DoILike CiteULike?

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My tag cloud from my CiteULike.

So first off, let’s talk about my tags. My most obvious tags are knowledge-management, knowledge, knowledge-creation, knowledge-sharing, and knowledge-transfer. That makes complete sense for a body of work I read for a knowledge management class.

Secondly, what patterns do I have? I noticed a pattern of REPETITIVE WORD-descriptor. For example, I used knowledge-WORD and organizational-WORD a lot (knowledge-management, knowledge-creation, knowledge-sharing, knowledge-transfer. knowledge-production, knowledge-society AND organizational-adaptation, organizational-change, organizational-knowledge, organizational-knowledge-management, organizational-learning). I think that’s an easy way to make sure I don’t use duplicate tags that mean the same thing. Like instead of saying knowledge-sharing and sharing-of-knowledge or organization-processes and processes-in-organizations I know that if there is the word ‘knowledge’ or ‘organization/al’ in the potential tag, I always start with the main word to keep the tags simplified (even if they make more sense the other way).

Another pattern I see is tagging unusual or weird words that may seem minor but are what stood out to me in the articles and will make me remember them. For example, I read multiple case studies but tagged the ones that I liked as ‘case study’. I tagged the two papers I read that referenced the same Xerox study as ‘xerox’. I tagged the paper about rural ICTs as ‘rural’ to remember. Same with ‘urban-planning’.

This isn’t a pattern but I did create tags before I read the papers and then went back and changed them or added to them after I read the articles in order to make sure they were completely accurate.

Lastly, what methodology would I suggest for future tagging? I would suggest a little more pre-planning. I would tag before reading like I did and would, if possible, tag as a collection so that you keep your tagging schema in mind OR I would write the schema down so it can be referenced. This is especially important as you first are starting to create your methodology so you don’t have to fix as many mistakes.

I also believe that you can’t really have too many tags but after looking at my tag cloud, I realize that, after the fact, I don’t know what some of these mean. That means I need to either a) be more explicit in my tagging so I have context or b) don’t use as many random tags. I think I would lean towards being more explicit because having more avenues to organize knowledge is a good thing to me.

I like tagging to be a mix of planning and organic thought. I like to go in with a vague plan and structure and then let the information I’m tagging guide me into how it wants to be tagged. I always find that a pattern emerges, whether you intend it to or not.

In the end, for my first try at tagging academic work (but definitely not tagging period because I’m an avid Tumblr user), I like it went pretty well. I ThinkILike CiteULike. 😀

Words: 490
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Knowledge Management Systems

I probably should have read these articles BEFORE I wrote my 15 page paper on knowledge management systems but alas, hindsight is 20/20. The first article I want to talk about is Chalmeta and Grangel’s² article. Their KM-IRIS methodology is intense. Like, really really intense and dense. Like has been the case in most of my college career, this article was hard for me to get into. The 5 ‘lessons’ that were in the case study did speak to me however. They really reiterated a lot of the topics I’ve covered this semester and over my entire graduate tenure.

1. In order for enterprises to integrate knowledge management effectively with all their existing business processes, both management and employees must understand and assimilate the strategic business value of KM. These key participants must understand that KM is not simply a technological strategy but rather a business strategy that is essential for the success of their individual departments and of the organization as a whole.

I really liked this because I made clear that in order for KM and a KMS (or any new technology, really) to work, everyone must be on the same page. The understand must be there that this new technology has worth to each person using it. The technology must be explained and their input taken or they will reject the technology purely because they don’t understand it.

2. The knowledge-oriented business model is seldom practised and poorly known, regardless of whether we are talking about an operational or management level.

Education. KM isn’t an automatic thing. It doesn’t come naturally to most people and even those it comes naturally too, the skill still needs to be honed. Educate the management about KM and how it works AND WHY it is important to them. Management does not want to do something, in my experience, unless it is shown to be beneficial to them.

3. Historical factors like culture, power, etc., condition people and companies not to share knowledge.

When trying to implement knowledge management (and record management) into a business, the organizational culture MUST be taken into account. Organizational structure and power dynamics within the organization may need to be nicely addressed in order for KM and a new KMS to be supported.

 4. There is a need for more scientific production showing KM methodologies and business experiences. As Blair (2002) says, experts learn from case studies.

Case studies and papers need to be written so burden of proof doesn’t have to fall on the poor sods just trying to do their job and help their organization manage their knowledge by convincing the management to give them the funds to do so.

5. The need to encourage the training of staff in KM. It has been shown that staff training programs do not include the participation of employees in courses or other types of events related to KM.

Not just the management needs to understand KM.  Everyone in the organization does because everyone in the organization plays a role in KM. If a manager is telling you to do something, wouldn’t you like to know why? Training the staff in KM will help smooth the process and engage everyone in KM.

Now, Alavi and Lender¹. It felt very similar in style and content to Chalmeta and Grangel but that just may be me. The fourth lesson from Chalmeta and Grangel talks about case studies being needed and Alavi and Lender state:

Research is now needed that moves beyond the source and state to consider the conditions that facilitate knowledge creation. (pg. 126)

They also mention the barriers to KM and how organizational culture must shift, like Chalmeta and Grangel talks about.
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Pillet and Carillo³ brings up the illustrious Web 2.0 again. I love their research model. It’s model that when you look at it, it just makes sense (which is rare for me). I love it’s simplicity and the fact that it still says a lot. It shows the relationship between habits and the perceived attitudes towards knowledge sharing.

I thought that Yuan, Zhao, Liao, and Chi’s4 concept of using generations of ICTS to help support knowledge sharing in organizations was interesting. Instead of trying to create one ICT for all, they have different generations of ICTs (e-mail to instant messaging and telephone to video conferencing) and making sure they work together and knowledge sharing is happening.

Words:

Words I Had to Look Up

None this week! Isn’t that cool?!


References

¹ Alavi, M. and Leidner, D. E. (2001). Review: Knowledge management and
knowledge management systems: Conceptual foundations and research
issues. MIS Quarterly, 25(1):107–136.

² Chalmeta, R. and Grangel, R. (2008). Methodology for the implementation
of knowledge management systems. J. Am. Soc. Inf. Sci., 59(5):742–755.

³ Pillet, J.-C. and Carillo, K. D. (2016). Email-free collaboration: An
exploratory study on the formation of new work habits among knowledge
workers. International Journal of Information Management,
36(1):113–125.

Yuan, Y. C., Zhao, X., Liao, Q., and Chi, C. (2013). The use of different
information and communication technologies to support knowledge
sharing in organizations: From e-mail to micro-blogging. Journal of the
American Society for Information Science and Technology,
64(8):1659–1670.