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What Is Information Seeking?

By Matthew Edgar · Last Updated: April 08, 2024

At the most basic level, generative AI and websites are both tools that help people seek information. The information will range from learning about a particular subject, reading a review of a local business, finding a product to purchase, finding a game to play, finding people to connect with, finding a video to watch, and more. Whatever it is, people turn to technology to help them get that information.

For the last thirty years, search engines and websites have increasingly been the predominant choice to find that information, but generative AI tools also help users find information. How do we describe information seeking and based on that description, how do we compare generative AI tools and websites? There are many theories describing the information seeking process, but some are more relevant to the question of how websites and search engines compare to generative AI. Understanding these can help us understand the questions to ask when developing our generative AI strategies.

Information Search Process

In papers published in the early 1990s, Carol Kuhlthau studied and described the Information Search Process. This process is related to accomplishing long-term goals and was initially related to research papers in an academic setting, though it applies to many different types of information searches. Along with explaining the various stages of seeking information, this process also analyzes the user’s feelings, thoughts and actions when seeking information.

There are six stages of Kuhlthau’s Information Search Process: initiation, selection, exploration, formation, collection, and presentation.

  • Initiation: The information search process begins when the user realizes there is some piece of information they are lacking. At this point, the search topic is still vague, but the searcher knows there is some piece of information they want to or need to find. This stage is categorized by a high degree of uncertainty.
  • Selection: After initiating the search process, the searcher begins to narrow down the information domain or space to be analyzed. This is where constraints are applied; “I am searching for this, not that”. Kuhlthau’s research found that there is an increase in optimism and uncertainty begins to fade as the searcher shifts into seeking more relevant information.
  • Exploration: The search process is underway with the searcher reading, processing, and evaluating new sources of information. The searcher’s goal is to try to make sense of the new knowledge being acquired and determine how that information fits alongside previous knowledge about the subject. Uncertainty begins to return, along with confusion and doubt. Kuhlthau noted this is the most difficult stage of the process and may lead to abandonment.
  • Formulation: The search process becomes more focused in the fourth stage of the Information Search Process. The searcher develops a greater understanding of the search topic and forms their own perspective on the topic. Feelings of uncertainty decrease as the searcher grows more confident.
  • Collection: Now that the search topic is more focused, the searcher begins to collect additional information sources to extend or support what has already been found. The searcher is not after general information but wants more specific, relevant information.
  • Presentation: In the final stage of the process, the searcher brings all the information together to summarize the information. The searcher may be disappointed if the information initially sought could not be found or, in the reverse, satisfied if the information could be found. Depending on the situation, the searcher may prepare a presentation about the findings for others. Since not everybody searching for information will present the information found, this stage has also been referred to as “Action” instead of “Presentation” to capture the idea that the searcher must now act on the newly acquired information in some way.
Kuhlthau’s Information Search Process – Source

While this process was developed before Google and long before generative AI, the process still provides interesting insight into the process of using Google or generative AI tools. However, the way this process maps to search and generative AI differs.

All six steps are part of the process of conducting a search on Google. The user enters  a search query and the search engine returns a list of results. The user then needs to review the list of available websites, deciding which to select. The user needs to explore the websites selected, deciding which websites offer the most useful information. After visiting enough websites, the user begins to formulate a clear perspective but may visit a few more websites to confirm they have sufficient information. The user will then act on that information—an action in this case is likely to involve converting on a website, such as making a purchase, filling out a form or signing up for a free trial.

In contrast, not all steps are visible to the user when conversing with generative AI tools. The user initiates the process by entering a prompt. However, the generative AI tool returns a conversation instead of a list of information. That is, the AI tool did most of the selection and exploration on behalf of the user. The user can ask follow-up questions to refine the information returned. This forms the user’s perspective and helps the user collect additional information about the subject. It is less clear how the final stage, of presentation or action represents on generative AI tools. In some cases, the generative AI tool may suggest additional things for the user to do. In other cases, the user will use the information returned by the generative AI tool outside of the conversation.

