The Effects of Work Meaningfulness and Autonomy on Radically Innovative Behavior: A Neural Network Approach

Objective: The primary objective of this study was to utilize an advanced artificial neural network approach to investigate the complex, non-linear predictive effects of work meaningfulness and job autonomy on radically innovative behavior among professional employees.Methods and Materials: A quantitative, cross-sectional design was employed, utilizing a purposive sample of Polish professionals operating within the high-technology, engineering, and manufacturing sectors. Data were gathered using adapted, standardized psychometric instruments measured on a five-point Likert scale, with all measures demonstrating robust internal reliability (Cronbach’s ). To accurately model intricate, non-linear behavioral relationships without relying on restrictive parametric assumptions, the data were analyzed using a Multilayer Perceptron artificial neural network. The dataset was systematically partitioned into training ( ), testing ( ), and holdout ( ) subsets to rigorously validate the computational model’s predictive accuracy and isolate predictor strength via sensitivity analysis.Findings: The descriptive analysis confirmed that the sample ( ) reported significant positive correlations among all variables ( ). The artificial neural network demonstrated excellent model fit and explanatory power, successfully accounting for approximately of the total variance in radically innovative behavior ( ). The normalized sensitivity analysis revealed that job autonomy is the paramount predictor of radical innovation, yielding an absolute importance of and a normalized importance of . Work meaningfulness emerged as a highly critical secondary predictor, demonstrating an absolute importance of and a normalized importance of .Conclusion: Structural job autonomy is the absolute foundational prerequisite for enabling radically innovative behavior, while the cultivation of work meaningfulness provides the indispensable psychological drive and intrinsic resilience required to sustain these complex, paradigm-shifting efforts. Objective: The primary objective of this study was to utilize an advanced artificial neural network approach to investigate the complex, non-linear predictive effects of work meaningfulness and job autonomy on radically innovative behavior among professional employees. Methods and Materials: A quantitative, cross-sectional design was employed, utilizing a purposive sample of Polish professionals operating within the high-technology, engineering, and manufacturing sectors. Data were gathered using adapted, standardized psychometric instruments measured on a five-point Likert scale, with all measures demonstrating robust internal reliability (Cronbach’s ). To accurately model intricate, non-linear behavioral relationships without relying on restrictive parametric assumptions, the data were analyzed using a Multilayer Perceptron artificial neural network. The dataset was systematically partitioned into training ( ), testing ( ), and holdout ( ) subsets to rigorously validate the computational model’s predictive accuracy and isolate predictor strength via sensitivity analysis. Findings: The descriptive analysis confirmed that the sample ( ) reported significant positive correlations among all variables ( ). The artificial neural network demonstrated excellent model fit and explanatory power, successfully accounting for approximately of the total variance in radically innovative behavior ( ). The normalized sensitivity analysis revealed that job autonomy is the paramount predictor of radical innovation, yielding an absolute importance of and a normalized importance of . Work meaningfulness emerged as a highly critical secondary predictor, demonstrating an absolute importance of and a normalized importance of . Conclusion: Structural job autonomy is the absolute foundational prerequisite for enabling radically innovative behavior, while the cultivation of work meaningfulness provides the indispensable psychological drive and intrinsic resilience required to sustain these complex, paradigm-shifting efforts.

کلیدواژه ها:

Radically Innovative Behavior ، Work Meaningfulness ، Job Autonomy