Stefan Thalmann

University of Graz | KFU Graz

Literature reviews play a crucial role in Information Systems (IS) research. However, scholars have expressed concerns regarding the reproducibility of their results and the quality of documentation. The involvement of human reproducers in these reviews is often hindered by the time-consuming nature of the procedures. The emergence of Large Languag...

Data-driven technologies changed the way how service innovation is conducted in organisations. The literature discusses the potential of Data-Driven Service Innovation (DDSI) processes, but it is not clear yet what tool support for DDSI looks like. This structured literature review examines the tool support for each phase of DDSI processes. We foun...

Data-driven business models imply the inter-organisational exchange of data or similar value objects. Data science methods enable organisations to discover patterns and eventually knowledge from data. Further, by training machine learning models, knowledge is materialised in those models. Thus, organisations might risk the exposure of competitive k...

Research in various fields is currently experiencing challenges regarding the reproducibility of results. This problem is also prevalent in machine learning (ML) research. The issue arises primarily due to unpublished data and/or source code and the sensitivity of ML training conditions. Although different solutions have been proposed to address th...

Finding suitable participants is a big challenge for health research and is considered a significant barrier. Research referral portals (RRPs) matching participants with requirements of researchers are intended to overcome this barrier. Here, the willingness to share health data is the key success factor for this data-driven matching process. Howev...

Purpose This paper aims to clarify how organizations manage their participation in networks to share and jointly create knowledge but also risk unwanted knowledge spillovers at the same time. As formal governance, trust and observation are less applicable in informal networks, the authors need to understand how members address the need to protect k...

In times of interconnected and digitalized supply chains (SCs), managing knowledge risks is challenging. As sharing data is associated to the risk of unintentional disclosure of competitive knowledge, SC partners must balance knowledge sharing and protection. However, knowledge risks can inhibit knowledge sharing and therefore harm the SC managemen...

Partial discharges, mainly caused by an insufficient drying processes or different types of contamination, reduce the lifetime of a transformer, and thus lead to expensive rework costs. Herein, a decision support system for partial discharges of transformers occurring in oven and filling processes is introduced. Based on machine learning (ML), part...

While the uptake of AI and ML has been rising in recent years, SMEs still face various adoption challenges. In contrast to large enterprises, SMEs struggle to adopt AI as already the identification of suitable AI use cases requires substantial technical expertise. At the same time, productivity tools like AutoML promise easy access to AI capabiliti...

Despite the high potential of artificial intelligence (AI), its actual adoption in recruiting is low. Explanations for this discrepancy are scarce. Hence, this paper presents an exploratory interview study investigating HR professionals’ beliefs about AI to examine their impact on use cases and barriers and to identify the reasons that lead to the...

aTrain is an open-source and offline tool for transcribing audio data in multiple languages with CPU and NVIDIA GPU support. It is specifically designed for researchers using qualitative data generated from various forms of speech interactions with research participants. aTrain requires no programming skills, runs on most computers, does not requir...

The fourth industrial revolution (Industry 4.0) is significantly changing industrial work by introducing interconnected machines and leveraging Artificial Intelligence (AI) to collect and analyze vast amounts of data. This technological advancement has led to increased reliance on AI systems, with some decisions being made automatically. However, t...

The use of conversational agents (CAs) based on artificial intelligence (AI) is becoming more common in the field of recruiting. Organizations are now adopting AI-based CAs for applicant (pre-)selection, but negative news coverage and especially the black box character of AI, has hindered adoption. So far, little is known about the contextual facto...

Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system's recommendation. As this so-called automation bias contradicts ethical and...

Taking the multi-component perspective in Predictive Maintenance (PdM) is one promising approach to improve prediction quality. Therefore, detection and modeling of interdependencies within systems are important, especially as systems become more complex and personalized. However , existing solutions in PdM mostly focus on a single-component perspe...

Making sense of the vast amounts of data generated by modern production operations—and thus realizing the full potential of digitization—requires adequate means of data analysis. In this regard, data mining represents the employment of statistical methods to look for patterns in data. Predictive analytics then puts the thus gathered knowledge to go...

Data-driven business models are becoming increasingly important and have been applied successfully in the service sector. However, due to the challenges associated with utilizing data-driven technologies to identify service innovations, these have received little attention so far. Researchers and practitioners have primarily focused on understandin...

