@inproceedings{VladovaUllrichSultanowetal.2023, author = {Vladova, Gergana and Ullrich, Andr{\´e} and Sultanow, Eldar and Tobolla, Marinho and Sebrak, Sebastian and Czarnecki, Christian and Brockmann, Carsten}, title = {Visual analytics for knowledge management}, series = {Informatik 2023}, booktitle = {Informatik 2023}, editor = {Klein, Maike and Krupka, Daniel and Winter, Cornelia and Wohlgemuth, Volker}, publisher = {Gesellschaft f{\"u}r Informatik e.V. (GI)}, address = {Bonn}, isbn = {978-3-88579-731-9}, issn = {1617-5468}, doi = {10.18420/inf2023_187}, pages = {1851 -- 1870}, year = {2023}, abstract = {The management of knowledge in organizations considers both established long-term processes and cooperation in agile project teams. Since knowledge can be both tacit and explicit, its transfer from the individual to the organizational knowledge base poses a challenge in organizations. This challenge increases when the fluctuation of knowledge carriers is exceptionally high. Especially in large projects in which external consultants are involved, there is a risk that critical, company-relevant knowledge generated in the project will leave the company with the external knowledge carrier and thus be lost. In this paper, we show the advantages of an early warning system for knowledge management to avoid this loss. In particular, the potential of visual analytics in the context of knowledge management systems is presented and discussed. We present a project for the development of a business-critical software system and discuss the first implementations and results.}, language = {en} } @inproceedings{GrumBlunkRojahnetal.2020, author = {Grum, Marcus and Blunk, Oliver and Rojahn, Marcel and Fettke, Peter and Gronau, Norbert}, title = {Research challenges of knowledge modelling and the outline of a research agenda}, series = {Knowledge in digital age : IFKAD 2020}, booktitle = {Knowledge in digital age : IFKAD 2020}, publisher = {The Arts of Business Institute}, address = {Matera, Italy}, isbn = {978-88-96687-13-0}, issn = {2280-787X}, year = {2020}, language = {en} } @incollection{GrumGronau2021, author = {Grum, Marcus and Gronau, Norbert}, title = {Quantification of knowledge transfers}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_13}, pages = {224 -- 242}, year = {2021}, abstract = {Faced with the triad of time-cost-quality, the realization of knowledge-intensive tasks at economic conditions is not trivial. Since the number of knowledge-intensive processes is increasing more and more nowadays, the efficient design of knowledge transfers at business processes as well as the target-oriented improvement of them is essential, so that process outcomes satisfy high quality criteria and economic requirements. This particularly challenges knowledge management, aiming for the assignment of ideal manifestations of influence factors on knowledge transfers to a certain task. Faced with first attempts of knowledge transfer-based process improvements [1], this paper continues research about the quantitative examination of knowledge transfers and presents a ready-to-go experiment design that is able to examine quality of knowledge transfers empirically and is suitable to examine knowledge transfers on a quantitative level. Its use is proven by the example of four influence factors, which namely are stickiness, complexity, competence and time pressure.}, language = {en} } @incollection{Gronau2021, author = {Gronau, Norbert}, title = {Modeling the handling of knowledge for Industry 4.0}, series = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, volume = {422}, booktitle = {Business modeling and software design : 11th International Symposium, BMSD 2021, Sofia, Bulgaria, July 5-7, 2021, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing}, address = {Cham}, isbn = {978-3-030-79975-5}, doi = {10.1007/978-3-030-79976-2_12}, pages = {207 -- 223}, year = {2021}, abstract = {Industry 4.0, i.e. the connection of cyber-physical systems via the Internet in production and logistics, leads to considerable changes in the socio-technical system of the factory. The effects range from a considerable need for further training, which is exacerbated by the current shortage of skilled workers, to an opening of the previously inaccessible boundaries of the factory to third-party access, an increasing merging of office IT and manufacturing IT, and a new understanding of what machines can do with their data. This results in new requirements for the modeling, analysis and design of information processing and performance mapping business processes. In the past, procedures were developed under the name of "process-oriented knowledge management" with which the exchange and use of knowledge in business processes could be represented, analyzed and improved. However, these approaches were limited to the office environment. A method that makes it possible to document, analyze and jointly optimize the new possibilities of knowledge processing by using artificial intelligence and machine learning in production and logistics in the same way and in a manner compatible with the approach in the office environment does not exist so far. The extension of the modeling language KMDL, which is described in this paper, will contribute to close this research gap. This paper describes first approaches for an analysis and design method for a knowledge management integrating man and machine in the age of Industry 4.0.