In several design science research (DSR) frameworks and methods, the need for generalising problemsand solutions has been emphasised. For example, various DSR studies present generalised designprinciples as nascent theories. Due to the creative and contextual nature of DSR, several scholars reportthat generalisation can be challenging. The challenge is particularly valid for single qualitative studiesbecause the design of an artefact always reflects the circumstances in the context where it is to be used.Therefore, the purpose of this study is to present guidelines regarding generalisation. The suggestedguidelines are: strategically plan the generalisation process, validate the generalised knowledge toaugment the reusability and find an appropriate generalisation level. The formulation of the guidelinesis based on the analyses of three DSR projects and insights from previous knowledge. Our knowledgecontribution consists of prescriptive knowledge (guidelines) as a complement to existing explanativeand descriptive knowledge.

The problem we address in this paper is that the potential impact of Large Language Models (LLMs) on the research practice in information systems is not well understood. The focus has been on how LLMs could support literature review processes. Therefore, this paper aims to advance knowledge on how Large Language Models (LLMs) could support knowledge exploration through literature reviews. The knowledge contribution consists of meta-requirements that inform the design of LLM-based tools assisting knowledge exploration. The meta-requirements are theoretically justified by scrutinizing established IS literature review methodologies, reported challenges of LLMs and design process experiences. Furthermore, we introduce an LLM supported literature review process model that maps the relationships between the meta-requirements and specific phases of the process model. This work contributes to the field by providing a foundation for designing transparent, controllable, and resource-efficient tools for knowledge exploration, and supporting the rigor of knowledge exploration in information systems research. 

Action Design Research (ADR) is a well-known research method within Design Science Research (DSR). An essential characteristic of the ADR method is the need for researcher-practitioner collaboration (RPC). While there is abundant research on RPC regarding information systems projects in general concerning explanatory and normative knowledge, there is very limited prescriptive knowledge on how to execute RPC in ADR projects. Successful collaboration in ADR projects is imperative since the development of socio-technical IT artefacts requires frequent interaction in organisational contexts. However, RPC can be hard to manage due to competing interests. Therefore, the purpose of this paper is to present prescriptive knowledge for how to manage RPC. We have analysed a collaborative ADR project consisting of several researchers and practitioners. Based on a grounded theory approach, we have developed theoretical models based on challenges identified in an ADR project. The models provide prescriptive knowledge regarding: shape the IT artefact based on organisational intervention, exploit the mutual dependency between developing design principles and IT artefacts, and contextualise and generalise learning. Each model involves logical relationships between: conditions for the challenges, actions taken to address the challenges and consequences of the actions taken. The guidelines were deducted from the models and consist of recommendations that could be considered in future ADR projects.

Design science research (DSR) aims to generate knowledge about innovative solutions to real-world problems. DSR studies usually involve inductive, deductive or abductive inference strategies to generate design knowledge. However, applying these strategies has often remained implicit, which means that the transparency of the research processes could be questioned (e.g. openness, honesty). This study focuses on abductive DSR, which is often regarded as a combination of inductive and deductive strategies. Based on our literature review, we can conclude that there is a lack of prescriptive support for how to conduct abductive DSR. We have also identified that the concept of abduction is omitted in well- known DSR frameworks/methods. To address this research gap, our study applies a qualitative content analysis to analyse two abductive DSR PhD theses. The analysis outcome is a conceptual model describing central relationships and an abductive DSR process model illustrating movements between the theoretical and empirical domains. 

One essential characteristic of the Action Design Research (ADR) method is researcher-practitioner collaboration (RPC). The purpose of this paper is to present theoretical models for RPC collaboration in ADR projects. The models involve conditions, actions, and consequences concerning RPC challenges. A grounded theory approach was applied to identify the RPC challenges. The challenges were identified in a collaborative ADR project consisting of four researchers and nine organizations from the industry sector. The identified main challenges are: researcher intervention in practitioner contexts, reciprocal shaping between artifacts and design principles emerged from theory and practice, and researcher and practitioner learning. The contribution to practice, which consists of future ADR projects involving collaboration between researchers and practitioners, involves specific actions to be taken.

