Information Theory and Design, Essay Example

Individuals’ behavior for information sharing and exchanging will be guided by personal characteristics and the environment they are in. and there are many information behavior theories and models that focus on individuals. Some researchers have applied these theories to investigation of information behavior (IB). Discuss two or three major IB models and theories used in it? How could these apply to your research?

There are key differences between collaborative information behavior and individuals information behavior, but in order to fully understand these differences a clear understanding of behavioral information studies a whole must be established. The first line of thinking associated with information behavior studies is to understand the distinct difference between a model and a theory. A theory is a body of principles, or generalizations established in association with a particular field of study or practice and the forming of theoretical content is recognized as an intellectual discipline in itself. Theory is also defined as a select group of assumptions, rules, principles of procedures structured to analyzes a set of phenomena, nature or behavior. A model, on the other hand, is a tool based on theoretical ideals. In his text on theories associated with information behavior, Fisher et al (2005)., breaks down individual information behavioral theory into a model by hypothesizing four select explanations that serve as driving factors for why individuals behave the way they do based on the information they have. The first explanation the author give is that 1) “in decision making, people make a good enough decision to meet their needs, and do not necessarily consider all the possible, or knowable, options” (Fisher et al., 2005, p.5). Fisher is keen to point out that the individual is focused primarily on the bare minimum necessary for survival and to continue on living life. He attributes much of this factor number 2) which is that “people underestimate the value of what they do not know, and overestimate the value of what they do know” (Fisher et al., 2005, p.5). He further supports this claim by pointing out most individuals are incapable of imagining new information that they are unaware of, specifically what that information could be or how they might behave based on being equipped with unknown information. On the other hand, information they do know is very real to them, clear, vivid seemingly absolute and irrefutable in nature. Fisher attributes this complexity to why most people under-invest in seeking out new information. Another reason why individuals tend to avoid seeking new information is noted in factor 3) “gaining new knowledge may be emotionally threatening in some cases” (Fisher et al, 2005, p.5). He justifies this concept by pointing out that most people base their perceived identity on the body of knowledge they posses, and changing this knowledge can poses a threat to one’s sense of self. The final factor in Fishers model of individual information behavior is the fact that 4) “information is not tangible” but objects are tangible. He points this out to reiterate another reason why individuals under-invest seeking out new information. He argues they rather pursue tangible objects.  It is within this context that smart phones and other mobile devices in collaboration with the internet and social media sites influence the individual information behavior of consumers with content. Fisher et al (2005) further points out that, “even the eternal verities of forms of writing—the book, the journal, the newspaper article—are being shaken up in the new world of Internet information. Under these circumstances, we should perhaps not be surprised that basic metatheoretical assumptions about what research is or should be are also breaking down” (Fisher et al., 2005, p.5). No interaction better exemplifies the nature of individual information behavior than the response individuals makes to content to which they are exposed. It represents information retrieval in its rawest form. As Bates points out, this interaction is one of the key causes of innovation in society, “because of the linguistic, psychological, cognitive, social, and technical complexities of information retrieval, each increase in size of the information source or database requires different solutions; scalability is a fundamental problem in this field. I believe that at some point a historian will show that the information explosion (with us since the invention of printing) has driven most of the major innovations in information organization and access”(Bates, 1999, p. 1049). Social media, specifically through the use of mobile devices, has become more readily available to information seekers. As the method  of accessing information through mobile devices is most convenient in its application, the notion that there is a popular trend in social behavior in regards to its use aligns with Wagner and Berger’s (1985) view that individuals are naturally drawn to the type of research and thinking that works best for them. The effect this has on the growth of select epistemological sciences is that it hinders objectivity or alternative ways of thinking. Fisher et al (2005) addresses this concept stating that “heaven help the psychological doctoral student who wanted to take a qualitative approach back in the heyday of behaviorism, for example.  Many talented people were forced out, simply because they had the wrong cognitive style for the intellectual spirit of the times” (Fisher, 2005, p.8). The authors go on to point out that the current state of affairs within  the discipline of intellectual research is that there is a tolerance of alternative approaches, while at the same time the natural tendency for individuals to identify their preferred method of information seeking as the one true form, and disregard the efficacy of all others, is still prevalent. In 1985 when David Wagner and Joseph Berger assessed the nature of this behavioral phenomenon within the field of socially, they wanted to establish where or not sociological theories were capable of growing.  The authors found that, while certain approaches to information seeking within the field of sociology were expanding, for the most part growth was hidden.

In addressing the path through which human knowledge is acquired Bawden (2007 notes that, “information may be seen in the physical domain as patterns of organised complexity of matter and energy, with the added implication that information, and perhaps meaning and consciousness, may underlie and suffuse the physical universe to a remarkable extent” (Bawden, 2007, p.317). He goes on to point out that, “in the biological domain, meaning-in-context emerges from the self-organised complexity of biological organisms. In the human domain, understanding emerges from the complex interactions of World 2, the mental product of the human consciousness, with World 3, the social product of recorded human knowledge”(Bawden, 2007, p.317). Essentially what Bawden is saying is that through the world’s impact on context, in which individuals live, the individual extracts meaning. This meaning is taken from the recorded knowledge attained throughout one’s lifetime. The interaction individuals have with content, specifically an individually reading text, or writing text for another, is detailed by belkin, as he notes that “within the context and presuppositions outlined, one can construct a communication system for Information Retrival. In this system, a generator, such as an author, decides to communicate for some reason and to some audience some aspect of his or her state of knowledge or “image” of the world. What the generator knows about this topic is modified by beliefs, intentions, values and so on, and specially by knowledge of the intended audience and context of communication”(Belkin, 1980, p.135). This results in the creation of a form of communication, but at Bateson (1972) puts it, the communication is mundande and reduntant. Bateson argues that “At the digital end of this scale all the theorems of information theory have their full force, but at the ostensive and analogic end they are meaningless”(Bateson, 1972, p.296).

