Designing and Deploying Advanced Knowledge Systems Minitrack
Permanent URI for this collection
The objective of this minitrack is to contribute to the body of knowledge that helps academics and practitioners to:
- investigate how IT can enable knowledge creation, retention, transfer and application,
- design, deploy and evaluate advanced knowledge systems,
- explore and leverage appropriate project management methods and tools for designing and deploying knowledge systems, and
- study changing knowledge processes and structures in various (e.g., inter-organizational) levels of analysis.
Researchers and practitioners interested in submitting papers to this minitrack are encouraged to explore (1) how IT can enhance or facilitate the creation, retention, transfer, and application of knowledge; (2) the design, evaluation, and deployment of knowledge systems that integrate emerging technologies like social media, mashups, and ubiquitous IT; (3) project management methods and tools involved in the design and deployment of knowledge systems; and/or (4) changes in knowledge processes and structures due to the use of these technologies. We welcome papers that study IT-enabled knowledge management processes from both an intra- and inter-organizational perspective as well as papers that present an integrative view spanning the entire life-cycle of knowledge systems – from knowledge systems design through deployment to retirement.
Topics relevant for submissions include, but are not limited to, the following:
- Theoretical models and empirical research describing how IT can enable the creation, retention, transfer and/or application of tacit and/or explicit knowledge
- Development of frameworks for classifying IT-enabled knowledge management processes
- Methodologies, tools, processes, and technologies for developing knowledge systems
- Management of design and deployment projects of knowledge systems
- Empirical studies in designing and using knowledge systems
- Case studies focusing on the implementation of knowledge management technologies (e.g., virtual reality, social media, expert systems, data analytics, machine learning, e-learning) and processes
- Systems design for social knowledge creation and use (e.g. social media system architectures)
- Incorporating and/or integrating knowledge services and mashups, social media, Web 2.0, cloud computing, and/or ubiquitous technologies in knowledge systems
- The design, evaluation, and/or use of processes, semantic technologies, knowledge retrieval and representation methods, and/or systems to map, track, and/or visualize social networks and/or work systems in order to facilitate knowledge creation and sharing and quick problem solving (e.g., when unexpected coordination breakdowns emerge)
- Design processes, representations, and/or kernel (reference) theories for co-designing and/or co-evolving work systems and knowledge systems
- Design science and design theory research in knowledge systems design and deployment
- The organizational and/or inter-organizational value of tacit knowledge management
- Boundary objects in various forms of IT
- Issues in, limitations of, and barriers to tacit knowledge management
Stefan Smolnik (Primary Contact)
University of Hagen
Université du Québec à Montréal, Canada
W. David Holford
Université du Québec à Montréal, Canada
University of Jyväskylä
ItemRe-Examining the Jennex Olfman Knowledge Management Success Model( 2017-01-04)The Jennex and Olfman KM success model was first published at HICSS in 2004 and in the International Journal of Knowledge Management in 2006. Since then there has been many technology changes and innovations as well as further research on KM success. This paper re-examines the Jennex Olfman model and suggests a newer model that incorporates the past ten years of research and technology innovation.
ItemQuery Generation as Result Aggregation for Knowledge Representation( 2017-01-04)Knowledge representations have greatly enhanced the fundamental human problem of information search, profoundly changing representations of queries and database information for various retrieval tasks. Despite new technologies, little thought has been given in the field of query recommendation – recommending keyword queries to end users – to a holistic approach that recommends constructed queries from relevant snippets of information; pre-existing queries are used instead. Can we instead determine relevant information a user should see and aggregate it into a query? We construct a general framework leveraging various retrieval architectures to aggregate relevant information into a natural language query for recommendation. We test this framework in text retrieval, aggregating text snippets and comparing output queries to user generated queries. We show that an algorithm can generate queries more closely resembling the original and give effective retrieval results. Our simple approach shows promise for also leveraging knowledge structures to generate effective query recommendations.