PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 29, 20260 citationsOpen Access

Configurational Determinants of Metanoia: Integrating Johannine Soteriology into a Knowledge-Attitude-Practice (KAP) Framework via The Haryono 2³ Configurational Model

View Full Paper
HSHaryono Saputro

Key Points

  • To develop and validate a model for predicting metanoia using a configurational approach.
  • Developed the Haryono 2³ Configurational Model.
  • Integrated the Knowledge-Attitude-Practice (KAP) framework with the Johannine triad.
  • Employed a 2³ factorial design to identify behavioral profiles.
  • Utilized binary logistic regression to analyze configurations.
  • Identified eight distinct behavioral profiles that influence transformation.
  • Revealed specific configurations of 'Way-Truth-Life' that determine transformation probability.
  • Provided a scalable tool for leaders to diagnose structural issues and design interventions.

Abstract

This working paper develops and tests the Haryono 2³ Configurational Model, a robust configurational framework designed to quantify and predict Metanoia (transformational change) within organizational and spiritual contexts. By synthesizing the Knowledge-Attitude-Practice (KAP) framework with the Johannine triad (The Way, The Truth, The Life), this study employs a 2³ factorial design to identify eight distinct behavioral profiles. Using binary logistic regression, the model moves beyond linear assumptions to reveal how specific configurations of "Way-Truth-Life" determine the probability of genuine personal or organizational transformation (Y=1). The result is a scalable diagnostic tool that allows leaders to identify structural bottlenecks and develop evidence-based Standard Operating Procedures (SOPs) for behavioral intervention.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Haryono Saputro (2026) studied this question.

synapsesocial.com/papers/69c8c35cde0f0f753b39e138https://doi.org/10.5281/zenodo.19248509
Ask AI
Helpful
Bookmark
Share
View Full Paper