Digital asset-seeking behavior is the process of searching and retrieving assets toward some goal within a given context. Examining the overall process as well as analyzing its individual components is of the utmost importance to DAM professionals. Knowing how our constituents think, feel, and act when looking for digital material will help us effectively choose, design and tailor systems. Its study will help us to determine what assets are needed, why they are needed, the way they are expected to be found and retrieved, and how they will be used. Although there has been little written specifically about asset-seeking behavior outside of academia, the research conducted by other information professionals about information-seeking behavior is relevant. Marchionini (1995) introduced six major factors that play a significant role in asset-seeking behavior: the seeker, task, search system, domain, setting, and search outcomes (p. 9). Asset use should also be added to this list.
There are several prominent Library and Information Science theories to help us understand such a varied and complex topic. Just as there is no single way we look for digital assets, there is no ‘correct’ information-seeking behavior theory. As Cleveland and Cleveland (2013) wrote, there is
“…no such thing as a one-size-fits-all model for searching for information” (p. 103).
Information behavior is not a mechanical process but an intellectual journey that involves conscious human thinking. Studies have centered on the use of forms of literature (books, journals, etc.), non-library uses, cognitive and affective processes, motives, and user-interfaces. Of these, several theories stand out.
Kuhlthau conceived of the Information Search Process (ISP) model that consists of six stages: initiation, selection, exploration, formulation, collection, and presentation. The first stage involves the realization of a need for knowledge to gain understanding. The topic and how to learn about it are unfocused. The level of uncertainty is directly proportionate to the gap in seeker understanding and is the primary motivator for research. Selection is where the topic becomes clearer and search strategies are considered. An exploration phase follows, whereby the seeker gains general knowledge about the topic. The researcher is then able to formulate their information need further and becomes less uncertain. Specific information about the topic, having now been clearly defined, is collected and leads to uncertainty being further reduced. Finally, the search is completed and the researcher turns their attention to reporting on the gathered information. The model has helped me determine the best time to approach clients for a consultation, also known as Kuhlthau’s zone of intervention. Similarly Belkin’s Anomalous States of Knowledge (ASK) model describes the states through which an information seeker must pass to clarify and articulate their search. Belkin and Kuhlthau’s theories have obvious implications to digital asset management system design; they must support the researcher through each step of the information-seeking process.
Bates proposed the berrypicking search model, which posits that knowledge is scattered throughout resources rather than found clumped together. Furthermore it is based on the idea that search is an iterative process, where queries evolve over the course of the search. In this way chunks of information are gathered until the information need is satisfied. During this process users rely on several types of resources and strategies. Likewise, Dervin (1998) proposed a theory called sense-making whereby the researcher creates, seeks, uses, and rejects the information they find to reduce the initial information gap.
In 1949 Zipf called for the study of human behavior by examining natural phenomena, in much the same way we study biology. Zipf presented the Principle of Least Effort that attempted to explain why
“we tend to take the path that entails the least effort” (Cleveland, 2013, p. 99).
This theory can be applied to Digital Asset Management by explaining why many users tend to consult a search engine or book before they call someone knowledgeable on the subject. Further, the principle could help explain the importance of a taxonomy, which reduces concepts in a given corpus to a list of subject terms. Although, many have argued against Zipf’s theory, one study showed that the more important a task, the more users relied on sources local to them rather than consult an authoritative source (Xu, Tan & Yang as referenced by Rubin, R., 2010).
In Marchionini’s seminal work Information Seeking in Electronic Environments, the author discussed many theories regarding human-computer interaction. One theory states that whereas browsing is opportunistic and depends on the recognition of relevant information, searching relies on an iterative approach and is done in batches (Marchionini, 1995). Ensuring users have the ability to do both will increase the likelihood they find what they need.
DAM systems must facilitate the ways users search otherwise they will be avoided. Besides helping us to develop a DAM system’s interface, understanding our clientele’s asset-seeking behavior will help us develop better reference service, form an accurate user model, build a useful metadata schema and controlled vocabulary, and establish appropriate indexing and abstracting guidelines. As such it is imperative that DAM professionals study and write about asset-seeking behavior in varied contexts. In addition to providing us with useful insights into the process, the published work will encourage system vendors to modify their systems to better suit the needs of their varied user base.
Cleveland, D. B. (2013). Introduction to indexing and abstracting (Fourth edition.). Santa Barbara, California: Libraries Unlimited, An Imprint of ABC-CLIO, LLC.
Dervin, B. (1998). Sense-making theory and practice: an overview of user interests in knowledge seeking and use. Journal of Knowledge Management, 2(2), 36–46. doi:10.1108/13673279810249369
Information behavior theories. (2014, August 16). Retrieved October 26, 2014, from http://liswiki.org/wiki/Information_behavior_theories
Marchionini, G. (1995). Information Seeking in Electronic Environments. Cambridge: Cambridge University Press. Retrieved from http://books.google.co.uk/
Rubin, R. (2010). Foundations of library and information science (3rd ed.). New York, NY: Neal-Schuman Publishers, Inc.