Why should we investigate knowledge risks incidents? - Lessons from four cases.

In a knowledge-based economy, knowledge has become the most important source for competitive advantage. Thus, organizations spend more attention on the protection of knowledge and also research on knowledge protection has gained increasing attention in the past years. However, knowledge protection research mainly focuses on the design of preventive measures and little is published about real incidents or reactive measures. Learning from failure and from incidents is important to improve current practice. This paper reflects on four cases of real knowledge risk incidents. We discuss ways to prevent or delay knowledge spillovers and the importance of knowing the threats in order to prevent them. In addition to preventive measures, we highlight that companies need to have reactive measures in place. Finally, based on our insights we discuss why analyzing incidents in addition to identified threats is important for practice as well as academia

Enforcing Organizational Knowledge Protection: An Investigation of Currently Applied Measures

Nowadays, organizations increasingly pay attention to protecting their data and information but at the same time the protection of their knowledge is neglected or underdeveloped in many cases. To maintain an organization’s competitive advantage, organisational risk management should pay more attention to the protection of knowledge. Scholarly knowledge management literature mainly concentrated on the facilitation of knowledge sharing and widely neglected this topic so far. In this paper we present results of a knowledge café that we ran with 18 IT professionals to investigate the current state of knowledge protection practice. It turned out that some organizational measures are applied in a rather uncoordinated manner, that only few technical measures are applied. Further, the performance measurement of knowledge protection lacks behind

The Ends of Knowledge Sharing in Networks: Using Information Technology to Start Knowledge Protection

Organisations need networks to leverage external knowledge, particularly for SMEs with their limited resources. Organisations use networks for knowledge sharing to foster innovation. This use of networks bears risks like the unwanted spill-over of knowledge. Consequently, organisa-tions need to balance sharing and protecting knowledge. While scholars have extensively inves-tigated the sharing perspective, they have so far neglected knowledge protection in network set-tings and especially the interplay between sharing and protection. This paper illuminates the motives and practices of network members switching from open sharing to stronger protection on the basis of 53 interviews with members from 10 SME networks. We describe three patterns of switching behaviour and explain how the interviewees adapt the use of collaborative IT to manage the switches. Employees switch from sharing to being open to (a) a certain extent, (b) a certain group, or (c) a certain topic. We find that the three types of switching behaviour are re-lated to network characteristics and to corresponding adaptions in using collaborative IT. Col-laborative IT does not necessarily hamper knowledge protection, but adapted use can support both knowledge sharing and knowledge protection. We argue that organisations should develop protection capabilities to manage the switches

A Framework to Identify Data Governance Requirements in Open Data Ecosystems

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 requirements of the three stakeholder groups combined are not clear and especially how they can be integrated in one DG concept. This paper develops a framework, supporting elicitation of DG requirements in ODEs. The framework builds on a series of stakeholder workshops and literature research resulting in DG requirements and DG mechanisms. The resulting framework includes five main dimensions: (1) data usability, (2) ethical and legal compliance, (3) data lineage, (4) data access and specified data use, and (5) organizational design

How large manufacturing firms understand the impact of digitization: ALearning Perspective

Digitization is currently one of the major factors changing society and the business world. Most research focused on the technical issues of this change, but also employees and especially the way how they learn changes dramatically. In this paper, we are interested in exploring the perspectives of decision makers in huge manufacturing companies on current challenges in organizing learning and knowledge distribution in digitized manufacturing environments. Moreover, we investigated the change process and challenges of implementing new knowledge and learning processes. To this purpose, we have conducted 24 interviews with senior representatives of large manufacturing companies from Austria, Germany, Italy, Liechtenstein and Switzerland. Our exploratory study shows that decision makers perceive significant changes in work practice of manufacturing due to digitization and they currently plan changes in organizational training and knowledge distribution processes in response. Due to the lack of best practices, companies focus very much on technological advancements. The delivery of knowledge just-in-time directly into work practice is a favorite approach. Overall, digital learning services are growing and new requirements regarding compliance, quality management and organisational culture arise

Capturing Practices of Knowledge Work for Information Systems Design

Despite abundant tools and systems claiming support for knowledge work, many have failed to be accepted by users. Designing information systems (ISs) for knowledge work is a challenging task, but results on how knowledge work is actually performed is scarce and so are instruments that help to translate results into artefacts useful for IS design. This paper takes the perspective of work practices and proposes an approach to collaboratively study and analyze practices of knowledge work. The approach uses stereotypes of users, called personas, in order to inform IS design activities. The persona concept is enriched with respect to behaviour concerning practices of knowledge work. Furthermore, a procedure for selecting primary personas out of a set of personas is suggested based on cluster analysis. The approach is illustrated with the case of a collaborative ethnographically-informed study of seven organizations in four European countries. The proposed approach is the more suitable, the more innovative, big and diverse the project, the planned product, the developers and the target group are. User-centered design activities benefit from personas by reduced effort for involving end-users and a continuous focus on characteristics of critical users and their way of performing practices of knowledge work

An Approach for Secure Data Transmission in a Distributed Production Environment

The exchange of data along the supply chain can be viewed as one of the key characteristics of advanced manufacturing concepts, frequently labeled as industry 4.0 . Intelligent products produced in shorter life cycles, increasing cost and quality pressures from global supply chains, increasingly complex regulatory requirements, as well as decreasing costs of advanced sensors are major drivers for this trend. Large amounts of data generated as a by-product of this trend represents an opportunity for advanced data analytics. However, the exchange of data across organizational boundaries bears also the risks of being in the focus of cyber-attacks. In this paper, we tackle the challenge of securing the data transfer in an Industry 4.0 environment. We first identify the security requirements within our use case. Based on these requirements, we present an approach for secure data transmission and discuss how our solution meets the identified requirements

Decision support for multi-component systems: visualizing interdependencies for predictive maintenance

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 identify interdependencies is using visualization. To the best of our knowledge, existing research on visualizing interdependencies is not applied to multi-component systems (MCS) so far. Further, it is not clear how visualization approaches can provide suitable decision support to identify interdependencies in PdM tasks. We evaluate three key visualization approaches to represent interdependencies in the context of PdM for MCS using a crowd-sourced design study in a questionnaire survey involving 530 participants. Based on our study, we were able to rank these approaches based on performance and usability for our given PdM task. The multi-line approach outperformed other approaches with respect to performance

Take the aTrain. Introducing an Interface for the Accessible Transcription of Interviews

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 require an internet connection, and was verified not to upload data to any server. aTrain combines OpenAI's Whisper model with speaker recognition to provide output that integrates with the popular qualitative data analysis software tools MAXQDA and ATLAS.ti. It has an easy-to-use graphical interface and is provided as a Windows-App through the Microsoft Store allowing for simple installation by researchers. The source code is freely available on GitHub. Having developed aTrain with a focus on speed on local computers, we show that the transcription time on current mobile CPUs is around 2 to 3 times the duration of the audio file using the highest-accuracy transcription models. If an entry-level graphics card is available, the transcription speed increases to 20% of the audio duration.Comment: Install via Microsoft store: apps.microsoft.com/store/detail/atrain/9N15Q44SZNS2. Github: github.com/BANDAS-Center/aTrai