐depth research into the causes of failure. In this paper, an action research (AR) project is described in a major motor manufacturer (WWM) focusing on one of their key knowledge processes (Design for Manufacturabilty) which was regarded as dysfunctional. The paper has two aims: to explore the substantive factors underlying this malaise, and to demonstrate how the rigour and respectability of AR as a research tool can be strengthened by the use of grounded theory (GT) methodology. Using the latter, a model was produced relating the effectiveness of the DFM process to a set of success factors. These factors resonate with key issues described in previous KM research, namely the need for a shared knowledge repository, a formal KM process, and a culture disposed towards knowledge sharing and re‐use. The model provided a rigorous platform for designing interventions to improve the DFM process. Whilst technical changes (creating infrastructure, defining process) are readily feasible, bringing about cultural change is less tractable and process improvements in WWM have tended to focus more on the former than the latter. As well as providing generalisable insights into KM success and failure, the paper also demonstrates the valuable role that GT can make in informing the design of organisational interventions, in evaluating their impact, and in adding rigour to the theory generation dimension of AR.

In contrast to the behavioural paradigm of IS research, design science seeks to develop a body of practically‐oriented knowledge which will directly aid the design, implementation, and use of information technologies and systems. Design science, however, remains a minority practice. The purpose of this paper is to argue the case for its more widespread adoption, especially so in research on innovation.

To this end, the authors report an example of design science in action. Two experiments are reported, both concerning the design of the user interface for domestic heating systems. Of note is the use of a medium‐fidelity laboratory simulation (“microworld”) in this work.

Two specific substantive findings results. First that ecologically designed feedback, embodying a strong mapping between task goals and system status, produces superior task performance. Second, that predictive decision aids provide clear benefits over other forms of user support, such as advisory systems.

Whilst arguing the general case for design science, the study shows that there are formidable barriers to its wider promulgation. These include the challenges of constructing realistic meta‐artefacts, compounded by the complex, modal and uncertain nature of design theory itself. The practical value of the microworld paradigm is also confirmed.

Although research in this field has largely addressed the workplace, here the paper addresses the domestic realm. Further novelty derives from the use of the microworld approach. The argument that design science should draw more on the proven methods of “good design” (e.g. prototyping, user participation) in terms of its own praxis is also noteworthy.

This paper seeks to critique the notion of diffusionism.

The notion of diffusionism provides a general way of understanding innovation and human progress. It is pervasive within IS research and practice. Generically, diffusionism denotes an asymmetrical view of innovation as originating exclusively in “progressive” centres, from which it spreads through an essentially passive recipient community. This model is pernicious, as it privileges an élite few over the majority, with the innovator/imitator dichotomy presented as natural, moral and inevitable. This paper analyses the notion of diffusionism in information systems (IS).

The failure to find any empirical support for diffusionism reveals both its mythical character and its ideological rationale in lending moral legitimacy to colonialistic projects. Empirical examples demonstrate both the ubiquity of the diffusionist mindset in IS research and practice, and its linkage to pseudo‐colonial activities in the home domain.

The paper concludes by arguing for a more critical approach within IS research on innovation, the use of richer, process‐based theories, and greater partnership with practitioners in order to close the research/practice gap.

This paper describes the findings of a field study that explores the process of information systems (IS) development in a large organization. The paper argues that traditional IS development methodologies are treated primarily as a necessary fiction to present an image of control or to provide a symbolic status, and are too mechanistic to be of much use in the detailed, day‐to‐day organization of systems developers’ activities. By drawing on the insights gained from this study, the paper outlines some implications for IS development methodologies. A secondary purpose of the paper is to illustrate the use of an “ecological” research approach to IS development as advocated by Shneiderman and Carroll.

This paper aims to take a new look at how scenarios are produced and used. It does so from a perspective that is unusual in the field: network pragmatism.

This paper takes a conceptual approach.

A network pragmatist account allows scenarios to play an important role in actions designed to secure specific futures for organisations. It, thus, endows them with micro-political force. Any scenario that fails to exert this force will wither and, ultimately, die, but it can be resuscitated. With its demise in the networked world, a scenario can assume a more partial and private existence, shaping the affections, loyalties and actions of notable individuals.

