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Existing literature acknowledges information systems development (ISD) to be a complex activity. This complexity is magnified by the continuous changes in user requirements due to changing organizational needs in changing external competitive environments. Research findings show that, if this increasing complexity is not managed appropriately, information systems fail. The paper thus aims to portray the sources of complexity related to ISD and to suggest the use of complexity theory as a frame of reference, analyzing its implications on information system design and development to deal with the emergent nature of IS.

Conceptual analysis and review of relevant literature.

This article provides a conceptual model explaining how top‐down “official” and bottom‐up “emergent” co‐evolutionary adaptations of information systems design with changing user requirements will result in more effective system design and operation. At the heart of this model are seven first principles of adaptive success drawn from foundational biological and social science theory: adaptive tension, requisite complexity, change rate, modular design, positive feedback, causal intricacy, and coordination rhythm. These principles, translated into the ISD context, outline how IS professionals can use them to better enable the co‐evolutionary adaptation of ISD projects to changing stakeholder interests and broader environmental changes.

This paper considers and recognizes the different sources of complexity related to ISD before suggesting how they could be better dealt with. It develops a framework for change to deal with the emergent nature of ISD and enable more expeditious co‐evolutionary adaptation.

To give an overview of the papers contained in this Special Issue.

Looks at how each of the papers reflects the theme of the Special Issue, “Complexity and IT design and evolution”.

The collection of papers in this Special Issue addresses complexity, drawing on multi‐faceted, multi‐theoretical lines of inquiry.

Frameworks from complexity science, institutional theory, social science, philosophy, and recent thinking in science and technology studies (STS) are used as theoretical lenses to conceptualize and analyze complexity in IS and to offer ways to mitigate it.

This study aims to uncover the constraints of automation and the affordances of augmentation related to implementing artificial intelligence (AI)-powered systems across different task types: mechanical, thinking and feeling.

Qualitative study involving 45 interviews with various stakeholders in artistic gymnastics, for which AI-powered systems for the judging process are currently developed and tested. Stakeholders include judges, gymnasts, coaches and a technology vendor.

We identify perceived constraints of automation, such as too much mechanization, preciseness and inability of the system to evaluate artistry or to provide human interaction. Moreover, we find that the complexity and impreciseness of the rules prevent automation. In addition, we identify affordances of augmentation such as speedier, fault-less, more accurate and objective evaluation. Moreover, augmentation affords to provide an explanation, which in turn may decrease the number of decision disputes.

While the unique context of our study is revealing, the generalizability of our specific findings still needs to be established. However, the approach of considering task types is readily applicable in other contexts.

Our research provides useful insights for organizations that consider implementing AI for evaluation in terms of possible constraints, risks and implications of automation for the organizational practices and human agents while suggesting augmented AI-human work as a more beneficial approach in the long term.

Our granular approach provides a novel point of view on AI implementation, as our findings challenge the notion of full automation of mechanical and partial automation of thinking tasks. Therefore, we put forward augmentation as the most viable AI implementation approach. In addition, we developed a rich understanding of the perception of various stakeholders with a similar institutional background, which responds to recent calls in socio-technical research.

This paper proposes to rethink the concepts of relevance and usefulness and their relation to the theory–practice gap in management research.

On the basis of the cognitive-linguistic relevance theory or inferential pragmatics, supplemented by insights from information science, we define relevance as a general conceptual category, while reserving usefulness for the instrumental application in a particular case.

There is no reason to hold onto the difference between theoretical and practical relevance, nor to distinguish between instrumental and conceptual relevance.

This novel approach will help to clarify the confusion in the field and contribute to a better understanding of the added value of management research.

This paper aims to find and rank the barriers of the four knowledge management (KM) processes including generation, storage, distribution and application in the gas and petroleum sector.

Reviewing the literature of KM and organizational learning, this paper extracted all of the barriers which impede KM processes. Then it designed a questionnaire for validating, ranking and categorizing barriers. Totally, 190 completed questionnaires were gathered from 26 gas and petroleum companies in Iran. Some statistical tests such as T, Friedman, Kruskal–Wallis and Mann–Whitney were used for analyzing data.

Findings reviewed the current literature of KM barriers, validated and ranked the barriers of knowledge generation, storage, distribution and application separately. The importance of knowledge generation and knowledge application barriers were significantly different between gas and petroleum companies. Hence they were disjointedly ranked for gas and petroleum. Finally, KM barriers were ranked according to their contribution to KM processes and the average mean of their importance in KM processes.

From the practical point of view, this paper suggests managers of gas and petroleum companies to emphasize solving high-priority barriers according to the KM process which they are focused on. Furthermore, the study provides a checklist that can be used as an assessment tool for evaluating KM processes considering barriers.

This paper finds the importance of each barrier for each of the four KM processes and ranks the “critical barriers” according to their contribution to four KM processes in the gas and petroleum sector.

The purpose of this paper is to develop a new ontology for knowledge management (KM) technologies, determining the relationships between these technologies and classification of them.

The study applies NOY methodology – named after Natalya F. Noy who initiated this methodology. Protégé software and ontology web language are used for building the ontology. The presented ontology is evaluated with abbreviation and consistency criteria and knowledge retrieval of KM technologies by experts.

All the main concepts in the scope of KM technologies are extracted from existing literature. There are 241 words, 49 out of them are domain concepts, eight terms are about taxonomic and non-taxonomic relations, one term relates to data property and 183 terms are instances. These terms are used to develop KM technologies’ ontology based on three factors: facilitating KM processes, supporting KM strategies and the position of technology in the KM technology stage model. The presented ontology is created a common understanding in the field of KM technologies.

