One of the most important concepts that were covered in the State Farm case study was the fact that high-risk intersections do not only pose a risk for the drivers and their physical and mental health, but they also increase the cost of accidents for insurance companies. This ultimately means that State Farm, without addressing the most problematic intersections, would have to spend its own resources to reimburse drivers recurrently. This model of organizational behavior would not benefit an insurance company in any way because it should be as proactive as possible when reaching out to respective drivers (Geistfeld, 2017). As for the essential construct that was touched upon within the case study, it should be noted that State Farm could seriously contribute to the process of making the roads significantly safer through the interface of altered intersections, with traffic lights and additional stop signs added to the intersections where there were none.
Also, after reading the case study, one might propose the following hypothesis:
H1 – a careful examination of the most dangerous intersections within a specific radius might help insurance companies analyze the accident occurrence rate and align it against previous insurance claims.
This hypothesis might be utilized by State Farm to distinguish three main types of risks and assign those to the most dangerous intersections. The latter could be identified with the help of gathering information from police reports. Therefore, the hypothesis should hint at the possibility of taking a preventive approach to automotive accidents since both State Farm and insured drivers are mutually interested in protecting their assets from recurrent damage. There would be nothing strange or inappropriate about the company asking for additional reports from local law enforcement agencies, so the hypothesis proposed above may be deemed valid.
Speaking of the methodology applied by State Farm’s research department, it should be noted that the company did an outstanding job trying to analyze how intersections could be distinguished based on the number and severity of accidents alone. Even though many other companies tried to do it in the past, State Farm turned out to be more successful because State Farm’s research unit pulled information from a variety of databases instead of consulting just one source (Sheehan et al., 2017). This was the essential modification to the company’s research strategy that allowed State Farm to scrutinize traffic and police reports or even stabilize the potential threats related to road geometry. Ultimately, State Farm was able to identify the most dangerous intersections but, most importantly, gain insight into how the increasingly high rate of accidents could be disrupted.
The role of transportation engineers could not be discounted as well. The best way to describe the situation from the State Farm’s managers’ point of view would be to adopt the following Biblical quote: “to answer before listening – that is folly and shame” (English Standard Version Bible, 2001, Proverbs 18:13). This particular adage might play an important role in the activities initiated by State Farm because the company might need to listen to the concerns of transport engineers prior to coming out with serious updates to its research methodology or insurance process. There are no identical traffic accidents, but additional information would not hurt State Farm in the case where the company would like to prevent further accidents while also gathering all sorts of data to help transportation engineers do their job properly (Wach, 2016). The best way to collect all the required information would be to partner with a variety of stakeholders such as drivers, law enforcement officers, or even pedestrians. Therefore, transportation engineers would get the opportunity to speak out while also having enough room to contribute to the developing traffic safety program.
Another important question is whether it would be useful to use traffic volume counts during the 2003 study for State Farm. Judging from the company’s approach to accidents, it would be a reasonable addition to the existing data sets, as every other bit of information would aid the company in painting a bigger picture for every stakeholder, from State Farm executives and police officers to civilians and drivers (Fountas et al., 2018). The company should not avoid different sources of statistics when trying to prevent future traffic accidents. Nevertheless, additional numerical evidence could interfere with the existing findings and make some of State Farm’s claims invalid or create supplementary limitations for the company’s research.
The biggest concern that one might have in regard to the information listed in the case is the lack of material obtained from drivers and other individuals related to the traffic. State Farm should be more diligent in terms of deploying feedback forms for its customers and regular bystanders because they might represent the biggest under-researched depository of accident-related information (Hsu et al., 2016). This idea also resonates with State Farm mostly paying attention to statistics and figures while, in reality, it would be essential to look at how other traffic participants see different intersections and the notion of road safety in general. Ultimately, the company is going to develop an all-around approach to the problem of traffic accidents and see how most of those could be prevented instead of having to deal with grave consequences such as human demise.
English Standard Version Bible. (2001). ESV Online. Web.
Fountas, G., Anastasopoulos, P. C., & Mannering, F. L. (2018). Analysis of vehicle accident-injury severities: A comparison of segment-versus accident-based latent class ordered probit models with class-probability functions. Analytic Methods in Accident Research, 18, 15-32.
Geistfeld, M. A. (2017). A roadmap for autonomous vehicles: State tort liability, automobile insurance, and federal safety regulation. California Law Review, 105, 1611.
Hsu, Y. C., Chou, P. L., & Shiu, Y. M. (2016). An examination of the relationship between vehicle insurance purchase and the frequency of accidents. Asia Pacific Management Review, 21(4), 231-238.
Sheehan, B., Murphy, F., Ryan, C., Mullins, M., & Liu, H. Y. (2017). Semi-autonomous vehicle motor insurance: A Bayesian Network risk transfer approach. Transportation Research Part C: Emerging Technologies, 82, 124-137.
Wach, W. (2016). Calculation reliability in vehicle accident reconstruction. Forensic Science International, 263, 27-38.