The Influence of Demographic Variables on the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA)

Rakesh Gangadharaiah (), Johnell O. Brooks, Patrick J. Rosopa, Lisa Boor, Kristin Kolodge, Joseph Paul, Haotian Su and Yunyi Jia
Additional contact information
Rakesh Gangadharaiah: Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA
Johnell O. Brooks: Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA
Patrick J. Rosopa: Department of Psychology, Clemson University, Clemson, SC 29634, USA
Lisa Boor: J.D. Power, Troy, MI 48083, USA
Kristin Kolodge: J.D. Power, Troy, MI 48083, USA
Joseph Paul: Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA
Haotian Su: Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA
Yunyi Jia: Department of Automotive Engineering, Clemson University, Greenville, SC 29607, USA

Sustainability, 2025, vol. 17, issue 9, 1-43

Abstract: Building on our prior research with a national survey sample of 5385 US participants, the Pooled Rideshare Acceptance Model (PRAM) was built upon two factor analyses. This exploratory study extends the PRAM framework using the Pooled Rideshare Acceptance Model Multigroup Analyses (PRAMMA) to examine how 16 demographic variables influence and interact with the acceptance of Pooled Rideshare (PR), filling a gap in understanding user segmentation and personalization. Using a national sample of 5385 US participants, this methodological approach allowed for the evaluation of how PRAM variables such as safety, privacy, service experience, and environmental impact vary across diverse groups, including gender, generation, driver’s license, rideshare experience, education level, employment status, household size, number of children, income, vehicle ownership, and typical commuting practices. Factors such as convenience, comfort, and passenger safety did not show significant differences across the moderators, suggesting their universal importance across all demographics. Furthermore, geographical differences did not significantly impact the relationships within the model, suggesting consistent relationships across different regions. The findings highlight the need to move beyond a “one size fits all” approach, demonstrating that tailored strategies may be crucial for enhancing the adoption and satisfaction of PR services among various demographic groups. The analyses provide valuable insight for policymakers and rideshare companies looking to optimize their services and increase user engagement in PR.

Keywords: transportation network companies (TNCs); ride-hailing; multigroup analysis; structural equation model (SEM); rideshare; moderator (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/17/9/4196/pdf (application/pdf)
https://www.mdpi.com/2071-1050/17/9/4196/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:17:y:2025:i:9:p:4196-:d:1650195

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().