knowledge management what you can learn from scientific research

KNOWLEDGE MANAGEMENT WHAT YOU CAN LEARN FROM SCIENTIFIC RESEARCH

2 Vrije Universiteit Amsterdam

RESEARCH QUESTIONS

• Can you expect synergistically effects of online knowledge communities and (technological) knowledge systems?

• What are main motivations for online knowledge sharing?• Do interventions – such as status rewards, tangible

reward or target setting – have positive effects on the utilization of knowledge management systems?

• What are features of successful knowledge management systems?


SYNERGY SOCIAL MEDIA AND KMS?

Social media not replacing formal learning, but

• Allow quickly finding experts and contactdetails • e.g. via profiles and connections

• Enriches – existing – offline sharing• Promotion of available knowledge/documents• Discover trends• Collect tacit knowledge (e.g. blogs and wikis)• Socializing features stimulate offline knowledge sharing

Kane et al 2010, Jarle et al 2009


BENEFITS ONLINE KNOWLEDGE SHARING

• Enable integration for newcomers• Improve collaboration of geographical dispersed people• Improve identification with firm• Directed information seeking beyond the borders of own

unit (exploration)• Allow finding out what has happened elsewhere and

seeking to replicate or (commonly) adapt in own context (exploitation)

Ardichvili et al 2003, Ardichvili 2008, Dearing et al 2011


CONDITIONS EFFECTIVE ONLINE KNOWLEDGE SHARING

• Peers, share educational backgrounds and interpretive frameworks that permit them to interpret the information in similar and compatible ways

• Clear guidelines on type of knowledge to be shared and confidentiality considerations

• Sociolizing features• Quality content (which is a plee for moderation)• Richness of media• Simple approval and security procedures• Supportive organisational culture, trust, cultivation• Userfriendly tools

Chetty et al 2012, Ardichvili et al 2003, Ardichvili 2008, Jarle et al 2009, Chen 2007, Agterberg et al 2010, Iversone t al 2002


BARRIERS ONLINE KNOWLEDGE SHARING

• Lack of motivation to acquire knowledge, is a more important barrier than in offline knowledge sharing

• Less effective for unexperienced newcomers• Limited number of social interaction ties• Organizational control• No willingness to adopt others’ knowledge (not invented

here)• Legal issues (IPR, privacy)

Hildrum 2009, Chen 2007, Agterberg et al 2010, Dearing et al 2011


MOTIVATIONS ONLINE KNOWLEDGE SHARING - 1

Motivations for contribution of knowledge+ Social obligation (public good, feeling obliged to give back)+ Reputation, career enhancements— Losing face + Emotional benefits (boosting self-esteem, enjoyment)+ Material gains+ Expected reciprocity+ Social belonging

Ardichvili et al 2003, Ardichvili 2008


MOTIVATIONS ONLINE KNOWLEDGE SHARING - 2

Motivations for acquiring knowledge+ Direct use of information (e.g. problem solving)+ Strong intrinsic motivation, e.g. intellectual benefits (developing

expertise, extention of perspective)+ Social belonging

Motivations for participating in computer-mediated communication+ Immediate access information (always available)

Although motivations for contributing and using information are also relevant for offline knowledge sharing, motivations may differ in online setting e.g. due to the influence of not knowing the knowledge receivers

Ardichvili et al 2003, Ardichvili 2008, Hildrum 2009


REWARD SYSTEMS

Measures to reach full potential of online knowledge sharing• Promotion systems that take into account community contributions • Reward structures that encourage (informal) collaboration

Discussion on effects of rewards• Counterproductive• Positive effects reported, specifically for public recognition

Hildrum 2009, Chetty et al 2012


INITIAL VERSUS CONTINUANCE KNOWLEDGE SHARING

Initial knowledge sharing• Ease of use• Compatibility to work procedures

Continued knowledge sharing• Ease of use less important• Satisfaction on prior use, mainly dependent on purposive value and

self-discovery• Enjoyment an important driver• Post usage social interaction ties

Battacherjee et al 2008; Cheung et al 2009, Chiu et al 2011, Chen 2007, Jina et al 2010


MEASURING SUCCESSFULNESS KMS

1. Information quality: relevance, timeliness, and completeness of information/knowledge provided by the KMS

2. System quality: consistency of user interface, ease of use, response rates in interactive systems, and accuracy of codified business processes

3. Service quality: how well subject matter experts and KMS managers support the KMS

4. Perceived usefulness: degree to which a person believes that use of the system enhances his/her job performance

5. User satisfaction: an affective state representing an emotional reaction to the KMS use experience

6. Net benefits: consequent enhancement of individual effectiveness and overall organizational effectiveness

Chen 2009


SUCCESSFUL FEATURES KMS – 1

Static and structured model KMS for information supporting routine tasks• Intrinsic motivation and creative behavior have negative impact• Control is key

Dynamic model for non-routine and unstructured sensemaking• Intrinsic motivation of participants key• Control undermines sharing behavior

In practice mostly a combination of both models required

Malholtra 2002


FEATURES SUCCESFUL KMS - 2

Capture knowledge a.s.a.p.• Post-project storage may result in leaking of knowledge• Facilitates immediate use

Capture not only facts but also experiences (experience factory)

Matturo et al 2010


FEATURES SUCCESFUL KMS - 3

People tagging• Outsourcing of HR processes/360 degree appraisals• Appears to be heavily influenced by self-tagging behavior• Absence of guidelines and rules and the lack of semantics, allow

different interpretations of tags• Quality influenced by relation/distance of tagger?• Popularity measure instead of experience/thoughtleadership

measure?• May require a complex moderating system?

