Search

Particularly young researchers face challenges in organizing large design science research (DSR) projects and often struggle to capture, communicate, and reflect on important components to produce purposeful outcomes. Making informed decisions at the project start, such as selecting suitable kernel theories and development procedures, is of great relevance because they affect the entire design process and the resulting design products. Although DSR can produce different types of outcomes, from more situational artifacts to more abstract design knowledge, scholars point to the need for generalizing insights collected in such projects to advance the knowledge base. As design principles are among the prevailing forms of such design knowledge, this paper builds a visual inquiry tool--represented as a canvas--that navigates researchers through common components for crafting design principles and leverages collaborative reflections on essential project decisions. To build our canvas, we adapt inquiry-based learning (IBL) guidelines and visual inquiry tools to DSR education. Evaluations with doctoral students revealed promising indications for the canvas's applicability and usefulness in guiding iterative DSR projects, reflecting on basic components, and communicating work-in-progress to other scholars and practice. Overall, we complement the body of DSR literature by providing an educational visual inquiry tool for producing design principles.

Today's Information Systems (IS) design research projects pursue digital innovation to conquer complex societal challenges. Many of these projects reach out beyond disciplinary and organisational boundaries, as evident in interdisciplinary consortia and academia‐industry collaboration. The design activities in each project differ based on contextual requirements and the team's underlying design logic. As diversity increases, shared understanding is essential for project success. Established design research methodologies need complementary tools to support design researchers in configuring their design activities and representing them faithfully, dimensions that contribute to a shared understanding. This article presents Baustein as an instance of such design tools. Baustein is tailorable to the contextual requirements of each design research project, comprising an ensemble of card‐deck, ready‐made configurations, and a manual. To ensure theoretical and practical relevance, the design of Baustein is based on primary empirical data (workshop and interviews with 16 IS design researchers) and a literature analysis of 99 published IS design research projects. We demonstrate its proof‐of‐value through three main evaluation episodes, altogether involving over 110 IS design researchers. With Baustein, design research teams can balance the trade‐off between creative messiness and standardised configurations of design activities. [ABSTRACT FROM AUTHOR]

Digital platforms disrupt today’s industries with novel offers and business models. Despite their revolutionizing impact, over 80% of platforms fail. To better understand what differentiates failing from successful platforms, we identified 30 platform success factors. These range from corporate value integration, where the platform strategy gets defined and the platform’s value definition, to the platform architecture covering all essential IT considerations and best practices making, e.g., innovation and personalization possible. To make these success factors usable in practice, we integrated all 30 success factors into the wellestablished Business Model Canvas (BMC) widely adopted for business model design. This method was chosen to test the applicability and usefulness of the platform success factors in a widely used vehicle and thus ease our workshop study. The success factors are promising, as demonstrated in our workshop study.

One of Design Science Research's (DSR) principal purposes is to generate and codify design knowledge. Codification in DSR is done by providing clear chunks of prescriptive knowledge that guide the design of future solutions, including instructions on how to design (parts of) artifacts. Although various codification mechanisms have emerged over the last years, design principles are among the most prominent mechanisms. Yet, distinguishing between different codification mechanisms is often blurry, hindering designers from making informed decisions regarding appropriate mechanisms for their research aim and leveraging the full potential of the prescriptive knowledge. We seek to bridge the challenge of selecting from the fuzzy array of codification mechanisms by proposing an inductively generated solution space. We provide a taxonomy to organize essential elements of prescriptive knowledge based on an analysis of design-oriented literature in four meta-dimensions (i.e., communication, application, development, and justification). These meta-dimensions make transparent how codified prescriptive design knowledge works. Overall, the taxonomy guides designers in reflecting on and selecting from the set of suitable elements for their statements. Also, providing a synthesis of options for codifying prescriptive design knowledge will simplify the identification and advance the positioning of DSR contributions. [ABSTRACT FROM AUTHOR]