}, language = {en} } @article{ChujfiLaRocheMeinel2017, author = {Chujfi-La-Roche, Salim and Meinel, Christoph}, title = {Matching cognitively sympathetic individual styles to develop collective intelligence in digital communities}, series = {AI \& society : the journal of human-centred systems and machine intelligence}, volume = {35}, journal = {AI \& society : the journal of human-centred systems and machine intelligence}, number = {1}, publisher = {Springer}, address = {New York}, issn = {0951-5666}, doi = {10.1007/s00146-017-0780-x}, pages = {5 -- 15}, year = {2017}, abstract = {Creation, collection and retention of knowledge in digital communities is an activity that currently requires being explicitly targeted as a secure method of keeping intellectual capital growing in the digital era. In particular, we consider it relevant to analyze and evaluate the empathetic cognitive personalities and behaviors that individuals now have with the change from face-to-face communication (F2F) to computer-mediated communication (CMC) online. This document proposes a cyber-humanistic approach to enhance the traditional SECI knowledge management model. A cognitive perception is added to its cyclical process following design thinking interaction, exemplary for improvement of the method in which knowledge is continuously created, converted and shared. In building a cognitive-centered model, we specifically focus on the effective identification and response to cognitive stimulation of individuals, as they are the intellectual generators and multiplicators of knowledge in the online environment. Our target is to identify how geographically distributed-digital-organizations should align the individual's cognitive abilities to promote iteration and improve interaction as a reliable stimulant of collective intelligence. The new model focuses on analyzing the four different stages of knowledge processing, where individuals with sympathetic cognitive personalities can significantly boost knowledge creation in a virtual social system. For organizations, this means that multidisciplinary individuals can maximize their extensive potential, by externalizing their knowledge in the correct stage of the knowledge creation process, and by collaborating with their appropriate sympathetically cognitive remote peers.}, language = {en} } @incollection{Grum2020, author = {Grum, Marcus}, title = {Managing human and artificial knowledge bearers}, series = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings}, booktitle = {Business modeling and software design : 10th International Symposium, BMSD 2020, Berlin, Germany, July 6-8, 2020, Proceedings}, editor = {Shishkov, Boris}, publisher = {Springer International Publishing AG}, address = {Cham}, isbn = {978-3-030-52305-3}, doi = {10.1007/978-3-030-52306-0_12}, pages = {182 -- 201}, year = {2020}, abstract = {As part of the digitization, the role of artificial systems as new actors in knowledge-intensive processes requires to recognize them as a new form of knowledge bearers side by side with traditional knowledge bearers, such as individuals, groups, organizations. By now, artificial intelligence (AI) methods were used in knowledge management (KM) for knowledge discovery, for the reinterpreting of information, and recent works focus on the studying of different AI technologies implementation for knowledge management, like big data, ontology-based methods and intelligent agents [1]. However, a lack of holistic management approach is present, that considers artificial systems as knowledge bearers. The paper therefore designs a new kind of KM approach, that integrates the technical level of knowledge and manifests as Neuronal KM (NKM). Superimposing traditional KM approaches with the NKM, the Symbiotic Knowledge Management (SKM) is conceptualized furthermore, so that human as well as artificial kinds of knowledge bearers can be managed as symbiosis. First use cases demonstrate the new KM, NKM and SKM approaches in a proof-of-concept and exemplify their differences.}, language = {en} } @phdthesis{Fischer2020, author = {Fischer, Caroline}, title = {Knowledge Sharing in the Public Sector}, school = {Universit{\"a}t Potsdam}, pages = {xiii, 222}, year = {2020}, abstract = {This dissertation examines the activity of knowledge sharing by public employees in the workplace. Building on the Rubicon model of human behavior formation, I use a threefold approach to analyze the knowledge-sharing process: public employees' motivation to share knowledge, their intention to share, and knowledge sharing behavior as such. The first article maps the knowledge-sharing behavior of public employees. It builds a typology of behavioral patterns and shows that public employees mainly share their knowledge responsively and directly with a knowledge receiver rather than an information medium. The second article elaborates on the construct of knowledge-sharing motivation and develops a scale to measure this kind of work motivation in a selective and domain-specific way. Data from three studies indicate three dimensions of knowledge-sharing motivation, namely appreciation, growth and altruism, and tangible rewards. Based on these dimensions, the third article analyzes whether the satisfaction of public employees' underlying needs can foster ther knowledge-sharing intention. The study indicates that both tested treatments (appreciation by co-workers, benefits in a performance appraisal) positively affect knowledge-sharing intention if it is explicit knowledge that ought to be shared. However, no effects of either treatment can be found if implicit knowledge is shared. Hence, to foster sharing of explicit knowledge, the analyzed motivation-enhancing rewards can be used in public management practice. To enhance implicit knowledge sharing, ability- and opportunity-enhancing management instruments are discussed. All in all, this dissertation integrates a micro-level perspective on human knowledge sharing into a meso-level perspective on organizational knowledge management. It adds to the literature on workplace behaviors of public employees and knowledge management and aims to incorporate knowledge sharing and management into the public administration and management literature.