The revised Network and Information Security Directive (NIS2) aims to achieve a high level of common cybersecurity across the European Union. Several stakeholders, including member states, supervisory authorities, and critical infrastructure service providers, are expected to support this effort by ensuring a high level of information security. Part of increasing security levels involves implementing risk management measures by service providers, but also an evaluation of the security situation and its changes is necessary from the perspective of each stakeholder. Previous research has described NIS2-related activities through user stories that encompass six types of stakeholders with their respective goals in relation to the security level evaluation of organizations. In this article, we examine the real-life implementation of these security evaluation user stories and demonstrate how the framework for security level evaluation (F4SLE) can be utilized to achieve NIS2 compliance within this narrowed scope of security level evaluation. The advantage of F4SLE is that the data can be collected once and then reused to satisfy different stakeholders without imposing an additional reporting burden on entities that must be NIS2-compliant.

Monitoring and testing of different water parameters play a vital role in water safety. The traditional analytical instruments of water testing are labor-intensive, require trained staff, and are difficult to operate in the field, owing to their non-portability. Considering these issues, the present work reports the development of an Internet of Things (IoT) enabled low-cost multi-parameter water testing system. The proposed system can measure the turbidity, pH, temperature, and conductivity properties of water samples. The proposed system was developed around a low-cost Arduino UNO R3 microcontroller. The system was designed in such a way that it can operate on a 12V rechargeable battery. Therefore, the system can be moved to the field, and important water parameters can be measured. The total cost of the entire system was comparatively low, suggesting the proposed system can be used by economically weak communities. The system was tested with real water samples, and the results are compared with commercial water parameters testing systems. The results of the proposed system are well in agreement with the commercial systems, suggesting its effectiveness for on and off-field water analysis of resource-limited areas.

In global competition, tangible resources as a differentiating factor of competitive advantage are losing their power. The subject of intangibles and knowledge management is associated with high economic performance and, thereby, becomes increasingly significant in strategy implementation. This article explores how Enterprise Modeling (EM) can be used to mitigate the gap between the theoretical stands on the importance of the intangibles and their practical application. In particular, the study presents the application of the Fractal Enterprise Model (FEM) in strategic resource analysis. This research aims to propose a double-hierarchy resource modeling method using FEM and the patterns developed using this method. Additionally, this article presents an example of the method’s application in a real case. The business situation concerns the structural change within the product development processes in pursuit of strategic leadership. The study deploys the Design Science Research (DSR) methodology and generates new knowledge about EM application in intangible resource management.

Data analytics projects have become a common accomplishment in many enterprises. However, establishing a data analytics project requires consideration of many factors that are not always recognized at the very beginning of the project. This study seeks to identify what generic requirements must be defined for data analytics projects and what analytics project attributes need to be addressed by these requirements. It proposes a requirements template for the generic requirements of the analytics projects. The template is intended to be used to reduce the complexity of starting the analytics projects by providing a checklist of requirements to be considered at the beginning of the project. The template is derived from analyzing 16 data analytics project reports for descriptive, diagnostic, predictive, and prescriptive analytics tasks. The template is then validated by analyzing its compliance with 20 analytics projects within the real estate domain using the corresponding research articles.

With the latest advances in natural language processing technologies, multi-participant conversation summarization features are now embedded in the most widely used collaboration platforms offered by industry leaders such as Microsoft, Google, and Zoom. This allows employees to streamline their work and increase efficiency by summarizing long chat threads. In this study, an attempt has been made to perceive summarized chat conversations as a tool for knowledge discovery and reusable information extraction within an organization in general or during projects. To this end, recent scientific articles have been reviewed to identify the most effective techniques and approaches for summarizing chat threads and conversations alongside the challenges and peculiarities of collaborative text-based communication. In addition, significant attention has been paid to the further utilization of the extracted information to represent the knowledge for further reuse.

