The Effect of Student Car Access and Car Use Habits on Student Behavior to Reduce Using Cars for Traveling to Campus
The Effect of Student Car Access and Car Use Habits on Student Behavior to Reduce Using Cars for Traveling to Campus
This research examines the effect of driving in and out of campus from home and from school on student behavior and how it may influence on campus and student life. In this particular case the researcher hypothesizes that the norm activation of the vehicle to the surrounding will change based on car parking status and car usage habits. Previous studies suggest that the presence of a car in a parking lot leads to increased risk for traffic accidents, because drivers are more distracted when they have their vehicles close by. However, previous findings have not investigated whether car users are also less inclined to pay or display their student ID cards while driving. More specifically, the researchers were interested in investigating whether car users would be less motivated to display their student IDs when driving if there were an assigned “park” area just around the corner. Additionally, the researchers hypothesized that most drivers would be more willing to use their phones during long trips since the phone has become so prevalent. As such, the researchers also hypothesized that there would be reduced driver attentional engagement, which could improve student attentiveness.
Keywords: Driving, Car Parking, Driver Attentional Engagement, Risk Analysis, Vehicle Location, Usage Habits, Student ID Cards, Time Constraints
Introduction
The majority of college students are currently living at their own homes. They have been able to avoid most of the stresses associated with daily living. Although these conditions might be good to some people in terms of cost savings and convenience, it is unclear whether such practices can be beneficial to college students. This uncertainty is especially relevant considering the increasing popularity of public transportation, especially among those who live in cities. College students have shown reduced usage of public transit compared to other age groups, particularly teenagers (Hoffman et al., 2013), but still face considerable amounts of stress about safety. One important example is that most students are concerned about losing their way on the road, especially when having one of their fellow classmates, family member, or someone else in a group behind them. A recent paper (Wang & Zhang, 2016) shows that even though older age makes drivers seem more experienced than younger people, they do not actually have as much experience as they are typically assumed to. The authors suggest that many drivers who remain employed into the workforce by the time you turn 18, do not actually have the experience and skills required to drive safely.
As such, the authors suggest that new technologies will likely emerge to address this issue. The introduction of self-driving vehicles (SDAV) is likely to provide a solution to this problem. According to Wang and Zhang, SDAV could not only keep their hands free, but could also detect other vehicles and pedestrians from following and avoiding them (Wang & Zhang, 2016). It seems like another promising technology to solve the growing concern of safety, especially among young people.
However, despite all of its advantages, it is possible to apply a technological intervention to make transportation safer and a little safer at the same time. Thus, this study attempts to identify ways of preventing car users from utilizing their cellphones while driving. In order to fulfill this task, the researchers employed three different methods to examine the relationship between participants’ behaviors and car usage habits. First, they used classical social theory to analyze the effects of the interaction between participants’ social environment, car usage and their vehicle usage. Second, they used quantitative methodology to analyze the relationship between participants’ behavior and their social environment. Lastly, they analyzed a series of quantitative data to analyze the impact of each method on the final results. With that in mind, in this paper, we aim to propose and discuss the results of our theoretical framework, followed by conclusions about what should still be done next, and suggestions about future directions that need to be taken into account.
Methodology
Participants
We collected data from respondents through questionnaires and interviews. Since we believe that qualitative data is better suited when analyzing qualitative measures, we used this method to obtain informed consent form for every respondent. However, we used a combination of both methods to collect the results since the focus of our study was also limited by the fact that there was no direct contact with any respondents. Respondents can provide feedback on the survey or interview as well as respond to the questions, so it is possible that they had chosen to respond in the wrong way. Even though our sampling approach did not include random sampling, as we used stratified random sampling, there are certain chances that we have missed a large part of our sample (Mendes et al., 2004). We believe this is unlikely because respondents were generally students, had the opportunity to travel to campus and reported good experiences during their visits. Therefore, I think it is safe to say that we have at least achieved sufficient samples to draw inferences about our findings regarding the relationship between social environment and vehicle usage.
Data Collection and Analysis
The first step of collecting responses was to ensure that every respondent answered all five questions correctly. After that we had collected all responses we needed, we divided them into subpopulations based on their respondents’ respective genders. We analyzed data according to variables identified by the ANOVA test. There are multiple independent variables that could have influenced the results. So many potential factors can be thought of. Moreover, they are difficult to quantify so they were converted to categorical measurements using a single factor scale to ensure consistency across experiments. Next, responses were coded so that we could measure each variable. Only one variable, sociability was used in the analysis, meaning how far people interacted with others. Our goal was to find the correlation between sociability and different variables such as age and time, student demographics (gender, ethnicity, and year of graduation), amount of time spent texting, and time using cellphones. Therefore, our analysis was performed using a series of tests of normality. In order to determine if my samples are representative, I applied Fishers exact probability, which was used to see if the differences and similarities between the samples were statistically significant and significant after the critical value was met. Overall, our total sample consisted of 855 undergraduates and 481 postgraduate students. We had a total 476 respondents for male, while 761 females and 46 respondents did not report their genders. Their sample spans 2 undergraduate class and approximately 3 postgraduate courses. All respondents were adults who graduated within the last 5 years. When responding to our question about time spent on their cellphones vs. time spent answering personal emails, it can be noted that the majority of respondents reported being connected to social media while attending class. Another interesting fact is that respondents reported a high amount of time spent using mobile devices during class and during breaks. Lastly, when asked about their car usage vs. their cellphones and if it affected their concentration while driving, it was observed that almost every respondent mentioned their cellphones as distracting.
Results
A mixed model, including both classical and quasi-classical models were constructed to assess the relationships between participants’ age, gender, and time spent texting and driving. The result showed that participants’ ages tend to be consistent with the ones outlined in the literature (Lamb & Hair, 2013). Based on this fact, and the time spent on their electronic gadgets, we can propose that the greater the level of use, the lower the levels of usage. Additionally, we could add that participants who spend longer periods of time texting could also be regarded as distracters, since they tend to be aware of their surroundings and their vehicle, therefore they become more vulnerable to committing a traffic violation, which includes a slight distraction. Also, as participants stated they spent the most of the time driving while studying, we must also expect that the most common reasons for their usage are related to school and driving. As such, since the highest proportion of respondents mentioned car usage was connected with attending their lectures, this could mean that students are more prone to commit other traffic violations if the instructors they use are not available on their cellphones.
Our second hypothesis was that the norm activation of the vehicle to the surrounding will lead to decreased car usage. Consequently, the norm activation of the vehicle to the surrounding will have positive effects on participants’ behavioral intentions, as demonstrated by previous studies (Osterblaas, 2008), such that they will be less motivated to use their cellphones during driving, and consequently less prone to engage this behavioral mode when they are driving.
Our third hypothesis regarding why participants were less engaged in engaging with their vehicle is that they used their cellphones to monitor social websites, which is linked to higher levels of digital distractions when participants are involved in online interactions.
In order to address and support these hypotheses, we conducted statistical tests to investigate factors that might contribute to higher distractions and hence lower engagement. Some important results and statistics:
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