The Impact of Data Models and Task Complexity on End User Performance: An Experimental Investigation

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Prashant Palvia, Joe Rosenthal Excellence Professor and Director of the McDowell Research Center for Global IT Management (Creator)
Institution
The University of North Carolina at Greensboro (UNCG )
Web Site: http://library.uncg.edu/

Abstract: The purpose of this study was to investigate similarities and differences in the quality of data representations produced by end-users using the relational model (RM), the extended entity-relationship model (EERM), and the object-oriented model (OOM). By performing laboratory experiments using MIS major students, quality was evaluated on five constructs of a data model (i.e. entity/object, descriptor, identifier, relationship and generalization hierarchy) and six facets of a relationship (i.e. unary one-to-one, unary one-to-many, binary one-to-one, binary one-to-many, binary many-to-many and ternary many-to-many-to-many).The research focused on two major issues: data model design and data model conversion. The first issue investigated the differences in user performance between the RM, the EERM and the OOM. The second investigated the differences in user performance between the RM and the relational conversions of the EERM and the OOM models. For the first issue, EERM and OOM scored much higher than the RM in correctness scores of binary one-to-many and binary many-to-many relationships, but only the EERM led to significance. The RM and OOM scored much higher than EERM for unary one-to-one relationships, however, only the RM resulted in significance. The OOM required significantly less time for task completion than EERM. For the second issue, RM and the relational conversion of OOM scored significantly higher than the relational conversion of EERM for unary one-to-one relationships.

Additional Information

Publication
International Journal of Human-Computer Studies
Language: English
Date: 2000
Keywords
data models, end user performance, data representation, conceptual modeling

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