Artificial intelligence (AI)-based decision support systems (DSS) have been promoted for their high potential for many application domains. However, the adoption of such AI-based DSS is still low in practice. The AI-expert-centric development process and domain experts' challenges in exploring suitable AI use cases and communicating their requireme...

Link: http://www.worldcat.org/oclc/1357147336 Digitalization risks in supply chains (SCs) are increasing, not least due to the COVID 19 crisis. Due to a lack of know-how and scarcity of resources, these are particularly difficult for micro, small and medium-sized enterprises (SMEs) to manage. The increase in cyberattacks, e.g., also increases the...

“Digital? Sicher!” is a free educational game designed to build students’ digital competences in cybersecurity, privacy, tracking and datafication. The target group are students aged 14–16, although the educational game can be used by younger or older students. The game was co-designed in Austria by an interdisciplinary team together with 18 indust...

“Digital? Sicher!” ist ein kostenloses digitales Lernspiel, das SchülerInnen ein tieferes Verständnis für Themen wie Cybersecurity, Privatsphäre, Tracking oder Datafication vermitteln soll. Die Zielgruppe sind SchülerInnen im Alter von 14-16 Jahren, obwohl das Lernspiel auch von jüngeren oder älteren SchülerInnen verwendet werden kann. Das Spiel wu...

Artificial Intelligence (AI) becomes increasingly common, but adoption in sensitive use cases lacks due to the black-box character of AI hindering auditing and trust-building. Explainable AI (XAI) promises to make AI transparent, allowing for auditing and increasing user trust. However, in sensitive use cases maximizing trust is not the goal, rathe...

Artificial Intelligence (AI) promises huge potential for businesses but due to its black-box character has also substantial drawbacks. This is a particular challenge in regulated use cases, where software needs to be certified or validated before deployment. Traditional software documentation is not sufficient to provide the required evidence to au...

Modeling complex processes like ironmaking is a demanding task. Approaches based on machine learning (ML) and especially deep learning (DL) offer a considerable option that can complement traditional first principles approaches. They can model complex connections between many input variables and with the possibility to retrain a model, they can adj...

With the digital transformation in manufacturing, Predictive Maintenance (PdM) is increasingly proposed as an approach to increase the efficiency of manufacturing processes. However, system complexity increases due to mass customization, shorter product life cycles, and many component variants within a manufacturing system. So far, PdM mainly focus...

Predictive Maintenance (PdM) is one of the most important applications of advanced data science in Industry 4.0, aiming to facilitate manufacturing processes. To build PdM models, sufficient data, such as condition monitoring and maintenance data of the industrial application, are required. However, collecting maintenance data is complex and challe...

Although research on risk management (RM) in small- and medium-sized enterprises (SMEs) in general and regarding supply chains (SCs) has increased recently, our understanding is still rather fragmented and underdeveloped. This refers particularly to new types of risks such as dynamic crises or emerging risks associated with digital transformation (...

This study aims to reflect on a list of libraries providing decision support to AI models. The goal is to assist in finding suitable libraries that support visual explainability and interpretability of the output of their AI model. Especially in sensitive application areas, such as medicine, this is crucial for understanding the decision-making pro...

Industry 4.0 radically alters manufacturing organization and management, fostering collection and analysis of increasing amounts of data. Advanced data analytics, such as machine learning (ML), are essential for implementing Industry 4.0 and obtaining insights regarding production, better decision support, and enhanced manufacturing quality and sus...

Taking dependencies between components seriously and considering the multi-component perspective instead of the single-system perspective could help to improve the results of predictive maintenance (PdM). However, modeling and identifying the interdependencies in complex industrial systems is challenging. A way to tackle this challenge and to ident...

Artificial Intelligence (AI) is adopted in many businesses. However, adoption lacks behind for use cases with regulatory or compliance requirements, as validation and auditing of AI is still unresolved. AI's opaqueness (i.e., "black box") makes the validation challenging for auditors. Explainable AI (XAI) is the proposed technical countermeasure th...

Open data and open data ecosystems (ODEs) are important for stakeholders from science, businesses, and the broader society. However, concerns about data sharing and data handling are significant adoption barriers of ODEs that reduce stakeholder participation and thus the success of the initiative. Data governance (DG) is proposed as solution, but r...

