The faster a user can transition from typing a query into a search field to typing a number in a credit card field, the happier are both user (customer) and business.
Not surprisingly, “Seek and you shall find” has been the catchphrase of successful search engine vendors. This maxim encapsulates the main thrust of successful interaction within the commercial domain: task A (seek) directly leads to outcome B (find). This interaction model is vectorial—its directionality is governed by the wish to achieve the optimal outcome.
Based on the vectorial interaction model, successful commercial search engines eliminate the vast landscape of the Web by providing patrons with a 'relevant' result set, quickly — on the first results screen and often in the top record. The fact that these search engines simultaneously provide an entirely unusable result-set of hundreds of thousands of records further emphasizes the underlying goal of providing the single relevant match for the search.
Given a result-set of workable dimensions (e.g. 25 links), the user would have been likely to explore it, but the paradoxical combination of results presented by the commercial search engines does not offer the user real choices. Rather, it conditions the user to view the action of searching and its outcome as inseparable, providing an instantly gratifying experience packaged as a volitional choice process.
Different from the “Seek and you shall find” vectorial model, is a model of user-collection interaction characterized by the phrase “Seek so you can find”. Here, the relationship between task A (seek) and task B (find) is more reciprocal and dynamic. It suggests a spiral interaction model in which the outcome emerges as a synthesis enabled by the search process. Whereas the vectorial interaction model works well for commercial search engines, I believe that the spiral interaction model is more appropriate for the academic and scholarly domains.
Commercial search engines help users find a needle in a haystack—and this has never been easier. Google’s ability is amazing even to those who understand its underlying technology. Nonetheless, finding a needle in a haystack (vectorial model) is not, in fact, such a big deal when you have the right equipment—a magnet in the case of this analogy. Only the needle attaches to the magnet while the haystack becomes immaterial.
Most research and academic electronic collections, on the other hand, are serving patrons who are interested in finding a piece of hay in the haystack (spiral model). The magnet becomes useless; the key functions of assigning relevancy and ranking of information become dependent on human capacities such as critical evaluation, synthesis, and decision making.
First posed on: Thursday, September 14, 2006
Not surprisingly, “Seek and you shall find” has been the catchphrase of successful search engine vendors. This maxim encapsulates the main thrust of successful interaction within the commercial domain: task A (seek) directly leads to outcome B (find). This interaction model is vectorial—its directionality is governed by the wish to achieve the optimal outcome.
Based on the vectorial interaction model, successful commercial search engines eliminate the vast landscape of the Web by providing patrons with a 'relevant' result set, quickly — on the first results screen and often in the top record. The fact that these search engines simultaneously provide an entirely unusable result-set of hundreds of thousands of records further emphasizes the underlying goal of providing the single relevant match for the search.
Given a result-set of workable dimensions (e.g. 25 links), the user would have been likely to explore it, but the paradoxical combination of results presented by the commercial search engines does not offer the user real choices. Rather, it conditions the user to view the action of searching and its outcome as inseparable, providing an instantly gratifying experience packaged as a volitional choice process.
Different from the “Seek and you shall find” vectorial model, is a model of user-collection interaction characterized by the phrase “Seek so you can find”. Here, the relationship between task A (seek) and task B (find) is more reciprocal and dynamic. It suggests a spiral interaction model in which the outcome emerges as a synthesis enabled by the search process. Whereas the vectorial interaction model works well for commercial search engines, I believe that the spiral interaction model is more appropriate for the academic and scholarly domains.
Commercial search engines help users find a needle in a haystack—and this has never been easier. Google’s ability is amazing even to those who understand its underlying technology. Nonetheless, finding a needle in a haystack (vectorial model) is not, in fact, such a big deal when you have the right equipment—a magnet in the case of this analogy. Only the needle attaches to the magnet while the haystack becomes immaterial.
Most research and academic electronic collections, on the other hand, are serving patrons who are interested in finding a piece of hay in the haystack (spiral model). The magnet becomes useless; the key functions of assigning relevancy and ranking of information become dependent on human capacities such as critical evaluation, synthesis, and decision making.
First posed on: Thursday, September 14, 2006
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