Effect of Different Database Structure Representations, Query Languages, and Task Characteristics on Information Retrieval

INTRODUCTION The accounting and business communities increasingly rely on database applications to convert raw data into useful business information (Hayes and Hunton, 2000). Today, a majority of accounting information systems use relational databases (Hooper and Page, 1996). In the past, information systems (IS) professionals, not end-users, were responsible for query tasks (Borthick, 1992). Recently, this function has shifted to end-users as accounting systems use more easily accessible databases (Hooper and Page, 1996). End-users (e.g., accountants, auditors, and managers) must understand the database structure and be able to use the available query language to transform accounting data into useful business information (Leitheiser and March, 1996). Accounting end-users must be able to discern whether the potential information is available and then retrieve it (Borthick, 1992). A database structure representation communicates the availability of accounting data (Dunn and Grabski, 2002). This representation details the stored data items and their logical organization. Examples include the entity-relationship (ER) model and the relational model. Accessing data of interest requires knowledge of a database query language. Examples include query-by-example (QBE) and structured query language (SQL). The IS literature is unclear concerning how database representation type, query tool type, and user characteristic affect end-user performance in query construction tasks. According to Dunn and Grabski (2002, 168), "prior research has not examined these factors or their interactions in a systematic way, as this is a relatively new research field. " They propose that these factors be studied further to learn more about their combined effects on query writing performance. Past empirical research on information retrieval typically compares one representation model to another, while keeping the language constant (Lochovsky and Tsichritzis, 1977; Jih et al., 1989; Davis, 1990; Chan et al., 1993; Leitheiser and March, 1996), or compares one query language against another without regard to the database representation (Reisner et al., 1975; Chamberlin et al., 1976; Zloof, 1977; Greenblatt and Waxman, 1978; Reisner, 1981; Yen and Scamell, 1993). Only one study manipulated both the database structure representation and the query language (Chan et al., 1993). The current study manipulates both the data model and the query language. Chan et al. (1993), however, did not investigate the interaction between the two factors. The current study solves this problem by analyzing the main effects as well as the interaction of the different factors affecting end-user query performance. Prior research on query languages, which typically holds the data model constant, suggests that a graphical tool is easier to use, but only for nonprogrammers (Reisner et al., 1975). Prior research on data models indicates overwhelming superiority of the graphical version of the data model for database designing tasks (Batra et al., 1990). Results were inconsistent for those studies that manipulated the data model while investigating user performance in query writing tasks (Jih et al., 1989; Chan et al., 1993). None of these studies examines the use of QBE as a retrieval technique. Therefore, this study includes both SQL and QBE as query languages. Accountants, auditors, and managers use a variety of query tools, depending on their database management system. In addition, all prior experiments except Amer (1993) used IS students as participants. Therefore, this study uses accounting and MIS students as proxies for potential DBMS end-users. This is the first study that incorporates user characteristics in the research model. This study investigates the influence of database structure representation, query language, and task characteristics on user performance in the information retrieval process. Organizations often implement formal training programs to teach their new hires and experienced employees the skills they will need, including the ability to efficiently and effectively query a database. …