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Towards Sociotechnical Management of Inner-Organisational Knowledge Transfer
- 1. Towards Sociotechnical Management of Inner- Organisational Knowledge Transfer Research-in-Progress Report Jonas Oppenlaender, Jesse J. Benjamin, Claudia Müller-Birn Multikonferenz Wirtschaftsinformatik, March 7, 2018
- 3. 3 Over-arching Goal Increase internal knowledge transfer in research organisations
- 4. 4 Internal Knowledge Transfer: Exchange of information between members of the organisation • knowledge exchange • knowledge sharing
- 5. 5 Technology Organi- sation Culture Factors Influencing the Internal Transfer of Knowledge
- 6. 6 • Knowledge sharing culture Factors Influencing the Internal Transfer of Knowledge Need: Management of the knowledge relationships Technology Organi- sation Culture
- 7. 7 • Knowledge sharing culture • Focus on external knowledge transfer • Weak position of knowledge transfer managers Factors Influencing the Internal Transfer of Knowledge Technology Organi- sation Culture Need: Strengthened position of internal knowledge managers
- 8. 8 Factors Influencing the Internal Transfer of Knowledge • Knowledge sharing culture • Focus on external knowledge transfer • Weak position of knowledge transfer managers • Interdisciplinary systems TechnologyOrgani- sation Culture Need: Interdisciplinary and easy-to-use knowledge bases
- 9. 9 Research Networking Systems (RNS) • Primary user group: individual researchers • Purpose: Help researchers find collaborators Schleyer, T., Butler, B. S., Song, M., and Spallek, H. (2012): Conceptualizing and advancing research networking systems. ACM Trans. Comput.-Hum. Interact. 19(1), Article 2, 26 pages. Loki
- 10. 10Screenshot of VIVO installation at TIB, Hannover, https://vivo.tib.eu/fis/. Last accessed: 2018-02-28. VIVO
- 11. 11 Harvard Catalyst Profiles RNS
- 12. 12 Limitations of RNS Limited insights due to being based on a narrow set of data sources (basic personal information; bibliographic data) 1 2 3 Only explicit information; discovering implicit relationships in the data is time-intensive and requires cognitive effort Self-service repository model that assumes knowledge transfer by mere publication of data
- 13. 13 Goals / Solutions Go beyond what current RNS are offering Integrate data from different sources and interview researchers 1
- 14. 14 Goals / Solutions Go beyond what current RNS are offering Integrate data from different sources and interview researchers 1 2 Materialize the implicit opportunities for internal knowledge transfer Augment the system with reasoning capabilities
- 15. 15 Goals / Solutions Go beyond what current RNS are offering Integrate data from different sources and interview researchers 1 2 3 Materialize the implicit opportunities for internal knowledge transfer Augment the system with reasoning capabilities Support the management of internal knowledge transfer Establish a dedicated Knowledge Transfer Manager position
- 17. 17 Reasoning2
- 19. 19 Re- searcher R1 Project P1 Re- searcher R2 Project P2 member of member of Knowledge Transfer Example
- 20. 20 Re- searcher R1 Project P1 Re- searcher R2 Project P2 applied to member of member of Knowledge Transfer Example studies Research Method M Research Object O1 Research Object O2
- 21. 21 studies Re- searcher R1 Project P1 Re- searcher R2 Project P2related to related to applied to T1 …T2 T Tn Taxonomy of research topics member of member of Knowledge Transfer Example Research Method M Research Object O1 Research Object O2
- 22. 22 Re- searcher R1 Project P1 Re- searcher R2 Project P2related to related to applied to T1 …T2 T Tn potential for knowledge transfer synergy member of member of Knowledge Transfer Example studies Research Method M Research Object O1 Research Object O2 Taxonomy of research topics
- 24. 24 Current State of Project
- 25. 25 Current State of Project (1/3) • Data acquisition phase • Identification of data sources • Data integration
- 26. 26 Current State of Project (2/3) Ontology Basis: Research Core Dataset* https://fub-hcc.github.io/IKON-ontology * Biesenbender, S., Hornbostel, S. (2016): The Research Core Dataset for the German Science System: Developing Standards for an Integrated Management of Research Information. Scientometrics, 108(1), 401–412
- 27. 27 Current State of Project (3/3) Design and implementation of visualisation
- 28. 28 Next Steps Implementation at Research Museum of Natural History, Berlin, Leibniz Institute for Evolution and Biodiversity Science web application & 65” multi-touch display
- 29. 29 Questions Towards Socio-technical Management of Inner- organisational Knowledge Transfer Multikonferenz Wirtschaftsinformatik, March 7, 2018 Jonas Oppenlaender, Jesse J. Benjamin, Claudia Müller-Birn Research-in-Progress Report p. 1 p. 2 p. 3 p. 4 p. 6 p. 7 p. 8 p. 17p. 16p. 15p. 12p. 11p. 10p. 9 p. 18 p. 22 p. 23 p. 25 p. 26 p. 27 p. 28
