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<p>Leadership must integrate Spiritual Intelligence Quotient (SQ) beyond cognitive and emotional realms to sustain and ethically lead in increasingly complex organizational contexts. Without a flexible tool to assess spirituality and turn it into leadership behavior, current models overlook or undermeasure it in process. Existing models lack causal and reinforcing growth modeling sets, structural representation of qualitative SQ traits, and contextual domain generalizability. The paper proposes a five-phase structured analytical approach to integrate SQ into TFL to address these issues for different scenarios. SECGE begins graph-based embedding with unstructured SQ data and relational graph convolutional networks. The Moral-Spiritual Latent Variational Context Encoder (MoSLaCE) uses moral priors to map spiritual traits into latent spaces matching the Multifactor Leadership Questionnaire (MLQ). CTAM-SIS mimics adaptive leadership over domain metadata via transformer-based attention. Finally, LAGReS will optimize long-term Spirituality and Leadership Set blends using deep reinforcement learning. An integrated leadership paradigm that supports ethical consistency and is context-adaptive and dynamic is needed. These methods will bridge the subjective-objective split in SQ research and produce scalable leadership development models.</p>

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Design of an adaptive multistage approach for modeling spiritual intelligence in transformational leadership contexts

  • 期刊论文

  • 发表日期：

    2025年12月27日

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  • Jaee Jogalekar,
  • Reena Mahapatra Lenka

摘要

Leadership must integrate Spiritual Intelligence Quotient (SQ) beyond cognitive and emotional realms to sustain and ethically lead in increasingly complex organizational contexts. Without a flexible tool to assess spirituality and turn it into leadership behavior, current models overlook or undermeasure it in process. Existing models lack causal and reinforcing growth modeling sets, structural representation of qualitative SQ traits, and contextual domain generalizability. The paper proposes a five-phase structured analytical approach to integrate SQ into TFL to address these issues for different scenarios. SECGE begins graph-based embedding with unstructured SQ data and relational graph convolutional networks. The Moral-Spiritual Latent Variational Context Encoder (MoSLaCE) uses moral priors to map spiritual traits into latent spaces matching the Multifactor Leadership Questionnaire (MLQ). CTAM-SIS mimics adaptive leadership over domain metadata via transformer-based attention. Finally, LAGReS will optimize long-term Spirituality and Leadership Set blends using deep reinforcement learning. An integrated leadership paradigm that supports ethical consistency and is context-adaptive and dynamic is needed. These methods will bridge the subjective-objective split in SQ research and produce scalable leadership development models.