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Steven D. Sheetz

Researcher at Virginia Tech

Publications -  63
Citations -  2062

Steven D. Sheetz is an academic researcher from Virginia Tech. The author has contributed to research in topics: Social media & Information system. The author has an hindex of 18, co-authored 63 publications receiving 1888 citations. Previous affiliations of Steven D. Sheetz include Pamplin College of Business & University of Colorado Boulder.

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Social media use by government: From the routine to the critical

TL;DR: Findings from a exploratory study conducted with government officials in Arlington, VA between June and December 2010 are presented, with the broad goal of understanding social media use by government officials as well as community organizations, businesses, and the public at large.
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The adoption of software measures: A technology acceptance model (TAM) perspective

TL;DR: The model is based on the technology acceptance model (TAM) and operationalizes the perceived usefulness construct according to the ''desirable properties of software measures'' and provides guidance for software engineers in selecting among different software measures and for software metrics coordinators who are planning measurement programs.
Proceedings ArticleDOI

Social media use by government: from the routine to the critical

TL;DR: Findings from a pilot study conducted between June and December 2010 with government officials in Arlington, Virginia with a view to understanding the use of social media by government officials as well as community organizations, businesses and the public are presented.
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A software complexity model of object-oriented systems

TL;DR: The model defines the software complexity of OO systems at the variable, method, object, and system levels, and identifies measures that account for the cohesion and coupling aspects of the system.
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Group cognitive mapping: a methodology and system for capturing and evaluating managerial and organizational cognition

TL;DR: A cognitive mapping based methodology and system is described and demonstrated that eliminates the merging problem, supports data collection, and provides data analyses to uncover both individual and collective cognitive maps.