Humans at the center!? Analyzing digital workplace characteristics and their impact on truck drivers’ perceived workload

Purpose The aim of this study is to examine the relationship between job characteristics such as mental job requirements, decision-making authorities, variety of skills, (social) interactions, and perceived workload using the example of truck drivers from a work humanization perspective. Design/methodology/approach A questionnaire was conducted with 221 truck drivers in Germany. Findings This study makes strong theoretical and empirical contributions to the existing body of knowledge. First, it enhances the understanding of perceived workload in the digital work environments of truck drivers. Second, it augments the theoretical approach of human-centered work design in the context of digital transformation and especially in digital road freight transport by emphasizing the critical role of worker empowerment and technology commitment for the successful implementation of digital technologies. Lastly, it helps to understand the importance of the human factor in ensuring robust supply chains. Originality/value The transportation sector, particularly road freight, plays a pivotal role in maintaining robust supply chains and economic stability. Concurrently, the sector is undergoing substantial transformations driven by digitalization, which is fundamentally altering the work environment of truck drivers. Existing research underscores the importance of the human factor for successful use of digital technologies thereby ensuring high performance. However, a comprehensive analysis of human needs in digital work environments in relation to perceived workload remains an unresolved issue.

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Publication Details

Journal
The International Journal of Logistics Management
Published
2026-03-13
DOI
https://doi.org/10.1108/ijlm-01-2025-0046
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
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article

Humans at the center!? Analyzing digital workplace characteristics and their impact on truck drivers’ perceived workload

Sarah Maria Straub
The International Journal of Logistics Management
Human-Automation Interaction and Safety
article

Humans at the center!? Analyzing digital workplace characteristics and their impact on truck drivers’ perceived workload

Sarah Maria Straub
article en

Abstract

Purpose The aim of this study is to examine the relationship between job characteristics such as mental job requirements, decision-making authorities, variety of skills, (social) interactions, and perceived workload using the example of truck drivers from a work humanization perspective. Design/methodology/approach A questionnaire was conducted with 221 truck drivers in Germany. Findings This study makes strong theoretical and empirical contributions to the existing body of knowledge. First, it enhances the understanding of perceived workload in the digital work environments of truck drivers. Second, it augments the theoretical approach of human-centered work design in the context of digital transformation and especially in digital road freight transport by emphasizing the critical role of worker empowerment and technology commitment for the successful implementation of digital technologies. Lastly, it helps to understand the importance of the human factor in ensuring robust supply chains. Originality/value The transportation sector, particularly road freight, plays a pivotal role in maintaining robust supply chains and economic stability. Concurrently, the sector is undergoing substantial transformations driven by digitalization, which is fundamentally altering the work environment of truck drivers. Existing research underscores the importance of the human factor for successful use of digital technologies thereby ensuring high performance. However, a comprehensive analysis of human needs in digital work environments in relation to perceived workload remains an unresolved issue.

The International Journal of Logistics ManagementVol. 37(7)
University of Hohenheim (DE)
Openalex Percentile: Top 25%
Human-Automation Interaction and Safety
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