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This study analyzes the influence of organizational culture on the perception of emotional pay, incorporating gender as a modifying variable. The objective was to identify how these variables interact to affect organizational dynamics, addressing gaps in previous literature, especially in Latin American contexts. A quantitative empirical design was employed using a Multi-Layer Perceptron (MLP) to model the relationships between emotional pay, organizational culture types (clan, adhocracy, market, hierarchy) and gender. The sample included 114 cases, divided into training (63.4%) and test (36.6%) sets, with iterative optimization of the model using hyperbolic and sigmoid tangent activation functions. The results showed that emotional pay significantly impacts organizational culture, with a low relative error (0.985 in training; 1.030 in test). Gender moderated this relationship, showing differences in perception, particularly in hierarchical cultures, where higher prediction errors were observed. This suggests that women and men value distinctively the non-monetary components of emotional salience. The findings highlight the usefulness of the MLP in capturing complex interactions and underscore the need for gender-sensitive organizational policies. Future research could delve deeper into other demographic factors and improve the model with deep learning techniques.

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Decoding Emotional Salary: How Gender Perceptions Shape Organizational Culture. An Analysis Based on Perceptron-Type Neural Networks

  • 会议论文

  • 发表日期：

    2026年

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  • Ángel Villarroel,
  • Angelita Romero-Poveda,
  • Marlon Tinajero,
  • Evelyn Tovar-Molina

摘要

This study analyzes the influence of organizational culture on the perception of emotional pay, incorporating gender as a modifying variable. The objective was to identify how these variables interact to affect organizational dynamics, addressing gaps in previous literature, especially in Latin American contexts. A quantitative empirical design was employed using a Multi-Layer Perceptron (MLP) to model the relationships between emotional pay, organizational culture types (clan, adhocracy, market, hierarchy) and gender. The sample included 114 cases, divided into training (63.4%) and test (36.6%) sets, with iterative optimization of the model using hyperbolic and sigmoid tangent activation functions. The results showed that emotional pay significantly impacts organizational culture, with a low relative error (0.985 in training; 1.030 in test). Gender moderated this relationship, showing differences in perception, particularly in hierarchical cultures, where higher prediction errors were observed. This suggests that women and men value distinctively the non-monetary components of emotional salience. The findings highlight the usefulness of the MLP in capturing complex interactions and underscore the need for gender-sensitive organizational policies. Future research could delve deeper into other demographic factors and improve the model with deep learning techniques.