The Role of IoT Devices in Sustainable Car Expenses in the Context of the Intelligent Mobility: A Comparative Approach
Abstract
:1. Introduction
- The objective of the Delphi method was to identify which determinants they believed had the highest impact on the money that drivers spent on their cars. Once these determinants were ranked by all the expert participants and an agreement reached following various rounds of the Delphi method, it was determined that technology has a salient role in forecasting drivers’ expenditure on their cars, outperforming other determinants such as marketing campaigns or previous knowledge of the driver.
- To confirm the outcomes from the exploratory analysis, a questionnaire was created to obtain evidence from everyday drivers. The survey was conducted to evaluate and compare the spending level of drivers who own a connected car with regards to those who have non-connected conventional cars.A sample of 556 drivers was gathered, where the drivers were divided into two groups: people with assistive technology in their cars (connected cars) and drivers of conventional cars without such technology incorporated.
2. Literature Review
2.1. Connected Car Services
2.2. Variables Affecting Cars’ Expenses
2.3. The Role of Technology in Reducing Price and Environmental Costs
3. Methodology
3.1. First Phase: Delphi Method Adapted within a COVID-19 Context
- They had to first answer whether they agreed or not with the variables included in Group A (top-5 averaged determinants averaging the responses from the first phase). We also presented to them a Group B of the least-voted determinants.
- If they answered positively, they had to rank their personal Group A determinants according to their expert criteria where a determinant placed in the first position meant the best ranking for them and the one in the fifth position was considered the least important.
- If they had answered in the negative to the previous question, they then had to suggest a new ranking, with the possibility of combining or changing a variable from one group to another (Groups A and B, see Table 3).
3.2. Second Phase: Comparative Survey Method
4. Results
4.1. Delphi Results
- 4.
- A total of 24 experts (one of them did not respond at this stage, so we decided to continue with 24 experts instead of 25 in order to not stop the process) responded with their determinants and these were examined, curated, grouped, adjusted, and finally formatted in a table (see Table 1).
- 5.
- A total of 22 experts, out of the remaining 24, responded to the questionnaire to reach an initial agreement when it comes to prioritizing which determinants were more or less important. We, therefore, had an attrition rate of three people who did not respond to the questionnaire after several gentle reminders. The results of the first averaged ranking among the 22 experts can be observed in Table 2. Again, the information was input into a personal database of the research team for further analysis.
4.2. Survey Results
5. Discussion and Limitations
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
SEC 1 | Vehicle Data |
---|---|
Q1 | What is the brand of your vehicle? |
Q2 | What year is your vehicle? |
Q3 | How long have you had your vehicle? |
Q4 | How many kilometres do you drive per year in your vehicle? |
SEC 2 | Mobility and consumption data |
Q1 | How much money do you spend per month on Petrol |
Q2 | How much money do you spend per year on insurance for your main vehicle? |
Q3 | How much money do you spend per year on maintenance to your vehicle (change of oil, filters, tyres and other incidentals)? |
Q4 | How much money do you think you spend per year on your vehicle (including petrol, insurance, taxes, repairs, maintenance, parking, tolls, fines, etc.)? |
Q5 | How much money do you think could be saved per year through a recommendation IT embedded system? |
Q6 | How much money do you think you could save per year on your vehicle expenses by using a recommendation system based on real needs at any given time? |
SEC 3 | Perception of vehicle expenses |
Q1 | What percentage of the household expenses is used for vehicle expenses? |
Q2 | Do you often use promotions or special offers to try to save money on vehicle expenses? |
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Determinant No. | Determinant Description |
---|---|
Determinant 1 | On-board technology with warning messages generated by the system could influence decision-making over car expenses (showing potential failures and maintenance reminders). |
Determinant 2 | Promotions launched by Original Equipment Manufacturers (OEM) and the automotive industry could influence decision-making for vehicle maintenance and expenditures. |
Determinant 3 | Marketing, communications, and promotions are elements that could actively influence a driver’s maintenance expenditure decision-making. |
Determinant 4 | Marketing campaigns generated by automotive brands and dealerships could influence maintenance expenditure decision-making. |
Determinant 5 | Point-of-sale distance could actively influence drivers’ maintenance expenditure decision-making. |
Determinant 6 | The vehicle’s type of use (professional or personal, weekday or weekend) could actively influence drivers’ maintenance expenditure decision-making. |
Determinant 7 | Apps and websites in the field of cars could actively influence drivers’ maintenance expenditure decision-making. |
Determinant 8 | Recommendations from family or close friends could actively influence drivers’ maintenance expenditure decision-making. |
Determinant 9 | Comments and recommendations from friends in the automotive field could actively influence drivers’ maintenance expenditure decision-making. |