}, language = {en} } @misc{KlugeGronau2018, author = {Kluge, Annette and Gronau, Norbert}, title = {Intentional forgetting in organizations}, series = {Frontiers in psychology}, volume = {9}, journal = {Frontiers in psychology}, publisher = {Frontiers Research Foundation}, address = {Lausanne}, issn = {1664-1078}, doi = {10.3389/fpsyg.2018.00051}, pages = {17}, year = {2018}, abstract = {To cope with the already large, and ever increasing, amount of information stored in organizational memory, "forgetting," as an important human memory process, might be transferred to the organizational context. Especially in intentionally planned change processes (e.g., change management), forgetting is an important precondition to impede the recall of obsolete routines and adapt to new strategic objectives accompanied by new organizational routines. We first comprehensively review the literature on the need for organizational forgetting and particularly on accidental vs. intentional forgetting. We discuss the current state of the art of theory and empirical evidence on forgetting from cognitive psychology in order to infer mechanisms applicable to the organizational context. In this respect, we emphasize retrieval theories and the relevance of retrieval cues important for forgetting. Subsequently, we transfer the empirical evidence that the elimination of retrieval cues leads to faster forgetting to the forgetting of organizational routines, as routines are part of organizational memory. We then propose a classification of cues (context, sensory, business process-related cues) that are relevant in the forgetting of routines, and discuss a meta-cue called the "situational strength" cue, which is relevant if cues of an old and a new routine are present simultaneously. Based on the classification as business process-related cues (information, team, task, object cues), we propose mechanisms to accelerate forgetting by eliminating specific cues based on the empirical and theoretical state of the art. We conclude that in intentional organizational change processes, the elimination of cues to accelerate forgetting should be used in change management practices.}, language = {en} } @article{Fischer2022, author = {Fischer, Caroline}, title = {Incentives can't buy me knowledge}, series = {Review of public personnel administration}, volume = {42}, journal = {Review of public personnel administration}, number = {2}, publisher = {Sage}, address = {London}, issn = {0734-371X}, doi = {10.1177/0734371X20986839}, pages = {368 -- 389}, year = {2022}, abstract = {This study examines whether incentives affect public employees' intention to share knowledge. Tested incentives satisfy needs for either achievement or appreciation. Both treatments were tested on implicit as well as explicit knowledge sharing. A 2 x 3 factorial survey experiment was designed to observe within-person and between-person effects. Data were collected from public employees in the core administration and healthcare sector (n = 623) in 2018. The analysis indicates that both treatments positively affect knowledge-sharing intention if it is explicit knowledge that ought to be shared. However, no effects of either treatment can be found in either type of knowledge sharing. No negative effect of the tested incentives on knowledge sharing was observed. Hence, incentives might not harm knowledge sharing but also do not pay off in organizational practice. In contrast to these motivation-enhancing human resource practices, ability and opportunity-enhancing practices should be tested to foster knowledge sharing.}, language = {en} } @phdthesis{ChujfiLaRoche2020, author = {Chujfi-La-Roche, Salim}, title = {Human Cognition and natural Language Processing in the Digitally Mediated Environment}, school = {Universit{\"a}t Potsdam}, pages = {148}, year = {2020}, abstract = {Organizations continue to assemble and rely upon teams of remote workers as an essential element of their business strategy; however, knowledge processing is particular difficult in such isolated, largely digitally mediated settings. The great challenge for a knowledge-based organization lies not in how individuals should interact using technology but in how to achieve effective cooperation and knowledge exchange. Currently more attention has been paid to technology and the difficulties machines have processing natural language and less to studies of the human aspect—the influence of our own individual cognitive abilities and preferences on the processing of information when interacting online. This thesis draws on four scientific domains involved in the process of interpreting and processing massive, unstructured data—knowledge management, linguistics, cognitive science, and artificial intelligence—to build a model that offers a reliable way to address the ambiguous nature of language and improve workers' digitally mediated interactions. Human communication can be discouragingly imprecise and is characterized by a strong linguistic ambiguity; this represents an enormous challenge for the computer analysis of natural language. In this thesis, I propose and develop a new data interpretation layer for the processing of natural language based on the human cognitive preferences of the conversants themselves. Such a semantic analysis merges information derived both from the content and from the associated social and individual contexts, as well as the social dynamics that emerge online. At the same time, assessment taxonomies are used to analyze online comportment at the individual and community level in order to successfully identify characteristics leading to greater effectiveness of communication. Measurement patterns for identifying effective methods of individual interaction with regard to individual cognitive and learning preferences are also evaluated; a novel Cyber-Cognitive Identity (CCI)—a perceptual profile of an individual's cognitive and learning styles—is proposed. Accommodation of such cognitive preferences can greatly facilitate knowledge management in the geographically dispersed and collaborative digital environment. Use of the CCI is proposed for cognitively labeled Latent Dirichlet Allocation (CLLDA), a novel method for automatically labeling and clustering knowledge that does not rely solely on probabilistic methods, but rather on a fusion of machine learning algorithms and the cognitive identities of the associated individuals interacting in a digitally mediated environment. Advantages include: a greater perspicuity of dynamic and meaningful cognitive rules leading to greater tagging accuracy and a higher content portability at the sentence, document, and corpus level with respect to digital communication.}, language = {en} }