Purpose Action design research (ADR) has become widely accepted as a prominent research method within information systems when managing design-oriented research projects. One purpose of the ADR method is to provide methodological guidance for the building of IT artefacts. However, several scholars have reported a lack of guidance of method support at the micro level. This article aims to complement the macro level of the ADR method by integrating prescriptive method support at the micro level. Design/methodology/approach A qualitative approach including direct content analysis. An empirical ADR project was analysed in order to identify method support that could be integrated into the ADR method. Findings Method support at the micro level was identified for all the stages of the ADR method. The method support consists of procedural support, guiding concepts, and various techniques for the documentation of project tasks stated in the ADR method. Research limitations/implications The contribution to theory consists of aspects concerning the integration of macro and micro levels: relationships between normative and prescriptive support, continuous focus shifts, and method completeness. Practical implications The contribution to practice consists of explicit suggestions for method support that could be integrated into the ADR method. Originality/value This study extends previously provided knowledge by offering empirical evidence concerning theoretical constructions consisting of explicit relationships between ADR tasks and integrated method support, and elaboration on the integration of macro and micro levels.

Advancements within artificial intelligence (AI) enable organisations to reformulate strategies forexploiting data in order to refine their business models, make better decisions and maintain acompetitive advantage. We recognise the technical advantages of AI. However, our view is that thetechnical perspective as a base for decision-making is necessary but insufficient. Several studies inhuman science report that essential human knowledge and competencies that affect decision making arenot represented in AI systems. Based on this observation, we have developed design principles fordeveloping decision-support systems (DSS) that combine human intelligence (HI) with AI. The designprinciples are: design for amplified decision-making, design for unbiased decision-making and designfor human and AI learning. The design principles constitute the scientific contribution to the emergentfield of Human-Centred AI. The contribution to practice consists of a DSS (a digital prototype) thatsupports the combination of HI and AI.

Aim/Purpose: The concept of lifelong learning has attracted a lot of interest for several years. One reason is the rapid development of digital technology. The purpose of this paper is to create knowledge supporting the development of education for lifelong learning. 

Background: On meeting the growing demand for lifelong learning, universities face several challenges. One challenge is that the curriculum seldom is designed for lifelong learning. At the same time, several universities want to be attractive education partners for adult students with work experiences. There is a lack of normative and prescriptive support that can guide the development of education concerning lifelong learning.

Methodology: Design science research, interviews, grounded theory and root-cause analysis.

Contribution: Contribution to practice: A master program in Information Systems that supports lifelong learning. Contribution to theory: Advancements on design knowledge that can guide the development of education programs concerning rapid advancements in digital technology.

Findings: Five design principles: consider rapid development of digital technology, balance time-consuming bureaucratic procedures with companies’ demands for speedy access to new modern courses, simplify procedures for students applying with work experience qualifications, implement plans for competence development of teachers, and base courses on rigour and relevance. The main conclusion is that the rapid technological development causes universities to face a strategic imperative to broaden access to lifelong learning. We can also conclude that close academic-practitioner collaboration will support the design of lifelong learning.

Impact on Society: Lifelong learning improves social inclusion, active citizenship, personal development, competitiveness, and employability.

Future Research: Further validation of the design principles in order to create knowledge that can support the development of education for lifelong learning.

Aim/Purpose

Due to the rapid development of digital technology, create knowledge to support the development of education for lifelong learning.

Background

There is a lack of normative and prescriptive support that can guide the development of education concerning lifelong learning.

Methodology

Design science research, interviews, grounded theory and root-cause analysis.

Contribution

Contribution to practice: A master program in Information Systems that supports lifelong learning. Contribution to theory: Advancements on design knowledge that can guide the development of education programs concerning rapid advancements in digital technology.

Findings

Five design principles: consider rapid development of digital technology, balance time-consuming bureaucratic procedures with companies’ demands for speedy access to modern courses, simplify procedures for students applying with work experience qualifications, implement plans for competence development of teachers, and base courses on rigour and relevance.

Recommendations for Practitioners

Companies could enter the whole education program or select interesting courses or course modules.

Recommendation for Researchers

The design principles should be considered when research on guidance concerning lifelong learning for adult learners is conducted.

Impact on Society

Lifelong learning enhances social inclusion, active citizenship and personal development, as well as competitiveness and employability.

Future Research

Further validation of the design principles in order to create knowledge that can support the development of education for lifelong learning.