Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country
Abstract
:1. Introduction
2. Literature Review
3. Methodology
3.1. Stated Preference Survey
3.1.1. SP Survey Design Considerations
- Level balance: The level balance means maintaining the attribute levels of each attribute, so they come about with equal frequency. It reflects the desired property, although it can influence the statistical efficiency of the design [45].
- Design type (orthogonality): The design type selection for scenario generation is more important either to choose a full factorial design or partial factorial design. Full factorial designs estimate all possible combinations and make the design more optimal. In contrast, fractional factorial design chooses the random selection of a full factorial design. The design should also preserve the orthogonal properties. The main aim of orthogonality is for independent variables to remain uncorrelated and to reduce multicollinearity [46].
- Minimum overlap: The attribute level should not repeat itself in the choice sets. The minimum overlap means that the occurrence of the attributes levels itself in each scenario remains at a minimum. Overlap provides a means for simplifying choice questions by reducing the number of attribute differences that respondents must evaluate [47].
- Utility balance: The design of the fractional factorial experiment of the SP survey should present an accurate estimation of the number of choices set. There should be a clear difference and a vivid set of questions for each set of respondents [48].
3.1.2. Experimental Design of SP Survey
3.2. Data Collection
3.3. Survey Sample
3.4. Methods Used
- Decision-makers: Decision-makers can be individual persons or households. Governments or firms or any decision-making units that possess preferences or tastes over alternatives.
- Alternatives: Alternatives are the products or services or course of action over which decisions are being made. The set of alternatives should be feasible for decision-makers. For example, private car, public transport, taxi, etc.
- Attributes: Attributes represent the characteristics, values, and properties of alternatives that make the alternatives useful, for example, travel cost, travel time, privacy, and comfortability.
- Attributes Levels: The value that is assigned to attributes is called attributes levels. The levels are fixed as realistic market values which capture the individual perception. For example, the fare level takes the average of fares concerning time. This depends on the analyst’s decision.
- Decision rule: The decision rule refers to the principles and criteria used to assist the traveler in making a decision. A discrete choice model uses the random utility maximization theory as the decision rule.
3.4.1. Multinomial Logit Model
3.4.2. Nested Logit Model
3.4.3. Random Parameter Logit Model
4. Results
4.1. Descriptive Statistical Analysis
4.2. Models’ Estimation Results
4.3. Socioeconomic Variables Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
References
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Alternatives | Attributes | |||||
---|---|---|---|---|---|---|
Levels | Travel Time (Min) | Travel Cost (PKR) | Waiting Time (Min) | Parking Cost (PKR/hour) | Privacy | |
Car-sharing | 1 | 20 | 200 | 3 | 0 | Low |
2 | 25 | 250 | 6 | 0 | Medium | |
3 | 30 | 300 | 9 | 0 | High | |
Taxi | 1 | 25 | 350 | 4 | 0 | Low |
2 | 30 | 400 | 8 | 0 | Medium | |
3 | 35 | 450 | 12 | 0 | High | |
Private Car | 1 | 15 | 180 | 0 | 0 | Low |
2 | 20 | 230 | 0 | 20 | Medium | |
3 | 25 | 280 | 0 | 50 | High |
Explanatory Variables | Category | Percentage of Sample |
---|---|---|
(N = 242) | ||
Gender | Male | 72% |
Female | 28% | |
