The impact of knowledge absorptive capacity on the success of knowledge transfers in alliances: The mediating role of interorganizational link strength

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Knowledge transfer success in alliances can establish competitive advantages by enhancing an organization’s ability to identify, assimilate, and apply new knowledge, commonly referred to as absorptive capacity of knowledge. This process is facilitated through interorganizational relationships, connections, or links characterized by trust, reciprocity, and frequent interactions, which improve the flow of knowledge between partners. This study aims to investigate the impact of the absorptive capacity of knowledge on the success of knowledge transfer, with interorganizational link strength considered as a mediating variable. The analysis was conducted with an initial sample of 154 organizations from the agro-industry cluster in the Santander region of Colombia. Data were collected using a structured Likert-scale questionnaire, targeting leaders and project managers involved in collaborative initiatives. Structural equation modeling (SEM) was employed to analyze the data and test the proposed hypotheses. The results reveal that absorptive capacity significantly and positively influences both the success of knowledge transfer and interorganizational link strength. Besides, interorganizational link strength has a positive and significant impact on the success of knowledge transfer. Furthermore, interorganizational link strength mediates the relationship between absorptive capacity and knowledge transfer success. The study demonstrates that enhancing a firm’s absorptive capacity of knowledge improves technology transfer outcomes in collaborative alliances. Also, it reveals that strong interorganizational relationships play a significant role in facilitating knowledge exchange. Together, these findings offer new insights into the synergistic interplay between absorptive capacity and link strength as key enablers of the success of knowledge transfers.

Acknowledgment
We gratefully acknowledge the support of the “IIoT Platform for Training Professionals in Fourth Industrial Revolution Technologies” project (code 2824 at the Universidad Industrial de Santander). This project’s resources and expertise in knowledge transfer processes were instrumental in the success of our study. Special thanks to the project team for their invaluable contributions.

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    • Figure 1. Theoretical framework
    • Figure 2. Final adjusted structural model
    • Table 1. Definitions of operational variables
    • Table 2. Cronbach’s alpha, average variance extracted (AVE), composite reliability (CR), and factor loading
    • Table 3. Goodness of fit
    • Table 4. Path coefficients
    • Conceptualization
      Hugo E. Martínez-Ardila, Monica Duenas
    • Formal Analysis
      Hugo E. Martínez-Ardila, Monica Duenas
    • Investigation
      Hugo E. Martínez-Ardila, Monica Duenas, Luis Becerra
    • Methodology
      Hugo E. Martínez-Ardila, Monica Duenas
    • Project administration
      Hugo E. Martínez-Ardila, Luis Becerra
    • Supervision
      Hugo E. Martínez-Ardila, Luis Becerra
    • Validation
      Hugo E. Martínez-Ardila, Monica Duenas
    • Visualization
      Hugo E. Martínez-Ardila, Monica Duenas
    • Writing – original draft
      Hugo E. Martínez-Ardila, Monica Duenas
    • Writing – review & editing
      Hugo E. Martínez-Ardila, Luis Becerra
    • Data curation
      Monica Duenas
    • Funding acquisition
      Luis Becerra
    • Resources
      Luis Becerra