A Typological Analysis of US Transportation and Logistics Jobs: Automation and Prospects

Chaodong Han, James Otto and Martin Dresner

Transportation Journal, 2019, vol. 58, issue 4, 323-341

Abstract: Based on the O*NET job database, this research performs a typological analysis of US transportation and logistics jobs using two variables—the originality requirement of workers and the decision‐making freedom by work design. Based on cluster analysis, we find that Express Lane jobs command the highest average pay due to the greatest worker originality required, have the most decision‐making freedom allowed and are least vulnerable to automation. In contrast, Gridlock jobs are paid least on average due to the lowest worker originality, have the least decision‐making freedom, and are most vulnerable to automation. In the middle range fall Slow Lane and Bumpy Road jobs, due to less decision‐making freedom and lower worker originality required, respectively. Policy and managerial implications concerning training and work redesign are discussed in the context of technological advancements and automation.

Date: 2019
References: Add references at CitEc
Citations:

Downloads: (external link)
https://doi.org/10.5325/transportationj.58.4.0323

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:wly:transj:v:58:y:2019:i:4:p:323-341

Access Statistics for this article

More articles in Transportation Journal from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().