This poster introduces MLmorph, an innovative method integrating morphological modeling with machine learning to enhance decision-making in management. MLmorph bridges deductive exploration and empirical validation, enabling the analysis of complex, nonlinear relationships among variables. Demonstrated through a reward-based crowdfunding case study, it identified optimal configurations, such as October as the best general launch month and March for crafts. A key feature of MLmorph is its interactive model, allowing real-time configuration adjustments and dynamic scenario exploration, making it a practical decision support tool. This study highlights MLmorph's potential for consulting and management research, emphasizing its adaptability to diverse organizational contexts. Future research aims to expand MLmorph's applications and incorporate broader datasets, including audiovisual materials, to enrich its decision space.
