Why leaders are important for cross-functional teams: Moderating role of supportive leadership on knowledge hiding

  • Received May 13, 2022;
    Accepted July 27, 2022;
    Published August 8, 2022
  • Author(s)
  • DOI
  • Article Info
    Volume 20 2022, Issue #3, pp. 178-191
  • Cited by
    2 articles

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This work is licensed under a Creative Commons Attribution 4.0 International License

Knowledge exchange has been a critical factor for cross-functional teams to master different tasks and problems and promote innovation. Cross-functional teams rely on the direct cooperation of senior employees from different departments, often with converging aims, leadership, culture, and communication. However, with the ever-increasing complexity in business decisions, decision-makers invested in the manufacturing industry sector need the support of a diverse team as an advisory tool to put well-thought measures into effect. The aim of this study is to analyze how cross-functional teams in commerce and industry rely on different key performance indicators to limit knowledge hiding. This paper conducted a quantitative study of 130 individual participants working in cross-functional teams in Germany. It also adapted multiple linear regression and used a conceptual model impacting the relationship between team performance, trust, and organizational citizenship behavior, including the moderating role of leadership. The disruptive effect of knowledge hiding was contextualized. The results indicate that team performance is directly affected by the selected variables. Furthermore, it is limited to knowledge hiding, while trust and the use of adequate leadership help to retain knowledge retention. Lastly, organizational citizenship behavior was found as the paramount factor, supported by individually tailored leadership methods, to foster information exchange and thereby promote organization-wide learning.

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    • Figure 1. Conceptual model
    • Figure 2. Moderating effect of OCB
    • Table 1. Descriptive data
    • Table 2. Linear regression
    • Table 3. Hierarchical linear regression
    • Table A1. Questionnaire
    • Conceptualization
      Anh Don Ton
    • Data curation
      Anh Don Ton
    • Formal Analysis
      Anh Don Ton
    • Investigation
      Anh Don Ton, Laszlo Hammerl
    • Methodology
      Anh Don Ton, Laszlo Hammerl
    • Project administration
      Anh Don Ton, Gabor Szabo-Szentgroti
    • Software
      Anh Don Ton
    • Validation
      Anh Don Ton
    • Visualization
      Anh Don Ton
    • Writing – original draft
      Anh Don Ton, Laszlo Hammerl
    • Writing – review & editing
      Laszlo Hammerl, Dennis Weber, Oliver Kremer, Gabor Szabo-Szentgroti
    • Funding acquisition
      Dennis Weber, Oliver Kremer
    • Resources
      Dennis Weber, Oliver Kremer
    • Supervision
      Gabor Szabo-Szentgroti