Agile software development has become widespread, particularly in small team settings, because of its emphasis on user value and adaptability. Scaling agile practices in large organizations, however, presents challenges. As organizations grow, projects become complex, requiring control mechanisms to ensure quality deliverables, align outputs across teams, and connect them to organizational goals. Scaling agile frameworks provide structured approaches to coordinate teams and maintain alignment while preserving team autonomy. Yet, applying these frameworks rigidly can reduce flexibility, overlook organizational context, and disrupt the balance between autonomy and alignment. This study examines how different control mechanisms, both formal controls such as structured processes, rules, and performance metrics, and informal controls such as trust, social norms, and mutual adjustment, can foster organizational alignment while preserving team autonomy in large organizations that implement scaling agile frameworks. A multi-method approach was adopted, combining a systematic literature review with an interpretive case study. The literature review synthesized prior research and produced eight theoretical propositions on the dynamic interaction of control, alignment, and autonomy. The case study was conducted in the IT department of a multinational organization transitioning from waterfall to agile, offering an empirical setting to explore how these propositions unfold in practice. Our findings show that formal controls, such as adherence to architectural standards, provide structure and support alignment. Informal mechanisms, including communities of practice, peer coaching, and visibility-based incentives, enhance accountability and autonomy. However, rigid separation of formal and informal controls is restrictive. More integrated hybrid approaches that combine agile methods with structured processes, supported by parallel decision-making bodies, address inter-team dependencies while maintaining flexibility. Large organizations should adopt context-sensitive strategies when applying scaling agile frameworks. Control mechanisms need to be tailored to organizational needs, maturity, and conditions, supported by clear communication, cultural commitment, and adaptive governance.

To sustain competitiveness in contemporary, fast-paced markets, organizations increasingly focus on innovating their business models to enhance current value propositions or to explore novel sources of value creation. However, business model innovation is a complex task, characterized by shifting characteristics in terms of uncertainty, data availability and its impact on decision making. To cope with such challenges, business model evaluation is advocated to make sense of novel business models and to support decision making. Key performance indicators (KPIs) are frequently used in business model evaluation to structure the performance assessment of these models and to evaluate their strategic implications, in turn aiding business model decision making. However, given the shifting characteristics of the innovation process, the application and effectiveness of KPIs depend significantly on how such KPIs are defined. The techniques proposed in the existing literature typically generate or use quantitatively oriented KPIs, which are not well-suited for the early phases of the business model innovation process. Therefore, following a design science research methodology, we have developed a novel method for defining business model KPIs, taking into account the characteristics of the innovation process, offering holistic support toward decision making. Building on theory on linguistic summarization, we use a set of structured templates to define qualitative KPIs that are suitable to support early-phase decision making. In addition, we show how these KPIs can be gradually quantified to support later phases of the innovation process. We have evaluated our method by applying it in two real-life business cases, interviewing 13 industry experts to assess its utility.

We propose the use of linguistic summarization concept to support business model design evaluation. In contrast to traditional linguistic summarization, we do not infer these linguistic summaries from data, but use their structure to express intentions or conditions for stakeholders to participate in the designed business model. Early phase business model design is highly uncertain, for which it is often difficult to quantify under what conditions the business model design would be acceptable for the stakeholders involved. Through generation of intentional linguistic summaries (ILS), stakeholders can make explicit under what conditions a certain business model design would be acceptable. Therefore ILS can allow a discussion on benefits and costs without focusing too much on details, which are rarely available in the early phases of business model design. These intentional linguistic summaries can consequently be used as soft KPIs to be validated once the business model is operational.

End-user acceptance is considered as a significant factor influencing the success of enterprise information system (EIS) implementations and operations. This study conceptualizes three aspects of EIS user interfaces (UIs), namely information overload, control familiarity, and UI fit, and proposes a model to understand their effect on two major factors that are considered to influence the end-user acceptance of these systems: EIS end user’s performance expectancy and effort expectancy. We developed a theoretical model and multiitem scales for the proposed EIS UI characteristics and tested the model empirically with data from a survey performed with a sample 98 EIS end users. The results from our test provide evidence for the key role that EIS UI design plays in the end user’s performance and effort expectancy.

In this paper, we present a method for the development and implementation of Value Based Care Pathways. The method is based on an integration of three existing care pathway development methods, the concepts of the Value Based Healthcare paradigm, and practical knowledge obtained from interviews with experts in care pathway development in practice. This paper furthermore presents an interactive toolbox that facilitates the use of the method in practice and an expert evaluation of the toolbox.

Abstract Although the agile software development approaches have gained wide acceptance in practice, the concerns regarding the scalability and integration of agile practices in traditional large‐scale system development projects are prevailing. Scaled Agile Framework (SAFe) has emerged as a solution to address some of these concerns. Despite few encouraging results, case studies indicate several challenges of SAFe adoption. Currently, there is a lack of a well‐structured gradual approach for establishing SAFe. Before and during SAFe adoption, organizations can benefit greatly from a uniform model for assessing the current progress, and establishing a roadmap for the initiative. To address this need, we developed a maturity model that provides guidance for software developing organizations in defining a roadmap for adopting SAFe. The model can also be used to assess the level of SAFe adoption. We took an existing agile maturity model as a basis for agile practices and extended it with practices that are key to SAFe. The model was developed and refined with industry experts using the Delphi technique. A case study was conducted in a large organization where we evaluated the model by applying it to assess the level of SAFe adoption. © 2016 The Authors. Journal of Software: Evolution and Process Published by John Wiley & Sons Ltd.

Today's enterprises demand a high degree of compliance of business processes to meet laws and regulations, such as Sarbanes-Oxley and Basel II. Compliance should be enforced during all phases of business process lifecycle, from the phases of analysis and design to deployment, monitoring and evaluation. In this paper, a taxonomy of compliance constraints for business processes is introduced based on the notion of compliance patterns. Patterns facilitate the formal specification of compliance constraints that enable their verification and analysis against business process models. This taxonomy serves as the backbone of the root-cause analysis, which is conducted to reason about and eventually to resolve design-time compliance violations, by providing appropriate guidelines as remedies to alleviate design-time compliance deviations. We have developed and integrated a set of tools to observe and evaluate the applicability of our approach, and experiment with it in case studies.