- 30. 30Titel, Datum
- 31. 31 Methodology Design Science Adapted from Peffers, K., Tuunanen, T., Rothenberger, M.A., Chatterjee, S. (2007): A Design Science Research Methodology for Information Systems Research. J. Manag. Inf. Syst., 24(3), 45–77 Motivation / Problem Objective of solution Design & Develop- ment Demon- stration Eval- uation Communi- cation
- 32. 32 Evaluation (Outlook) • Quantitative and qualitative • Web analytics • Usability testing • Pre- and post implementation surveys • Interviews with researchers, administration staff and managers • Comparison with similar visualisations, e.g. • Data Monolith (EPFL) • Max Planck Research Networks (MPI) • Harvard Catalyst Profiles • VIVO • ...
Editor's Notes
- Research practice has become more data-intensive over the last few decades. Research is also becoming more collaborative and interdisciplinary. As a result, collaboration with the right individuals, teams, and institutions is increasingly crucial for scientific progress. Additionally, science has shifted towards a result-oriented model. Research organisations are increasingly often evaluated by their output. This increases the pressure and the work load of scientists. In turn, this leaves less time for networking and knowledge sharing. In this complex context with many challenging questions, we focus on one challenge: the Internal Transfer of Knowledge, a critical success factor for research organisations. In our research-in-progress project, we partner with the Research Museum of Natural History in Berlin, the Leibniz Institute for Evolution and Biodiversity Science. ( Largest of its kind in Germany )
- We aim to increase the internal transfer of knowledge at research organisations such as the research museum.
- We take a communication theory perspective: The Internal Knowledge Transfer is realized via the exchange of information between members of the organisation In this light, the internal transfer of knowledge is akin to ‘knowledge exchange‘ and ‘knowledge sharing‘.
- Many factors influencing the internal transfer of knowledge at research organisations. The three main factors are culture, organisation, and technology.
- Culture: The knowledge sharing culture is one of the most important factors that may impede internal transfer of knowledge. The knowledge sharing culture varies between scientific disciplines. In some disciplines, knowledge hiding & knowledge hoarding behaviour may be common. This highlghts the need for active management of knowledge relationships.
- Organisation: Strong commercial focus: Emphasis is placed on knowledge transfer to external parties („technology transfer“) Absence of management of internal knowledge transfer If managed: Knowledge managers have „dual identity“: Serve both external and internal stakeholders Weak position of knowledge managers in the organisation The influence that the knowledge managers can exert on members of the research organisation is therefore limited. This highlights the need to strengthen the position of the internal knowledge managers.
- Technology: Absence of interdisciplinary systems to support internal knowledge transfer between disciplines. Either: General knowledge bases that do not go in sufficient depth. Or: Discipline-specific knowledge bases maintained in silos with limited access. Technology as an enabler to change both the Organisation and its Culture Organisations therefore increasingly turn to Information and Communication Technology to handle and manage their scientific activities. A class of systems that I would like to introduce are Research Networking Systems.
- A class of systems that I would like to introduce are Research Networking Systems. Researchers are unlikely to encounter new knowledge in their immediate personal networks. Expanding a researcher‘s knowledge requires expanding the individual‘s social network. RNS are a technology that cater to this need. RNS are: Web-based knowledge management systems Unlike (Current) Research Information Systems, which primarily serve auditing and reporting purposes, RNS have researchers as their primary user group. Facilitate discovery of potential collaborators. (Help researchers identify and choose collaborators.) Help in establishing interpersonal ties Extend the individuals’ reach beyond personal networks Examples: VIVO Catalyst Profiles – Harvard University Loki – University of Iowa ResearcherID – Thomson Reuters
- Beta installation of VIVO TIB – Leibniz Information Centre for Science and Technology and University Library Person-centered view System follows a repository model Predominantly a text-based approach, similar to sites like Researchgate. The system is successful in offering basic information about a single person. However, system fails to connect profiles in meaningful ways Only basic visualisations, such as co-authorship and co-investigator networks.
