To support the penetration of cargo cycles in commercial freight transport, research by Narayanan et al. aims at identifying the significant influencing factors for their purchase, using data from Europe’s largest cargo cycle testing project, “Ich entlaste Städte.”
This is achieved by developing binary logit models for the intention to purchase (stated at the end of a 3-month vehicle trial) and the actual purchase decision made (queried three months or later, after the end of the trial). Before estimating the logit models, latent variables were constructed using explanatory factor analysis.
Based on the estimation results, factors influencing the purchase decision include catchment area of cargo cycle trips, daily usage during the trial phase, trial phase season, type of cargo cycle tested, mode substituted by cargo cycles during the trial phase, and business sector. Furthermore, four other factors, latent variables constructed through exploratory factor analysis, are found to have significant influence: perception of operational, cost, and additional benefits, as well as the importance of deterioration of conditions for conventional vehicles.
Based on the influence of these factors, policy measures are suggested under the following categories: (i) Regulation, (ii) Infrastructure, (iii) Finance, (iv) Campaigns, and (v) Trial schemes. In addition, the project’s data shows a difference between the intention and the actual purchase decision (around 50% higher intent compared to realized purchase). This implies a need to convert intention to the actual decision when making conclusions based on intention.
A comparison made between the binary logit models of intention and the actual purchase decision brings out the reality that hard facts like the deteriorating conditions influence the latter (e.g., vehicle access restrictions) for conventional vehicles, while operational concerns towards cargo cycles influence the former. This observation suggests the necessity of formulating measures to foster the market penetration of cargo cycles.
Longtail bikes and heavy load trikes are more suited for commercial light vehicle users. Therefore, vehicle manufacturers and policymakers are advised to target the development of these models to substitute light commercial vehicles. The researchers note that lower purchase costs, without possibly lower maintenance costs, may not result in intended cargo cycle penetration. Hence, robust and efficient cargo cycle designs are required to ensure lower maintenance costs.
Although trial schemes may not ensure the purchase of cargo cycles by every participating organization, they are practical tools in reducing the negative reservations towards cargo cycles and hence, support their penetration. Cities are suggested to implement push measures such as regulative frameworks deteriorating the conditions for conventional vehicles and pull measures such as improving the operational benefits and perception of the soft benefits and implementation of trial schemes, along with ensuring robust and efficient cargo cycle designs.
Within the context of Germany, there exists hardly any comprehensive data about commercial transport. The potential use of cargo bikes in Germany is between 8 to 23% of the commercial trips. This potential could be improved with a combination of other measures, such as urban consolidation centers. The successful penetration of the cargo cycles can reduce the use of conventional vehicles and decrease the negative externalities (e.g., local emissions) caused by German commercial transport, thereby supporting the aim of the nation to become climate-neutral by 2045. The federal government has already begun to implement initiatives, such as the provision of a 25% purchase price subsidy and the introduction of new traffic signs for cargo cycles, the positive effects of which can already be perceived, i.e., the continuous growth of the cargo cycle market 2020.
Source: Narayanan, S., Gruber, J., Liedtke, G., & Antoniou, C. (2022). Purchase intention and actual purchase of cargo cycles: Influencing factors and policy insights.Transportation Research Part A: Policy and Practice, 155, 31-45.