Three important determinants of user performance for database retrieval

Chan, Hock Chuan; Tan, Bernard C. Y.; Wei, Kwok Kee

doi:10.1006/ijhc.1999.0272

Cited by 31 publications

(15 citation statements)

References 27 publications

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“…The success of a search is commonly defined in terms of its performance (correctness or accuracy, time) and the searcher's attitudes (confidence and satisfaction) (Chan, Tan, & Wei, 1999;Te'eni & Feldman, 2001). The theoretical framework for our research focuses on those factors that ultimately affect the successfulness of the search process via their effects on performance and attitudes, as represented in Figure 1.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Training for Web search: Will it get you in shape?

Lucas

Topi

2004

J. Am. Soc. Inf. Sci.

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. Two of the experimental treatments were based on the type of training the users received. The first of these treatments was the presence or absence of training on the principles of the use of Boolean operators, henceforth referred to as logic training. The second was the presence or absence of training that focused on the specific characteristics of the assisted search interface, which we will refer to as interface training. The third treatment was the type of interface used by the participants, namely, simple or assisted. The impacts of logic and interface training and the type of search interface used on correctness, time, satisfaction, and confidence were measured and analyzed. Given that time is money, Web searching can be a very expensive proposition. Even with the best search technology, the usefulness of search results depends on the searcher's ability to use that technology effectively. In an effort to improve this ability, our research investigates the effects of logic training, interface training, and the type of search interface on the search process. In a study with 145 participants, we found that even limited training in basic Boolean logic improved performance with a simple search interface. Surprisingly, for users of an interface that assisted them in forming syntactically correct Boolean queries, performance was negatively affected by logic training and unaffected by interface training. Use of the assisted interface itself, however, resulted in strong improvements in performance over use of the simple interface. In addition to being useful for search engine providers, these findings are important for all companies that rely heavily on search for critical aspects of their operations, in that they demonstrate simple means by which the search experience can be improved for their employees and customers.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Training for Web search: Will it get you in shape?

Lucas

Topi

2004

J. Am. Soc. Inf. Sci.

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“…Results obtained by several researchers demonstrate that as task complexity increases, productivity, k, decreases. [4][5][6]8,11 Another parameter related to learning behaviour is the estimate of prior expertise, which is by some estimated based on initial performance level, where higher task complexity is associated with a lower levels of initial performance. 7,10 This suggests the effects of task complexity on initial expertise, p, wherein higher task complexity is associated with lower mean initial expertise, p.…”

Section: Task Complexity Effects On L/f Parametersmentioning

confidence: 99%

“…In addition, several researchers concluded that the L/F parameters of each individual are dependent on the complexity of the task s/he performs. [3][4][5][6][7][8][9][10][11][12][13] In general, a worker's productivity at a particular time is a function of past individual L/F. Where disruptions and workplace interventions occur frequently in production lines, L/F phenomena have become more important for determining productivity.…”

Section: Introductionmentioning

confidence: 99%

Learning and forgetting-based worker selection for tasks of varying complexity

Nembhard

Osothsilp

2005

Journal of the Operational Research Society

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This paper presents an approach for selecting workers for tasks of varying complexity based on individual learning and forgetting characteristics in order to improve system productivity. The performance of a learning and forgetting-based selection (LFBS) policy is examined using simulation and compared to a baseline policy representing criteria used in practice. The effects of factors including worker redundancy and task-tenure on productivity are also examined in the environment of continuously staffed independent tasks. Results demonstrate that the LFBs policy significantly improves productivity relative to common practice and suggests that lower levels of redundancy and shorter tasktenures tend to mitigate some of the negative effects of forgetting.

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“…In particular, extensions of the ER (Extended‐Entity‐Relationship—EER) model emphasize greater semantic details in the data requirements. The EER model has been frequently included in many human factor studies (Batra, Hoffer, & Bostrom, 1990; Bock & Ryan, 1993; Chan, Tan, & Wei, 1999; Chan, Wei, & Siau, 1993; Liao & Palvia, 2000; Shoval & Frumermann, 1994; Siau, Wand, & Benbasat, 1997). Some studies compared the EER model with other conceptual models, while some other studies compared the EER model with the traditional models, usually the relational model.…”

Section: Introductionmentioning

confidence: 99%

An evaluation of novice end‐user computing performance: Data modeling, query writing, and comprehension

Chan

Teo

Zeng

2005

J. Am. Soc. Inf. Sci.

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End-user computing has become a well-established aspect of enterprise database systems today. End-user computing performance depends on the user-database interface, in which the data model and query language are major components. We examined three prominent data models-the relational model, the Extended-EntityRelationship (EER) model, and the Object-Oriented (OO) model-and their query languages in a rigorous and systematic experiment to evaluate their effects on novice end-user computing performance in the context of database design and data manipulation. In addition, relationships among the performances for different tasks (modeling, query writing, query comprehension) were postulated with the use of a cognitive model for the query process, and are tested in the experiment. Structural Equation Modeling (SEM) techniques were used to examine the multiple causal relationships simultaneously. The findings indicate that the EER and OO models overwhelmingly outperformed the relational model in terms of accuracy for both database design and data manipulation. The associations between tasks suggest that data modeling techniques would enhance query writing correctness, and query writing ability would contribute to query comprehension. This study provides a better and thorough understanding of the inter-relationships among these data modeling and task factors. Our findings have significant implications for novice end-user training and development.

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Three important determinants of user performance for database retrieval

Cited by 31 publications

References 27 publications

Training for Web search: Will it get you in shape?

Training for Web search: Will it get you in shape?

Learning and forgetting-based worker selection for tasks of varying complexity

An evaluation of novice end‐user computing performance: Data modeling, query writing, and comprehension

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