Age groups (Years) | 18–29 | 40% |
30–39 | 35% | |
40–49 | 15% | |
50–60 | 7% | |
Over 60 | 3% | |
Education level | Under high school | 10% |
High school | 28% | |
Bachelor’s degree | 46% | |
Master’s degree and above | 16% | |
Occupation | Student | 35% |
Unemployed | 4% | |
Employed | 53% | |
Entrepreneur | 8% | |
Monthly income (PKR) | <30,000 | 38% |
31,000–60,000 | 22% | |
61,000–90,000 | 27% | |
91,000–120,000 | 8% | |
Over 120,000 | 5% | |
Marital status | Married | 39% |
Single | 61% | |
Frequency of travel in one week | 0–2 days | 43% |
3–5 days | 57% | |
Car ownership | Have | 59% |
Do not have | 41% | |
Driving license | Have | 58% |
Do not have | 42% |
Variables | Multinomial Logit Model | Nested Logit Model | Random Parameter Logit Model | |||||||
---|---|---|---|---|---|---|---|---|---|---|
Car Sharing | Private Car | Taxi | Car Sharing | Private Car | Taxi | Car Sharing | Private Car | Taxi | ||
Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | Coef. (t-Value) | ||
β0 | Constant | −0.538 (−2.31 *) | −1.90 (−6.00 ***) | _ | −0.454 (−3.66 ***) | −1.80 (−7.32 ***) | _ | −0.575 (−2.39 *) | −2.07 (−5.73 ***) | _ |
β age | Age 1 = > 39-year-old, 0 = otherwise | 0.341 (2.03 *) | 0.287 (1.62) | _ | 0.144 (1.94′) | 0.141 (1.05) | _ | 0.336 (1.97 *) | 0.284 (1.54) | _ |
β edu | Education 1 = Bachelor’s and Master’s degree and above, 0 = otherwise | −0.304 (−2.31 *) | −2.271 (−1.89′) | _ | −0.134 (−2.20 *) | −0.120 (−1.04) | _ | −0.312 (−2.32 *) | −0.277 (−1.84′) | _ |
β inc | Monthly income 1 = > 60,000 PKR, 0 = otherwise | 0.355 (2.57 *) | 0.355 (2.42 *) | _ | 0.138 (2.09 *) | 0.219 (1.89 ′) | _ | 0.379 (2.67 **) | 0.380 (2.45 *) | _ |
β drlic | Driver’s license 1 = Yes, 0 = No | 0.434 (2.15 *) | 0.643 (3.05 **) | _ | 0.150 (1.60) | 0.424 (2.52 *) | _ | 0.456 (2.22 *) | 0.690 (3.09 **) | _ |
β trlwk | Frequency of travel in a week 1 = > 2 day, 0 = otherwise | 0.573 (4.74 ***) | 0.585 (4.35 ***) | _ | 0.230 (3.01 **) | 0.353 (3.11 **) | _ | 0.586 (4.74 ***) | 0.613 (4.29 ***) | _ |
β priv | Privacy | 0.253 (6.97 ***) | 0.156 (4.17 ***) | 0.253 (6.72 ***) | ||||||
β prkc | Parking cost | 3.01 × 10−3 (1.25) | 6.37 × 10−4 (0.26) | 3.12 × 10−3 (1.24) | ||||||
β wt | Waiting time | −8.17 × 10−2 (−5.97 ***) | −5.83 × 10−2 (−4.81 ***) | −8.58 × 10−2 (−5.80 ***) | ||||||
β tc | Travel cost | −2.53 × 10−3 (−2.62 **) | −2.15 × 10−3 (−3.73 ***) | −2.89 × 10−3 (−2.77 **) | ||||||
β tt | Travel time | −5.01 × 10−2 (−6.91 ***) | −2.66 × 10−2 (−3.41 ***) | −5.62 × 10−2 (−6.00 ***) | ||||||
Nesting coefficient | _ | 2.55 (3.91 ***) | _ | |||||||
σ | Travel time Std. dev. | _ | _ | 5.63 × 10−2 (2.30 *) | ||||||
Observations: 2178 Initial Log-likelihood = −2392.778 Final Log-likelihood = −2170.716 = 0.093 | Observations: 2178 Initial Log-likelihood = −2392.778 Final Log-likelihood = −2161.229 = 0.097 | Observations: 2178 Initial Log-likelihood = −2392.778 Final Log-likelihood = −2169.740 = 0.093 Halton draws = 500 |
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Safdar, M.; Jamal, A.; Al-Ahmadi, H.M.; Rahman, M.T.; Almoshaogeh, M. Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country. Sustainability 2022, 14, 2778. https://doi.org/10.3390/su14052778
Safdar M, Jamal A, Al-Ahmadi HM, Rahman MT, Almoshaogeh M. Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country. Sustainability. 2022; 14(5):2778. https://doi.org/10.3390/su14052778
Chicago/Turabian StyleSafdar, Muhammad, Arshad Jamal, Hassan M. Al-Ahmadi, Muhammad Tauhidur Rahman, and Meshal Almoshaogeh. 2022. "Analysis of the Influential Factors towards Adoption of Car-Sharing: A Case Study of a Megacity in a Developing Country" Sustainability 14, no. 5: 2778. https://doi.org/10.3390/su14052778