Harmony between culture, work style, work preferences, mental health, and performance of Generation Z

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Type of the article: Research Article

Abstract
The entry of Generation Z into organizations brings new expectations regarding how performance is defined in the workplace. Performance is no longer assessed solely on results, but also on alignment with values, psychological well-being, and preferences, thus requiring adjustments in performance management practices. This study aims to analyze the influence of employee preferences, work styles, organizational culture, and mental health on the performance of Generation Z employees in startup companies. A quantitative approach was employed using primary data collected through questionnaires distributed to 160 Generation Z employees working in startup companies in Medan City, Indonesia, selected through purposive sampling. Data were analyzed using structural equation modeling (SEM) to test both direct and indirect relationships. The results show that work preferences (p = 0.000), work style (p = 0.002), and organizational culture (p = 0.000) have a positive and significant effect on the performance of Generation Z. In addition, work preferences (p = 0.000), work style (p = 0.000), and organizational culture (p = 0.008) also have a significant effect on mental health, while mental health has a significant effect on performance (p = 0.004). Mental health was found to mediate the influence of work preferences (p = 0.017) and work style (p = 0.018) on performance but did not mediate the influence of organizational culture on performance (p = 0.058). This study contributes novelty by demonstrating that mental health functions as a selective mediator in shaping Generation Z performance in startup settings, thereby enriching theoretical perspectives on young employee performance formation.

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    • Figure 1. Research framework
    • Figure 2. Structural model
    • Table 1. Participants’ overview
    • Table 2. Discriminant validity
    • Table 3. Multicollinearity testing
    • Table 4. Hypotheses testing
    • Table A1. Factor loadings, path coefficient (α) values, rho A reliability, composite reliability (CR), and average variance extracted (AVE)
    • Conceptualization
      Elisabet Siahaan, Sri Annisa, Lasma Siahaan
    • Data curation
      Elisabet Siahaan, Sri Annisa, Ewy Ritonga, Lasma Siahaan
    • Formal Analysis
      Elisabet Siahaan, Sri Annisa, Ewy Ritonga, Hafiszah Ismail
    • Methodology
      Elisabet Siahaan, Sri Annisa, Ewy Ritonga
    • Project administration
      Elisabet Siahaan, Sri Annisa, Hafiszah Ismail
    • Supervision
      Elisabet Siahaan, Hafiszah Ismail, Lasma Siahaan
    • Validation
      Elisabet Siahaan, Sri Annisa, Hafiszah Ismail, Lasma Siahaan
    • Visualization
      Elisabet Siahaan, Ewy Ritonga, Hafiszah Ismail, Lasma Siahaan
    • Writing – original draft
      Elisabet Siahaan, Sri Annisa, Ewy Ritonga
    • Writing – review & editing
      Elisabet Siahaan, Sri Annisa, Hafiszah Ismail, Lasma Siahaan
    • Investigation
      Sri Annisa, Ewy Ritonga, Lasma Siahaan
    • Software
      Sri Annisa
    • Resources
      Ewy Ritonga, Hafiszah Ismail