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Vijay Gurbaxani

Researcher at University of California, Irvine

Publications -  58
Citations -  11860

Vijay Gurbaxani is an academic researcher from University of California, Irvine. The author has contributed to research in topics: Information system & Information technology. The author has an hindex of 28, co-authored 57 publications receiving 11311 citations. Previous affiliations of Vijay Gurbaxani include University of California & Saint Petersburg State University.

Papers
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Proceedings ArticleDOI

Disaggregating the return on investment to IT capital

TL;DR: Gurbaxani et al. as mentioned in this paper presented a system architecture for information technology at the University of California, Irvine, which has been supported by grants from the CISE/IIS/CSS Division of the U.S. National Science Foundation and the NSF Industry/University Cooperative Research Center (CISE/EEC) to the Center for Research on Information Technology and Organizations (CRITO).
Proceedings Article

Information systems in manufacturing coordination: economic and social perspectives

TL;DR: Kling, Rob; Kraemer, Kenneth L.; Allen, Jonathan; Bakos, Yannis; Gurbaxani, Viijay; King, John L. as mentioned in this paper
Journal Article

Information Systems in Manufacturing Coordination: Economic and Social Perspectives

TL;DR: Information systems inManufacturing Coordination and Social Perspectives: ECONOMIC and SOCIAL PERSPECTIVES working paper #AIM-002.
Proceedings Article

A Multi-dimensional Assessment of the Contribution of Information Technology to Firm Performance.

TL;DR: In this article, the impact of IT on intermediate business processes can be used to measure IT business value, using a process-oriented framework proposed by Mooney, Gurbaxani and Kraemer (1995).
Journal ArticleDOI

Research Report-Modeling vs. Forecasting: The Case of Information Systems Spending

TL;DR: It is argued that the objectives and premises of extrapolation techniques are so fundamentally different from those of positive modeling that the evaluation of positive models using the criteria of "black box" forecasting approaches is inadequate.