Braun et al 2012


CHALLENGES IN SOCIAL TAGGING

• Cold start: empty profiles hamper tagging• Missing auto-completion or suggestion support• Existing tags hampers adding new tagging• Divided opinions on occurring non-professional and

negative information• Uncomfortable feelings on the anonymity of contributor • Fear of transparency; e.g. being associated with non-

comfortable topics• Missing opt in and opt out opportunities• Missing control of tags that were assigned to oneself and

their visibility.

Braun et al 2012


PHASING ONLINE KNOWLEDGE SHARING COMMUNITY

• Phase 1: quantity• Get as much as possible input

• Phase 2: quality• Reusable input have to be extracted

• Phase3: measurement• Measuring reusable knowledge (Return on Investment)

Chetty et al 2012


FACTS ONLINE SHARING BEHAVIOR

Wikipedia• 350 million readers per months• 1 million authors• 10% of authors produce 90% of new content• Prosocial values distinguish between authors and readers• Authors are more likely trendsetters but not opinion-leaders (latter

may be explained by factual and objective character of Wikipedia)

Jadin et al 2013


LITERATURE – 1

Agterberg, M., van den Hooff, B., Huysman, M., Soekijad, M. (2010). Keeping the wheels turning: The dynamics of managing networks of practice. Journal of Management Studies. 47 (1).

Ardichvili, A., Page, V., Wentling, T. (2003). Motivation and barriers to participation in virtual knowledge-sharing communities of practice. Journal of Knowledge Management. 7 (1) 64 – 77.

Ardichvili, A. (2008). Motivators, barriers, and enablers: learning and knowledge sharing in virtual Communities of Practice. Advances in Developing Human Resources. 10 (4), 541 – 554.

Battacherjee, A. , Perols, J., Sanford, C. (2008). Information technology continuance: A theoretic extension and empirical test. Journal of Computer Information Systems. Fall 2008, 17 – 26.

Braun, S., Kunzmann, C., Schmidt. A. (2012). Semantic people tagging and ontology maturing: An enterprise social media approach to competence management. International Journal of Knowledge and Learning. 8 (1), 86 – 111.

Chen, I. (2007). The factors influencing members’ continuance intentions in professional virtual communities – a longitudinal study. Journal of Information Science. 33 (4) 451 – 467.

Chen, I. (2009). Social capital, IT capability, and the success of Knowledge Management Systems. Knowledge Management & E-Learning: An International Journal. 1 (1), 36 – 50.


LITERATURE – 2

Chetty, L., Mearns, M. (2012). Using communities of practice towards the next level of knowledge-management maturity. Journal of Information Management. http://www.sajim.co.za/index.php/SAJIM/article/viewFile/503/558#3

Cheung, C., Lee, M. (2009). Understanding the sustainability of a virtual community: model development and empirical test. Journal of Information Science. 35 (3), 279 – 298.

Chiu, C., Wang, E., Shih, F., Fan, Y. (2011). Understanding knowledge sharing in virtual communities: An integration of expectancy disconfirmation and justice theories. Online Information Review. 35 (1), 134 – 153.

Dearing, J., Greene, S., Stewart, W., Williams, A. (2011). If we only knew what we know: principles for knowledge sharing across people, practices, and platforms. TBM. 1, 15 – 25.

Hildrum, J. (2009). Sharing Tacit Knowledge Online: A Case Study of e-Learning in Cisco's Network of System Integrator Partner Firms: Research Paper. Industry and Innovation. 16(2), 197 – 218.

Iverson, J., Mcphee, R. (2002). Knowledge management in Communities of Practice: Being true to the communicative character of knowledge. Management Communication Quarterly. 16 (2), 259 – 256.


LITERATURE – 3

Jadin, T., Gnambs, T., Batinic, B. (2013). Personality traits and knowledge sharing in online communities. Computers in Human Behavior. 29, 210 – 216.

Jina, X., Lee, M., Cheung, C. (2010). Predicting continuance in online communities: model development and empirical test. Behaviour & Information Technology. 29 (4), 383 – 394.

Kane, K., Robinson, J., Berge, Z. (2010). Tapping into social networking: Collaborating enhances both knowledge management and e-learning. The journal of information and knowledge management systems. 40 (1), 62 – 70.

Malholtra, Y. (2002). Why knowledge management systems fail? Enablers and constraints of knowledge management in human enterprises. In: Handbook on Knowledge Management. Edited by Holsapple, C. Springer Verlag, Heidelberg.

Matturro, G., Silva, A. (2010). A model for capturing and managing software engineering knowledge and experience. Journal of Universal Computer Science. 16(3), 479 – 505.

Young, M., Tseng, F. (2008). Interplay between physical and virtual settings for online interpersonal trust formation in knowledge-sharing practice. CyberPsychology & Behavior. 11(1), 55 – 64.

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knowledge management what you can learn from scientific research

Documents

tacit knowledge

type of knowledge

agterberg et

jarle et

dearing et

scientific research

main motivations

intellectual benefits