Case Management Modeling and Notation (CMMN) and iStar are two distinct, multi-purposed modeling techniques that may be used to represent organizational challenges at separate levels of abstraction. CMMN, a flexible process-oriented technique, aims to extract knowledge that enhances the representational capacity of activity flows for a specific case. Conversely, the iStar framework adopts a goal-oriented modeling approach, effectively capturing the interplay of social actors and their influence on the attainment of organizational objectives. While prior studies have explored methods for integrating these techniques to attain a more comprehensive understanding of the organizational landscape, these remain primarily associated with a distinct level of the organizational matrix (i.e., CMMN for operations and tactics, and iStar for more strategic aspects). As such, any effort to evaluate their semantic proximity appears fragmented, as it deals only with the partial association of specific notations and elements. This article describes the conduct of a dual-purposed literature review to identify specific criteria that might accommodate a more holistic assessment of the two modeling techniques; these criteria are then employed as a guiding framework to construct a set of propositions that articulate the areas of complementarity and/or divergence between these two techniques, as identified in previous research. These propositions are subsequently subjected to validation by domain experts, leveraging a real-world case study in the educational domain. The results show that there can be areas of semantic convergence between the two techniques, suggesting their parallel use to effectively model complex domain problems. Overall, the present study aims to crystallize an approach for conducting complex modeling comparisons that transcends technical considerations.

Environmental, social, and governance accounting (ESGA) aids organizations in achieving their sustainability goals through continuous improvement. Suitable method selection is crucial to prevent rework, additional expenses, trivial outcomes, and reduced confidence in sustainability practices. The current ESGA method selection process lacks comprehensive consideration of alternatives and criteria, occasionally resulting in suboptimal choices. This work aims to achieve optimal ESGA by engineering a method selection framework. The research approach is based on the design cycle, where engineering decisions are informed by empirical evidence. The main findings are that the framework, which includes a decision model and a supporting information system, can reduce the chances of organizations selecting an unsuitable method, whilst sparing decision-making managers time and effort. Firstly, the reusable elements of the framework can help managers of any organization select a suitable method more efficiently since they do not have to produce these elements themselves. Secondly, the results demonstrate how selection frameworks and tools can aid organizations in navigating the complex ESG accounting landscape. Lastly, this study lowers the barrier for organizational impact management; in particular, for measuring and reporting ESG impact, which is a rigorous assessment of the organization’s progress towards sustainable development goals.

Model driven engineering aims to shorten the development cycle by focusing on abstractions and partially automating code generation. We long lived in the myth of automatic Model Driven Development (MDD) with promising approaches, techniques, and tools. Describing models should be a main concern in software development as well as model verification and model transformation to get running applications from high level models. We revisit the subject of MDD through the prism of experimentation and open mindness. In this article, we explore assistance for the stepwise transition from the model to the code to reduce the time between the analysis model and implementation. The current state of practice requires methods and tools. We provide a general process and detailed transformation specifications where reverse-engineering may play its part. We advocate a model transformation approach in which transformations remain simple, the complexity lies in the process of transformation that is adaptable and configurable. We demonstrate the usefulness, and scalability of our proposed MDD process by conducting experiments. We conduct experiments within a simple case study in software automation systems. It is both representative and scalable. The models are written in UML; the transformations are implemented mainly using ATL, and the programs are deployed on Android and Lego EV3. Last we report the lessons learned from experimentation for future community work.

Nowadays, there is an increasing need to rapidly build more realistic models to solve environmental problems in an interdisciplinary context. In particular, agent-based and spatial modeling have proven to be useful for understanding land use and land cover change processes. Both approaches include simulation platforms often used in several research domains to develop models explaining and analyzing complex phenomena. Domain experts generally use an ad hoc approach for model development, which relies on a code-and-fix life cycle, going from a prototype model through progressive refinement. This adaptive approach does not capture systematically actors’ knowledge and their interactions with the environment. The development and maintenance of resulting models become cumbersome and time-consuming. In this article, we propose an actor and architecture-driven approach that relies on relevant existing methods and satisfies the needs of spatially explicit agent-based modeling and implementation. We have designed an Agent Global Experiment framework incorporating a meta-model built from actor, agent architecture, and spatial concepts to produce an initial model from specifications provided by domain experts and system analysts. An engine is built as a tool to support model transformation. Domain knowledge including spatial specifications is summarized in a class diagram which is later transformed into the agent-based model. Finally, the XML file representing the model produced is used as input in the transformation process leading to code. This approach is illustrated on a hunting and population dynamic model to generate a running code for GAMA, an agent-based and spatially explicit simulation platform.