Scrapbook - Unit 4

Wilson discusses many models for having information needs in his paper, but the most interesting to me was the stress and coping model from psychology. He goes on to quote Miller and Mangan (1983) who report that stress is related to how much information an individual has - someone with a good deal of information about a stress-inducing activity will be less stressed than someone with a lack of information. It may not happen every time, but in most cases you can see the connection between stress and information needs. For instance, I have to write a book chapter on virtual management (a true story) that's due in a few weeks and I need information on the topic. I am VERY stressed out about my information need. My coping mechanism for now is knowing that I have professors I can go to and ask for help finding sources. Stress and coping was a theme I explored in my undergraduate honors thesis on the gender gap in technology. The connection here is that even if you think you don't know something (when you do) you are stressed out because of your alleged information need. The female IS/CS students I interviewed felt inferior to the males they had programming courses with because they thought that they weren't as good at coding and math as their male peers were. In reality, their grades were equal, sometimes superior to the males. They thought they had an information need to learn how to code and were thus very stressed out about it. This is an interesting way to look at information needs and it gives the topic a refreshingly new perspective. I think Wilson has the right idea - information science is about every discipline. It seems that looking to our foundational disciplines (so to speak) - psychology, computer science, communication, and library science - we can look at information needs from a different angle. This can only help us, as information professionals, better serve our users.

Solomon notes two studies that discuss the autopoietic characteristics of information systems. Dr. Barreau's 'make do' article discusses how people take the technologies that mostly fit their situation and work around them (sometimes providing the user with more work than they originally had) in order to 'make do' with the technology. Bailey's work talks about hospitals dealing with technology and patient records to deal with secondary information needs. Both of these examples show that technology is not a one-size fits all solution. Consulting firms typically take a technological solution that worked well at one company and implement at another company of similar stature. Most of the time this does not seem to be a good solution and causing what Dr. Barreau calls 'making do'. All in all, I think the important thing to remember with this article and all of our foundations article is that information contexts are people specific. That is to say, everyone brings his or her own information seeking biases with them when they search. They each have unique experiences that have an effect on their information searching processes. As we design information systems we have to remember that the same solution (even the same work around) does not work for each individual. If I'm designing a search engine for the ACM digital library, for example, I know that I can develop a lot of features like thesauri and advanced searching capabilities because my target audience is information professionals. However, designing a mainstream search engine (like Google) is completely different and much more difficult because you don't know your audience. They're successful because they keep things simple. My point is, know your audience as much as possible and understand that they all have different information needs and even though you can't please everyone, hopefully you can design a search engine that helps people bridge the gaps between information needs and knowledge (as outlined in Dervin's sense-making theory).

From all of our foundational readings I have learned that designing systems to solving information seeking behavior is not a one size fits all solution. In all of the readings it has been reiterated that everyone brings their own personal beliefs, values, and experiences to the table. All of these factors affect information seeking behavior. I particularly like Chatman's rounding theory and bringing in the idea that we all create our own small worlds where certain information is important to us and particular information sources have more merit than others. Whenever I think about designing a solution for the general public's information seeking needs, I think of Google. Google has a simple interface and admittedly does not produce the best results, but they produce results that satisfy their user population. An academic search engine like LexisNexis on the other hand, has a very specific group they are targeting and thus can use more advanced search features for their experienced users. Personally, I think Google has the hardest job of solving a wide variety of information seeking problems for their users: people go to Google to find answers to academic, work, and recreational problems and Google must find a way to bridge this gap for a huge range of people - their audience includes a wide variety of people with different types of information needs. From all of these readings I can take away the fact that while you can't solve everyone's problem you can know your target audience and work towards helping them bridge the gap between a lack of information and a state of knowledge.