The process can be used to describe searching for information with generative AI and websites, but what does it tell us about which technology people will prefer? That question is not answered directly by this model. However, one of the main takeaways of Kuhlthau’s Information Search Process is that people want to move toward clarity and gain more focus. So, the question is how do we help people do that?

We already know that clearly written, highly focused websites rank better in search results and drive more conversions. This process explains why that is. We also know that generative AI can deliver highly focused and clearly written responses to prompts. This process also explains why people prefer generative AI responses. If we want our websites to compete against generative AI, then our websites need to help people move through the Information Search Process as effortlessly as possible.

Evolving Search and Berrypicking

The challenge with many theories about information seeking, including Kuhlthau’s Information Search Process, is that the process described is cleaner and tidier than many real-life scenarios. Kuhlthau and others described information searching within an academic context, which is different than how people look for information outside of academia. Searching for information about a new pair of shoes to purchase is not equivalent to searching for information related to a term paper.

Marcia J. Bates addressed these challenges and described an alternative in a 1989 paper called “evolving search”. Evolving search describes these real-life, non-academic scenarios of searching for information where the search is more iterative and non-linear. A user conducts an initial search, reviews the information returned to that search, changes the search, reviews the next set of information returned, and so on.

The changes to the search are not mere refinements. Instead, evolving search means that everything about the search process is continually shifting. Users will adjust the search query, adjust search techniques, change the information space being researched, and even rework the entire search process. Searchers move between different sources of information adjusting their search paths and queries based on what they discover along the way.

As part of this, evolving search emphasizes that information is collected in pieces from multiple sources. Because of this, Bates compared the evolving search approach to picking berries off multiple bushes. Berries are scattered across the bushes, not in bunches. Also, to acquire enough berries, berries must be picked from several different bushes. In the same way, information is picked from multiple sources and not gathered from a single source.

Bate’s Berrypicking Model – Source

Most Google searches are a form of berrypicking. A search is rarely a linear process moving from vague information to more concrete information. Instead, after the user enters in an initial search query and reviews some of the initial websites returned, the user will have new information and form new thoughts about what information they need. The user will return to Google and enter in a new query based on the new information. The new query may be a completely different question, not just a refinement of the original query. The user may veer off from Google entirely and browse for additional information on another website.

Some conversations with generative AI can be described as a form of berrypicking. Reviewing the response to the user’s initial prompt leads the user to think of another prompt. That subsequent prompt may be a refinement, moving through a linear process of removing uncertainty as described by the Information Search Process. Often, though, the next prompt is not a refinement or clarification of the original prompt but something else entirely. The user’s intent with the prompt is to explore more aspects of the subject and gather more information. The conversation continues until a satisfying amount of information has been gathered.

Given that generative AI, search engines and websites can be used to find information iteratively, it is possible that generative AI could replace websites and search engines altogether. Instead of gathering information from multiple websites, a user could gather information from multiple conversations with multiple chatbots. Of course, Bates’ evolving search theory is consistent with using conversations, search results, and websites together to retrieve the needed information.

Berrypicking could either describe a way generative AI will compete with search engines and websites but also could describe a way these tools could complement each other. As we think about forming strategies for generative AI, the question is where do users want to use each tool as part of finding information? This requires talking to your users and understanding when they will converse with generative AI, when they want to conduct a search, and when they want to visit your website. Will users do all those things or will users prefer one technology to another?

Information Foraging

What linear and non-linear information seeking frameworks do not fully answer is what strategies people employ when searching for information. What is it that causes people to move through the Information Search Process described by Kuhlthau? What is it that causes people to iterate through multiple searches in Bates’ berrypicking model?

To explain the strategies behind information seeking, Peter Pirolli and Stuart Card introduced information foraging theory in a 1999 paper. Information foraging has become one of the more popular and influential theories to describe the strategies behind how people use search engines and seek information on websites.

