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Jayesh Prasad

dblp:25/6261 · DBLP profile ↗
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6ranked-venue papers
0as first author
0since 2021 · last 2000
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Software engineering, systems software and programming languages · 4Artificial intelligence and machine learning · 1Human-computer interaction and ubiquitous computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
1 paper
Empirical software engineering · 100%
Human-computer interaction and pervasive computing
1 paper
Usability and user experience research · 77% User interface design and tools · 23%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Empirical software engineering
software effort estimation
0.011998
A Causal Model for Software Cost Estimating Error · IEEE Trans. Software Eng. 1998
Usability and user experience research
technology acceptance
0.011996
Training experiences and usage intentions: a field study of a graphical user interface · Int. J. Hum. Comput. Stud. 1996
Empirical software engineering
causal inference
0.011998
A Causal Model for Software Cost Estimating Error · IEEE Trans. Software Eng. 1998
User interface design and tools
graphical user interface
0.011996
Training experiences and usage intentions: a field study of a graphical user interface · Int. J. Hum. Comput. Stud. 1996

Methods — techniques the papers use, named apart from their topics

structural equation modeling · 0.0causal analysis · 0.0field study · 0.0
YearPublicationVenuePosition
2000 Software management and cost estimating error
Albert L. Lederer, Jayesh Prasad
J. Syst. Softw.2
1998 The antecedents and consequents of user perceptions in information technology adoption
Jayesh Prasad
Decis. Support Syst.2
1998 A Causal Model for Software Cost Estimating Error
abstract
Software cost estimation is an important concern for software managers and other software professionals. The hypothesized model in this research suggests that an organization's use of an estimate influences its estimating practices which influence both the basis of the estimating process and the accuracy of the estimate. The model also suggests that the estimating basis directly influences the accuracy of the estimate. A study of business information systems managers and professionals at 112 different organizations using causal analysis with the Equations Modeling System (EQS) refined the model. The refined model shows that no managerial practice in this study discourages the use of intuition, guessing and personal memory in cost estimating. Although user commitment and accountability appear to foster algorithm-based estimating, such an algorithmic basis does not portend greater accuracy. Only one managerial practice-the use of the estimate in performance evaluations of software managers and professionals-presages greater accuracy. By implication, the research suggests somewhat ironically that the most effective approach to improve estimating accuracy may be to make estimators, developers and managers more accountable for the estimate even though it may be impossible to direct them explicitly on how to produce a more accurate one.
Albert L. Lederer, Jayesh Prasad
IEEE Trans. Software Eng.2
1997 Targeting COBOL Programmers for C Training: The Role of Job Insecurity and Organizational Tenure
Jayesh Prasad
J. Syst. Softw.2
1996 Training experiences and usage intentions: a field study of a graphical user interface
Jayesh Prasad, Michael C. Zanino
Int. J. Hum. Comput. Stud.2
1995 Causes of inaccurate software development cost estimates
Albert L. Lederer, Jayesh Prasad
J. Syst. Softw.2