Exploring Purposes of Using Taxonomies

Taxonomies are artifacts that can be used for numerous purposes, including gap spotting, decision-making, and theory building. Despite the variety of usage purposes, we can observe that designers state that their taxonomies help to ‘classify something’; leaving the full potential of taxonomies rather untapped. In order to lay attention on questions of for what taxonomies can be used, this short paper (1) raises awareness of the actual problem space and motivate the relevance of an overview of taxonomy use purposes, (2) outlines the overall project’s research design to identify and structure the set of use purposes, and (3) proposes preliminary purposes extracted from analyzing a corpus of articles that built upon—and use—previously published taxonomies. In doing this, we seek to complement available methodological guidance to make more informed decisions in terms of a taxonomy’s usage potential

Proposing A Supply Chain Analytics Reference Model As Performance Enabler

Nowadays firms have to react quickly to changing markets creating a need for accurate forecasts of demand and supply. In a data-rich environment as it is within the field of supply chain management, much information needs to be stored, processed, and transformed for decision making. To deal with the increasing amounts of data, firms must be aware of chances in supply chain management such as supply chain analytic capabilities to stay agile, flexible, and make use of (complex) data. Supply chain analytics can predict patterns and trends, even in high velocity markets in real-time supporting decision making by using supply chain analytic tools based on data. The benefits of successfully implementing supply chain analytic processes are enormous and result in competitive advantages for companies such as lowering costs while increasing revenues. As many companies fail to apply supply chain analytic processes and tools, this paper examines the challenges, benefits, and factors for the introduction of supply chain analytics using the input-output model

The Beauty of Messiness: A Flexible Tool for Design Principle Projects

Abstracting and formalizing knowledge collected throughout a design science research (DSR) project is important to inform the design of future artifacts. Design principles are one of the prevailing forms to capture design-relevant knowledge and guide both research and practice to build new artifacts. Although today’s DSR projects are often agile and creative, they require a minimum structure to ensure rigor. In this paper, we set out to master the tradeoff between creative messiness and fully standardized design endeavors by presenting a situational tool in the form of a card deck. We report on the building of a design tool and its demonstration via two illustrative examples. Overall, we complement the valuable body of DSR frameworks and introduce a flexible and configurable tool capable of taking into account specific project situations

Praxisforschungsnetzwerke - Eine explorative Studie mit Akteuren der ökologischen Landwirtschaft in Hessen

In der Praxisforschung werden LandwirtInnen als aktiv Handelnde in den Forschungsprozess eingebunden. LandwirtInnen, BeraterInnen und ForscherInnen gestalten Fragen und Inhalte gemeinsam. Dadurch entstehen Synergieeffekte, die Innovationen für eine nachhaltige Landwirtschaft hervorbringen. Durch langjährige Zusammenarbeit in Netzwerken kann erfolgsentscheidenes Vertrauen zwischen den Akteuren aufgebaut werden. In der vorliegenden Untersuchung wurden im April 2018 acht Akteure aus Hessen (LandwirtInnen, BeraterInnen und ForscherInnen) zur Praxisforschung in Hessen befragt. Hierzu wurde die Methode der Fokusgruppendiskussion genutzt. Die befragten Akteure nehmen eine Expertenstellung innerhalb ihrer Akteursgruppe ein. Die Analyse der Fokusgruppendiskussion zeigt, dass zur Zeit nur wenig Praxisforschung in Hessen betrieben wird und die Akteursgruppen schlecht untereinander vernetzt sind. Die Rahmenbedingungen für intensivere Zusammenarbeit sind sehr gut: Finanzmittel stehen zur Verfügung (Ökoaktionsplan), es gibt motivierte Akteure, es sind viele Universitäten ansässig und erfolgreiche EIP-Projekte bieten eine gute Ausgangsbasis. Um Praxisforschung im ökologischen Landbau in Hessen zu etablieren, werden AnsprechpartnerInnen und eine entsprechende Institutionalisierung benötigt. Die VÖL (Vereinigung Ökologischer Landbau in Hessen e.V.) bietet interessante Voraussetzungen für die Koordination eines Praxisforschungsnetzwerks. Die zur Zeit limitierte Versuchstechnik, eingeschränkte zeitliche Kapazitäten und räumliche Distanzen müssen dabei ebenso realistisch berücksichtigt werden wie eine faire Kostenverteilung

Synthesizing a Solution Space for Prescriptive Design Knowledge Codification

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

Artificial Intelligence for Sign Language Translation – A Design Science Research Study

Although our digitalized society is able to foster social inclusion and integration, there are still numerous communities having unequal opportunities. This is also the case with deaf people. About 750,000 deaf people only in the European Union and over 4 million people in the United States face daily challenges in terms of communication and participation, such as in leisure activities but more importantly in emergencies too. To provide equal environments and allow people with hearing handicaps to communicate in their native language, this paper presents an AI-based sign language translator. We adopted a transformer neural network capable of analyzing over 500 data points from a person’s gestures and face to translate sign language into text. We have designed a machine learning pipeline that enables the translator to evolve, build new datasets, and train sign language recognition models. As proof of concept, we instantiated a sign language interpreter for an emergency call with over 200 phrases. The overall goal is to support people with hearing inabilities by enabling them to participate in economic, social, political, and cultural life

Guiding Design Principle Projects: A Canvas for Young Design Science Researchers

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

Understanding Value-based Pricing for Industrial Internet of Things

Digital industrial platforms are increasingly implemented as an inter-organizational digital infrastructure to unlock numerous sources of value. Establishing an appropriate pricing strategy is pivotal for platform providers’ success. To profit from these value sources, existing research considers value-based pricing as a superior and customer-oriented pricing strategy, enabling platform organizations to capitalize on the technological platform integration in the manufacturing domain. However, little is known about value-based pricing in the Industrial Internet of Things (IIoT) and how platform organizations understand and establish it. To advance the understanding of value-based pricing at the nexus of IIoT and digital platforms, we report on a qualitative study spanning 19 interviews with digital industrial platform providers. Our preliminary findings incorporate core characteristics, preconditions, outcomes, challenges, and guiding principles experienced by the experts, connecting the fragmented research streams on value-based pricing and digital platforms in IIoT and supporting platform organizations in crafting financially sustainable business models