Stimulators of third-party logistics performance of supply chains in the Nigerian manufacturing industry

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The COVID-19 disruption of supply chains has motivated manufacturing companies in Nigeria to build and maintain supply chain visibility, robustness, and resilience to remain third-party logistics providers. It is vital to have an adequate understanding of third-party logistics performance drivers. Most studies have concentrated on third-party logistics capability, while few others explored the impact of relational governance structures on performance. However, studies examining the synergy between third-party logistics capability and relationship management are scarce. The purpose of this study is to investigate the stimulators of third-party logistics performance in the Nigerian manufacturing industry. A descriptive survey, e-mail questionnaire, and PLS-SEM approach was used to collect and analyze the data from a sample of 364 manufacturing companies in Nigeria. The findings indicated that relationship management has a significant positive association with third-party logistics capability (β= 0.785, t = 3.457, p < 0.001); relationship management has a significant negative association with supply chain risk (β= –0.209, t = 4.149, p < 0.001); third-party logistics capability has a significant negative association with supply chain risk (β = –0.620, t = 3.199, p < 0.001); supply chain risk has a significant negative association with logistics performance (β= –0.695 t = 5.396, p < 0.001). Hence, relationship management, third-party logistics capability, and supply chain risk are drivers of third-party logistics performance. Therefore, supply chain partners should manage their relationships to strengthen third-party logistics capability and reduce all kinds of uncertainties and risks.

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    • Figure 1. Proposed research model
    • Figure 2. PLS-SEM output of hypothesized relationships and the structural model
    • Table 1. Items’ factor loadings, reliability, and validity (AVE)
    • Table 2. Construct correlations and discriminant validity
    • Table 3. Estimated results of the structural model and hypotheses tests outputs
    • Conceptualization
      Cajetan Ewuzie, Geraldine Ugwuonah, Ebere Okocha, Agu Okoro Agu
    • Data curation
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo
    • Formal Analysis
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Agu Okoro Agu
    • Funding acquisition
      Cajetan Ewuzie, Ebere Okocha, Agu Okoro Agu
    • Investigation
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha, Agu Okoro Agu
    • Methodology
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha, Agu Okoro Agu
    • Project administration
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha, Agu Okoro Agu
    • Resources
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha, Agu Okoro Agu
    • Software
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha
    • Validation
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha, Agu Okoro Agu
    • Visualization
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha
    • Writing – original draft
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Ebere Okocha
    • Writing – review & editing
      Cajetan Ewuzie, Geraldine Ugwuonah, Victor O. Okolo, Agu Okoro Agu