- Harvard University‘s Catalyst Profiles goes one step further than VIVO‘s basic approach offers broader range of visualisations recommendations based on a person‘s research area, co-authorships, and geographic location However, Uses the same data sources as VIVO (mainly bibliographic information) Because the system uses automated methods of aggregating information from existing data repositories, it does not consider the knowledge of the members of the organisation.
- 1: The RNS systems are primarily based on bibliographic data. Provides researchers and managers only limited insights into the research activities and competencies. 2: The RNS only offer explicit information. Users need to browse profiles to discover implicit information. Finding implicit interrelationships between projects, resources and people in such repositories is a time-intensive process and requires a high cognitive effort. Example: “Who else at a department has experience with a specific research method applied in a specific context?“ 3: The RNS follow a self-service repository model. This model assumes knowledge transfer as a result of the publication of the information on the RNS. However, successful knowledge transfer is not guaranteed through the provision of these systems.
- Follow a Design Science approach --------------------------------------- 1: Goal: Our goal is to go beyond what current RNS are offering We will provide new perspectives useful and actionable insights into the research activities and competencies of the organisation. Solution: We will achieve this by Integrating data from different sources Acquiring additional data via interviews with researchers
- 2: Goal: Establish new relationships in the data. Uncover opportunities for internal knowledge transfer that were previously implicit. Solution: Augment the system with reasoning capabilities to uncover opportunities for internal knowledge transfer Identify new relationships in the data Cross-reference and visually represent the patterns in these data sets Prevent cognitive overload Faciliate knowledge sharing by presenting the user with new opportunities for networking
- 3: Goal: Support the management of internal knowledge transfer Help overcome the “stickiness” that impedes the transfer of knowledge in research organisations Solution: Establish a dedicated Knowledge Transfer Manager position Provide actionable insights and decision support Facilitate taking steps towards matching members of the organisation managing the scientific collaboration and the inner-organisational knowledge transfer at the research organisation.
- Our main source of information that differentiates us from other systems is interview data. We will however also integrate data from other sources, such as research data, scientific publications and data from external sources. We use the Linked Data paradigm to integrate the data. Linked Data will also be the input for our visualisation.
- The data will be routed through a reasoner. Using rule reasoning and machine learning, we will identify new relationships in the data. I will give an example of this implicit information in two slides.
- We embed a dedicated Knowledge Transfer Manager in our process. Implicit information is routed to this manager. Manager decides which information is relevant and approves it. As a result, the manager receives a deep insight into the organisation. The manager may use the information for decision support and relationship management.
- Illustrative example
- Via the taxonomy that we are creating, we can infer a new relationship between the two researchers.
- Given this information, Researcher 2 may want to contact researcher 1 to talk about the research method M. The result is a knowledge transfer between the researcher and a synergy between the two projects.
- We follow a hybrid approach. Researcher side: ------------------- Researchers may use the visualisation on their own terms. This will encourage individuals to communicate and share their knowledge directly with other individuals. We also encourage the researchers to contribute and codify their knowledge. Manager side: ----------------- Provide insight into the research organisation to the Knowledge Transfer Manager. Based on this information that we provide to this manager, the manager may take steps towards facilitating the internal transfer of knowledge between researchers and projects.
- We are in the data acquisition phase. We conduct interviews with researchers at our partner institution. We work on identifying new data sources which we will integrate. Research Questions in this Phase: What are the data sources available in research organisations that can be integrated? What relationships can be inferred between the entities of the organisation, based on the data sources?
- We released a first version of the ontology which will form the basis of our system. The ontology is based on the ”Research Core Dataset” a description of the German Science system recommended by the German Council of Science and Humanities. The ontology will contain Research projects Scientific artifacts Scientific publications objects Research activities Expertise Competencies Research infrastructure ... and other concepts.
- We have designed and implemented a first prototype of a visualisation. This prototype will be iteratively and incrementally be improved. Research questions: - How can we present the inferred relationships to produce insights that are useful and actionable for both managers and researchers?
- Following the Design Science approach, the next steps will be the implementation of the system in its context of use at our partner institution. We will implement the visualisation as a web application and on a 65“ multi-touch display.