Purpose Artificial intelligence (AI) is currently one of the most disruptive technologies and can be applied in many different use cases. However, applying AI in regulated environments is challenging, as it is currently not clear how to achieve and assess the fairness, accountability and transparency (FAT) of AI. Documentation is one promising gove...

Digital supply chains (SCs) and data-centric collaborations have boosted data exchange between companies and, combined with recent advancements in data science, have brought a new type of knowledge risks . This paper presents an exploratory interview study investigating knowledge risks in data-centric collaborations. The aim is to gain insights int...

The digitalization of manufacturing involves machines equipped with sensors that collect, produce, and exchange data machine-to-machine and machine-to-human in real-time. As the data generated within a production process can be massive and overwhelming for human users, support is needed to understand and explore this data, and drive decisions from...

Digital transformation fundamentally changes established practices in public and private sector. Hence, it represents an opportunity to improve the value creation processes (e.g., “industry 4.0”) and to rethink how to address customers' needs such as “data-driven business models” and “Mobility-as-a-Service”. Dependable, collaborative and autonomous...

End-of-line (EoL) testing is performed to determine product quality by ensuring reliable performance. Even though low-quality products may pass EoL testing, they have a high probability of failure over time. Analyzing product usage data can help to improve EoL testing in this regard. However, current approaches do not consider usage data for this p...

Unternehmen wie Google oder Amazon haben auf beeindruckende Weise die Wirkung und Bedeutung datenbasierter Geschäftsmodelle aufgezeigt. Im Zuge einer steigenden Bewusstseinsbildung für diesen Trend und der zunehmenden Digitalisierung, versuchen mehr und mehr Unternehmen datenbasierte Geschäftsmodelle zu entwickeln. Dies geschieht dabei häufig als E...

Zusammenfassung Die Geschichte des Instituts für Informationswissenschaft an der Universität Graz in Österreich von seiner Gründung 1987 bis zur Fusionierung mit anderen Instituten im Jahr 2020 wird beschrieben und mit Daten aus bibliometrischen Analysen der Publikationen ergänzt. Abschließend wird ein Ausblick auf die Einbindung der Informationswi...

Organisations participate in collaborative projects that include competitors for a number of strategic reasons, even whilst knowing that this requires them to consider both knowledge sharing and knowledge protection throughout collaboration. In this paper, we investigated which knowledge protection practices representatives of organizations employ...

Purpose This paper aims to report an interview study investigating knowledge protection practices in a collaborative research and innovation project centred around the semi-conductor industry. The authors explore which and how knowledge protection practices are applied and zoom in on a particular one to investigate the perspective of three stakehol...

Artificial intelligence (AI) is receiving increasing attention in business and society. In banking, the first applications of AI were successful; however, AI is mainly applied in investment banking and backend services without customer contact. AI in commercial banking with its focus on customer interaction has received little attention so far. Int...

One of the most prominent use cases of a digitized industry is predictive maintenance. Advances in sensor and data technology enable continuous condition monitoring, thus, extending the opportunities for predictive maintenance. However, so far, most approaches stick to a simplistic paradigm viewing industrial systems as a single-component system (S...

When organizations create new knowledge and work practices as a reaction to challenges they face, they often have difficulty to adopt these new practices “on the ground”. One of the reasons is that in these cases, individual informal learning and collective knowledge creation are often insufficiently connected. In this paper, we investigate knowled...

Industrial product testing is frequently performed in cycles, resulting in cycle-dependent test data. Monitoring the condition of products under test involves analysis of large and complex test data sets. Main tasks are to detect anomalies and dependencies between observation variables, which appears to be challenging to engineers. In this paper, w...

Digitalization reshapes production in a sense that production processes are required to be more flexible and more interconnected to produce products in smaller lot sizes. This makes the process improvement much more challenging, as traditional approaches, which are based on the learning curve, are difficult to apply. Data-driven technologies promis...

Business Analytics as Part of Marketing Education in Business Schools Settings 7th International Big Data and Analytics Education Conference Maryland, USA https://na.eventscloud.com/ehome/372646/home

Current trends in manufacturing lead to more intelligent products, produced in global supply chains in shorter cycles, taking more and complex requirements into account. To manage this increasing complexity, cognitive decision support systems, building on data analytic approaches and focusing on the product life cycle, stages seem a promising appro...

