Browsing by Author "Marcus, Alfred"

Now showing 1 - 3 of 3
  • Thumbnail Image
    Item
    ISO 9000's Effects on Accident Reduction in the U.S. Motor Carrier Industry
    (2003-06-01) Naveh, Eitan; Marcus, Alfred; Allen, Gove
    This report aims at establishing a correlation between the voluntary ISO 9000 certification by motor carriers and traffic safety. The report shows that improvement of operating and quality performance improved safety performance. As part of the analysis, several ISO 9000 certified and non-certified motor carrier companies were compared based on their social and financial performance prior to, during and after the certification process. The authors have shown that there is a positive increase in performance during and after the certification process. The number of motor carrier companies certified by ISO 9000 is limited and unique. The uniqueness arises from the fact that the certified organizations haul specialized products such as automobiles and hazardous materials. That shows that most organizations are certified due to mandatory reasons. In conclusion, the observations and results have thrown light in a new direction that indicates that there exists a significant relationship between the quality assurance and safety performance of a motor carrier organization. The authors feel that further research is necessary to investigate of the circumstances under which ISO 9000 and other programs for upgrading motor carrier performance help lead to increase in safety.
  • Thumbnail Image
    Item
    Path Creation and Learning in the Clean Tech Industry
    (2010) Marcus, Alfred
    Clean tech – the production of electricity and fuels with a smaller environmental impact –saw a mini-investment boom occurred in the first decade of the 21st century. This study investigates the degree to which the strategies of clean tech investors varied over time in response to learning from investment successes and failures and from changes in public policy. The literature on path dependence predicts, all else equal, that initial patterns persist into the future. The past character of the investments will continue into the future without much alteration. Our model suggests that this pattern can be broken based on the feedback that investors receive from successful or unsuccessful rounds of venture capital funding and from the changes in the global economy, energy prices, and the clean tech polices of global governments. Thus, there is another perspective that should be applied to the strategic choices that investors in this domain make, that is learning theory. Its predictions would be that adjustments in strategic choices will take place based on factors included in our model.
  • Thumbnail Image
    Item
    A Re-assessment of Road Accident Data-Analysis Policy: Applying Theory from Involuntary, High-Consequence, Low-Probability Events like Nuclear Power Plant Meltdowns to Voluntary, Low-Consequence, High-Probability Events like Traffic Accidents
    (2002-02-01) Naveh, Eitan; Marcus, Alfred
    More people are injured and die annually from motor vehicle accidents than from less commonly occurring events like nuclear power plant meltdowns. Unlike motor vehicle accidents, however, incidents at nuclear power plants and in commercial aviation are thoroughly scrutinized and analyzed, and the information fed back to operators, to determine how such disasters can be prevented. Roughly parallel systems should be in place in the traffic safety system, where both the professional driver and the average driver need to be more aware of road hazards and the decisions they should make to avoid them. This report examines the literature on involuntary, high-consequence, low-probability (IHL) events like nuclear power plant meltdowns to determine what can be applied to the problem of voluntary, low-consequence, high-probability (VLH) events like motor vehicle accidents. It examines five closely related literatures on IHL events: "normal" accident theory, system reliability theory, high reliable organizations theory, complexity and tight coupling theory, and a theory of feedback and learning (band-of-accident theory). Based on these theories, the researchers developed and tested a series of propositions to explain traffic injuries and fatalities. They carried out logistic regression analyses, examining driving conditions and decisions drivers make as factors that can lead to fatalities and injuries, then characterized and described the models, found in state crash data publications, that traffic safety officials use for understanding fatalities and injuries. These models were compared with the instructional material that is used in state driving educational manuals in order to investigate how to improve the collection and use of road traffic safety data based on analysis of the existing data and its use. Through the investigation, the researchers found that the most significant condition leading to a fatality or an injury was driving on a rural road, and the most significant decision was choosing not to use a seat belt. How factors combine to cause fatalities and injuries was also examined. For example, a combination of risky driver behavior at stop and yield signs was significantly related to both fatalities and injuries. Similarly, a combination of illegal speed and alcohol use was significantly related to both fatalities and injuries. Overall, the fatality model explained about 2 percent of the variance and the injury model explained about 12 percent of the variance. In the investigation of state driving instruction manuals, the researchers discovered that about one-third of the pages in a typical manual were devoted to factors that traffic safety officials consider to be the main reasons for fatalities and injuries. Although the current data collected in Minnesota, when analyzed, provided a number of powerful predictors of fatalities and injuries relating to the conditions a driver faces and the actions that drivers take, overall the data's ability to explain crash severity could be better. Improved theory can inform data collection and result in more powerful predictive models that could be used in programs to educate drivers.