Theory def (Fisher, 2005)

24 October 2010

A system of assumptions, accepted principles, and rules of procedure devised to analyze, predict, or otherwise explain the nature or behavior of a specified set of phenomena. (American Heritage Dictionary, 1969). (See also Reynolds, 1971.)

(Fisher, 2005, s. 2)


Resistance to innovations (Fisher, 2005)

24 October 2010

Diffusion is the process by which an innovation is communicated through channels over time among members of a social system (Rogers, 2003).

An innovation is an idea, practice, or object that is perceived as new by an individual or other unit of adoption (Rogers, 2003). Knowing of an innovation creates uncertainty in the mind and the potential of a new idea impels an individual to learn more about the innovation. Once information seeking activities reduce uncertainty about expectations to a comfortable level, a decision concerning adoption is made. If adopted, further evaluation about the effects of the innovation is carried out. Thus, the innovation-decision process is essentially an information-seeking and processing activity in which an individual is motivated to reduce uncertainty about relative advantages and disadvantages of an innovation (Rogers, 2003).

The main questions typically asked are: What is the innovation? How does it work? Why does it work? What are its consequences? and, What will be its advantages and disadvantages in my situation? (Rogers, 2003).

According to Rogers (2003), the following perceived characteristics of  innovations help to explain their different rates of adoption:

  • Relative advantage, or the degree to which an innovation is perceived as better than the former idea. This may be measured in economic terms, but social prestige, convenience, and satisfaction are also important factors.
  • Compatibility, or the degree to which an innovation is perceived as being consistent with the existing values, past experiences, and needs of potential adopters. An idea that is incompatible with the values and norms of a social system will not be adopted as rapidly as an innovation that is more compatible.
  • Trialability , or the degree to which an innovation may be experimented with before adoption. New ideas that can be tested in increments will generally be adopted more quickly than those that cannot.
  • Observability, or the degree to which the results of an innovation are visible to others. The easier it is to see the results of an innovation, the more likely it is to be adopted.

Time is a crucial element in three aspects of the diffusion process: 1) the innovation-decision process by which an individual passes from first knowledge of an innovation through to its adoption or rejection; 2) the innovativeness of an individual, that is, the timeliness with which an innovation is adopted compared with other members in the system; and, 3) the rate of adoption of an innovation, usually measured as the number of members of the system who adopt the innovation in a given time period.

In the innovation-decision process, an individual passes from knowledge (first knowledge of an innovation) to persuasion (formation of an attitude toward the innovation) to decision (the decision to adopt or reject) to implementation (actual use of the innovation) and finally to confirmation (commitment to adopt).

(Fisher, 2005, p. 118)

The free rider problem and tradegy of the commons (Fisher, 2005)

24 October 2010

There are two classes of social dilemmas especially relevant to information behavior: the free rider problem and the tragedy of the commons. These dilemma situations refer to opposite sides of the same coin. In the free rider problem actors are tempted to not contribute to a group good, while in the tragedy of the commons actors are tempted to consume a good without consideration of how their use degrades that good. The free rider problem arises when actors want to enjoy a collective good, without contributing the resources necessary to create or maintain it. The seminal statements come from extensions of the rational actor model (derived from economics) to political and social problems of collective action (Olson, 1965), theories of group solidarity (Hechter, 1987), and the enforcement of norms (Coleman, 1990). The free rider problem is seen as a pervasive challenge for any collective good providing group, and is especially problematic when contribution is voluntary and groups are informal. This is often the case in online groups, so studying provision of collective goods in these settings promises to bring new insight into this general question.

The tragedy of the commons (Hardin, 1968; Ostrom, 1990) occurs when individual consumption of a collective good degrades the quality of that resource for all. When the resource is large none of the individuals feel the negative effects of their own actions, thus they have no incentive to curtail their own consumption (Yamagishi, 1995). The tragedy of the commons highlights different ways that consumption of a resource interferes with the ability of others to use that resource. Bandwidth consumption due to excessive downloading is a clear example in the online setting (Huberman, Rajan, &. Lucose, 1997). However, one of the major strengths of electronic resources is that they greatly reduce problems of rivalness (only one person can read a book at a time), degradation (Web pages don’t wear out), and crowding (for most uses additional users do not noticeably degrade the quality of online information resources). However, there are online social spaces like Usenet groups, e-mail lists, and blogs that may have a social carrying capacity where excessive use by some may degrade the resource for others.

