Impact of digitalization on the attractiveness of employee recruitment and retention in Moroccan companies

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The relevant evolution of social networks and the expansion of digitalization has led to significant changes in the classical processes used by Moroccan companies in different fields such as marketing, human resources management, etc. This paper investigates the effects of digitalization on the attractiveness of Moroccan companies in terms of recruitment and safeguarding these constructs by using structural equation models according to the PLS approach. The study was carried out to touch 74 companies in different sectors. The study showed positive relationships between management support, digitalization, and recruitment performance (defined as the attractiveness of a company for recruitment and federalization of employees). The results show that the T-statistics are equal to 67.55, 6.862, and 5.941, respectively. The Q² value is 0.884 for scanning and 0.937 for performance, which means that the model is predictive in nature. The GoF is 1.388, which means that model is sufficiently large for the overall validity of the PLS model. While jobseeker behavior and competitive intensity did not affect recruitment performance because the test T-statistics is less than 1.64, the two factors have no moderating effect as the p-values are 0.228 and 0.082, respectively, exceeding the threshold of 0.05.

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    • Figure 1. Research framework
    • Figure 2. Measurement models, factor loadings, and coefficient values
    • Figure 3. Direct relationships from the bootstrap
    • Table 1. Item definitions
    • Table 2. Fornell and Larcker criterion
    • Table 3. Determining BETAs and test results
    • Table 4. R2-values
    • Table 5. Specific indirect effects
    • Table 6. Moderating effect of the behavior of jobseekers
    • Table 7. Moderating effect of competitive intensity
    • Table B1. Discriminant validity – cross-loading
    • Table B2. Fk2 values
    • Table B3. GoF calculation element
    • Table B4. Total effects
    • Table B5. Lower leverage and upper leverage
    • Conceptualization
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Data curation
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Formal Analysis
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Funding acquisition
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Investigation
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Methodology
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Resources
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Software
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Supervision
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Visualization
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine
    • Writing – original draft
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji
    • Writing – review & editing
      Mohamed Habachi, Zakia Nouira, Cheklekbire Malainine, Omar Hajaji