In the last years many companies made substantial investments in digitization of production and started collecting a lot of data. However, the big question arises how to make sense of all these data and to create competitive advantage? In this regard maintenance is an ever-urged topic and seems to be a low hanging fruit to realize benefits from ana...

Learning analytics deals with tools and methods for analyzing and detecting patterns in order to support learners while learning in formal as well as informal learning settings. In this work, we present the results of two focus groups in which the effects of a learning resource recommender system and a dashboard based on analytics for everyday lear...

Traditionally, professional learning for senior professionals is organized around face-2-face trainings. Virtual trainings seem to offer an opportunity to reduce costs related to travel and travel time. In this paper we present a comparative case study that investigates the differences between traditional face-2-face trainings in physical reality,...

In the Big Data era, people can access vast amounts of information, but often lack the time, strategies and tools to efficiently extract the necessary knowledge from it. Research and innovation staff needs to effectively obtain an overview of publications, patents, products, funding opportunities, etc., to derive an innovation strategy. The MOVING...

The implicit, spontaneous and hidden nature of informal learning in addition to the large and less predictable number of application scenarios challenge the evaluation of learning technology. A further challenge for evaluation is added if a user-centred design method had been employed that already had involved users in large numbers and has led to...

Digital transformation revolutionises the way people work not only in office settings but also in physical work settings such as manufacturing or construction. New ways of combining digital and physical innovations and intensified inter-organisational collaborations are key characteristics for success. Knowledge sharing becomes increasingly importa...

The goal of AFEL is to develop, pilot and evaluate methods and applications , which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multidisciplinary , industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve in...

The creation of digital innovations requires active participation and knowledge sharing on behalf of all collaboration partners in inter-organisational settings. However, while the participants collaborate, they also have their own interests and as they are competitors in many cases, they have to protect their competitive knowledge. Collaboration t...

The concentration of knowledge development around the economy’s big players and into few regions leads to rising inequalities of knowledge distribution. Due to shorter innovation cycles, more and more knowledge is ephemeral. To stay competitive, both trends force organizations to absorb increasingly more distant knowledge faster and with less oppor...

In times of globalization, also workforce needs to be able to go global. This holds true especially for technical experts holding an exclusive expertise. Together with a global manufacturing company, we addressed the challenge of being able to send staff into foreign countries for managing technical projects in the foreign language. We developed a...

This study explored the application scenarios of a mobile app called Ach So! for workplace learning of construction work apprentices. The mobile application was used for piloting new technology-enhanced learning practices in vocational apprenticeship training at construction sites in Finland and in a training center in Germany. Semi-structured focu...

Organizational risk management should not only rely on protecting data and information but also on protecting knowledge which is underdeveloped in many cases or measures are applied in an uncoordinated, dispersed way. Therefore, we propose a consistent top-down translation from the organizational risk management goals to implemented controls to ove...

The digitisation of the industry is currently mainly a technological topic for researchers as well as for practitioners. However, also employees face major challenges as their work environments change dramatically. For employers, the main challenge is on how to teach and train their employees so that they can acquire the required knowledge to solve...

he amount of documented organizational knowledge steadily increases as well as the amount of knowledge available from external sources. At the same time the need for innovation at the workplace also increases and poses the challenge to support employee's workplace learning. Knowledge analytics seems to be a promising approach to help employees to s...

The financial sector is characterized as knowledge intensive with knowledge as the key source of competitive advantage. The introduction of social media within the organizational environment has raised the number of risks that can lead to knowledge leakage and thus to a loss of competitive edge. We investigated knowledge risks arising from the use...

Knowledge transfer between employees is a primary concern in organizations. Employees create or acquire content that partially represents knowledge. These knowledge elements are specific to the context in and for which they are created and rarely address the learning needs of other employees in other work situations. Organizations therefore need to...

This is the integrated presentation that covers the reports on the Construction sector pilots of the Learning Layers project during the third year of the project. It was presented in the Year 3 review meeting in December 2015.

This paper explores how informal learning can be assessed in the work environment which bears difficulties, as informal learning is largely invisible and people lack awareness of informal learning. We perform an exploratory case study involving 24 healthcare professionals representing small and medium sized enterprises (SME) in six healthcare netwo...