Information foraging takes an evolutionary and ecological perspective on information gathering, comparing the process to an animal foraging for food. When foraging for information, the objective is to maximize gains of valuable information while also reducing the costs of gaining that information. To reduce costs, people will balance resource costs (like time or attention) against the opportunity costs (the value of the information). If the cost of obtaining the information is too high, people will not continue obtaining that information. For example, people may want to find a product to purchase but the user interface may be overly difficult to use, so people will abandon the website.

Pirolli and Card’s paper also discusses how people move between multiple information patches. An information patch is a collection of information sources. An information patch could be a Google search result, a conversation with ChatGPT, or an article on a website. To forage for information, people will typically move between multiple information patches. This is similar to Bates’ evolving search theory where people move between multiple sources of information to find all the desired information.

As part of balancing costs and benefits, people need to decide when to stay with the same information patch and when to move to a different information patch. A user may have conducted a search on Google and found that several of the search results are relevant to the query. The user may stay with that Google search result for several minutes and open several of the links listed. If the search results end up proving unsatisfactory, the cost of staying in that information patch is too high. So, the user may conduct a new search instead, moving to a new information patch.

There is a third option instead of staying with a patch or moving: enrichment. Pirolli and Card define enrichment as the process of refining an existing information patch. Moving to a new patch may be more costly than simply refining the existing search. For example, a user may conduct a search on an ecommerce website. The initial results list products somewhat relevant to what the user was looking for but not a perfect match. Changing the search entirely could lead to even less relevant results, increasing the cost of switching to a new information patch. Instead of switching, the user can filter the initial search results, such as limiting the search results to products within a certain price range or products meeting some other criteria. There is a cost associated with applying the filter but, importantly, that cost is less than the cost of conducting an entirely new search.

Pirolli and Card’s enrichment is a key part of the move from the Exploration to Formulation phases of Kuhlthau’s Information Search Process; enriching an information space demonstrates a move toward clarity and a greater focus within the search. The cost of enrichment versus switching also helps explain the thought process behind why people move from one query to another in Bates’ berrypicking theory.

Information foraging also describes how people use generative AI tools. A conversation with an AI tool is an information patch. After users enter a prompt and retrieve some information, users must decide whether to stay with that conversation and refine it with follow-up prompts (a process of enrichment) or if it would be better to switch to a new information patch. That switch might be a new conversation with the generative AI tool, but it may also involve moving from a generative AI tool to a website or to a search engine.

Understanding information foraging is critical to formulating an appropriate generative AI strategy. The key question is which tool better reduces the cost of seeking information relative to the value of the information returned? Do websites and search engines help users get sufficiently valuable information at a lower cost than generative AI tools? Or is generative AI less costly? This requires understanding what prompts your customers may use with generative AI and if those prompts are generally helpful—or at least if they are more helpful than a search result or an article on your website.

Information foraging theory demonstrates that users will prefer whichever tool delivers valuable information at the lowest cost. The goal should be to do everything you can to lower the cost of retrieving information on your website to hopefully encourage people to visit instead of using generative AI. Of course, if people find generative AI is simpler, you need to know which prompts people are using and do your best to ensure your company surfaces in those conversations.

Key Takeaways

People do not want to use a search engine.

People do not want to visit a website.

People do not want to converse with generative AI.

People want to get information.

People will use whatever tool helps them get that information as easily as possible. Information foraging explains that people want to get information at the lowest cost (cost is measured by the amount of time and attention required). The Information Search Process explains people want to move toward clarity and focus. To compete with generative AI requires delivering information in the simplest way possible and making that information as clear as possible.

It may not always be a competition. Berrypicking explains people will iterate through a complicated search process to find relevant information. Generative AI, websites, and search engines can all be a part of that iteration—along with social media, forums, email, chat, books, and more. To help users find information related to your business, you need to do more than rank highly in search results; you need to make sure your company is present in as many places as your customers use to find information.

Need Help?

If you need help figuring out how to prepare for generative AI and the way it will change SEO, contact me. We can work together to determine how your users are using generative AI tools and what the implications are for your business.

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