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Crowd Logistics Platform’s Informative Support to Logistics Performance: Scale Development and Empirical Examination

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  • Mingyu Zhang

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Yuhuan Xia

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Shuang Li

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Wenbing Wu

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

  • Shuxiang Wang

    (School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China)

Abstract

Using the organization information processing theory, we explored the process through which the informative support of crowd logistics platforms affects logistics performance. After collecting data from 321 respondents from two crowd logistics companies in China, we proposed and tested the theoretical framework empirically using SEM. To conduct the empirical study, we developed scales for platform’s informative support and the degree of logistics resources-demand match, respectively. The results indicate that a platform’s informative support improves logistics performance via two mediators, i.e., logistics resources-demand match and logistics agility. Moreover, a platform’s ease of use moderates the indirect process through which its informative support promotes logistics performance via logistics resources-demand match. However, a platform’s ease of use has no significant effect on the indirect process of its informative support affecting logistics performance via logistics agility. This paper extends our understanding on how the informative support of crowd logistics platforms predicts logistics performance.

Suggested Citation

  • Mingyu Zhang & Yuhuan Xia & Shuang Li & Wenbing Wu & Shuxiang Wang, 2019. "Crowd Logistics Platform’s Informative Support to Logistics Performance: Scale Development and Empirical Examination," Sustainability, MDPI, vol. 11(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:2:p:451-:d:198258
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    1. Jagienka Rześny-Cieplińska & Agnieszka Szmelter-Jarosz, 2019. "Assessment of the Crowd Logistics Solutions—The Stakeholders’ Analysis Approach," Sustainability, MDPI, vol. 11(19), pages 1-26, September.
    2. Agnieszka Szmelter-Jarosz & Jagienka Rześny-Cieplińska, 2019. "Priorities of Urban Transport System Stakeholders According to Crowd Logistics Solutions in City Areas. A Sustainability Perspective," Sustainability, MDPI, vol. 12(1), pages 1-19, December.
    3. Jun Lee & Dong-Han Kim, 2022. "Urban Forest Visit Motivation Scale: Development and Validation," Sustainability, MDPI, vol. 15(1), pages 1-19, December.
    4. Marco Bortolini & Francesca Calabrese & Francesco Gabriele Galizia, 2022. "Crowd Logistics: A Survey of Successful Applications and Implementation Potential in Northern Italy," Sustainability, MDPI, vol. 14(24), pages 1-17, December.
    5. Aleksandra Stanković & Milan Andrejić & Vukašin Pajić & Milorad Kilibarda & Dragan Djurdjević, 2023. "A Novel Survey-QFD-WASPAS Methodological Approach for Designing Crowd Storage Platforms: A Case Study of Serbia," Sustainability, MDPI, vol. 15(10), pages 1-32, May.

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