Business Source Complete: Its Functionalities in the Light of Information Retrieval Theories

(Examination of the Database Interface)

Business Source Complete is a robust and premium scholarly, full-text business database. Combined with Regional Business News Plus, Business Source Complete is a comprehensive source of company information for academic researchers. The database covers all business disciplines including marketing, management, MIS, POM, accounting, finance, and economics (https://www.lib.ncsu.edu/guide/business/databases/business-source-complete).As of this writing, it boasts more than 2,100 active full-text journals and magazines, nearly 1,300 active full-text peer-reviewed journals, nearly 770 active full-text peer-reviewed journals with no embargo, and more than 840 active full-text journals indexed in Web of Science or Scopus.

The Business Source Complete (BSC) is hosted by EBSCOhost which is considered as one of the powerful online reference systems or research databases accessible via internet. Having said this, the BSC offers its new and experienced users with intuitive research platform with variety of resources to refine search results. It has basic and advanced search options which save time and be more efficient. This system observes the Zipf’s Principle of Least Effort which means everyone will adopt a course of action that will involve the expenditure of the probably least average of his work. Thus, the search interface is easy to navigate as shown below:

Source: http://researchguides.library.vanderbilt.edu/journals

These illustrations present the use of various retrieval models such as the exact match models namely boolean retrieval, standard Boolean, narrowing & broadening techniques, and extended Boolean, as well as the best match models like statistical models, vector space model, probabilistic model, and latent semantic indexing. It deploys complex algorithms for best-match keyword search however complex it may seem, the Business Search Interface (BSI) is specialised to users which gives them high level of control over the development of their search strategies. It also features easy access to search options thru the variety of options presented in the search result. The user has the capability to personalise his/her result list by saving the articles relevant to the search by simply clicking the to folder icon next to the title. Furthermore, the user can save the articles thru the sign-in link in the top tool bar to create or access the personal application account. This feature facilitates personalisation of search as well as retention of user’s search history. Why is this relevant? Abstracting & Indexing services play a critical role in supporting a personalized end-user experience. These services enable the leveraging of abstract/index data within the relevance ranking algorithm, as well as the mapping of terms across multiple thesauri (Collins,2015).

Another important feature of the BSC interface is the availability of browse links which provide quick access to industry information and profiles which are relevant to the search. These links connect to relevant information search and retrieval. How relevant the results maybe is another dilemma that a user may face in the information retrieval stage. That is why the probabilistic information retrieval model comes to mind. It is when if we have some known relevant and nonrelevant documents, then we can straightforwardly start to estimate the probability of a term appearing in a relevant document, and that this could be the basis of a classifier that decides whether documents are relevant or not. “Users start with information needs, which they translate into query representations. Similarly, there are documents, which are converted into document representations. Based on these two representations, a system tries to determine how well documents satisfy information needs. In the Boolean or vector space models of information retrieval, matching is done in a formally defined but semantically imprecise calculus of index terms. Given only a query, an IR system has an uncertain understanding of the information need. Given the query and document representations, a system has an uncertain guess of whether a document has content relevant to the information need. Probability theory provides a principled foundation for such reasoning under uncertainty (Manning, Raghavan, & Schutze, 2008).” The challenge has always been knowing the most relevant or the best answer to an information need. The user must be able to specifically state the information need as there are largely available sources of information for retrieval.  

Finally, whether the user is new or experienced may feel positively about the EBSCO search interface on BSC but there are still usability issues caused by either the user’s competency or incompetency on information search and information retrieval, or it may be the confusions on the overly designed source types, incompatible/obsolete formats or just the exaggerated drop-down entries, relevancy rankings and retrieval modes. These issues can be further studied focusing mainly on the USER or the user in the forefront and the whole experience provided in the information search and retrieval.

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