This approach generates novel propositions that question the adequacy of currently dominant cognitive theories. However, it has yet to be tested empirically.

Pragmatist reading of scenarios that is proposed is not only distinct but also only ever partial. This work emphasises that a holistic account of scenario lives needs multiple theoretical perspectives.

Business process re‐engineering (BPR) is certainly one of the latest buzzwords and is the subject of great interest and also great controversy. Organizations need to shake themselves out of complacency to close competitive gaps and achieve superior performance standards ‐ the reason why many have embarked on huge BPR projects. In view of the high risks associated with radical change, there are, however, many problems associated with BPR. For some BPR is going off the rails before it is properly understood, and many BPR exercises are not delivering the goods. Sometimes, organizations are expecting “quick fixes”, thus displaying their lack of understanding of a complex system. It is unreasonable to expect quick results when so much change is involved, especially when these business processes involve not only machines, but also people. Many believe, such as Mumford, that the management of change is the largest task in re‐engineering. Many people perceive re‐engineering as a threat to both their methods and their jobs. Owing to this recognition, many authors concentrate on the need to take account of the human side of re‐engineering, in particular the management of organizational change.

The nature of technologies that are recognised as Artificial Intelligence (AI) has continually changed over time to be something more advanced than other technologies. Despite the fluidity of understanding of AI, the most common theme that has stuck with AI is ‘human-like decision making’. Advancements in processing power, coupled with big data technologies, gave rise to highly accurate prediction algorithms. Analytical techniques which use multi-layered neural networks such as machine learning and deep learning have emerged as the drivers of these AI-based applications. Due to easy access and growing information workforce, these algorithms are extensively used in a plethora of industries ranging from healthcare, transportation, finance, legal systems, to even military. AI-tools have the potential to transform industries and societies through automation. Conversely, the undesirable or negative consequences of AI-tools have harmed their respective organisations in social, financial and legal spheres. As the use of these algorithms propagates in the industry, the AI-based decisions have the potential to affect large portions of the population, sometimes involving vulnerable groups in society. This chapter presents an overview of AI’s use in organisations by discussing the following: first, it discusses the core components of AI. Second, the chapter discusses common goals organisations can achieve with AI. Third, it examines different types of AI. Fourth, it discusses unintended consequences that may take place in organisations due to the use of AI. Fifth, it discusses vulnerabilities that may arise from AI systems. Lastly, this chapter offers some recommendations for industries to consider regarding the development and implementation of AI systems.

The literature about the development of information systems tends to concentrate on methodologies, techniques and tools. There is significant published research about the potential negative aspects of using methodologies and tools (along with that discussing their potential benefits). Techniques, on the other hand, are seen largely as benign, very often as simple aids to help carry out a task, and are used in many methodologies. They might be seen as supporting the collection, collation, analysis, representation or communication of information about system requirements and attributes (or a combination of these). However, it is argued in this paper that techniques also have negative aspects and there are as many dangers in their use as in using methodologies and tools. In particular, techniques may restrict understanding by framing the ways of thinking about the problem situation. In other words, people’s understanding of a problem can be profoundly influenced by how the problem is presented to them by the technique. Different development techniques can represent the same problem situation differently, and the way in which it is represented has considerable potential for influencing problem understanding and resultant decision making. Drawing on the cognitive psychology literature enables one to show how specific visual and linguistic characteristics of techniques may influence problem understanding. In addition, examining the taken‐for‐granted paradigm of a particular technique provides a further dimension influencing problem understanding. This knowledge of visual/language and paradigm attributes is applied to over 80 techniques used to a greater or lesser extent in IS development, indicating how different types of technique are likely to influence problem cognition. This serves two purposes. First, it exposes potential biases of a particular technique and makes users aware of the potential dangers. Second, the overall categorization may provide guidance to users in selecting appropriate techniques and combinations of techniques to help reduce any negative framing influences, provide a more holistic view of a problem situation and support a more appropriate problem‐learning environment.