Table of Contents
Abstract
The Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model (Anwar et al., 2023) was developed for a study that integrated the push-pull-mooring (PPM) model and institutional theory. This study aimed to examine urban commuters’ intentions to switch to urban public transport in the post-COVID-19 era and the perceived trade-offs between green actions and personal health protection. The proposed items for the instrument were adapted from previous research (e.g., Sajjad, Chu et al., 2020; Zhang et al., 2014; Shiu et al., 2011). To ensure face and content validity, the authors consulted with three experts in marketing, public health, and environmental psychology. The resulting measure was administered to respondents from metropolitan areas in the United States of America and evaluated using structural equation modeling. The study reported findings concerning factor structure, reliability, and validity.
Keywords
Affordability; Choice Uncertainty; Green Self-Identity; Hygiene Concern; Normative Environment; Post-COVID-19 Era; Regulative Environment; Switching Intentions Towards Public Transport; Push-Pull-Mooring; Institutional Theory
Authors
Anwar, Muhammad Azfar; Dhir, Amandeep; Jabeen, Fauzia; Zhang, Qingyu; Siddiquei, Ahmad Nabeel
Purpose
The purpose of this measurement model is to assess the direct influence of hygiene concerns on commuters’ switching intentions towards public transportation. Additionally, it aims to evaluate the moderating effect of these concerns on the associations of push and pull factors with switching intentions towards public transport in a post-COVID-19 era.
Validity
Convergent and Discriminant Validity: Regarding convergent validity, the AVEs ranged from 0.51 to 0.84, which is above the acceptable value of 0.50 (Fornell & Larcker, 1981). Furthermore, the results from HTMT testing were below the upper cut-off of 0.85, thereby confirming discriminant validity.
Reliability
Internal Consistency: The Composite Reliability (CR) values ranged from 0.72 to 0.95, which is well above the recommended cut-off of 0.70 (Hair et al., 2010).
Factor Analysis
Common Method Bias: Harman’s single-factor analysis was conducted to check for common method bias (Podsakoff et al., 2003). The maximum variance extracted from a single construct was 17.91%, which is below the upper cut-off limit of 50% recommended by Podsakoff et al. (2003). Consequently, it was determined that the data were normally distributed and free from multicollinearity and common method bias.
Confirmatory Factor Analysis: Confirmatory Factor Analysis (CFA) revealed acceptable goodness-of-fit indices for the measurement model (chi-square (𝜒²)/degree of freedom (df) = 1.60, CFI = 0.98, TLI = 0.98, RMSEA = 0.03).
Instrument: The Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model
Test Type: Original
Format: Items are rated using a 5-point Likert scale.
Language Available: English
Population Group: Human (Male and Female)
Age Group: Adulthood (18 yrs & older), including Young Adulthood (18-29 yrs), Thirties (30-39 yrs), and Middle Age (40-64 yrs).
Population Details: The respondents were Public Transportation Consumers located in the United States.
Test Methodology: Test Validity, Content Validity, Convergent Validity, Discriminant Validity, Test Reliability, Internal Consistency, Factor Analysis, Confirmatory Factor Analysis, Measurement Model, Structural Equation Modeling.
Number of Items: This is a 24-item measure.
Constructs: Regulative environment (REG); Affordability (AFF); Hygiene concern (HC); Choice uncertainty (CU); Green self-identity (GSI); Normative environment (NOE); Switching intentions towards public transport (SWI).
Keywords
Choice Behavior; Consumer Attitudes; Consumer Behavior; Costs and Cost Analysis; Environmental Attitudes; Hygiene; Public Transportation; Theories; Uncertainty; Behavioral Intention; Consumer Ethics; COVID-19; Public Health Attitudes; Consumer Measures
Authors
Author ORCID Identifier:
Dhir, Amandeep: http://orcid.org/0000-0002-6006-6058
Affiliation and Email Addresses:
Anwar, Muhammad Azfar: Shenzhen University, College of Management, Research Institute of Business Analytics and Supply Chain Management. Email: [email protected]
Dhir, Amandeep: Norwegian School of Hotel Management, Faculty of Social Sciences. Email: [email protected]
Jabeen, Fauzia: Abu Dhabi University, College of Business. Email: [email protected]
Zhang, Qingyu: Shenzhen University, College of Management, Research Institute of Business Analytics and Supply Chain Management. Email: [email protected]
Siddiquei, Ahmad Nabeel: Bond University, Bond Business School. Email: [email protected]
Correspondence Address:
Zhang, Qingyu: Shenzhen University, College of Management, Research Institute of Business Analytics and Supply Chain Management, Shenzhen, China, [email protected]
Permissions & Fee and Test Year
Permissions: May be used for Research/Teaching.