The key elements through which individual information behavior occurs is generally basic, as Reddy and Jansen’s (2008) not in their assessment of both individual and collaborative behavior information in the clinical setting, “we found that much of the individual information seeking took the form of simple questions and answers. For instance, a physician asked ‘‘what is the protocol for an apnea test?’’ This was a simple question that was answered by another physician” (Reddy and Jansesn, 2008, p.256). “if students fail to enter a program within their first year of enrollment, they are considered to be much less likely to enter one at all. This also means they are less likely to earn credentials (Jenkins & Cho, 2012).” The authors go on to note that one of the core problems that prevents students entering into community college from achieving their goals is that as they enter into the program of study many are sidetracked with remedial courses that have nothing to do with their objective, and with many of these course they don’t receive course credit. The authors state that, “among younger students, a majority take at least one developmental course. However, community college developmental instruction is generally narrowly focused on helping students take and pass college-level math and English courses rather than preparing them for success in college-level programs of study more generally (Jenkins & Cho, 2012).” All of this means an effective orientation program for nursing students will involve intensive focus on avoiding the obstacles to detracting from receiving an AA in nursing and continuing on to the next level. Half the battle is just initiating students in their field of study early on, as not doing so could result in could result in course incompletion.

In further assessment of how Reddy and Jansen (2008) contrast the differences between individual information behavior (IIB) and collaborative information behavior (CIB), the authors identify information systems as well as information seekers as agents in resolving an information problem. They note that agents exist in both collaborative information scenarios as well as individual information scenarios, but they further point out that, “agents are entities that must interact to address the information problem. At the IIB level, there are usually a small number of agents, typically one person and one or more information systems. As the problem becomes more complex, the numbers of agents involved increases. Typically, in a collaborative setting, each team member usually has different expertise” (Reddy and Jansen, 2008, p.267). The authors attribute the emergence of collaborative information behavior, specifically when conditions are most complex, to need as information problems grow in complexity there arises an incapacity of individuals to resolve the problems on their own. This can also be attributed to the fact that unlike individual information behavior, which relies on usually one individuals interacting with information systems, collaborative in formation behavior represents a collection of system agents each bringing to the table their own distinct brand of information content. Team members in collaborative environments are also more adept at identifying which agent possess the needed information to resolve information problems.

Individual Versus Collaborative Information Behavior

The above chart reveals that there is a clear relationship between individual information behavior seeking and that of collaborative information behavior. One where there is a transition from individual information behavior and collaborative information behavior and a point where an overlap is triggered. The authors note that, “these two factors interplay simultaneously across problems, agents, and interactions. The interplay of the complexity of the problem, the number of agents interacting, and the nature of these interactions initiates a trigger that transforms the context from IIB to CIB” (Reddy and Jansen, 2008, p256). This trigger that occurs giving birth to an urgent need for collaborative information behavior to takeover is attributed to the fact that on the individual level, the information problem solving is simplistic when compared to the collaborative level. As the information problem becomes more complex, the need to collaborate becomes more pronounced. Despite this need for collaboration as the complexity of information problems rise, there are still examples of cases where individuals exposed to conditions of high pressure still perform individually without collaborations. This can especially be seen in the business world with certain CEOs.

Business Information and business information systems management, entail a wide range aspects related to managing information, all of which fall under the guides of information science and the interaction between individual information behavior and collaborative. Some are more technical, while others rely on leadership capabilities and establishing social networks and relationships. Alden M. Hayashi’s (2001), “When to Trust Your Gut,”  he assess how CEOs utilize information formulated in group settings to make singular decisions. He notes that “Many Top Executives say they routinely make big decisions without relying on any logical analysis. Instead, they call upon their ‘intuition,’ ‘gut instinct,’ ‘hunches,” or “inner voice”—but they can’t describe the process much more than that” (Hayashi, 2001, p.169). He argues that certain CEOs identify a subliminal connection between problems that need to be solved and decision making. While he identifies this subliminal connection a routine aspect of decision making, Hayashi is keen to point out that these top level executives are always making these gut decisions within the context of group think environments. The key complexity with the idea of the ‘gut instinct’ of inspired decision making is that it appears to come from the sky, from a divine force, and few give the process credit for being a genuine form of evaluating actual data to extract a plausible solution.  Throughout the piece Hayashi (2001) cites the intuitive exploits of many top level corporate executives, like as Bob Lutz Executive Manager of Chrysler Automotives, who followed his gut instinct in the decision to create the concept for Chrysler’s Dodge Viper, despite a score of data advising him otherwise. If anything this may reveal that problem complexity is relative. He also talks about Robert Pittman Media Executive of AOL and the method he utilizes to extract value driven decisions from information.  The author notes,“AOL’s Pittman couldn’t agree more. ‘Staring at market data is like looking at a jigsaw puzzle,’ he says. ‘You have to figure out what the picture is. What does it all mean? It’s not just a bunch of data. There’s a message in there.’ This is why Pittman routinely loads himself up with as much data as possible” (Hayashi, 2001, p.169). Pittman’s use of his own mind borders on the technical. He literally has a technical use of his mind because he treats it like a computer where he can insert data and then he trust his mind to do the proper calculating and produce the best results. This type of decision making process believes that so long as one has complete faith in their mind’s ability to ‘cross index’ the material it absorbs, then the more data one exposes themselves to, the more likely will have informed decisions.