The baseline prediction in collective action dilemmas is that the deficient equilibrium will dominate in the absence of mechanisms that alter the payoff structure. Identifying and explaining the operation of such mechanisms has been the subject of a wide range of research. Contribution, cooperation, and trust are more likely to emerge when actors know that they will interact in the future, i.e., repeat games cast a “shadow of the future” (Murnighan &. Roth, 1983). Reputation effects are reason this shadow effects dilemma situations. Actors “do the right thing” in order to protect their reputation, an important mechanism for generating trust in online settings (Kollock, 1999). In-group membership due to identities based on roles or on group affiliation can foster contribution when embedded in social ties (Snow, Zurcher & Exland-Olson, 1980). Social ties, that is, the connections between individuals, are an important reason that people contribute to collective goods (McAdam & Paulson, 1993). Finally, selective incentives, in the form of valued goods (especially social incentives like approval), are only available to those that contribute and are important foundations for sustaining contribution (Coleman, 1990; Hechter, 1987). In the absence of the mechanisms like those described above, cooperation and contribution are harder to sustain, and are more likely to devolve into defection.

(Fisher, 2005, p. 94ff)

Affective Load Theory (ALT), (Fisher, 2005)

24 October 2010

Affective load theory (ALT) is a social-behavioral perspective on the thoughts and feelings of individuals while engaged in information behavior (lB). ALT provides empirical methods for identifying affective states of users that disrupt ongoing cognitive operations (James & Nahl, 1986). Once a disruptive affective state is identified, coping assistance services (CAS) can be provided to encourage users to mitigate disruptive states to achieve task success. ALT identifies underlying habits of thinking and feeling while engaging in information behavior, and clarifies the details of information retrieval from a user perspective. There are three essential ideas in applying social-behavioral psychology to IB:

  1. The mental activity of information users, both cognitive and affective, is defined as behavior (Martin &. Briggs, 1986). For instance, “thinking of a search word” or “feeling motivated to finish a task” are behaviors. Global control of the affective over the cognitive operates at general and specific levels. At the general level of control people possess motivational states such as optimism or pessimism prior to a search. At the specific level of control we experience micro-behaviors that involve search strategy such as inspecting a list, thinking of a synonym, or recalling an item that has been seen before. A search task or session involves hundreds of individual cognitive micro-behaviors, each one connected to an affective state that maintains or interrupts it (Nahl, 1997). Affective states are organized in a top-down hierarchy and can be reliably measured through concurrent self-reports about expectations, satisfactions, and acceptance during continuous cognitive activity.
  2. Affective behavior initiates, maintains, and terminates cognitive behavior (Isen, Daubman & Gorgolione, 1987; Carver & Scheier, 2001). For instance, when searchers lose the motivation to continue a task, they begin thinking about something else. Or, if they unexpectedly find some new information they want, they switch activity midstream. The new affective behavior interrupts and takes over the ongoing activity and continues in a new direction with new cognitive activity. This managerial or directive function of affective behavior over cognitive, makes it desirable in information environments to employ self-monitoring techniques to keep track of the affective behavior of users (Nahl, 1996, 1998).
  3. Affective behavior operates within a binary-value system: on/off or positive/negative. Cognitive behavior operates through a multivalue logic. Therefore, affective behavior is measured with bi-polar scales and cognitive behavior is measured with multiple-choice, matching or fill-in items. Content analysis and protocol analysis of concurrent verbal reports are used to identify affective and cognitive behavior patterns during search tasks (Nahl, 2001).

The behavioral approach to information use is attracting increasing interest among information scientists. However, sufficient attention is not given to the three essential elements outlined above. The focus has been on cognitive behavior and more recently, on how affective behavior is also important to consider. Nahl’s social-behavioral theory of affective load makes explicit the need to create a methodological connection between each cognitive behavior and its affective support or control state. Affective load theory was developed by analyzing concurrent self-reports of searchers and learners in conjunction with quantitative ratings filled out by searchers while engaged in searching and problem-solving. To achieve high reliability, it is critical to obtain concurrent rather than recollected data. Nahl’s ALT theory is emerging from a 20-year research program. One area of application has been to identify affective dimensions like self-efficacy and optimism that help searchers perform better. Currently ALT research focuses on how diverse affective behaviors interact to produce an effective coping style when searchers feel challenged by uncertainty.

ALT proposes that all information behavior involves affective states that provide specific goal-directionality and motivation to support cognitive activity. Affective load ( AL) is operationally defined as uncertainty (U) multiplied by felt time pressure (TP). Uncertainty is defined as the combined degrees of irritation, frustration, anxiety, and rage (Nahl, 2004).