Commercial Use: No
Fee: No
Test Year: 2023
Web Site: https://creativecommons.org/licenses/by/4.0/
References
Anwar, M. A., Dhir, A., Jabeen, F., Zhang, Q., & Siddiquei, A. N. (2023). Unconventional green transport innovations in the post-COVID-19 era. A trade-off between green actions and personal health protection. Journal of Business Research, 155(Part A), Article 113442. https://doi.org/10.1016/j.jbusres.2022.113442
Items of the The Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model
| Study measures | Measurement items |
| Regulative environment (REG) <br> (Sajjad, Chu et al., 2020) | REG1. Government organisations in this country assist citizens to use public transport with safety measures. <br> REG2. The government deploys additional staff/volunteers to guide and assist in adopting safety measures. <br> REG3. Local and national governments have special support available for individuals who want to adopt safety measures (i.e. hand sanitiser). <br> REG4. Even in case of not having personal protective equipment, the transport service provider assists in adopting health-protective measures (i.e. hand sanitiser and face masks). |
| Affordability (AFF) <br> (H. Zhang et al., 2014) | AFF1. It is expensive to use public transport. <br> AFF2. It is expensive to buy a daypass that I can use to use public transport every day. <br> AFF3. The traveling fare for using public transport is not reasonable. |
| Hygiene concern (HC) <br> (Shiu et al., 2011) | HC1. I avoid touching the open surfaces in public transport. <br> HC2. I wash my hands immediately after coughing, sneezing and rubbing my nose on public transport. <br> HC3. I cover my mouth when coughing and sneezing on public transport. |
| Choice uncertainty (CU) | CU1. I am often not certain about which model of transport I choose. <br> CU2. I am often not certain about which type of public transport I choose (share-bike, taxi or bus). <br> CU3. I am often not certain about the particular time when I will travel (peak hours, off-peak hours). <br> CU4. I am often not certain about choosing a time for using less crowded public transport. |
| Green self-identity (GSI) <br> (Tung et al., 2017) | GSI1. I think of myself as an ‘environmental consumer’. <br> GSI2. I am a socially responsible consumer. |
| Normative environment (NOE) <br> (Sajjad, Chu et al., 2020) | NOE1. When I am faced with difficulties, some people are always on my side with me. <br> NOE2. When I am faced with difficulties, some people comfort and encourage me. <br> NOE3. When I am faced with difficulties, some people listen to me talk about my private feelings. <br> NOE4. When I am faced with difficulties, some people express interest and concern in my wellbeing. |
| Switching intentions towards public transport (SWI) <br> (Jung et al., 2017) | SWI1. I am intending to switch to public transport in the near future. <br> SWI2. I have a plan to switch to public transport in the near future. <br> SWI3. I want to switch to public transport in the near future. <br> SWI4. It is likely that I will switch to public transport in the near future. |
Note. Items are rated using a five-point Likert scale ranging from 1 (Strongly disagree) to 5 (Strongly agree).
Cite this article
Mohammed looti (2026). Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model Inventory. PSYCHOLOGICAL SCALES. Retrieved from https://scales.arabpsychology.com/s/drivers-of-urban-public-transportation-adoption-in-the-post-covid-19-era-model-inventory/
Mohammed looti. "Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model Inventory." PSYCHOLOGICAL SCALES, 6 Apr. 2026, https://scales.arabpsychology.com/s/drivers-of-urban-public-transportation-adoption-in-the-post-covid-19-era-model-inventory/.
Mohammed looti. "Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model Inventory." PSYCHOLOGICAL SCALES, 2026. https://scales.arabpsychology.com/s/drivers-of-urban-public-transportation-adoption-in-the-post-covid-19-era-model-inventory/.
Mohammed looti (2026) 'Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model Inventory', PSYCHOLOGICAL SCALES. Available at: https://scales.arabpsychology.com/s/drivers-of-urban-public-transportation-adoption-in-the-post-covid-19-era-model-inventory/.
[1] Mohammed looti, "Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model Inventory," PSYCHOLOGICAL SCALES, vol. X, no. Y, ص Z-Z, April, 2026.
Mohammed looti. Drivers of Urban Public Transportation Adoption in the Post-COVID-19 Era–Model Inventory. PSYCHOLOGICAL SCALES. 2026;vol(issue):pages.