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This issue of CSIMQ includes five articles selected on the topics of complexity management and knowledge management in enterprises from various perspectives and towards diverse goals: the economic and societal perspective seeking strategy fulfillment with the help of enterprise architecture management, the operational performance perspective, which requires grounding management decisions on data insights, and the human aspect perspective to facilitate collaboration, creativity and innovation. Methodologically, the issue reports a mix of conceptual modeling and analytics approaches, suggesting an emerging requirement to balance or alternate, in complexity management, abstraction-based analysis, and data-oriented analytics.

The 38th issue of CSIMQ comprises four articles selected by guest issue editors on topics related to novel decision support methods or model-based frameworks for evolving or evaluating information systems. Design-oriented research is the dominant approach in these works, balancing technical design decisions insights with empirical evaluation cases. Three of the selected articles contribute with decision-support methods or frameworks – for ESG (Environment-Social-Governance) accounting, for democratized decision services, and for information security management. The fourth article revisits UML-based model-driven software development from a new perspective.

This article frames Conceptual Modeling education as a design problem, in the sense of the Design Science research framework, motivated by student preconceptions and oversimplifications causing a gap between how the discipline is perceived at the bachelor level and the holistic understanding of model value that is required for research work. The treatment to this design problem must comprise teaching approaches and artifacts capable of positioning Conceptual Modeling as a standalone discipline having a value proposition for any application domain, rather than a technique subordinated to other disciplines. The underpinning thesis is that modeling languages should be primarily understood as purposeful knowledge schemas that can be subjected to agile adaptations in support of model-driven systems or knowledge processes, by analogy to how a database schema is evolved in response to changing requirements of a data-driven system or data analytics needs. This thesis is supported by enablers provided by the Open Models Laboratory and the Agile Modeling Method Engineering framework – resources that support the development of treatments to the design problem framed by the article.

The 25th issue of CSIMQ presents four articles that investigate novel enablers for information systems development. Three of the selected articles focus on conceptual modeling methods, whereas the fourth one investigates the readiness of SMEs for Industry 4.0 adoption. Model-driven engineering was both tailored (on method level) and applied in two of the papers, taking a Design Science approach to develop proofs-of-concept for the notion of “technology-specific modeling”, a particular flavor of domain-specific modeling – more precisely, technology-specific modeling languages reflect the specificity of a technological ecosystem or pattern (e. g. REST APIs, MVC Web development frameworks). The third paper's contribution may also be seen as a modeling method, although it is not framed as such – it focuses on a design viewpoint and its theoretical applicability in business scenarios. This method proposes a chemistry-inspired technique for optimizing enterprise collaboration patterns based on their KPI effects. Finally, the last paper takes a systematic literature review approach to investigate Industry 4.0 adoption at SME level through the lens of a modified version of the IMPULS assessment model.

The Internet has become a place where millions ofpeople around the globe access a vast amount of information.In the last years, a new technological revolution took placethrough the development of the mobile technology - includingmobile phones, personal digital assistants (PDAs), andhandheld computers. This modern revolution provides aninnovative medium of communication and internet access.Consumers and organizations have benefited and aresupposed to benefit even more in the future from the featuresof m-commerce technologies through these mobileapplications, such as mobile websites, advertising, portalaccess points, information and news services, entertainmentand games, education and training etc. On the other hand,this greatly increases heterogeneity and raises issues for Websites that try to adapt to users preferences and needs. Thepurpose of this article is to develop a deeper understanding ofadaptability and personalization techniques over the internet,subordinated to the so called “delivery context”, whichdesignates the hardware and software context from which acertain user accesses a Web site. The companies, in theircompetition for consumers’ dollars are more and more awarethat it is no longer sufficient to just place the products orservices offers onto the Internet and expect the sales to rise.The e-commerce website designing is undergoing a significantrevolution. It has become an important strategy to designwebsites that are able to provide contents that are matched tothe customer’s technology, needs or preferences.

The paper proposes a model for automating enterprise resource planning data semantization through formal concept derivation based on the ERP relational database existing in most companies with an underlying management support software infrastructure. The problem is stated in the context of a convergence between the Semantic Web and the clustering technique derived from formal concept analysis. As the society shifts from a data-driven cybernetic system towards a knowledgedriven one, databases must be backed up by knowledge management systems, thus ERP databases should be extended with semantic capabilities, specifically explicit descriptions of relationships and conceptualization of the entities detected in the relational model. For human resources management, this opens new potential for talent and competence management, by employing artificial intelligence tools over datasets that are currently exploited for low-level, operational management, usually in querying and reporting scenarios.