Lack of specific documentary about logic behind decision making and prioritizing criteria in choosing KM technologies.

Uploading the presented ontology in the web environment provides a platform for knowledge sharing between experts from around the world. In addition, it helps to decide on the choice of KM technologies based on KM processes and KM strategy.

Among the many categories of KM technologies in literature, there is no classifying according to several criteria simultaneously. This paper contributes to filling this gap and considers KM processes, KM strategy and stages of growth for KM technologies simultaneously to choice the KM technologies and also there exists no formal ontology regarding KM technologies. This study has tried to propose a formal KM technologies’ ontology.

This paper aims to identify the key issues that healthcare knowledge-intensive organizations (KIPOs) should focus on to define themselves as socioenvironmentally and governance responsible for integrating environmental, social, and governance (ESG) logic into their business strategy. At the same time, this provides an understanding of how healthcare KIPOs contribute to achieving the Sustainable Development Goals of the 2030 Agenda.

Taking a cue from the model developed by the World Economic Forum, an “ESG Processing Map” was constructed to identify qualitative disclosures that a healthcare company should consider when implementing sustainability logic. The aspects investigated were processed, considering national and international standards, frameworks and disclosures. The social network analysis technique was used to systemize and combine the outcomes of these processes and analyze their consistency with sustainable development.

Through the “ESG Processing Map,” 13 areas of action and 27 topics specific to the health sector were defined on which to intervene in sustainability in order to concretely help HCOs to place specific corrective and improvement actions over time concerning socioenvironmental and governance aspects.

The paper provides contribute, on the one hand, to enriching and updating the academic literature on ESG logic in a still underexplored field and, on the other hand, to provide these types of organizations with a “compass” to guide and orient their business strategies towards sustainability.

Artificial intelligence (AI) is currently one of the most disruptive technologies and can be applied in many different use cases. However, applying AI in regulated environments is challenging, as it is currently not clear how to achieve and assess the fairness, accountability and transparency (FAT) of AI. Documentation is one promising governance mechanism to ensure that AI is FAT when it is applied in practice. However, due to the nature of AI, documentation standards from software engineering are not suitable to collect the required evidence. Even though FAT AI is called for by lawmakers, academics and practitioners, suitable guidelines on how to document AI are not available. This interview study aims to investigate the requirements for AI documentations.

A total of 16 interviews were conducted with senior employees from companies in the banking and IT industry as well as with consultants. The interviews were then analyzed using an informed-inductive coding approach.

The authors found five requirements for AI documentation, taking the specific nature of AI into account. The interviews show that documenting AI is not a purely technical task, but also requires engineers to present information on how the AI is understandably integrated into the business process.

This paper benefits from the unique insights of senior employees into the documentation of AI.

The purpose of this conceptual paper is to explore the role of strategic leadership under conditions of uncertainty and unpredictability. The authors argue that highly improbable, but high-impact events require the upper echelons of management, traditionally the custodians of strategy formulation to offer a new kind of strategic leadership focused on new mindsets, organizational capabilities, more in tune with high uncertainty and unpredictability.

Drawing on strategic leadership, and complexity leadership theory, the authors review the literature and present a conceptual framework for exploring the nature of strategic leadership under uncertainty. The authors conceptualize organizations as complex adaptive systems and discuss the imperatives for developing new mental models for emergent leadership.

Strategic leaders have a key role to play in preparing their organizations for episodic disruptions. These include developing their adaptive capabilities and building resilient organizations to ensure their organizations cannot only bounce back after a disruption but have the capacity for transformation to new fitness levels when necessary. Strategic leaders must engage with complexity leadership by seeing their organizations as complex adaptive systems, reconfigure their leadership approaches and organizations to build strategic adaptive capability.

This is a conceptual paper and the authors cannot make any claims of causality.

Organizational leaders need to reconfigure their mental models and leadership approaches to reflect the new normal of uncertainty and unpredictability. Developing the strategic adaptive capability of organizations should prepare them for dealing with high impact events. To assure business continuity in the face of disruptions requires building flexible, adaptable business models.

The paper focuses on how managers can offer strategic leadership for a new normal that challenges some of our most cherished leadership and strategic management paradigms. The authors explore the new mental models and leadership models in an era of great uncertainty.

The evolution of Knowledge-Management (KM)-related literature has highlighted that Knowledge Management Systems (KMSs) have undergone massive changes in collaborative environments. Information-Systems-enabled KM seems to be the necessary response to the recent challenges posed by globalisation and technology dynamics to both large companies (LCs) and small and medium enterprises (SMEs).

This paper provides a systematic review about KMSs to offer an analytical overview of their role in supporting innovative forms of knowledge translation occurring in collaborative relationships. A sample of 129 papers was selected and analysed according to three perspectives: unit of analysis (LCs, SMEs), phases of the KM process (adoption, translation) and topic area (KM Practices, KM Tools, KMSs).

The findings highlight five literature gaps: (1) the role of KM practices supporting knowledge translation; (2) the impact of the alignment among KM practices, firm's complexity, dimension and culture on KM process; (3) the effect of KM tools on knowledge translation; (4) the variety of KMSs exploited in both LCs and SMEs; and (5) the alignment between organisational structure and information systems in KM context. Accordingly, 13 research questions were formulated.

The proposed research questions define a formal research agenda that could steer further research efforts about the KMS topic for improving the body of knowledge in the KM field. Scientific literature is currently lacking a contribution assessing the role of KMSs in supporting innovative forms of knowledge translation that occur in collaborative relationships.