Filtering by
- All Subjects: Insurance
- All Subjects: Senior Living
- Creators: Schiller, Christoph
- Creators: Archer, Melissa
- Resource Type: Text
To gauge interest in the TTR, a stated preference survey was developed and distributed throughout the Phoenix-metropolitan area. Over 2,200 responses were gathered with about 805 being completed. Exploratory data analysis of the data included a descriptive analysis regarding individual and household demographic variables, HOV usage and satisfaction levels, HOT usage and interests, and TTR interests. Cross-tabulation analysis is further conducted to examine trends and correlations between variables, if any.
Because most survey takers were in Arizona, the majority (53%) of respondents were unfamiliar with HOT lanes and their practices. This may have had an impact on the interest in the TTR, although it was not apparent when looking at the cross-tabulation between HOT knowledge and TTR interest. The concept of the HOT lane and “paying to travel” itself may have turned people away from the TTR option. Therefore, similar surveys implementing new HOT pricing strategies should be deployed where current HOT practices are already in existence. Moreover, introducing the TTR concept to current HOT users may also receive valuable feedback in its future deployment.
Further analysis will include the weighting of data to account for sample bias, an exploration of the stated preference scenarios to determine what factors were significant in peoples’ choices, and a predictive model of those choices based on demographic information.
The insurance industry is a multibillion-dollar industry, yet it lags far behind other industries like banking and big tech in its adaptation of automation. I experienced this first-hand as an intern at State Farm. I completed a project that was a massive data entry job and made it into a process that took clicking three buttons to finish. Although just one example, it was clear that State Farm as well as the insurance industry in general are not utilizing automation and machine learning. The adaptation of automation and machine learning will have internal and external benefits for insurance companies like increased efficiencies in business processes and increased customer satisfaction. However, to realize these external and internal benefits, companies, like State Farm, must implement an adhocratic culture where risk taking is incentivized, and companies must invest resources into their underwriting processes, rather through internal investment or an acquisition, to automate the process.
Through research, interviews, and analysis, our paper provides the local community with a resource that offers a comprehensive collection of insight into the Mirabella at ASU Life Plan Community and the projected impact it will have on the City of Tempe and Arizona State University.
Through research, interviews, and analysis, our paper provides the local community with a resource that offers a comprehensive collection of insight into the Mirabella at ASU Life Plan Community and the projected impact it will have on the City of Tempe and Arizona State University.