Determinant 10 | New transport policies (e.g., 30 km/h zones) and their impact on the environment could actively influence on drivers’ maintenance expenditure decision-making. |
Averaged Order | Determinant No. |
---|---|
1 | Determinant 4 |
2 | Determinant 5 |
3 | Determinant 2 |
4 | Determinant 3 |
5 | Determinant 1 |
6 | Determinant 6 |
7 | Determinant 7 |
8 | Determinant 8 |
9 | Determinant 9 |
10 | Determinant 10 |
Group A | ||
---|---|---|
Averaged order | A | Description |
1 | A1 | Marketing campaigns generated by automotive brands and dealerships could influence maintenance expenditure decision-making. |
2 | A2 | Point-of-sale distance could actively influence drivers’ maintenance expenditure decision-making. |
3 | A2 | Promotions launched by OEMs and the automotive industry could influence decision-making for vehicle maintenance and expenditures. |
4 | A4 | Marketing, communications, and promotions are elements that could actively influence drivers’ maintenance expenditure decision-making. |
5 | A5 | Onboard technology with warning messages generated by the system could influence car-expense-related decision-making (showing potential failures and maintenance reminders). |
Group B | ||
Averaged order | B | Description |
6 | B1 | The vehicle’s type of use (professional or personal, weekday or weekend) could actively influence drivers’ maintenance expenditure decision-making. |
7 | B2 | Apps and websites in the field of cars could actively influence drivers’ maintenance expenditure decision-making. |
8 | B3 | Recommendations from family or close friends could actively influence drivers’ maintenance expenditure decision-making. |
9 | B4 | Comments and recommendations from friends in the automotive field could actively influence drivers’ maintenance expenditure decision-making. |
10 | B5 | New transport policies (e.g., 30 km/h zones) and their impact on the environment could actively influence drivers’ maintenance expenditure decision-making. |
Ranking | Short Description | Determinant | Determinant Full Description |
---|---|---|---|
1 | Driver Artefact System (DAS) | A5 | On-board technology with warning messages generated by the system could influence car expenses decision-making (showing potential failures and maintenance reminders). |
2 | Proximity | A2 | Point of sale distance that could actively influence drivers’ maintenance expenditure decision-making. |
3 | General Marketing | A4 | Marketing, communications, and promotions are elements that could actively influence drivers’ maintenance expenditure decision-making. |
4 | OEM Marketing | A1 | Marketing campaigns generated by automotive brands and dealerships could influence maintenance expenditure decision-making. |
5 | Promotions | A3 | Promotions launched by OEMs and the automotive industry could influence decision-making for vehicle maintenance and expenditures. |
Question | Connected Car | Non-Connected Car |
---|---|---|
Q1-Vehicle Brand | 20 most popular brands in | 20 most popular brands in |
Q2-Vehicle year (median) | 2014 | 2013 |
Q3- How long have you had the vehicle? | 5.82 | 5.58 |
Q4-No. of km/year (mean) | 13,103.82 | 12,786.5 |
Q1 (Petrol) | Q2 (Insurance) | Q3 (Maintenance) | Q4 (Car Expenditures) | Q5 (Recommendations) | Q6 (Savings) | ∑ Q | |
---|---|---|---|---|---|---|---|
Mean | 43.55 | 398.59 | 291.41 | 1516.61 | 32.97 | 55.43 | 1.18 |
Standard Error | 0.66 | 8.91 | 7.96 | 33.09 | 2.26 | 3.14 | 0.00 |
Standard Deviation | 11.69 | 157.71 | 140.78 | 585.50 | 39.82 | 55.64 | 0.07 |
Sample Variance | 136.55 | 24,872.38 | 19,817.89 | 342,816.06 | 1585.62 | 3095.41 | 0.00 |
Confidence Level (99.0%) | 1.71 | 23.10 | 20.62 | 85.77 | 5.86 | 8.15 | 0.01 |
Q1 (Petrol) | Q2 (Insurance) | Q3 (Maintenance) | Q4 (Car Expenditures) | Q5 (Recommendations) | Q6 (Savings) | ∑ Q | |
---|---|---|---|---|---|---|---|
Mean | 40.18 | 396.63 | 306.06 | 1508.33 | 21.72 | 60.67 | 2.39 |
Standard Error | 0.97 | 12.85 | 11.56 | 42.96 | 2.32 | 3.83 | 1.19 |
Standard Deviation | 16.31 | 215.86 | 194.15 | 721.49 | 38.98 | 64.28 | 18.93 |
Sample Variance | 266.09 | 46,595.37 | 37,693.70 | 520,544.19 | 1519.82 | 4132.46 | 358.46 |
Confidence Level (99.0%) | 2.52 | 33.34 | 29.98 | 111.42 | 6.02 | 9.93 | 3.10 |
Connected Cars per Year (n = 302) | Non-Connected Cars per Year (n =252) | |
---|---|---|
Total Expenses | EUR 2758.0 | EUR 2979.8 |
Average expenditure per km travelled | EUR 0.210209 | EUR 0.22752 |
Average expenditure per km travelled weighted to the confidence level of the given responses | EUR 0.183214 | EUR 0.19555 |
Perception of Vehicle Expenses | Connected Cars | Non-Connected Cars |
---|---|---|
Q1 (% of expenses) | 2.12 | 2.43 |
Q2 (Promotions) | 2.81 | 2.57 |
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Goikoetxea-Gonzalez, J.; Casado-Mansilla, D.; López-de-Ipiña, D. The Role of IoT Devices in Sustainable Car Expenses in the Context of the Intelligent Mobility: A Comparative Approach. Appl. Sci. 2022, 12, 1080. https://doi.org/10.3390/app12031080
Goikoetxea-Gonzalez J, Casado-Mansilla D, López-de-Ipiña D. The Role of IoT Devices in Sustainable Car Expenses in the Context of the Intelligent Mobility: A Comparative Approach. Applied Sciences. 2022; 12(3):1080. https://doi.org/10.3390/app12031080
Chicago/Turabian StyleGoikoetxea-Gonzalez, Javier, Diego Casado-Mansilla, and Diego López-de-Ipiña. 2022. "The Role of IoT Devices in Sustainable Car Expenses in the Context of the Intelligent Mobility: A Comparative Approach" Applied Sciences 12, no. 3: 1080. https://doi.org/10.3390/app12031080
APA StyleGoikoetxea-Gonzalez, J., Casado-Mansilla, D., & López-de-Ipiña, D. (2022). The Role of IoT Devices in Sustainable Car Expenses in the Context of the Intelligent Mobility: A Comparative Approach. Applied Sciences, 12(3), 1080. https://doi.org/10.3390/app12031080