In the modern rapidly evolving business environments, organizations must continuously adapt their capabilities to remain competitive. The constant need for changing capabilities results in a plethora, not only of capabilities, but also of their variations. Several approaches for capability analysis exist, yet, the variability aspect of organizational capabilities has not been explored in depth. The aim of this paper is to gain insight into capability variability, by exploring integration potentials between capability modeling and variability modeling. In particular, the KYKLOS method, which is specialized for modeling changing capabilities, is utilized in combination with the Orthogonal Variability Model, which is specialized for capturing the variability aspect in software engineering. The study explores the theoretical foundations of each component, outlines the potentials of integrating them into a unified approach, and demonstrates its application using a case study. This integration is explored as a means to improve understanding and framing of capability variability and its boundaries.

This research note explores the future research agenda for Capability-Based Management (CBM) and the capability concept, building on discussions from the International Workshop on the Foundations and Applications of Capabilities in Enterprises, Transformations, and ESG Initiatives (FACETE). The note identifies two primary subdomains for advancement: establishing a harmonized foundation for the Capability and Capability Map concepts, and refining CBM practices. Key topics include developing dynamic, context-aware definitions and models; formalizing relationships; and improving the discovery, identification, and scalability of capabilities. Additionally, the note emphasizes the importance of clarifying capability ownership within complex organizational ecosystems. By addressing these areas, the research note calls for academics to conduct research that can enhance both the theoretical underpinnings and practical applications of CBM, fostering greater clarity and efficacy in strategy formulation and implementation, and managing the operational configuration of an organization.

In alignment with the ongoing digitalization in all aspects of science and society, the legal domain is experiencing a significant growth in the volume of produced documents. Manual approaches for analysis of legal corpora, like Intellectual Property case decisions, are deemed resource-intensive, in terms of time and effort. As a response, we present the CLAIM tool, which has been developed combining Model-Driven Development with a Large Language Model. As a Design Science Research project, the tool’s development has been based on the needs of its stakeholders, which have been elicited in participatory modeling workshops. The prototype version presented in this paper includes two main phases of analysis; information extraction and semantic information retrieval. Its functionality is briefly demonstrated by analyzing trademark cancellation cases which have been filed in bad faith.

As digitalization increases its pace, the legal field produces far more text than manual analysis can keep up with. Parsing large bodies of material such as Intellectual Property rulings by hand is both slow and labor-intensive, to the point where it is considered unfeasible. In this paper, the CLAIM tool, developed to tackle this challenge by blending conceptual modeling with a large language model, is presented and evaluated. Conceived as a Design Science artifact, the tool was motivated by stakeholder requirements gathered through participatory modeling workshops. The prototype runs in two consecutive stages: automated information extraction followed by semantic querying. Its capabilities are illustrated on a corpus of EU trademark-cancellation decisions brought on grounds of bad-faith filing. CLAIM’s evaluation was based on user-testing and interviews, resulting in a set of highly positive responses.

Modern modeling methods are developed in various ways, some following method engineering approaches and principles, others in more ad-hoc ways based on experience, observation, or by exploration of innovative ideas. In all cases, the assessment of the method quality and fitness to the different situations in which it has to serve is a challenge that the method engineers have to face. In this paper, we aim to share knowledge concerning the process of development of modeling methods with an emphasis on systematic approach to verification, validation, and evaluation (VVE). The goal of the paper is to: (1) clarify the differences and key activities and practices of method VVE, (2) to analyze how these have been applied to several modeling methods, and (3) to capture and share key best practices in this process.

The concept of capability, despite the fact of having gradually been established as a valuable construct for business analysis and design, remains vaguely defined and classified. A wide spectrum of diverse definitions and perspectives exists in the literature, and this has led to an equally wide and diverse spectrum of typologies based on different aspects and focal points. Synthesizing a variety of such perspectives, which is the aim of this paper, has the potential to improve the understanding around the concept of capability and enhance the means to communicate its value. A literature review has been performed in order to identify a set of existing capability typologies and classifications, and the elicited set has been synthesized and visualized in order to provide a basis for a capability ontology, derived from the existing perspectives in the literature.

The nature of modern organizations needs to be increasingly adaptive, since they are dealing with a constant demand to respond to stimuli derived from the dynamic environments they operate in. Changing their capabilities is a common response, and this makes capability management a vital aspect of organizational survivability. To date, there are no approaches specifically designed to address this specific situation. KYKLOS and Compass are two interlinked approaches of different complexity, a DSML and a canvas, developed to support capability change. As recently developed methods, they lacked formal demonstration and evaluation; therefore, the goal of this article is to present the demonstration and evaluation of the two approaches by their stakeholders, in particular, business and modeling experts. A case study in a Swedish company in the ERP system consulting domain that is undergoing changes in its sales and consulting capabilities related to evolving customer requirements has been used to demonstrate and evaluate the two approaches. The process consisted of two evaluation cycles. The first cycle concerned KYKLOS and used two categories of evaluators, the business experts and the modeling experts. While the modeling experts evaluated positively the method, the business experts had difficulties associated with its ease of use and adoption. This resulted in the development of Compass, which was evaluated by business experts during the second evaluation cycle. Compass was evaluated more positively in terms of the difficult aspects, but the challenge is ongoing and motivates further future research.

In the continuously and rapidly evolving operational landscape of modern organizations, capabilities are a valuable conceptual asset. Capability thinking and management facilitates the analysis of the value produced by an organization. As a result, capability management plays an essential role in assessing and enhancing an organization’s capabilities to align with dynamic contextual factors and internal needs. Within capability management, a variety of analysis types exists, focusing on different ways to explore and examine capability improvement potentials, like mapping, modeling, and maturity assessment, with several specific approaches associated to each type. However, existing approaches for capability analysis often focus on single perspectives while omitting to provide support for the capability aspects that need to be taken into consideration before the initiation of the analysis. This study aims to explore the potentials for the development of a framework for pre-analyzing capabilities, which can holistically add value to all types of capability analysis. A set of essential capability aspects, such as discovery, demarcation, size, and ownership, that need to be identified and clarified before the initiation of an analysis are suggested towards the development of a generic capability pre-analysis framework.