The influence of assessment on training to improve productivity of construction companies

  • Received July 15, 2022;
    Accepted December 12, 2022;
    Published February 7, 2023
  • Author(s)
  • DOI
    http://dx.doi.org/10.21511/ppm.21(1).2023.15
  • Article Info
    Volume 21 2023, Issue #1, pp. 169-182
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This work is licensed under a Creative Commons Attribution 4.0 International License

The study investigated the influence of assessment on training to improve productivity of construction companies. This is important for the construction industry, which plays a critical role in a country’s economic development in a continuously shifting business world, entrenching globalization, and a technology-driven economy. The investigation employed a cross-sectional descriptive quantitative design after receiving 234 responses from builders, artisans, general workers, and technicians of construction sites in Gauteng Province, South Africa. Empirical data were analyzed using STATA 14 assisted by the ‘medsem’ package. The results of the exploratory and confirmatory factor analysis confirmed that rework in operations (rework), optimum utilization of equipment (utilization), use of modern equipment (modernization), and identification of defects in raw material (defects) could collectively determine productivity. The AVE value was higher than 0.5 (AVE = 0.523-0.665), with all factors reliable (CR = 0.761-0.869) and the heterotrait-monotrait criterion (HTMT) ≤ 0.85 (HTMT = 0.162-0.652). Assessment has a mediation effect on theoretical and on-the-job training with productivity measures (utilization, modernization, and defects). For on-the-job training, assessment showed a complete mediation effect on modernization (effect size of 98.8% and RID = 84.6). In contrast, for theoretical training, defects showed the highest mediation (effect size = 64.3% and RID = 1.804). The implication is that well-trained employees are critical in construction sites as they can improve productivity.

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    • Figure 1. Theoretical model
    • Table 1. Demographic profile of the respondents
    • Table 2. Descriptive statistics of training items
    • Table 3. Exploratory factor analysis of training items
    • Table 4. Descriptive statistics of productivity items
    • Table 5. Exploratory factor analysis of productivity items
    • Table 6. HTMT for a measure of discriminant validity
    • Table 7. Correlation matrix of the training and productivity factors
    • Table A1. Direct and indirect effects of the competency assessment on training and productivity
    • Conceptualization
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Formal Analysis
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Investigation
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Methodology
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Software
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Supervision
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Validation
      Matolwandile M. Mtotywa
    • Visualization
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Writing – original draft
      Matolwandile M. Mtotywa
    • Writing – review & editing
      Matolwandile M. Mtotywa, Sandile Mdlalose
    • Data curation
      Sandile Mdlalose
    • Project administration
      Sandile Mdlalose
    • Resources
      Sandile Mdlalose