To decide whether to visit a page, people take into account how much relevant information they are likely to find on that page relative to t
To decide whether to visit a page, people take into account how much relevant information they are likely to find on that page relative to the effort involved in extracting that info.
Information foraging is the fundamental theory of how people navigate on the web to satisfy an information need. It essentially says that, when users have a certain information goal, they assess the information that they can extract from any candidate source of information relative to the cost involved in extracting that information and choose one or several candidate sources so that they maximize the ratio:
Rate of gain = Information value / Cost associated with obtaining that information
information foraging explains why people don’t scroll mindlessly or click on every single link on the page: because they attempt to maximize the rate of gain and get as much relevant information in as little time as possible. Scrolling or clicking a lot more would probably gain the user more information, but in the user’s estimation, the rate-of-gain ratio would decrease, because the numerator (the information value) would increase too little compared to the increase in the denominator (the interaction cost associated to getting the information).
For a given task, they do know however how much real time they spent so far and how much relevant content they were able to get. In order to choose what source to look at next, they estimate how the rate of gain will change if they choose to explore a specific information source
When people attempt to acquire information for an information need, they often look at multiple sources of information (or information patches). At any point in time, they know the real information value they have gotten so far from all the patches they’ve already visited and also the real effort (or time) spent to gather that information. To decide whether a new patch is worth exploring, they estimate how the rate of information gain will change if they choose that patch. The estimate is based the on cues that they receive from the patch about the info value of that patch (information scent) and the perceived effort needed to extract that info. It’s possible that these estimates are in fact wrong and that the real information value and effort associated with a patch are different than the user-perceived information value (or information scent) and effort.
Information scent - Similarly, as a user searches for information on the web, she judges the webpages she encounters based on how well suited they are for her goal. Each source of information thus emits a “scent” — a signal that tells the forager how likely it is that it contains what she needs.
When a person lands on that very page, the scent is given by the title, images, and the information that is easily visible above the fold. If the user is searching for dish towels and lands on a site with pictures of strawberries, beer, and candy, she may assume that this page is unlikely to contain what she needs simply because the scent points into a different direction.
When a person looks at a link to a page, the scent is given by all the words and images associated with that link. Thus, the same person looking for dish towels may be strongly attracted by a link called Kitchen linens next to an apron, a kitchen glove, and several dish towels.
There are two types of costs associated with obtaining information: (1) the actual time and effort involved in extracting the information from the various information sources, and (2) the opportunity cost — resulting from foregoing the benefits of exploring other documents in favor of the chosen ones.
Between-patch activities: Gathering information sources (i.e., patches).
Within-patch activities: Inspecting each patch to extract information from it.
An enrichment refers to a user interaction, behavior, or strategy that aims to maximize the utility of the information foraging. It can happen either between patches or within patches.
Behavior enrichments are the tools that users already have acquired and that help them extract information efficiently. These behaviors are adaptations that evolved over time and that proved to be successful in many situations in the past.
Interaction enrichments require extra effort from the user — these are the tools that the user needs to build on the spot, in order to maximize the efficiency of the information-foraging task.
For example, the user can spend time to think of specific keywords that best describe her query, hoping to increase the likelihood of relevant search results. Or she can set a lot of filters. Both of these actions are between-patch enrichments.
Within patches, people can also use strategies such as the use of within-page search to quickly locate content that is relevant to them.
Enrichments are risky for the user for two reasons: not only it is the case that some enrichments require users to pay an extra interaction cost or take upon themselves a larger cognitive load, but there is a chance that they won’t be right for the task. For example, a user using an F-pattern to scan a webpage may miss important concepts that don’t app.
When people look for information on the web, they attempt to maximize the rate of information gain over time: they want to get as much information as possible in the minimum amount of time. To reduce time, they estimate the information value of a page based on its information scent and use enrichments such as learned behaviors or interactions to improve the chance of quickly getting what they need in their information foraging.