AL = U (irritation + frustration+ anxiety + rage) x TP

Affective load is high when people operate with ineffective cognitive behaviors. For example, cognitive ambiguity, uncertainty, or information overload attract affective behaviors that are negative and counterproductive to the searcher’s goal. For instance, a search that appears to yield no relevant results after some attempts is cognitively disorienting, as represented by such thoughts as, ‘Tm no good at this” or “This is so frustrating! ”

At other times, searchers are able to engage affective coping strategies when faced with cognitive load and uncertainty. For instance, “I’ll just keep going until I find something” or “I’m positive I can find what I need in another database.” These verbal expressions are standard and recurrent within a population of searchers, and because they are learned cultural habits, can be termed “learned affective norms” (LANs). Negative LANs disrupt cognitive strategies, interrupt the search, and often terminate it prematurely, while positive LANs provide persistence and integration to cognitive strategies. In general, negative LANs increase AL and appear in the form of uncertainty, anxiety, frustration, low expectations, pessimism, low self-efficacy, low task completion motivation, low satisfaction, low system acceptance, and other disruptive symptoms that interfere with a positive outcome. On the other hand, positive LANs decrease AL because they provide better coping strategies to manage ambiguity and cognitive load. Support and counseling interventions can be triggered when affective load rises above a specified level. Knowledge about the affective environment of searchers will also be helpful in search instruction. More research is needed on how the affective information environment of searchers impinges on their cognitive activity to strengthen information system services and design.

(Fisher, 2005, p. 39ff)

Principle of least effort def (Fisher, 2005)

17 October 2010

The Principle of Least Effort, is probably the most solid result in all of information-seeking research. Specifically, we have found that people invest little in seeking information, preferring easy-to-use, accessible sources to sources of known high quality that are less easy to use and/or less accessible.

Ease of use and accessibility of information seem to be more important to people than quality of information. But what is the explanation for this phenomenon? Why are people unwilling to invest that little bit of extra energy in order to get information that they themselves would acknowledge is of better quality?

1) People “satisfice” in all realms of life, including information seeking. The idea of satisficing comes from Simon (1976), who argued that in decision-making, people make a good enough decision to meet their needs, and do not necessarily consider all possible, or knowable, options. Translated to the language of LIS, for example, using Dervin’s concept of “Sense-Making” (Dervin, 1983, 1999), we could hypothesize that people make sense of their situations based on what they know and can learn easily. Their Sense-Making need only be adequate to continue with life; it does not need to be so perfect or extensive as to enable them to make sense of everything.

2) People underestimate the value of what they do not know, and over-estimate the value of what they do know. People have difficulty imagining what the new information would be that they do not know, while what they do know is vivid and real to them. Consequently, they under-invest in information seeking. See Gilovich, Griffin, & Kahneman (2002) and Kahneman & Tversky (2000) for work on distortions in decision-making and choice.

3) Gaining new knowledge may be emotionally threatening in some cases. Gregory Bateson once described what he called “value-seeking” and “information-seeking” (Ruesch &. Bateson, 1968, pp. 178-I79). In value, seeking, a person has an idea in mind of something that he or she wants. Suppose one wants some eggs and. toast to eat, for example. One then goes out into the world, does various things involving chickens, grain, cooking, and baking, with the end. result that one has a breakfast of eggs and toast. Thus, one has done things to parts of the world in order to make the world match the plan one has in mind. In information seeking, on the other hand, according to Bateson, the directionality is reversed; one acquires information from the world in order to impress it on one’s own mind.

However, new knowledge can always bring surprises, sometimes uncomfortable ones. If “we are what we know,” if our sense of self is based, in part, on our body of knowledge of the world, then to change that knowledge may be threatening to our sense of self.

4) Information is not tangible, and objects are. Intangible things seem less real to us, therefore less valuable. Consequently, we invest more in acquiring tangible than intangible things.

Each hypothesis above is not a complete explanation … For instance, Simon’s satisficing may be, in effect, another name for Zipf’s Principle of Least Effort (1949). Poole (I985) believed his results fit well with Zipf’s earlier work. Zipf had a more extensively conceptualized understanding of least effort, one that constitutes a preliminary explanation, i.e., theory, and which contributes to a better understand_ ing of least effort than we usually articulate in LIS. To Zipf, according to Poole, least effort was technically the “least average rate of probable work” (Poole, 1985, p. 90). That is, people do not just minimize current work associated with some activity, because they could eventually do a total of much more work in the end. Rather, they make a considered estimate of all likely work associated with a given effort, now and in the future, and do the amount of work now that they estimate will best reduce their overall effort, now and later combined (Poole, 1985).


“Principle of Least Effort” has been so widely observed that we were able to make confident predictions about where else it might appear as well. But we still had no explanation, no theory as to why this phenomenon occurs (except possibly in Zipf’s original research, 1949). We hypothesized four possible explanations, and considered ways in which these theories could be tested. Testing might then lead to further tentative theories that would explain this phenomenon still more deeply.

(Fisher, 2005, s. 4ff)