Browsing by Author "DeHart-Davis, Leisha"

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    Formalization and consistency heighten organizational rule following: Experimental and survey evidence
    (Public Administration, 2018) Borry, Erin L.; DeHart-Davis, Leisha; Kaufmann, Wesley; Merritt, Cullen C.; Mohr, Zachary; Tummers, Lars
    This study examines the attributes of organizational rules that influence rule following. Rule following fosters organizational effectiveness by aligning individual behaviors with organizational preference. While a range of theoretical explanations has been offered for rule following, the characteristics of rule design and implementation have received less empirical attention. Borrowing from the green tape theory of effective rules, this study examines the influence of two particular characteristics—rule formalization and rule consistency—on rule following. Three studies, which include two vignette experiments and a survey of two local government organizations, provide the data for the research. The results suggest that rule formalization and rule consistency independently increase rule following, with mixed evidence of interaction effects. The broad implication is that public managers must attend to both rule design and implementation to foster organizational rule following.
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    A Quasi-Experimental Evaluation of High Emitter Non-Compliance and its Impact on Vehicular Tailpipe Emissions in Atlanta, 1997-2001
    (2006-01) Zia, Asim; Norton, Bryan G.; Noonan, Douglas S.; Rodgers, Michael O.; DeHart-Davis, Leisha
    A quasi-experimental evaluation is employed to assess the compliance behavior of high emitters in response to Atlanta’s Inspection and Maintenance program between 1997 and 2001 and to predict the impact of compliance behavior on vehicular tailpipe emissions of ozone precursors, such as carbon monoxide, hydrocarbons and nitrogen oxide. Remote sensing data of a sample of approximately 0.8 million observations of on-road vehicles are matched with IM program data and vehicle registration data to identify the compliant and non-compliant high emitters. A mixed-pool time-series regression analysis is carried out to predict changes in the vehicular tailpipe emissions due to the compliance and non-compliance of the high emitters in the Atlanta airshed.