A dialectic view on Open Innovation

The paradigm of Open-Innovation allows software companies new forms of interactive innovation and its diffusion across socio-cultural boundaries. This process constitutes and is constituted by a heterogeneous network of interacting actors. In this interaction, seeds for innovation will be created and have to be adopted by the participants of the respective network. This paper studies the concept of Open Innovation from a dialectic perspective on innovation seeds, which regards diffusion and adoption as intertwined. Traditionally, innovation research mainly focuses on transferring processes, but in order to reflect on the interactive character of Open Innovation across socio-cultural boundaries, one has to enlarge this perspective. In this paper we have developed a theoretic model which integrates also the aspect of translation and transformation. Based on this theoretical understanding we have figured out competences to adopt innovation seeds that have been developed in a crosscultural setting. At the end of the paper we show how this model can be used to study empirically the behavior of a software company adopting externally created seeds

Explainable product backorder prediction exploiting CNN: Introducing explainable models in businesses

Due to expected positive impacts on business, the application of artificial intelligence has been widely increased. The decision-making procedures of those models are often complex and not easily understandable to the company’s stakeholders, i.e. the people having to follow up on recommendations or try to understand automated decisions of a system. This opaqueness and black-box nature might hinder adoption, as users struggle to make sense and trust the predictions of AI models. Recent research on eXplainable Artificial Intelligence (XAI) focused mainly on explaining the models to AI experts with the purpose of debugging and improving the performance of the models. In this article, we explore how such systems could be made explainable to the stakeholders. For doing so, we propose a new convolutional neural network (CNN)-based explainable predictive model for product backorder prediction in inventory management. Backorders are orders that customers place for products that are currently not in stock. The company now takes the risk to produce or acquire the backordered products while in the meantime, customers can cancel their orders if that takes too long, leaving the company with unsold items in their inventory. Hence, for their strategic inventory management, companies need to make decisions based on assumptions. Our argument is that these tasks can be improved by offering explanations for AI recommendations. Hence, our research investigates how such explanations could be provided, employing Shapley additive explanations to explain the overall models’ priority in decision-making. Besides that, we introduce locally interpretable surrogate models that can explain any individual prediction of a model. The experimental results demonstrate effectiveness in predicting backorders in terms of standard evaluation metrics and outperform known related works with AUC 0.9489. Our approach demonstrates how current limitations of predictive technologies can be addressed in the business domain

Fostering Continuous User Participation by Embedding a Communication Support Tool in User Interfaces

This paper critically reviews previous IS literature on user participation and argues that the literature is mainly empirically or normatively oriented and lacks design research on developing system prototypes in order to foster continuous user participation. It then contributes to the current research by introducing a system prototype, a communication tool that enables users to participate while using their application systems in their work contexts. The prototype provides different communication channels for supporting user-designer communications and knowledge sharing among users with respect to application usage. When integrated in the interface of an application system, the tool can help to adapt and redesign the application. The initial evaluation of the communication tool within the context of an application system indicates its usefulness and usability

The Automation of the Taxi Industry – Taxi Drivers’ Expectations and Attitudes Towards the Future of their Work

Advocates of autonomous driving predict that the occupation of taxi driver could be made obsolete by shared autonomous vehicles (SAV) in the long term. Conducting interviews with German taxi drivers, we investigate how they perceive the changes caused by advancing automation for the future of their business. Our study contributes insights into how the work of taxi drivers could change given the advent of autonomous driving: While the task of driving could be taken over by SAVs for standard trips, taxi drivers are certain that other areas of their work such as providing supplementary services and assistance to passengers would constitute a limit to such forms of automation, but probably involving a shifting role for the taxi drivers, one which focuses on the sociality of the work. Our findings illustrate how taxi drivers see the future of their work, suggesting design implications for tools that take various forms of assistance into account, and demonstrating how important it is to consider taxi drivers in the co-design of future taxis and SAV services

Potentials of energy consumption measurements in office environments

Reducing energy consumption is one of the most pursued economic and ecologic challenges concerning societies as a whole, individuals and organizations alike. While politics start taking measures for energy turnaround and smart home energy monitors are becoming popular, few studies have touched on sustainability in office environments so far, though they account for almost every second workplace in modern economics. In this paper, we present findings of two parallel studies in an organizational context using behavioral change oriented strategies to raise energy awareness. Next to demonstrating potentials, it shows that energy feedback needs must fit to the local organizational context to succeed and should consider typical work patterns to foster accountability of consumption

Towards an Appropriation Infrastructure: Supporting User Creativity in IT Adoption

Research on the adoption of information systems (IS) often stated technology as a fixed entity. Following the ’practical turn’ in IS we argue that information technology artefacts are mainly ’cultural artefacts’, which are shaped in a social process of appropriation where software usage is accompanied by processes of interpretation, negotiation or change in organizations. We elaborate on a (neo-)Marxian interpretation of appropriation from a design-oriented perspective in order to investigate the possibilities of technological support of activities of appropriation work. To capture the different facets of appropriation, we combine theoretical concepts of social capital and activity-based learning. With the help of this theoretical orientation, we systemize empirical evidence from several research projects in order to detect recurring patterns. We use these patterns to develop a generic architecture for actively supporting the social activity of appropriating the cultural artefact in context of its usage

Role-based Eco-info Systems: An Organizational Theoretical View of Sustainable HCI at Work

So far, sustainable HCI has mainly focused on the domestic context, but there is a growing body of work looking at the organizational context. As in the domestic context, these works still rest on psy-chological theories for behaviour change used for the domestic context. We supplement this view with an organizational theory-informed approach that adopts organizational roles as a key element. We will show how a role-based analysis could be applied to uncover information needs and to give em-ployee’s eco-feedback, which is linked to their tasks at hand. We illustrate the approach on a qualita-tive case study that was part of a broader, on-going action research conducted in a German produc-tion company

Trust your guts: fostering embodied knowledge and sustainable practices through voice interaction

Despite various attempts to prevent food waste and motivate conscious food handling, household members find it difficult to correctly assess the edibility of food. With the rise of ambient voice assistants, we did a design case study to support households’ in situ decision-making process in collaboration with our voice agent prototype, Fischer Fritz. Therefore, we conducted 15 contextual inquiries to understand food practices at home. Furthermore, we interviewed six fish experts to inform the design of our voice agent on how to guide consumers and teach food literacy. Finally, we created a prototype and discussed with 15 consumers its impact and capability to convey embodied knowledge to the human that is engaged as sensor. Our design research goes beyond current Human-Food Interaction automation approaches by emphasizing the human-food relationship in technology design and demonstrating future complementary human-agent collaboration with the aim to increase humans’ competence to sense, think, and act

Unveiling Black-boxes: Explainable Deep Learning Models for Patent Classification

Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classification rely on deep neural networks (DNNs), which are complex and often considered black-boxes due to their opaque decision-making processes. In this paper, we propose a novel deep explainable patent classification framework by introducing layer-wise relevance propagation (LRP) to provide human-understandable explanations for predictions. We train several DNN models, including Bi-LSTM, CNN, and CNN-BiLSTM, and propagate the predictions backward from the output layer up to the input layer of the model to identify the relevance of words for individual predictions. Considering the relevance score, we then generate explanations by visualizing relevant words for the predicted patent class. Experimental results on two datasets comprising two-million patent texts demonstrate high performance in terms of various evaluation measures. The explanations generated for each prediction highlight important relevant words that align with the predicted class, making the prediction more understandable. Explainable systems have the potential to facilitate the adoption of complex AI-enabled methods for patent classification in real-world applications.Comment: This is the pre-print of the submitted manuscript on the World Conference on eXplainable Artificial Intelligence (xAI2023), Lisbon, Portugal. The published manuscript can be found here https://doi.org/10.1007/978-3-031-44067-0_2

Stakeholder-Analyse zum Einsatz IIoT-basierter Frischeinformationen in der Lebensmittelindustrie

Eine Herausforderung bei der Implementierung des industriellen Internet of Things (IIoT) besteht darin, Mehrwerte in Wertschöpfungsketten zu identifizieren, um darauf aufbauend Lösungen nutzerzentriert zu gestalten. Dieser Beitrag stellt das Forschungsprojekt FreshIndex vor, bei dem diese Herausforderung durch eine Kombination aus Stakeholder-Analyse und User-Centered-Design-Methoden adressiert wurde. Ziel des Projekts ist es, eine IIoT-basierte Lösung zum Monitoring der Kühlkette in der Lebensmittelindustrie zu entwickeln. Hierzu ist es wichtig zu wissen, welche Nutzer/-innen mit den Daten in Berührung kommen und welche Erfahrungen, Fähigkeiten, Anforderungen und Wünsche sie mitbringen. Die Berücksichtigung dieser Aspekte ist relevant für den Erfolg der Konzeption, Implementierung und des Betriebs eines IIoT-Systems. So können nützliche und handhabbare Produktideen generiert und Anwendungen gestaltet werden, die von Mitarbeiter/-innen und Konsument/-innen angenommen werden. IIoT schließt somit die lokale Verwendbarkeit von Daten entlang der Wertschöpfungskette ein und beschränkt sich nicht auf zentrale Verfügbarkeit von Daten